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

PubMed

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

2016-08-01

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

Estimation of Sensory Pork Loin Tenderness Using Warner-Bratzler Shear Force and Texture Profile Analysis Measurements

PubMed Central

Choe, Jee-Hwan; Choi, Mi-Hee; Rhee, Min-Suk; Kim, Byoung-Chul

2016-01-01

This study investigated the degree to which instrumental measurements explain the variation in pork loin tenderness as assessed by the sensory evaluation of trained panelists. Warner-Bratzler shear force (WBS) had a significant relationship with the sensory tenderness variables, such as softness, initial tenderness, chewiness, and rate of breakdown. In a regression analysis, WBS could account variations in these sensory variables, though only to a limited proportion of variation. On the other hand, three parameters from texture profile analysis (TPA)—hardness, gumminess, and chewiness—were significantly correlated with all sensory evaluation variables. In particular, from the result of stepwise regression analysis, TPA hardness alone explained over 15% of variation in all sensory evaluation variables, with the exception of perceptible residue. Based on these results, TPA analysis was found to be better than WBS measurement, with the TPA parameter hardness likely to prove particularly useful, in terms of predicting pork loin tenderness as rated by trained panelists. However, sensory evaluation should be conducted to investigate practical pork tenderness perceived by consumer, because both instrumental measurements could explain only a small portion (less than 20%) of the variability in sensory evaluation. PMID:26954174

Estimation of Sensory Pork Loin Tenderness Using Warner-Bratzler Shear Force and Texture Profile Analysis Measurements.

PubMed

Choe, Jee-Hwan; Choi, Mi-Hee; Rhee, Min-Suk; Kim, Byoung-Chul

2016-07-01

This study investigated the degree to which instrumental measurements explain the variation in pork loin tenderness as assessed by the sensory evaluation of trained panelists. Warner-Bratzler shear force (WBS) had a significant relationship with the sensory tenderness variables, such as softness, initial tenderness, chewiness, and rate of breakdown. In a regression analysis, WBS could account variations in these sensory variables, though only to a limited proportion of variation. On the other hand, three parameters from texture profile analysis (TPA)-hardness, gumminess, and chewiness-were significantly correlated with all sensory evaluation variables. In particular, from the result of stepwise regression analysis, TPA hardness alone explained over 15% of variation in all sensory evaluation variables, with the exception of perceptible residue. Based on these results, TPA analysis was found to be better than WBS measurement, with the TPA parameter hardness likely to prove particularly useful, in terms of predicting pork loin tenderness as rated by trained panelists. However, sensory evaluation should be conducted to investigate practical pork tenderness perceived by consumer, because both instrumental measurements could explain only a small portion (less than 20%) of the variability in sensory evaluation.

Density dependence and climate effects in Rocky Mountain elk: an application of regression with instrumental variables for population time series with sampling error.

PubMed

Creel, Scott; Creel, Michael

2009-11-01

1. Sampling error in annual estimates of population size creates two widely recognized problems for the analysis of population growth. First, if sampling error is mistakenly treated as process error, one obtains inflated estimates of the variation in true population trajectories (Staples, Taper & Dennis 2004). Second, treating sampling error as process error is thought to overestimate the importance of density dependence in population growth (Viljugrein et al. 2005; Dennis et al. 2006). 2. In ecology, state-space models are used to account for sampling error when estimating the effects of density and other variables on population growth (Staples et al. 2004; Dennis et al. 2006). In econometrics, regression with instrumental variables is a well-established method that addresses the problem of correlation between regressors and the error term, but requires fewer assumptions than state-space models (Davidson & MacKinnon 1993; Cameron & Trivedi 2005). 3. We used instrumental variables to account for sampling error and fit a generalized linear model to 472 annual observations of population size for 35 Elk Management Units in Montana, from 1928 to 2004. We compared this model with state-space models fit with the likelihood function of Dennis et al. (2006). We discuss the general advantages and disadvantages of each method. Briefly, regression with instrumental variables is valid with fewer distributional assumptions, but state-space models are more efficient when their distributional assumptions are met. 4. Both methods found that population growth was negatively related to population density and winter snow accumulation. Summer rainfall and wolf (Canis lupus) presence had much weaker effects on elk (Cervus elaphus) dynamics [though limitation by wolves is strong in some elk populations with well-established wolf populations (Creel et al. 2007; Creel & Christianson 2008)]. 5. Coupled with predictions for Montana from global and regional climate models, our results predict a substantial reduction in the limiting effect of snow accumulation on Montana elk populations in the coming decades. If other limiting factors do not operate with greater force, population growth rates would increase substantially.

Prediction of future falls in a community dwelling older adult population using instrumented balance and gait analysis.

PubMed

Bauer, C M; Gröger, I; Rupprecht, R; Marcar, V L; Gaßmann, K G

2016-04-01

The role of instrumented balance and gait assessment when screening for prospective fallers is currently a topic of controversial discussion. This study analyzed the association between variables derived from static posturography, instrumented gait analysis and clinical assessments with the occurrence of prospective falls in a sample of community dwelling older people. In this study 84 older people were analyzed. Based on a prospective occurrence of falls, participants were categorized into fallers and non-fallers. Variables derived from clinical assessments, static posturography and instrumented gait analysis were evaluated with respect to the association with the occurrence of prospective falls using a forward stepwise, binary, logistic regression procedure. Fallers displayed a significantly shorter single support time during walking while counting backwards, increased mediolateral to anteroposterior sway amplitude ratio, increased fast mediolateral oscillations and a larger coefficient (Coeff) of sway direction during various static posturography tests. Previous falls were insignificantly associated with the occurrence of prospective falls. Variables derived from posturography and instrumented gait analysis showed significant associations with the occurrence of prospective falls in a sample of community dwelling older adults.

The impact of extracurricular activities participation on youth delinquent behaviors: An instrumental variables approach.

PubMed

Han, Sehee; Lee, Jonathan; Park, Kyung-Gook

2017-07-01

The purpose of this study was to examine the association between extracurricular activities (EA) participation and youth delinquency while tackling an endogeneity problem of EA participation. Using survey data of 12th graders in South Korea (n = 1943), this study employed an instrumental variables approach to address the self-selection problem of EA participation as the data for this study was based on an observational study design. We found a positive association between EA participation and youth delinquency based on conventional regression analysis. By contrast, we found a negative association between EA participation and youth delinquency based on an instrumental variables approach. These results indicate that caution should be exercised when we interpret the effect of EA participation on youth delinquency based on observational study designs. Copyright © 2017 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

Exploration of an oculometer-based model of pilot workload

NASA Technical Reports Server (NTRS)

Krebs, M. J.; Wingert, J. W.; Cunningham, T.

1977-01-01

Potential relationships between eye behavior and pilot workload are discussed. A Honeywell Mark IIA oculometer was used to obtain the eye data in a fixed base transport aircraft simulation facility. The data were analyzed to determine those parameters of eye behavior which were related to changes in level of task difficulty of the simulated manual approach and landing on instruments. A number of trends and relationships between eye variables and pilot ratings were found. A preliminary equation was written based on the results of a stepwise linear regression. High variability in time spent on various instruments was related to differences in scanning strategy among pilots. A more detailed analysis of individual runs by individual pilots was performed to investigate the source of this variability more closely. Results indicated a high degree of intra-pilot variability in instrument scanning. No consistent workload related trends were found. Pupil diameter which had demonstrated a strong relationship to task difficulty was extensively re-exmained.

Quasi-Experimental Designs for Causal Inference

ERIC Educational Resources Information Center

Kim, Yongnam; Steiner, Peter

2016-01-01

When randomized experiments are infeasible, quasi-experimental designs can be exploited to evaluate causal treatment effects. The strongest quasi-experimental designs for causal inference are regression discontinuity designs, instrumental variable designs, matching and propensity score designs, and comparative interrupted time series designs. This…

Network Mendelian randomization: using genetic variants as instrumental variables to investigate mediation in causal pathways

PubMed Central

Burgess, Stephen; Daniel, Rhian M; Butterworth, Adam S; Thompson, Simon G

2015-01-01

Background: Mendelian randomization uses genetic variants, assumed to be instrumental variables for a particular exposure, to estimate the causal effect of that exposure on an outcome. If the instrumental variable criteria are satisfied, the resulting estimator is consistent even in the presence of unmeasured confounding and reverse causation. Methods: We extend the Mendelian randomization paradigm to investigate more complex networks of relationships between variables, in particular where some of the effect of an exposure on the outcome may operate through an intermediate variable (a mediator). If instrumental variables for the exposure and mediator are available, direct and indirect effects of the exposure on the outcome can be estimated, for example using either a regression-based method or structural equation models. The direction of effect between the exposure and a possible mediator can also be assessed. Methods are illustrated in an applied example considering causal relationships between body mass index, C-reactive protein and uric acid. Results: These estimators are consistent in the presence of unmeasured confounding if, in addition to the instrumental variable assumptions, the effects of both the exposure on the mediator and the mediator on the outcome are homogeneous across individuals and linear without interactions. Nevertheless, a simulation study demonstrates that even considerable heterogeneity in these effects does not lead to bias in the estimates. Conclusions: These methods can be used to estimate direct and indirect causal effects in a mediation setting, and have potential for the investigation of more complex networks between multiple interrelated exposures and disease outcomes. PMID:25150977

Measuring Ventilatory Activity with Structured Light Plethysmography (SLP) Reduces Instrumental Observer Effect and Preserves Tidal Breathing Variability in Healthy and COPD

PubMed Central

Niérat, Marie-Cécile; Dubé, Bruno-Pierre; Llontop, Claudia; Bellocq, Agnès; Layachi Ben Mohamed, Lila; Rivals, Isabelle; Straus, Christian; Similowski, Thomas; Laveneziana, Pierantonio

2017-01-01

The use of a mouthpiece to measure ventilatory flow with a pneumotachograph (PNT) introduces a major perturbation to breathing (“instrumental/observer effect”) and suffices to modify the respiratory behavior. Structured light plethysmography (SLP) is a non-contact method of assessment of breathing pattern during tidal breathing. Firstly, we validated the SLP measurements by comparing timing components of the ventilatory pattern obtained by SLP vs. PNT under the same condition; secondly, we compared SLP to SLP+PNT measurements of breathing pattern to evaluate the disruption of breathing pattern and breathing variability in healthy and COPD subjects. Measurements were taken during tidal breathing with SLP alone and SLP+PNT recording in 30 COPD and healthy subjects. Measurements included: respiratory frequency (Rf), inspiratory, expiratory, and total breath time/duration (Ti, Te, and Tt). Passing-Bablok regression analysis was used to evaluate the interchangeability of timing components of the ventilatory pattern (Rf, Ti, Te, and Tt) between measurements performed under the following experimental conditions: SLP vs. PNT, SLP+PNT vs. SLP, and SLP+PNT vs. PNT. The variability of different ventilatory variables was assessed through their coefficients of variation (CVs). In healthy: according to Passing-Bablok regression, Rf, TI, TE and TT were interchangeable between measurements obtained under the three experimental conditions (SLP vs. PNT, SLP+PNT vs. SLP, and SLP+PNT vs. PNT). All the CVs describing “traditional” ventilatory variables (Rf, Ti, Te, Ti/Te, and Ti/Tt) were significantly smaller in SLP+PNT condition. This was not the case for more “specific” SLP-derived variables. In COPD: according to Passing-Bablok regression, Rf, TI, TE, and TT were interchangeable between measurements obtained under SLP vs. PNT and SLP+PNT vs. PNT, whereas only Rf, TE, and TT were interchangeable between measurements obtained under SLP+PNT vs. SLP. However, most discrete variables were significantly different between the SLP and SLP+PNT conditions and CVs were significantly lower when COPD patients were assessed in the SLP+PNT condition. Measuring ventilatory activity with SLP preserves resting tidal breathing variability, reduces instrumental observer effect and avoids any disruptions in breathing pattern induced by the use of PNT-mouthpiece-nose-clip combination. PMID:28572773

Measuring Ventilatory Activity with Structured Light Plethysmography (SLP) Reduces Instrumental Observer Effect and Preserves Tidal Breathing Variability in Healthy and COPD.

PubMed

Niérat, Marie-Cécile; Dubé, Bruno-Pierre; Llontop, Claudia; Bellocq, Agnès; Layachi Ben Mohamed, Lila; Rivals, Isabelle; Straus, Christian; Similowski, Thomas; Laveneziana, Pierantonio

2017-01-01

The use of a mouthpiece to measure ventilatory flow with a pneumotachograph (PNT) introduces a major perturbation to breathing ("instrumental/observer effect") and suffices to modify the respiratory behavior. Structured light plethysmography (SLP) is a non-contact method of assessment of breathing pattern during tidal breathing. Firstly, we validated the SLP measurements by comparing timing components of the ventilatory pattern obtained by SLP vs. PNT under the same condition; secondly, we compared SLP to SLP+PNT measurements of breathing pattern to evaluate the disruption of breathing pattern and breathing variability in healthy and COPD subjects. Measurements were taken during tidal breathing with SLP alone and SLP+PNT recording in 30 COPD and healthy subjects. Measurements included: respiratory frequency (R f ), inspiratory, expiratory, and total breath time/duration (Ti, Te, and Tt). Passing-Bablok regression analysis was used to evaluate the interchangeability of timing components of the ventilatory pattern (R f , Ti, Te, and Tt) between measurements performed under the following experimental conditions: SLP vs. PNT, SLP+PNT vs. SLP, and SLP+PNT vs. PNT. The variability of different ventilatory variables was assessed through their coefficients of variation (CVs). In healthy: according to Passing-Bablok regression, Rf, TI, TE and TT were interchangeable between measurements obtained under the three experimental conditions (SLP vs. PNT, SLP+PNT vs. SLP, and SLP+PNT vs. PNT). All the CVs describing "traditional" ventilatory variables (R f , Ti, Te, Ti/Te, and Ti/Tt) were significantly smaller in SLP+PNT condition. This was not the case for more "specific" SLP-derived variables. In COPD: according to Passing-Bablok regression, Rf, TI, TE, and TT were interchangeable between measurements obtained under SLP vs. PNT and SLP+PNT vs. PNT, whereas only Rf, TE, and TT were interchangeable between measurements obtained under SLP+PNT vs. SLP. However, most discrete variables were significantly different between the SLP and SLP+PNT conditions and CVs were significantly lower when COPD patients were assessed in the SLP+PNT condition. Measuring ventilatory activity with SLP preserves resting tidal breathing variability, reduces instrumental observer effect and avoids any disruptions in breathing pattern induced by the use of PNT-mouthpiece-nose-clip combination.

Using instrumental variables to estimate a Cox's proportional hazards regression subject to additive confounding

PubMed Central

Tosteson, Tor D.; Morden, Nancy E.; Stukel, Therese A.; O'Malley, A. James

2014-01-01

The estimation of treatment effects is one of the primary goals of statistics in medicine. Estimation based on observational studies is subject to confounding. Statistical methods for controlling bias due to confounding include regression adjustment, propensity scores and inverse probability weighted estimators. These methods require that all confounders are recorded in the data. The method of instrumental variables (IVs) can eliminate bias in observational studies even in the absence of information on confounders. We propose a method for integrating IVs within the framework of Cox's proportional hazards model and demonstrate the conditions under which it recovers the causal effect of treatment. The methodology is based on the approximate orthogonality of an instrument with unobserved confounders among those at risk. We derive an estimator as the solution to an estimating equation that resembles the score equation of the partial likelihood in much the same way as the traditional IV estimator resembles the normal equations. To justify this IV estimator for a Cox model we perform simulations to evaluate its operating characteristics. Finally, we apply the estimator to an observational study of the effect of coronary catheterization on survival. PMID:25506259

Using instrumental variables to estimate a Cox's proportional hazards regression subject to additive confounding.

PubMed

MacKenzie, Todd A; Tosteson, Tor D; Morden, Nancy E; Stukel, Therese A; O'Malley, A James

2014-06-01

The estimation of treatment effects is one of the primary goals of statistics in medicine. Estimation based on observational studies is subject to confounding. Statistical methods for controlling bias due to confounding include regression adjustment, propensity scores and inverse probability weighted estimators. These methods require that all confounders are recorded in the data. The method of instrumental variables (IVs) can eliminate bias in observational studies even in the absence of information on confounders. We propose a method for integrating IVs within the framework of Cox's proportional hazards model and demonstrate the conditions under which it recovers the causal effect of treatment. The methodology is based on the approximate orthogonality of an instrument with unobserved confounders among those at risk. We derive an estimator as the solution to an estimating equation that resembles the score equation of the partial likelihood in much the same way as the traditional IV estimator resembles the normal equations. To justify this IV estimator for a Cox model we perform simulations to evaluate its operating characteristics. Finally, we apply the estimator to an observational study of the effect of coronary catheterization on survival.

Estimation of Chinese surface NO2 concentrations combining satellite data and Land Use Regression

NASA Astrophysics Data System (ADS)

Anand, J.; Monks, P.

2016-12-01

Monitoring surface-level air quality is often limited by in-situ instrument placement and issues arising from harmonisation over long timescales. Satellite instruments can offer a synoptic view of regional pollution sources, but in many cases only a total or tropospheric column can be measured. In this work a new technique of estimating surface NO2 combining both satellite and in-situ data is presented, in which a Land Use Regression (LUR) model is used to create high resolution pollution maps based on known predictor variables such as population density, road networks, and land cover. By employing a mixed effects approach, it is possible to take advantage of the spatiotemporal variability in the satellite-derived column densities to account for daily and regional variations in surface NO2 caused by factors such as temperature, elevation, and wind advection. In this work, surface NO2 maps are modelled over the North China Plain and Pearl River Delta during high-pollution episodes by combining in-situ measurements and tropospheric columns from the Ozone Monitoring Instrument (OMI). The modelled concentrations show good agreement with in-situ data and surface NO2 concentrations derived from the MACC-II global reanalysis.

Investigation on Motorcyclist Riding Behaviour at Curve Entry Using Instrumented Motorcycle

PubMed Central

Yuen, Choon Wah; Karim, Mohamed Rehan; Saifizul, Ahmad

2014-01-01

This paper details the study on the changes in riding behaviour, such as changes in speed as well as the brake force and throttle force applied, when motorcyclists ride over a curve section road using an instrumented motorcycle. In this study, an instrumented motorcycle equipped with various types of sensors, on-board cameras, and data loggers, was developed in order to collect the riding data on the study site. Results from the statistical analysis showed that riding characteristics, such as changes in speed, brake force, and throttle force applied, are influenced by the distance from the curve entry, riding experience, and travel mileage of the riders. A structural equation modeling was used to study the impact of these variables on the change of riding behaviour in curve entry section. Four regression equations are formed to study the relationship between four dependent variables, which are speed, throttle force, front brake force, and rear brake force applied with the independent variables. PMID:24523660

Graphical Models for Quasi-Experimental Designs

ERIC Educational Resources Information Center

Steiner, Peter M.; Kim, Yongnam; Hall, Courtney E.; Su, Dan

2017-01-01

Randomized controlled trials (RCTs) and quasi-experimental designs like regression discontinuity (RD) designs, instrumental variable (IV) designs, and matching and propensity score (PS) designs are frequently used for inferring causal effects. It is well known that the features of these designs facilitate the identification of a causal estimand…

College quality and hourly wages: evidence from the self-revelation model, sibling models and instrumental variables.

PubMed

Borgen, Nicolai T

2014-11-01

This paper addresses the recent discussion on confounding in the returns to college quality literature using the Norwegian case. The main advantage of studying Norway is the quality of the data. Norwegian administrative data provide information on college applications, family relations and a rich set of control variables for all Norwegian citizens applying to college between 1997 and 2004 (N = 141,319) and their succeeding wages between 2003 and 2010 (676,079 person-year observations). With these data, this paper uses a subset of the models that have rendered mixed findings in the literature in order to investigate to what extent confounding biases the returns to college quality. I compare estimates obtained using standard regression models to estimates obtained using the self-revelation model of Dale and Krueger (2002), a sibling fixed effects model and the instrumental variable model used by Long (2008). Using these methods, I consistently find increasing returns to college quality over the course of students' work careers, with positive returns only later in students' work careers. I conclude that the standard regression estimate provides a reasonable estimate of the returns to college quality. Copyright © 2014 Elsevier Inc. All rights reserved.

Using the Nobel Laureates in Economics to Teach Quantitative Methods

ERIC Educational Resources Information Center

Becker, William E.; Greene, William H.

2005-01-01

The authors show how the work of Nobel Laureates in economics can enhance student understanding and bring them up to date on topics such as probability, uncertainty and decision theory, hypothesis testing, regression to the mean, instrumental variable techniques, discrete choice modeling, and time-series analysis. (Contains 2 notes.)

Combining data visualization and statistical approaches for interpreting measurements and meta-data: Integrating heatmaps, variable clustering, and mixed regression models

EPA Science Inventory

The advent of new higher throughput analytical instrumentation has put a strain on interpreting and explaining the results from complex studies. Contemporary human, environmental, and biomonitoring data sets are comprised of tens or hundreds of analytes, multiple repeat measures...

Regularization Methods for High-Dimensional Instrumental Variables Regression With an Application to Genetical Genomics

PubMed Central

Lin, Wei; Feng, Rui; Li, Hongzhe

2014-01-01

In genetical genomics studies, it is important to jointly analyze gene expression data and genetic variants in exploring their associations with complex traits, where the dimensionality of gene expressions and genetic variants can both be much larger than the sample size. Motivated by such modern applications, we consider the problem of variable selection and estimation in high-dimensional sparse instrumental variables models. To overcome the difficulty of high dimensionality and unknown optimal instruments, we propose a two-stage regularization framework for identifying and estimating important covariate effects while selecting and estimating optimal instruments. The methodology extends the classical two-stage least squares estimator to high dimensions by exploiting sparsity using sparsity-inducing penalty functions in both stages. The resulting procedure is efficiently implemented by coordinate descent optimization. For the representative L1 regularization and a class of concave regularization methods, we establish estimation, prediction, and model selection properties of the two-stage regularized estimators in the high-dimensional setting where the dimensionality of co-variates and instruments are both allowed to grow exponentially with the sample size. The practical performance of the proposed method is evaluated by simulation studies and its usefulness is illustrated by an analysis of mouse obesity data. Supplementary materials for this article are available online. PMID:26392642

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.

The Use of Linear Instrumental Variables Methods in Health Services Research and Health Economics: A Cautionary Note

PubMed Central

Terza, Joseph V; Bradford, W David; Dismuke, Clara E

2008-01-01

Objective To investigate potential bias in the use of the conventional linear instrumental variables (IV) method for the estimation of causal effects in inherently nonlinear regression settings. Data Sources Smoking Supplement to the 1979 National Health Interview Survey, National Longitudinal Alcohol Epidemiologic Survey, and simulated data. Study Design Potential bias from the use of the linear IV method in nonlinear models is assessed via simulation studies and real world data analyses in two commonly encountered regression setting: (1) models with a nonnegative outcome (e.g., a count) and a continuous endogenous regressor; and (2) models with a binary outcome and a binary endogenous regressor. Principle Findings The simulation analyses show that substantial bias in the estimation of causal effects can result from applying the conventional IV method in inherently nonlinear regression settings. Moreover, the bias is not attenuated as the sample size increases. This point is further illustrated in the survey data analyses in which IV-based estimates of the relevant causal effects diverge substantially from those obtained with appropriate nonlinear estimation methods. Conclusions We offer this research as a cautionary note to those who would opt for the use of linear specifications in inherently nonlinear settings involving endogeneity. PMID:18546544

Literature review of some selected types of results and statistical analyses of total-ozone data. [for the ozonosphere

NASA Technical Reports Server (NTRS)

Myers, R. H.

1976-01-01

The depletion of ozone in the stratosphere is examined, and causes for the depletion are cited. Ground station and satellite measurements of ozone, which are taken on a worldwide basis, are discussed. Instruments used in ozone measurement are discussed, such as the Dobson spectrophotometer, which is credited with providing the longest and most extensive series of observations for ground based observation of stratospheric ozone. Other ground based instruments used to measure ozone are also discussed. The statistical differences of ground based measurements of ozone from these different instruments are compared to each other, and to satellite measurements. Mathematical methods (i.e., trend analysis or linear regression analysis) of analyzing the variability of ozone concentration with respect to time and lattitude are described. Various time series models which can be employed in accounting for ozone concentration variability are examined.

Development and validation of the neck dissection impairment index: a quality of life measure.

PubMed

Taylor, Rodney J; Chepeha, Judith C; Teknos, Theodoros N; Bradford, Carol R; Sharma, Pramod K; Terrell, Jeffrey E; Hogikyan, Norman D; Wolf, Gregory T; Chepeha, Douglas B

2002-01-01

To validate a health-related quality-of-life (QOL) instrument for patients following neck dissection and to identify the factors that affect QOL following neck dissection. Cross-sectional validation study. The outpatient clinic of a tertiary care cancer center. Convenience sample of 54 patients previously treated for head and neck cancer who underwent a selective neck dissection or modified radical neck dissection (64 total neck dissections). Patients had a minimum postoperative convalescence of 11 months. Thirty-two underwent accessory nerve-sparing modified radical neck dissection, and 32 underwent selective neck dissection. A 10-item, self-report instrument, the Neck Dissection Impairment Index (NDII), was developed and validated. Reliability was evaluated with test-retest correlation and internal consistency using the Cronbach alpha coefficient. Convergent validity was assessed using the 36-Item Short-Form Health Survey (SF-36) and the Constant Shoulder Scale, a shoulder function test. Multiple variable regression was used to determine variables that most affected QOL following neck dissection The 10-item NDII test-retest correlation was 0.91 (P<.001) with an internal consistency Cronbach alpha coefficient of.95. The NDII correlated with the Constant Shoulder Scale (r = 0.85, P<.001) and with the SF-36 physical functioning (r = 0.50, P<.001) and role-physical functioning (r = 0.60, P<.001) domains. Using multiple variable regression, the variables that contributed most to QOL score were patient's age and weight, radiation treatment, and neck dissection type. The NDII is a valid, reliable instrument for assessing neck dissection impairment. Patient's age, weight, radiation treatment, and neck dissection type were important factors that affect QOL following neck dissection.

An extended Kalman-Bucy filter for atmospheric temperature profile retrieval with a passive microwave sounder

NASA Technical Reports Server (NTRS)

Ledsham, W. H.; Staelin, D. H.

1978-01-01

An extended Kalman-Bucy filter has been implemented for atmospheric temperature profile retrievals from observations made using the Scanned Microwave Spectrometer (SCAMS) instrument carried on the Nimbus 6 satellite. This filter has the advantage that it requires neither stationary statistics in the underlying processes nor linear production of the observed variables from the variables to be estimated. This extended Kalman-Bucy filter has yielded significant performance improvement relative to multiple regression retrieval methods. A multi-spot extended Kalman-Bucy filter has also been developed in which the temperature profiles at a number of scan angles in a scanning instrument are retrieved simultaneously. These multi-spot retrievals are shown to outperform the single-spot Kalman retrievals.

Revising Our Thinking about the Relationship between Maternal Labor Supply and Preschool

ERIC Educational Resources Information Center

Fitzpatrick, Maria Donovan

2012-01-01

Many argue that childcare costs limit the labor supply of mothers, though existing evidence has been mixed. Using a child's eligibility for public kindergarten in a regression discontinuity instrumental variables framework, I estimate how use of a particular subsidy, public school, affects maternal labor supply. I find public school enrollment…

Assessing the Impact of Drug Use on Hospital Costs

PubMed Central

Stuart, Bruce C; Doshi, Jalpa A; Terza, Joseph V

2009-01-01

Objective To assess whether outpatient prescription drug utilization produces offsets in the cost of hospitalization for Medicare beneficiaries. Data Sources/Study Setting The study analyzed a sample (N=3,101) of community-dwelling fee-for-service U.S. Medicare beneficiaries drawn from the 1999 and 2000 Medicare Current Beneficiary Surveys. Study Design Using a two-part model specification, we regressed any hospital admission (part 1: probit) and hospital spending by those with one or more admissions (part 2: nonlinear least squares regression) on drug use in a standard model with strong covariate controls and a residual inclusion instrumental variable (IV) model using an exogenous measure of drug coverage as the instrument. Principal Findings The covariate control model predicted that each additional prescription drug used (mean=30) raised hospital spending by $16 (p<.001). The residual inclusion IV model prediction was that each additional prescription fill reduced hospital spending by $104 (p<.001). Conclusions The findings indicate that drug use is associated with cost offsets in hospitalization among Medicare beneficiaries, once omitted variable bias is corrected using an IV technique appropriate for nonlinear applications. PMID:18783453

CIEL*a*b* color space predictive models for colorimetry devices--analysis of perfume quality.

PubMed

Korifi, Rabia; Le Dréau, Yveline; Antinelli, Jean-François; Valls, Robert; Dupuy, Nathalie

2013-01-30

Color perception plays a major role in the consumer evaluation of perfume quality. Consumers need first to be entirely satisfied with the sensory properties of products, before other quality dimensions become relevant. The evaluation of complex mixtures color presents a challenge even for modern analytical techniques. A variety of instruments are available for color measurement. They can be classified as tristimulus colorimeters and spectrophotometers. Obsolescence of the electronics of old tristimulus colorimeter arises from the difficulty in finding repair parts and leads to its replacement by more modern instruments. High quality levels in color measurement, i.e., accuracy and reliability in color control are the major advantages of the new generation of color instrumentation, the integrating sphere spectrophotometer. Two models of spectrophotometer were tested in transmittance mode, employing the d/0° geometry. The CIEL(*)a(*)b(*) color space parameters were measured with each instrument for 380 samples of raw materials and bases used in the perfume compositions. The results were graphically compared between the colorimeter device and the spectrophotometer devices. All color space parameters obtained with the colorimeter were used as dependent variables to generate regression equations with values obtained from the spectrophotometers. The data was statistically analyzed to create predictive model between the reference and the target instruments through two methods. The first method uses linear regression analysis and the second method consists of partial least square regression (PLS) on each component. Copyright © 2012 Elsevier B.V. All rights reserved.

Functional assessment using Constant's Shoulder Scale after modified radical and selective neck dissection.

PubMed

Chepeha, Douglas B; Taylor, Rodney J; Chepeha, Judith C; Teknos, Theodoros N; Bradford, Carol R; Sharma, Pramod K; Terrell, Jeffrey E; Wolf, Gregory T

2002-05-01

Constant's Shoulder Scale is a validated and widely applied instrument for assessment of shoulder function. We used this instrument to assess which treatment and demographic variables contribute to shoulder dysfunction after neck dissection in head and neck cancer patients. A convenience sample of 54 patients with 64 neck dissections and minimum follow-up of 11 months were evaluated. Thirty-two accessory nerve-sparing modified radical (MRND) and 32 selective neck (SND) dissections were performed. Multivariable regression analysis was used to determine the variables that were predictive for shoulder dysfunction. Clinical variables included age, time from surgery, handedness, weight, radiation therapy, neck dissection type, tumor stage, and site. Patients receiving MRND had significantly worse shoulder function than patients with SND (p =.0007). Radiation therapy contributed negatively, whereas weight contributed positively (p =.0001). The critical factors contributing to shoulder dysfunction after neck dissection were weight, radiation therapy, and neck dissection type. Copyright 2002 Wiley Periodicals, Inc.

Performance evaluation of the microINR® point-of-care INR-testing system.

PubMed

Joubert, J; van Zyl, M C; Raubenheimer, J

2018-04-01

Point-of-care International Normalised Ratio (INR) testing is used frequently. We evaluated the microINR ® POC system for accuracy, precision and measurement repeatability, and investigated instrument and test chip variability and error rates. Venous blood INRs of 210 patients on warfarin were obtained with Thromborel ® S on the Sysmex CS-2100i ® analyser and compared with capillary blood microINR ® values. Precision was assessed using control materials. Measurement repeatability was calculated on 51 duplicate finger-prick INRs. Triplicate finger-prick INRs using three different instruments (30 patients) and three different test chip lots (29 patients) were used to evaluate instrument and test chip variability. Linear regression analysis of microINR ® and Sysmex CS2100i ® values showed a correlation coefficient of 0.96 (P < .0001) and a positive proportional bias of 4.4%. Dosage concordance was 93.8% and clinical agreement 95.7%. All acceptance criteria based on ISO standard 17593:2007 system accuracy requirements were met. Control material coefficients of variation (CV) varied from 6.2% to 16.7%. The capillary blood measurement repeatability CV was 7.5%. No significant instrument (P = .93) or test chip (P = .81) variability was found, and the error rate was low (2.8%). The microINR ® instrument is accurate and precise for monitoring warfarin therapy. © 2017 John Wiley & Sons Ltd.

The Effect of Private Insurance on the Health of Older, Working Age Adults: Evidence from the Health and Retirement Study

PubMed Central

Dor, Avi; Sudano, Joseph; Baker, David W

2006-01-01

Objective Primarily, to determine if the presence of private insurance leads to improved health status, as measured by a survey-based health score. Secondarily, to explore sensitivity of estimates to adjustments for endogeneity. The study focuses on adults in late middle age who are nearing entry into Medicare. Data Sources The analysis file is drawn from the Health and Retirement Study, a national survey of relatively older adults in the labor force. The dependent variable, an index of 5 health outcome items, was obtained from the 1996 survey. Independent variables were obtained from the 1992 survey. State-level instrumental variables were obtained from the Area Resources File and the TAXSIM file. The final sample consists of 9,034 individuals of which 1,540 were uninsured. Study Design Estimation addresses endogeneity of the insurance participation decision in health score regressions. In addition to ordinary least squares (OLS), two models are tested: an instrumental variables (IV) model, and a model with endogenous treatment effects due to Heckman (1978). Insurance participation and health behaviors enter with a lag to allow their effects to dissipate over time. Separate regressions were run for groupings of chronic conditions. Principal Findings The OLS model results in statistically significant albeit small effects of insurance on the computed health score, but the results may be downward biased. Adjusting for endogeneity using state-level instrumental variables yields up to a six-fold increase in the insurance effect. Results are consistent across IV and treatment effects models, and for major groupings of medical conditions. The insurance effect appears to be in the range of about 2–11 percent. There appear to be no significant differences in the insurance effect for subgroups with and without major chronic conditions. Conclusions Extending insurance coverage to working age adults may result in improved health. By conjecture, policies aimed at expanding coverage to this population may lead to improved health at retirement and entry to Medicare, potentially leading to savings. However, further research is needed to determine whether similar results are found when alternative measures of overall health or health scores are used. Future research should also explore the use of alternative instrumental variables. Preliminary results provide no justification for targeting certain subgroups with susceptibility to certain chronic conditions rather than broad policy interventions. PMID:16704511

Ability-Grouping and Academic Inequality: Evidence from Rule-Based Student Assignments. NBER Working Paper No. 14911

ERIC Educational Resources Information Center

Jackson, C. Kirabo

2009-01-01

In Trinidad and Tobago students are assigned to secondary schools after fifth grade based on achievement tests, leading to large differences in the school environments to which students of differing initial levels of achievement are exposed. Using both a regression discontinuity design and rule-based instrumental variables to address…

Modeling Outcomes with Floor or Ceiling Effects: An Introduction to the Tobit Model

ERIC Educational Resources Information Center

McBee, Matthew

2010-01-01

In gifted education research, it is common for outcome variables to exhibit strong floor or ceiling effects due to insufficient range of measurement of many instruments when used with gifted populations. Common statistical methods (e.g., analysis of variance, linear regression) produce biased estimates when such effects are present. In practice,…

Promoting motivation through mode of instruction: The relationship between use of affective teaching techniques and motivation to learn science

NASA Astrophysics Data System (ADS)

Sanchez Rivera, Yamil

The purpose of this study is to add to what we know about the affective domain and to create a valid instrument for future studies. The Motivation to Learn Science (MLS) Inventory is based on Krathwohl's Taxonomy of Affective Behaviors (Krathwohl et al., 1964). The results of the Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) demonstrated that the MLS Inventory is a valid and reliable instrument. Therefore, the MLS Inventory is a uni-dimensional instrument composed of 9 items with convergent validity (no divergence). The instrument had a high Chronbach Alpha value of .898 during the EFA analysis and .919 with the CFA analysis. Factor loadings on the 9 items ranged from .617 to .800. Standardized regression weights ranged from .639 to .835 in the CFA analysis. Various indices (RMSEA = .033; NFI = .987; GFI = .985; CFI = 1.000) demonstrated a good fitness of the proposed model. Hierarchical linear modeling was used to statistical analyze data where students' motivation to learn science scores (level-1) were nested within teachers (level-2). The analysis was geared toward identifying if teachers' use of affective behavior (a level-2 classroom variable) was significantly related with students' MLS scores (level-1 criterion variable). Model testing proceeded in three phases: intercept-only model, means-as-outcome model, and a random-regression coefficient model. The intercept-only model revealed an intra-class correlation coefficient of .224 with an estimated reliability of .726. Therefore, data suggested that only 22.4% of the variance in MLS scores is between-classes and the remaining 77.6% is at the student-level. Due to the significant variance in MLS scores, X2(62.756, p<.0001), teachers' TAB scores were added as a level-2 predictor. The regression coefficient was non-significant (p>.05). Therefore, the teachers' self-reported use of affective behaviors was not a significant predictor of students' motivation to learn science.

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

PubMed

Wojan, Timothy R; Nichols, Bonnie

2018-01-01

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

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

PubMed Central

Nichols, Bonnie

2018-01-01

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

Estimating the effects of wages on obesity.

PubMed

Kim, DaeHwan; Leigh, John Paul

2010-05-01

To estimate the effects of wages on obesity and body mass. Data on household heads, aged 20 to 65 years, with full-time jobs, were drawn from the Panel Study of Income Dynamics for 2003 to 2007. The Panel Study of Income Dynamics is a nationally representative sample. Instrumental variables (IV) for wages were created using knowledge of computer software and state legal minimum wages. Least squares (linear regression) with corrected standard errors were used to estimate the equations. Statistical tests revealed both instruments were strong and tests for over-identifying restrictions were favorable. Wages were found to be predictive (P < 0.05) of obesity and body mass in regressions both before and after applying IVs. Coefficient estimates suggested stronger effects in the IV models. Results are consistent with the hypothesis that low wages increase obesity prevalence and body mass.

Insomnia and hypnotic medications are associated with suicidal ideation in a community population.

PubMed

Pigeon, Wilfred R; Woosley, Julie A; Lichstein, Kenneth L

2014-01-01

Suicidal ideation (SI), a significant predictor of suicide, is associated with sleep disturbance, which is seldom assessed using stringent diagnostic criteria and validated sleep instruments in community samples. Cross-sectional data, including sleep diaries and validated instruments, from 767 community adults were used to identify variables associated with SI and subsequently entered into a regression model to predict SI. Suicidal ideation was endorsed by 9.3% of the sample. This group differed from non-ideators on several variables, but only insomnia diagnosis, depression severity, and hypnotic medication use predicted SI. Findings confirm an association of insomnia with SI using stringent criteria and controlling for depression. If treating insomnia is a conceivable pathway to reduce SI, the apparent risk posed by hypnotics may limit treatment options.

Outcomes of deliveries by family physicians or obstetricians: a population-based cohort study using an instrumental variable

PubMed Central

Aubrey-Bassler, Kris; Cullen, Richard M.; Simms, Alvin; Asghari, Shabnam; Crane, Joan; Wang, Peizhong Peter; Godwin, Marshall

2015-01-01

Background: Previous research has suggested that obstetric outcomes are similar for deliveries by family physicians and obstetricians, but many of these studies were small, and none of them adjusted for unmeasured selection bias. We compared obstetric outcomes between these provider types using an econometric method designed to adjust for unobserved confounding. Methods: We performed a retrospective population-based cohort study of all Canadian (except Quebec) hospital births with delivery by family physicians and obstetricians at more than 20 weeks gestational age, with birth weight greater than 500 g, between Apr. 1, 2006, and Mar. 31, 2009. The primary outcomes were the relative risks of in-hospital perinatal death and a composite of maternal mortality and major morbidity assessed with multivariable logistic regression and instrumental variable–adjusted multivariable regression. Results: After exclusions, there were 3600 perinatal deaths and 14 394 cases of maternal morbidity among 799 823 infants and 793 053 mothers at 390 hospitals. For deliveries by family physicians v. obstetricians, the relative risk of perinatal mortality was 0.98 (95% confidence interval [CI] 0.85–1.14) and of maternal morbidity was 0.81 (95% CI 0.70–0.94) according to logistic regression. The respective relative risks were 0.97 (95% CI 0.58–1.64) and 1.13 (95% CI 0.65–1.95) according to instrumental variable methods. Interpretation: After adjusting for both observed and unobserved confounders, we found a similar risk of perinatal mortality and adverse maternal outcome for obstetric deliveries by family physicians and obstetricians. Whether there are differences between these groups for other outcomes remains to be seen. PMID:26303244

[A correlational study on uncertainty, mastery and appraisal of uncertainty in hospitalized children's mothers].

PubMed

Yoo, Kyung Hee

2007-06-01

This study was conducted to investigate the correlation among uncertainty, mastery and appraisal of uncertainty in hospitalized children's mothers. Self report questionnaires were used to measure the variables. Variables were uncertainty, mastery and appraisal of uncertainty. In data analysis, the SPSSWIN 12.0 program was utilized for descriptive statistics, Pearson's correlation coefficients, and regression analysis. Reliability of the instruments was cronbach's alpha=.84~.94. Mastery negatively correlated with uncertainty(r=-.444, p=.000) and danger appraisal of uncertainty(r=-.514, p=.000). In regression of danger appraisal of uncertainty, uncertainty and mastery were significant predictors explaining 39.9%. Mastery was a significant mediating factor between uncertainty and danger appraisal of uncertainty in hospitalized children's mothers. Therefore, nursing interventions which improve mastery must be developed for hospitalized children's mothers.

Cocaine Dependence Treatment Data: Methods for Measurement Error Problems With Predictors Derived From Stationary Stochastic Processes

PubMed Central

Guan, Yongtao; Li, Yehua; Sinha, Rajita

2011-01-01

In a cocaine dependence treatment study, we use linear and nonlinear regression models to model posttreatment cocaine craving scores and first cocaine relapse time. A subset of the covariates are summary statistics derived from baseline daily cocaine use trajectories, such as baseline cocaine use frequency and average daily use amount. These summary statistics are subject to estimation error and can therefore cause biased estimators for the regression coefficients. Unlike classical measurement error problems, the error we encounter here is heteroscedastic with an unknown distribution, and there are no replicates for the error-prone variables or instrumental variables. We propose two robust methods to correct for the bias: a computationally efficient method-of-moments-based method for linear regression models and a subsampling extrapolation method that is generally applicable to both linear and nonlinear regression models. Simulations and an application to the cocaine dependence treatment data are used to illustrate the efficacy of the proposed methods. Asymptotic theory and variance estimation for the proposed subsampling extrapolation method and some additional simulation results are described in the online supplementary material. PMID:21984854

The Difference That One Year of Schooling Makes for Russian Schoolchildren. Based on PISA 2009: Reading

ERIC Educational Resources Information Center

Tiumeneva, Yu. A.; Kuzmina, Ju. V.

2015-01-01

The PISA 2009 data (in reading) investigated the effectiveness of one year of schooling in seven countries: Russia, Czech Republic, Hungary, Slovakia, Germany, Canada, and Brazil. We used an instrumental variable, which allowed us to estimate the effect of one year of schooling through the fuzzy method of regression discontinuity. The analysis was…

The Fixed-Effects Model in Returns to Schooling and Its Application to Community Colleges: A Methodological Note

ERIC Educational Resources Information Center

Dynarski, Susan; Jacob, Brian; Kreisman, Daniel

2016-01-01

The purpose of this note is to develop insight into the performance of the individual fixed-effects model when used to estimate wage returns to postsecondary schooling. We focus our attention on the returns to attending and completing community college. While other methods (instrumental variables, regression discontinuity) have been used to…

Combating Unmeasured Confounding in Cross-Sectional Studies: Evaluating Instrumental-Variable and Heckman Selection Models

PubMed Central

DeMaris, Alfred

2014-01-01

Unmeasured confounding is the principal threat to unbiased estimation of treatment “effects” (i.e., regression parameters for binary regressors) in nonexperimental research. It refers to unmeasured characteristics of individuals that lead them both to be in a particular “treatment” category and to register higher or lower values than others on a response variable. In this article, I introduce readers to 2 econometric techniques designed to control the problem, with a particular emphasis on the Heckman selection model (HSM). Both techniques can be used with only cross-sectional data. Using a Monte Carlo experiment, I compare the performance of instrumental-variable regression (IVR) and HSM to that of ordinary least squares (OLS) under conditions with treatment and unmeasured confounding both present and absent. I find HSM generally to outperform IVR with respect to mean-square-error of treatment estimates, as well as power for detecting either a treatment effect or unobserved confounding. However, both HSM and IVR require a large sample to be fully effective. The use of HSM and IVR in tandem with OLS to untangle unobserved confounding bias in cross-sectional data is further demonstrated with an empirical application. Using data from the 2006–2010 General Social Survey (National Opinion Research Center, 2014), I examine the association between being married and subjective well-being. PMID:25110904

The effects of competition on premiums: using United Healthcare's 2015 entry into Affordable Care Act's marketplaces as an instrumental variable.

PubMed

Agirdas, Cagdas; Krebs, Robert J; Yano, Masato

2018-01-08

One goal of the Affordable Care Act is to increase insurance coverage by improving competition and lowering premiums. To facilitate this goal, the federal government enacted online marketplaces in the 395 rating areas spanning 34 states that chose not to establish their own state-run marketplaces. Few multivariate regression studies analyzing the effects of competition on premiums suffer from endogeneity, due to simultaneity and omitted variable biases. However, United Healthcare's decision to enter these marketplaces in 2015 provides the researcher with an opportunity to address this endogeneity problem. Exploiting the variation caused by United Healthcare's entry decision as an instrument for competition, we study the impact of competition on premiums during the first 2 years of these marketplaces. Combining panel data from five different sources and controlling for 12 variables, we find that one more insurer in a rating area leads to a 6.97% reduction in the second-lowest-priced silver plan premium, which is larger than the estimated effects in existing literature. Furthermore, we run a threshold analysis and find that competition's effects on premiums become statistically insignificant if there are four or more insurers in a rating area. These findings are robust to alternative measures of premiums, inclusion of a non-linear term in the regression models and a county-level analysis.

Analysis of an Environmental Exposure Health Questionnaire in a Metropolitan Minority Population Utilizing Logistic Regression and Support Vector Machines

PubMed Central

Chen, Chau-Kuang; Bruce, Michelle; Tyler, Lauren; Brown, Claudine; Garrett, Angelica; Goggins, Susan; Lewis-Polite, Brandy; Weriwoh, Mirabel L; Juarez, Paul D.; Hood, Darryl B.; Skelton, Tyler

2014-01-01

The goal of this study was to analyze a 54-item instrument for assessment of perception of exposure to environmental contaminants within the context of the built environment, or exposome. This exposome was defined in five domains to include 1) home and hobby, 2) school, 3) community, 4) occupation, and 5) exposure history. Interviews were conducted with child-bearing-age minority women at Metro Nashville General Hospital at Meharry Medical College. Data were analyzed utilizing DTReg software for Support Vector Machine (SVM) modeling followed by an SPSS package for a logistic regression model. The target (outcome) variable of interest was respondent's residence by ZIP code. The results demonstrate that the rank order of important variables with respect to SVM modeling versus traditional logistic regression models is almost identical. This is the first study documenting that SVM analysis has discriminate power for determination of higher-ordered spatial relationships on an environmental exposure history questionnaire. PMID:23395953

Analysis of an environmental exposure health questionnaire in a metropolitan minority population utilizing logistic regression and Support Vector Machines.

PubMed

Chen, Chau-Kuang; Bruce, Michelle; Tyler, Lauren; Brown, Claudine; Garrett, Angelica; Goggins, Susan; Lewis-Polite, Brandy; Weriwoh, Mirabel L; Juarez, Paul D; Hood, Darryl B; Skelton, Tyler

2013-02-01

The goal of this study was to analyze a 54-item instrument for assessment of perception of exposure to environmental contaminants within the context of the built environment, or exposome. This exposome was defined in five domains to include 1) home and hobby, 2) school, 3) community, 4) occupation, and 5) exposure history. Interviews were conducted with child-bearing-age minority women at Metro Nashville General Hospital at Meharry Medical College. Data were analyzed utilizing DTReg software for Support Vector Machine (SVM) modeling followed by an SPSS package for a logistic regression model. The target (outcome) variable of interest was respondent's residence by ZIP code. The results demonstrate that the rank order of important variables with respect to SVM modeling versus traditional logistic regression models is almost identical. This is the first study documenting that SVM analysis has discriminate power for determination of higher-ordered spatial relationships on an environmental exposure history questionnaire.

A critical re-evaluation of the regression model specification in the US D1 EQ-5D value function

PubMed Central

2012-01-01

Background The EQ-5D is a generic health-related quality of life instrument (five dimensions with three levels, 243 health states), used extensively in cost-utility/cost-effectiveness analyses. EQ-5D health states are assigned values on a scale anchored in perfect health (1) and death (0). The dominant procedure for defining values for EQ-5D health states involves regression modeling. These regression models have typically included a constant term, interpreted as the utility loss associated with any movement away from perfect health. The authors of the United States EQ-5D valuation study replaced this constant with a variable, D1, which corresponds to the number of impaired dimensions beyond the first. The aim of this study was to illustrate how the use of the D1 variable in place of a constant is problematic. Methods We compared the original D1 regression model with a mathematically equivalent model with a constant term. Comparisons included implications for the magnitude and statistical significance of the coefficients, multicollinearity (variance inflation factors, or VIFs), number of calculation steps needed to determine tariff values, and consequences for tariff interpretation. Results Using the D1 variable in place of a constant shifted all dummy variable coefficients away from zero by the value of the constant, greatly increased the multicollinearity of the model (maximum VIF of 113.2 vs. 21.2), and increased the mean number of calculation steps required to determine health state values. Discussion Using the D1 variable in place of a constant constitutes an unnecessary complication of the model, obscures the fact that at least two of the main effect dummy variables are statistically nonsignificant, and complicates and biases interpretation of the tariff algorithm. PMID:22244261

A critical re-evaluation of the regression model specification in the US D1 EQ-5D value function.

PubMed

Rand-Hendriksen, Kim; Augestad, Liv A; Dahl, Fredrik A

2012-01-13

The EQ-5D is a generic health-related quality of life instrument (five dimensions with three levels, 243 health states), used extensively in cost-utility/cost-effectiveness analyses. EQ-5D health states are assigned values on a scale anchored in perfect health (1) and death (0).The dominant procedure for defining values for EQ-5D health states involves regression modeling. These regression models have typically included a constant term, interpreted as the utility loss associated with any movement away from perfect health. The authors of the United States EQ-5D valuation study replaced this constant with a variable, D1, which corresponds to the number of impaired dimensions beyond the first. The aim of this study was to illustrate how the use of the D1 variable in place of a constant is problematic. We compared the original D1 regression model with a mathematically equivalent model with a constant term. Comparisons included implications for the magnitude and statistical significance of the coefficients, multicollinearity (variance inflation factors, or VIFs), number of calculation steps needed to determine tariff values, and consequences for tariff interpretation. Using the D1 variable in place of a constant shifted all dummy variable coefficients away from zero by the value of the constant, greatly increased the multicollinearity of the model (maximum VIF of 113.2 vs. 21.2), and increased the mean number of calculation steps required to determine health state values. Using the D1 variable in place of a constant constitutes an unnecessary complication of the model, obscures the fact that at least two of the main effect dummy variables are statistically nonsignificant, and complicates and biases interpretation of the tariff algorithm.

Calibration of the dietary data obtained from the Brazilian center of the Natural History of HPV Infection in Men study: the HIM Study.

PubMed

Teixeira, Juliana Araujo; Baggio, Maria Luiza; Fisberg, Regina Mara; Marchioni, Dirce Maria Lobo

2010-12-01

The objective of this study was to estimate the regressions calibration for the dietary data that were measured using the quantitative food frequency questionnaire (QFFQ) in the Natural History of HPV Infection in Men: the HIM Study in Brazil. A sample of 98 individuals from the HIM study answered one QFFQ and three 24-hour recalls (24HR) at interviews. The calibration was performed using linear regression analysis in which the 24HR was the dependent variable and the QFFQ was the independent variable. Age, body mass index, physical activity, income and schooling were used as adjustment variables in the models. The geometric means between the 24HR and the calibration-corrected QFFQ were statistically equal. The dispersion graphs between the instruments demonstrate increased correlation after making the correction, although there is greater dispersion of the points with worse explanatory power of the models. Identification of the regressions calibration for the dietary data of the HIM study will make it possible to estimate the effect of the diet on HPV infection, corrected for the measurement error of the QFFQ.

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

PubMed

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

2014-01-01

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

Poverty and Child Development: A Longitudinal Study of the Impact of the Earned Income Tax Credit

PubMed Central

Hamad, Rita; Rehkopf, David H.

2016-01-01

Although adverse socioeconomic conditions are correlated with worse child health and development, the effects of poverty-alleviation policies are less understood. We examined the associations of the Earned Income Tax Credit (EITC) on child development and used an instrumental variable approach to estimate the potential impacts of income. We used data from the US National Longitudinal Survey of Youth (n = 8,186) during 1986–2000 to examine effects on the Behavioral Problems Index (BPI) and Home Observation Measurement of the Environment inventory (HOME) scores. We conducted 2 analyses. In the first, we used multivariate linear regressions with child-level fixed effects to examine the association of EITC payment size with BPI and HOME scores; in the second, we used EITC payment size as an instrument to estimate the associations of income with BPI and HOME scores. In linear regression models, higher EITC payments were associated with improved short-term BPI scores (per $1,000, β = −0.57; P = 0.04). In instrumental variable analyses, higher income was associated with improved short-term BPI scores (per $1,000, β = −0.47; P = 0.01) and medium-term HOME scores (per $1,000, β = 0.64; P = 0.02). Our results suggest that both EITC benefits and higher income are associated with modest but meaningful improvements in child development. These findings provide valuable information for health researchers and policymakers for improving child health and development. PMID:27056961

Maternal overprotection score of the Parental Bonding Instrument predicts the outcome of cognitive behavior therapy by trainees for depression.

PubMed

Asano, Motoshi; Esaki, Kosei; Wakamatsu, Aya; Kitajima, Tomoko; Narita, Tomohiro; Naitoh, Hiroshi; Ozaki, Norio; Iwata, Nakao

2013-07-01

The purpose of this study was to predict the outcome of cognitive behavior therapy (CBT) by trainees for major depressive disorder (MDD) based on the Parental Bonding Instrument (PBI). The hypothesis was that the higher level of care and/or lower level of overprotection score would predict a favorable outcome of CBT by trainees. The subjects were all outpatients with MDD treated with CBT as a training case. All the subjects were asked to fill out the Japanese version of the PBI before commencing the course of psychotherapy. The difference between the first and the last Beck Depression Inventory (BDI) score was used to represent the improvement of the intensity of depression by CBT. In order to predict improvement (the difference of the BDI scores) as the objective variable, multiple regression analysis was performed using maternal overprotection score and baseline BDI score as the explanatory variables. The multiple regression model was significant (P = 0.0026) and partial regression coefficient for the maternal overprotection score and the baseline BDI was -0.73 (P = 0.0046) and 0.88 (P = 0.0092), respectively. Therefore, when a patient's maternal overprotection score of the PBI was lower, a better outcome of CBT was expected. The hypothesis was partially supported. This result would be useful in determining indications for CBT by trainees for patients with MDD. © 2013 The Authors. Psychiatry and Clinical Neurosciences © 2013 Japanese Society of Psychiatry and Neurology.

Influence of therapist competence and quantity of cognitive behavioural therapy on suicidal behaviour and inpatient hospitalisation in a randomised controlled trial in borderline personality disorder: further analyses of treatment effects in the BOSCOT study.

PubMed

Norrie, John; Davidson, Kate; Tata, Philip; Gumley, Andrew

2013-09-01

We investigated the treatment effects reported from a high-quality randomized controlled trial of cognitive behavioural therapy (CBT) for 106 people with borderline personality disorder attending community-based clinics in the UK National Health Service - the BOSCOT trial. Specifically, we examined whether the amount of therapy and therapist competence had an impact on our primary outcome, the number of suicidal acts, using instrumental variables regression modelling. Randomized controlled trial. Participants from across three sites (London, Glasgow, and Ayrshire/Arran) were randomized equally to CBT for personality disorders (CBTpd) plus Treatment as Usual or to Treatment as Usual. Treatment as Usual varied between sites and individuals, but was consistent with routine treatment in the UK National Health Service at the time. CBTpd comprised an average 16 sessions (range 0-35) over 12 months. We used instrumental variable regression modelling to estimate the impact of quantity and quality of therapy received (recording activities and behaviours that took place after randomization) on number of suicidal acts and inpatient psychiatric hospitalization. A total of 101 participants provided full outcome data at 2 years post randomization. The previously reported intention-to-treat (ITT) results showed on average a reduction of 0.91 (95% confidence interval 0.15-1.67) suicidal acts over 2 years for those randomized to CBT. By incorporating the influence of quantity of therapy and therapist competence, we show that this estimate of the effect of CBTpd could be approximately two to three times greater for those receiving the right amount of therapy from a competent therapist. Trials should routinely control for and collect data on both quantity of therapy and therapist competence, which can be used, via instrumental variable regression modelling, to estimate treatment effects for optimal delivery of therapy. Such estimates complement rather than replace the ITT results, which are properly the principal analysis results from such trials. © 2013 The British Psychological Society.

The Effect of Birth Weight on Academic Performance: Instrumental Variable Analysis.

PubMed

Lin, Shi Lin; Leung, Gabriel Matthew; Schooling, C Mary

2017-05-01

Observationally, lower birth weight is usually associated with poorer academic performance; whether this association is causal or the result of confounding is unknown. To investigate this question, we obtained an effect estimate, which can have a causal interpretation under specific assumptions, of birth weight on educational attainment using instrumental variable analysis based on single nucleotide polymorphisms determining birth weight combined with results from the Social Science Genetic Association Consortium study of 126,559 Caucasians. We similarly obtained an estimate of the effect of birth weight on academic performance in 4,067 adolescents from Hong Kong's (Chinese) Children of 1997 birth cohort (1997-2016), using twin status as an instrumental variable. Birth weight was not associated with years of schooling (per 100-g increase in birth weight, -0.006 years, 95% confidence interval (CI): -0.02, 0.01) or college completion (odds ratio = 1.00, 95% CI: 0.96, 1.03). Birth weight was also unrelated to academic performance in adolescents (per 100-g increase in birth weight, -0.004 grade, 95% CI: -0.04, 0.04) using instrumental variable analysis, although conventional regression gave a small positive association (0.02 higher grade, 95% CI: 0.01, 0.03). Observed associations of birth weight with academic performance may not be causal, suggesting that interventions should focus on the contextual factors generating this correlation. © The Author 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Resource utilization in home health care: results of a prospective study.

PubMed

Trisolini, M G; Thomas, C P; Cashman, S B; Payne, S M

1994-01-01

Resource utilization in home health care has become an issue of concern due to rising costs and recent initiatives to develop prospective payment systems for home health care. A number of issues remain unresolved for the development of prospective reimbursement in this sector, including the types of variables to be included as payment variables and appropriate measures of resource use. This study supplements previous work on home health case-mix by analyzing the factors affecting one aspect of resource use for skilled nursing visits--visit length--and explores the usefulness of several specially collected variables which are not routinely available in administrative records. A data collection instrument was developed with a focus group of skilled nurses, identifying a range of variables hypothesized to affect visit length. Five categories of variables were studied using multiple regression analysis: provider-related; patient's socio-economic status; patient's clinical status; patient's support services; and visit-specific. The final regression model identifies 9 variables which significantly affect visit time. Five of the 9 are visit-specific variables, a significant finding since these are not routinely collected. Case-mix systems which include visit time as a measure of resource use will need to investigate visit-specific variables, as this study indicates they could have the largest influence on visit time. Two other types of resources used in home health care, supplies and security drivers, were also investigated in less detail.

[Income inequality, corruption, and life expectancy at birth in Mexico].

PubMed

Idrovo, Alvaro Javier

2005-01-01

To ascertain if the effect of income inequality on life expectancy at birth in Mexico is mediated by corruption, used as a proxy of social capital. An ecological study was carried out with the 32 Mexican federative entities. Global and by sex correlations between life expectancy at birth were estimated by federative entity with the Gini coefficient, the Corruption and Good Government Index, the percentage of Catholics, and the percentage of the population speaking indigenous language. Robust linear regressions, with and without instrumental variables, were used to explore if corruption acts as intermediate variable in the studied relationship. Negative correlations with Spearman's rho near to -0.60 (p < 0.05) and greater than -0.66 (p < 0.05) between life expectancy at birth, the Gini coefficient and the population speaking indigenous language, respectively, were observed. Moreover, the Corruption and Good Government Index correlated with men's life expectancy at birth with Spearman's rho -0.3592 (p < 0.05). Regressions with instruments were more consistent than conventional ones and they show a strong negative effect (p < 0.05) of income inequality on life expectancy at birth. This effect was greater among men. The findings suggest a negative effect of income inequality on life expectancy at birth in Mexico, mediated by corruption levels and other related cultural factors.

Analyzing the impact of public transit usage on obesity.

PubMed

She, Zhaowei; King, Douglas M; Jacobson, Sheldon H

2017-06-01

The objective of this paper is to estimate the impact of county-level public transit usage on obesity prevalence in the United States and assess the potential for public transit usage as an intervention for obesity. This study adopts an instrumental regression approach to implicitly control for potential selection bias due to possible differences in commuting preferences among obese and non-obese populations. United States health data from the 2009 Behavioral Risk Factor Surveillance System and transportation data from the 2009 National Household Travel Survey are aggregated and matched at the county level. County-level public transit accessibility and vehicle ownership rates are chosen as instrumental variables to implicitly control for unobservable commuting preferences. The results of this instrumental regression analysis suggest that a one percent increase in county population usage of public transit is associated with a 0.221 percent decrease in county population obesity prevalence at the α=0.01 statistical significance level, when commuting preferences, amount of non-travel physical activity, education level, health resource, and distribution of income are fixed. Hence, this study provides empirical support for the effectiveness of encouraging public transit usage as an intervention strategy for obesity. Copyright © 2017 Elsevier Inc. All rights reserved.

Factors associated with the health status of internally displaced persons in northern Uganda

PubMed Central

Roberts, B; Ocaka, K Felix; Browne, J; Oyok, T; Sondorp, E

2009-01-01

Background: Globally, there are over 24 million internally displaced persons (IDPs) who have fled their homes due to violence and insecurity but who remain within their own country. There have been up to 2 million IDPs in northern Uganda alone. The objective of this study was to investigate factors associated with mental and physical health status of IDPs in northern Uganda. Methods: A cross-sectional survey was conducted in November 2006 in IDP camps in the Gulu and Amuru districts of northern Uganda. The study outcome of physical and mental health was measured using the SF-8 instrument, which produces physical (PCS) and mental (MCS) component summary measures. Independent demographic, socio-economic, and trauma exposure (using the Harvard Trauma Questionnaire) variables were also measured. Multivariate regression linear regression analysis was conducted to investigate associations of the independent variables on the PCS and MCS outcomes. Results: 1206 interviews were completed. The respective mean PCS and MCS scores were 42.2 (95% CI 41.32 to 43.10) and 39.3 (95% CI 38.42 to 40.13), well below the instrument norm of 50, indicating poor health. Variables with negative associations with physical or mental health included gender, age, marital status, income, distance of camp from home areas, food security, soap availability, and sense of safety in the camp. A number of individual trauma variables and the frequency of trauma exposure also had negative associations with physical and mental health. Conclusions: This study provides evidence on the impact on health of deprivation of basic goods and services, traumatic events, and fear and uncertainty amongst displaced and crisis affected populations. PMID:19028730

An Exploratory Study of the Effects of Work Environment Variables on Job Satisfaction Among Chinese Prison Staff.

PubMed

Jiang, Shanhe; Lambert, Eric G; Liu, Jianhong; Zhang, Jinwu

2018-05-01

Job satisfaction has been linked to many positive outcomes, such as greater work performance, increased organizational commitment, reduced job burnout, decreased absenteeism, and lower turnover intent/turnover. A substantial body of research has examined how work environment variables are linked to job satisfaction among U.S. correctional staff; far less research has examined prison staff in non-Western nations, especially China. Using survey data collected from two prisons in Guangzhou, China, this study investigated the level of job satisfaction among prison staff and how personal characteristics (i.e., gender, tenure, age, and educational level) and work environment variables (i.e., perceived dangerousness of the job, job variety, supervision, instrumental communication, and input into decision making) affect job satisfaction. The findings from ordinary least squares regression equations indicated that the work environment variables explained a greater proportion of the variance in the job satisfaction measure than the personal characteristics. In the full multivariate regression model, gender was the only personal characteristic to have a significant association with job satisfaction, with female staff reporting higher satisfaction. Input into decision making and job variety had significant positive associations, whereas dangerousness had a significant negative relationship with job satisfaction.

Approximate Dynamic Programming Algorithms for United States Air Force Officer Sustainment

DTIC Science & Technology

2015-03-26

level of correction needed. While paying bonuses has an easily calculable cost, RIFs have more subtle costs. Mone (1994) discovered that in a steady...a regression is performed utilizing instrumental variables to minimize Bellman error. This algorithm uses a set of basis functions to approximate the...transitioned to an all-volunteer force. Charnes et al. (1972) utilize a goal programming model for General Schedule civilian manpower management in the

Comparison of partial least squares and lasso regression techniques as applied to laser-induced breakdown spectroscopy of geological samples

NASA Astrophysics Data System (ADS)

Dyar, M. D.; Carmosino, M. L.; Breves, E. A.; Ozanne, M. V.; Clegg, S. M.; Wiens, R. C.

2012-04-01

A remote laser-induced breakdown spectrometer (LIBS) designed to simulate the ChemCam instrument on the Mars Science Laboratory Rover Curiosity was used to probe 100 geologic samples at a 9-m standoff distance. ChemCam consists of an integrated remote LIBS instrument that will probe samples up to 7 m from the mast of the rover and a remote micro-imager (RMI) that will record context images. The elemental compositions of 100 igneous and highly-metamorphosed rocks are determined with LIBS using three variations of multivariate analysis, with a goal of improving the analytical accuracy. Two forms of partial least squares (PLS) regression are employed with finely-tuned parameters: PLS-1 regresses a single response variable (elemental concentration) against the observation variables (spectra, or intensity at each of 6144 spectrometer channels), while PLS-2 simultaneously regresses multiple response variables (concentrations of the ten major elements in rocks) against the observation predictor variables, taking advantage of natural correlations between elements. Those results are contrasted with those from the multivariate regression technique of the least absolute shrinkage and selection operator (lasso), which is a penalized shrunken regression method that selects the specific channels for each element that explain the most variance in the concentration of that element. To make this comparison, we use results of cross-validation and of held-out testing, and employ unscaled and uncentered spectral intensity data because all of the input variables are already in the same units. Results demonstrate that the lasso, PLS-1, and PLS-2 all yield comparable results in terms of accuracy for this dataset. However, the interpretability of these methods differs greatly in terms of fundamental understanding of LIBS emissions. PLS techniques generate principal components, linear combinations of intensities at any number of spectrometer channels, which explain as much variance in the response variables as possible while avoiding multicollinearity between principal components. When the selected number of principal components is projected back into the original feature space of the spectra, 6144 correlation coefficients are generated, a small fraction of which are mathematically significant to the regression. In contrast, the lasso models require only a small number (< 24) of non-zero correlation coefficients (β values) to determine the concentration of each of the ten major elements. Causality between the positively-correlated emission lines chosen by the lasso and the elemental concentration was examined. In general, the higher the lasso coefficient (β), the greater the likelihood that the selected line results from an emission of that element. Emission lines with negative β values should arise from elements that are anti-correlated with the element being predicted. For elements except Fe, Al, Ti, and P, the lasso-selected wavelength with the highest β value corresponds to the element being predicted, e.g. 559.8 nm for neutral Ca. However, the specific lines chosen by the lasso with positive β values are not always those from the element being predicted. Other wavelengths and the elements that most strongly correlate with them to predict concentration are obviously related to known geochemical correlations or close overlap of emission lines, while others must result from matrix effects. Use of the lasso technique thus directly informs our understanding of the underlying physical processes that give rise to LIBS emissions by determining which lines can best represent concentration, and which lines from other elements are causing matrix effects.

Individual Differences in Toddlers' Prosociality: Experiences in Early Relationships Explain Variability in Prosocial Behavior.

PubMed

Newton, Emily K; Thompson, Ross A; Goodman, Miranda

2016-11-01

Latent class logistic regression analysis was used to investigate sources of individual differences in profiles of prosocial behavior. Eighty-seven 18-month-olds were observed in tasks assessing sharing with a neutral adult, instrumentally helping a neutral adult, and instrumentally helping a sad adult. Maternal mental state language (MSL) and maternal sensitivity were also assessed. Despite differing motivational demands across tasks, we found consistency in children's prosocial behavior with three latent classes: no prosocial behavior, moderate prosocial behavior, and frequent instrumental helping across emotional situations. Maternal sensitivity, MSL, and their interaction predicted toddlers' membership in the classes. These findings evidence moderate consistency in early prosocial behaviors and suggest that these capacities are motivated in early relationships with caregivers. © 2016 The Authors. Child Development © 2016 Society for Research in Child Development, Inc.

Association of Body Mass Index with Depression, Anxiety and Suicide—An Instrumental Variable Analysis of the HUNT Study

PubMed Central

Bjørngaard, Johan Håkon; Carslake, David; Lund Nilsen, Tom Ivar; Linthorst, Astrid C. E.; Davey Smith, George; Gunnell, David; Romundstad, Pål Richard

2015-01-01

Objective While high body mass index is associated with an increased risk of depression and anxiety, cumulative evidence indicates that it is a protective factor for suicide. The associations from conventional observational studies of body mass index with mental health outcomes are likely to be influenced by reverse causality or confounding by ill-health. In the present study, we investigated the associations between offspring body mass index and parental anxiety, depression and suicide in order to avoid problems with reverse causality and confounding by ill-health. Methods We used data from 32,457 mother-offspring and 27,753 father-offspring pairs from the Norwegian HUNT-study. Anxiety and depression were assessed using the Hospital Anxiety and Depression Scale and suicide death from national registers. Associations between offspring and own body mass index and symptoms of anxiety and depression and suicide mortality were estimated using logistic and Cox regression. Causal effect estimates were estimated with a two sample instrument variable approach using offspring body mass index as an instrument for parental body mass index. Results Both own and offspring body mass index were positively associated with depression, while the results did not indicate any substantial association between body mass index and anxiety. Although precision was low, suicide mortality was inversely associated with own body mass index and the results from the analysis using offspring body mass index supported these results. Adjusted odds ratios per standard deviation body mass index from the instrumental variable analysis were 1.22 (95% CI: 1.05, 1.43) for depression, 1.10 (95% CI: 0.95, 1.27) for anxiety, and the instrumental variable estimated hazard ratios for suicide was 0.69 (95% CI: 0.30, 1.63). Conclusion The present study’s results indicate that suicide mortality is inversely associated with body mass index. We also found support for a positive association between body mass index and depression, but not for anxiety. PMID:26167892

Association of Body Mass Index with Depression, Anxiety and Suicide-An Instrumental Variable Analysis of the HUNT Study.

PubMed

Bjørngaard, Johan Håkon; Carslake, David; Lund Nilsen, Tom Ivar; Linthorst, Astrid C E; Davey Smith, George; Gunnell, David; Romundstad, Pål Richard

2015-01-01

While high body mass index is associated with an increased risk of depression and anxiety, cumulative evidence indicates that it is a protective factor for suicide. The associations from conventional observational studies of body mass index with mental health outcomes are likely to be influenced by reverse causality or confounding by ill-health. In the present study, we investigated the associations between offspring body mass index and parental anxiety, depression and suicide in order to avoid problems with reverse causality and confounding by ill-health. We used data from 32,457 mother-offspring and 27,753 father-offspring pairs from the Norwegian HUNT-study. Anxiety and depression were assessed using the Hospital Anxiety and Depression Scale and suicide death from national registers. Associations between offspring and own body mass index and symptoms of anxiety and depression and suicide mortality were estimated using logistic and Cox regression. Causal effect estimates were estimated with a two sample instrument variable approach using offspring body mass index as an instrument for parental body mass index. Both own and offspring body mass index were positively associated with depression, while the results did not indicate any substantial association between body mass index and anxiety. Although precision was low, suicide mortality was inversely associated with own body mass index and the results from the analysis using offspring body mass index supported these results. Adjusted odds ratios per standard deviation body mass index from the instrumental variable analysis were 1.22 (95% CI: 1.05, 1.43) for depression, 1.10 (95% CI: 0.95, 1.27) for anxiety, and the instrumental variable estimated hazard ratios for suicide was 0.69 (95% CI: 0.30, 1.63). The present study's results indicate that suicide mortality is inversely associated with body mass index. We also found support for a positive association between body mass index and depression, but not for anxiety.

Influence of therapist competence and quantity of cognitive behavioural therapy on suicidal behaviour and inpatient hospitalisation in a randomised controlled trial in borderline personality disorder: Further analyses of treatment effects in the BOSCOT study

PubMed Central

Norrie, John; Davidson, Kate; Tata, Philip; Gumley, Andrew

2013-01-01

Objectives We investigated the treatment effects reported from a high-quality randomized controlled trial of cognitive behavioural therapy (CBT) for 106 people with borderline personality disorder attending community-based clinics in the UK National Health Service – the BOSCOT trial. Specifically, we examined whether the amount of therapy and therapist competence had an impact on our primary outcome, the number of suicidal acts†, using instrumental variables regression modelling. Design Randomized controlled trial. Participants from across three sites (London, Glasgow, and Ayrshire/Arran) were randomized equally to CBT for personality disorders (CBTpd) plus Treatment as Usual or to Treatment as Usual. Treatment as Usual varied between sites and individuals, but was consistent with routine treatment in the UK National Health Service at the time. CBTpd comprised an average 16 sessions (range 0–35) over 12 months. Method We used instrumental variable regression modelling to estimate the impact of quantity and quality of therapy received (recording activities and behaviours that took place after randomization) on number of suicidal acts and inpatient psychiatric hospitalization. Results A total of 101 participants provided full outcome data at 2 years post randomization. The previously reported intention-to-treat (ITT) results showed on average a reduction of 0.91 (95% confidence interval 0.15–1.67) suicidal acts over 2 years for those randomized to CBT. By incorporating the influence of quantity of therapy and therapist competence, we show that this estimate of the effect of CBTpd could be approximately two to three times greater for those receiving the right amount of therapy from a competent therapist. Conclusions Trials should routinely control for and collect data on both quantity of therapy and therapist competence, which can be used, via instrumental variable regression modelling, to estimate treatment effects for optimal delivery of therapy. Such estimates complement rather than replace the ITT results, which are properly the principal analysis results from such trials. Practitioner points Assessing the impact of the quantity and quality of therapy (competence of therapists) is complex. More competent therapists, trained in CBTpd, may significantly reduce the number of suicidal act in patients with borderline personality disorder. PMID:23420622

Measuring Networking as an Outcome Variable in Undergraduate Research Experiences

PubMed Central

Hanauer, David I.; Hatfull, Graham

2015-01-01

The aim of this paper is to propose, present, and validate a simple survey instrument to measure student conversational networking. The tool consists of five items that cover personal and professional social networks, and its basic principle is the self-reporting of degrees of conversation, with a range of specific discussion partners. The networking instrument was validated in three studies. The basic psychometric characteristics of the scales were established by conducting a factor analysis and evaluating internal consistency using Cronbach’s alpha. The second study used a known-groups comparison and involved comparing outcomes for networking scales between two different undergraduate laboratory courses (one involving a specific effort to enhance networking). The final study looked at potential relationships between specific networking items and the established psychosocial variable of project ownership through a series of binary logistic regressions. Overall, the data from the three studies indicate that the networking scales have high internal consistency (α = 0.88), consist of a unitary dimension, can significantly differentiate between research experiences with low and high networking designs, and are related to project ownership scales. The ramifications of the networking instrument for student retention, the enhancement of public scientific literacy, and the differentiation of laboratory courses are discussed. PMID:26538387

Characterizing error distributions for MISR and MODIS optical depth data

NASA Astrophysics Data System (ADS)

Paradise, S.; Braverman, A.; Kahn, R.; Wilson, B.

2008-12-01

The Multi-angle Imaging SpectroRadiometer (MISR) and Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA's EOS satellites collect massive, long term data records on aerosol amounts and particle properties. MISR and MODIS have different but complementary sampling characteristics. In order to realize maximum scientific benefit from these data, the nature of their error distributions must be quantified and understood so that discrepancies between them can be rectified and their information combined in the most beneficial way. By 'error' we mean all sources of discrepancies between the true value of the quantity of interest and the measured value, including instrument measurement errors, artifacts of retrieval algorithms, and differential spatial and temporal sampling characteristics. Previously in [Paradise et al., Fall AGU 2007: A12A-05] we presented a unified, global analysis and comparison of MISR and MODIS measurement biases and variances over lives of the missions. We used AErosol RObotic NETwork (AERONET) data as ground truth and evaluated MISR and MODIS optical depth distributions relative to AERONET using simple linear regression. However, AERONET data are themselves instrumental measurements subject to sources of uncertainty. In this talk, we discuss results from an improved analysis of MISR and MODIS error distributions that uses errors-in-variables regression, accounting for uncertainties in both the dependent and independent variables. We demonstrate on optical depth data, but the method is generally applicable to other aerosol properties as well.

Variable selection in near-infrared spectroscopy: benchmarking of feature selection methods on biodiesel data.

PubMed

Balabin, Roman M; Smirnov, Sergey V

2011-04-29

During the past several years, near-infrared (near-IR/NIR) spectroscopy has increasingly been adopted as an analytical tool in various fields from petroleum to biomedical sectors. The NIR spectrum (above 4000 cm(-1)) of a sample is typically measured by modern instruments at a few hundred of wavelengths. Recently, considerable effort has been directed towards developing procedures to identify variables (wavelengths) that contribute useful information. Variable selection (VS) or feature selection, also called frequency selection or wavelength selection, is a critical step in data analysis for vibrational spectroscopy (infrared, Raman, or NIRS). In this paper, we compare the performance of 16 different feature selection methods for the prediction of properties of biodiesel fuel, including density, viscosity, methanol content, and water concentration. The feature selection algorithms tested include stepwise multiple linear regression (MLR-step), interval partial least squares regression (iPLS), backward iPLS (BiPLS), forward iPLS (FiPLS), moving window partial least squares regression (MWPLS), (modified) changeable size moving window partial least squares (CSMWPLS/MCSMWPLSR), searching combination moving window partial least squares (SCMWPLS), successive projections algorithm (SPA), uninformative variable elimination (UVE, including UVE-SPA), simulated annealing (SA), back-propagation artificial neural networks (BP-ANN), Kohonen artificial neural network (K-ANN), and genetic algorithms (GAs, including GA-iPLS). Two linear techniques for calibration model building, namely multiple linear regression (MLR) and partial least squares regression/projection to latent structures (PLS/PLSR), are used for the evaluation of biofuel properties. A comparison with a non-linear calibration model, artificial neural networks (ANN-MLP), is also provided. Discussion of gasoline, ethanol-gasoline (bioethanol), and diesel fuel data is presented. The results of other spectroscopic techniques application, such as Raman, ultraviolet-visible (UV-vis), or nuclear magnetic resonance (NMR) spectroscopies, can be greatly improved by an appropriate feature selection choice. Copyright © 2011 Elsevier B.V. All rights reserved.

Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption.

PubMed

Hartwig, Fernando Pires; Davey Smith, George; Bowden, Jack

2017-12-01

Mendelian randomization (MR) is being increasingly used to strengthen causal inference in observational studies. Availability of summary data of genetic associations for a variety of phenotypes from large genome-wide association studies (GWAS) allows straightforward application of MR using summary data methods, typically in a two-sample design. In addition to the conventional inverse variance weighting (IVW) method, recently developed summary data MR methods, such as the MR-Egger and weighted median approaches, allow a relaxation of the instrumental variable assumptions. Here, a new method - the mode-based estimate (MBE) - is proposed to obtain a single causal effect estimate from multiple genetic instruments. The MBE is consistent when the largest number of similar (identical in infinite samples) individual-instrument causal effect estimates comes from valid instruments, even if the majority of instruments are invalid. We evaluate the performance of the method in simulations designed to mimic the two-sample summary data setting, and demonstrate its use by investigating the causal effect of plasma lipid fractions and urate levels on coronary heart disease risk. The MBE presented less bias and lower type-I error rates than other methods under the null in many situations. Its power to detect a causal effect was smaller compared with the IVW and weighted median methods, but was larger than that of MR-Egger regression, with sample size requirements typically smaller than those available from GWAS consortia. The MBE relaxes the instrumental variable assumptions, and should be used in combination with other approaches in sensitivity analyses. © The Author 2017. Published by Oxford University Press on behalf of the International Epidemiological Association

The impact of fashion competence and achievement motivation toward college student’s working readiness on “Cipta Karya” subject

NASA Astrophysics Data System (ADS)

Marniati; Wibawa, S. C.

2018-01-01

This experiment aimed to know the rate of college student’s working readiness of fashion’s program study to perform ‘Cipta Karya’ related to cognitive readiness, manner readiness and skill readiness from a variable of fashion’s workmanship and achievement motivation. The subject of the experiment was 43 college students who took Cipta Karya subject. Method of collecting data used questionnaire with five alternative answers to Likert ratio model. Data analysis technique used path analysis (double regression). The instrument validity test used product moment correlation while for instrument reliability used Alpha Cronbach’s grade. The results showed (1) fashion competence was taking effect significantly on working readiness for ‘Cipta Karya’ (2) achievement motivation is taking effect significantly on working readiness for ‘cipta karya’ (3) both variables are positive. This means that fashion competence and achievement motivation have a positive effect on working readiness for ‘cipta karya’ performance.

Parental representations in drug-dependent patients and their parents.

PubMed

Torresani, S; Favaretto, E; Zimmermann, C

2000-01-01

The Parental Bonding Instrument (PBI), a measure of perceived parental care and protection, was administered to drug-dependent patients and their parents with the aim to assess the reliability of the instrument in such samples and to compare the parental representations across generations. Ninety drug-dependent patients and 44 mothers and 35 fathers participated. Reliability indices were calculated, and parental representations of parents and their offspring were compared. Linear regression analyses were performed with the patient's PBI score as the dependent variable and the mother's and father's PBI scores as predictor variables. The reliability indices were highly satisfactory and varied between 0.61 and 0.91. The parental bonding of patients, fathers, and mothers was similar. All three groups reported high maternal and paternal control and low maternal care, a pattern characteristic of an "affectionless control" rearing style. Maternal care received by the fathers and paternal protection received by the mothers predicted the care and protection they themselves gave to their drug-dependent offspring.

Poverty and Child Development: A Longitudinal Study of the Impact of the Earned Income Tax Credit.

PubMed

Hamad, Rita; Rehkopf, David H

2016-05-01

Although adverse socioeconomic conditions are correlated with worse child health and development, the effects of poverty-alleviation policies are less understood. We examined the associations of the Earned Income Tax Credit (EITC) on child development and used an instrumental variable approach to estimate the potential impacts of income. We used data from the US National Longitudinal Survey of Youth (n = 8,186) during 1986-2000 to examine effects on the Behavioral Problems Index (BPI) and Home Observation Measurement of the Environment inventory (HOME) scores. We conducted 2 analyses. In the first, we used multivariate linear regressions with child-level fixed effects to examine the association of EITC payment size with BPI and HOME scores; in the second, we used EITC payment size as an instrument to estimate the associations of income with BPI and HOME scores. In linear regression models, higher EITC payments were associated with improved short-term BPI scores (per $1,000, β = -0.57; P = 0.04). In instrumental variable analyses, higher income was associated with improved short-term BPI scores (per $1,000, β = -0.47; P = 0.01) and medium-term HOME scores (per $1,000, β = 0.64; P = 0.02). Our results suggest that both EITC benefits and higher income are associated with modest but meaningful improvements in child development. These findings provide valuable information for health researchers and policymakers for improving child health and development. © The Author 2016. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Too much ado about instrumental variable approach: is the cure worse than the disease?

PubMed

Baser, Onur

2009-01-01

To review the efficacy of instrumental variable (IV) models in addressing a variety of assumption violations to ensure standard ordinary least squares (OLS) estimates are consistent. IV models gained popularity in outcomes research because of their ability to consistently estimate the average causal effects even in the presence of unmeasured confounding. However, in order for this consistent estimation to be achieved, several conditions must hold. In this article, we provide an overview of the IV approach, examine possible tests to check the prerequisite conditions, and illustrate how weak instruments may produce inconsistent and inefficient results. We use two IVs and apply Shea's partial R-square method, the Anderson canonical correlation, and Cragg-Donald tests to check for weak instruments. Hall-Peixe tests are applied to see if any of these instruments are redundant in the analysis. A total of 14,952 asthma patients from the MarketScan Commercial Claims and Encounters Database were examined in this study. Patient health care was provided under a variety of fee-for-service, fully capitated, and partially capitated health plans, including preferred provider organizations, point of service plans, indemnity plans, and health maintenance organizations. We used controller-reliever copay ratio and physician practice/prescribing patterns as an instrument. We demonstrated that the former was a weak and redundant instrument producing inconsistent and inefficient estimates of the effect of treatment. The results were worse than the results from standard regression analysis. Despite the obvious benefit of IV models, the method should not be used blindly. Several strong conditions are required for these models to work, and each of them should be tested. Otherwise, bias and precision of the results will be statistically worse than the results achieved by simply using standard OLS.

Incorporating Measurement Error from Modeled Air Pollution Exposures into Epidemiological Analyses.

PubMed

Samoli, Evangelia; Butland, Barbara K

2017-12-01

Outdoor air pollution exposures used in epidemiological studies are commonly predicted from spatiotemporal models incorporating limited measurements, temporal factors, geographic information system variables, and/or satellite data. Measurement error in these exposure estimates leads to imprecise estimation of health effects and their standard errors. We reviewed methods for measurement error correction that have been applied in epidemiological studies that use model-derived air pollution data. We identified seven cohort studies and one panel study that have employed measurement error correction methods. These methods included regression calibration, risk set regression calibration, regression calibration with instrumental variables, the simulation extrapolation approach (SIMEX), and methods under the non-parametric or parameter bootstrap. Corrections resulted in small increases in the absolute magnitude of the health effect estimate and its standard error under most scenarios. Limited application of measurement error correction methods in air pollution studies may be attributed to the absence of exposure validation data and the methodological complexity of the proposed methods. Future epidemiological studies should consider in their design phase the requirements for the measurement error correction method to be later applied, while methodological advances are needed under the multi-pollutants setting.

Response of Nuclear Power Plant Instrumentation Cables Exposed to Fire Conditions.

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

Muna, Alice Baca; LaFleur, Chris Bensdotter; Brooks, Dusty Marie

This report presents the results of instrumentation cable tests sponsored by the US Nuclear Regulatory Commission (NRC) Office of Nuclear Regulatory Research and performed at Sandia National Laboratories (SNL). The goal of the tests was to assess thermal and electrical response behavior under fire-exposure conditions for instrumentation cables and circuits. The test objective was to assess how severe radiant heating conditions surrounding an instrumentation cable affect current or voltage signals in an instrumentation circuit. A total of thirty-nine small-scale tests were conducted. Ten different instrumentation cables were tested, ranging from one conductor to eight-twisted pairs. Because the focus of themore » tests was thermoset (TS) cables, only two of the ten cables had thermoplastic (TP) insulation and jacket material and the remaining eight cables were one of three different TS insulation and jacket material. Two instrumentation cables from previous cable fire testing were included, one TS and one TP. Three test circuits were used to simulate instrumentation circuits present in nuclear power plants: a 4–20 mA current loop, a 10–50 mA current loop and a 1–5 VDC voltage loop. A regression analysis was conducted to determine key variables affecting signal leakage time.« less

Scalable Regression Tree Learning on Hadoop using OpenPlanet

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

Yin, Wei; Simmhan, Yogesh; Prasanna, Viktor

As scientific and engineering domains attempt to effectively analyze the deluge of data arriving from sensors and instruments, machine learning is becoming a key data mining tool to build prediction models. Regression tree is a popular learning model that combines decision trees and linear regression to forecast numerical target variables based on a set of input features. Map Reduce is well suited for addressing such data intensive learning applications, and a proprietary regression tree algorithm, PLANET, using MapReduce has been proposed earlier. In this paper, we describe an open source implement of this algorithm, OpenPlanet, on the Hadoop framework usingmore » a hybrid approach. Further, we evaluate the performance of OpenPlanet using realworld datasets from the Smart Power Grid domain to perform energy use forecasting, and propose tuning strategies of Hadoop parameters to improve the performance of the default configuration by 75% for a training dataset of 17 million tuples on a 64-core Hadoop cluster on FutureGrid.« less

Automated gait and balance parameters diagnose and correlate with severity in Parkinson disease.

PubMed

Dewey, D Campbell; Miocinovic, Svjetlana; Bernstein, Ira; Khemani, Pravin; Dewey, Richard B; Querry, Ross; Chitnis, Shilpa; Dewey, Richard B

2014-10-15

To assess the suitability of instrumented gait and balance measures for diagnosis and estimation of disease severity in PD. Each subject performed iTUG (instrumented Timed-Up-and-Go) and iSway (instrumented Sway) using the APDM(®) Mobility Lab. MDS-UPDRS parts II and III, a postural instability and gait disorder (PIGD) score, the mobility subscale of the PDQ-39, and Hoehn & Yahr stage were measured in the PD cohort. Two sets of gait and balance variables were defined by high correlation with diagnosis or disease severity and were evaluated using multiple linear and logistic regressions, ROC analyses, and t-tests. 135 PD subjects and 66 age-matched controls were evaluated in this prospective cohort study. We found that both iTUG and iSway variables differentiated PD subjects from controls (area under the ROC curve was 0.82 and 0.75 respectively) and correlated with all PD severity measures (R(2) ranging from 0.18 to 0.61). Objective exam-based scores correlated more strongly with iTUG than iSway. The chosen set of iTUG variables was abnormal in very mild disease. Age and gender influenced gait and balance parameters and were therefore controlled in all analyses. Our study identified sets of iTUG and iSway variables which correlate with PD severity measures and differentiate PD subjects from controls. These gait and balance measures could potentially serve as markers of PD progression and are under evaluation for this purpose in the ongoing NIH Parkinson Disease Biomarker Program. Copyright © 2014 Elsevier B.V. All rights reserved.

Automated Gait and Balance Parameters Diagnose and Correlate with Severity in Parkinson Disease

PubMed Central

Dewey, Daniel C.; Miocinovic, Svjetlana; Bernstein, Ira; Khemani, Pravin; Dewey, Richard B.; Querry, Ross; Chitnis, Shilpa; Dewey, Richard B.

2014-01-01

Objective To assess the suitability of instrumented gait and balance measures for diagnosis and estimation of disease severity in PD. Methods Each subject performed iTUG (instrumented Timed-Up-and-Go) and iSway (instrumented Sway) using the APDM® Mobility Lab. MDS-UPDRS parts II and III, a postural instability and gait disorder (PIGD) score, the mobility subscale of the PDQ-39, and Hoehn & Yahr stage were measured in the PD cohort. Two sets of gait and balance variables were defined by high correlation with diagnosis or disease severity and were evaluated using multiple linear and logistic regressions, ROC analyses, and t-tests. Results 135 PD subjects and 66 age-matched controls were evaluated in this prospective cohort study. We found that both iTUG and iSway variables differentiated PD subjects from controls (area under the ROC curve was 0.82 and 0.75 respectively) and correlated with all PD severity measures (R2 ranging from 0.18 to 0.61). Objective exam-based scores correlated more strongly with iTUG than iSway. The chosen set of iTUG variables was abnormal in very mild disease. Age and gender influenced gait and balance parameters and were therefore controlled in all analyses. Interpretation Our study identified sets of iTUG and iSway variables which correlate with PD severity measures and differentiate PD subjects from controls. These gait and balance measures could potentially serve as markers of PD progression and are under evaluation for this purpose in the ongoing NIH Parkinson Disease Biomarker Program. PMID:25082782

Joint nonparametric correction estimator for excess relative risk regression in survival analysis with exposure measurement error

PubMed Central

Wang, Ching-Yun; Cullings, Harry; Song, Xiao; Kopecky, Kenneth J.

2017-01-01

SUMMARY Observational epidemiological studies often confront the problem of estimating exposure-disease relationships when the exposure is not measured exactly. In the paper, we investigate exposure measurement error in excess relative risk regression, which is a widely used model in radiation exposure effect research. In the study cohort, a surrogate variable is available for the true unobserved exposure variable. The surrogate variable satisfies a generalized version of the classical additive measurement error model, but it may or may not have repeated measurements. In addition, an instrumental variable is available for individuals in a subset of the whole cohort. We develop a nonparametric correction (NPC) estimator using data from the subcohort, and further propose a joint nonparametric correction (JNPC) estimator using all observed data to adjust for exposure measurement error. An optimal linear combination estimator of JNPC and NPC is further developed. The proposed estimators are nonparametric, which are consistent without imposing a covariate or error distribution, and are robust to heteroscedastic errors. Finite sample performance is examined via a simulation study. We apply the developed methods to data from the Radiation Effects Research Foundation, in which chromosome aberration is used to adjust for the effects of radiation dose measurement error on the estimation of radiation dose responses. PMID:29354018

A quantile regression model for failure-time data with time-dependent covariates

PubMed Central

Gorfine, Malka; Goldberg, Yair; Ritov, Ya’acov

2017-01-01

Summary Since survival data occur over time, often important covariates that we wish to consider also change over time. Such covariates are referred as time-dependent covariates. Quantile regression offers flexible modeling of survival data by allowing the covariates to vary with quantiles. This article provides a novel quantile regression model accommodating time-dependent covariates, for analyzing survival data subject to right censoring. Our simple estimation technique assumes the existence of instrumental variables. In addition, we present a doubly-robust estimator in the sense of Robins and Rotnitzky (1992, Recovery of information and adjustment for dependent censoring using surrogate markers. In: Jewell, N. P., Dietz, K. and Farewell, V. T. (editors), AIDS Epidemiology. Boston: Birkhaäuser, pp. 297–331.). The asymptotic properties of the estimators are rigorously studied. Finite-sample properties are demonstrated by a simulation study. The utility of the proposed methodology is demonstrated using the Stanford heart transplant dataset. PMID:27485534

How Financial Literacy Affects Household Wealth Accumulation.

PubMed

Behrman, Jere R; Mitchell, Olivia S; Soo, Cindy K; Bravo, David

2012-05-01

This study isolates the causal effects of financial literacy and schooling on wealth accumulation using a new household dataset and an instrumental variables (IV) approach. Financial literacy and schooling attainment are both strongly positively associated with wealth outcomes in linear regression models, whereas the IV estimates reveal even more potent effects of financial literacy. They also indicate that the schooling effect only becomes positive when interacted with financial literacy. Estimated impacts are substantial enough to imply that investments in financial literacy could have large wealth payoffs.

How Financial Literacy Affects Household Wealth Accumulation

PubMed Central

Behrman, Jere R.; Mitchell, Olivia S.; Soo, Cindy K.; Bravo, David

2012-01-01

This study isolates the causal effects of financial literacy and schooling on wealth accumulation using a new household dataset and an instrumental variables (IV) approach. Financial literacy and schooling attainment are both strongly positively associated with wealth outcomes in linear regression models, whereas the IV estimates reveal even more potent effects of financial literacy. They also indicate that the schooling effect only becomes positive when interacted with financial literacy. Estimated impacts are substantial enough to imply that investments in financial literacy could have large wealth payoffs. PMID:23355747

Predicting Use of Nurse Care Coordination by Older Adults With Chronic Conditions.

PubMed

Vanderboom, Catherine E; Holland, Diane E; Mandrekar, Jay; Lohse, Christine M; Witwer, Stephanie G; Hunt, Vicki L

2017-07-01

To be effective, nurse care coordination must be targeted at individuals who will use the service. The purpose of this study was to identify variables that predicted use of care coordination by primary care patients. Data on the potential predictor variables were obtained from patient interviews, the electronic health record, and an administrative database of 178 adults eligible for care coordination. Use of care coordination was obtained from an administrative database. A multivariable logistic regression model was developed using a bootstrap sampling approach. Variables predicting use of care coordination were dependence in both activities of daily living (ADL) and instrumental activities of daily living (IADL; odds ratio [OR] = 5.30, p = .002), independent for ADL but dependent for IADL (OR = 2.68, p = .01), and number of prescription medications (OR = 1.12, p = .002). Consideration of these variables may improve identification of patients to target for care coordination.

[Observations and significance of extrasystole in very young athletes].

PubMed

Rossini, G; Mazzoli, M; Dalmastri, G; Crescimbeni, L; Berti, P; Arata, G; Losi, G; Martines, G

1982-01-01

80 very young football players (from 8 to 12) have been examined for three months by some clinical and instrumental cardiologic tests (starting E.C.G. and after graduated stresses on a football court). The starting E.C.G. showed variable extresystolic arrhythmias in 8 subjects, without any sure signs of a cardiopathy, to point out by deeper tests (such as polygraphic, echocardiographic test and rx heart teleradiography). The above-mentioned arrhythmias felt the effects of training variably, since they regressed in 6 cases, however two subjects needed a pharmacological intervention. They are still talking over the meaning to give to extrasystolic arrhythmias in very young people in evaluation of attitude to agonism and in programming training.

Cooperation without culture? The null effect of generalized trust on intentional homicide: a cross-national panel analysis, 1995-2009.

PubMed

Robbins, Blaine

2013-01-01

Sociologists, political scientists, and economists all suggest that culture plays a pivotal role in the development of large-scale cooperation. In this study, I used generalized trust as a measure of culture to explore if and how culture impacts intentional homicide, my operationalization of cooperation. I compiled multiple cross-national data sets and used pooled time-series linear regression, single-equation instrumental-variables linear regression, and fixed- and random-effects estimation techniques on an unbalanced panel of 118 countries and 232 observations spread over a 15-year time period. Results suggest that culture and large-scale cooperation form a tenuous relationship, while economic factors such as development, inequality, and geopolitics appear to drive large-scale cooperation.

Duration of untreated psychosis and clinical outcomes of first episode psychosis: An observational and an instrumental variables analysis.

PubMed

Sullivan, Sarah A; Carroll, Robert; Peters, Tim J; Amos, Tim; Jones, Peter B; Marshall, Max; Birchwood, Max; Fowler, David; Johnson, Sonia; Fisher, Helen L; Major, Barnaby; Rahaman, Nikola; Joyce, John; Chamberlain-Kent, Nick; Lawrence, Jo; Moran, Paul; Tilling, Kate

2018-04-26

Duration of untreated psychosis (DUP) is considered as a key prognostic variable in psychosis. Yet, it is unclear whether a longer DUP causes worse outcomes or whether reported associations have alternative explanations. Data from 2 cohorts of patients with first episode psychosis were used (n = 2134). Measures of DUP were assessed at baseline and outcomes at 12 months. Regression models were used to investigate the associations between DUP and outcomes. We also investigated whether any associations were replicated using instrumental variables (IV) analysis to reduce the effect of residual confounding and measurement bias. There were associations between DUP per 1-year increase and positive psychotic symptoms (7.0% in symptom score increase 95% confidence interval (CI) 4.0%, 10.0%, P < .001), worse recovery (risk difference [RD] 0.78, 95%, CI 0.68, 0.83, P < .001) and worse global functioning (0.62 decrease in functioning score 95% CI -1.19, -0.04, P = .035). There was no evidence of an association with negative psychotic symptoms (1.0%, 95%, CI -2.0%, 5.0%, P = .455). The IV analysis showed weaker evidence of associations in the same direction between DUP per 1-year increase and positive psychotic symptoms, recovery and global functioning. However, there was evidence of an inverse association with negative psychotic symptoms (decrease of 15.0% in symptom score 95% CI -26.0%, -3.0%, P = .016). We have confirmed previous findings of a positive association between positive psychotic symptoms, global functioning and recovery and DUP using regression analysis. IV analysis shows some support for these findings. Future investigation using IV analysis should be repeated in large data sets. © 2018 John Wiley & Sons Australia, Ltd.

Functional assessment of chronic illness therapy—the fatigue scale exhibits stronger associations with clinical parameters in chronic dialysis patients compared to other fatigue-assessing instruments

PubMed Central

Chao, Chia-Ter; Huang, Jenq-Wen

2016-01-01

Background. Patients with end-stage renal disease (ESRD) have a high symptom burden, among which fatigue is highly prevalent. Many fatigue-assessing instruments exist, but comparisons among instruments in this patient population have yet to be investigated. Methods. ESRD patients under chronic hemodialysis were prospectively enrolled and seven types of fatigue instruments were administered: Brief Fatigue Inventory (BFI), Functional Assessment of Chronic Illness Therapy–Fatigue (FACIT-F), Fatigue Severity Scale (FSS), Lee Fatigue Scale (LFS), Fatigue Questionnaire (FQ), Fatigue Symptom Inventory (FSI), and Short-Form 36-Vitality (SF36-V). Using these instruments, we investigated the correlation between fatigue severity and clinical/biochemical parameters, including demographic/comorbidity profile, dialysis-related complications, and frailty severity. We used regression analysis with serum albumin and frailty severity as the dependent variables to investigate the independent correlations. Results. A total of 46 ESRD patients were enrolled (average age of 67 ± 11.6 years), and 50% of them had type 2 diabetes mellitus. Results from the seven tested instruments showed high correlation with each other. We found that the fatigue severity by FACIT-F was significantly associated with age (p = 0.03), serum albumin (p = 0.003) and creatinine (p = 0.02) levels, while SF36-V scores were also significantly associated with age (p = 0.02) and serum creatinine levels (p = 0.04). However, the fatigue severity measured by the FSS, FSI, FQ, BFI, and LFS did not exhibit these associations. Moreover, regression analysis showed that only FACIT-F scores were independently associated with serum albumin levels and frailty severity in ESRD patients. Conclusion. Among the seven fatigue-assessing instruments, only the FACIT-F yielded results that demonstrated significant and independent associations with important outcome-related features in ESRD patients. PMID:26998414

Poverty, Pregnancy, and Birth Outcomes: A Study of the Earned Income Tax Credit.

PubMed

Hamad, Rita; Rehkopf, David H

2015-09-01

Economic interventions are increasingly recognised as a mechanism to address perinatal health outcomes among disadvantaged groups. In the US, the earned income tax credit (EITC) is the largest poverty alleviation programme. Little is known about its effects on perinatal health among recipients and their children. We exploit quasi-random variation in the size of EITC payments to examine the effects of income on perinatal health. The study sample includes women surveyed in the 1979 National Longitudinal Survey of Youth (n = 2985) and their children born during 1986-2000 (n = 4683). Outcome variables include utilisation of prenatal and postnatal care, use of alcohol and tobacco during pregnancy, term birth, birthweight, and breast-feeding status. We first examine the health effects of both household income and EITC payment size using multivariable linear regressions. We then employ instrumental variables analysis to estimate the causal effect of income on perinatal health, using EITC payment size as an instrument for household income. We find that EITC payment size is associated with better levels of several indicators of perinatal health. Instrumental variables analysis, however, does not reveal a causal association between household income and these health measures. Our findings suggest that associations between income and perinatal health may be confounded by unobserved characteristics, but that EITC income improves perinatal health. Future studies should continue to explore the impacts of economic interventions on perinatal health outcomes, and investigate how different forms of income transfers may have different impacts. © 2015 John Wiley & Sons Ltd.

The role of psychological processes in estimates of stuttering severity.

PubMed

Manning, Walter; Gayle Beck, J

2013-12-01

To examine the associations of trait anxiety (STAI), social anxiety (SIAS), depression (BDI-II), and personality features (ADP-IV) with three measures of stuttering severity: %SS, Stuttering Severity, Instrument, and the Overall Assessment of the Speaker's Experience of Stuttering. Fifty adults with a history of stuttering served as participants. Participant scores on trait, anxiety, social anxiety, depression, and personality features were entered into a regression analysis, with the criterion variables (DVs) being: %SS, SSI-3, OASES total score. In order to explore the OASES, further, each of the four OASES subscales were also examined. A separate regression was conducted for, each dependent variable. The OASES total score model was significant (p<.0001) and revealed that social anxiety and, trait anxiety were the only significant predictors, with medium effect sizes noted for both variables. In contrast, percent syllables stuttered and the SSI were not significantly associated with psychological, variables, suggesting that anxiety may not always be related to overt indicators of stuttering. Depression and personality dysfunction were not significantly associated with any measure of, stuttering severity. Anxiety in the form of social and trait anxiety are significantly associated with stuttering, severity as indicated by the OASES. Traditional procedures for assigning severity ratings to individuals, who stutter based on percent syllables stuttered and the Stuttering Severity Instrument are not, significantly related to psychological processes central to the stuttering experience. Depression and, personality characteristics do not meaningfully account for stuttering. The reader will be able to: (a) differentiate forms of anxiety that are likely to be associated with stuttering (b) understand the importance of determining features of stuttering that go beyond the obvious, surface characteristics of stuttering frequency, and (c) discuss the important clinical and theoretical implications for understanding the degree of psychological dysfunction that is likely to be characteristic of those who stutter. Copyright © 2013 Elsevier Inc. All rights reserved.

Anesthesia Technique and Outcomes of Mechanical Thrombectomy in Patients With Acute Ischemic Stroke.

PubMed

Bekelis, Kimon; Missios, Symeon; MacKenzie, Todd A; Tjoumakaris, Stavropoula; Jabbour, Pascal

2017-02-01

The impact of anesthesia technique on the outcomes of mechanical thrombectomy for acute ischemic stroke remains an issue of debate. We investigated the association of general anesthesia with outcomes in patients undergoing mechanical thrombectomy for ischemic stroke. We performed a cohort study involving patients undergoing mechanical thrombectomy for ischemic stroke from 2009 to 2013, who were registered in the New York Statewide Planning and Research Cooperative System database. An instrumental variable (hospital rate of general anesthesia) analysis was used to simulate the effects of randomization and investigate the association of anesthesia technique with case-fatality and length of stay. Among 1174 patients, 441 (37.6%) underwent general anesthesia and 733 (62.4%) underwent conscious sedation. Using an instrumental variable analysis, we identified that general anesthesia was associated with a 6.4% increased case-fatality (95% confidence interval, 1.9%-11.0%) and 8.4 days longer length of stay (95% confidence interval, 2.9-14.0) in comparison to conscious sedation. This corresponded to 15 patients needing to be treated with conscious sedation to prevent 1 death. Our results were robust in sensitivity analysis with mixed effects regression and propensity score-adjusted regression models. Using a comprehensive all-payer cohort of acute ischemic stroke patients undergoing mechanical thrombectomy in New York State, we identified an association of general anesthesia with increased case-fatality and length of stay. These considerations should be taken into account when standardizing acute stroke care. © 2017 American Heart Association, Inc.

Factors explaining children's responses to intravenous needle insertions.

PubMed

McCarthy, Ann Marie; Kleiber, Charmaine; Hanrahan, Kirsten; Zimmerman, M Bridget; Westhus, Nina; Allen, Susan

2010-01-01

Previous research shows that numerous child, parent, and procedural variables affect children's distress responses to procedures. Cognitive-behavioral interventions such as distraction are effective in reducing pain and distress for many children undergoing these procedures. The purpose of this report was to examine child, parent, and procedural variables that explain child distress during a scheduled intravenous insertion when parents are distraction coaches for their children. A total of 542 children, between 4 and 10 years of age, and their parents participated. Child age, gender, diagnosis, and ethnicity were measured by questions developed for this study. Standardized instruments were used to measure child experience with procedures, temperament, ability to attend, anxiety, coping style, and pain sensitivity. Questions were developed to measure parent variables, including ethnicity, gender, previous experiences, and expectations, and procedural variables, including use of topical anesthetics and difficulty of procedure. Standardized instruments were used to measure parenting style and parent anxiety, whereas a new instrument was developed to measure parent performance of distraction. Children's distress responses were measured with the Observation Scale of Behavioral Distress-Revised (behavioral), salivary cortisol (biological), Oucher Pain Scale (self-report), and parent report of child distress (parent report). Regression methods were used for data analyses. Variables explaining behavioral, child-report and parent-report measures include child age, typical coping response, and parent expectation of distress (p < .01). Level of parents' distraction coaching explained a significant portion of behavioral, biological, and parent-report distress measures (p < .05). Child impulsivity and special assistance at school also significantly explained child self-report of pain (p < .05). Additional variables explaining cortisol response were child's distress in the morning before clinic, diagnoses of attention deficit hyperactivity disorder or anxiety disorder, and timing of preparation for the clinic visit. The findings can be used to identify children at risk for high distress during procedures. This is the first study to find a relationship between child behavioral distress and level of parent distraction coaching.

Prevention of Proximal Junctional Kyphosis: Are Polyaxial Pedicle Screws Superior to Monoaxial Pedicle Screws at the Upper Instrumented Vertebrae?

PubMed

Wang, Hui; Ding, Wenyuan; Ma, Lei; Zhang, Lijun; Yang, Dalong

2017-05-01

Evidence regarding whether the polyaxial pedicle screws at the upper instrumented vertebrae (UIV) are superior to monoaxial pedicle screws in prevention of proximal junctional kyphosis (PJK) is not clear. The aim of this study was therefore to explore the influence of different types of pedicle screws at UIV on the incidence of PJK. We reviewed retrospectively 242 patients surgically treated with instrumented segmental posterior spinal fusion at a minimum of 4 motion segments. Polyaxial pedicle screws were used at UIV in 125 patients (polyaxial group), and monoaxial pedicle screws were used at UIV in 117 patients (monoaxial group). According to the occurrence of PJK at final follow-up, patients in both the polyaxial and monoaxial groups were then divided into 2 subgroups: PJK and no proximal junctional kyphosis (NPJK). To investigate the risk factors of PJK, 2 categorized variables were analyzed statistically: 1) patient characteristics: age, sex, body mass index (BMI), bone mineral density (BMD), sagittal vertical axis (SVA), thoracic kyphosis, thoracolumbar junctional angle, lumbar lordosis (LL), pelvic incidence, pelvic tilt, and sacral slope. 2) Surgical variables: Changes of radiographic parameters include the SVA, thoracic kyphosis, thoracolumbar junctional, LL, pelvic incidence, pelvic tilt, sacral slope, pedicle-upper end plate angle, the number of instrumented levels, and the most proximal and distal levels of the instrumentation. PJK was developed in 26 of 117 patients (22.2%) in the monoaxial group and 30 of 125 patients (24.0%) in the polyaxial group. Until the final follow-up, there was no significant difference in the incidence of PJK (χ2 = 0.107, P = 0.734) between the monoaxial and polyaxial groups. There was no significant difference in patient characteristics and surgical variables between the 2 groups, except the proximal junctional angle change (P = 0.031). In the monoaxial group, there were no significant differences in patient characteristics between the PJK and NPJK subgroups, except BMI (P = 0.042) and BMD (P = 0.037). There were no significant differences in change of radiographic parameters, except SVA change (P = 0.036), proximal junctional angle change (P = 0.029), LL change (P = 0.025), and lower instrumented vertebrae location (P = 0.036). Multivariate logistic regression analysis revealed that obesity, osteoporosis, lower instrumented vertebra at sacrum, and LL change >10 degrees were independently associated with PJK. In the polyaxial group, there were no significant differences in patient characteristics between the PJK and NPJK subgroups, except BMI (P = 0.032) and BMD (P = 0.040). There were no significant differences in change of radiographic parameters between the PJK and NPJK subgroups, except P-UP angle (P = 0.037) and lower instrumented vertebrae location (P = 0.017). Multivariate logistic regression analysis revealed that obesity, osteoporosis, and lower instrumented vertebra at sacrum were independently associated with PJK. Polyaxial pedicle screws at UIV is not superior to monoaxial pedicle screws in prevention of PJK. Obesity, osteoporosis, and lower instrumented vertebra at sacrum are risk factors for PJK in all the patients. Excessive LL reconstruction is the unique risk factor of PJK when monoaxial pedicle screws were used at UIV. Copyright © 2017 Elsevier Inc. All rights reserved.

The use of a combination of instrumental methods to assess change in sensory crispness during storage of a "Honeycrisp" apple breeding family.

PubMed

Chang, Hsueh-Yuan; Vickers, Zata M; Tong, Cindy B S

2018-04-01

Loss of crispness in apple fruit during storage reduces the fruit's fresh sensation and consumer acceptance. Apple varieties that maintain crispness thus have higher potential for longer-term consumer appeal. To efficiently phenotype crispness, several instrumental methods have been tested, but variable results were obtained when different apple varieties were assayed. To extend these studies, we assessed the extent to which instrumental measurements correlate to and predict sensory crispness, with a focus on crispness maintenance. We used an apple breeding family derived from a cross between "Honeycrisp" and "MN1764," which segregates for crispness maintenance. Three types of instrumental measurements (puncture, snapping, and mechanical-acoustic tests) and sensory evaluation were performed on fruit at harvest and after 8 weeks of cold storage. Overall, 20 genotypes from the family and the 2 parents were characterized by 19 force and acoustic measures. In general, crispness was more related to force than to acoustic measures. Force linear distance and maximum force as measured by the mechanical-acoustic test were best correlated with sensory crispness and change in crispness, respectively. The correlations varied by apple genotype. The best multiple linear regression model to predict change in sensory crispness between harvest and storage of fruit of this breeding family incorporated both force and acoustic measures. This work compared the abilities of instrumental tests to predict sensory crispness maintenance of apple fruit. The use of an instrumental method that is highly correlated to sensory crispness evaluation can enhance the efficiency and reduce the cost of measuring crispness for breeding purposes. This study showed that sensory crispness and change in crispness after storage of an apple breeding family were reliably predicted with a combination of instrumental measurements and multiple variable analyses. The strategy potentially can be applied to other apple varieties for more accurate interpretation of crispness maintenance measured instrumentally. © 2018 Wiley Periodicals, Inc.

Factors associated with the income distribution of full-time physicians: a quantile regression approach.

PubMed

Shih, Ya-Chen Tina; Konrad, Thomas R

2007-10-01

Physician income is generally high, but quite variable; hence, physicians have divergent perspectives regarding health policy initiatives and market reforms that could affect their incomes. We investigated factors underlying the distribution of income within the physician population. Full-time physicians (N=10,777) from the restricted version of the 1996-1997 Community Tracking Study Physician Survey (CTS-PS), 1996 Area Resource File, and 1996 health maintenance organization penetration data. We conducted separate analyses for primary care physicians (PCPs) and specialists. We employed least square and quantile regression models to examine factors associated with physician incomes at the mean and at various points of the income distribution, respectively. We accounted for the complex survey design for the CTS-PS data using appropriate weighted procedures and explored endogeneity using an instrumental variables method. We detected widespread and subtle effects of many variables on physician incomes at different points (10th, 25th, 75th, and 90th percentiles) in the distribution that were undetected when employing regression estimations focusing on only the means or medians. Our findings show that the effects of managed care penetration are demonstrable at the mean of specialist incomes, but are more pronounced at higher levels. Conversely, a gender gap in earnings occurs at all levels of income of both PCPs and specialists, but is more pronounced at lower income levels. The quantile regression technique offers an analytical tool to evaluate policy effects beyond the means. A longitudinal application of this approach may enable health policy makers to identify winners and losers among segments of the physician workforce and assess how market dynamics and health policy initiatives affect the overall physician income distribution over various time intervals.

Factors Associated with the Income Distribution of Full-Time Physicians: A Quantile Regression Approach

PubMed Central

Shih, Ya-Chen Tina; Konrad, Thomas R

2007-01-01

Objective Physician income is generally high, but quite variable; hence, physicians have divergent perspectives regarding health policy initiatives and market reforms that could affect their incomes. We investigated factors underlying the distribution of income within the physician population. Data Sources Full-time physicians (N=10,777) from the restricted version of the 1996–1997 Community Tracking Study Physician Survey (CTS-PS), 1996 Area Resource File, and 1996 health maintenance organization penetration data. Study Design We conducted separate analyses for primary care physicians (PCPs) and specialists. We employed least square and quantile regression models to examine factors associated with physician incomes at the mean and at various points of the income distribution, respectively. We accounted for the complex survey design for the CTS-PS data using appropriate weighted procedures and explored endogeneity using an instrumental variables method. Principal Findings We detected widespread and subtle effects of many variables on physician incomes at different points (10th, 25th, 75th, and 90th percentiles) in the distribution that were undetected when employing regression estimations focusing on only the means or medians. Our findings show that the effects of managed care penetration are demonstrable at the mean of specialist incomes, but are more pronounced at higher levels. Conversely, a gender gap in earnings occurs at all levels of income of both PCPs and specialists, but is more pronounced at lower income levels. Conclusions The quantile regression technique offers an analytical tool to evaluate policy effects beyond the means. A longitudinal application of this approach may enable health policy makers to identify winners and losers among segments of the physician workforce and assess how market dynamics and health policy initiatives affect the overall physician income distribution over various time intervals. PMID:17850525

Estimating Uncertainty in Long Term Total Ozone Records from Multiple Sources

NASA Technical Reports Server (NTRS)

Frith, Stacey M.; Stolarski, Richard S.; Kramarova, Natalya; McPeters, Richard D.

2014-01-01

Total ozone measurements derived from the TOMS and SBUV backscattered solar UV instrument series cover the period from late 1978 to the present. As the SBUV series of instruments comes to an end, we look to the 10 years of data from the AURA Ozone Monitoring Instrument (OMI) and two years of data from the Ozone Mapping Profiler Suite (OMPS) on board the Suomi National Polar-orbiting Partnership satellite to continue the record. When combining these records to construct a single long-term data set for analysis we must estimate the uncertainty in the record resulting from potential biases and drifts in the individual measurement records. In this study we present a Monte Carlo analysis used to estimate uncertainties in the Merged Ozone Dataset (MOD), constructed from the Version 8.6 SBUV2 series of instruments. We extend this analysis to incorporate OMI and OMPS total ozone data into the record and investigate the impact of multiple overlapping measurements on the estimated error. We also present an updated column ozone trend analysis and compare the size of statistical error (error from variability not explained by our linear regression model) to that from instrument uncertainty.

Relationship between clinical fieldwork educator performance and health professional students' perceptions of their practice education learning environments.

PubMed

Brown, Ted; Williams, Brett; Lynch, Marty

2013-12-01

The Dundee Ready Education Environment Measure, Clinical Teaching Effectiveness Instrument, and Clinical Learning Environment Inventory were completed by 548 undergraduate students (54.5% response rate) enrolled in eight health professional bachelor degree courses. Regression analysis was used to investigate the significant predictors of the Clinical Teaching Effectiveness Instrument with the Dundee Ready Education Environment Measure and Clinical Learning Environment Inventory subscales as independent variables. The results indicated that the Dundee Ready Education Environment Measure and Clinical Learning Environment Inventory Actual version subscale scores explained 44% of the total variance in the Clinical Teaching Effectiveness Instrument score. The Dundee Ready Education Environment Measure subscale Academic Self-Perception explained 1.1% of the variance in the Clinical Teaching Effectiveness Instrument score. The Clinical Learning Environment Inventory Actual subscales accounted for the following variance percentages in the Clinical Teaching Effectiveness Instrument score: personalization, 1.1%; satisfaction, 1.7%; task orientation, 5.1%; and innovation, 6.2%. Aspects of the clinical learning environment appear to be predictive of the effectiveness of the clinical teaching that students experience. Fieldwork educator performance might be a significant contributing factor toward student skill development and practitioner success. © 2013 Wiley Publishing Asia Pty Ltd.

The power of siblings and caregivers: under-explored types of social support among children affected by HIV and AIDS.

PubMed

Sharer, Melissa; Cluver, Lucie; Shields, Joseph J; Ahearn, Frederick

2016-03-01

Children affected by HIV and AIDS have significantly higher rates of mental health problems than unaffected children. There is a need for research to examine how social support functions as a source of resiliency for children in high HIV-prevalence settings such as South Africa. The purpose of this research was to explore how family social support relates to depression, anxiety, and post-traumatic stress (PTS). Using the ecological model as a frame, data were drawn from a 2011 cross-sectional study of 1380 children classified as either orphaned by AIDS and/or living with an AIDS sick family member. The children were from high-poverty, high HIV-prevalent rural and urban communities in South Africa. Social support was analyzed in depth by examining the source (e.g. caregiver, sibling) and the type (e.g. emotional, instrumental, quality). These variables were entered into multiple regression analyses to estimate the most parsimonious regression models to show the relationships between social support and depression, anxiety, and PTS symptoms among the children. Siblings emerged as the most consistent source of social support on mental health. Overall caregiver and sibling support explained 13% variance in depression, 12% in anxiety, and 11% in PTS. Emotional support was the most frequent type of social support associated with mental health in all regression models, with higher levels of quality and instrumental support having the strongest relation to positive mental health outcomes. Although instrumental and quality support from siblings were related to positive mental health, unexpectedly, the higher the level of emotional support received from a sibling resulted in the child reporting more symptoms of depression, anxiety, and PTS. The opposite was true for emotional support provided via caregivers, higher levels of this support was related to lower levels of all mental health symptoms. Sex was significant in all regressions, indicating the presence of moderation.

The power of siblings and caregivers: under-explored types of social support among children affected by HIV and AIDS

PubMed Central

Sharer, Melissa; Cluver, Lucie; Shields, Joseph J.; Ahearn, Frederick

2016-01-01

ABSTRACT Children affected by HIV and AIDS have significantly higher rates of mental health problems than unaffected children. There is a need for research to examine how social support functions as a source of resiliency for children in high HIV-prevalence settings such as South Africa. The purpose of this research was to explore how family social support relates to depression, anxiety, and post-traumatic stress (PTS). Using the ecological model as a frame, data were drawn from a 2011 cross-sectional study of 1380 children classified as either orphaned by AIDS and/or living with an AIDS sick family member. The children were from high-poverty, high HIV-prevalent rural and urban communities in South Africa. Social support was analyzed in depth by examining the source (e.g. caregiver, sibling) and the type (e.g. emotional, instrumental, quality). These variables were entered into multiple regression analyses to estimate the most parsimonious regression models to show the relationships between social support and depression, anxiety, and PTS symptoms among the children. Siblings emerged as the most consistent source of social support on mental health. Overall caregiver and sibling support explained 13% variance in depression, 12% in anxiety, and 11% in PTS. Emotional support was the most frequent type of social support associated with mental health in all regression models, with higher levels of quality and instrumental support having the strongest relation to positive mental health outcomes. Although instrumental and quality support from siblings were related to positive mental health, unexpectedly, the higher the level of emotional support received from a sibling resulted in the child reporting more symptoms of depression, anxiety, and PTS. The opposite was true for emotional support provided via caregivers, higher levels of this support was related to lower levels of all mental health symptoms. Sex was significant in all regressions, indicating the presence of moderation. PMID:27392006

Cooperation without Culture? The Null Effect of Generalized Trust on Intentional Homicide: A Cross-National Panel Analysis, 1995–2009

PubMed Central

Robbins, Blaine

2013-01-01

Sociologists, political scientists, and economists all suggest that culture plays a pivotal role in the development of large-scale cooperation. In this study, I used generalized trust as a measure of culture to explore if and how culture impacts intentional homicide, my operationalization of cooperation. I compiled multiple cross-national data sets and used pooled time-series linear regression, single-equation instrumental-variables linear regression, and fixed- and random-effects estimation techniques on an unbalanced panel of 118 countries and 232 observations spread over a 15-year time period. Results suggest that culture and large-scale cooperation form a tenuous relationship, while economic factors such as development, inequality, and geopolitics appear to drive large-scale cooperation. PMID:23527211

Cognitive functioning and everyday problem solving in older adults.

PubMed

Burton, Catherine L; Strauss, Esther; Hultsch, David F; Hunter, Michael A

2006-09-01

The relationship between cognitive functioning and a performance-based measure of everyday problem-solving, the Everyday Problems Test (EPT), thought to index instrumental activities of daily living (IADL), was examined in 291 community-dwelling non-demented older adults. Performance on the EPT was found to vary according to age, cognitive status, and education. Hierarchical regression analyses revealed that, after adjusting for demographic and health variables, measures of cognitive functioning accounted for 23.6% of the variance in EPT performance. In particular, measures of global cognitive status, cognitive decline, speed of processing, executive functioning, episodic memory, and verbal ability were significant predictors of EPT performance. These findings suggest that cognitive functioning along with demographic variables are important determinants of everyday problem-solving.

How the 2SLS/IV estimator can handle equality constraints in structural equation models: a system-of-equations approach.

PubMed

Nestler, Steffen

2014-05-01

Parameters in structural equation models are typically estimated using the maximum likelihood (ML) approach. Bollen (1996) proposed an alternative non-iterative, equation-by-equation estimator that uses instrumental variables. Although this two-stage least squares/instrumental variables (2SLS/IV) estimator has good statistical properties, one problem with its application is that parameter equality constraints cannot be imposed. This paper presents a mathematical solution to this problem that is based on an extension of the 2SLS/IV approach to a system of equations. We present an example in which our approach was used to examine strong longitudinal measurement invariance. We also investigated the new approach in a simulation study that compared it with ML in the examination of the equality of two latent regression coefficients and strong measurement invariance. Overall, the results show that the suggested approach is a useful extension of the original 2SLS/IV estimator and allows for the effective handling of equality constraints in structural equation models. © 2013 The British Psychological Society.

The Choice of Screening Instrument Matters: The Case of Problematic Cannabis Use Screening in Spanish Population of Adolescents

PubMed Central

Domingo-Salvany, Antónia; Barrio Anta, Gregorio; Sánchez Mañez, Amparo; Llorens Aleixandre, Noelia; Brime Beteta, Begoña; Vicente, Julián

2013-01-01

The aim of this study was to examine the feasibility of problem cannabis use screening instruments administration within wide school surveys, their psychometric properties, overlaps, and relationships with other variables. Students from 7 Spanish regions, aged 14–18, who attended secondary schools were sampled by two-stage cluster sampling (net sample 14,589). Standardized, anonymous questionnaire including DSM-IV cannabis abuse criteria, Cannabis Abuse Screening Test (CAST), and Severity of Dependence Scale (SDS) was self-completed with paper and pencil in the selected classrooms. Data was analysed using classical psychometric theory, bivariate tests, and multinomial logistic regression analysis. Not responding to instruments' items (10.5–12.3%) was associated with reporting less frequent cannabis use. The instruments overlapped partially, with 16.1% of positives being positive on all three. SDS was more likely to identify younger users with lower frequency of use who thought habitual cannabis use posed a considerable problem. CAST positivity was associated with frequent cannabis use and related problems. It is feasible to use short psychometric scales in wide school surveys, but one must carefully choose the screening instrument, as different instruments identify different groups of users. These may correspond to different types of problematic cannabis use; however, measurement bias seems to play a role too. PMID:25969832

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.

HIV-related stigma in pregnancy and early postpartum of mothers living with HIV in Ontario, Canada.

PubMed

Ion, Allyson; Wagner, Anne C; Greene, Saara; Loutfy, Mona R

2017-02-01

HIV-related stigma is associated with many psychological challenges; however, minimal research has explored how perceived HIV-related stigma intersects with psychosocial issues that mothers living with HIV may experience including depression, perceived stress and social isolation. The present study aims to describe the correlates and predictors of HIV-related stigma in a cohort of women living with HIV (WLWH) from across Ontario, Canada during pregnancy and early postpartum. From March 2011 to December 2012, WLWH ≥ 18 years (n = 77) completed a study instrument measuring independent variables including sociodemographic characteristics, perceived stress, depression symptoms, social isolation, social support and perceived racism in the third trimester and 3, 6 and 12 months postpartum. Multivariable linear regression was employed to explore the relationship between HIV-related stigma and multiple independent variables. HIV-related stigma generally increased from pregnancy to postpartum; however, there were no significant differences in HIV-related stigma across all study time points. In multivariable regression, depression symptoms and perceived racism were significant predictors of overall HIV-related stigma from pregnancy to postpartum. The present analysis contributes to our understanding of HIV-related stigma throughout the pregnancy-motherhood trajectory for WLWH including the interactional relationship between HIV-related stigma and other psychosocial variables, most notably, depression and racism.

Meta-Analysis of the Reasoned Action Approach (RAA) to Understanding Health Behaviors.

PubMed

McEachan, Rosemary; Taylor, Natalie; Harrison, Reema; Lawton, Rebecca; Gardner, Peter; Conner, Mark

2016-08-01

Reasoned action approach (RAA) includes subcomponents of attitude (experiential/instrumental), perceived norm (injunctive/descriptive), and perceived behavioral control (capacity/autonomy) to predict intention and behavior. To provide a meta-analysis of the RAA for health behaviors focusing on comparing the pairs of RAA subcomponents and differences between health protection and health-risk behaviors. The present research reports a meta-analysis of correlational tests of RAA subcomponents, examination of moderators, and combined effects of subcomponents on intention and behavior. Regressions were used to predict intention and behavior based on data from studies measuring all variables. Capacity and experiential attitude had large, and other constructs had small-medium-sized correlations with intention; all constructs except autonomy were significant independent predictors of intention in regressions. Intention, capacity, and experiential attitude had medium-large, and other constructs had small-medium-sized correlations with behavior; intention, capacity, experiential attitude, and descriptive norm were significant independent predictors of behavior in regressions. The RAA subcomponents have utility in predicting and understanding health behaviors.

The relationship between cigarette taxes and child maltreatment.

PubMed

McLaughlin, Michael

2018-05-01

Prior research suggests that income and child maltreatment are related, but questions remain about the specific types of economic factors that affect the risk of maltreatment. The need to understand the role of economics in child welfare is critical, given the significant public health costs of child maltreatment. One factor that has been overlooked is regressive taxation. This study addresses this need by examining whether state-level changes in cigarette tax rates predict changes in state-level child maltreatment rates. The results of both a fixed effects (FE) and a fixed effects instrumental variables (FE-IV) estimator show that increases in state cigarette tax rates are followed by increases in child abuse and neglect. An additional test finds that increases in the sales tax (another tax deemed to be regressive) also predict increases in child maltreatment rates. Taken as a whole, the findings suggest that regressive taxes have a significant effect on the risk of child maltreatment. Copyright © 2018 Elsevier Ltd. All rights reserved.

Revisiting the relationship between managed care and hospital consolidation.

PubMed

Town, Robert J; Wholey, Douglas; Feldman, Roger; Burns, Lawton R

2007-02-01

This paper analyzes whether the rise in managed care during the 1990s caused the increase in hospital concentration. We assemble data from the American Hospital Association, InterStudy and government censuses from 1990 to 2000. We employ linear regression analyses on long differenced data to estimate the impact of managed care penetration on hospital consolidation. Instrumental variable analogs of these regressions are also analyzed to control for potential endogeneity. All data are from secondary sources merged at the level of the Health Care Services Area. In 1990, the mean population-weighted hospital Herfindahl-Hirschman index (HHI) in a Health Services Area was .19. By 2000, the HHI had risen to .26. Most of this increase in hospital concentration is due to hospital consolidation. Over the same time frame HMO penetration increased three fold. However, our regression analysis strongly implies that the rise of managed care did not cause the hospital consolidation wave. This finding is robust to a number of different specifications.

Revisiting the Relationship between Managed Care and Hospital Consolidation

PubMed Central

Town, Robert J; Wholey, Douglas; Feldman, Roger; Burns, Lawton R

2007-01-01

Objective This paper analyzes whether the rise in managed care during the 1990s caused the increase in hospital concentration. Data Sources We assemble data from the American Hospital Association, InterStudy and government censuses from 1990 to 2000. Study Design We employ linear regression analyses on long differenced data to estimate the impact of managed care penetration on hospital consolidation. Instrumental variable analogs of these regressions are also analyzed to control for potential endogeneity. Data Collection All data are from secondary sources merged at the level of the Health Care Services Area. Principle Findings In 1990, the mean population-weighted hospital Herfindahl–Hirschman index (HHI) in a Health Services Area was .19. By 2000, the HHI had risen to .26. Most of this increase in hospital concentration is due to hospital consolidation. Over the same time frame HMO penetration increased three fold. However, our regression analysis strongly implies that the rise of managed care did not cause the hospital consolidation wave. This finding is robust to a number of different specifications. PMID:17355590

Intercomparison of NO3 radical detection instruments in the atmosphere simulation chamber SAPHIR

NASA Astrophysics Data System (ADS)

Dorn, H.-P.; Apodaca, R. L.; Ball, S. M.; Brauers, T.; Brown, S. S.; Crowley, J. N.; Dubé, W. P.; Fuchs, H.; Häseler, R.; Heitmann, U.; Jones, R. L.; Kiendler-Scharr, A.; Labazan, I.; Langridge, J. M.; Meinen, J.; Mentel, T. F.; Platt, U.; Pöhler, D.; Rohrer, F.; Ruth, A. A.; Schlosser, E.; Schuster, G.; Shillings, A. J. L.; Simpson, W. R.; Thieser, J.; Tillmann, R.; Varma, R.; Venables, D. S.; Wahner, A.

2013-05-01

The detection of atmospheric NO3 radicals is still challenging owing to its low mixing ratios (≈ 1 to 300 pptv) in the troposphere. While long-path differential optical absorption spectroscopy (DOAS) has been a well-established NO3 detection approach for over 25 yr, newly sensitive techniques have been developed in the past decade. This publication outlines the results of the first comprehensive intercomparison of seven instruments developed for the spectroscopic detection of tropospheric NO3. Four instruments were based on cavity ring-down spectroscopy (CRDS), two utilised open-path cavity-enhanced absorption spectroscopy (CEAS), and one applied "classical" long-path DOAS. The intercomparison campaign "NO3Comp" was held at the atmosphere simulation chamber SAPHIR in Jülich (Germany) in June 2007. Twelve experiments were performed in the well-mixed chamber for variable concentrations of NO3, N2O5, NO2, hydrocarbons, and water vapour, in the absence and in the presence of inorganic or organic aerosol. The overall precision of the cavity instruments varied between 0.5 and 5 pptv for integration times of 1 s to 5 min; that of the DOAS instrument was 9 pptv for an acquisition time of 1 min. The NO3 data of all instruments correlated excellently with the NOAA-CRDS instrument, which was selected as the common reference because of its superb sensitivity, high time resolution, and most comprehensive data coverage. The median of the coefficient of determination (r2) over all experiments of the campaign (60 correlations) is r2 = 0.981 (quartile 1 (Q1): 0.949; quartile 3 (Q3): 0.994; min/max: 0.540/0.999). The linear regression analysis of the campaign data set yielded very small intercepts (median: 1.1 pptv; Q1/Q3: -1.1/2.6 pptv; min/max: -14.1/28.0 pptv), and the slopes of the regression lines were close to unity (median: 1.01; Q1/Q3: 0.92/1.10; min/max: 0.72/1.36). The deviation of individual regression slopes from unity was always within the combined accuracies of each instrument pair. The very good correspondence between the NO3 measurements by all instruments for aerosol-free experiments indicates that the losses of NO3 in the inlet of the instruments were determined reliably by the participants for the corresponding conditions. In the presence of inorganic or organic aerosol, however, differences in the measured NO3 mixing ratios were detectable among the instruments. In individual experiments the discrepancies increased with time, pointing to additional NO3 radical losses by aerosol deposited onto the filters or on the inlet walls of the instruments. Instruments using DOAS analyses showed no significant effect of aerosol on the detection of NO3. No hint of a cross interference of NO2 was found. The effect of non-Lambert-Beer behaviour of water vapour absorption lines on the accuracy of the NO3 detection by broadband techniques was small and well controlled. The NO3Comp campaign demonstrated the high quality, reliability and robustness of performance of current state-of-the-art instrumentation for NO3 detection.

Dependence for basic and instrumental activities of daily living after hip fractures.

PubMed

González-Zabaleta, Jorge; Pita-Fernandez, Salvador; Seoane-Pillado, Teresa; López-Calviño, Beatriz; Gonzalez-Zabaleta, Jose Luis

2015-01-01

The objective of the study is to determine basic activities of daily living (Barthel Index) and instrumental activities of daily living (Lawton-Brody Index) before and after hip fracture. Follow-up study of patients (n=100) with hip fracture, operated at Complejo Hospitalario Universitario de A Coruña (Spain). Period January/2009-December/2011. Demographic characteristic of the patients, Charlson Index, Glomerular filtration rate, Barthel index, Lawton index, type of proximal femur fracture and surgical treatment delay were recorded. Multivariate regression was performed. Informed patient consent and ethical review approval were obtained. Before fracture were independent for activities of daily living (ADL) a 38.0%, at 90 days were 15.4%. The Barthel index score decreased from 75.2±28.2 to 56.5±31.8) (p<0.0001). If we consider the age, gender, comorbidity (Charlson index), renal function, fracture type and surgical delay objectify the only independent variable to predict dependency effect is age. If we also consider the Barthel score objectify the variable that significantly modifies that score at 90 days is the baseline value of the index. The prevalence of independence for instrumental activities of daily living (IADL) at the baseline moment is 11% and at 90 days is decreased to 2.2%. There is a decrease in the independence effect in all activities. The variable predictor of independence for all activities after taking into consideration age, sex, comorbidity, fracture type, surgical delay and renal function is the baseline score of the Barthel and Lawton index. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Poverty, Pregnancy, and Birth Outcomes: A Study of the Earned Income Tax Credit

PubMed Central

Rehkopf, David H.

2015-01-01

Background Economic interventions are increasingly recognized as a mechanism to address perinatal health outcomes among disadvantaged groups. In the United States, the earned income tax credit (EITC) is the largest poverty alleviation program. Little is known about its effects on perinatal health among recipients and their children. We exploit quasi-random variation in the size of EITC payments over time to examine the effects of income on perinatal health. Methods The study sample includes women surveyed in the 1979 National Longitudinal Survey of Youth (N=2,985) and their children born during 1986–2000 (N=4,683). Outcome variables include utilization of prenatal and postnatal care, use of alcohol and tobacco during pregnancy, term birth, birthweight, and breast-feeding status. We examine the health effects of both household income and EITC payment size using multivariable linear regressions. We employ instrumental variables analysis to estimate the causal effect of income on perinatal health, using EITC payment size as an instrument for household income. Results We find that household income and EITC payment size are associated with improvements in several indicators of perinatal health. Instrumental variables analysis, however, does not reveal a causal association between household income and these health measures. Conclusions Our findings suggest that associations between income and perinatal health may be confounded by unobserved characteristics, but that EITC income improves perinatal health. Future studies should continue to explore the impacts of economic interventions on perinatal health outcomes, and investigate how different forms of income transfers may have different impacts. PMID:26212041

Modified Regression Correlation Coefficient for Poisson Regression Model

NASA Astrophysics Data System (ADS)

Kaengthong, Nattacha; Domthong, Uthumporn

2017-09-01

This study gives attention to indicators in predictive power of the Generalized Linear Model (GLM) which are widely used; however, often having some restrictions. We are interested in regression correlation coefficient for a Poisson regression model. This is a measure of predictive power, and defined by the relationship between the dependent variable (Y) and the expected value of the dependent variable given the independent variables [E(Y|X)] for the Poisson regression model. The dependent variable is distributed as Poisson. The purpose of this research was modifying regression correlation coefficient for Poisson regression model. We also compare the proposed modified regression correlation coefficient with the traditional regression correlation coefficient in the case of two or more independent variables, and having multicollinearity in independent variables. The result shows that the proposed regression correlation coefficient is better than the traditional regression correlation coefficient based on Bias and the Root Mean Square Error (RMSE).

Effect of a community intervention programme promoting social interactions on functional disability prevention for older adults: propensity score matching and instrumental variable analyses, JAGES Taketoyo study.

PubMed

Hikichi, Hiroyuki; Kondo, Naoki; Kondo, Katsunori; Aida, Jun; Takeda, Tokunori; Kawachi, Ichiro

2015-09-01

The efficacy of promoting social interactions to improve the health of older adults is not fully established due to residual confounding and selection bias. The government of Taketoyo town, Aichi Prefecture, Japan, developed a resident-centred community intervention programme called 'community salons', providing opportunities for social interactions among local older residents. To evaluate the impact of the programme, we conducted questionnaire surveys for all older residents of Taketoyo. We carried out a baseline survey in July 2006 (prior to the introduction of the programme) and assessed the onset of functional disability during March 2012. We analysed the data of 2421 older people. In addition to the standard Cox proportional hazard regression, we conducted Cox regression with propensity score matching (PSM) and an instrumental variable (IV) analysis, using the number of community salons within a radius of 350 m from the participant's home as an instrument. In the 5 years after the first salon was launched, the salon participants showed a 6.3% lower incidence of functional disability compared with non-participants. Even adjusting for sex, age, equivalent income, educational attainment, higher level activities of daily living and depression, the Cox adjusted HR for becoming disabled was 0.49 (95% CI 0.33 to 0.72). Similar results were observed using PSM (HR 0.52, 95% CI 0.33 to 0.83) and IV-Cox analysis (HR 0.50, 95% CI 0.34 to 0.74). A community health promotion programme focused on increasing social interactions among older adults may be effective in preventing the onset of disability. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

The Differential role of parenting, peers, and temperament for explaining interindividual differences in 18-months-olds' comforting and helping.

PubMed

Schuhmacher, Nils; Collard, Jenny; Kärtner, Joscha

2017-02-01

This study analyzes temperamental and social correlates of 18-month-olds' (N=58) instrumental helping (i.e., handing over out-of-reach objects) and comforting (i.e., alleviating experimenter's distress). While out-of-reach helping as a basic type of prosocial behavior was not associated with any of the social and temperamental variables, comforting was associated with maternal responsible parenting, day care attendance, and temperamental fear, accounting for 34% of the total variance in a corresponding regression model. The data of the present study suggest that, while simple instrumental helping seems to be a robust developmental phenomenon, comforting is associated with specific social experiences and child temperament that constitute interindividual differences and thereby help to explain the domain-specific development of prosociality. Copyright © 2017 Elsevier Inc. All rights reserved.

Assessing the impact of natural policy experiments on socioeconomic inequalities in health: how to apply commonly used quantitative analytical methods?

PubMed

Hu, Yannan; van Lenthe, Frank J; Hoffmann, Rasmus; van Hedel, Karen; Mackenbach, Johan P

2017-04-20

The scientific evidence-base for policies to tackle health inequalities is limited. Natural policy experiments (NPE) have drawn increasing attention as a means to evaluating the effects of policies on health. Several analytical methods can be used to evaluate the outcomes of NPEs in terms of average population health, but it is unclear whether they can also be used to assess the outcomes of NPEs in terms of health inequalities. The aim of this study therefore was to assess whether, and to demonstrate how, a number of commonly used analytical methods for the evaluation of NPEs can be applied to quantify the effect of policies on health inequalities. We identified seven quantitative analytical methods for the evaluation of NPEs: regression adjustment, propensity score matching, difference-in-differences analysis, fixed effects analysis, instrumental variable analysis, regression discontinuity and interrupted time-series. We assessed whether these methods can be used to quantify the effect of policies on the magnitude of health inequalities either by conducting a stratified analysis or by including an interaction term, and illustrated both approaches in a fictitious numerical example. All seven methods can be used to quantify the equity impact of policies on absolute and relative inequalities in health by conducting an analysis stratified by socioeconomic position, and all but one (propensity score matching) can be used to quantify equity impacts by inclusion of an interaction term between socioeconomic position and policy exposure. Methods commonly used in economics and econometrics for the evaluation of NPEs can also be applied to assess the equity impact of policies, and our illustrations provide guidance on how to do this appropriately. The low external validity of results from instrumental variable analysis and regression discontinuity makes these methods less desirable for assessing policy effects on population-level health inequalities. Increased use of the methods in social epidemiology will help to build an evidence base to support policy making in the area of health inequalities.

Toward a clearer portrayal of confounding bias in instrumental variable applications.

PubMed

Jackson, John W; Swanson, Sonja A

2015-07-01

Recommendations for reporting instrumental variable analyses often include presenting the balance of covariates across levels of the proposed instrument and levels of the treatment. However, such presentation can be misleading as relatively small imbalances among covariates across levels of the instrument can result in greater bias because of bias amplification. We introduce bias plots and bias component plots as alternative tools for understanding biases in instrumental variable analyses. Using previously published data on proposed preference-based, geography-based, and distance-based instruments, we demonstrate why presenting covariate balance alone can be problematic, and how bias component plots can provide more accurate context for bias from omitting a covariate from an instrumental variable versus non-instrumental variable analysis. These plots can also provide relevant comparisons of different proposed instruments considered in the same data. Adaptable code is provided for creating the plots.

Impact of Rehabilitation on Outcomes in Patients With Ischemic Stroke: A Nationwide Retrospective Cohort Study in Japan.

PubMed

Yagi, Maiko; Yasunaga, Hideo; Matsui, Hiroki; Morita, Kojiro; Fushimi, Kiyohide; Fujimoto, Masashi; Koyama, Teruyuki; Fujitani, Junko

2017-03-01

We aimed to examine the concurrent effects of timing and intensity of rehabilitation on improving activities of daily living (ADL) among patients with ischemic stroke. Using the Japanese Diagnosis Procedure Combination inpatient database, we retrospectively analyzed consecutive patients with ischemic stroke at admission who received rehabilitation (n=100 719) from April 2012 to March 2014. Early rehabilitation was defined as that starting within 3 days after admission. The average rehabilitation intensity per day was calculated as the total units of rehabilitation during hospitalization divided by the length of hospital stay. A multivariable logistic regression analysis with multiple imputation and an instrumental variable analysis were performed to examine the association of early and intensive rehabilitation with the proportion of improved ADL score. The proportion of improved ADL score was higher in the early and intensive rehabilitation group. The multivariable logistic regression analysis showed that significant improvements in ADL were observed for early rehabilitation (odds ratio: 1.08; 95% confidence interval: 1.04-1.13; P <0.01) and intensive rehabilitation of >5.0 U/d (odds ratio: 1.87; 95% confidence interval: 1.69-2.07; P <0.01). The instrumental variable analysis showed that an increased proportion of improved ADL was associated with early rehabilitation (risk difference: 2.8%; 95% confidence interval: 2.0-3.4%; P <0.001) and intensive rehabilitation (risk difference: 5.6%; 95% confidence interval: 4.6-6.6%; P <0.001). The present results suggested that early and intensive rehabilitation improved ADL during hospitalization in patients with ischemic stroke. © 2017 American Heart Association, Inc.

Hierarchical cluster-based partial least squares regression (HC-PLSR) is an efficient tool for metamodelling of nonlinear dynamic models.

PubMed

Tøndel, Kristin; Indahl, Ulf G; Gjuvsland, Arne B; Vik, Jon Olav; Hunter, Peter; Omholt, Stig W; Martens, Harald

2011-06-01

Deterministic dynamic models of complex biological systems contain a large number of parameters and state variables, related through nonlinear differential equations with various types of feedback. A metamodel of such a dynamic model is a statistical approximation model that maps variation in parameters and initial conditions (inputs) to variation in features of the trajectories of the state variables (outputs) throughout the entire biologically relevant input space. A sufficiently accurate mapping can be exploited both instrumentally and epistemically. Multivariate regression methodology is a commonly used approach for emulating dynamic models. However, when the input-output relations are highly nonlinear or non-monotone, a standard linear regression approach is prone to give suboptimal results. We therefore hypothesised that a more accurate mapping can be obtained by locally linear or locally polynomial regression. We present here a new method for local regression modelling, Hierarchical Cluster-based PLS regression (HC-PLSR), where fuzzy C-means clustering is used to separate the data set into parts according to the structure of the response surface. We compare the metamodelling performance of HC-PLSR with polynomial partial least squares regression (PLSR) and ordinary least squares (OLS) regression on various systems: six different gene regulatory network models with various types of feedback, a deterministic mathematical model of the mammalian circadian clock and a model of the mouse ventricular myocyte function. Our results indicate that multivariate regression is well suited for emulating dynamic models in systems biology. The hierarchical approach turned out to be superior to both polynomial PLSR and OLS regression in all three test cases. The advantage, in terms of explained variance and prediction accuracy, was largest in systems with highly nonlinear functional relationships and in systems with positive feedback loops. HC-PLSR is a promising approach for metamodelling in systems biology, especially for highly nonlinear or non-monotone parameter to phenotype maps. The algorithm can be flexibly adjusted to suit the complexity of the dynamic model behaviour, inviting automation in the metamodelling of complex systems.

Hierarchical Cluster-based Partial Least Squares Regression (HC-PLSR) is an efficient tool for metamodelling of nonlinear dynamic models

PubMed Central

2011-01-01

Background Deterministic dynamic models of complex biological systems contain a large number of parameters and state variables, related through nonlinear differential equations with various types of feedback. A metamodel of such a dynamic model is a statistical approximation model that maps variation in parameters and initial conditions (inputs) to variation in features of the trajectories of the state variables (outputs) throughout the entire biologically relevant input space. A sufficiently accurate mapping can be exploited both instrumentally and epistemically. Multivariate regression methodology is a commonly used approach for emulating dynamic models. However, when the input-output relations are highly nonlinear or non-monotone, a standard linear regression approach is prone to give suboptimal results. We therefore hypothesised that a more accurate mapping can be obtained by locally linear or locally polynomial regression. We present here a new method for local regression modelling, Hierarchical Cluster-based PLS regression (HC-PLSR), where fuzzy C-means clustering is used to separate the data set into parts according to the structure of the response surface. We compare the metamodelling performance of HC-PLSR with polynomial partial least squares regression (PLSR) and ordinary least squares (OLS) regression on various systems: six different gene regulatory network models with various types of feedback, a deterministic mathematical model of the mammalian circadian clock and a model of the mouse ventricular myocyte function. Results Our results indicate that multivariate regression is well suited for emulating dynamic models in systems biology. The hierarchical approach turned out to be superior to both polynomial PLSR and OLS regression in all three test cases. The advantage, in terms of explained variance and prediction accuracy, was largest in systems with highly nonlinear functional relationships and in systems with positive feedback loops. Conclusions HC-PLSR is a promising approach for metamodelling in systems biology, especially for highly nonlinear or non-monotone parameter to phenotype maps. The algorithm can be flexibly adjusted to suit the complexity of the dynamic model behaviour, inviting automation in the metamodelling of complex systems. PMID:21627852

Classification of Dust Days by Satellite Remotely Sensed Aerosol Products

NASA Technical Reports Server (NTRS)

Sorek-Hammer, M.; Cohen, A.; Levy, Robert C.; Ziv, B.; Broday, D. M.

2013-01-01

Considerable progress in satellite remote sensing (SRS) of dust particles has been seen in the last decade. From an environmental health perspective, such an event detection, after linking it to ground particulate matter (PM) concentrations, can proxy acute exposure to respirable particles of certain properties (i.e. size, composition, and toxicity). Being affected considerably by atmospheric dust, previous studies in the Eastern Mediterranean, and in Israel in particular, have focused on mechanistic and synoptic prediction, classification, and characterization of dust events. In particular, a scheme for identifying dust days (DD) in Israel based on ground PM10 (particulate matter of size smaller than 10 nm) measurements has been suggested, which has been validated by compositional analysis. This scheme requires information regarding ground PM10 levels, which is naturally limited in places with sparse ground-monitoring coverage. In such cases, SRS may be an efficient and cost-effective alternative to ground measurements. This work demonstrates a new model for identifying DD and non-DD (NDD) over Israel based on an integration of aerosol products from different satellite platforms (Moderate Resolution Imaging Spectroradiometer (MODIS) and Ozone Monitoring Instrument (OMI)). Analysis of ground-monitoring data from 2007 to 2008 in southern Israel revealed 67 DD, with more than 88 percent occurring during winter and spring. A Classification and Regression Tree (CART) model that was applied to a database containing ground monitoring (the dependent variable) and SRS aerosol product (the independent variables) records revealed an optimal set of binary variables for the identification of DD. These variables are combinations of the following primary variables: the calendar month, ground-level relative humidity (RH), the aerosol optical depth (AOD) from MODIS, and the aerosol absorbing index (AAI) from OMI. A logistic regression that uses these variables, coded as binary variables, demonstrated 93.2 percent correct classifications of DD and NDD. Evaluation of the combined CART-logistic regression scheme in an adjacent geographical region (Gush Dan) demonstrated good results. Using SRS aerosol products for DD and NDD, identification may enable us to distinguish between health, ecological, and environmental effects that result from exposure to these distinct particle populations.

Childhood antecedents of adult sense of belonging.

PubMed

Hagerty, Bonnie M; Williams, Reg Arthur; Oe, Hiroaki

2002-07-01

Sense of belonging has been proposed to be a basic human need, and deficits in sense of belonging have been linked to problems in social and psychological functioning. Yet, there is little evidence about what early life experiences contribute to sense of belonging. The purpose of this study was to examine potential childhood antecedents of adult sense of belonging. The sample consisted of 362 community college students ranging in age from 18 to 72 years, with a mean age of 26 years. Measures included the Sense of Belonging Instrument, the Parental Bonding Instrument, and the Childhood Adversity and Adolescent Deviance Instrument. Multiple regression analysis was used to correlate childhood antecedents with adult sense of belonging. The final reduced model included 12 variables, which accounted for 25% of the variance in sense of belonging. Significant positive antecedents with a relationship with sense of belonging were perceived caring by both mother and father while growing up, participation in high school athletic activity, and parental divorce. Significant negative variables with a relationship with sense of belonging included perceived overprotection of father, high school pregnancy, family financial problems while growing up, incest, and homosexuality. Knowledge of these factors should influence interventions with families regarding child-rearing and parenting practices, mediating the effects of crises during childhood such as divorce and teen pregnancy, and the interpersonal growth needs of teenagers. Copyright 2002 Wiley Periodicals, Inc.

Two-Stage Bayesian Model Averaging in Endogenous Variable Models*

PubMed Central

Lenkoski, Alex; Eicher, Theo S.; Raftery, Adrian E.

2013-01-01

Economic modeling in the presence of endogeneity is subject to model uncertainty at both the instrument and covariate level. We propose a Two-Stage Bayesian Model Averaging (2SBMA) methodology that extends the Two-Stage Least Squares (2SLS) estimator. By constructing a Two-Stage Unit Information Prior in the endogenous variable model, we are able to efficiently combine established methods for addressing model uncertainty in regression models with the classic technique of 2SLS. To assess the validity of instruments in the 2SBMA context, we develop Bayesian tests of the identification restriction that are based on model averaged posterior predictive p-values. A simulation study showed that 2SBMA has the ability to recover structure in both the instrument and covariate set, and substantially improves the sharpness of resulting coefficient estimates in comparison to 2SLS using the full specification in an automatic fashion. Due to the increased parsimony of the 2SBMA estimate, the Bayesian Sargan test had a power of 50 percent in detecting a violation of the exogeneity assumption, while the method based on 2SLS using the full specification had negligible power. We apply our approach to the problem of development accounting, and find support not only for institutions, but also for geography and integration as development determinants, once both model uncertainty and endogeneity have been jointly addressed. PMID:24223471

Threats to the Internal Validity of Experimental and Quasi-Experimental Research in Healthcare.

PubMed

Flannelly, Kevin J; Flannelly, Laura T; Jankowski, Katherine R B

2018-01-01

The article defines, describes, and discusses the seven threats to the internal validity of experiments discussed by Donald T. Campbell in his classic 1957 article: history, maturation, testing, instrument decay, statistical regression, selection, and mortality. These concepts are said to be threats to the internal validity of experiments because they pose alternate explanations for the apparent causal relationship between the independent variable and dependent variable of an experiment if they are not adequately controlled. A series of simple diagrams illustrate three pre-experimental designs and three true experimental designs discussed by Campbell in 1957 and several quasi-experimental designs described in his book written with Julian C. Stanley in 1966. The current article explains why each design controls for or fails to control for these seven threats to internal validity.

Falsification Testing of Instrumental Variables Methods for Comparative Effectiveness Research.

PubMed

Pizer, Steven D

2016-04-01

To demonstrate how falsification tests can be used to evaluate instrumental variables methods applicable to a wide variety of comparative effectiveness research questions. Brief conceptual review of instrumental variables and falsification testing principles and techniques accompanied by an empirical application. Sample STATA code related to the empirical application is provided in the Appendix. Comparative long-term risks of sulfonylureas and thiazolidinediones for management of type 2 diabetes. Outcomes include mortality and hospitalization for an ambulatory care-sensitive condition. Prescribing pattern variations are used as instrumental variables. Falsification testing is an easily computed and powerful way to evaluate the validity of the key assumption underlying instrumental variables analysis. If falsification tests are used, instrumental variables techniques can help answer a multitude of important clinical questions. © Health Research and Educational Trust.

Tutorial in Biostatistics: Instrumental Variable Methods for Causal Inference*

PubMed Central

Baiocchi, Michael; Cheng, Jing; Small, Dylan S.

2014-01-01

A goal of many health studies is to determine the causal effect of a treatment or intervention on health outcomes. Often, it is not ethically or practically possible to conduct a perfectly randomized experiment and instead an observational study must be used. A major challenge to the validity of observational studies is the possibility of unmeasured confounding (i.e., unmeasured ways in which the treatment and control groups differ before treatment administration which also affect the outcome). Instrumental variables analysis is a method for controlling for unmeasured confounding. This type of analysis requires the measurement of a valid instrumental variable, which is a variable that (i) is independent of the unmeasured confounding; (ii) affects the treatment; and (iii) affects the outcome only indirectly through its effect on the treatment. This tutorial discusses the types of causal effects that can be estimated by instrumental variables analysis; the assumptions needed for instrumental variables analysis to provide valid estimates of causal effects and sensitivity analysis for those assumptions; methods of estimation of causal effects using instrumental variables; and sources of instrumental variables in health studies. PMID:24599889

Long-term reliability of the Athabasca River (Alberta, Canada) as the water source for oil sands mining

PubMed Central

Sauchyn, David J.; St-Jacques, Jeannine-Marie; Luckman, Brian H.

2015-01-01

Exploitation of the Alberta oil sands, the world’s third-largest crude oil reserve, requires fresh water from the Athabasca River, an allocation of 4.4% of the mean annual flow. This allocation takes into account seasonal fluctuations but not long-term climatic variability and change. This paper examines the decadal-scale variability in river discharge in the Athabasca River Basin (ARB) with (i) a generalized least-squares (GLS) regression analysis of the trend and variability in gauged flow and (ii) a 900-y tree-ring reconstruction of the water-year flow of the Athabasca River at Athabasca, Alberta. The GLS analysis removes confounding transient trends related to the Pacific Decadal Oscillation (PDO) and Pacific North American mode (PNA). It shows long-term declining flows throughout the ARB. The tree-ring record reveals a larger range of flows and severity of hydrologic deficits than those captured by the instrumental records that are the basis for surface water allocation. It includes periods of sustained low flow of multiple decades in duration, suggesting the influence of the PDO and PNA teleconnections. These results together demonstrate that low-frequency variability must be considered in ARB water allocation, which has not been the case. We show that the current and projected surface water allocations from the Athabasca River for the exploitation of the Alberta oil sands are based on an untenable assumption of the representativeness of the short instrumental record. PMID:26392554

Instrumental variable applications using nursing home prescribing preferences in comparative effectiveness research.

PubMed

Huybrechts, Krista F; Gerhard, Tobias; Franklin, Jessica M; Levin, Raisa; Crystal, Stephen; Schneeweiss, Sebastian

2014-08-01

Nursing home residents are of particular interest for comparative effectiveness research given their susceptibility to adverse treatment effects and systematic exclusion from trials. However, the risk of residual confounding because of unmeasured markers of declining health using conventional analytic methods is high. We evaluated the validity of instrumental variable (IV) methods based on nursing home prescribing preference to mitigate such confounding, using psychotropic medications to manage behavioral problems in dementia as a case study. A cohort using linked data from Medicaid, Medicare, Minimum Data Set, and Online Survey, Certification and Reporting for 2001-2004 was established. Dual-eligible patients ≥65 years who initiated psychotropic medication use after admission were selected. Nursing home prescribing preference was characterized using mixed-effects logistic regression models. The plausibility of IV assumptions was explored, and the association between psychotropic medication class and 180-day mortality was estimated. High-prescribing and low-prescribing nursing homes differed by a factor of 2. Each preference-based IV measure described a substantial proportion of variation in psychotropic medication choice (β(IV → treatment): 0.22-0.36). Measured patient characteristics were well balanced across patient groups based on instrument status (52% average reduction in Mahalanobis distance). There was no evidence that instrument status was associated with markers of nursing home quality of care. Findings indicate that IV analyses using nursing home prescribing preference may be a useful approach in comparative effectiveness studies, and should extend naturally to analyses including untreated comparison groups, which are of great scientific interest but subject to even stronger confounding. Copyright © 2014 John Wiley & Sons, Ltd.

Improved estimation of PM2.5 using Lagrangian satellite-measured aerosol optical depth

NASA Astrophysics Data System (ADS)

Olivas Saunders, Rolando

Suspended particulate matter (aerosols) with aerodynamic diameters less than 2.5 mum (PM2.5) has negative effects on human health, plays an important role in climate change and also causes the corrosion of structures by acid deposition. Accurate estimates of PM2.5 concentrations are thus relevant in air quality, epidemiology, cloud microphysics and climate forcing studies. Aerosol optical depth (AOD) retrieved by the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument has been used as an empirical predictor to estimate ground-level concentrations of PM2.5 . These estimates usually have large uncertainties and errors. The main objective of this work is to assess the value of using upwind (Lagrangian) MODIS-AOD as predictors in empirical models of PM2.5. The upwind locations of the Lagrangian AOD were estimated using modeled backward air trajectories. Since the specification of an arrival elevation is somewhat arbitrary, trajectories were calculated to arrive at four different elevations at ten measurement sites within the continental United States. A systematic examination revealed trajectory model calculations to be sensitive to starting elevation. With a 500 m difference in starting elevation, the 48-hr mean horizontal separation of trajectory endpoints was 326 km. When the difference in starting elevation was doubled and tripled to 1000 m and 1500m, the mean horizontal separation of trajectory endpoints approximately doubled and tripled to 627 km and 886 km, respectively. A seasonal dependence of this sensitivity was also found: the smallest mean horizontal separation of trajectory endpoints was exhibited during the summer and the largest separations during the winter. A daily average AOD product was generated and coupled to the trajectory model in order to determine AOD values upwind of the measurement sites during the period 2003-2007. Empirical models that included in situ AOD and upwind AOD as predictors of PM2.5 were generated by multivariate linear regressions using the least squares method. The multivariate models showed improved performance over the single variable regression (PM2.5 and in situ AOD) models. The statistical significance of the improvement of the multivariate models over the single variable regression models was tested using the extra sum of squares principle. In many cases, even when the R-squared was high for the multivariate models, the improvement over the single models was not statistically significant. The R-squared of these multivariate models varied with respect to seasons, with the best performance occurring during the summer months. A set of seasonal categorical variables was included in the regressions to exploit this variability. The multivariate regression models that included these categorical seasonal variables performed better than the models that didn't account for seasonal variability. Furthermore, 71% of these regressions exhibited improvement over the single variable models that was statistically significant at a 95% confidence level.

Cortical thickness maturation and duration of music training: health-promoting activities shape brain development.

PubMed

Hudziak, James J; Albaugh, Matthew D; Ducharme, Simon; Karama, Sherif; Spottswood, Margaret; Crehan, Eileen; Evans, Alan C; Botteron, Kelly N

2014-11-01

To assess the extent to which playing a musical instrument is associated with cortical thickness development among healthy youths. Participants were part of the National Institutes of Health (NIH) Magnetic Resonance Imaging (MRI) Study of Normal Brain Development. This study followed a longitudinal design such that participants underwent MRI scanning and behavioral testing on up to 3 separate visits, occurring at 2-year intervals. MRI, IQ, and music training data were available for 232 youths (334 scans), ranging from 6 to 18 years of age. Cortical thickness was regressed against the number of years that each youth had played a musical instrument. Next, thickness was regressed against an "Age × Years of Playing" interaction term. Age, gender, total brain volume, and scanner were controlled for in analyses. Participant ID was entered as a random effect to account for within-person dependence. False discovery rate correction was applied (p ≤ .05). There was no association between thickness and years playing a musical instrument. The "Age × Years of Playing" interaction was associated with thickness in motor, premotor, and supplementary motor cortices, as well as prefrontal and parietal cortices. Follow-up analysis revealed that music training was associated with an increased rate of thickness maturation. Results were largely unchanged when IQ and handedness were included as covariates. Playing a musical instrument was associated with more rapid cortical thickness maturation within areas implicated in motor planning and coordination, visuospatial ability, and emotion and impulse regulation. However, given the quasi-experimental nature of this study, we cannot rule out the influence of confounding variables. Copyright © 2014 American Academy of Child and Adolescent Psychiatry. Published by Elsevier Inc. All rights reserved.

[Instruments for quantitative methods of nursing research].

PubMed

Vellone, E

2000-01-01

Instruments for quantitative nursing research are a mean to objectify and measure a variable or a phenomenon in the scientific research. There are direct instruments to measure concrete variables and indirect instruments to measure abstract concepts (Burns, Grove, 1997). Indirect instruments measure the attributes by which a concept is made of. Furthermore, there are instruments for physiologic variables (e.g. for the weight), observational instruments (Check-lists e Rating Scales), interviews, questionnaires, diaries and the scales (Check-lists, Rating Scales, Likert Scales, Semantic Differential Scales e Visual Anologue Scales). The choice to select an instrument or another one depends on the research question and design. Instruments research are very useful in research both to describe the variables and to see statistical significant relationships. Very carefully should be their use in the clinical practice for diagnostic assessment.

A Spanish-language patient safety questionnaire to measure medical and nursing students' attitudes and knowledge.

PubMed

Mira, José J; Navarro, Isabel M; Guilabert, Mercedes; Poblete, Rodrigo; Franco, Astolfo L; Jiménez, Pilar; Aquino, Margarita; Fernández-Trujillo, Francisco J; Lorenzo, Susana; Vitaller, Julián; de Valle, Yohana Díaz; Aibar, Carlos; Aranaz, Jesús M; De Pedro, José A

2015-08-01

To design and validate a questionnaire for assessing attitudes and knowledge about patient safety using a sample of medical and nursing students undergoing clinical training in Spain and four countries in Latin America. In this cross-sectional study, a literature review was carried out and total of 786 medical and nursing students were surveyed at eight universities from five countries (Chile, Colombia, El Salvador, Guatemala, and Spain) to develop and refine a Spanish-language questionnaire on knowledge and attitudes about patient safety. The scope of the questionnaire was based on five dimensions (factors) presented in studies related to patient safety culture found in PubMed and Scopus. Based on the five factors, 25 reactive items were developed. Composite reliability indexes and Cronbach's alpha statistics were estimated for each factor, and confirmatory factor analysis was conducted to assess validity. After a pilot test, the questionnaire was refined using confirmatory models, maximum-likelihood estimation, and the variance-covariance matrix (as input). Multiple linear regression models were used to confirm external validity, considering variables related to patient safety culture as dependent variables and the five factors as independent variables. The final instrument was a structured five-point Likert self-administered survey (the "Latino Student Patient Safety Questionnaire") consisting of 21 items grouped into five factors. Compound reliability indexes (Cronbach's alpha statistic) calculated for the five factors were about 0.7 or higher. The results of the multiple linear regression analyses indicated good model fit (goodness-of-fit index: 0.9). Item-total correlations were higher than 0.3 in all cases. The convergent-discriminant validity was adequate. The questionnaire designed and validated in this study assesses nursing and medical students' attitudes and knowledge about patient safety. This instrument could be used to indirectly evaluate whether or not students in health disciplines are acquiring and thus likely to put into practice the professional skills currently considered most appropriate for patient safety.

Suwannee River flow variability 1550-2005 CE reconstructed from a multispecies tree-ring network

NASA Astrophysics Data System (ADS)

Harley, Grant L.; Maxwell, Justin T.; Larson, Evan; Grissino-Mayer, Henri D.; Henderson, Joseph; Huffman, Jean

2017-01-01

Understanding the long-term natural flow regime of rivers enables resource managers to more accurately model water level variability. Models for managing water resources are important in Florida where population increase is escalating demand on water resources and infrastructure. The Suwannee River is the second largest river system in Florida and the least impacted by anthropogenic disturbance. We used new and existing tree-ring chronologies from multiple species to reconstruct mean March-October discharge for the Suwannee River during the period 1550-2005 CE and place the short period of instrumental flows (since 1927 CE) into historical context. We used a nested principal components regression method to maximize the use of chronologies with varying time coverage in the network. Modeled streamflow estimates indicated that instrumental period flow conditions do not adequately capture the full range of Suwannee River flow variability beyond the observational period. Although extreme dry and wet events occurred in the gage record, pluvials and droughts that eclipse the intensity and duration of instrumental events occurred during the 16-19th centuries. The most prolonged and severe dry conditions during the past 450 years occurred during the 1560s CE. In this prolonged drought period mean flow was estimated at 17% of the mean instrumental period flow. Significant peaks in spectral density at 2-7, 10, 45, and 85-year periodicities indicated the important influence of coupled oceanic-atmospheric processes on Suwannee River streamflow over the past four centuries, though the strength of these periodicities varied over time. Future water planning based on current flow expectations could prove devastating to natural and human systems if a prolonged and severe drought mirroring the 16th and 18th century events occurred. Future work in the region will focus on updating existing tree-ring chronologies and developing new collections from moisture-sensitive sites to improve understandings of past hydroclimate in the region.

Error-in-variables models in calibration

NASA Astrophysics Data System (ADS)

Lira, I.; Grientschnig, D.

2017-12-01

In many calibration operations, the stimuli applied to the measuring system or instrument under test are derived from measurement standards whose values may be considered to be perfectly known. In that case, it is assumed that calibration uncertainty arises solely from inexact measurement of the responses, from imperfect control of the calibration process and from the possible inaccuracy of the calibration model. However, the premise that the stimuli are completely known is never strictly fulfilled and in some instances it may be grossly inadequate. Then, error-in-variables (EIV) regression models have to be employed. In metrology, these models have been approached mostly from the frequentist perspective. In contrast, not much guidance is available on their Bayesian analysis. In this paper, we first present a brief summary of the conventional statistical techniques that have been developed to deal with EIV models in calibration. We then proceed to discuss the alternative Bayesian framework under some simplifying assumptions. Through a detailed example about the calibration of an instrument for measuring flow rates, we provide advice on how the user of the calibration function should employ the latter framework for inferring the stimulus acting on the calibrated device when, in use, a certain response is measured.

Pre-flight calibration and initial data processing for the ChemCam laser-induced breakdown spectroscopy instrument on the Mars Science Laboratory rover

USGS Publications Warehouse

Wiens, R.C.; Maurice, S.; Lasue, J.; Forni, O.; Anderson, R.B.; Clegg, S.; Bender, S.; Blaney, D.; Barraclough, B.L.; Cousin, A.; DeFlores, L.; Delapp, D.; Dyar, M.D.; Fabre, C.; Gasnault, O.; Lanza, N.; Mazoyer, J.; Melikechi, N.; Meslin, P.-Y.; Newsom, H.; Ollila, A.; Perez, R.; Tokar, R.; Vaniman, D.

2013-01-01

The ChemCam instrument package on the Mars Science Laboratory rover, Curiosity, is the first planetary science instrument to employ laser-induced breakdown spectroscopy (LIBS) to determine the compositions of geological samples on another planet. Pre-processing of the spectra involves subtracting the ambient light background, removing noise, removing the electron continuum, calibrating for the wavelength, correcting for the variable distance to the target, and applying a wavelength-dependent correction for the instrument response. Further processing of the data uses multivariate and univariate comparisons with a LIBS spectral library developed prior to launch as well as comparisons with several on-board standards post-landing. The level-2 data products include semi-quantitative abundances derived from partial least squares regression. A LIBS spectral library was developed using 69 rock standards in the form of pressed powder disks, glasses, and ceramics to minimize heterogeneity on the scale of the observation (350–550 μm dia.). The standards covered typical compositional ranges of igneous materials and also included sulfates, carbonates, and phyllosilicates. The provenance and elemental and mineralogical compositions of these standards are described. Spectral characteristics of this data set are presented, including the size distribution and integrated irradiances of the plasmas, and a proxy for plasma temperature as a function of distance from the instrument. Two laboratory-based clones of ChemCam reside in Los Alamos and Toulouse for the purpose of adding new spectra to the database as the need arises. Sensitivity to differences in wavelength correlation to spectral channels and spectral resolution has been investigated, indicating that spectral registration needs to be within half a pixel and resolution needs to match within 1.5 to 2.6 pixels. Absolute errors are tabulated for derived compositions of each major element in each standard using PLS regression. Sources of errors are investigated and discussed, and methods for improving the analytical accuracy of compositions derived from ChemCam spectra are discussed.

Eliminating Survivor Bias in Two-stage Instrumental Variable Estimators.

PubMed

Vansteelandt, Stijn; Walter, Stefan; Tchetgen Tchetgen, Eric

2018-07-01

Mendelian randomization studies commonly focus on elderly populations. This makes the instrumental variables analysis of such studies sensitive to survivor bias, a type of selection bias. A particular concern is that the instrumental variable conditions, even when valid for the source population, may be violated for the selective population of individuals who survive the onset of the study. This is potentially very damaging because Mendelian randomization studies are known to be sensitive to bias due to even minor violations of the instrumental variable conditions. Interestingly, the instrumental variable conditions continue to hold within certain risk sets of individuals who are still alive at a given age when the instrument and unmeasured confounders exert additive effects on the exposure, and moreover, the exposure and unmeasured confounders exert additive effects on the hazard of death. In this article, we will exploit this property to derive a two-stage instrumental variable estimator for the effect of exposure on mortality, which is insulated against the above described selection bias under these additivity assumptions.

Unitary Response Regression Models

ERIC Educational Resources Information Center

Lipovetsky, S.

2007-01-01

The dependent variable in a regular linear regression is a numerical variable, and in a logistic regression it is a binary or categorical variable. In these models the dependent variable has varying values. However, there are problems yielding an identity output of a constant value which can also be modelled in a linear or logistic regression with…

Impact of Financial Incentives for Prenatal Care on Birth Outcomes and Spending

PubMed Central

Rosenthal, Meredith B; Li, Zhonghe; Robertson, Audra D; Milstein, Arnold

2009-01-01

Objective To evaluate the impact of offering US$100 each to patients and their obstetricians or midwives for timely and comprehensive prenatal care on low birth weight, neonatal intensive care admissions, and total pediatric health care spending in the first year of life. Data Sources/Study Setting Claims and enrollment profiles of the predominantly low-income and Hispanic participants of a union-sponsored, health insurance plan from 1998 to 2001. Study Design Panel data analysis of outcomes and spending for participants and nonparticipants using instrumental variables to account for selection bias. Data Collection/Abstraction Methods Data provided were analyzed using t-tests and chi-squared tests to compare maternal characteristics and birth outcomes for incentive program participants and nonparticipants, with and without instrumental variables to address selection bias. Adjusted variables were analyzed using logistic regression models. Principle Findings Participation in the incentive program was significantly associated with lower odds of neonatal intensive care unit admission (0.45; 95 percent CI, 0.23–0.88) and spending in the first year of life (estimated elasticity of −0.07; 95 percent CI, −0.12 to −0.01), but not low birth weight (0.53; 95 percent CI, 0.23–1.18). Conclusion The use of patient and physician incentives may be an effective mechanism for improving use of recommended prenatal care and associated outcomes, particularly among low-income women. PMID:19619248

Identifying the key factors in increasing recycling and reducing residual household waste: a case study of the Flemish region of Belgium.

PubMed

Gellynck, X; Jacobsen, R; Verhelst, P

2011-10-01

The competent waste authority in the Flemish region of Belgium created the 'Implementation plan household waste 2003-2007' and the 'Implementation plan sustainable management 2010-2015' to comply with EU regulation. It incorporates European and regional requirements and describes strategies, goals, actions and instruments for the collection and treatment of household waste. The central mandatory goal is to reduce and maintain the amount of residual household waste to 150 kg per capita per year between 2010-2015. In literature, a reasonable body of information has been published on the effectiveness and efficiency of a variety of policy instruments, but the information is complex, often contradictory and difficult to interpret. The objective of this paper is to identify, through the development of a binary logistic regression model, those variables of the waste collection scheme that help municipalities to reach the mandatory 150 kg goal. The model covers a number of variables for household characteristics, provision of recycling services, frequency of waste collection and charging for waste services. This paper, however, is not about waste prevention and reuse. The dataset originates from 2003. Four out of 12 variables in the model contributed significantly: income per capita, cost of residual waste collection, collection frequency and separate curbside collection of organic waste. Copyright © 2011 Elsevier 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.

Examining the relationship between adolescent sexual risk-taking and perceptions of monitoring, communication, and parenting styles.

PubMed

Huebner, Angela J; Howell, Laurie W

2003-08-01

To examine the relationship between adolescent sexual risk-taking and perception of parental monitoring, frequency of parent-adolescent communication, and parenting style. The influences of gender, age, and ethnicity are also of interest. Data were collected from 7th-12th grade students in six rural, ethnically diverse school located in adjacent counties in a Southeastern state. A 174-item instrument assessed adolescent perceptions, behaviors and attitudes. Youth who had engaged in sexual intercourse (n = 1160) were included in the analyses. Logistic regression analyses were conducted to identify parenting practices that predicted high versus low-risk sex (defined by number of partners and use of condoms). Variables included parental monitoring, parent-adolescent communication, parenting style, parenting process interaction effects and interaction effects among these three parenting processes and gender, age and ethnicity. Analyses included frequencies, cross-tabulations and logistic regression. Parental monitoring, parental monitoring by parent-adolescent communication and parenting style by ethnicity were significant predictors of sexual risk-taking. No gender or age interactions were noted. Parental monitoring, parent-adolescent communication and parenting style are all important variables to consider when examining sexual risk-taking among adolescents.

Influence of operator experience on canal preparation time when using the rotary Ni-Ti ProFile system in simulated curved canals.

PubMed

Mesgouez, C; Rilliard, F; Matossian, L; Nassiri, K; Mandel, E

2003-03-01

The aim of this study was to determine the influence of operator experience on the time needed for canal preparation when using a rotary nickel-titanium (Ni-Ti) system. A total of 100 simulated curved canals in resin blocks were used. Four operators prepared a total of 25 canals each. The operators included practitioners with prior experience of the preparation technique, and practitioners with no experience. The working length for each instrument was precisely predetermined. All canals were instrumented with rotary Ni-Ti ProFile Variable Taper Series 29 engine-driven instruments using a high-torque handpiece (Maillefer, Ballaigues, Switzerland). The time taken to prepare each canal was recorded. Significant differences between the operators were analysed using the Student's t-test and the Kruskall-Wallis and Dunn nonparametric tests. Comparison of canal preparation times demonstrated a statistically significant difference between the four operators (P < 0.001). In the inexperienced group, a significant linear regression between canal number and preparation time occurred. Time required for canal preparation was inversely related to operator experience.

Neuropsychological test performance and prediction of functional capacities among Spanish-speaking and English-speaking patients with dementia.

PubMed

Loewenstein, D A; Rubert, M P; Argüelles, T; Duara, R

1995-03-01

Neuropsychological measures have been widely used by clinicians to assist them in making judgments regarding a cognitively impaired patient's ability to independently perform important activities of daily living. However, important questions have been raised concerning the degree to which neuropsychological instruments can predict a broad array of specific functional capacities required in the home environment. In the present study, we examined 127 English-speaking and 56 Spanish-speaking patients with Alzheimer's disease (AD) and determined the extent to which various neuropsychological measures and demographic variables were predictive of performance on functional measures administered within the clinical setting. Among English-speaking AD patients, Block Design and Digit-Span of the WAIS-R, as well as tests of language were among the strongest predictors of functional performance. For Spanish-speakers, Block Design, The Mini-Mental State Evaluation (MMSE) and Digit Span had the optimal predictive power. When stepwise regression was conducted on the entire sample of 183 subjects, ethnicity emerged as a statistically significant predictor variable on one of the seven functional tests (writing a check). Despite the predictive power of several of the neuropsychological measures for both groups, most of the variability in objective functional performance could not be explained in our regression models. As a result, it would appear prudent to include functional measures as part of a comprehensive neuropsychological evaluation for dementia.

Case-related factors affecting cutting errors of the proximal tibia in total knee arthroplasty assessed by computer navigation.

PubMed

Tsukeoka, Tadashi; Tsuneizumi, Yoshikazu; Yoshino, Kensuke; Suzuki, Mashiko

2018-05-01

The aim of this study was to determine factors that contribute to bone cutting errors of conventional instrumentation for tibial resection in total knee arthroplasty (TKA) as assessed by an image-free navigation system. The hypothesis is that preoperative varus alignment is a significant contributory factor to tibial bone cutting errors. This was a prospective study of a consecutive series of 72 TKAs. The amount of the tibial first-cut errors with reference to the planned cutting plane in both coronal and sagittal planes was measured by an image-free computer navigation system. Multiple regression models were developed with the amount of tibial cutting error in the coronal and sagittal planes as dependent variables and sex, age, disease, height, body mass index, preoperative alignment, patellar height (Insall-Salvati ratio) and preoperative flexion angle as independent variables. Multiple regression analysis showed that sex (male gender) (R = 0.25 p = 0.047) and preoperative varus alignment (R = 0.42, p = 0.001) were positively associated with varus tibial cutting errors in the coronal plane. In the sagittal plane, none of the independent variables was significant. When performing TKA in varus deformity, careful confirmation of the bone cutting surface should be performed to avoid varus alignment. The results of this study suggest technical considerations that can help a surgeon achieve more accurate component placement. IV.

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

PubMed

Atri, Ashutosh; Sharma, Manoj; Cottrell, Randall

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

Development and Validation of the Work-Related Well-Being Index: Analysis of the Federal Employee Viewpoint Survey.

PubMed

Eaton, Jennifer L; Mohr, David C; Hodgson, Michael J; McPhaul, Kathleen M

2018-02-01

To describe development and validation of the work-related well-being (WRWB) index. Principal components analysis was performed using Federal Employee Viewpoint Survey (FEVS) data (N = 392,752) to extract variables representing worker well-being constructs. Confirmatory factor analysis was performed to verify factor structure. To validate the WRWB index, we used multiple regression analysis to examine relationships with burnout associated outcomes. Principal Components Analysis identified three positive psychology constructs: "Work Positivity", "Co-worker Relationships", and "Work Mastery". An 11 item index explaining 63.5% of variance was achieved. The structural equation model provided a very good fit to the data. Higher WRWB scores were positively associated with all three employee experience measures examined in regression models. The new WRWB index shows promise as a valid and widely accessible instrument to assess worker well-being.

Development and Validation of the Work-Related Well-Being Index: Analysis of the Federal Employee Viewpoint Survey (FEVS).

PubMed

Eaton, Jennifer L; Mohr, David C; Hodgson, Michael J; McPhaul, Kathleen M

2017-10-11

To describe development and validation of the Work-Related Well-Being (WRWB) Index. Principal Components Analysis was performed using Federal Employee Viewpoint Survey (FEVS) data (N = 392,752) to extract variables representing worker well-being constructs. Confirmatory factor analysis was performed to verify factor structure. To validate the WRWB index, we used multiple regression analysis to examine relationships with burnout associated outcomes. PCA identified three positive psychology constructs: "Work Positivity", "Co-worker Relationships", and "Work Mastery". An 11 item index explaining 63.5% of variance was achieved. The structural equation model provided a very good fit to the data. Higher WRWB scores were positively associated with all 3 employee experience measures examined in regression models. The new WRWB index shows promise as a valid and widely accessible instrument to assess worker well-being.

The Relationship of Clinical Nurses' Perceptions of Structural and Psychological Empowerment and Engagement on Their Unit.

PubMed

DiNapoli, Jean Marie; O'Flaherty, Deirdre; Musil, Carol; Clavelle, Joanne T; Fitzpatrick, Joyce J

2016-02-01

The purpose of this study was to describe relationships between structural empowerment, psychological empowerment, and engagement among clinical nurses. Empowerment and engagement are key drivers of retention and quality in healthcare. Creating an empowering culture and an engaged staff supports initiatives that are essential for positive work environments. A survey of 280 nurses in a national conference was conducted using the Conditions of Work Effectiveness, Psychological Empowerment Instrument, and the Utrecht Work Engagement Scale. Pearson correlation coefficients and multiple regression analysis were used to determine relationships between demographic data and study variables. Overall, nurses had high perceptions of structural empowerment and psychological empowerment and were moderately engaged. Also, significant positive relationships were found between the key study variables. Results show positive correlations between empowerment and perceived engagement among clinical nurses.

Above ground biomass and tree species richness estimation with airborne lidar in tropical Ghana forests

NASA Astrophysics Data System (ADS)

Vaglio Laurin, Gaia; Puletti, Nicola; Chen, Qi; Corona, Piermaria; Papale, Dario; Valentini, Riccardo

2016-10-01

Estimates of forest aboveground biomass are fundamental for carbon monitoring and accounting; delivering information at very high spatial resolution is especially valuable for local management, conservation and selective logging purposes. In tropical areas, hosting large biomass and biodiversity resources which are often threatened by unsustainable anthropogenic pressures, frequent forest resources monitoring is needed. Lidar is a powerful tool to estimate aboveground biomass at fine resolution; however its application in tropical forests has been limited, with high variability in the accuracy of results. Lidar pulses scan the forest vertical profile, and can provide structure information which is also linked to biodiversity. In the last decade the remote sensing of biodiversity has received great attention, but few studies focused on the use of lidar for assessing tree species richness in tropical forests. This research aims at estimating aboveground biomass and tree species richness using discrete return airborne lidar in Ghana forests. We tested an advanced statistical technique, Multivariate Adaptive Regression Splines (MARS), which does not require assumptions on data distribution or on the relationships between variables, being suitable for studying ecological variables. We compared the MARS regression results with those obtained by multilinear regression and found that both algorithms were effective, but MARS provided higher accuracy either for biomass (R2 = 0.72) and species richness (R2 = 0.64). We also noted strong correlation between biodiversity and biomass field values. Even if the forest areas under analysis are limited in extent and represent peculiar ecosystems, the preliminary indications produced by our study suggest that instrument such as lidar, specifically useful for pinpointing forest structure, can also be exploited as a support for tree species richness assessment.

The influence of market deregulation on fast food consumption and body mass index: a cross-national time series analysis.

PubMed

De Vogli, Roberto; Kouvonen, Anne; Gimeno, David

2014-02-01

To investigate the effect of fast food consumption on mean population body mass index (BMI) and explore the possible influence of market deregulation on fast food consumption and BMI. The within-country association between fast food consumption and BMI in 25 high-income member countries of the Organisation for Economic Co-operation and Development between 1999 and 2008 was explored through multivariate panel regression models, after adjustment for per capita gross domestic product, urbanization, trade openness, lifestyle indicators and other covariates. The possible mediating effect of annual per capita intake of soft drinks, animal fats and total calories on the association between fast food consumption and BMI was also analysed. Two-stage least squares regression models were conducted, using economic freedom as an instrumental variable, to study the causal effect of fast food consumption on BMI. After adjustment for covariates, each 1-unit increase in annual fast food transactions per capita was associated with an increase of 0.033 kg/m2 in age-standardized BMI (95% confidence interval, CI: 0.013-0.052). Only the intake of soft drinks--not animal fat or total calories--mediated the observed association (β: 0.030; 95% CI: 0.010-0.050). Economic freedom was an independent predictor of fast food consumption (β: 0.27; 95% CI: 0.16-0.37). When economic freedom was used as an instrumental variable, the association between fast food and BMI weakened but remained significant (β: 0.023; 95% CI: 0.001-0.045). Fast food consumption is an independent predictor of mean BMI in high-income countries. Market deregulation policies may contribute to the obesity epidemic by facilitating the spread of fast food.

The association of sociodemographic and objectively-assessed disease variables with fatigue in systemic sclerosis: an analysis of 785 Canadian Scleroderma Research Group Registry patients.

PubMed

Levis, Brooke; Kwakkenbos, Linda; Hudson, Marie; Baron, Murray; Thombs, Brett D

2017-02-01

Fatigue is prevalent among patients with systemic sclerosis (SSc). To date, studies investigating fatigue in SSc have been hampered by the instruments used to measure fatigue in SSc and have included patient-reported rather than objectively-rated measures of disease. The Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F) scale is a validated measure for assessing fatigue in SSc that, compared to other instruments, provides good coverage of the full range of the fatigue spectrum. The objective of this study was to assess sociodemographic and objectively-rated disease-related associates of fatigue, as measured by the FACIT-F, in a large sample of patients with SSc. Fatigue was assessed using the FACIT-F scale. Disease severity was assessed using Medsger's severity scale. Multivariable linear regression was performed to assess the independent associations between sociodemographic and medical variables and fatigue. Among 785 patients, the mean FACIT-F score was 32.2 (SD = 12.1). Being age 40-49 (reference = 60+; standardized regression coefficient (β) = -0.11), less than post-secondary education (β = 0.07), having more medical comorbidities (β = -0.11) and more severe muscle (β = -0.10), gastrointestinal (β = -0.15), lung (β = -0.13), and general system disease severity (β = -0.13) were independently associated with more fatigue (p < 0.05). Fatigue in SSc was independently associated with more severe disease. These data contribute to a better understanding of fatigue in SSc and help inform patient-centered research in SSc.

Implementation of Instrumental Variable Bounds for Data Missing Not at Random.

PubMed

Marden, Jessica R; Wang, Linbo; Tchetgen, Eric J Tchetgen; Walter, Stefan; Glymour, M Maria; Wirth, Kathleen E

2018-05-01

Instrumental variables are routinely used to recover a consistent estimator of an exposure causal effect in the presence of unmeasured confounding. Instrumental variable approaches to account for nonignorable missing data also exist but are less familiar to epidemiologists. Like instrumental variables for exposure causal effects, instrumental variables for missing data rely on exclusion restriction and instrumental variable relevance assumptions. Yet these two conditions alone are insufficient for point identification. For estimation, researchers have invoked a third assumption, typically involving fairly restrictive parametric constraints. Inferences can be sensitive to these parametric assumptions, which are typically not empirically testable. The purpose of our article is to discuss another approach for leveraging a valid instrumental variable. Although the approach is insufficient for nonparametric identification, it can nonetheless provide informative inferences about the presence, direction, and magnitude of selection bias, without invoking a third untestable parametric assumption. An important contribution of this article is an Excel spreadsheet tool that can be used to obtain empirical evidence of selection bias and calculate bounds and corresponding Bayesian 95% credible intervals for a nonidentifiable population proportion. For illustrative purposes, we used the spreadsheet tool to analyze HIV prevalence data collected by the 2007 Zambia Demographic and Health Survey (DHS).

Income and Child Maltreatment in Unmarried Families: Evidence from the Earned Income Tax Credit.

PubMed

Berger, Lawrence M; Font, Sarah A; Slack, Kristen S; Waldfogel, Jane

2017-12-01

This study estimates the associations of income with both (self-reported) child protective services (CPS) involvement and parenting behaviors that proxy for child abuse and neglect risk among unmarried families. Our primary strategy follows the instrumental variables (IV) approach employed by Dahl and Lochner (2012), which leverages variation between states and over time in the generosity of the total state and federal Earned Income Tax Credit for which a family is eligible to identify exogenous variation in family income. As a robustness check, we also estimate standard OLS regressions (linear probability models), reduced form OLS regressions, and OLS regressions with the inclusion of a control function (each with and without family-specific fixed effects). Our micro-level data are drawn from the Fragile Families and Child Wellbeing Study, a longitudinal birth-cohort of relatively disadvantaged urban children who have been followed from birth to age nine. Results suggest that an exogenous increase in income is associated with reductions in behaviorally-approximated child neglect and CPS involvement, particularly among low-income single-mother families.

Estimators for longitudinal latent exposure models: examining measurement model assumptions.

PubMed

Sánchez, Brisa N; Kim, Sehee; Sammel, Mary D

2017-06-15

Latent variable (LV) models are increasingly being used in environmental epidemiology as a way to summarize multiple environmental exposures and thus minimize statistical concerns that arise in multiple regression. LV models may be especially useful when multivariate exposures are collected repeatedly over time. LV models can accommodate a variety of assumptions but, at the same time, present the user with many choices for model specification particularly in the case of exposure data collected repeatedly over time. For instance, the user could assume conditional independence of observed exposure biomarkers given the latent exposure and, in the case of longitudinal latent exposure variables, time invariance of the measurement model. Choosing which assumptions to relax is not always straightforward. We were motivated by a study of prenatal lead exposure and mental development, where assumptions of the measurement model for the time-changing longitudinal exposure have appreciable impact on (maximum-likelihood) inferences about the health effects of lead exposure. Although we were not particularly interested in characterizing the change of the LV itself, imposing a longitudinal LV structure on the repeated multivariate exposure measures could result in high efficiency gains for the exposure-disease association. We examine the biases of maximum likelihood estimators when assumptions about the measurement model for the longitudinal latent exposure variable are violated. We adapt existing instrumental variable estimators to the case of longitudinal exposures and propose them as an alternative to estimate the health effects of a time-changing latent predictor. We show that instrumental variable estimators remain unbiased for a wide range of data generating models and have advantages in terms of mean squared error. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Independent variable complexity for regional regression of the flow duration curve in ungauged basins

NASA Astrophysics Data System (ADS)

Fouad, Geoffrey; Skupin, André; Hope, Allen

2016-04-01

The flow duration curve (FDC) is one of the most widely used tools to quantify streamflow. Its percentile flows are often required for water resource applications, but these values must be predicted for ungauged basins with insufficient or no streamflow data. Regional regression is a commonly used approach for predicting percentile flows that involves identifying hydrologic regions and calibrating regression models to each region. The independent variables used to describe the physiographic and climatic setting of the basins are a critical component of regional regression, yet few studies have investigated their effect on resulting predictions. In this study, the complexity of the independent variables needed for regional regression is investigated. Different levels of variable complexity are applied for a regional regression consisting of 918 basins in the US. Both the hydrologic regions and regression models are determined according to the different sets of variables, and the accuracy of resulting predictions is assessed. The different sets of variables include (1) a simple set of three variables strongly tied to the FDC (mean annual precipitation, potential evapotranspiration, and baseflow index), (2) a traditional set of variables describing the average physiographic and climatic conditions of the basins, and (3) a more complex set of variables extending the traditional variables to include statistics describing the distribution of physiographic data and temporal components of climatic data. The latter set of variables is not typically used in regional regression, and is evaluated for its potential to predict percentile flows. The simplest set of only three variables performed similarly to the other more complex sets of variables. Traditional variables used to describe climate, topography, and soil offered little more to the predictions, and the experimental set of variables describing the distribution of basin data in more detail did not improve predictions. These results are largely reflective of cross-correlation existing in hydrologic datasets, and highlight the limited predictive power of many traditionally used variables for regional regression. A parsimonious approach including fewer variables chosen based on their connection to streamflow may be more efficient than a data mining approach including many different variables. Future regional regression studies may benefit from having a hydrologic rationale for including different variables and attempting to create new variables related to streamflow.

Evaluating the sensitivity of EQ-5D in a sample of patients with type 2 diabetes mellitus in two tertiary health care facilities in Nigeria.

PubMed

Ekwunife, Obinna Ikechukwu; Ezenduka, Charles C; Uzoma, Bede Emeka

2016-01-12

The EQ-5D instrument is arguably the most well-known and commonly used generic measure of health status internationally. Although the instrument has been employed in outcomes studies of diabetes mellitus in many countries, it has not yet been used in Nigeria. This study was carried out to assess the sensitivity of the EQ-5D instrument in a sample of Nigerian patients with type 2 diabetes mellitus (T2DM). A cross-sectional study was conducted using the EQ-5D instrument to assess the self-reported quality of life of patients with T2DM attending two tertiary healthcare facilities in south eastern Nigeria consenting patients completed the questionnaire while waiting to see a doctor. A priori hypotheses were examined using multiple regression analysis to model the relationship between the dependent variables (EQ VAS and EQ-5D Index) and hypothesized independent variables. A total of 226 patients with T2DM participated in the study. The average age of participants was 57 years (standard deviation 10 years) and 61.1% were male. The EQ VAS score and EQ-5D index averaged 66.19 (standard deviation 15.42) and 0.78 (standard deviation 0.21) respectively. Number of diabetic complications, number of co-morbidities, patient's age and being educated predicted EQ VAS score by -6.76, -6.15, -0.22, and 4.51 respectively. Also, number of diabetic complications, number of co-morbidities, patient's age and being educated predicted EQ-5D index by -0.12, -0.07, -0.003, and 0.06 respectively.. Our findings indicate that the EQ-5D could adequately capture the burden of type 2 diabetes and related complications among Nigerian patients.

Phonation Quotient in Women: A Measure of Vocal Efficiency Using Three Aerodynamic Instruments.

PubMed

Joshi, Ashwini; Watts, Christopher R

2017-03-01

The purpose of this study was to examine measures of vital capacity and phonation quotient across three age groups in women using three different aerodynamic instruments representing low-tech and high-tech options. This study has a prospective, repeated measures design. Fifteen women in each age group of 25-39 years, 40-59 years, and 60-79 years were assessed using maximum phonation time and vital capacity obtained from three aerodynamic instruments: a handheld analog windmill type spirometer, a handheld digital spirometer, and the Phonatory Aerodynamic System (PAS), Model 6600. Phonation quotient was calculated using vital capacity from each instrument. Analyses of variance were performed to test for main effects of the instruments and age on vital capacity and derived phonation quotient. Pearson product moment correlation was performed to assess measurement reliability (parallel forms) between the instruments. Regression equations, scatterplots, and coefficients of determination were also calculated. Statistically significant differences were found in vital capacity measures for the digital spirometer compared with the windmill-type spirometer and PAS across age groups. Strong positive correlations were present between all three instruments for both vital capacity and derived phonation quotient measurements. Measurement precision for the digital spirometer was lower than the windmill spirometer compared with the PAS. However, all three instruments had strong measurement reliability. Additionally, age did not have an effect on the measurement across instruments. These results are consistent with previous literature reporting data from male speakers and support the use of low-tech options for measurement of basic aerodynamic variables associated with voice production. Copyright © 2017 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

Social Media Use and Well-Being in People with Physical Disabilities: Influence of SNS and Online Community Uses on Social Support, Depression, and Psychological Disposition.

PubMed

Lee, H Erin; Cho, Jaehee

2018-04-13

This study examined the relationships across social media use, social support, depression, and general psychological disposition among people with movement or mobility disabilities in Korea. First, with survey data (n = 91) collected from users of social network sites (SNSs) and online communities, hypotheses regarding positive associations between intensity of an individual's engagement in social media and four different types of social support-emotional, instrumental, informational, and appraisal support-were tested as well as hypotheses regarding mediation effects of the social support variables in the association between social media use and depression. Second, through focus group interviews (n = 15), influences of social media use on social support were more thoroughly explored as well as their influences on general psychological disposition. Results from hierarchical regression analyses confirmed that both intensity of SNS use and online community use significantly predicted instrumental, informational, and appraisal support, while they did not predict emotional support. Further regression and Sobel tests showed that higher levels of intensity of SNS use and of online community use both led to lower levels of depression through the mediation of instrumental and informational support. Analysis of the interviews further revealed the positive roles of social media use in building social support and healthy psychological dispositions. However, analysis also revealed some negative consequences of and limitations to social media use for those with physical disabilities. These findings expand our knowledge of the context and implications of engaging in online social activities for people with physical disabilities.

Sadism and Violent Reoffending in Sexual Offenders.

PubMed

Eher, Reinhard; Schilling, Frank; Hansmann, Brigitte; Pumberger, Tanja; Nitschke, Joachim; Habermeyer, Elmar; Mokros, Andreas

2016-02-01

A diagnosis of sadism in sexual offenders is commonly regarded as indicative of high risk for violent reoffending. The purpose of the current two studies was to evaluate whether sadism is indeed associated with higher rates of violent (including sexual) reoffending. In Study 1 (meta-analysis), the rate of violent and sexual recidivism was assessed across seven samples of male sex offenders (total N = 2,169) as a function of diagnoses of sexual sadism. In Study 2 (N = 768) the outcome (violent recidivism yes/no) was regressed on sadism, along with behavioral indicators of sexually sadistic offending, and scores from violence risk assessment instruments. In Study 1 (meta-analysis), the overall risk of sadists compared with nonsadists with respect to violent (including sexual contact) reoffending was slightly elevated (by a factor of 1.18), yet not significantly increased. Similarly, the risk of sexual reoffending among sadists was slightly, but not significantly, higher than among nonsadists (factor 1.38). According to Study 2, only a measure of sadistic behavior, not the clinical diagnosis, was associated with violent reoffending. This association, however, was not present once age and customary risk assessment instruments for violence risk were included in the regression. A clinical diagnosis of sexual sadism and behavioral measures of sadism are related to the risk of violent reoffending in sexual offenders. These associations, however, are weak and do not hold once variables relevant for the prediction of violence are controlled for. At the individual level, the risk for future violence in sadists can therefore be adequately described by customary risk assessment instruments. © The Author(s) 2015.

Instrumental intelligent test of food sensory quality as mimic of human panel test combining multiple cross-perception sensors and data fusion.

PubMed

Ouyang, Qin; Zhao, Jiewen; Chen, Quansheng

2014-09-02

Instrumental test of food quality using perception sensors instead of human panel test is attracting massive attention recently. A novel cross-perception multi-sensors data fusion imitating multiple mammal perception was proposed for the instrumental test in this work. First, three mimic sensors of electronic eye, electronic nose and electronic tongue were used in sequence for data acquisition of rice wine samples. Then all data from the three different sensors were preprocessed and merged. Next, three cross-perception variables i.e., color, aroma and taste, were constructed using principal components analysis (PCA) and multiple linear regression (MLR) which were used as the input of models. MLR, back-propagation artificial neural network (BPANN) and support vector machine (SVM) were comparatively used for modeling, and the instrumental test was achieved for the comprehensive quality of samples. Results showed the proposed cross-perception multi-sensors data fusion presented obvious superiority to the traditional data fusion methodologies, also achieved a high correlation coefficient (>90%) with the human panel test results. This work demonstrated that the instrumental test based on the cross-perception multi-sensors data fusion can actually mimic the human test behavior, therefore is of great significance to ensure the quality of products and decrease the loss of the manufacturers. Copyright © 2014 Elsevier B.V. All rights reserved.

Kepler AutoRegressive Planet Search

NASA Astrophysics Data System (ADS)

Feigelson, Eric

NASA's Kepler mission is the source of more exoplanets than any other instrument, but the discovery depends on complex statistical analysis procedures embedded in the Kepler pipeline. A particular challenge is mitigating irregular stellar variability without loss of sensitivity to faint periodic planetary transits. This proposal presents a two-stage alternative analysis procedure. First, parametric autoregressive ARFIMA models, commonly used in econometrics, remove most of the stellar variations. Second, a novel matched filter is used to create a periodogram from which transit-like periodicities are identified. This analysis procedure, the Kepler AutoRegressive Planet Search (KARPS), is confirming most of the Kepler Objects of Interest and is expected to identify additional planetary candidates. The proposed research will complete application of the KARPS methodology to the prime Kepler mission light curves of 200,000: stars, and compare the results with Kepler Objects of Interest obtained with the Kepler pipeline. We will then conduct a variety of astronomical studies based on the KARPS results. Important subsamples will be extracted including Habitable Zone planets, hot super-Earths, grazing-transit hot Jupiters, and multi-planet systems. Groundbased spectroscopy of poorly studied candidates will be performed to better characterize the host stars. Studies of stellar variability will then be pursued based on KARPS analysis. The autocorrelation function and nonstationarity measures will be used to identify spotted stars at different stages of autoregressive modeling. Periodic variables with folded light curves inconsistent with planetary transits will be identified; they may be eclipsing or mutually-illuminating binary star systems. Classification of stellar variables with KARPS-derived statistical properties will be attempted. KARPS procedures will then be applied to archived K2 data to identify planetary transits and characterize stellar variability.

Bias and Bias Correction in Multi-Site Instrumental Variables Analysis of Heterogeneous Mediator Effects

ERIC Educational Resources Information Center

Reardon, Sean F.; Unlu, Faith; Zhu, Pei; Bloom, Howard

2013-01-01

We explore the use of instrumental variables (IV) analysis with a multi-site randomized trial to estimate the effect of a mediating variable on an outcome in cases where it can be assumed that the observed mediator is the only mechanism linking treatment assignment to outcomes, as assumption known in the instrumental variables literature as the…

Correspondence between EQ-5D health state classifications and EQ VAS scores.

PubMed

Whynes, David K

2008-11-07

The EQ-5D health-related quality of life instrument comprises a health state classification followed by a health evaluation using a visual analogue scale (VAS). The EQ-5D has been employed frequently in economic evaluations, yet the relationship between the two parts of the instrument remains ill-understood. In this paper, we examine the correspondence between VAS scores and health state classifications for a large sample, and identify variables which contribute to determining the VAS scores independently of the health states as classified. A UK trial of management of low-grade abnormalities detected on screening for cervical pre-cancer (TOMBOLA) provided EQ-5D data for over 3,000 women. Information on distress and multi-dimensional health locus of control had been collected using other instruments. A linear regression model was fitted, with VAS score as the dependent variable. Independent variables comprised EQ-5D health state classifications, distress, locus of control, and socio-demographic characteristics. Equivalent EQ-5D and distress data, collected at twelve months, were available for over 2,000 of the women, enabling us to predict changes in VAS score over time from changes in EQ-5D classification and distress. In addition to EQ-5D health state classification, VAS score was influenced by the subject's perceived locus of control, and by her age, educational attainment, ethnic origin and smoking behaviour. Although the EQ-5D classification includes a distress dimension, the independent measure of distress was an additional determinant of VAS score. Changes in VAS score over time were explained by changes in both EQ-5D severities and distress. Women allocated to the experimental management arm of the trial reported an increase in VAS score, independently of any changes in health state and distress. In this sample, EQ VAS scores were predictable from the EQ-5D health state classification, although there also existed other group variables which contributed systematically and independently towards determining such scores. These variables comprised psychological disposition, socio-demographic factors such as age and education, clinically-important distress, and the clinical intervention itself. ISRCTN34841617.

Power and instrument strength requirements for Mendelian randomization studies using multiple genetic variants.

PubMed

Pierce, Brandon L; Ahsan, Habibul; Vanderweele, Tyler J

2011-06-01

Mendelian Randomization (MR) studies assess the causality of an exposure-disease association using genetic determinants [i.e. instrumental variables (IVs)] of the exposure. Power and IV strength requirements for MR studies using multiple genetic variants have not been explored. We simulated cohort data sets consisting of a normally distributed disease trait, a normally distributed exposure, which affects this trait and a biallelic genetic variant that affects the exposure. We estimated power to detect an effect of exposure on disease for varying allele frequencies, effect sizes and samples sizes (using two-stage least squares regression on 10,000 data sets-Stage 1 is a regression of exposure on the variant. Stage 2 is a regression of disease on the fitted exposure). Similar analyses were conducted using multiple genetic variants (5, 10, 20) as independent or combined IVs. We assessed IV strength using the first-stage F statistic. Simulations of realistic scenarios indicate that MR studies will require large (n > 1000), often very large (n > 10,000), sample sizes. In many cases, so-called 'weak IV' problems arise when using multiple variants as independent IVs (even with as few as five), resulting in biased effect estimates. Combining genetic factors into fewer IVs results in modest power decreases, but alleviates weak IV problems. Ideal methods for combining genetic factors depend upon knowledge of the genetic architecture underlying the exposure. The feasibility of well-powered, unbiased MR studies will depend upon the amount of variance in the exposure that can be explained by known genetic factors and the 'strength' of the IV set derived from these genetic factors.

[Detecting the moisture content of forest surface soil based on the microwave remote sensing technology.

PubMed

Li, Ming Ze; Gao, Yuan Ke; Di, Xue Ying; Fan, Wen Yi

2016-03-01

The moisture content of forest surface soil is an important parameter in forest ecosystems. It is practically significant for forest ecosystem related research to use microwave remote sensing technology for rapid and accurate estimation of the moisture content of forest surface soil. With the aid of TDR-300 soil moisture content measuring instrument, the moisture contents of forest surface soils of 120 sample plots at Tahe Forestry Bureau of Daxing'anling region in Heilongjiang Province were measured. Taking the moisture content of forest surface soil as the dependent variable and the polarization decomposition parameters of C band Quad-pol SAR data as independent variables, two types of quantitative estimation models (multilinear regression model and BP-neural network model) for predicting moisture content of forest surface soils were developed. The spatial distribution of moisture content of forest surface soil on the regional scale was then derived with model inversion. Results showed that the model precision was 86.0% and 89.4% with RMSE of 3.0% and 2.7% for the multilinear regression model and the BP-neural network model, respectively. It indicated that the BP-neural network model had a better performance than the multilinear regression model in quantitative estimation of the moisture content of forest surface soil. The spatial distribution of forest surface soil moisture content in the study area was then obtained by using the BP neural network model simulation with the Quad-pol SAR data.

Modelling fourier regression for time series data- a case study: modelling inflation in foods sector in Indonesia

NASA Astrophysics Data System (ADS)

Prahutama, Alan; Suparti; Wahyu Utami, Tiani

2018-03-01

Regression analysis is an analysis to model the relationship between response variables and predictor variables. The parametric approach to the regression model is very strict with the assumption, but nonparametric regression model isn’t need assumption of model. Time series data is the data of a variable that is observed based on a certain time, so if the time series data wanted to be modeled by regression, then we should determined the response and predictor variables first. Determination of the response variable in time series is variable in t-th (yt), while the predictor variable is a significant lag. In nonparametric regression modeling, one developing approach is to use the Fourier series approach. One of the advantages of nonparametric regression approach using Fourier series is able to overcome data having trigonometric distribution. In modeling using Fourier series needs parameter of K. To determine the number of K can be used Generalized Cross Validation method. In inflation modeling for the transportation sector, communication and financial services using Fourier series yields an optimal K of 120 parameters with R-square 99%. Whereas if it was modeled by multiple linear regression yield R-square 90%.

Variable Selection for Nonparametric Quantile Regression via Smoothing Spline AN OVA

PubMed Central

Lin, Chen-Yen; Bondell, Howard; Zhang, Hao Helen; Zou, Hui

2014-01-01

Quantile regression provides a more thorough view of the effect of covariates on a response. Nonparametric quantile regression has become a viable alternative to avoid restrictive parametric assumption. The problem of variable selection for quantile regression is challenging, since important variables can influence various quantiles in different ways. We tackle the problem via regularization in the context of smoothing spline ANOVA models. The proposed sparse nonparametric quantile regression (SNQR) can identify important variables and provide flexible estimates for quantiles. Our numerical study suggests the promising performance of the new procedure in variable selection and function estimation. Supplementary materials for this article are available online. PMID:24554792

Power calculator for instrumental variable analysis in pharmacoepidemiology

PubMed Central

Walker, Venexia M; Davies, Neil M; Windmeijer, Frank; Burgess, Stephen; Martin, Richard M

2017-01-01

Abstract Background Instrumental variable analysis, for example with physicians’ prescribing preferences as an instrument for medications issued in primary care, is an increasingly popular method in the field of pharmacoepidemiology. Existing power calculators for studies using instrumental variable analysis, such as Mendelian randomization power calculators, do not allow for the structure of research questions in this field. This is because the analysis in pharmacoepidemiology will typically have stronger instruments and detect larger causal effects than in other fields. Consequently, there is a need for dedicated power calculators for pharmacoepidemiological research. Methods and Results The formula for calculating the power of a study using instrumental variable analysis in the context of pharmacoepidemiology is derived before being validated by a simulation study. The formula is applicable for studies using a single binary instrument to analyse the causal effect of a binary exposure on a continuous outcome. An online calculator, as well as packages in both R and Stata, are provided for the implementation of the formula by others. Conclusions The statistical power of instrumental variable analysis in pharmacoepidemiological studies to detect a clinically meaningful treatment effect is an important consideration. Research questions in this field have distinct structures that must be accounted for when calculating power. The formula presented differs from existing instrumental variable power formulae due to its parametrization, which is designed specifically for ease of use by pharmacoepidemiologists. PMID:28575313

Quasi-experimental study designs series-paper 7: assessing the assumptions.

PubMed

Bärnighausen, Till; Oldenburg, Catherine; Tugwell, Peter; Bommer, Christian; Ebert, Cara; Barreto, Mauricio; Djimeu, Eric; Haber, Noah; Waddington, Hugh; Rockers, Peter; Sianesi, Barbara; Bor, Jacob; Fink, Günther; Valentine, Jeffrey; Tanner, Jeffrey; Stanley, Tom; Sierra, Eduardo; Tchetgen, Eric Tchetgen; Atun, Rifat; Vollmer, Sebastian

2017-09-01

Quasi-experimental designs are gaining popularity in epidemiology and health systems research-in particular for the evaluation of health care practice, programs, and policy-because they allow strong causal inferences without randomized controlled experiments. We describe the concepts underlying five important quasi-experimental designs: Instrumental Variables, Regression Discontinuity, Interrupted Time Series, Fixed Effects, and Difference-in-Differences designs. We illustrate each of the designs with an example from health research. We then describe the assumptions required for each of the designs to ensure valid causal inference and discuss the tests available to examine the assumptions. Copyright © 2017 Elsevier Inc. All rights reserved.

Compliance-Effect Correlation Bias in Instrumental Variables Estimators

ERIC Educational Resources Information Center

Reardon, Sean F.

2010-01-01

Instrumental variable estimators hold the promise of enabling researchers to estimate the effects of educational treatments that are not (or cannot be) randomly assigned but that may be affected by randomly assigned interventions. Examples of the use of instrumental variables in such cases are increasingly common in educational and social science…

Model Robust Calibration: Method and Application to Electronically-Scanned Pressure Transducers

NASA Technical Reports Server (NTRS)

Walker, Eric L.; Starnes, B. Alden; Birch, Jeffery B.; Mays, James E.

2010-01-01

This article presents the application of a recently developed statistical regression method to the controlled instrument calibration problem. The statistical method of Model Robust Regression (MRR), developed by Mays, Birch, and Starnes, is shown to improve instrument calibration by reducing the reliance of the calibration on a predetermined parametric (e.g. polynomial, exponential, logarithmic) model. This is accomplished by allowing fits from the predetermined parametric model to be augmented by a certain portion of a fit to the residuals from the initial regression using a nonparametric (locally parametric) regression technique. The method is demonstrated for the absolute scale calibration of silicon-based pressure transducers.

Outcomes of Posterolateral Fusion with and without Instrumentation and of Interbody Fusion for Isthmic Spondylolisthesis: A Prospective Study.

PubMed

Endler, Peter; Ekman, Per; Möller, Hans; Gerdhem, Paul

2017-05-03

Various methods for the treatment of isthmic spondylolisthesis are available. The aim of this study was to compare outcomes after posterolateral fusion without instrumentation, posterolateral fusion with instrumentation, and interbody fusion. The Swedish Spine Register was used to identify 765 patients who had been operated on for isthmic spondylolisthesis and had at least preoperative and 2-year outcome data; 586 of them had longer follow-up (a mean of 6.9 years). The outcome measures were a global assessment of leg and back pain, the Oswestry Disability Index (ODI), the EuroQol-5 Dimensions (EQ-5D) Questionnaire, the Short Form-36 (SF-36), a visual analog scale (VAS) for back and leg pain, and satisfaction with treatment. Data on additional lumbar spine surgery was searched for in the register, with the mean duration of follow-up for this variable being 10.6 years after the index procedure. Statistical analyses were performed with analysis of covariance or competing-risks proportional hazards regression, adjusted for baseline differences in the studied variables, smoking, employment status, and level of fusion. Posterolateral fusion without instrumentation was performed in 102 patients; posterolateral fusion with instrumentation, in 452; and interbody fusion, in 211. At 1 year, improvement was reported in the global assessment for back pain by 54% of the patients who had posterolateral fusion without instrumentation, 68% of those treated with posterolateral fusion with instrumentation, and 70% of those treated with interbody fusion (p = 0.009). The VAS for back pain and reported satisfaction with treatment showed similar patterns (p = 0.003 and p = 0.017, respectively), whereas other outcomes did not differ among the treatment groups at 1 year. At 2 years, the global assessment for back pain indicated improvement in 57% of the patients who had undergone posterolateral fusion without instrumentation, 70% of those who had posterolateral fusion with instrumentation, and 71% of those treated with interbody fusion (p = 0.022). There were no significant outcome differences at the mean 6.9-year follow-up interval. There was an increased hazard ratio for additional lumbar spine surgery after interbody fusion (4.34; 95% confidence interval [CI] = 1.71 to 11.03) and posterolateral fusion with instrumentation (2.56; 95% CI = 1.02 to 6.42) compared with after posterolateral fusion without instrumentation (1.00; reference). Fusion with instrumentation, with or without interbody fusion, was associated with more improvement in back pain scores and higher satisfaction with treatment compared with fusion without instrumentation at 1 year, but the difference was attenuated with longer follow-up. Fusion with instrumentation was associated with a significantly higher risk of additional spine surgery. Therapeutic Level III. See Instructions for Authors for a complete description of levels of evidence.

Liquid Medication Dosing Errors in Children: Role of Provider Counseling Strategies

PubMed Central

Yin, H. Shonna; Dreyer, Benard P.; Moreira, Hannah A.; van Schaick, Linda; Rodriguez, Luis; Boettger, Susanne; Mendelsohn, Alan L.

2014-01-01

Objective To examine the degree to which recommended provider counseling strategies, including advanced communication techniques and dosing instrument provision, are associated with reductions in parent liquid medication dosing errors. Methods Cross-sectional analysis of baseline data on provider communication and dosing instrument provision from a study of a health literacy intervention to reduce medication errors. Parents whose children (<9 years) were seen in two urban public hospital pediatric emergency departments (EDs) and were prescribed daily dose liquid medications self-reported whether they received counseling about their child’s medication, including advanced strategies (teachback, drawings/pictures, demonstration, showback) and receipt of a dosing instrument. Primary dependent variable: observed dosing error (>20% deviation from prescribed). Multivariate logistic regression analyses performed, controlling for: parent age, language, country, ethnicity, socioeconomic status, education, health literacy (Short Test of Functional Health Literacy in Adults); child age, chronic disease status; site. Results Of 287 parents, 41.1% made dosing errors. Advanced counseling and instrument provision in the ED were reported by 33.1% and 19.2%, respectively; 15.0% reported both. Advanced counseling and instrument provision in the ED were associated with decreased errors (30.5 vs. 46.4%, p=0.01; 21.8 vs. 45.7%, p=0.001). In adjusted analyses, ED advanced counseling in combination with instrument provision was associated with a decreased odds of error compared to receiving neither (AOR 0.3; 95% CI 0.1–0.7); advanced counseling alone and instrument alone were not significantly associated with odds of error. Conclusion Provider use of advanced counseling strategies and dosing instrument provision may be especially effective in reducing errors when used together. PMID:24767779

Liquid medication dosing errors in children: role of provider counseling strategies.

PubMed

Yin, H Shonna; Dreyer, Benard P; Moreira, Hannah A; van Schaick, Linda; Rodriguez, Luis; Boettger, Susanne; Mendelsohn, Alan L

2014-01-01

To examine the degree to which recommended provider counseling strategies, including advanced communication techniques and dosing instrument provision, are associated with reductions in parent liquid medication dosing errors. Cross-sectional analysis of baseline data on provider communication and dosing instrument provision from a study of a health literacy intervention to reduce medication errors. Parents whose children (<9 years) were seen in 2 urban public hospital pediatric emergency departments (EDs) and were prescribed daily dose liquid medications self-reported whether they received counseling about their child's medication, including advanced strategies (teachback, drawings/pictures, demonstration, showback) and receipt of a dosing instrument. The primary dependent variable was observed dosing error (>20% deviation from prescribed). Multivariate logistic regression analyses were performed, controlling for parent age, language, country, ethnicity, socioeconomic status, education, health literacy (Short Test of Functional Health Literacy in Adults); child age, chronic disease status; and site. Of 287 parents, 41.1% made dosing errors. Advanced counseling and instrument provision in the ED were reported by 33.1% and 19.2%, respectively; 15.0% reported both. Advanced counseling and instrument provision in the ED were associated with decreased errors (30.5 vs. 46.4%, P = .01; 21.8 vs. 45.7%, P = .001). In adjusted analyses, ED advanced counseling in combination with instrument provision was associated with a decreased odds of error compared to receiving neither (adjusted odds ratio 0.3; 95% confidence interval 0.1-0.7); advanced counseling alone and instrument alone were not significantly associated with odds of error. Provider use of advanced counseling strategies and dosing instrument provision may be especially effective in reducing errors when used together. Copyright © 2014 Academic Pediatric Association. Published by Elsevier Inc. All rights reserved.

An Assessment of Science Teachers' Perceptions of Secondary School Environments in Taiwan

NASA Astrophysics Data System (ADS)

Huang, Shwu-Yong L.

2006-01-01

This study investigates the psychosocial environments of secondary schools from science teachers’ perspectives, as well as associated variables. Using a sample of 900 secondary science teachers from 52 schools in Taiwan, the results attest to the validity and reliability of the instrument, the Science Teacher School Environment Questionnaire, and its ability to differentiate among schools. The descriptive results show that a majority of science teachers positively perceived their school environments. The teachers reported high collegiality, good teacher student relations, effective principal leadership, strong professional interest, and low work pressure—but also low staff freedom. Multiple regression results further indicate that policy-relevant variables like school level, school location, and teachers’ intentions to stay in teaching were associated with science teachers’ perceptions of their school environments. Qualitative data analysis based on interviews of 34 science teachers confirmed and enriched these findings.

Bivariate least squares linear regression: Towards a unified analytic formalism. I. Functional models

NASA Astrophysics Data System (ADS)

Caimmi, R.

2011-08-01

Concerning bivariate least squares linear regression, the classical approach pursued for functional models in earlier attempts ( York, 1966, 1969) is reviewed using a new formalism in terms of deviation (matrix) traces which, for unweighted data, reduce to usual quantities leaving aside an unessential (but dimensional) multiplicative factor. Within the framework of classical error models, the dependent variable relates to the independent variable according to the usual additive model. The classes of linear models considered are regression lines in the general case of correlated errors in X and in Y for weighted data, and in the opposite limiting situations of (i) uncorrelated errors in X and in Y, and (ii) completely correlated errors in X and in Y. The special case of (C) generalized orthogonal regression is considered in detail together with well known subcases, namely: (Y) errors in X negligible (ideally null) with respect to errors in Y; (X) errors in Y negligible (ideally null) with respect to errors in X; (O) genuine orthogonal regression; (R) reduced major-axis regression. In the limit of unweighted data, the results determined for functional models are compared with their counterparts related to extreme structural models i.e. the instrumental scatter is negligible (ideally null) with respect to the intrinsic scatter ( Isobe et al., 1990; Feigelson and Babu, 1992). While regression line slope and intercept estimators for functional and structural models necessarily coincide, the contrary holds for related variance estimators even if the residuals obey a Gaussian distribution, with the exception of Y models. An example of astronomical application is considered, concerning the [O/H]-[Fe/H] empirical relations deduced from five samples related to different stars and/or different methods of oxygen abundance determination. For selected samples and assigned methods, different regression models yield consistent results within the errors (∓ σ) for both heteroscedastic and homoscedastic data. Conversely, samples related to different methods produce discrepant results, due to the presence of (still undetected) systematic errors, which implies no definitive statement can be made at present. A comparison is also made between different expressions of regression line slope and intercept variance estimators, where fractional discrepancies are found to be not exceeding a few percent, which grows up to about 20% in the presence of large dispersion data. An extension of the formalism to structural models is left to a forthcoming paper.

Modelling infant mortality rate in Central Java, Indonesia use generalized poisson regression method

NASA Astrophysics Data System (ADS)

Prahutama, Alan; Sudarno

2018-05-01

The infant mortality rate is the number of deaths under one year of age occurring among the live births in a given geographical area during a given year, per 1,000 live births occurring among the population of the given geographical area during the same year. This problem needs to be addressed because it is an important element of a country’s economic development. High infant mortality rate will disrupt the stability of a country as it relates to the sustainability of the population in the country. One of regression model that can be used to analyze the relationship between dependent variable Y in the form of discrete data and independent variable X is Poisson regression model. Recently The regression modeling used for data with dependent variable is discrete, among others, poisson regression, negative binomial regression and generalized poisson regression. In this research, generalized poisson regression modeling gives better AIC value than poisson regression. The most significant variable is the Number of health facilities (X1), while the variable that gives the most influence to infant mortality rate is the average breastfeeding (X9).

Use of instrumental variables in the analysis of generalized linear models in the presence of unmeasured confounding with applications to epidemiological research.

PubMed

Johnston, K M; Gustafson, P; Levy, A R; Grootendorst, P

2008-04-30

A major, often unstated, concern of researchers carrying out epidemiological studies of medical therapy is the potential impact on validity if estimates of treatment are biased due to unmeasured confounders. One technique for obtaining consistent estimates of treatment effects in the presence of unmeasured confounders is instrumental variables analysis (IVA). This technique has been well developed in the econometrics literature and is being increasingly used in epidemiological studies. However, the approach to IVA that is most commonly used in such studies is based on linear models, while many epidemiological applications make use of non-linear models, specifically generalized linear models (GLMs) such as logistic or Poisson regression. Here we present a simple method for applying IVA within the class of GLMs using the generalized method of moments approach. We explore some of the theoretical properties of the method and illustrate its use within both a simulation example and an epidemiological study where unmeasured confounding is suspected to be present. We estimate the effects of beta-blocker therapy on one-year all-cause mortality after an incident hospitalization for heart failure, in the absence of data describing disease severity, which is believed to be a confounder. 2008 John Wiley & Sons, Ltd

Changes in photochemically significant solar UV spectral irradiance as estimated by the composite Mg II index and scale factors

NASA Technical Reports Server (NTRS)

Deland, Matthew T.; Cebula, Richard P.

1994-01-01

Quantitative assessment of the impact of solar ultraviolet irradiance variations on stratospheric ozone abundances currently requires the use of proxy indicators. The Mg II core-to-wing index has been developed as an indicator of solar UV activity between 175-400 nm that is independent of most instrument artifacts, and measures solar variability on both rotational and solar cycle time scales. Linear regression fits have been used to merge the individual Mg II index data sets from the Nimbus-7, NOAA-9, and NOAA-11 instruments onto a single reference scale. The change in 27-dayrunning average of the composite Mg II index from solar maximum to solar minimum is approximately 8 percent for solar cycle 21, and approximately 9 percent for solar cycle 22 through January 1992. Scaling factors based on the short-term variations in the Mg II index and solar irradiance data sets have been developed to estimate solar variability at mid-UV and near-UV wavelengths. Near 205 nm, where solar irradiance variations are important for stratospheric photo-chemistry and dynamics, the estimated change in irradiance during solar cycle 22 is approximately 10 percent using the composite Mg II index and scale factors.

Estimating the association between metabolic risk factors and marijuana use in U.S. adults using data from the continuous National Health and Nutrition Examination Survey.

PubMed

Thompson, Christin Ann; Hay, Joel W

2015-07-01

More research is needed on the health effects of marijuana use. Results of previous studies indicate that marijuana could alleviate certain factors of metabolic syndrome, such as obesity. Data on 6281 persons from National Health and Nutrition Examination Survey from 2005 to 2012 were used to estimate the effect of marijuana use on cardiometabolic risk factors. The reliability of ordinary least squares (OLS) regression models was tested by replacing marijuana use as the risk factor of interest with alcohol and carbohydrate consumption. Instrumental variable methods were used to account for the potential endogeneity of marijuana use. OLS models show lower fasting insulin, insulin resistance, body mass index, and waist circumference in users compared with nonusers. However, when alcohol and carbohydrate intake substitute for marijuana use in OLS models, similar metabolic benefits are estimated. The Durbin-Wu-Hausman tests provide evidence of endogeneity of marijuana use in OLS models, but instrumental variables models do not yield significant estimates for marijuana use. These findings challenge the robustness of OLS estimates of a positive relationship between marijuana use and fasting insulin, insulin resistance, body mass index, and waist circumference. Copyright © 2015 Elsevier Inc. All rights reserved.

Quality of life, human insecurity, and distress among Palestinians in the Gaza Strip before and after the Winter 2008-2009 Israeli war.

PubMed

Hammoudeh, Weeam; Hogan, Dennis; Giacaman, Rita

2013-11-01

This study investigates changes in the quality of life (QoL) of Gaza Palestinians before and after the Israeli winter 2008-2009 war using the World Health Organization's WHOQOL-Bref; the extent to which this instrument adequately measures changing situations; and its responsiveness to locally developed human insecurity and distress measures appropriate for context. Ordinary least squares regression analysis was performed to detect how demographic and socioeconomic variables usually associated with QoL were associated with human insecurity and distress. We estimated the usual baseline model for the three QoL domains, and a second set of models including these standard variables and human insecurity and distress to assess how personal exposure to political violence affects QoL. No difference between the quality of life scores in 2005 and 2009 was found, with results suggesting lack of sensitivity of WHOQOL-Bref in capturing changes resulting from intensification of preexisting political violence. Results show that human insecurity and individual distress significantly increased in 2009 compared to 2005. Results indicate that a political domain may provide further understanding of and possibly increase the sensitivity of the instrument to detect changes in the Qol of Palestinians and possibly other populations experiencing intensified political violence.

Characteristics predicting laparoscopic skill in medical students: nine years' experience in a single center.

PubMed

Nomura, Tsutomu; Matsutani, Takeshi; Hagiwara, Nobutoshi; Fujita, Itsuo; Nakamura, Yoshiharu; Kanazawa, Yoshikazu; Makino, Hiroshi; Mamada, Yasuhiro; Fujikura, Terumichi; Miyashita, Masao; Uchida, Eiji

2018-01-01

We introduced laparoscopic simulator training for medical students in 2007. This study was designed to identify factors that predict the laparoscopic skill of medical students, to identify intergenerational differences in abilities, and to estimate the variability of results in each training group. Our ultimate goal was to determine the optimal educational program for teaching laparoscopic surgery to medical students. Between 2007 and 2015, a total of 270 fifth-year medical students were enrolled in this observational study. Before training, the participants were asked questions about their interest in laparoscopic surgery, experience with playing video games, confidence about driving, and manual dexterity. After the training, aspects of their competence (execution time, instrument path length, and economy of instrument movement) were assessed. Multiple regression analysis identified significant effects of manual dexterity, gender, and confidence about driving on the results of the training. The training results have significantly improved over recent years. The variability among the results in each training group was relatively small. We identified the characteristics of medical students with excellent laparoscopic skills. We observed educational benefits from interactions between medical students within each training group. Our study suggests that selection and grouping are important to the success of modern programs designed to train medical students in laparoscopic surgery.

A Polychoric Instrumental Variable (PIV) Estimator for Structural Equation Models with Categorical Variables

ERIC Educational Resources Information Center

Bollen, Kenneth A.; Maydeu-Olivares, Albert

2007-01-01

This paper presents a new polychoric instrumental variable (PIV) estimator to use in structural equation models (SEMs) with categorical observed variables. The PIV estimator is a generalization of Bollen's (Psychometrika 61:109-121, 1996) 2SLS/IV estimator for continuous variables to categorical endogenous variables. We derive the PIV estimator…

[From clinical judgment to linear regression model.

PubMed

Palacios-Cruz, Lino; Pérez, Marcela; Rivas-Ruiz, Rodolfo; Talavera, Juan O

2013-01-01

When we think about mathematical models, such as linear regression model, we think that these terms are only used by those engaged in research, a notion that is far from the truth. Legendre described the first mathematical model in 1805, and Galton introduced the formal term in 1886. Linear regression is one of the most commonly used regression models in clinical practice. It is useful to predict or show the relationship between two or more variables as long as the dependent variable is quantitative and has normal distribution. Stated in another way, the regression is used to predict a measure based on the knowledge of at least one other variable. Linear regression has as it's first objective to determine the slope or inclination of the regression line: Y = a + bx, where "a" is the intercept or regression constant and it is equivalent to "Y" value when "X" equals 0 and "b" (also called slope) indicates the increase or decrease that occurs when the variable "x" increases or decreases in one unit. In the regression line, "b" is called regression coefficient. The coefficient of determination (R 2 ) indicates the importance of independent variables in the outcome.

Satellite NO 2 retrievals suggest China has exceeded its NO x reduction goals from the twelfth Five-Year Plan

DOE PAGES

de Foy, Benjamin; Lu, Zifeng; Streets, David G.

2016-10-27

China’s twelfth Five-Year Plan included pollution control measures with a goal of reducing national emissions of nitrogen oxides (NO x) by 10% by 2015 compared with 2010. Multiple linear regression analysis was used on 11-year time series of all nitrogen dioxide (NO 2) pixels from the Ozone Monitoring Instrument (OMI) over 18 NO 2 hotspots in China. The regression analysis accounted for variations in meteorology, pixel resolution, seasonal effects, weekday variability and year-to-year variability. The NO 2 trends suggested that there was an increase in NO 2 columns in most areas from 2005 to around 2011 which was followed bymore » a strong decrease continuing through 2015. The satellite results were in good agreement with the annual official NO x emission inventories which were available up until 2014. We show the value of evaluating trends in emission inventories using satellite retrievals. It further shows that recent control strategies were effective in reducing emissions and that recent economic transformations in China may be having an effect on NO 2 columns. The satellite information for 2015 suggests that emissions have continued to decrease since the latest inventories available and have surpassed the goals of the twelfth Five-Year Plan.« less

The effect of the water tariff structures on the water consumption in Mallorcan hotels

NASA Astrophysics Data System (ADS)

Deyà-Tortella, Bartolomé; Garcia, Celso; Nilsson, William; Tirado, Dolores

2016-08-01

Tourism increases water demand, especially in coastal areas and on islands, and can also cause water shortages during the dry season and the degradation of the water supply. The aim of this study is to evaluate the impact of water price structures on hotel water consumption on the island of Mallorca (Spain). All tourist municipalities on the island use different pricing structures, such as flat or block rates, and different tariffs. This exogenous variation is used to evaluate the effect of prices on water consumption for a sample of 134 hotels. The discontinuity of the water tariff structure and the fixed rate, which depends on the number of hotel beds, generate endogeneity problems. We propose an econometric model, an instrumental variable quantile regression for within artificial blocks transformed data, to solve both problems. The coefficients corresponding to the price variables are not found to be significantly different from zero. The sign of the effect is negative, but the magnitude is negligible: a 1% increase in all prices would reduce consumption by an average of only 0.024%. This result is probably due to the small share of water costs with respect to the total hotel operational costs (around 4%). Our regression model concludes that the introduction of water-saving initiatives constitutes an effective way to reduce consumption.

Satellite NO 2 retrievals suggest China has exceeded its NO x reduction goals from the twelfth Five-Year Plan

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

de Foy, Benjamin; Lu, Zifeng; Streets, David G.

China’s twelfth Five-Year Plan included pollution control measures with a goal of reducing national emissions of nitrogen oxides (NO x) by 10% by 2015 compared with 2010. Multiple linear regression analysis was used on 11-year time series of all nitrogen dioxide (NO 2) pixels from the Ozone Monitoring Instrument (OMI) over 18 NO 2 hotspots in China. The regression analysis accounted for variations in meteorology, pixel resolution, seasonal effects, weekday variability and year-to-year variability. The NO 2 trends suggested that there was an increase in NO 2 columns in most areas from 2005 to around 2011 which was followed bymore » a strong decrease continuing through 2015. The satellite results were in good agreement with the annual official NO x emission inventories which were available up until 2014. We show the value of evaluating trends in emission inventories using satellite retrievals. It further shows that recent control strategies were effective in reducing emissions and that recent economic transformations in China may be having an effect on NO 2 columns. The satellite information for 2015 suggests that emissions have continued to decrease since the latest inventories available and have surpassed the goals of the twelfth Five-Year Plan.« less

Cognitive and physical functions related to the level of supervision and dependence in the toileting of stroke patients.

PubMed

Sato, Atsushi; Okuda, Yutaka; Fujita, Takaaki; Kimura, Norihiko; Hoshina, Noriyuki; Kato, Sayaka; Tanaka, Shigenari

2016-01-01

This study aimed to clarify which cognitive and physical factors are associated with the need for toileting assistance in stroke patients and to calculate cut-off values for discriminating between independent supervision and dependent toileting ability. This cross-sectional study included 163 first-stroke patients in nine convalescent rehabilitation wards. Based on their FIM Ⓡ instrument score for toileting, the patients were divided into an independent-supervision group and a dependent group. Multiple logistic regression analysis and receiver operating characteristic analysis were performed to identify factors related to toileting performance. The Minimental State Examination (MMSE); the Stroke Impairment Assessment Set (SIAS) score for the affected lower limb, speech, and visuospatial functions; and the Functional Assessment for Control of Trunk (FACT) were analyzed as independent variables. The multiple logistic regression analysis showed that the FIM Ⓡ instrument score for toileting was associated with the SIAS score for the affected lower limb function, MMSE, and FACT. On receiver operating characteristic analysis, the SIAS score for the affected lower limb function cut-off value was 8/7 points, the MMSE cut-off value was 25/24 points, and the FACT cut-off value was 14/13 points. Affected lower limb function, cognitive function, and trunk function were related with the need for toileting assistance. These cut-off values may be useful for judging whether toileting assistance is needed in stroke patients.

Developing and evaluating a relevant and feasible instrument for measuring health literacy of Canadian high school students.

PubMed

Wu, Amery D; Begoray, Deborah L; Macdonald, Marjorie; Wharf Higgins, Joan; Frankish, Jim; Kwan, Brenda; Fung, Winny; Rootman, Irving

2010-12-01

Health literacy has come to play a critical role in health education and promotion, yet it is poorly understood in adolescents and few measurement tools exist. Standardized instruments to measure health literacy in adults assume it to be a derivative of general literacy. This paper reports on the development and the early-stage validation of a health literacy tool for high school students that measured skills to understand and evaluate health information. A systematic process was used to develop, score and validate items. Questionnaire data were collected from 275, primarily 10th grade students in three secondary schools in Vancouver, Canada that reflected variation in demographic profile. Forty-eight percent were male, and 69.1% spoke a language other than English. Bivariate correlations between background variables and the domain and overall health literacy scores were calculated. A regression model was developed using 15 explanatory variables. The R(2) value was 0.567. Key findings were that lower scores were achieved by males, students speaking a second language other than English, those who immigrated to Canada at a later age and those who skipped school more often. Unlike in general literacy where the family factors of mother's education and family affluence both played significant roles, these two factors failed to predict the health literacy of our school-aged sample. The most significant contributions of this work include the creation of an instrument for measuring adolescent health literacy and further emphasizing the distinction between health literacy and general literacy.

Measuring Networking as an Outcome Variable in Undergraduate Research Experiences.

PubMed

Hanauer, David I; Hatfull, Graham

2015-01-01

The aim of this paper is to propose, present, and validate a simple survey instrument to measure student conversational networking. The tool consists of five items that cover personal and professional social networks, and its basic principle is the self-reporting of degrees of conversation, with a range of specific discussion partners. The networking instrument was validated in three studies. The basic psychometric characteristics of the scales were established by conducting a factor analysis and evaluating internal consistency using Cronbach's alpha. The second study used a known-groups comparison and involved comparing outcomes for networking scales between two different undergraduate laboratory courses (one involving a specific effort to enhance networking). The final study looked at potential relationships between specific networking items and the established psychosocial variable of project ownership through a series of binary logistic regressions. Overall, the data from the three studies indicate that the networking scales have high internal consistency (α = 0.88), consist of a unitary dimension, can significantly differentiate between research experiences with low and high networking designs, and are related to project ownership scales. The ramifications of the networking instrument for student retention, the enhancement of public scientific literacy, and the differentiation of laboratory courses are discussed. © 2015 D. I. Hanauer and G. Hatfull. CBE—Life Sciences Education © 2015 The American Society for Cell Biology. This article is distributed by The American Society for Cell Biology under license from the author(s). It is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

Censored Quantile Instrumental Variable Estimates of the Price Elasticity of Expenditure on Medical Care.

PubMed

Kowalski, Amanda

2016-01-02

Efforts to control medical care costs depend critically on how individuals respond to prices. I estimate the price elasticity of expenditure on medical care using a censored quantile instrumental variable (CQIV) estimator. CQIV allows estimates to vary across the conditional expenditure distribution, relaxes traditional censored model assumptions, and addresses endogeneity with an instrumental variable. My instrumental variable strategy uses a family member's injury to induce variation in an individual's own price. Across the conditional deciles of the expenditure distribution, I find elasticities that vary from -0.76 to -1.49, which are an order of magnitude larger than previous estimates.

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.

The Impact of Childhood Obesity on Health and Health Service Use.

PubMed

Kinge, Jonas Minet; Morris, Stephen

2018-06-01

To test the impact of obesity on health and health care use in children, by the use of various methods to account for reverse causality and omitted variables. Fifteen rounds of the Health Survey for England (1998-2013), which is representative of children and adolescents in England. We use three methods to account for reverse causality and omitted variables in the relationship between BMI and health/health service use: regression with individual, parent, and household control variables; sibling fixed effects; and instrumental variables based on genetic variation in weight. We include all children and adolescents aged 4-18 years old. We find that obesity has a statistically significant and negative impact on self-rated health and a positive impact on health service use in girls, boys, younger children (aged 4-12), and adolescents (aged 13-18). The findings are comparable in each model in both boys and girls. Using econometric methods, we have mitigated several confounding factors affecting the impact of obesity in childhood on health and health service use. Our findings suggest that obesity has severe consequences for health and health service use even among children. © Health Research and Educational Trust.

Measuring the Impact of Financial Intermediation: Linking Contract Theory to Econometric Policy Evaluation *

PubMed Central

Townsend, Robert M.; Urzua, Sergio S.

2010-01-01

We study the impact that financial intermediation can have on productivity through the alleviation of credit constraints in occupation choice and/or an improved allocation of risk, using both static and dynamic structural models as well as reduced form OLS and IV regressions. Our goal in this paper is to bring these two strands of the literature together. Even though, under certain assumptions, IV regressions can recover accurately the true model-generated local average treatment effect, these are quantitatively different, in order of magnitude and even sign, from other policy impact parameters (e.g., ATE and TT). We also show that laying out clearly alternative models can guide the search for instruments. On the other hand adding more margins of decision, i.e., occupation choice and intermediation jointly, or adding more periods with promised utilities as key state variables, as in optimal multi-period contracts, can cause the misinterpretation of IV as the causal effect of interest. PMID:20436953

Desire thinking: A risk factor for binge eating?

PubMed

Spada, Marcantonio M; Caselli, Gabriele; Fernie, Bruce A; Manfredi, Chiara; Boccaletti, Fabio; Dallari, Giulia; Gandini, Federica; Pinna, Eleonora; Ruggiero, Giovanni M; Sassaroli, Sandra

2015-08-01

In the current study we explored the role of desire thinking in predicting binge eating independently of Body Mass Index, negative affect and irrational food beliefs. A sample of binge eaters (n=77) and a sample of non-binge eaters (n=185) completed the following self-report instruments: Hospital Anxiety and Depression Scale, Irrational Food Beliefs Scale, Desire Thinking Questionnaire, and Binge Eating Scale. Mann-Whitney U tests revealed that all variable scores were significantly higher for binge eaters than non-binge eaters. A logistic regression analysis indicated that verbal perseveration was a predictor of classification as a binge eater over and above Body Mass Index, negative affect and irrational food beliefs. A hierarchical regression analysis, on the combined sample, indicated that verbal perseveration predicted levels of binge eating independently of Body Mass Index, negative affect and irrational food beliefs. These results highlight the possible role of desire thinking as a risk factor for binge eating. Copyright © 2015 Elsevier Ltd. All rights reserved.

Latent Class Models in action: bridging social capital & Internet usage.

PubMed

Neves, Barbara Barbosa; Fonseca, Jaime R S

2015-03-01

This paper explores how Latent Class Models (LCM) can be applied in social research, when the basic assumptions of regression models cannot be validated. We examine the usefulness of this method with data collected from a study on the relationship between bridging social capital and the Internet. Social capital is defined here as the resources that are potentially available in one's social ties. Bridging is a dimension of social capital, usually related to weak ties (acquaintances), and a source of instrumental resources such as information. The study surveyed a stratified random sample of 417 inhabitants of Lisbon, Portugal. We used LCM to create the variable bridging social capital, but also to estimate the relationship between bridging social capital and Internet usage when we encountered convergence problems with the logistic regression analysis. We conclude by showing a positive relationship between bridging and Internet usage, and by discussing the potential of LCM for social science research. Copyright © 2014 Elsevier Inc. All rights reserved.

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.

Comparative effectiveness research in cancer with observational data.

PubMed

Giordano, Sharon H

2015-01-01

Observational studies are increasingly being used for comparative effectiveness research. These studies can have the greatest impact when randomized trials are not feasible or when randomized studies have not included the population or outcomes of interest. However, careful attention must be paid to study design to minimize the likelihood of selection biases. Analytic techniques, such as multivariable regression modeling, propensity score analysis, and instrumental variable analysis, also can also be used to help address confounding. Oncology has many existing large and clinically rich observational databases that can be used for comparative effectiveness research. With careful study design, observational studies can produce valid results to assess the benefits and harms of a treatment or intervention in representative real-world populations.

The relationship between happiness and health: evidence from Italy.

PubMed

Sabatini, Fabio

2014-08-01

We test the relationship between happiness and self-rated health in Italy. The analysis relies on a unique dataset collected through the administration of a questionnaire to a representative sample (n = 817) of the population of the Italian Province of Trento in March 2011. Based on probit regressions and instrumental variables estimates, we find that happiness is strongly correlated with perceived good health, after controlling for a number of relevant socio-economic phenomena. Health inequalities based on income, work status and education are relatively contained with respect to the rest of Italy. As expected, this scales down the role of social relationships. Copyright © 2014 Elsevier Ltd. All rights reserved.

Linking the variability of atmospheric carbon monoxide to climate modes in the Southern Hemisphere

NASA Astrophysics Data System (ADS)

Buchholz, Rebecca; Monks, Sarah; Hammerling, Dorit; Worden, Helen; Deeter, Merritt; Emmons, Louisa; Edwards, David

2017-04-01

Biomass burning is a major driver of atmospheric carbon monoxide (CO) variability in the Southern Hemisphere. The magnitude of emissions, such as CO, from biomass burning is connected to climate through both the availability and dryness of fuel. We investigate the link between CO and climate using satellite measured CO and climate indices. Observations of total column CO from the satellite instrument MOPITT are used to build a record of interannual variability in CO since 2001. Four biomass burning regions in the Southern Hemisphere are explored. Data driven relationships are determined between CO and climate indices for the climate modes: El Niño Southern Oscillation (ENSO); the Indian Ocean Dipole (IOD); the Tropical Southern Atlantic (TSA); and the Southern Annular Mode (SAM). Stepwise forward and backward regression is used to select the best statistical model from combinations of lagged indices. We find evidence for the importance of first-order interaction terms of the climate modes when explaining CO variability. Implications of the model results are discussed for the Maritime Southeast Asia and Australasia regions. We also draw on the chemistry-climate model CAM-chem to explain the source contribution as well as the relative contributions of emissions and meteorology to CO variability.

Introduction to the use of regression models in epidemiology.

PubMed

Bender, Ralf

2009-01-01

Regression modeling is one of the most important statistical techniques used in analytical epidemiology. By means of regression models the effect of one or several explanatory variables (e.g., exposures, subject characteristics, risk factors) on a response variable such as mortality or cancer can be investigated. From multiple regression models, adjusted effect estimates can be obtained that take the effect of potential confounders into account. Regression methods can be applied in all epidemiologic study designs so that they represent a universal tool for data analysis in epidemiology. Different kinds of regression models have been developed in dependence on the measurement scale of the response variable and the study design. The most important methods are linear regression for continuous outcomes, logistic regression for binary outcomes, Cox regression for time-to-event data, and Poisson regression for frequencies and rates. This chapter provides a nontechnical introduction to these regression models with illustrating examples from cancer research.

Development of an instrument for community-level health related social capital among Japanese older people: The JAGES Project.

PubMed

Saito, Masashige; Kondo, Naoki; Aida, Jun; Kawachi, Ichiro; Koyama, Shihoko; Ojima, Toshiyuki; Kondo, Katsunori

2017-05-01

We developed and validated an instrument to measure community-level social capital based on data derived from older community dwellers in Japan. We used cross-sectional data from the Japan Gerontological Evaluation Study, a nationwide survey involving 123,760 functionally independent older people nested within 702 communities (i.e., school districts). We conducted exploratory and confirmatory factor analyses on survey items to determine the items in a multi-dimensional scale to measure community social capital. Internal consistency was checked with Cronbach's alpha. Convergent construct validity was assessed via correlating the scale with health outcomes. From 53 candidate variables, 11 community-level variables were extracted: participation in volunteer groups, sports groups, hobby activities, study or cultural groups, and activities for teaching specific skills; trust, norms of reciprocity, and attachment to one's community; received emotional support; provided emotional support; and received instrumental support. Using factor analysis, these variables were determined to belong to three sub-scales: civic participation (eigenvalue = 3.317, α = 0.797), social cohesion (eigenvalue = 2.633, α = 0.853), and reciprocity (eigenvalue = 1.424, α = 0.732). Confirmatory factor analysis indicated the goodness of fit of this model. Multilevel Poisson regression analysis revealed that civic participation score was robustly associated with individual subjective health (Self-Rated Health: prevalence ratio [PR] 0.96; 95% confidence interval [CI], 0.94-0.98; Geriatric Depression Scale [GDS]: PR 0.95; 95% CI, 0.93-0.97). Reciprocity score was also associated with individual GDS (PR 0.98; 95% CI, 0.96-1.00). Social cohesion score was not consistently associated with individual health indicators. Our scale for measuring social capital at the community level might be useful for future studies of older community dwellers. Copyright © 2016 The Authors. Production and hosting by Elsevier B.V. All rights reserved.

Definition of the limit of quantification in the presence of instrumental and non-instrumental errors. Comparison among various definitions applied to the calibration of zinc by inductively coupled plasma-mass spectrometry

NASA Astrophysics Data System (ADS)

Badocco, Denis; Lavagnini, Irma; Mondin, Andrea; Favaro, Gabriella; Pastore, Paolo

2015-12-01

The limit of quantification (LOQ) in the presence of instrumental and non-instrumental errors was proposed. It was theoretically defined combining the two-component variance regression and LOQ schemas already present in the literature and applied to the calibration of zinc by the ICP-MS technique. At low concentration levels, the two-component variance LOQ definition should be always used above all when a clean room is not available. Three LOQ definitions were accounted for. One of them in the concentration and two in the signal domain. The LOQ computed in the concentration domain, proposed by Currie, was completed by adding the third order terms in the Taylor expansion because they are of the same order of magnitude of the second ones so that they cannot be neglected. In this context, the error propagation was simplified by eliminating the correlation contributions by using independent random variables. Among the signal domain definitions, a particular attention was devoted to the recently proposed approach based on at least one significant digit in the measurement. The relative LOQ values resulted very large in preventing the quantitative analysis. It was found that the Currie schemas in the signal and concentration domains gave similar LOQ values but the former formulation is to be preferred as more easily computable.

An improved multiple linear regression and data analysis computer program package

NASA Technical Reports Server (NTRS)

Sidik, S. M.

1972-01-01

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

Censored Quantile Instrumental Variable Estimates of the Price Elasticity of Expenditure on Medical Care

PubMed Central

Kowalski, Amanda

2015-01-01

Efforts to control medical care costs depend critically on how individuals respond to prices. I estimate the price elasticity of expenditure on medical care using a censored quantile instrumental variable (CQIV) estimator. CQIV allows estimates to vary across the conditional expenditure distribution, relaxes traditional censored model assumptions, and addresses endogeneity with an instrumental variable. My instrumental variable strategy uses a family member’s injury to induce variation in an individual’s own price. Across the conditional deciles of the expenditure distribution, I find elasticities that vary from −0.76 to −1.49, which are an order of magnitude larger than previous estimates. PMID:26977117

Logic regression and its extensions.

PubMed

Schwender, Holger; Ruczinski, Ingo

2010-01-01

Logic regression is an adaptive classification and regression procedure, initially developed to reveal interacting single nucleotide polymorphisms (SNPs) in genetic association studies. In general, this approach can be used in any setting with binary predictors, when the interaction of these covariates is of primary interest. Logic regression searches for Boolean (logic) combinations of binary variables that best explain the variability in the outcome variable, and thus, reveals variables and interactions that are associated with the response and/or have predictive capabilities. The logic expressions are embedded in a generalized linear regression framework, and thus, logic regression can handle a variety of outcome types, such as binary responses in case-control studies, numeric responses, and time-to-event data. In this chapter, we provide an introduction to the logic regression methodology, list some applications in public health and medicine, and summarize some of the direct extensions and modifications of logic regression that have been proposed in the literature. Copyright © 2010 Elsevier Inc. All rights reserved.

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…

Tutorial on Using Regression Models with Count Outcomes Using R

ERIC Educational Resources Information Center

Beaujean, A. Alexander; Morgan, Grant B.

2016-01-01

Education researchers often study count variables, such as times a student reached a goal, discipline referrals, and absences. Most researchers that study these variables use typical regression methods (i.e., ordinary least-squares) either with or without transforming the count variables. In either case, using typical regression for count data can…

Comparing the analytical performances of Micro-NIR and FT-NIR spectrometers in the evaluation of acerola fruit quality, using PLS and SVM regression algorithms.

PubMed

Malegori, Cristina; Nascimento Marques, Emanuel José; de Freitas, Sergio Tonetto; Pimentel, Maria Fernanda; Pasquini, Celio; Casiraghi, Ernestina

2017-04-01

The main goal of this study was to investigate the analytical performances of a state-of-the-art device, one of the smallest dispersion NIR spectrometers on the market (MicroNIR 1700), making a critical comparison with a benchtop FT-NIR spectrometer in the evaluation of the prediction accuracy. In particular, the aim of this study was to estimate in a non-destructive manner, titratable acidity and ascorbic acid content in acerola fruit during ripening, in a view of direct applicability in field of this new miniaturised handheld device. Acerola (Malpighia emarginata DC.) is a super-fruit characterised by a considerable amount of ascorbic acid, ranging from 1.0% to 4.5%. However, during ripening, acerola colour changes and the fruit may lose as much as half of its ascorbic acid content. Because the variability of chemical parameters followed a non-strictly linear profile, two different regression algorithms were compared: PLS and SVM. Regression models obtained with Micro-NIR spectra give better results using SVM algorithm, for both ascorbic acid and titratable acidity estimation. FT-NIR data give comparable results using both SVM and PLS algorithms, with lower errors for SVM regression. The prediction ability of the two instruments was statistically compared using the Passing-Bablok regression algorithm; the outcomes are critically discussed together with the regression models, showing the suitability of the portable Micro-NIR for in field monitoring of chemical parameters of interest in acerola fruits. Copyright © 2016 Elsevier B.V. All rights reserved.

A gentle introduction to quantile regression for ecologists

USGS Publications Warehouse

Cade, B.S.; Noon, B.R.

2003-01-01

Quantile regression is a way to estimate the conditional quantiles of a response variable distribution in the linear model that provides a more complete view of possible causal relationships between variables in ecological processes. Typically, all the factors that affect ecological processes are not measured and included in the statistical models used to investigate relationships between variables associated with those processes. As a consequence, there may be a weak or no predictive relationship between the mean of the response variable (y) distribution and the measured predictive factors (X). Yet there may be stronger, useful predictive relationships with other parts of the response variable distribution. This primer relates quantile regression estimates to prediction intervals in parametric error distribution regression models (eg least squares), and discusses the ordering characteristics, interval nature, sampling variation, weighting, and interpretation of the estimates for homogeneous and heterogeneous regression models.

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.

Instrumental Variable Analysis with a Nonlinear Exposure–Outcome Relationship

PubMed Central

Davies, Neil M.; Thompson, Simon G.

2014-01-01

Background: Instrumental variable methods can estimate the causal effect of an exposure on an outcome using observational data. Many instrumental variable methods assume that the exposure–outcome relation is linear, but in practice this assumption is often in doubt, or perhaps the shape of the relation is a target for investigation. We investigate this issue in the context of Mendelian randomization, the use of genetic variants as instrumental variables. Methods: Using simulations, we demonstrate the performance of a simple linear instrumental variable method when the true shape of the exposure–outcome relation is not linear. We also present a novel method for estimating the effect of the exposure on the outcome within strata of the exposure distribution. This enables the estimation of localized average causal effects within quantile groups of the exposure or as a continuous function of the exposure using a sliding window approach. Results: Our simulations suggest that linear instrumental variable estimates approximate a population-averaged causal effect. This is the average difference in the outcome if the exposure for every individual in the population is increased by a fixed amount. Estimates of localized average causal effects reveal the shape of the exposure–outcome relation for a variety of models. These methods are used to investigate the relations between body mass index and a range of cardiovascular risk factors. Conclusions: Nonlinear exposure–outcome relations should not be a barrier to instrumental variable analyses. When the exposure–outcome relation is not linear, either a population-averaged causal effect or the shape of the exposure–outcome relation can be estimated. PMID:25166881

Parameters Estimation of Geographically Weighted Ordinal Logistic Regression (GWOLR) Model

NASA Astrophysics Data System (ADS)

Zuhdi, Shaifudin; Retno Sari Saputro, Dewi; Widyaningsih, Purnami

2017-06-01

A regression model is the representation of relationship between independent variable and dependent variable. The dependent variable has categories used in the logistic regression model to calculate odds on. The logistic regression model for dependent variable has levels in the logistics regression model is ordinal. GWOLR model is an ordinal logistic regression model influenced the geographical location of the observation site. Parameters estimation in the model needed to determine the value of a population based on sample. The purpose of this research is to parameters estimation of GWOLR model using R software. Parameter estimation uses the data amount of dengue fever patients in Semarang City. Observation units used are 144 villages in Semarang City. The results of research get GWOLR model locally for each village and to know probability of number dengue fever patient categories.

Cognitive and attitudinal predictors related to graphing achievement among pre-service elementary teachers

NASA Astrophysics Data System (ADS)

Szyjka, Sebastian P.

The purpose of this study was to determine the extent to which six cognitive and attitudinal variables predicted pre-service elementary teachers' performance on line graphing. Predictors included Illinois teacher education basic skills sub-component scores in reading comprehension and mathematics, logical thinking performance scores, as well as measures of attitudes toward science, mathematics and graphing. This study also determined the strength of the relationship between each prospective predictor variable and the line graphing performance variable, as well as the extent to which measures of attitude towards science, mathematics and graphing mediated relationships between scores on mathematics, reading, logical thinking and line graphing. Ninety-four pre-service elementary education teachers enrolled in two different elementary science methods courses during the spring 2009 semester at Southern Illinois University Carbondale participated in this study. Each subject completed five different instruments designed to assess science, mathematics and graphing attitudes as well as logical thinking and graphing ability. Sixty subjects provided copies of primary basic skills score reports that listed subset scores for both reading comprehension and mathematics. The remaining scores were supplied by a faculty member who had access to a database from which the scores were drawn. Seven subjects, whose scores could not be found, were eliminated from final data analysis. Confirmatory factor analysis (CFA) was conducted in order to establish validity and reliability of the Questionnaire of Attitude Toward Line Graphs in Science (QALGS) instrument. CFA tested the statistical hypothesis that the five main factor structures within the Questionnaire of Attitude Toward Statistical Graphs (QASG) would be maintained in the revised QALGS. Stepwise Regression Analysis with backward elimination was conducted in order to generate a parsimonious and precise predictive model. This procedure allowed the researcher to explore the relationships among the affective and cognitive variables that were included in the regression analysis. The results for CFA indicated that the revised QALGS measure was sound in its psychometric properties when tested against the QASG. Reliability statistics indicated that the overall reliability for the 32 items in the QALGS was .90. The learning preferences construct had the lowest reliability (.67), while enjoyment (.89), confidence (.86) and usefulness (.77) constructs had moderate to high reliabilities. The first four measurement models fit the data well as indicated by the appropriate descriptive and statistical indices. However, the fifth measurement model did not fit the data well statistically, and only fit well with two descriptive indices. The results addressing the research question indicated that mathematical and logical thinking ability were significant predictors of line graph performance among the remaining group of variables. These predictors accounted for 41% of the total variability on the line graph performance variable. Partial correlation coefficients indicated that mathematics ability accounted for 20.5% of the variance on the line graphing performance variable when removing the effect of logical thinking. The logical thinking variable accounted for 4.7% of the variance on the line graphing performance variable when removing the effect of mathematics ability.

The Role of Leadership Support for Health Promotion in Employee Wellness Program Participation, Perceived Job Stress, and Health Behaviors.

PubMed

Hoert, Jennifer; Herd, Ann M; Hambrick, Marion

2018-05-01

The purpose of the study was to explore the relationship between leadership support for health promotion and job stress, wellness program participation, and health behaviors. A cross-sectional survey design was used. Four worksites with a range of wellness programs were selected for this study. Participants in this study were employees (n = 618) at 4 organizations (bank, private university, wholesale supplier, and public university) in the southeastern United States, each offering an employee wellness program. Response rates in each organization ranged from 3% to 34%. Leadership support for health promotion was measured with the Leading by Example instrument. Employee participation in wellness activities, job stress, and health behaviors were measured with multi-item scales. Correlation/regression analysis and descriptive statistics were used to analyze the relationships among the scaled variables. Employees reporting higher levels of leadership support for health promotion also reported higher levels of wellness activity participation, lower job stress, and greater levels of health behavior ( P = .001). To ascertain the amount of variance in health behaviors accounted for by the other variables in the study, a hierarchical regression analysis revealed a statistically significant model (model F 7,523 = 27.28; P = .001), with leadership support for health promotion (β = .19, t = 4.39, P = .001), wellness activity participation (β = .28, t = 6.95, P < .001), and job stress (β = -.27, t = -6.75, P ≤ .001) found to be significant predictors of health behaviors in the model. Exploratory regression analyses by organization revealed the focal variables as significant model predictors for only the 2 larger organizations with well-established wellness programs. Results from the study suggest that employees' perceptions of organizational leadership support for health promotion are related to their participation in wellness activities, perceived job stress levels, and health behaviors.

Regression Analysis with Dummy Variables: Use and Interpretation.

ERIC Educational Resources Information Center

Hinkle, Dennis E.; Oliver, J. Dale

1986-01-01

Multiple regression analysis (MRA) may be used when both continuous and categorical variables are included as independent research variables. The use of MRA with categorical variables involves dummy coding, that is, assigning zeros and ones to levels of categorical variables. Caution is urged in results interpretation. (Author/CH)

Bayesian Adaptive Lasso for Ordinal Regression with Latent Variables

ERIC Educational Resources Information Center

Feng, Xiang-Nan; Wu, Hao-Tian; Song, Xin-Yuan

2017-01-01

We consider an ordinal regression model with latent variables to investigate the effects of observable and latent explanatory variables on the ordinal responses of interest. Each latent variable is characterized by correlated observed variables through a confirmatory factor analysis model. We develop a Bayesian adaptive lasso procedure to conduct…

Occupant perception of indoor air and comfort in four hospitality environments.

PubMed

Moschandreas, D J; Chu, P

2002-01-01

This article reports on a survey of customer and staff perceptions of indoor air quality at two restaurants, a billiard hall, and a casino. The survey was conducted at each environment for 8 days: 2 weekend days on 2 consecutive weekends and 4 weekdays. Before and during the survey, each hospitality environment satisfied ventilation requirements set in ASHRAE Standard 62-1999, Ventilation for Acceptable Indoor Air. An objective of this study was to test the hypothesis: If a hospitality environment satisfies ASHRAE ventilation requirements, then the indoor air is acceptable, that is, fewer than 20% of the exposed occupants perceive the environment as unacceptable. A second objective was to develop a multiple regression model that predicts the dependent variable, the environment is acceptable, as a function of a number of independent perception variables. Occupant perception of environmental, comfort, and physical variables was measured using a questionnaire. This instrument was designed to be efficient and unobtrusive; subjects could complete it within 3 min. Significant differences of occupant environment perception were identified among customers and staff. The dependent variable, the environment is acceptable, is affected by temperature, occupant density, and occupant smoking status, odor perception, health conditions, sensitivity to chemicals, and enjoyment of activities. Depending on the hospitality environment, variation of independent variables explains as much as 77% of the variation of the dependent variable.

An improved strategy for regression of biophysical variables and Landsat ETM+ data.

Treesearch

Warren B. Cohen; Thomas K. Maiersperger; Stith T. Gower; David P. Turner

2003-01-01

Empirical models are important tools for relating field-measured biophysical variables to remote sensing data. Regression analysis has been a popular empirical method of linking these two types of data to provide continuous estimates for variables such as biomass, percent woody canopy cover, and leaf area index (LAI). Traditional methods of regression are not...

Health state utility instruments compared: inquiring into nonlinearity across EQ-5D-5L, SF-6D, HUI-3 and 15D.

PubMed

Gamst-Klaussen, Thor; Chen, Gang; Lamu, Admassu N; Olsen, Jan Abel

2016-07-01

Different health state utility (HSU) instruments produce different utilities for the same individuals, thereby compromising the intended comparability of economic evaluations of health care interventions. When developing crosswalks, previous studies have indicated nonlinear relationships. This paper inquires into the degree of nonlinearity across the four most widely used HSU-instruments and proposes exchange rates that differ depending on the severity levels of the health state utility scale. Overall, 7933 respondents from six countries, 1760 in a non-diagnosed healthy group and 6173 in seven disease groups, reported their health states using four different instruments: EQ-5D-5L, SF-6D, HUI-3 and 15D. Quantile regressions investigate the degree of nonlinear relationships between these instruments. To compare the instruments across different disease severities, we split the health state utility scale into utility intervals with 0.2 successive decrements in utility starting from perfect health at 1.00. Exchange rates (ERs) are calculated as the mean utility difference between two utility intervals on one HSU-instrument divided by the difference in mean utility on another HSU-instrument. Quantile regressions reveal significant nonlinear relationships across all four HSU-instruments. The degrees of nonlinearities differ, with a maximum degree of difference in the coefficients along the health state utility scale of 3.34 when SF-6D is regressed on EQ-5D. At the lower end of the health state utility scale, the exchange rate from SF-6D to EQ-5D is 2.11, whilst at the upper end it is 0.38. Comparisons at different utility levels illustrate the fallacy of using linear functions as crosswalks between HSU-instruments. The existence of nonlinear relationships between different HSU-instruments suggests that level-specific exchange rates should be used when converting a change in utility on the instrument used, onto a corresponding utility change had another instrument been used. Accounting for nonlinearities will increase the validity of the comparison for decision makers when faced with a choice between interventions whose calculations of QALY gains have been based on different HSU-instruments.

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.

Regression of an atlantoaxial rheumatoid pannus following posterior instrumented fusion.

PubMed

Bydon, Mohamad; Macki, Mohamed; Qadi, Mohamud; De la Garza-Ramos, Rafael; Kosztowski, Thomas A; Sciubba, Daniel M; Wolinsky, Jean-Paul; Witham, Timothy F; Gokaslan, Ziya L; Bydon, Ali

2015-10-01

Rheumatoid patients may develop a retrodental lesion (atlantoaxial rheumatoid pannus) that may cause cervical instability and/or neurological compromise. The objective is to characterize clinical and radiographic outcomes after posterior instrumented fusion for atlantoaxial rheumatoid pannus. We retrospectively reviewed all patients who underwent posterior fusions for an atlantoaxial rheumatoid pannus at a single institution. Both preoperative and postoperative imaging was available for all patients. Anterior or circumferential operations, non-atlantoaxial panni, or prior C1-C2 operations were excluded. Primary outcome measures included Nurick score, Ranawat score (neurologic status in patients with rheumatoid arthritis), pannus regression, and reoperation. Pannus volume was determined with axial and sagittal views on both preoperative and postoperative radiological images. Thirty patients surgically managed for an atlantoaxial rheumatoid pannus were followed for a mean of 24.43 months. Nine patients underwent posterior instrumented fusion alone, while 21 patients underwent posterior decompression and instrumented fusion. Following a posterior instrumented fusion in all 30 patients, the pannus statistically significantly regressed by 44.44%, from a mean volume of 1.26cm(3) to 0.70cm(3) (p<0.001), over 8.02 months. The Nurick score significantly improved from 2.40 to 0.60 (p<0.001), but the marginal improvement of 0.20 in the Ranawat score did not reach significance (p=0.312). Six patients (20%) required reoperations over a mean of 13.18 months. Reoperations were indicated for C1 instrumentation failure in four patients and pseudoarthrosis in two patients. Following posterior instrumented fusion, the pannus radiographically regressed by 44.44% over a mean of 8.02 months, and patients clinically improved per the Nurick score. The Ranawat score did not improve, and 20% of patients required reoperation over a mean of 13.18 months. The annualized reoperation rate was approximately 13.62%. Copyright © 2015 Elsevier B.V. All rights reserved.

Multi-Epoch Mid-Infrared Interferometric Observations of the Oxygen-rich Mira Variable Star RR Aql with the VLTI/MIDI Instrument

DTIC Science & Technology

2011-01-01

VLTI/ MIDI Instrument I. Karovicova,1,3 M. Wittkowski,1 D. A. Boboltz,2 E. Fossat,3 K. Ohnaka,4 and M. Scholz5,6 1European Southern Observatory...the oxygen-rich Mira variable RR Aql at 13 epochs covering 4 pulsation cycles with the MIDI instrument at the VLTI. We modeled the observed data...Variable Star RR Aql with the VLTI/ MIDI Instrument 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e

What are the predictor variables of social well-being among the medical science students?

PubMed

Javadi-Pashaki, Nazila; Darvishpour, Azar

2018-01-01

Individuals with social well-being can cope more successfully with major problems of social roles. Due to the social nature of human life, it cannot be ignored to pay attention the social aspect of health. The purpose of this study was to identify variables that predict the social well-being of medical students. A descriptive-analytical study was conducted on 489 medical science students of Gilan Province, the North of Iran, during May to September 2016. The samples were selected using quota sampling method. Research instrument was a questionnaire consisting of two parts: demographic section and Keyes social well-being questionnaire. Data analysis was done using SPSS software version 19 and with descriptive and inferential statistics (t-test, ANOVA, and linear regression). The results showed that majority of the students had average social well-being. Furthermore, a significant relationship between the academic degree ( P = 0.009), major ( P = 0.0001), the interest and field's satisfaction ( P = 0.0001), and social well-being was seen. The results of linear regression model showed that four variables (academic degree, major, group membership, and the interest and field's satisfaction) were significantly associated with the social well-being ( P < 0.05). The findings demonstrate that the different effects of the demographic factors on social well-being and the need for further consideration of these factors are obvious. Thus, health and education authorities are advised to pay attention students' academic degree, major, group membership, and the interest and field's satisfaction to upgrade and maintain the level of their social well-being.

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

PubMed Central

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

2012-01-01

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

Modeling the human development index and the percentage of poor people using quantile smoothing splines

NASA Astrophysics Data System (ADS)

Mulyani, Sri; Andriyana, Yudhie; Sudartianto

2017-03-01

Mean regression is a statistical method to explain the relationship between the response variable and the predictor variable based on the central tendency of the data (mean) of the response variable. The parameter estimation in mean regression (with Ordinary Least Square or OLS) generates a problem if we apply it to the data with a symmetric, fat-tailed, or containing outlier. Hence, an alternative method is necessary to be used to that kind of data, for example quantile regression method. The quantile regression is a robust technique to the outlier. This model can explain the relationship between the response variable and the predictor variable, not only on the central tendency of the data (median) but also on various quantile, in order to obtain complete information about that relationship. In this study, a quantile regression is developed with a nonparametric approach such as smoothing spline. Nonparametric approach is used if the prespecification model is difficult to determine, the relation between two variables follow the unknown function. We will apply that proposed method to poverty data. Here, we want to estimate the Percentage of Poor People as the response variable involving the Human Development Index (HDI) as the predictor variable.

Developing and testing a measurement tool for assessing predictors of breakfast consumption based on a health promotion model.

PubMed

Dehdari, Tahereh; Rahimi, Tahereh; Aryaeian, Naheed; Gohari, Mahmood Reza; Esfeh, Jabiz Modaresi

2014-01-01

To develop an instrument for measuring Health Promotion Model constructs in terms of breakfast consumption, and to identify the constructs that were predictors of breakfast consumption among Iranian female students. A questionnaire on Health Promotion Model variables was developed and potential predictors of breakfast consumption were assessed using this tool. One hundred female students, mean age 13 years (SD ± 1.2 years). Two middle schools from moderate-income areas in Qom, Iran. Health Promotion Model variables were assessed using a 58-item questionnaire. Breakfast consumption was also measured. Internal consistency (Cronbach alpha), content validity index, content validity ratio, multiple linear regression using stepwise method, and Pearson correlation. Content validity index and content validity ratio scores of the developed scale items were 0.89 and 0.93, respectively. Internal consistencies (range, .74-.91) of subscales were acceptable. Prior related behaviors, perceived barriers, self-efficacy, and competing demand and preferences were 4 constructs that could predict 63% variance of breakfast frequency per week among subjects. The instrument developed in this study may be a useful tool for researchers to explore factors affecting breakfast consumption among students. Students with a high level of self-efficacy, more prior related behavior, fewer perceived barriers, and fewer competing demands were most likely to regularly consume breakfast. Copyright © 2014 Society for Nutrition Education and Behavior. Published by Elsevier Inc. All rights reserved.

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

NASA Astrophysics Data System (ADS)

Lee, Hansang; Park, Minseok; Kim, Junmo

2017-03-01

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

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

ERIC Educational Resources Information Center

Woolley, Kristin K.

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

An instrumental variable random-coefficients model for binary outcomes

PubMed Central

Chesher, Andrew; Rosen, Adam M

2014-01-01

In this paper, we study a random-coefficients model for a binary outcome. We allow for the possibility that some or even all of the explanatory variables are arbitrarily correlated with the random coefficients, thus permitting endogeneity. We assume the existence of observed instrumental variables Z that are jointly independent with the random coefficients, although we place no structure on the joint determination of the endogenous variable X and instruments Z, as would be required for a control function approach. The model fits within the spectrum of generalized instrumental variable models, and we thus apply identification results from our previous studies of such models to the present context, demonstrating their use. Specifically, we characterize the identified set for the distribution of random coefficients in the binary response model with endogeneity via a collection of conditional moment inequalities, and we investigate the structure of these sets by way of numerical illustration. PMID:25798048

Developing and validating a measure of community capacity: Why volunteers make the best neighbours.

PubMed

Lovell, Sarah A; Gray, Andrew R; Boucher, Sara E

2015-05-01

Social support and community connectedness are key determinants of both mental and physical wellbeing. While social capital has been used to indicate the instrumental value of these social relationships, its broad and often competing definitions have hindered practical applications of the concept. Within the health promotion field, the related concept of community capacity, the ability of a group to identify and act on problems, has gained prominence (Labonte and Laverack, 2001). The goal of this study was to develop and validate a scale measuring community capacity including exploring its associations with socio-demographic and civic behaviour variables among the residents of four small (populations 1500-2000) high-deprivation towns in southern New Zealand. The full (41-item) scale was found to have strong internal consistency (Cronbach's alpha = 0.89) but a process of reducing the scale resulted in a shorter 26-item instrument with similar internal consistency (alpha 0.88). Subscales of the reduced instrument displayed at least marginally acceptable levels of internal consistency (0.62-0.77). Using linear regression models, differences in community capacity scores were found for selected criterion, namely time spent living in the location, local voting, and volunteering behaviour, although the first of these was no longer statistically significant in an adjusted model with potential confounders including age, sex, ethnicity, education, marital status, employment, household income, and religious beliefs. This provides support for the scale's concurrent validity. Differences were present between the four towns in unadjusted models and remained statistically significant in adjusted models (including variables mentioned above) suggesting, crucially, that even when such factors are accounted for, perceptions of one's community may still depend on place. Copyright © 2014. Published by Elsevier Ltd.

Pediatric outcomes data collection instrument scores in ambulatory children with cerebral palsy: an analysis by age groups and severity level.

PubMed

Barnes, Douglas; Linton, Judith L; Sullivan, Elroy; Bagley, Anita; Oeffinger, Donna; Abel, Mark; Damiano, Diane; Gorton, George; Nicholson, Diane; Romness, Mark; Rogers, Sarah; Tylkowski, Chester

2008-01-01

The Pediatric Outcomes Data Collection Instrument (PODCI) was developed in 1994 as a patient-based tool for use across a broad age range and wide array of musculoskeletal disorders, including children with cerebral palsy (CP). The purpose of this study was to establish means and SDs of the Parent PODCI measures by age groups and Gross Motor Function Classification System (GMFCS) levels for ambulatory children with CP. This instrument was one of several studied in a prospective, multicenter project of ambulatory patients with CP between the aged 4 and 18 years and GMFCS levels I through III. Participants included 338 boys and 221 girls at a mean age of 11.1 years, with 370 diplegic, 162 hemiplegic, and 27 quadriplegic. Both baseline and follow-up data sets of the completed Parent PODCI responses were statistically analyzed. Age was identified as a significant predictor of the PODCI measures of Upper Extremity Function, Transfers and Basic Mobility, Global Function, and Happiness With Physical Condition. Gross Motor Function Classification System levels was a significant predictor of Transfers and Basic Mobility, Sports and Physical Function, and Global Function. Pattern of involvement, sex, and prior orthopaedic surgery were not statistically significant predictors for any of the Parent PODCI measures. Mean and SD scores were calculated for age groups stratified by GMFCS levels. Analysis of the follow-up data set validated the findings derived from the baseline data. Linear regression equations were derived, with age as a continuous variable and GMFCS levels as a categorical variable, to be used for Parent PODCI predicted scores. The results of this study provide clinicians and researchers with a set of Parent PODCI values for comparison to age- and severity-matched populations of ambulatory patients with CP.

Borderline Personality Features in Students: the Predicting Role of Schema, Emotion Regulation, Dissociative Experience and Suicidal Ideation.

PubMed

Sajadi, Seyede Fateme; Arshadi, Nasrin; Zargar, Yadolla; Mehrabizade Honarmand, Mahnaz; Hajjari, Zahra

2015-06-01

Numerous studies have demonstrated that early maladaptive schemas, emotional dysregulation are supposed to be the defining core of borderline personality disorder. Many studies have also found a strong association between the diagnosis of borderline personality and the occurrence of suicide ideation and dissociative symptoms. The present study was designed to investigate the relationship between borderline personality features and schema, emotion regulation, dissociative experiences and suicidal ideation among high school students in Shiraz City, Iran. In this descriptive correlational study, 300 students (150 boys and 150 girls) were selected from the high schools in Shiraz, Iran, using the multi-stage random sampling. Data were collected using some instruments including borderline personality feature scale for children, young schema questionnaire-short form, difficulties in emotion-regulation scale (DERS), dissociative experience scale and beck suicide ideation scale. Data were analyzed using the Pearson correlation coefficient and multivariate regression analysis. The results showed a significant positive correlation between schema, emotion regulation, dissociative experiences and suicide ideation with borderline personality features. Moreover, the results of multivariate regression analysis suggested that among the studied variables, schema was the most effective predicting variable of borderline features (P < 0.001). The findings of this study are in accordance with findings from previous studies, and generally show a meaningful association between schema, emotion regulation, dissociative experiences, and suicide ideation with borderline personality features.

Mapping health outcome measures from a stroke registry to EQ-5D weights.

PubMed

Ghatnekar, Ola; Eriksson, Marie; Glader, Eva-Lotta

2013-03-07

To map health outcome related variables from a national register, not part of any validated instrument, with EQ-5D weights among stroke patients. We used two cross-sectional data sets including patient characteristics, outcome variables and EQ-5D weights from the national Swedish stroke register. Three regression techniques were used on the estimation set (n=272): ordinary least squares (OLS), Tobit, and censored least absolute deviation (CLAD). The regression coefficients for "dressing", "toileting", "mobility", "mood", "general health" and "proxy-responders" were applied to the validation set (n=272), and the performance was analysed with mean absolute error (MAE) and mean square error (MSE). The number of statistically significant coefficients varied by model, but all models generated consistent coefficients in terms of sign. Mean utility was underestimated in all models (least in OLS) and with lower variation (least in OLS) compared to the observed. The maximum attainable EQ-5D weight ranged from 0.90 (OLS) to 1.00 (Tobit and CLAD). Health states with utility weights <0.5 had greater errors than those with weights ≥ 0.5 (P<0.01). This study indicates that it is possible to map non-validated health outcome measures from a stroke register into preference-based utilities to study the development of stroke care over time, and to compare with other conditions in terms of utility.

Predicting Visual Distraction Using Driving Performance Data

PubMed Central

Kircher, Katja; Ahlstrom, Christer

2010-01-01

Behavioral variables are often used as performance indicators (PIs) of visual or internal distraction induced by secondary tasks. The objective of this study is to investigate whether visual distraction can be predicted by driving performance PIs in a naturalistic setting. Visual distraction is here defined by a gaze based real-time distraction detection algorithm called AttenD. Seven drivers used an instrumented vehicle for one month each in a small scale field operational test. For each of the visual distraction events detected by AttenD, seven PIs such as steering wheel reversal rate and throttle hold were calculated. Corresponding data were also calculated for time periods during which the drivers were classified as attentive. For each PI, means between distracted and attentive states were calculated using t-tests for different time-window sizes (2 – 40 s), and the window width with the smallest resulting p-value was selected as optimal. Based on the optimized PIs, logistic regression was used to predict whether the drivers were attentive or distracted. The logistic regression resulted in predictions which were 76 % correct (sensitivity = 77 % and specificity = 76 %). The conclusion is that there is a relationship between behavioral variables and visual distraction, but the relationship is not strong enough to accurately predict visual driver distraction. Instead, behavioral PIs are probably best suited as complementary to eye tracking based algorithms in order to make them more accurate and robust. PMID:21050615

Exploring the relationship between work environment, job satisfaction, and intent to stay of Jordanian nurses in underserved areas.

PubMed

AbuAlRub, Raeda; El-Jardali, Fadi; Jamal, Diana; Abu Al-Rub, Nawzat

2016-08-01

The aims of this study are to (1) examine the relationships between work environment, job satisfaction and intention to stay at work; and (2) explore the predicting factors of intention to stay at work among nurses in underserved areas. Developing and fostering creative work environment are paramount especially in underserved areas, where the work conditions present many challenges. A descriptive correlational design was utilized to collect data from 330 hospital nurses who worked in two underserved governorates in Jordan. A set of instruments were used to measure the variables of the study. The results showed a strong positive association between job satisfaction and work environment. The results of logistic regression indicated receiving housing, job satisfaction, and work environment were the predicting variables of the level of intention to stay at work. It is critical to improve work conditions and create a culture of supportive work environment in underserved area. Copyright © 2015 Elsevier Inc. All rights reserved.

The effects of types of social networks, perceived social support, and loneliness on the health of older people: accounting for the social context.

PubMed

Stephens, Christine; Alpass, Fiona; Towers, Andy; Stevenson, Brendan

2011-09-01

To use an ecological model of ageing (Berkman, Glass, Brissette, & Seeman, 2000) which includes upstream social context factors and downstream social support factors to examine the effects of social networks on health. Postal survey responses from a representative population sample of New Zealanders aged 55 to 70 years (N = 6,662). Correlations and multiple regression analyses provided support for a model in which social context contributes to social network type, which affects perceived social support and loneliness, and consequent mental and physical health. Ethnicity was related to social networks and health but this was largely accounted for by other contextual variables measuring socioeconomic status. Gender and age were also significant variables in the model. Social network type is a useful way to assess social integration within this model of cascading effects. More detailed information could be gained through the development of our network assessment instruments for older people.

The study of protein biomarkers to understand the biochemical processes underlying beef color development in young bulls.

PubMed

Gagaoua, Mohammed; Terlouw, E M Claudia; Picard, Brigitte

2017-12-01

This study investigates relationships between 21 biomarkers and meat color traits of Longissimus thoracis muscles of young Aberdeen Angus and Limousin bulls. The relationships found allowed to propose metabolic processes underlying meat color. The color coordinates were related with several biomarkers. The relationships were in some cases breed-dependent and the variability explained in the regression models varied between 31 and 56%. The correlations between biomarkers and color parameters were sometimes opposite between breeds. The PCA using the 21 biomarkers and the instrumental color coordinates showed that these variables discriminated efficiently between the two studied breeds. Results are coherent with earlier studies on other beef breeds showing that several proteins belonging to different but partly related biological pathways involved in muscle contraction, metabolism, heat stress and apoptosis are related to beef color. The results suggest that in future, biomarkers may be used to classify meat cuts sampled early post-mortem according to their forthcoming color. Copyright © 2017 Elsevier Ltd. All rights reserved.

The relative effectiveness of managed care penetration and the healthcare safety net in reducing avoidable hospitalizations.

PubMed

Pracht, Etienne E; Orban, Barbara L; Comins, Meg M; Large, John T; Asin-Oostburg, Virginia

2011-01-01

Avoidable hospitalizations represent a key indicator for access to, and the quality of, primary care. Therefore, understanding their behavior is essential in terms of management of healthcare resources and costs. This analysis examines the affect of 2 healthcare strategies on the rate of avoidable hospitalization, managed care and the healthcare safety net. The avoidable hospitalizations definition developed by Weissman et al. (1992) was used to identify relevant inpatient episodes. A 2-stage simultaneous equations multivariate regression model with instrumental variables was used to estimate the relative influence of HMO penetration and the composition of local hospital markets on the rate of avoidable hospitalizations. Control variables in the model include healthcare supply and demand, demographic, socioeconomic, and health status characteristics. Increased market presence of public hospitals significantly reduced avoidable hospitalizations. HMO penetration did not influence the rate of avoidable hospitalizations. The results suggest that public investments in healthcare facilities and infrastructure are more effective in reducing avoidable hospitalizations. © 2011 National Association for Healthcare Quality.

Physical fitness predicts technical-tactical and time-motion profile in simulated Judo and Brazilian Jiu-Jitsu matches.

PubMed

Coswig, Victor S; Gentil, Paulo; Bueno, João C A; Follmer, Bruno; Marques, Vitor A; Del Vecchio, Fabrício B

2018-01-01

Among combat sports, Judo and Brazilian Jiu-Jitsu (BJJ) present elevated physical fitness demands from the high-intensity intermittent efforts. However, information regarding how metabolic and neuromuscular physical fitness is associated with technical-tactical performance in Judo and BJJ fights is not available. This study aimed to relate indicators of physical fitness with combat performance variables in Judo and BJJ. The sample consisted of Judo ( n  = 16) and BJJ ( n  = 24) male athletes. At the first meeting, the physical tests were applied and, in the second, simulated fights were performed for later notational analysis. The main findings indicate: (i) high reproducibility of the proposed instrument and protocol used for notational analysis in a mobile device; (ii) differences in the technical-tactical and time-motion patterns between modalities; (iii) performance-related variables are different in Judo and BJJ; and (iv) regression models based on metabolic fitness variables may account for up to 53% of the variances in technical-tactical and/or time-motion variables in Judo and up to 31% in BJJ, whereas neuromuscular fitness models can reach values up to 44 and 73% of prediction in Judo and BJJ, respectively. When all components are combined, they can explain up to 90% of high intensity actions in Judo. In conclusion, performance prediction models in simulated combat indicate that anaerobic, aerobic and neuromuscular fitness variables contribute to explain time-motion variables associated with high intensity and technical-tactical variables in Judo and BJJ fights.

Multiresponse semiparametric regression for modelling the effect of regional socio-economic variables on the use of information technology

NASA Astrophysics Data System (ADS)

Wibowo, Wahyu; Wene, Chatrien; Budiantara, I. Nyoman; Permatasari, Erma Oktania

2017-03-01

Multiresponse semiparametric regression is simultaneous equation regression model and fusion of parametric and nonparametric model. The regression model comprise several models and each model has two components, parametric and nonparametric. The used model has linear function as parametric and polynomial truncated spline as nonparametric component. The model can handle both linearity and nonlinearity relationship between response and the sets of predictor variables. The aim of this paper is to demonstrate the application of the regression model for modeling of effect of regional socio-economic on use of information technology. More specific, the response variables are percentage of households has access to internet and percentage of households has personal computer. Then, predictor variables are percentage of literacy people, percentage of electrification and percentage of economic growth. Based on identification of the relationship between response and predictor variable, economic growth is treated as nonparametric predictor and the others are parametric predictors. The result shows that the multiresponse semiparametric regression can be applied well as indicate by the high coefficient determination, 90 percent.

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

NASA Astrophysics Data System (ADS)

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

2008-04-01

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

Investigating the Performance of Alternate Regression Weights by Studying All Possible Criteria in Regression Models with a Fixed Set of Predictors

ERIC Educational Resources Information Center

Waller, Niels; Jones, Jeff

2011-01-01

We describe methods for assessing all possible criteria (i.e., dependent variables) and subsets of criteria for regression models with a fixed set of predictors, x (where x is an n x 1 vector of independent variables). Our methods build upon the geometry of regression coefficients (hereafter called regression weights) in n-dimensional space. For a…

The influence of inequality on the standard of living: worldwide anthropometric evidence from the 19th and 20th centuries.

PubMed

Blum, Matthias

2013-12-01

We provide empirical evidence on the existence of the Pigou-Dalton principle. The latter indicates that aggregate welfare is - ceteris paribus - maximized when incomes of all individuals are equalized (and therefore marginal utility from income is as well). Using anthropometric panel data on 101 countries during the 19th and 20th centuries, we determine that there is a systematic negative and concave relationship between height inequality and average height. The robustness of this relationship is tested by means of several robustness checks, including two instrument variable regressions. These findings help to elucidate the impact of economic inequality on welfare. Copyright © 2012 Elsevier B.V. All rights reserved.

Weather variability and the incidence of cryptosporidiosis: comparison of time series poisson regression and SARIMA models.

PubMed

Hu, Wenbiao; Tong, Shilu; Mengersen, Kerrie; Connell, Des

2007-09-01

Few studies have examined the relationship between weather variables and cryptosporidiosis in Australia. This paper examines the potential impact of weather variability on the transmission of cryptosporidiosis and explores the possibility of developing an empirical forecast system. Data on weather variables, notified cryptosporidiosis cases, and population size in Brisbane were supplied by the Australian Bureau of Meteorology, Queensland Department of Health, and Australian Bureau of Statistics for the period of January 1, 1996-December 31, 2004, respectively. Time series Poisson regression and seasonal auto-regression integrated moving average (SARIMA) models were performed to examine the potential impact of weather variability on the transmission of cryptosporidiosis. Both the time series Poisson regression and SARIMA models show that seasonal and monthly maximum temperature at a prior moving average of 1 and 3 months were significantly associated with cryptosporidiosis disease. It suggests that there may be 50 more cases a year for an increase of 1 degrees C maximum temperature on average in Brisbane. Model assessments indicated that the SARIMA model had better predictive ability than the Poisson regression model (SARIMA: root mean square error (RMSE): 0.40, Akaike information criterion (AIC): -12.53; Poisson regression: RMSE: 0.54, AIC: -2.84). Furthermore, the analysis of residuals shows that the time series Poisson regression appeared to violate a modeling assumption, in that residual autocorrelation persisted. The results of this study suggest that weather variability (particularly maximum temperature) may have played a significant role in the transmission of cryptosporidiosis. A SARIMA model may be a better predictive model than a Poisson regression model in the assessment of the relationship between weather variability and the incidence of cryptosporidiosis.

Right adolescent idiopathic thoracic curve (Lenke 1 A and B): does cost of instrumentation and implant density improve radiographic and cosmetic parameters?

PubMed

Yang, Scott; Jones-Quaidoo, Sean M; Eager, Matthew; Griffin, Justin W; Reddi, Vasantha; Novicoff, Wendy; Shilt, Jeffrey; Bersusky, Ernesto; Defino, Helton; Ouellet, Jean; Arlet, Vincent

2011-07-01

In adolescent idiopathic scoliosis (AIS) there has been a shift towards increasing the number of implants and pedicle screws, which has not been proven to improve cosmetic correction. To evaluate if increasing cost of instrumentation correlates with cosmetic correction using clinical photographs. 58 Lenke 1A and B cases from a multicenter AIS database with at least 3 months follow-up of clinical photographs were used for analysis. Cosmetic parameters on PA and forward bending photographs included angular measurements of trunk shift, shoulder balance, rib hump, and ratio measurements of waist line asymmetry. Pre-op and follow-up X-rays were measured for coronal and sagittal deformity parameters. Cost density was calculated by dividing the total cost of instrumentation by the number of vertebrae being fused. Linear regression and spearman's correlation were used to correlate cost density to X-ray and photo outcomes. Three independent observers verified radiographic and cosmetic parameters for inter/interobserver variability analysis. Average pre-op Cobb angle and instrumented correction were 54° (SD 12.5) and 59% (SD 25) respectively. The average number of vertebrae fused was 10 (SD 1.9). The total cost of spinal instrumentation ranged from $6,769 to $21,274 (Mean $12,662, SD $3,858). There was a weak positive and statistically significant correlation between Cobb angle correction and cost density (r = 0.33, p = 0.01), and no correlation between Cobb angle correction of the uninstrumented lumbar spine and cost density (r = 0.15, p = 0.26). There was no significant correlation between all sagittal X-ray measurements or any of the photo parameters and cost density. There was good to excellent inter/intraobserver variability of all photographic parameters based on the intraclass correlation coefficient (ICC 0.74-0.98). Our method used to measure cosmesis had good to excellent inter/intraobserver variability, and may be an effective tool to objectively assess cosmesis from photographs. Since increasing cost density only improves mildly the Cobb angle correction of the main thoracic curve and not the correction of the uninstrumented spine or any of the cosmetic parameters, one should consider the cost of increasing implant density in Lenke 1A and B curves. In the area of rationalization of health care expenses, this study demonstrates that increasing the number of implants does not improve any relevant cosmetic or radiographic outcomes.

Change in active travel and changes in recreational and total physical activity in adults: longitudinal findings from the iConnect study

PubMed Central

2013-01-01

Background To better understand the health benefits of promoting active travel, it is important to understand the relationship between a change in active travel and changes in recreational and total physical activity. Methods These analyses, carried out in April 2012, use longitudinal data from 1628 adult respondents (mean age 54 years; 47% male) in the UK-based iConnect study. Travel and recreational physical activity were measured using detailed seven-day recall instruments. Adjusted linear regression models were fitted with change in active travel defined as ‘decreased’ (<−15 min/week), ‘maintained’ (±15 min/week) or ‘increased’ (>15 min/week) as the primary exposure variable and changes in (a) recreational and (b) total physical activity (min/week) as the primary outcome variables. Results Active travel increased in 32% (n=529), was maintained in 33% (n=534) and decreased in 35% (n=565) of respondents. Recreational physical activity decreased in all groups but this decrease was not greater in those whose active travel increased. Conversely, changes in active travel were associated with commensurate changes in total physical activity. Compared with those whose active travel remained unchanged, total physical activity decreased by 176.9 min/week in those whose active travel had decreased (adjusted regression coefficient −154.9, 95% CI −195.3 to −114.5) and was 112.2 min/week greater among those whose active travel had increased (adjusted regression coefficient 135.1, 95% CI 94.3 to 175.9). Conclusion An increase in active travel was associated with a commensurate increase in total physical activity and not a decrease in recreational physical activity. PMID:23445724

Improved intact soil-core carbon determination applying regression shrinkage and variable selection techniques to complete spectrum laser-induced breakdown spectroscopy (LIBS).

PubMed

Bricklemyer, Ross S; Brown, David J; Turk, Philip J; Clegg, Sam M

2013-10-01

Laser-induced breakdown spectroscopy (LIBS) provides a potential method for rapid, in situ soil C measurement. In previous research on the application of LIBS to intact soil cores, we hypothesized that ultraviolet (UV) spectrum LIBS (200-300 nm) might not provide sufficient elemental information to reliably discriminate between soil organic C (SOC) and inorganic C (IC). In this study, using a custom complete spectrum (245-925 nm) core-scanning LIBS instrument, we analyzed 60 intact soil cores from six wheat fields. Predictive multi-response partial least squares (PLS2) models using full and reduced spectrum LIBS were compared for directly determining soil total C (TC), IC, and SOC. Two regression shrinkage and variable selection approaches, the least absolute shrinkage and selection operator (LASSO) and sparse multivariate regression with covariance estimation (MRCE), were tested for soil C predictions and the identification of wavelengths important for soil C prediction. Using complete spectrum LIBS for PLS2 modeling reduced the calibration standard error of prediction (SEP) 15 and 19% for TC and IC, respectively, compared to UV spectrum LIBS. The LASSO and MRCE approaches provided significantly improved calibration accuracy and reduced SEP 32-55% over UV spectrum PLS2 models. We conclude that (1) complete spectrum LIBS is superior to UV spectrum LIBS for predicting soil C for intact soil cores without pretreatment; (2) LASSO and MRCE approaches provide improved calibration prediction accuracy over PLS2 but require additional testing with increased soil and target analyte diversity; and (3) measurement errors associated with analyzing intact cores (e.g., sample density and surface roughness) require further study and quantification.

Variable Selection for Regression Models of Percentile Flows

NASA Astrophysics Data System (ADS)

Fouad, G.

2017-12-01

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

A selective review of the first 20 years of instrumental variables models in health-services research and medicine.

PubMed

Cawley, John

2015-01-01

The method of instrumental variables (IV) is useful for estimating causal effects. Intuitively, it exploits exogenous variation in the treatment, sometimes called natural experiments or instruments. This study reviews the literature in health-services research and medical research that applies the method of instrumental variables, documents trends in its use, and offers examples of various types of instruments. A literature search of the PubMed and EconLit research databases for English-language journal articles published after 1990 yielded a total of 522 original research articles. Citations counts for each article were derived from the Web of Science. A selective review was conducted, with articles prioritized based on number of citations, validity and power of the instrument, and type of instrument. The average annual number of papers in health services research and medical research that apply the method of instrumental variables rose from 1.2 in 1991-1995 to 41.8 in 2006-2010. Commonly-used instruments (natural experiments) in health and medicine are relative distance to a medical care provider offering the treatment and the medical care provider's historic tendency to administer the treatment. Less common but still noteworthy instruments include randomization of treatment for reasons other than research, randomized encouragement to undertake the treatment, day of week of admission as an instrument for waiting time for surgery, and genes as an instrument for whether the respondent has a heritable condition. The use of the method of IV has increased dramatically in the past 20 years, and a wide range of instruments have been used. Applications of the method of IV have in several cases upended conventional wisdom that was based on correlations and led to important insights about health and healthcare. Future research should pursue new applications of existing instruments and search for new instruments that are powerful and valid.

Determination of riverbank erosion probability using Locally Weighted Logistic Regression

NASA Astrophysics Data System (ADS)

Ioannidou, Elena; Flori, Aikaterini; Varouchakis, Emmanouil A.; Giannakis, Georgios; Vozinaki, Anthi Eirini K.; Karatzas, George P.; Nikolaidis, Nikolaos

2015-04-01

Riverbank erosion is a natural geomorphologic process that affects the fluvial environment. The most important issue concerning riverbank erosion is the identification of the vulnerable locations. An alternative to the usual hydrodynamic models to predict vulnerable locations is to quantify the probability of erosion occurrence. This can be achieved by identifying the underlying relations between riverbank erosion and the geomorphological or hydrological variables that prevent or stimulate erosion. Thus, riverbank erosion can be determined by a regression model using independent variables that are considered to affect the erosion process. The impact of such variables may vary spatially, therefore, a non-stationary regression model is preferred instead of a stationary equivalent. Locally Weighted Regression (LWR) is proposed as a suitable choice. This method can be extended to predict the binary presence or absence of erosion based on a series of independent local variables by using the logistic regression model. It is referred to as Locally Weighted Logistic Regression (LWLR). Logistic regression is a type of regression analysis used for predicting the outcome of a categorical dependent variable (e.g. binary response) based on one or more predictor variables. The method can be combined with LWR to assign weights to local independent variables of the dependent one. LWR allows model parameters to vary over space in order to reflect spatial heterogeneity. The probabilities of the possible outcomes are modelled as a function of the independent variables using a logistic function. Logistic regression measures the relationship between a categorical dependent variable and, usually, one or several continuous independent variables by converting the dependent variable to probability scores. Then, a logistic regression is formed, which predicts success or failure of a given binary variable (e.g. erosion presence or absence) for any value of the independent variables. The erosion occurrence probability can be calculated in conjunction with the model deviance regarding the independent variables tested. The most straightforward measure for goodness of fit is the G statistic. It is a simple and effective way to study and evaluate the Logistic Regression model efficiency and the reliability of each independent variable. The developed statistical model is applied to the Koiliaris River Basin on the island of Crete, Greece. Two datasets of river bank slope, river cross-section width and indications of erosion were available for the analysis (12 and 8 locations). Two different types of spatial dependence functions, exponential and tricubic, were examined to determine the local spatial dependence of the independent variables at the measurement locations. The results show a significant improvement when the tricubic function is applied as the erosion probability is accurately predicted at all eight validation locations. Results for the model deviance show that cross-section width is more important than bank slope in the estimation of erosion probability along the Koiliaris riverbanks. The proposed statistical model is a useful tool that quantifies the erosion probability along the riverbanks and can be used to assist managing erosion and flooding events. Acknowledgements This work is part of an on-going THALES project (CYBERSENSORS - High Frequency Monitoring System for Integrated Water Resources Management of Rivers). The project has been co-financed by the European Union (European Social Fund - ESF) and Greek national funds through the Operational Program "Education and Lifelong Learning" of the National Strategic Reference Framework (NSRF) - Research Funding Program: THALES. Investing in knowledge society through the European Social Fund.

Predicting approach to homework in Primary school students.

PubMed

Valle, Antonio; Pan, Irene; Regueiro, Bibiana; Suárez, Natalia; Tuero, Ellián; Nunes, Ana R

2015-01-01

The goal of this research was to study the weight of student variables related to homework (intrinsic homework motivation, perceived homework instrumentality, homework attitude, time spent on homework, and homework time management) and context (teacher feedback on homework and parental homework support) in the prediction of approaches to homework. 535 students of the last three courses of primary education participated in the study. Data were analyzed with hierarchical regression models and path analysis. The results obtained suggest that students’ homework engagement (high or low) is related to students´ level of intrinsic motivation and positive attitude towards homework. Furthermore, it was also observed that students who manage their homework time well (and not necessarily those who spend more time) are more likely to show the deepest approach to homework. Parental support and teacher feedback on homework affect student homework engagement through their effect on the levels of intrinsic homework motivation (directly), and on homework attitude, homework time management, and perceived homework instrumentality (indirectly). Data also indicated a strong and significant relationship between parental and teacher involvement.

Development and evaluation of a case group concept for inpatients with mental disorders in Germany: using self-report and expert-rated instruments.

PubMed

Andreas, Sylke; Dirmaier, Jörg; Harfst, Timo; Kawski, Stephan; Koch, Uwe; Schulz, Holger

2009-03-01

The aim of this study was to evaluate a case-mix system to classify inpatients with mental disorders in Germany by means of self-report and expert-rated instruments. The use of case-mix systems enhances the transparency of performance and cost structure and can thus improve the quality of mental health care. We analysed a consecutive sample of 1677 inpatients with mental disorders from 11 hospitals using regression tree analysis. The model assigns patients to 17 groups, accounting for 17% of the variance for duration of stay. Patients with eating disorders had a longer duration of stay than patients with anxiety disorder, duration of mental illness of less than 3-5 years, lower levels of interpersonal problems and higher occupational position. The results showed that besides diagnosis, variables such as duration of illness and interpersonal problems are important for classifying inpatients with mental disorders. The results of the study should be critically reviewed regarding the empirical results of other studies and the appropriateness of case group concepts for inpatients with mental disorders.

Statistical analysis of the calibration procedure for personnel radiation measurement instruments

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

Bush, W.J.; Bengston, S.J.; Kalbeitzer, F.L.

1980-11-01

Thermoluminescent analyzer (TLA) calibration procedures were used to estimate personnel radiation exposure levels at the Idaho National Engineering Laboratory (INEL). A statistical analysis is presented herein based on data collected over a six month period in 1979 on four TLA's located in the Department of Energy (DOE) Radiological and Environmental Sciences Laboratory at the INEL. The data were collected according to the day-to-day procedure in effect at that time. Both gamma and beta radiation models are developed. Observed TLA readings of thermoluminescent dosimeters are correlated with known radiation levels. This correlation is then used to predict unknown radiation doses frommore » future analyzer readings of personnel thermoluminescent dosimeters. The statistical techniques applied in this analysis include weighted linear regression, estimation of systematic and random error variances, prediction interval estimation using Scheffe's theory of calibration, the estimation of the ratio of the means of two normal bivariate distributed random variables and their corresponding confidence limits according to Kendall and Stuart, tests of normality, experimental design, a comparison between instruments, and quality control.« less

Development of Software Sensors for Determining Total Phosphorus and Total Nitrogen in Waters

PubMed Central

Lee, Eunhyoung; Han, Sanghoon; Kim, Hyunook

2013-01-01

Total nitrogen (TN) and total phosphorus (TP) concentrations are important parameters to assess the quality of water bodies and are used as criteria to regulate the water quality of the effluent from a wastewater treatment plant (WWTP) in Korea. Therefore, continuous monitoring of TN and TP using in situ instruments is conducted nationwide in Korea. However, most in situ instruments in the market are expensive and require a time-consuming sample pretreatment step, which hinders the widespread use of in situ TN and TP monitoring. In this study, therefore, software sensors based on multiple-regression with a few easily in situ measurable water quality parameters were applied to estimate the TN and TP concentrations in a stream, a lake, combined sewer overflows (CSOs), and WWTP effluent. In general, the developed software sensors predicted TN and TP concentrations of the WWTP effluent and CSOs reasonably well. However, they showed relatively lower predictability for TN and TP concentrations of stream and lake waters, possibly because the water quality of stream and lake waters is more variable than that of WWTP effluent or CSOs. PMID:23307350

Determining the Attitudes of Undergraduate Students Having Vocational Music Education towards Individual Instrument Course According to Different Variables

ERIC Educational Resources Information Center

Uluçay, Taner

2017-01-01

This study was carried out in order to determine attitudes of undergraduate students who studied music vocationally towards the individual instrument course according to the variables of grade, gender, individual instrument and graduated high school type. The research data were obtained from 102 undergraduate students studying in Erzincan…

How Robust Is Linear Regression with Dummy Variables?

ERIC Educational Resources Information Center

Blankmeyer, Eric

2006-01-01

Researchers in education and the social sciences make extensive use of linear regression models in which the dependent variable is continuous-valued while the explanatory variables are a combination of continuous-valued regressors and dummy variables. The dummies partition the sample into groups, some of which may contain only a few observations.…

Instrumental variables estimation of exposure effects on a time-to-event endpoint using structural cumulative survival models.

PubMed

Martinussen, Torben; Vansteelandt, Stijn; Tchetgen Tchetgen, Eric J; Zucker, David M

2017-12-01

The use of instrumental variables for estimating the effect of an exposure on an outcome is popular in econometrics, and increasingly so in epidemiology. This increasing popularity may be attributed to the natural occurrence of instrumental variables in observational studies that incorporate elements of randomization, either by design or by nature (e.g., random inheritance of genes). Instrumental variables estimation of exposure effects is well established for continuous outcomes and to some extent for binary outcomes. It is, however, largely lacking for time-to-event outcomes because of complications due to censoring and survivorship bias. In this article, we make a novel proposal under a class of structural cumulative survival models which parameterize time-varying effects of a point exposure directly on the scale of the survival function; these models are essentially equivalent with a semi-parametric variant of the instrumental variables additive hazards model. We propose a class of recursive instrumental variable estimators for these exposure effects, and derive their large sample properties along with inferential tools. We examine the performance of the proposed method in simulation studies and illustrate it in a Mendelian randomization study to evaluate the effect of diabetes on mortality using data from the Health and Retirement Study. We further use the proposed method to investigate potential benefit from breast cancer screening on subsequent breast cancer mortality based on the HIP-study. © 2017, The International Biometric Society.

Incidence and risk factors for the progression of proximal junctional kyphosis in degenerative lumbar scoliosis following long instrumented posterior spinal fusion.

PubMed

Wang, Hui; Ma, Lei; Yang, Dalong; Wang, Tao; Yang, Sidong; Wang, Yanhong; Wang, Qian; Zhang, Feng; Ding, Wenyuan

2016-08-01

The aim of this study was to identify the prevalence of proximal junctional kyphosis (PJK) in degenerative lumbar scoliosis (DLS) following long instrumented posterior spinal fusion, and to search for predictable risk factors for the progression of junctional kyphosis.In total 98 DLS patients with a minimum 2-year follow-up were reviewed prospectively. According to the occurrence of PJK at the last follow-up, patients were divided into 2 groups: PJK group and non-PJK group. To investigate risk values for the progression of PJK, 3 categorized factors were analyzed statistically: patient characteristics-preoperative data of age, sex, body mass index (BMI), bone mineral density (BMD) were investigated; surgical variables-the most proximal and distal levels of the instrumentation, the number of instrumented levels; pre- and postoperative radiographic parameters include the scoliotic angle, sagittal vertical axis, thoracic kyphosis, thoracolumbar junctional angle, lumbar lordosis, pelvic incidence, pelvic tilt, and sacral slope.PJK was developed in 17 of 98 patients (17.3%) until to the final follow-up and were enrolled as the PJK group, and 81 patients without PJK at final follow-up were enrolled as the non-PJK group. There was no statistically significant difference between the 2 groups in age at operation (P = 0.900). The patient's sex was excluded in statistical analysis because of the predominance of female patients. There were statistically significant difference between the 2 groups in BMI ([25.5 ± 1.7] kg/m in the PJK group and [23.6 ± 1.9] kg/m in the non-PJK group, P < 0.001) and BMD ([-1.4 ± 0.8] g/cm in the PJK group and [-0.7 ± 0.3] g/cm in the non-PJK group, P < 0.001). No specific surgery-related variables were found to be associated with an increased risk of developing PJK, except when the most proximal instrumented vertebrae stopped at thoracolumbar junction (T11-L1). The upper instrumentation vertebrae (UIV) at thoracolumbar junction was more common in the PJK group than that in the non-PJK group (P = 0.007). No preoperative and early postoperative variable did reveal a statistically significant difference between the 2 groups. When included in a multivariate logistic regression model, BMI>25 kg/m, osteoporosis, and UIV at thoracolumbar junction were independently associated with PJK.In conclusion, osteoporosis, obesity, and UIV at thoracolumbar junction are risk factors for the development and progression of PJK in DLS patients following long instrumented posterior spinal fusion. Antiosteoporosis treatment extends the fusion level above the thoracolumbar region and controlling body weight before and after surgery could provide opportunities to reduce the rate of PJK and to improve therapeutic outcomes.

Factors associated with cigarette smoking among public school adolescents.

PubMed

Viana, Tatiana Barreto Pereira; Camargo, Climene Laura de; Gomes, Nadirlene Pereira; Felzemburgh, Ridalva Dias Martins; Mota, Rosana Santos; Lima, Carla Cristina Oliveira de Jesus

2018-01-01

Objective Estimating the prevalence of cigarette smoking and its association with sociodemographic variables, sexual initiation and experience with domestic violence among adolescents from public schools in Guanambi, Bahia, Brazil. Method A crosssectional study carried out with adolescents. Data were collected through interviews guided by a structured instrument, and analyzed according to descriptive and inferential statistics with multiple logistic regression. Results A total of 370 adolescents participated in the study. The prevalence of cigarette smoking was 17.6% and a statistically significant association was observed between the variables: age over 15 years (PR = 5.63 and 95% CI: 1.33 - 23.85), males (PR = 2.53 and 95% CI: 1.47 - 4.37), no reported religion (PR = 1.93 and 95% CI: 0.99 - 3.75), working (PR = 2.17 and 95% CI: 1.25 - 3.74), onset of sexual activity (PR = 10.64 and CI= 95%: 5.31 - 21.33) and experience of domestic violence (PR = 3.61 and 95% CI: 2.07 - 3.28). Conclusion The prevalence of cigarette smoking and the associated variables point to the need for intervention strategies among more vulnerable groups of adolescents, encompassing family involvement and assistance from teachers and health professionals, in particular nurses working in Primary Care.

Long-memory and the sea level-temperature relationship: a fractional cointegration approach.

PubMed

Ventosa-Santaulària, Daniel; Heres, David R; Martínez-Hernández, L Catalina

2014-01-01

Through thermal expansion of oceans and melting of land-based ice, global warming is very likely contributing to the sea level rise observed during the 20th century. The amount by which further increases in global average temperature could affect sea level is only known with large uncertainties due to the limited capacity of physics-based models to predict sea levels from global surface temperatures. Semi-empirical approaches have been implemented to estimate the statistical relationship between these two variables providing an alternative measure on which to base potentially disrupting impacts on coastal communities and ecosystems. However, only a few of these semi-empirical applications had addressed the spurious inference that is likely to be drawn when one nonstationary process is regressed on another. Furthermore, it has been shown that spurious effects are not eliminated by stationary processes when these possess strong long memory. Our results indicate that both global temperature and sea level indeed present the characteristics of long memory processes. Nevertheless, we find that these variables are fractionally cointegrated when sea-ice extent is incorporated as an instrumental variable for temperature which in our estimations has a statistically significant positive impact on global sea level.

Perceptions of control in adults with epilepsy.

PubMed

Gehlert, S

1994-01-01

That psychosocial problems are extant in epilepsy is evidenced by a suicide rate among epileptic persons five times that of the general population and an unemployment rate estimated to be more than twice that of the population as a whole. External perceptions of control secondary to repeated episodes of seizure activity that generalize to the social sphere have been implicated as causes of these problems. The hypothesis that individuals who continue to have seizures become more and more external in perceptions of control was tested by a survey mailed to a sample of individuals with epilepsy in a metropolitan area of the Midwest. Dependent variables were, scores on instruments measuring locus of control and attributional style. The independent variable was a measure of seizure control based on present age, age at onset, and length of time since last seizure. Gender, socioeconomic status, and certain parenting characteristics were included as control variables, as they are also known to affect perceptions of control. Analysis by multiple regression techniques supported the study's hypothesis when perceptions of control was conceptualized as learned helplessness for bad, but not for good, events. The hypothesis was not confirmed when perceptions of control was conceptualized as either general or health locus of control.

Height-income association in developing countries: Evidence from 14 countries.

PubMed

Patel, Pankaj C; Devaraj, Srikant

2017-12-28

The purpose of this study was to assess whether the height-income association is positive in developing countries, and whether income differences between shorter and taller individuals in developing countries are explained by differences in endowment (ie, taller individuals have a higher income than shorter individuals because of characteristics such as better social skills) or due to discrimination (ie, shorter individuals have a lower income despite having comparable characteristics). Instrumental variable regression, Oaxaca-Blinder decomposition, quantile regression, and quantile decomposition analyses were applied to a sample of 45 108 respondents from 14 developing countries represented in the Research on Early Life and Aging Trends and Effects (RELATE) study. For a one-centimeter increase in country- and sex-adjusted median height, real income adjusted for purchasing power parity increased by 1.37%. The income differential between shorter and taller individuals was explained by discrimination and not by differences in endowments; however, the effect of discrimination decreased at higher values of country- and sex-adjusted height. Taller individuals in developing countries may realize higher income despite having characteristics similar to those of shorter individuals. © 2017 Wiley Periodicals, Inc.

Domain-Invariant Partial-Least-Squares Regression.

PubMed

Nikzad-Langerodi, Ramin; Zellinger, Werner; Lughofer, Edwin; Saminger-Platz, Susanne

2018-05-11

Multivariate calibration models often fail to extrapolate beyond the calibration samples because of changes associated with the instrumental response, environmental condition, or sample matrix. Most of the current methods used to adapt a source calibration model to a target domain exclusively apply to calibration transfer between similar analytical devices, while generic methods for calibration-model adaptation are largely missing. To fill this gap, we here introduce domain-invariant partial-least-squares (di-PLS) regression, which extends ordinary PLS by a domain regularizer in order to align the source and target distributions in the latent-variable space. We show that a domain-invariant weight vector can be derived in closed form, which allows the integration of (partially) labeled data from the source and target domains as well as entirely unlabeled data from the latter. We test our approach on a simulated data set where the aim is to desensitize a source calibration model to an unknown interfering agent in the target domain (i.e., unsupervised model adaptation). In addition, we demonstrate unsupervised, semisupervised, and supervised model adaptation by di-PLS on two real-world near-infrared (NIR) spectroscopic data sets.

Calibrating the pixel-level Kepler imaging data with a causal data-driven model

NASA Astrophysics Data System (ADS)

Wang, Dun; Foreman-Mackey, Daniel; Hogg, David W.; Schölkopf, Bernhard

2015-01-01

In general, astronomical observations are affected by several kinds of noise, each with it's own causal source; there is photon noise, stochastic source variability, and residuals coming from imperfect calibration of the detector or telescope. In particular, the precision of NASA Kepler photometry for exoplanet science—the most precise photometric measurements of stars ever made—appears to be limited by unknown or untracked variations in spacecraft pointing and temperature, and unmodeled stellar variability. Here we present the Causal Pixel Model (CPM) for Kepler data, a data-driven model intended to capture variability but preserve transit signals. The CPM works at the pixel level (not the photometric measurement level); it can capture more fine-grained information about the variation of the spacecraft than is available in the pixel-summed aperture photometry. The basic idea is that CPM predicts each target pixel value from a large number of pixels of other stars sharing the instrument variabilities while not containing any information on possible transits at the target star. In addition, we use the target star's future and past (auto-regression). By appropriately separating the data into training and test sets, we ensure that information about any transit will be perfectly isolated from the fitting of the model. The method has four hyper-parameters (the number of predictor stars, the auto-regressive window size, and two L2-regularization amplitudes for model components), which we set by cross-validation. We determine a generic set of hyper-parameters that works well on most of the stars with 11≤V≤12 mag and apply the method to a corresponding set of target stars with known planet transits. We find that we can consistently outperform (for the purposes of exoplanet detection) the Kepler Pre-search Data Conditioning (PDC) method for exoplanet discovery, often improving the SNR by a factor of two. While we have not yet exhaustively tested the method at other magnitudes, we expect that it should be generally applicable, with positive consequences for subsequent exoplanet detection or stellar variability (in which case we must exclude the autoregressive part to preserve intrinsic variability).

Predictors of adherence to treatment by patients with coronary heart disease after percutaneous coronary intervention.

PubMed

Kähkönen, Outi; Saaranen, Terhi; Kankkunen, Päivi; Lamidi, Marja-Leena; Kyngäs, Helvi; Miettinen, Heikki

2018-03-01

To identify the predictors of adherence in patients with coronary heart disease after a percutaneous coronary intervention. Adherence is a key factor in preventing the progression of coronary heart disease. An analytical multihospital survey study. A survey of 416 postpercutaneous coronary intervention patients was conducted in 2013, using the Adherence of People with Chronic Disease Instrument. The instrument consists of 37 items measuring adherence and 18 items comprising sociodemographic, health behavioural and disease-specific factors. Adherence consisted of two mean sum variables: adherence to medication and a healthy lifestyle. Based on earlier studies, nine mean sum variables known to explain adherence were responsibility, cooperation, support from next of kin, sense of normality, motivation, results of care, support from nurses and physicians, and fear of complications. Frequencies and percentages were used to describe the data, cross-tabulation to find statistically significant background variables and multivariate logistic regression to confirm standardised predictors of adherence. Patients reported good adherence. However, there was inconsistency between adherence to a healthy lifestyle and health behaviours. Gender, close personal relationship, length of education, physical activity, vegetable and alcohol consumption, LDL cholesterol and duration of coronary heart disease without previous percutaneous coronary intervention were predictors of adherence. The predictive factors known to explain adherence to treatment were male gender, close personal relationship, longer education, lower LDL cholesterol and longer duration of coronary heart disease without previous percutaneous coronary intervention. Because a healthy lifestyle predicted factors known to explain adherence, these issues should be emphasised particularly for female patients not in a close personal relationship, with low education and a shorter coronary heart disease duration with previous coronary intervention. © 2017 John Wiley & Sons Ltd.

Perceived social support following percutaneous coronary intervention is a crucial factor in patients with coronary heart disease.

PubMed

Kähkönen, Outi; Kankkunen, Päivi; Miettinen, Heikki; Lamidi, Marja-Leena; Saaranen, Terhi

2017-05-01

To describe perceived social support among patients with coronary heart disease following percutaneous coronary intervention. A low level of social support is considered a risk factor for coronary heart disease in healthy individuals and reduces the likelihood that people diagnosed with coronary heart disease will have a good prognosis. A descriptive cross-sectional study. A survey of 416 patients was conducted in 2013. A self-report instrument, Social Support of People with Coronary Heart Disease, was used. The instrument comprises three dimensions of social support: informational, emotional, functional supports and 16 background variables. Data were analysed using descriptive statistics, factor analysis, mean sum variables and multivariate logistic regression. Perceived informational support was primarily high, but respondents' risk factors were not at the target level. The weakest items of informational support were advice on physical activity, continuum of care and rehabilitation. Regarding the items of emotional support, support from other cardiac patients was the weakest. The weakest item of functional support was respondents' sense of the healthcare professionals' care of patients coping with their disease. Background variables associated with perceived social support were gender, marital status, level of formal education, profession, physical activity, duration of coronary heart disease and previous myocardial infarction. Healthcare professionals should pay extra attention to women, single patients, physically inactive patients, those demonstrating a lower level of education, those with a longer duration of CHD, and respondents without previous acute myocardial infarction. Continuum of care and counselling are important to ensure especially among them. This study provides evidence that healthcare professionals should be more aware of the individual needs for social support among patients with coronary heart disease after percutaneous coronary intervention. © 2016 John Wiley & Sons Ltd.

Insomnia in long-term care facilities: a comparison of seven European countries and Israel: the Services and Health for Elderly in Long TERm care study.

PubMed

Gindin, Jacob; Shochat, Tamar; Chetrit, Angela; Epstein, Shulamit; Ben Israel, Yehoshua; Levi, Sarah; Onder, Graziano; Carpenter, Ian; Finne-Soveri, Harriet; van Hout, Hein; Henrard, Jean-Claude; Nikolaus, Thorsten; Topinkova, Eva; Fialová, Daniela; Bernabei, Roberto

2014-11-01

To assess insomnia and its correlates as part of the Services and Health for Elderly in Long TERm care (SHELTER) study, funded by the 7th Framework Programme of the European Union. Cross-cultural investigation. Long-term care facilities (LTCFs) in eight European countries (Czech Republic, France, Finland, Germany, England, the Netherlands, Italy) and one non-European country (Israel). Elderly residents (N = 4,156) of 57 LTCFs. Information on insomnia, age, sex, activities of daily living (ADLs), cognitive status, depression, major stressful life events, physical activity, fatigue, pain, and sleep medication use was extracted from the International Resident Assessment Instrument (interRAI)LTCF instrument. Rates of insomnia and its correlates were analyzed. Multivariate logistic regression was used to assess factors associated with insomnia, controlling for demographic variables. The prevalence of insomnia was 24% (range 13-30%), with significant differences between countries (P < .001). More insomnia complaints were reported in older than younger residents (P < .001). Higher rates of insomnia were associated with hypnosedatives and depression in all countries (P < .001) and with stressful life events, fatigue, and pain in most countries (P < .001). No associations were found between insomnia and ADLs, physical activity, or cognitive status. Age, depression, stressful life events, fatigue, pain and hypnosedatives were independent significant predictors of insomnia, controlling for all other variables and for country. Hypnosedatives and depression were strong predictors of insomnia beyond cultural differences. Overall, psychosocial variables were more strongly related to insomnia than functional and mental capacities. © 2014, Copyright the Authors Journal compilation © 2014, The American Geriatrics Society.

Functional Relationships and Regression Analysis.

ERIC Educational Resources Information Center

Preece, Peter F. W.

1978-01-01

Using a degenerate multivariate normal model for the distribution of organismic variables, the form of least-squares regression analysis required to estimate a linear functional relationship between variables is derived. It is suggested that the two conventional regression lines may be considered to describe functional, not merely statistical,…

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,…

The Detection and Interpretation of Interaction Effects between Continuous Variables in Multiple Regression.

ERIC Educational Resources Information Center

Jaccard, James; And Others

1990-01-01

Issues in the detection and interpretation of interaction effects between quantitative variables in multiple regression analysis are discussed. Recent discussions associated with problems of multicollinearity are reviewed in the context of the conditional nature of multiple regression with product terms. (TJH)

Continuous-variable quantum Gaussian process regression and quantum singular value decomposition of nonsparse low-rank matrices

NASA Astrophysics Data System (ADS)

Das, Siddhartha; Siopsis, George; Weedbrook, Christian

2018-02-01

With the significant advancement in quantum computation during the past couple of decades, the exploration of machine-learning subroutines using quantum strategies has become increasingly popular. Gaussian process regression is a widely used technique in supervised classical machine learning. Here we introduce an algorithm for Gaussian process regression using continuous-variable quantum systems that can be realized with technology based on photonic quantum computers under certain assumptions regarding distribution of data and availability of efficient quantum access. Our algorithm shows that by using a continuous-variable quantum computer a dramatic speedup in computing Gaussian process regression can be achieved, i.e., the possibility of exponentially reducing the time to compute. Furthermore, our results also include a continuous-variable quantum-assisted singular value decomposition method of nonsparse low rank matrices and forms an important subroutine in our Gaussian process regression algorithm.

A Linear Regression Model Identifying the Primary Factors Contributing to Maintenance Man Hours for the C-17 Globemaster III in the Air National Guard

DTIC Science & Technology

2012-06-15

Maintenance AFSCs ................................................................................................. 14 2. Variation Inflation Factors...total variability in the data. It is an indication of how much of the   20    variation in the data can be accounted for in the regression model. In... Variation Inflation Factors for each independent variable (predictor) as regressed against all of the other independent variables in the model. The

Advanced Statistics for Exotic Animal Practitioners.

PubMed

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

2017-09-01

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

Regression Analysis of Stage Variability for West-Central Florida Lakes

USGS Publications Warehouse

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

2008-01-01

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

The Attitudes of First Year Senior Secondary School Students toward Their Science Classes in the Sudan

NASA Astrophysics Data System (ADS)

Lado, Longun Moses

This study examined the influence of a set of relevant independent variables on students' decision to major in math or science disciplines, on the one hand, or arts or humanities disciplines, on the other. The independent variables of interest in the study were students' attitudes toward science, their gender, their socioeconomic status, their age, and the strength and direction of parents' and peers' influences on their academic decisions. The study answered five research questions that concerned students' intention in math or science, the association between students' attitudes and their choice to major in math or science, the extent to which parents' and peers' perspectives influence students' choice of major, and the influence of a combination of relevant variables on students' choice of major. The scholarly context for the study was literature relating to students' attitudes toward science and math, their likelihood of taking courses or majoring in science or math and various conditions influencing their attitudes and actions with respect to enrollment in science or math disciplines. This literature suggested that students' experiences, their gender, parents' and peers' influence, their socio-economic status, teachers' treatment of them, school curricula, school culture, and other variables may influence students' attitudes toward science and math and their decision regarding the study of these subjects. The study used a questionnaire comprised of 28 items to elicit information from students. Based upon cluster sampling of secondary schools, the researcher surveyed 1000 students from 10 secondary schools and received 987 responses. The researcher used SPSS to analyze students' responses. Descriptive statistics, logistic regression, and multiple regression analyses to provide findings that address the study's research questions. The following are the major findings from the study: (1) The instrument used to measure students' attitudes toward science and mathematics was not highly reliable, perhaps contributing to an attenuation of the relationship between attitude toward science and mathematics and choice of a science or mathematics major (rather than an arts or humanities major). (2) Far more students than the researcher had anticipated provided responses indicating that they planned to major in a science or mathematics discipline rather than an arts or humanities discipline. (3) Students' attitudes towards math and science were more favorable than the researcher anticipated based on findings from previous related studies. This result suggests the possibility of social desirability bias in students' responses. (4) Three significant predicator variables contributed to a significant logistic regression equation in which choice of science or mathematics major was the dependent variable: gender (negative association), attitude toward science and math (positive association), and peer influence 1 (positive association). Gender was the strongest predictor. (5) Five significant predictor variables contributed to a significant multiple linear regression equation in which attitude toward science and mathematics was the dependent variable: peer influence 1 (positive association), parent influence 1 (positive association), parent influence 2 (positive association), books in home (positive association), and peer influence 2 (positive association). The results reveal that among the targeted variables (gender, attitude, peer influence 1, peer influence 2, parent influence 1, parent influence 2, books in home, and age) only gender, peer influence 1, and attitude were significant predictors of students' major in math or science.

Correlation of PROMIS Physical Function and Pain CAT Instruments With Oswestry Disability Index and Neck Disability Index in Spine Patients.

PubMed

Papuga, Mark O; Mesfin, Addisu; Molinari, Robert; Rubery, Paul T

2016-07-15

A prospective and retrospective cross-sectional cohort analysis. The aim of this study was to show that Patient-Reported Outcomes Measurement Information System (PROMIS) computer adaptive testing (CAT) assessments for physical function and pain interference can be efficiently collected in a standard office visit and to evaluate these scores with scores from previously validated Oswestry Disability Index (ODI) and Neck Disability Index (NDI) providing evidence of convergent validity for use in patients with spine pathology. Spinal surgery outcomes are highly variable, and substantial debate continues regarding the role and value of spine surgery. The routine collection of patient-based outcomes instruments in spine surgery patients may inform this debate. Traditionally, the inefficiency associated with collecting standard validated instruments has been a barrier to routine use in outpatient clinics. We utilized several CAT instruments available through PROMIS and correlated these with the results obtained using "gold standard" legacy outcomes measurement instruments. All measurements were collected at a routine clinical visit. The ODI and the NDI assessments were used as "gold standard" comparisons for patient-reported outcomes. PROMIS CAT instruments required 4.5 ± 1.8 questions and took 35 ± 16 seconds to complete, compared with ODI/NDI requiring 10 questions and taking 188 ± 85 seconds when administered electronically. Linear regression analysis of retrospective scores involving a primary back complaint revealed moderate to strong correlations between ODI and PROMIS physical function with r values ranging from 0.5846 to 0.8907 depending on the specific assessment and patient subsets examined. Routine collection of physical function outcome measures in clinical practice offers the ability to inform and improve patient care. We have shown that several PROMIS CAT instruments can be efficiently administered during routine clinical visits. The moderate to strong correlations found validate the utility of computer adaptive testing when compared with the gold standard "static" legacy assessments. 4.

Peak flow regression equations For small, ungaged streams in Maine: Comparing map-based to field-based variables

USGS Publications Warehouse

Lombard, Pamela J.; Hodgkins, Glenn A.

2015-01-01

Regression equations to estimate peak streamflows with 1- to 500-year recurrence intervals (annual exceedance probabilities from 99 to 0.2 percent, respectively) were developed for small, ungaged streams in Maine. Equations presented here are the best available equations for estimating peak flows at ungaged basins in Maine with drainage areas from 0.3 to 12 square miles (mi2). Previously developed equations continue to be the best available equations for estimating peak flows for basin areas greater than 12 mi2. New equations presented here are based on streamflow records at 40 U.S. Geological Survey streamgages with a minimum of 10 years of recorded peak flows between 1963 and 2012. Ordinary least-squares regression techniques were used to determine the best explanatory variables for the regression equations. Traditional map-based explanatory variables were compared to variables requiring field measurements. Two field-based variables—culvert rust lines and bankfull channel widths—either were not commonly found or did not explain enough of the variability in the peak flows to warrant inclusion in the equations. The best explanatory variables were drainage area and percent basin wetlands; values for these variables were determined with a geographic information system. Generalized least-squares regression was used with these two variables to determine the equation coefficients and estimates of accuracy for the final equations.

Censored Hurdle Negative Binomial Regression (Case Study: Neonatorum Tetanus Case in Indonesia)

NASA Astrophysics Data System (ADS)

Yuli Rusdiana, Riza; Zain, Ismaini; Wulan Purnami, Santi

2017-06-01

Hurdle negative binomial model regression is a method that can be used for discreate dependent variable, excess zero and under- and overdispersion. It uses two parts approach. The first part estimates zero elements from dependent variable is zero hurdle model and the second part estimates not zero elements (non-negative integer) from dependent variable is called truncated negative binomial models. The discrete dependent variable in such cases is censored for some values. The type of censor that will be studied in this research is right censored. This study aims to obtain the parameter estimator hurdle negative binomial regression for right censored dependent variable. In the assessment of parameter estimation methods used Maximum Likelihood Estimator (MLE). Hurdle negative binomial model regression for right censored dependent variable is applied on the number of neonatorum tetanus cases in Indonesia. The type data is count data which contains zero values in some observations and other variety value. This study also aims to obtain the parameter estimator and test statistic censored hurdle negative binomial model. Based on the regression results, the factors that influence neonatorum tetanus case in Indonesia is the percentage of baby health care coverage and neonatal visits.

Rationale for hedging initiatives: Empirical evidence from the energy industry

NASA Astrophysics Data System (ADS)

Dhanarajata, Srirajata

Theory offers different rationales for hedging including (i) financial distress and bankruptcy cost, (ii) capacity to capture attractive investment opportunities, (iii) information asymmetry, (iv) economy of scale, (v) substitution for hedging, (vi) managerial risk aversion, and (vii) convexity of tax schedule. The purpose of this dissertation is to empirically test the explanatory power of the first five theoretical rationales on hedging done by oil and gas exploration and production (E&P) companies. The level of hedging is measured by the percentage of production effectively hedged, calculated based on the concept of delta and delta-gamma hedging. I employ Tobit regression, principal components, and panel data analysis on dependent and raw independent variables. Tobit regression is applied due to the fact that the dependent variable used in the analysis is non-negative. Principal component analysis helps to reduce the dimension of explanatory variables while panel data analysis combines/pools the data that is a combination of time-series and cross-sectional. Based on the empirical results, leverage level is consistently found to be a significant factor on hedging activities, either due to an attempt to avoid financial distress by the firm, or an attempt to control agency cost by debtholders, or both. The effect of capital expenditures and discretionary cash flows are both indeterminable due possibly to a potential mismatch in timing of realized cash flow items and hedging decision. Firm size is found to be positively related to hedging supporting economy of scale hypothesis, which is introduced in past literature, as well as the argument that large firm usually are more sophisticated and should be more willing and more comfortable to use hedge instruments than smaller firms.

O3 variability/trends in the troposphere from IASI observations in 2008-2017

NASA Astrophysics Data System (ADS)

Wespes, C.; Hurtmans, D.; Clerbaux, C.; Pierre-Francois, C.

2017-12-01

In this study, we describe the recent changes in the tropospheric ozone (O3) columns (TOCs) measured by the Infrared Atmospheric Sounding Interferometer (IASI) onboard the Metop satellites during the first ten years of the IASI operation (2008-2017). The instrument provides a unique dataset of vertically-resolved O3 profiles with a twice daily global coverage and a fairly good vertical resolution allowing us to monitor the year-to-year variability in the troposphere. The retrievals are performed using the FORLI software, a fast radiative transfer model based on the optimal estimation method, set up for near real time and large scale processing of IASI data. We differentiate trend characteristics from the seasonal and non-seasonal O3 variations captured by IASI in the troposphere by applying appropriate annual and seasonal multivariate regression models, which include important geophysical drivers of O3 variation (e.g. quasi biennial oscillations - QBO, El Niño/Southern Oscillation - ENSO, North Atlantic Oscillation-NAO) and a linear trend term, on time series of spatially gridded averaged O3. The performances of the regression models (annual vs seasonal) are first investigated. Given the large contribution of the interannual variability, we will then describe the effects of the main contributing O3 proxies (e.g. positive - or negatives - ENSO indexes measured during moderate to intense El Niño - or La Niña - episodes in the tropics) in addition to the adjusted O3 trend patterns. A special focus will be given over the Northern Hemisphere which is characterized by decreasing O3 precursor emissions (mainly over Europe and the US). FORLI O3-CO correlations patterns will also be discussed to evaluate the continental influence on the tropospheric O3 trends.

Derivation and validation of a multivariate model to predict mortality from pulmonary embolism with cancer: the POMPE-C tool

PubMed Central

Roy, Pierre-Marie; Than, Martin P.; Hernandez, Jackeline; Courtney, D. Mark; Jones, Alan E.; Penazola, Andrea; Pollack, Charles V.

2012-01-01

Background Clinical guidelines recommend risk stratification of patients with acute pulmonary embolism (PE). Active cancer increases risk of PE and worsens prognosis, but also causes incidental PE that may be discovered during cancer staging. No quantitative decision instrument has been derived specifically for patients with active cancer and PE. Methods Classification and regression technique was used to reduce 25 variables prospectively collected from 408 patients with AC and PE. Selected variables were transformed into a logistic regression model, termed POMPE-C, and compared with the pulmonary embolism severity index (PESI) score to predict the outcome variable of death within 30 days. Validation was performed in an independent sample of 182 patients with active cancer and PE. Results POMPE-C included eight predictors: body mass, heart rate >100, respiratory rate, SaO2%, respiratory distress, altered mental status, do not resuscitate status, and unilateral limb swelling. In the derivation set, the area under the ROC curve for POMPE-C was 0.84 (95% CI: 0.82-0.87), significantly greater than PESI (0.68, 0.60-0.76). In the validation sample, POMPE-C had an AUC of 0.86 (0.78-0.93). No patient with POMPE-C estimate ≤5% died within 30 days (0/50, 0-7%), whereas 10/13 (77%, 46-95%) with POMPE-C estimate >50% died within 30 days. Conclusion In patients with active cancer and PE, POMPE-C demonstrated good prognostic accuracy for 30 day mortality and better performance than PESI. If validated in a large sample, POMPE-C may provide a quantitative basis to decide treatment options for PE discovered during cancer staging and with advanced cancer. PMID:22475313

Econometrics in outcomes research: the use of instrumental variables.

PubMed

Newhouse, J P; McClellan, M

1998-01-01

We describe an econometric technique, instrumental variables, that can be useful in estimating the effectiveness of clinical treatments in situations when a controlled trial has not or cannot be done. This technique relies upon the existence of one or more variables that induce substantial variation in the treatment variable but have no direct effect on the outcome variable of interest. We illustrate the use of the technique with an application to aggressive treatment of acute myocardial infarction in the elderly.

Analysis of the labor productivity of enterprises via quantile regression

NASA Astrophysics Data System (ADS)

Türkan, Semra

2017-07-01

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

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

PubMed

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

2016-08-01

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

Biostatistics Series Module 6: Correlation and Linear Regression.

PubMed

Hazra, Avijit; Gogtay, Nithya

2016-01-01

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

Biostatistics Series Module 6: Correlation and Linear Regression

PubMed Central

Hazra, Avijit; Gogtay, Nithya

2016-01-01

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

Drought multiproxy reconstruction in the Czech Lands from AD 1500

NASA Astrophysics Data System (ADS)

Dobrovolný, Petr; Brázdil, Rudolf; Možný, Martin; Trnka, Miroslav; Rybníček, Michal; Kolář, Tomáš

2017-04-01

Whereas the air temperature variability in the past and recent climate is well understood, our knowledge on hydroclimate (drought/precipitation) from various proxy archives and instrumental measurements are sketchy and sometimes even contradictory. This is related to huge spatial and temporal hydroclimate variability that underlines the importance of detailed local/regional studies on long-term hydroclimate variability. We present main results of summer drought reconstruction for the territory of the Czech Republic (CR) spanning the last 500 years. Drought is represented by the Standardized Precipitation Evapotranspiration Index (SPEI). Summer (JJA) SPEI values calculated from various instrumental measurements from the CR and covering most of the 19th and 20th centuries represent the target data. Three different proxy archives were used for SPEI reconstruction: a) Central European monthly temperature and Czech seasonal precipitation index series derived from documentary evidence (1500-1854); b) grape harvest dates for the Czech Lands (1499-2012); c) oak (Quercus spp.) ring width chronologies from Bohemia (western part of the CR, 1500-2012). Linear regression with subsequent variance scaling were used for calibration in different time intervals covering mostly second part of the 19th and the first part of the 20th centuries. Response functions were further verified on independent proxy and target data. The strongest hydroclimate signal was found for grape harvest dates (more that 70% of explained variance) while oak ring width series show relatively weak reconstruction skill (30% of common variance between proxy and target data). The three SPEI reconstructions show several common features in their long-term variability. Distinctly dry periods cover the first half of the 16th century, which included an extremely dry 1540, and the years since the late 1970s. Higher humidity was characteristic for the second part of the 16th century and also for the turn of the 19th and 20th centuries.

Failure of a systemic lupus erythematosus response index developed from clinical trial data: lessons examined and learned.

PubMed

Forbess, L J; Bresee, C; Wallace, D J; Weisman, M H

2017-08-01

Background Our primary goal was to create an outcome change score index similar to a standard rheumatoid arthritis (RA) model utilizing real-world data in systemic lupus erythematosus (SLE) patients that occurred during their phase 3 trials with a Food and Drug Administration-approved drug. Methods We utilized raw data from trials of belimumab for the treatment of SLE. Data were split 80/20 into training/validation sets. Index variables present in a majority of patients and with face validity were selected. Variables were scored for each patient as percentage improvement from baseline after one year. The percentage of placebo- and drug-treated patients considered improved after the application of various criteria was ascertained. Logistic regression was employed to determine the ability of the new index to predict treatment assignment. Results A total of 1693 subjects had data for analyses. Eight variables were chosen: arthritis, rash, physician global assessment, fatigue, anti-double stranded DNA antibodies, C3, C4 and C-reactive protein. In the training dataset, ≥20% improvement in ≥4 of eight variables produced the largest difference between placebo- and drug-treated patients (22.1%) with an acceptable rate of improved placebo-treated patients (25%). This resulted in an odds ratio for belimumab (10 mg/kg) vs placebo of 2.7 (95% CI: 2.0-3.6; p < 0.001). However, in the validate dataset the odds ratio was not significant at 1.3 (95% CI: 0.8-2.2; p = 0.863). Conclusions The index created from training data did not achieve statistical significance when tested in the validation set. We have speculated why this happened. Is the lack of success of therapeutics for SLE caused by ineffective medications, study design and outcome instruments that fail to inform us, or is the heterogeneity of the disease too daunting? The lessons learned here can help direct future endeavors intended to improve SLE outcome instruments.

Mapping the spatial pattern of temperate forest above ground biomass by integrating airborne lidar with Radarsat-2 imagery via geostatistical models

NASA Astrophysics Data System (ADS)

Li, Wang; Niu, Zheng; Gao, Shuai; Wang, Cheng

2014-11-01

Light Detection and Ranging (LiDAR) and Synthetic Aperture Radar (SAR) are two competitive active remote sensing techniques in forest above ground biomass estimation, which is important for forest management and global climate change study. This study aims to further explore their capabilities in temperate forest above ground biomass (AGB) estimation by emphasizing the spatial auto-correlation of variables obtained from these two remote sensing tools, which is a usually overlooked aspect in remote sensing applications to vegetation studies. Remote sensing variables including airborne LiDAR metrics, backscattering coefficient for different SAR polarizations and their ratio variables for Radarsat-2 imagery were calculated. First, simple linear regression models (SLR) was established between the field-estimated above ground biomass and the remote sensing variables. Pearson's correlation coefficient (R2) was used to find which LiDAR metric showed the most significant correlation with the regression residuals and could be selected as co-variable in regression co-kriging (RCoKrig). Second, regression co-kriging was conducted by choosing the regression residuals as dependent variable and the LiDAR metric (Hmean) with highest R2 as co-variable. Third, above ground biomass over the study area was estimated using SLR model and RCoKrig model, respectively. The results for these two models were validated using the same ground points. Results showed that both of these two methods achieved satisfactory prediction accuracy, while regression co-kriging showed the lower estimation error. It is proved that regression co-kriging model is feasible and effective in mapping the spatial pattern of AGB in the temperate forest using Radarsat-2 data calibrated by airborne LiDAR metrics.

A fuzzy adaptive network approach to parameter estimation in cases where independent variables come from an exponential distribution

NASA Astrophysics Data System (ADS)

Dalkilic, Turkan Erbay; Apaydin, Aysen

2009-11-01

In a regression analysis, it is assumed that the observations come from a single class in a data cluster and the simple functional relationship between the dependent and independent variables can be expressed using the general model; Y=f(X)+[epsilon]. However; a data cluster may consist of a combination of observations that have different distributions that are derived from different clusters. When faced with issues of estimating a regression model for fuzzy inputs that have been derived from different distributions, this regression model has been termed the [`]switching regression model' and it is expressed with . Here li indicates the class number of each independent variable and p is indicative of the number of independent variables [J.R. Jang, ANFIS: Adaptive-network-based fuzzy inference system, IEEE Transaction on Systems, Man and Cybernetics 23 (3) (1993) 665-685; M. Michel, Fuzzy clustering and switching regression models using ambiguity and distance rejects, Fuzzy Sets and Systems 122 (2001) 363-399; E.Q. Richard, A new approach to estimating switching regressions, Journal of the American Statistical Association 67 (338) (1972) 306-310]. In this study, adaptive networks have been used to construct a model that has been formed by gathering obtained models. There are methods that suggest the class numbers of independent variables heuristically. Alternatively, in defining the optimal class number of independent variables, the use of suggested validity criterion for fuzzy clustering has been aimed. In the case that independent variables have an exponential distribution, an algorithm has been suggested for defining the unknown parameter of the switching regression model and for obtaining the estimated values after obtaining an optimal membership function, which is suitable for exponential distribution.

Statistical methods for biodosimetry in the presence of both Berkson and classical measurement error

NASA Astrophysics Data System (ADS)

Miller, Austin

In radiation epidemiology, the true dose received by those exposed cannot be assessed directly. Physical dosimetry uses a deterministic function of the source term, distance and shielding to estimate dose. For the atomic bomb survivors, the physical dosimetry system is well established. The classical measurement errors plaguing the location and shielding inputs to the physical dosimetry system are well known. Adjusting for the associated biases requires an estimate for the classical measurement error variance, for which no data-driven estimate exists. In this case, an instrumental variable solution is the most viable option to overcome the classical measurement error indeterminacy. Biological indicators of dose may serve as instrumental variables. Specification of the biodosimeter dose-response model requires identification of the radiosensitivity variables, for which we develop statistical definitions and variables. More recently, researchers have recognized Berkson error in the dose estimates, introduced by averaging assumptions for many components in the physical dosimetry system. We show that Berkson error induces a bias in the instrumental variable estimate of the dose-response coefficient, and then address the estimation problem. This model is specified by developing an instrumental variable mixed measurement error likelihood function, which is then maximized using a Monte Carlo EM Algorithm. These methods produce dose estimates that incorporate information from both physical and biological indicators of dose, as well as the first instrumental variable based data-driven estimate for the classical measurement error variance.

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

PubMed

Szekér, Szabolcs; Vathy-Fogarassy, Ágnes

2018-01-01

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

Variable Selection in Logistic Regression.

DTIC Science & Technology

1987-06-01

23 %. AUTIOR(.) S. CONTRACT OR GRANT NUMBE Rf.i %Z. D. Bai, P. R. Krishnaiah and . C. Zhao F49620-85- C-0008 " PERFORMING ORGANIZATION NAME AND AOORESS...d I7 IOK-TK- d 7 -I0 7’ VARIABLE SELECTION IN LOGISTIC REGRESSION Z. D. Bai, P. R. Krishnaiah and L. C. Zhao Center for Multivariate Analysis...University of Pittsburgh Center for Multivariate Analysis University of Pittsburgh Y !I VARIABLE SELECTION IN LOGISTIC REGRESSION Z- 0. Bai, P. R. Krishnaiah

Exploratory Study of Dimensions of Health-related Quality of Life in the General Population of South Korea.

PubMed

Kim, Seon-Ha; Jo, Min-Woo; Ock, Minsu; Lee, Sang-Il

2017-11-01

This study aimed to explore dimensions in addition to the 5 dimensions of the 5-level EQ-5D version (EQ-5D-5L) that could satisfactorily explain variation in health-related quality of life (HRQoL) in the general population of South Korea. Domains related to HRQoL were searched through a review of existing HRQoL instruments. Among the 28 potential dimensions, the 5 dimensions of the EQ-5D-5L and 7 additional dimensions (vision, hearing, communication, cognitive function, social relationships, vitality, and sleep) were included. A representative sample of 600 subjects was selected for the survey, which was administered through face-to-face interviews. Subjects were asked to report problems in 12 health dimensions at 5 levels, as well as their self-rated health status using the EuroQol visual analogue scale (EQ-VAS) and a 5-point Likert scale. Among subjects who reported no problems for any of the parameters in the EQ-5D-5L, we analyzed the frequencies of problems in the additional dimensions. A linear regression model with the EQ-VAS as the dependent variable was performed to identify additional significant dimensions. Among respondents who reported full health on the EQ-5D-5L (n=365), 32% reported a problem for at least 1 additional dimension, and 14% reported worse than moderate self-rated health. Regression analysis revealed a R2 of 0.228 for the original EQ-5D-5L dimensions, 0.200 for the new dimensions, and 0.263 for the 12 dimensions together. Among the added dimensions, vitality and sleep were significantly associated with EQ-VAS scores. This study identified significant dimensions for assessing self-rated health among members of the general public, in addition to the 5 dimensions of the EQ-5D-5L. These dimensions could be considered for inclusion in a new preference-based instrument or for developing a country-specific HRQoL instrument.

Independent contrasts and PGLS regression estimators are equivalent.

PubMed

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

2012-05-01

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

Intercomparison of near real time monitors of PM2.5 nitrate and sulfate at the U.S. Environmental Protection Agency Atlanta Supersite

NASA Astrophysics Data System (ADS)

Weber, R.; Orsini, D.; Duan, Y.; Baumann, K.; Kiang, C. S.; Chameides, W.; Lee, Y. N.; Brechtel, F.; Klotz, P.; Jongejan, P.; ten Brink, H.; Slanina, J.; Boring, C. B.; Genfa, Z.; Dasgupta, P.; Hering, S.; Stolzenburg, M.; Dutcher, D. D.; Edgerton, E.; Hartsell, B.; Solomon, P.; Tanner, R.

2003-04-01

Five new instruments for semicontinuous measurements of fine particle (PM2.5) nitrate and sulfate were deployed in the Atlanta Supersite Experiment during an intensive study in August 1999. The instruments measured bulk aerosol chemical composition at rates ranging from every 5 min to once per hour. The techniques included a filter sampling system with automated water extraction and online ion chromatographic (IC) analysis, two systems that directly collected particles into water for IC analysis, and two techniques that converted aerosol nitrate or sulfate either catalytically or by flash vaporization to gaseous products that were measured with gas analyzers. During the one-month study, 15-min integrated nitrate concentrations were low, ranging from about 0.1 to 3.5 μg m-3 with a mean value of 0.5 μg m-3. Ten-minute integrated sulfate concentrations varied between 0.3 and 40 μg m-3 with a mean of 14 μg m-3. By the end of the one-month study most instruments were in close agreement, with r-squared values between instrument pairs typically ranging from 0.7 to 0.94. Based on comparison between individual semicontinuous devices and 24-hour integrated filter measurements, most instruments were within 20-30% for nitrate (˜0.1-0.2 μg m-3) and 10-15% for sulfate (1-2 μg m-3). Within 95% confidence intervals, linear regression fits suggest that no biases existed between the semicontinuous techniques and the 24-hour integrated filter measurements of nitrate and sulfate;, however, for nitrate, the semicontinuous intercomparisons showed significantly less variability than intercomparisons amongst the 24-hour integrated filters.

Social communication skills of chiropractors: implications for professional practice.

PubMed

Marchiori, Dennis M; Henkin, Alan B; Hawk, Cheryl

2008-01-01

Social communication skills are critical in the health professions. The aim of this study was to measure and identify professional practice predictors of social communication skills of practicing chiropractors. The study population was derived from a group of doctors of chiropractic who participated in a practice-based research program. Participating chiropractors agreed to complete a survey detailing the chiropractor's sex, years in practice, practice type, size of the practice community, typical weekly practice volume, and an instrument to measure skills of social communication. Regression analysis was applied to identify associations between independent variables and responses to the social skills instrument. Results suggested that selected characteristics of clinical practice may be associated with clinician's social skills of communication. The weekly volume of patients to the practice emerged as a salient explanatory factor of overall social communication skills and as a factor individually for dimensions of social expressivity and social control. The practice arrangement (solo vs group) proved important in terms of respondent emotional control scores. Similarly, the solo vs group practice variable was associated with higher levels of emotional sensitivity; however, this association was mediated by the sex of the doctor of chiropractic; men reported lower levels of emotional sensitivity than women. The findings of this study suggest associations between dimensions of social communication skills, practice characteristics, practice arrangements, and sex that may inform the efforts of educators as they endeavor to better prepare health professionals for practice in a wide spectrum of settings.

The relationship of California's Medicaid reimbursement system to nurse staffing levels.

PubMed

Mukamel, Dana B; Kang, Taewoon; Collier, Eric; Harrington, Charlene

2012-10-01

Policy initiatives at the Federal and state level are aimed at increasing staffing in nursing homes. These include direct staffing standards, public reporting, and financial incentives. To examine the impact of California's Medicaid reimbursement for nursing homes which includes incentives directed at staffing. Two-stage limited-information maximum-likelihood regressions were used to model the relationship between staffing [registered nurses (RNs), licensed practical nurses, and certified nursing assistants hours per resident day] and the Medicaid payment rate, accounting for the specific structure of the payment system, endogeneity of payment and case-mix, and controlling for facility and market characteristics. A total of 927 California free-standing nursing homes in 2006. The model included facility characteristics (case-mix, size, ownership, and chain affiliation), market competition and excess demand, labor supply and wages, unemployment, and female employment. The instrumental variable for Medicaid reimbursement was the peer group payment rate for 7 geographical market areas, and the instrumental variables for resident case-mix were the average county revenues for professional therapy establishments and the percent of county population aged 65 and over. Consistent with the rate incentives and rational expectation behavior, expected nursing home reimbursement rates in 2008 were associated with increased RN staffing levels in 2006 but had no relationship with licensed practical nurse and certified nursing assistant staffing. The effect was estimated at 2 minutes per $10 increase in rate. The incentives in the Medicaid system impacted only RN staffing suggesting the need to improve the state's rate setting methodology.

Estimation and Bias Correction of Aerosol Abundance using Data-driven Machine Learning and Remote Sensing

NASA Technical Reports Server (NTRS)

Malakar, Nabin K.; Lary, D. L.; Moore, A.; Gencaga, D.; Roscoe, B.; Albayrak, Arif; Petrenko, Maksym; Wei, Jennifer

2012-01-01

Air quality information is increasingly becoming a public health concern, since some of the aerosol particles pose harmful effects to peoples health. One widely available metric of aerosol abundance is the aerosol optical depth (AOD). The AOD is the integrated light extinction coefficient over a vertical atmospheric column of unit cross section, which represents the extent to which the aerosols in that vertical profile prevent the transmission of light by absorption or scattering. The comparison between the AOD measured from the ground-based Aerosol Robotic Network (AERONET) system and the satellite MODIS instruments at 550 nm shows that there is a bias between the two data products. We performed a comprehensive analysis exploring possible factors which may be contributing to the inter-instrumental bias between MODIS and AERONET. The analysis used several measured variables, including the MODIS AOD, as input in order to train a neural network in regression mode to predict the AERONET AOD values. This not only allowed us to obtain an estimate, but also allowed us to infer the optimal sets of variables that played an important role in the prediction. In addition, we applied machine learning to infer the global abundance of ground level PM2.5 from the AOD data and other ancillary satellite and meteorology products. This research is part of our goal to provide air quality information, which can also be useful for global epidemiology studies.

A composite measure to explore visual disability in primary progressive multiple sclerosis.

PubMed

Poretto, Valentina; Petracca, Maria; Saiote, Catarina; Mormina, Enricomaria; Howard, Jonathan; Miller, Aaron; Lublin, Fred D; Inglese, Matilde

2017-01-01

Optical coherence tomography (OCT) and magnetic resonance imaging (MRI) can provide complementary information on visual system damage in multiple sclerosis (MS). The objective of this paper is to determine whether a composite OCT/MRI score, reflecting cumulative damage along the entire visual pathway, can predict visual deficits in primary progressive multiple sclerosis (PPMS). Twenty-five PPMS patients and 20 age-matched controls underwent neuro-ophthalmologic evaluation, spectral-domain OCT, and 3T brain MRI. Differences between groups were assessed by univariate general linear model and principal component analysis (PCA) grouped instrumental variables into main components. Linear regression analysis was used to assess the relationship between low-contrast visual acuity (LCVA), OCT/MRI-derived metrics and PCA-derived composite scores. PCA identified four main components explaining 80.69% of data variance. Considering each variable independently, LCVA 1.25% was significantly predicted by ganglion cell-inner plexiform layer (GCIPL) thickness, thalamic volume and optic radiation (OR) lesion volume (adjusted R 2 0.328, p  = 0.00004; adjusted R 2 0.187, p  = 0.002 and adjusted R 2 0.180, p  = 0.002). The PCA composite score of global visual pathway damage independently predicted both LCVA 1.25% (adjusted R 2 value 0.361, p  = 0.00001) and LCVA 2.50% (adjusted R 2 value 0.323, p  = 0.00003). A multiparametric score represents a more comprehensive and effective tool to explain visual disability than a single instrumental metric in PPMS.

[Application of regression tree in analyzing the effects of climate factors on NDVI in loess hilly area of Shaanxi Province].

PubMed

Liu, Yang; Lü, Yi-he; Zheng, Hai-feng; Chen, Li-ding

2010-05-01

Based on the 10-day SPOT VEGETATION NDVI data and the daily meteorological data from 1998 to 2007 in Yan' an City, the main meteorological variables affecting the annual and interannual variations of NDVI were determined by using regression tree. It was found that the effects of test meteorological variables on the variability of NDVI differed with seasons and time lags. Temperature and precipitation were the most important meteorological variables affecting the annual variation of NDVI, and the average highest temperature was the most important meteorological variable affecting the inter-annual variation of NDVI. Regression tree was very powerful in determining the key meteorological variables affecting NDVI variation, but could not build quantitative relations between NDVI and meteorological variables, which limited its further and wider application.

Moderation analysis using a two-level regression model.

PubMed

Yuan, Ke-Hai; Cheng, Ying; Maxwell, Scott

2014-10-01

Moderation analysis is widely used in social and behavioral research. The most commonly used model for moderation analysis is moderated multiple regression (MMR) in which the explanatory variables of the regression model include product terms, and the model is typically estimated by least squares (LS). This paper argues for a two-level regression model in which the regression coefficients of a criterion variable on predictors are further regressed on moderator variables. An algorithm for estimating the parameters of the two-level model by normal-distribution-based maximum likelihood (NML) is developed. Formulas for the standard errors (SEs) of the parameter estimates are provided and studied. Results indicate that, when heteroscedasticity exists, NML with the two-level model gives more efficient and more accurate parameter estimates than the LS analysis of the MMR model. When error variances are homoscedastic, NML with the two-level model leads to essentially the same results as LS with the MMR model. Most importantly, the two-level regression model permits estimating the percentage of variance of each regression coefficient that is due to moderator variables. When applied to data from General Social Surveys 1991, NML with the two-level model identified a significant moderation effect of race on the regression of job prestige on years of education while LS with the MMR model did not. An R package is also developed and documented to facilitate the application of the two-level model.

Influence of Strategy of Learning and Achievement Motivation of Learning Achievement Class VIII Students of State Junior High School in District Blitar

ERIC Educational Resources Information Center

Ayundawati, Dyah; Setyosari, Punaji; Susilo, Herawati; Sihkabuden

2016-01-01

This study aims for know influence of problem-based learning strategies and achievement motivation on learning achievement. The method used in this research is quantitative method. The instrument used in this study is two fold instruments to measure moderator variable (achievement motivation) and instruments to measure the dependent variable (the…

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

USGS Publications Warehouse

Ryberg, Karen R.

2007-01-01

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

Avoiding and Correcting Bias in Score-Based Latent Variable Regression with Discrete Manifest Items

ERIC Educational Resources Information Center

Lu, Irene R. R.; Thomas, D. Roland

2008-01-01

This article considers models involving a single structural equation with latent explanatory and/or latent dependent variables where discrete items are used to measure the latent variables. Our primary focus is the use of scores as proxies for the latent variables and carrying out ordinary least squares (OLS) regression on such scores to estimate…

Stock price forecasting for companies listed on Tehran stock exchange using multivariate adaptive regression splines model and semi-parametric splines technique

NASA Astrophysics Data System (ADS)

Rounaghi, Mohammad Mahdi; Abbaszadeh, Mohammad Reza; Arashi, Mohammad

2015-11-01

One of the most important topics of interest to investors is stock price changes. Investors whose goals are long term are sensitive to stock price and its changes and react to them. In this regard, we used multivariate adaptive regression splines (MARS) model and semi-parametric splines technique for predicting stock price in this study. The MARS model as a nonparametric method is an adaptive method for regression and it fits for problems with high dimensions and several variables. semi-parametric splines technique was used in this study. Smoothing splines is a nonparametric regression method. In this study, we used 40 variables (30 accounting variables and 10 economic variables) for predicting stock price using the MARS model and using semi-parametric splines technique. After investigating the models, we select 4 accounting variables (book value per share, predicted earnings per share, P/E ratio and risk) as influencing variables on predicting stock price using the MARS model. After fitting the semi-parametric splines technique, only 4 accounting variables (dividends, net EPS, EPS Forecast and P/E Ratio) were selected as variables effective in forecasting stock prices.

Physical fitness predicts technical-tactical and time-motion profile in simulated Judo and Brazilian Jiu-Jitsu matches

PubMed Central

Gentil, Paulo; Bueno, João C.A.; Follmer, Bruno; Marques, Vitor A.; Del Vecchio, Fabrício B.

2018-01-01

Background Among combat sports, Judo and Brazilian Jiu-Jitsu (BJJ) present elevated physical fitness demands from the high-intensity intermittent efforts. However, information regarding how metabolic and neuromuscular physical fitness is associated with technical-tactical performance in Judo and BJJ fights is not available. This study aimed to relate indicators of physical fitness with combat performance variables in Judo and BJJ. Methods The sample consisted of Judo (n = 16) and BJJ (n = 24) male athletes. At the first meeting, the physical tests were applied and, in the second, simulated fights were performed for later notational analysis. Results The main findings indicate: (i) high reproducibility of the proposed instrument and protocol used for notational analysis in a mobile device; (ii) differences in the technical-tactical and time-motion patterns between modalities; (iii) performance-related variables are different in Judo and BJJ; and (iv) regression models based on metabolic fitness variables may account for up to 53% of the variances in technical-tactical and/or time-motion variables in Judo and up to 31% in BJJ, whereas neuromuscular fitness models can reach values up to 44 and 73% of prediction in Judo and BJJ, respectively. When all components are combined, they can explain up to 90% of high intensity actions in Judo. Discussion In conclusion, performance prediction models in simulated combat indicate that anaerobic, aerobic and neuromuscular fitness variables contribute to explain time-motion variables associated with high intensity and technical-tactical variables in Judo and BJJ fights. PMID:29844991

An Effect Size for Regression Predictors in Meta-Analysis

ERIC Educational Resources Information Center

Aloe, Ariel M.; Becker, Betsy Jane

2012-01-01

A new effect size representing the predictive power of an independent variable from a multiple regression model is presented. The index, denoted as r[subscript sp], is the semipartial correlation of the predictor with the outcome of interest. This effect size can be computed when multiple predictor variables are included in the regression model…

RAWS II: A MULTIPLE REGRESSION ANALYSIS PROGRAM,

DTIC Science & Technology

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

Graphical Description of Johnson-Neyman Outcomes for Linear and Quadratic Regression Surfaces.

ERIC Educational Resources Information Center

Schafer, William D.; Wang, Yuh-Yin

A modification of the usual graphical representation of heterogeneous regressions is described that can aid in interpreting significant regions for linear or quadratic surfaces. The standard Johnson-Neyman graph is a bivariate plot with the criterion variable on the ordinate and the predictor variable on the abscissa. Regression surfaces are drawn…

Quantile regression applied to spectral distance decay

USGS Publications Warehouse

Rocchini, D.; Cade, B.S.

2008-01-01

Remotely sensed imagery has long been recognized as a powerful support for characterizing and estimating biodiversity. Spectral distance among sites has proven to be a powerful approach for detecting species composition variability. Regression analysis of species similarity versus spectral distance allows us to quantitatively estimate the amount of turnover in species composition with respect to spectral and ecological variability. In classical regression analysis, the residual sum of squares is minimized for the mean of the dependent variable distribution. However, many ecological data sets are characterized by a high number of zeroes that add noise to the regression model. Quantile regressions can be used to evaluate trend in the upper quantiles rather than a mean trend across the whole distribution of the dependent variable. In this letter, we used ordinary least squares (OLS) and quantile regressions to estimate the decay of species similarity versus spectral distance. The achieved decay rates were statistically nonzero (p < 0.01), considering both OLS and quantile regressions. Nonetheless, the OLS regression estimate of the mean decay rate was only half the decay rate indicated by the upper quantiles. Moreover, the intercept value, representing the similarity reached when the spectral distance approaches zero, was very low compared with the intercepts of the upper quantiles, which detected high species similarity when habitats are more similar. In this letter, we demonstrated the power of using quantile regressions applied to spectral distance decay to reveal species diversity patterns otherwise lost or underestimated by OLS regression. ?? 2008 IEEE.

Spectral distance decay: Assessing species beta-diversity by quantile regression

USGS Publications Warehouse

Rocchinl, D.; Nagendra, H.; Ghate, R.; Cade, B.S.

2009-01-01

Remotely sensed data represents key information for characterizing and estimating biodiversity. Spectral distance among sites has proven to be a powerful approach for detecting species composition variability. Regression analysis of species similarity versus spectral distance may allow us to quantitatively estimate how beta-diversity in species changes with respect to spectral and ecological variability. In classical regression analysis, the residual sum of squares is minimized for the mean of the dependent variable distribution. However, many ecological datasets are characterized by a high number of zeroes that can add noise to the regression model. Quantile regression can be used to evaluate trend in the upper quantiles rather than a mean trend across the whole distribution of the dependent variable. In this paper, we used ordinary least square (ols) and quantile regression to estimate the decay of species similarity versus spectral distance. The achieved decay rates were statistically nonzero (p < 0.05) considering both ols and quantile regression. Nonetheless, ols regression estimate of mean decay rate was only half the decay rate indicated by the upper quantiles. Moreover, the intercept value, representing the similarity reached when spectral distance approaches zero, was very low compared with the intercepts of upper quantiles, which detected high species similarity when habitats are more similar. In this paper we demonstrated the power of using quantile regressions applied to spectral distance decay in order to reveal species diversity patterns otherwise lost or underestimated by ordinary least square regression. ?? 2009 American Society for Photogrammetry and Remote Sensing.

AN IMPROVED STRATEGY FOR REGRESSION OF BIOPHYSICAL VARIABLES AND LANDSAT ETM+ DATA. (R828309)

EPA Science Inventory

Empirical models are important tools for relating field-measured biophysical variables to remote sensing data. Regression analysis has been a popular empirical method of linking these two types of data to provide continuous estimates for variables such as biomass, percent wood...

Predictors of comorbid personality disorders in patients with panic disorder with agoraphobia.

PubMed

Latas, M; Starcevic, V; Trajkovic, G; Bogojevic, G

2000-01-01

The aim of this study was to ascertain predictors of comorbid personality disorders in patients with panic disorder with agoraphobia (PDAG). Sixty consecutive outpatients with PDAG were administered the Structured Clinical Interview for DSM-IV Axis II Personality Disorders (SCID-II) for the purpose of diagnosing personality disorders. Logistic regressions were used to identify predictors of any comorbid personality disorder, any DSM-IV cluster A, cluster B, and cluster C personality disorder. Independent variables in these regressions were gender, age, duration of panic disorder (PD), severity of PDAG, and scores on self-report instruments that assess the patient's perception of their parents, childhood separation anxiety, and traumatic experiences. High levels of parental protection on the Parental Bonding Instrument (PBI), indicating a perception of the parents as overprotective and controlling, emerged as the only statistically significant predictor of any comorbid personality disorder. This finding was attributed to the association between parental overprotection and cluster B personality disorders, particularly borderline personality disorder. The duration of PD was a significant predictor of any cluster B and any cluster C personality disorder, suggesting that some of the cluster B and cluster C personality disorders may be a consequence of the long-lasting PDAG. Any cluster B personality disorder was also associated with younger age. In conclusion, despite a generally nonspecific nature of the relationship between parental overprotection in childhood and adult psychopathology, the findings of this study suggest some specificity for the association between parental overprotection in childhood and personality disturbance in PDAG patients, particularly cluster B personality disorders.

Changes in performance: a 5-year longitudinal study of participants in a multi-source feedback programme.

PubMed

Violato, Claudio; Lockyer, Jocelyn M; Fidler, Herta

2008-10-01

Multi-source feedback (MSF) enables performance data to be provided to doctors from patients, co-workers and medical colleagues. This study examined the evidence for the validity of MSF instruments for general practice, investigated changes in performance for doctors who participated twice, 5 years apart, and determined the association between change in performance and initial assessment and socio-demographic characteristics. Data for 250 doctors included three datasets per doctor from, respectively, 25 patients, eight co-workers and eight medical colleagues, collected on two occasions. There was high internal consistency (alpha > 0.90) and adequate generalisability (Ep(2) > 0.70). D study results indicate adequate generalisability coefficients for groups of eight assessors (medical colleagues, co-workers) and 25 patient surveys. Confirmatory factor analyses provided evidence for the validity of factors that were theoretically expected, meaningful and cohesive. Comparative fit indices were 0.91 for medical colleague data, 0.87 for co-worker data and 0.81 for patient data. Paired t-test analysis showed significant change between the two assessments from medical colleagues and co-workers, but not between the two patient surveys. Multiple linear regressions explained 2.1% of the variance at time 2 for medical colleagues, 21.4% of the variance for co-workers and 16.35% of the variance for patient assessments, with professionalism a key variable in all regressions. There is evidence for the construct validity of the instruments and for their stability over time. Upward changes in performance will occur, although their effect size is likely to be small to moderate.

Relationship between attention deficit hyperactive disorder symptoms and perceived parenting practices of school-age children.

PubMed

Kim, Dong Hee; Yoo, Il Young

2013-04-01

To examine the relationship between the perception on parenting practices and attention deficit hyperactivity disorder (ADHD) symptoms in school-age children. Psychosocial attention deficit hyperactivity disorder intervention approaches emphasise environmental risk factors at the individual, family and community level. Parenting variables are strongly related to attention deficit hyperactivity disorder symptom severity. A cross-sectional questionnaire survey. The participants were 747 children and their parents in two elementary schools. The instruments used were Korean Conners Abbreviated Parent Questionnaire and Korean version Maternal Behavior Research Instrument (measuring four dimensions of parenting practices: affection, autonomy, rejection, control). Descriptive and logistic regression analyses were performed. The rejective parenting practice was statistically significant in logistic regression controlling gender and age of children, family structure, maternal education level and socio-economic status. The rejection parenting is associated with attention deficit hyperactivity disorder symptoms in children (OR=1.356). These results suggest the importance of specific parenting educational programmes for parents to prevent and decrease attention deficit hyperactivity disorder symptoms. It would be more effective rather than focusing only on the child's attention deficit hyperactivity disorder symptoms, developing educational programmes for parents to prevent rejection parenting practice and improve parenting skills in the family system. When developing a treatment programme for children with attention deficit hyperactivity disorder, healthcare providers should consider not only the child's attention deficit hyperactivity disorder symptoms, but also the parenting practices. Comprehensive interventions designed to prevent rejection and improve parenting skills may be helpful in mitigating attention deficit hyperactivity disorder symptoms. © 2012 Blackwell Publishing Ltd.

Bias and Bias Correction in Multisite Instrumental Variables Analysis of Heterogeneous Mediator Effects

ERIC Educational Resources Information Center

Reardon, Sean F.; Unlu, Fatih; Zhu, Pei; Bloom, Howard S.

2014-01-01

We explore the use of instrumental variables (IV) analysis with a multisite randomized trial to estimate the effect of a mediating variable on an outcome in cases where it can be assumed that the observed mediator is the only mechanism linking treatment assignment to outcomes, an assumption known in the IV literature as the exclusion restriction.…

[Living with achondroplasia- how do young persons with disproportional short stature rate their quality of life and which factors are associated with quality of life?].

PubMed

Rohenkohl, Anja C; Sommer, Rachel; Bestges, Stephanie; Kahrs, Sabine; Klingebiel, Karl-Heinz; Bullinger, Monika; Quitmann, Julia

2015-11-01

Presently, little is known aqout the quality of life (QoL) as well as the strengths and difficulties of young people with achondroplasia. This study describes these patient-reported indicators and identifies possible correlates. At the invitation of a patient organization, a total of 89 short-statured patients aged 8 to 28 years and their parents participated in this study. QoL was assessed cross-sectionally with both generic and disease-specific instruments and the Strengths and Difficulties Questionnaire (SDQ) as a brief behavioral screening. In addition to descriptive analyses, patient data were compared with a reference population. Hierarchical regression analyses reflecting sociodemographic, clinical, and psychological variables were conducted to identify correlates of QoL. QoL and the strengths and difficulties of young patients with achondroplasia did not differ substantially from a healthy norm sample. However, the participants reported more behavioral problems and limitations in their physical and social QoL compared to patients with another short stature diagnosis. Strengths and difficulties, height-related beliefs, and social support correlated significantly with QoL. Adding psychological variables to the regression model increased the proportion of variance explained in QoL. Young persons with achondroplasia did not differ in their QoL and strengths and difficulties from healthy controls. Characteristics such as height appear less important for the self-perceived QoL than are strengths and difficulties and protective psychosocia~factors.

Parents' Perception of Satisfaction With Pediatric Nurse Practitioners' Care And Parental Intent to Adhere To Recommended Health Care Regimen.

PubMed

Kinder, Frances DiAnna

2016-01-01

The purposes of this study were to explore parents' perceptions of satisfaction with care from primary care pediatric nurse practitioners (PNPs) and to explore the relationships of the four components of parental satisfaction with parents' intent to adhere to recommended health care regimen. The study used a descriptive correlational research design. A convenience sample of 91 participants was recruited from practices in southeastern Pennsylvania. The 28-item, Parents' Perceptions of Satisfaction with Care from Pediatric Nurse Practitioners (PPSC-PNP) tool was developed to measure four components of satisfaction and overall satisfaction of parents with PNP care after the health visit. A 100 mm visual analog (VAS) scale measured parental intent to adhere to the care regimen recommended by the PNP. Parents' perceptions of overall satisfaction with care from PNPs and satisfaction with each of the four components (communication, clinical competence, caring behavior, and decisional control) were high as measured by the PPSC-PNP. Multiple regression analysis revealed that clinical competence had the strongest positive relationship with parental intent to adhere to PNP recommended health regimen and was the only variable to enter the regression equation. The findings of this study have implications for nursing practice. The PPSC-PNP instrument may be used with a variety of pediatric populations and settings as a benchmark for quality care. Clinical competence is important for the role of the PNP. Other variables of parental intent to adhere to the health regimen should be explored in future studies.

Mapping health outcome measures from a stroke registry to EQ-5D weights

PubMed Central

2013-01-01

Purpose To map health outcome related variables from a national register, not part of any validated instrument, with EQ-5D weights among stroke patients. Methods We used two cross-sectional data sets including patient characteristics, outcome variables and EQ-5D weights from the national Swedish stroke register. Three regression techniques were used on the estimation set (n = 272): ordinary least squares (OLS), Tobit, and censored least absolute deviation (CLAD). The regression coefficients for “dressing“, “toileting“, “mobility”, “mood”, “general health” and “proxy-responders” were applied to the validation set (n = 272), and the performance was analysed with mean absolute error (MAE) and mean square error (MSE). Results The number of statistically significant coefficients varied by model, but all models generated consistent coefficients in terms of sign. Mean utility was underestimated in all models (least in OLS) and with lower variation (least in OLS) compared to the observed. The maximum attainable EQ-5D weight ranged from 0.90 (OLS) to 1.00 (Tobit and CLAD). Health states with utility weights <0.5 had greater errors than those with weights ≥0.5 (P < 0.01). Conclusion This study indicates that it is possible to map non-validated health outcome measures from a stroke register into preference-based utilities to study the development of stroke care over time, and to compare with other conditions in terms of utility. PMID:23496957

Risk factors for unavoidable removal of instrumentation after surgical site infection of spine surgery

PubMed Central

Tominaga, Hiroyuki; Setoguchi, Takao; Kawamura, Hideki; Kawamura, Ichiro; Nagano, Satoshi; Abematsu, Masahiko; Tanabe, Fumito; Ishidou, Yasuhiro; Yamamoto, Takuya; Komiya, Setsuro

2016-01-01

Abstract Surgical site infection (SSI) after spine instrumentation is difficult to treat, and often requires removal of instrumentation. The removal of instrumentation after spine surgery is a severe complication that can lead to the deterioration of activities of daily living and poor prognosis. Although there are many reports on SSI after spine surgery, few reports have investigated the risk factors for the removal of instrumentation after spine surgery SSI. This study aimed to identify the risk factors for unavoidable removal of instrumentation after SSI of spine surgery. We retrospectively reviewed 511 patients who underwent spine surgery with instrumentation at Kagoshima University Hospital from January 2006 to December 2014. Risk factors associated with SSI were analyzed via multiple logistic regression analysis. Parameters of the group that needed instrumentation removal were compared with the group that did not require instrumentation removal using the Mann–Whitney U and Fisher's exact tests. The posterior approach was used in most cases (453 of 511 cases, 88.6%). SSI occurred in 16 of 511 cases (3.14%) of spine surgery with instrumentation. Multivariate logistic regression analysis identified 2 significant risk factors for SSI: operation time, and American Society of Anesthesiologists physical status classification ≥ 3. Twelve of the 16 patients with SSI (75%) were able to keep the instrumentation after SSI. Pseudarthrosis occurred in 2 of 4 cases (50%) after instrumentation removal. Risk factors identified for instrumentation removal after spine SSI were a greater number of past surgeries, low preoperative hemoglobin, high preoperative creatinine, high postoperative infection treatment score for the spine, and the presence of methicillin-resistant Staphylococcus aureus. In these high risk cases, attempts should be made to decrease the risk factors preoperatively, and careful postoperative monitoring should be conducted. PMID:27787365

Risk factors for unavoidable removal of instrumentation after surgical site infection of spine surgery: A retrospective case-control study.

PubMed

Tominaga, Hiroyuki; Setoguchi, Takao; Kawamura, Hideki; Kawamura, Ichiro; Nagano, Satoshi; Abematsu, Masahiko; Tanabe, Fumito; Ishidou, Yasuhiro; Yamamoto, Takuya; Komiya, Setsuro

2016-10-01

Surgical site infection (SSI) after spine instrumentation is difficult to treat, and often requires removal of instrumentation. The removal of instrumentation after spine surgery is a severe complication that can lead to the deterioration of activities of daily living and poor prognosis. Although there are many reports on SSI after spine surgery, few reports have investigated the risk factors for the removal of instrumentation after spine surgery SSI. This study aimed to identify the risk factors for unavoidable removal of instrumentation after SSI of spine surgery. We retrospectively reviewed 511 patients who underwent spine surgery with instrumentation at Kagoshima University Hospital from January 2006 to December 2014. Risk factors associated with SSI were analyzed via multiple logistic regression analysis. Parameters of the group that needed instrumentation removal were compared with the group that did not require instrumentation removal using the Mann-Whitney U and Fisher's exact tests. The posterior approach was used in most cases (453 of 511 cases, 88.6%). SSI occurred in 16 of 511 cases (3.14%) of spine surgery with instrumentation. Multivariate logistic regression analysis identified 2 significant risk factors for SSI: operation time, and American Society of Anesthesiologists physical status classification ≥ 3. Twelve of the 16 patients with SSI (75%) were able to keep the instrumentation after SSI. Pseudarthrosis occurred in 2 of 4 cases (50%) after instrumentation removal. Risk factors identified for instrumentation removal after spine SSI were a greater number of past surgeries, low preoperative hemoglobin, high preoperative creatinine, high postoperative infection treatment score for the spine, and the presence of methicillin-resistant Staphylococcus aureus. In these high risk cases, attempts should be made to decrease the risk factors preoperatively, and careful postoperative monitoring should be conducted.

Comparisons of estimates of annual exceedance-probability discharges for small drainage basins in Iowa, based on data through water year 2013

USGS Publications Warehouse

Eash, David A.

2015-01-01

An examination was conducted to understand why the 1987 single-variable RREs seem to provide better accuracy and less bias than either of the 2013 multi- or single-variable RREs. A comparison of 1-percent annual exceedance-probability regression lines for hydrologic regions 1-4 from the 1987 single-variable RREs and for flood regions 1-3 from the 2013 single-variable RREs indicates that the 1987 single-variable regional-regression lines generally have steeper slopes and lower discharges when compared to 2013 single-variable regional-regression lines for corresponding areas of Iowa. The combination of the definition of hydrologic regions, the lower discharges, and the steeper slopes of regression lines associated with the 1987 single-variable RREs seem to provide better accuracy and less bias when compared to the 2013 multi- or single-variable RREs; better accuracy and less bias was determined particularly for drainage areas less than 2 mi2, and also for some drainage areas between 2 and 20 mi2. The 2013 multi- and single-variable RREs are considered to provide better accuracy and less bias for larger drainage areas. Results of this study indicate that additional research is needed to address the curvilinear relation between drainage area and AEPDs for areas of Iowa.

Prediction of sickness absence: development of a screening instrument

PubMed Central

Duijts, S F A; Kant, IJ; Landeweerd, J A; Swaen, G M H

2006-01-01

Objectives To develop a concise screening instrument for early identification of employees at risk for sickness absence due to psychosocial health complaints. Methods Data from the Maastricht Cohort Study on “Fatigue at Work” were used to identify items to be associated with an increased risk of sickness absence. The analytical procedures univariate logistic regression, backward stepwise linear regression, and multiple logistic regression were successively applied. For both men and women, sum scores were calculated, and sensitivity and specificity rates of different cut‐off points on the screening instrument were defined. Results In women, results suggested that feeling depressed, having a burnout, being tired, being less interested in work, experiencing obligatory change in working days, and living alone, were strong predictors of sickness absence due to psychosocial health complaints. In men, statistically significant predictors were having a history of sickness absence, compulsive thinking, being mentally fatigued, finding it hard to relax, lack of supervisor support, and having no hobbies. A potential cut‐off point of 10 on the screening instrument resulted in a sensitivity score of 41.7% for women and 38.9% for men, and a specificity score of 91.3% for women and 90.6% for men. Conclusions This study shows that it is possible to identify predictive factors for sickness absence and to develop an instrument for early identification of employees at risk for sickness absence. The results of this study increase the possibility for both employers and policymakers to implement interventions directed at the prevention of sickness absence. PMID:16698807

Quantifying measurement uncertainty and spatial variability in the context of model evaluation

NASA Astrophysics Data System (ADS)

Choukulkar, A.; Brewer, A.; Pichugina, Y. L.; Bonin, T.; Banta, R. M.; Sandberg, S.; Weickmann, A. M.; Djalalova, I.; McCaffrey, K.; Bianco, L.; Wilczak, J. M.; Newman, J. F.; Draxl, C.; Lundquist, J. K.; Wharton, S.; Olson, J.; Kenyon, J.; Marquis, M.

2017-12-01

In an effort to improve wind forecasts for the wind energy sector, the Department of Energy and the NOAA funded the second Wind Forecast Improvement Project (WFIP2). As part of the WFIP2 field campaign, a large suite of in-situ and remote sensing instrumentation was deployed to the Columbia River Gorge in Oregon and Washington from October 2015 - March 2017. The array of instrumentation deployed included 915-MHz wind profiling radars, sodars, wind- profiling lidars, and scanning lidars. The role of these instruments was to provide wind measurements at high spatial and temporal resolution for model evaluation and improvement of model physics. To properly determine model errors, the uncertainties in instrument-model comparisons need to be quantified accurately. These uncertainties arise from several factors such as measurement uncertainty, spatial variability, and interpolation of model output to instrument locations, to name a few. In this presentation, we will introduce a formalism to quantify measurement uncertainty and spatial variability. The accuracy of this formalism will be tested using existing datasets such as the eXperimental Planetary boundary layer Instrumentation Assessment (XPIA) campaign. Finally, the uncertainties in wind measurement and the spatial variability estimates from the WFIP2 field campaign will be discussed to understand the challenges involved in model evaluation.

Advanced colorectal neoplasia risk stratification by penalized logistic regression.

PubMed

Lin, Yunzhi; Yu, Menggang; Wang, Sijian; Chappell, Richard; Imperiale, Thomas F

2016-08-01

Colorectal cancer is the second leading cause of death from cancer in the United States. To facilitate the efficiency of colorectal cancer screening, there is a need to stratify risk for colorectal cancer among the 90% of US residents who are considered "average risk." In this article, we investigate such risk stratification rules for advanced colorectal neoplasia (colorectal cancer and advanced, precancerous polyps). We use a recently completed large cohort study of subjects who underwent a first screening colonoscopy. Logistic regression models have been used in the literature to estimate the risk of advanced colorectal neoplasia based on quantifiable risk factors. However, logistic regression may be prone to overfitting and instability in variable selection. Since most of the risk factors in our study have several categories, it was tempting to collapse these categories into fewer risk groups. We propose a penalized logistic regression method that automatically and simultaneously selects variables, groups categories, and estimates their coefficients by penalizing the [Formula: see text]-norm of both the coefficients and their differences. Hence, it encourages sparsity in the categories, i.e. grouping of the categories, and sparsity in the variables, i.e. variable selection. We apply the penalized logistic regression method to our data. The important variables are selected, with close categories simultaneously grouped, by penalized regression models with and without the interactions terms. The models are validated with 10-fold cross-validation. The receiver operating characteristic curves of the penalized regression models dominate the receiver operating characteristic curve of naive logistic regressions, indicating a superior discriminative performance. © The Author(s) 2013.

Multinomial logistic regression modelling of obesity and overweight among primary school students in a rural area of Negeri Sembilan

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

Ghazali, Amirul Syafiq Mohd; Ali, Zalila; Noor, Norlida Mohd

Multinomial logistic regression is widely used to model the outcomes of a polytomous response variable, a categorical dependent variable with more than two categories. The model assumes that the conditional mean of the dependent categorical variables is the logistic function of an affine combination of predictor variables. Its procedure gives a number of logistic regression models that make specific comparisons of the response categories. When there are q categories of the response variable, the model consists of q-1 logit equations which are fitted simultaneously. The model is validated by variable selection procedures, tests of regression coefficients, a significant test ofmore » the overall model, goodness-of-fit measures, and validation of predicted probabilities using odds ratio. This study used the multinomial logistic regression model to investigate obesity and overweight among primary school students in a rural area on the basis of their demographic profiles, lifestyles and on the diet and food intake. The results indicated that obesity and overweight of students are related to gender, religion, sleep duration, time spent on electronic games, breakfast intake in a week, with whom meals are taken, protein intake, and also, the interaction between breakfast intake in a week with sleep duration, and the interaction between gender and protein intake.« less

Multinomial logistic regression modelling of obesity and overweight among primary school students in a rural area of Negeri Sembilan

NASA Astrophysics Data System (ADS)

Ghazali, Amirul Syafiq Mohd; Ali, Zalila; Noor, Norlida Mohd; Baharum, Adam

2015-10-01

Multinomial logistic regression is widely used to model the outcomes of a polytomous response variable, a categorical dependent variable with more than two categories. The model assumes that the conditional mean of the dependent categorical variables is the logistic function of an affine combination of predictor variables. Its procedure gives a number of logistic regression models that make specific comparisons of the response categories. When there are q categories of the response variable, the model consists of q-1 logit equations which are fitted simultaneously. The model is validated by variable selection procedures, tests of regression coefficients, a significant test of the overall model, goodness-of-fit measures, and validation of predicted probabilities using odds ratio. This study used the multinomial logistic regression model to investigate obesity and overweight among primary school students in a rural area on the basis of their demographic profiles, lifestyles and on the diet and food intake. The results indicated that obesity and overweight of students are related to gender, religion, sleep duration, time spent on electronic games, breakfast intake in a week, with whom meals are taken, protein intake, and also, the interaction between breakfast intake in a week with sleep duration, and the interaction between gender and protein intake.

THAT INSTRUMENT IS LOUSY! IN SEARCH OF AGREEMENT WHEN USING INSTRUMENTAL VARIABLES ESTIMATION IN SUBSTANCE USE RESEARCH

PubMed Central

Popovici, Ioana

2009-01-01

SUMMARY The primary statistical challenge that must be addressed when using cross-sectional data to estimate the consequences of consuming addictive substances is the likely endogeneity of substance use. While economists are in agreement on the need to consider potential endogeneity bias and the value of instrumental variables estimation, the selection of credible instruments is a topic of heated debate in the field. Rather than attempt to resolve this debate, our paper highlights the diversity of judgments about what constitutes appropriate instruments for substance use based on a comprehensive review of the economics literature since 1990. We then offer recommendations related to the selection of reliable instruments in future studies. PMID:20029936

Element enrichment factor calculation using grain-size distribution and functional data regression.

PubMed

Sierra, C; Ordóñez, C; Saavedra, A; Gallego, J R

2015-01-01

In environmental geochemistry studies it is common practice to normalize element concentrations in order to remove the effect of grain size. Linear regression with respect to a particular grain size or conservative element is a widely used method of normalization. In this paper, the utility of functional linear regression, in which the grain-size curve is the independent variable and the concentration of pollutant the dependent variable, is analyzed and applied to detrital sediment. After implementing functional linear regression and classical linear regression models to normalize and calculate enrichment factors, we concluded that the former regression technique has some advantages over the latter. First, functional linear regression directly considers the grain-size distribution of the samples as the explanatory variable. Second, as the regression coefficients are not constant values but functions depending on the grain size, it is easier to comprehend the relationship between grain size and pollutant concentration. Third, regularization can be introduced into the model in order to establish equilibrium between reliability of the data and smoothness of the solutions. Copyright © 2014 Elsevier Ltd. All rights reserved.

Performance of bias-correction methods for exposure measurement error using repeated measurements with and without missing data.

PubMed

Batistatou, Evridiki; McNamee, Roseanne

2012-12-10

It is known that measurement error leads to bias in assessing exposure effects, which can however, be corrected if independent replicates are available. For expensive replicates, two-stage (2S) studies that produce data 'missing by design', may be preferred over a single-stage (1S) study, because in the second stage, measurement of replicates is restricted to a sample of first-stage subjects. Motivated by an occupational study on the acute effect of carbon black exposure on respiratory morbidity, we compare the performance of several bias-correction methods for both designs in a simulation study: an instrumental variable method (EVROS IV) based on grouping strategies, which had been recommended especially when measurement error is large, the regression calibration and the simulation extrapolation methods. For the 2S design, either the problem of 'missing' data was ignored or the 'missing' data were imputed using multiple imputations. Both in 1S and 2S designs, in the case of small or moderate measurement error, regression calibration was shown to be the preferred approach in terms of root mean square error. For 2S designs, regression calibration as implemented by Stata software is not recommended in contrast to our implementation of this method; the 'problematic' implementation of regression calibration although substantially improved with use of multiple imputations. The EVROS IV method, under a good/fairly good grouping, outperforms the regression calibration approach in both design scenarios when exposure mismeasurement is severe. Both in 1S and 2S designs with moderate or large measurement error, simulation extrapolation severely failed to correct for bias. Copyright © 2012 John Wiley & Sons, Ltd.

Suppressor Variables: The Difference between "Is" versus "Acting As"

ERIC Educational Resources Information Center

Ludlow, Larry; Klein, Kelsey

2014-01-01

Correlated predictors in regression models are a fact of life in applied social science research. The extent to which they are correlated will influence the estimates and statistics associated with the other variables they are modeled along with. These effects, for example, may include enhanced regression coefficients for the other variables--a…

Regression Methods for Categorical Dependent Variables: Effects on a Model of Student College Choice

ERIC Educational Resources Information Center

Rapp, Kelly E.

2012-01-01

The use of categorical dependent variables with the classical linear regression model (CLRM) violates many of the model's assumptions and may result in biased estimates (Long, 1997; O'Connell, Goldstein, Rogers, & Peng, 2008). Many dependent variables of interest to educational researchers (e.g., professorial rank, educational attainment) are…

Causal Models with Unmeasured Variables: An Introduction to LISREL.

ERIC Educational Resources Information Center

Wolfle, Lee M.

Whenever one uses ordinary least squares regression, one is making an implicit assumption that all of the independent variables have been measured without error. Such an assumption is obviously unrealistic for most social data. One approach for estimating such regression models is to measure implied coefficients between latent variables for which…

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

PubMed

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

2003-06-01

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

Soil Cd, Cr, Cu, Ni, Pb and Zn sorption and retention models using SVM: Variable selection and competitive model.

PubMed

González Costa, J J; Reigosa, M J; Matías, J M; Covelo, E F

2017-09-01

The aim of this study was to model the sorption and retention of Cd, Cu, Ni, Pb and Zn in soils. To that extent, the sorption and retention of these metals were studied and the soil characterization was performed separately. Multiple stepwise regression was used to produce multivariate models with linear techniques and with support vector machines, all of which included 15 explanatory variables characterizing soils. When the R-squared values are represented, two different groups are noticed. Cr, Cu and Pb sorption and retention show a higher R-squared; the most explanatory variables being humified organic matter, Al oxides and, in some cases, cation-exchange capacity (CEC). The other group of metals (Cd, Ni and Zn) shows a lower R-squared, and clays are the most explanatory variables, including a percentage of vermiculite and slime. In some cases, quartz, plagioclase or hematite percentages also show some explanatory capacity. Support Vector Machine (SVM) regression shows that the different models are not as regular as in multiple regression in terms of number of variables, the regression for nickel adsorption being the one with the highest number of variables in its optimal model. On the other hand, there are cases where the most explanatory variables are the same for two metals, as it happens with Cd and Cr adsorption. A similar adsorption mechanism is thus postulated. These patterns of the introduction of variables in the model allow us to create explainability sequences. Those which are the most similar to the selectivity sequences obtained by Covelo (2005) are Mn oxides in multiple regression and change capacity in SVM. Among all the variables, the only one that is explanatory for all the metals after applying the maximum parsimony principle is the percentage of sand in the retention process. In the competitive model arising from the aforementioned sequences, the most intense competitiveness for the adsorption and retention of different metals appears between Cr and Cd, Cu and Zn in multiple regression; and between Cr and Cd in SVM regression. Copyright © 2017 Elsevier B.V. All rights reserved.

Psychosocial Assessment of Self-Harm Patients and Risk of Repeat Presentation: An Instrumental Variable Analysis Using Time of Hospital Presentation.

PubMed

Carroll, Robert; Metcalfe, Chris; Steeg, Sarah; Davies, Neil M; Cooper, Jayne; Kapur, Nav; Gunnell, David

2016-01-01

Clinical guidelines have recommended psychosocial assessment of self-harm patients for years, yet estimates of its impact on the risk of repeat self-harm vary. Assessing the association of psychosocial assessment with risk of repeat self-harm is challenging due to the effects of confounding by indication. We analysed data from a cohort study of 15,113 patients presenting to the emergency departments of three UK hospitals to investigate the association of psychosocial assessment with risk of repeat hospital presentation for self-harm. Time of day of hospital presentation was used as an instrument for psychosocial assessment, attempting to control for confounding by indication. Conventional regression analysis suggested psychosocial assessment was not associated with risk of repeat self-harm within 12 months (Risk Difference (RD) 0.00 95% confidence interval (95%CI) -0.01 to 0.02). In contrast, IV analysis suggested risk of repeat self-harm was reduced by 18% (RD -0.18, 95%CI -0.32 to -0.03) in those patients receiving a psychosocial assessment. However, the instrument of time of day did not remove all potential effects of confounding by indication, suggesting the IV effect estimate may be biased. We found that psychosocial assessments reduce risk of repeat self-harm. This is in-line with other non-randomised studies based on populations in which allocation to assessment was less subject to confounding by indication. However, as our instrument did not fully balance important confounders across time of day, the IV effect estimate should be interpreted with caution.

"Fibromyalgia and quality of life: mapping the revised fibromyalgia impact questionnaire to the preference-based instruments".

PubMed

Collado-Mateo, Daniel; Chen, Gang; Garcia-Gordillo, Miguel A; Iezzi, Angelo; Adsuar, José C; Olivares, Pedro R; Gusi, Narcis

2017-05-30

The revised version of the Fibromyalgia Impact Questionnaire (FIQR) is one of the most widely used specific questionnaires in FM studies. However, this questionnaire does not allow calculation of QALYs as it is not a preference-based measure. The aim of this study was to develop mapping algorithm which enable FIQR scores to be transformed into utility scores that can be used in the cost utility analyses. A cross-sectional survey was conducted. One hundred and 92 Spanish women with Fibromyalgia were asked to complete four general quality of life questionnaires, i.e. EQ-5D-5 L, 15D, AQoL-8D and SF-12, and one specific disease instrument, the FIQR. A direct mapping approach was adopted to derive mapping algorithms between the FIQR and each of the four multi-attribute utility (MAU) instruments. Health state utility was treated as the dependent variable in the regression analysis, whilst the FIQR score and age were predictors. The mean utility scores ranged from 0.47 (AQoL-8D) to 0.69 (15D). All correlations between the FIQR total score and MAU instruments utility scores were highly significant (p < 0.0001) with magnitudes larger than 0.5. Although very slight differences in the mean absolute error were found between ordinary least squares (OLS) estimator and generalized linear model (GLM), models based on GLM were better for EQ-5D-5 L, AQoL-8D and 15D. Mapping algorithms developed in this study enable the estimation of utility values from scores in a fibromyalgia specific questionnaire.

Carcass yield and meat quality in broilers fed with canola meal.

PubMed

Gopinger, E; Xavier, E G; Lemes, J S; Moraes, P O; Elias, M C; Roll, V F B

2014-01-01

1. This study evaluated the effects of canola meal in broiler diets on carcass yield, carcass composition, and instrumental and sensory analyses of meat. 2. A total of 320 one-day-old Cobb broilers were used in a 35-d experiment using a completely randomised design with 5 concentrations of canola meal (0, 10, 20, 30 and 40%) as a dietary substitute for soya bean meal. 3. Polynomial regression at 5% significance was used to evaluate the effects of canola meal content. The following variables were measured: carcass yield, chemical composition of meat, and instrumental and sensorial analyses. 4. The results showed that carcass yield exhibited a quadratic effect that was crescent to the level of 18% of canola meal based on the weight of the leg and a quadratic increase at concentrations up to 8.4% of canola meal based on the weight of the chest. The yield of the chest exhibited a linear behaviour. 5. The chemical composition of leg meat, instrumental analysis of breast meat and sensory characteristics of the breast meat was not significantly affected by the inclusion of canola meal. The chemical composition of the breast meat exhibited an increased linear effect in terms of dry matter and ether extract and a decreased linear behaviour in terms of the ash content. 6. In conclusion, soya bean meal can be substituted with canola meal at concentrations up to 20% of the total diet without affecting carcass yield, composition of meat or the instrumental or sensory characteristics of the meat of broilers.

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.

Treating pre-instrumental data as "missing" data: using a tree-ring-based paleoclimate record and imputations to reconstruct streamflow in the Missouri River Basin

NASA Astrophysics Data System (ADS)

Ho, M. W.; Lall, U.; Cook, E. R.

2015-12-01

Advances in paleoclimatology in the past few decades have provided opportunities to expand the temporal perspective of the hydrological and climatological variability across the world. The North American region is particularly fortunate in this respect where a relatively dense network of high resolution paleoclimate proxy records have been assembled. One such network is the annually-resolved Living Blended Drought Atlas (LBDA): a paleoclimate reconstruction of the Palmer Drought Severity Index (PDSI) that covers North America on a 0.5° × 0.5° grid based on tree-ring chronologies. However, the use of the LBDA to assess North American streamflow variability requires a model by which streamflow may be reconstructed. Paleoclimate reconstructions have typically used models that first seek to quantify the relationship between the paleoclimate variable and the environmental variable of interest before extrapolating the relationship back in time. In contrast, the pre-instrumental streamflow is here considered as "missing" data. A method of imputing the "missing" streamflow data, prior to the instrumental record, is applied through multiple imputation using chained equations for streamflow in the Missouri River Basin. In this method, the distribution of the instrumental streamflow and LBDA is used to estimate sets of plausible values for the "missing" streamflow data resulting in a ~600 year-long streamflow reconstruction. Past research into external climate forcings, oceanic-atmospheric variability and its teleconnections, and assessments of rare multi-centennial instrumental records demonstrate that large temporal oscillations in hydrological conditions are unlikely to be captured in most instrumental records. The reconstruction of multi-centennial records of streamflow will enable comprehensive assessments of current and future water resource infrastructure and operations under the existing scope of natural climate variability.

Optoelectronic instrumentation enhancement using data mining feedback for a 3D measurement system

NASA Astrophysics Data System (ADS)

Flores-Fuentes, Wendy; Sergiyenko, Oleg; Gonzalez-Navarro, Félix F.; Rivas-López, Moisés; Hernandez-Balbuena, Daniel; Rodríguez-Quiñonez, Julio C.; Tyrsa, Vera; Lindner, Lars

2016-12-01

3D measurement by a cyber-physical system based on optoelectronic scanning instrumentation has been enhanced by outliers and regression data mining feedback. The prototype has applications in (1) industrial manufacturing systems that include: robotic machinery, embedded vision, and motion control, (2) health care systems for measurement scanning, and (3) infrastructure by providing structural health monitoring. This paper presents new research performed in data processing of a 3D measurement vision sensing database. Outliers from multivariate data have been detected and removal to improve artificial intelligence regression algorithm results. Physical measurement error regression data has been used for 3D measurements error correction. Concluding, that the joint of physical phenomena, measurement and computation is an effectiveness action for feedback loops in the control of industrial, medical and civil tasks.

Field intercomparison of six different three-dimensional sonic anemometers

NASA Astrophysics Data System (ADS)

Mauder, Matthias; Zeeman, Matthias

2017-04-01

Although sonic anemometers have been used extensively for several decades in micrometeorological and ecological research, there is still some scientific debate about the measurement uncertainty of these instruments. This is due to the fact that an absolute reference for the measurement of turbulent wind fluctuations in the free atmosphere does not exist. In view of this lack we have conducted a field intercomparison experiment of six commonly used sonic anemometers from four major manufacturers. The models included Campbell CSAT3, Gill HS-50 and R3, METEK uSonic-3 Omni, R.M. Young 81000 and 81000RE. The experiment was conducted over a meadow at the TERENO/ICOS site De-Fen in southern Germany over a period of 16 days in June of 2016 in preparation of the ScaleX campaign. The measurement height was 3 m for all sensors, which were separated by 9 m from each other, each on its own tripod, in order to limit contamination of the turbulence measurements by neighbouring structures as much as possible. Moreover, the data were filtered for potentially disturbed wind sectors, and the high-frequency data from all instruments were treated with the same post-processing algorithm. In this presentation, we compare the results for various turbulence statistics from all sensors. These include mean horizontal wind speed, standard deviations of vertical wind velocity and sonic temperature, friction velocity and the covariance between vertical wind velocity and sonic temperature. Quantitative measures of uncertainty were derived from these results. We find that biases and regression intercepts are generally very small for all sensors and all computed variables, except for the temperature measurements of the two Gill sonic anemometers (HS and R3), which are known to suffer from a transducer-temperature dependence of the sonic temperature measurement. The comparability of the instruments is not always as good, which means that there is some scatter but the errors compensate at least partly. The best overall agreement between the different instruments was found for the variables "mean wind speed" and "buoyancy flux", which reflects that the sensors are optimized for measuring these quantities.

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.

Intraindividual Variability in Executive Functions but Not Speed of Processing or Conflict Resolution Predicts Performance Differences in Gait Speed in Older Adults

PubMed Central

Mahoney, Jeannette; Verghese, Joe

2014-01-01

Background. The relationship between executive functions (EF) and gait speed is well established. However, with the exception of dual tasking, the key components of EF that predict differences in gait performance have not been determined. Therefore, the current study was designed to determine whether processing speed, conflict resolution, and intraindividual variability in EF predicted variance in gait performance in single- and dual-task conditions. Methods. Participants were 234 nondemented older adults (mean age 76.48 years; 55% women) enrolled in a community-based cohort study. Gait speed was assessed using an instrumented walkway during single- and dual-task conditions. The flanker task was used to assess EF. Results. Results from the linear mixed effects model showed that (a) dual-task interference caused a significant dual-task cost in gait speed (estimate = 35.99; 95% CI = 33.19–38.80) and (b) of the cognitive predictors, only intraindividual variability was associated with gait speed (estimate = −.606; 95% CI = −1.11 to −.10). In unadjusted analyses, the three EF measures were related to gait speed in single- and dual-task conditions. However, in fully adjusted linear regression analysis, only intraindividual variability predicted performance differences in gait speed during dual tasking (B = −.901; 95% CI = −1.557 to −.245). Conclusion. Among the three EF measures assessed, intraindividual variability but not speed of processing or conflict resolution predicted performance differences in gait speed. PMID:24285744

Determinants of Skin Cancer Preventive Behaviors Among Rural Farmers in Iran: an Application of Protection Motivation Theory.

PubMed

Babazadeh, Towhid; Nadrian, Haidar; Banayejeddi, Morteza; Rezapour, Baratali

2017-09-01

Skin cancer is one of the most prevalent cancers, worldwide, which happens more among those with more sunlight exposure like farmers. The aim of this study was to explore the determinants of skin cancer preventive behaviors (SCPBs) among rural farmers using Protection Motivation Theory (PMT). In this cross-sectional study, multistage random sampling was employed to enroll 238 farmers referring to rural health houses (HH) in Chaldoran County, Iran. A valid and reliable instrument based on PMT variables was used. Significant correlations were found between all PMT variables with SCPBs (p < 0.05). Hierarchical multiple linear regressions were performed with Protection Motivation and SCPBs as outcome variables. Predictors for these two outcome variables were classified in two different blocks according to their natures. Demographic characteristics (p > 0.05) and PMT constructs (p < 0.001) explained 3 and 63.6 % of the observed variance in Protection Motivation, respectively. Also, no significant effect was found on SCPBs by demographic variables, in the first block (∆R 2  = 0.025); however, in the second block, Perceived Susceptibility (p = 0.000), Rewards (p = 0.022), Self-efficacy (p = 0.000), and Response Cost (p = 0.001) were significant predictors of SCPBs (∆R 2  = 0.432). Health care providers may consider PMT as a framework for developing educational interventions aiming at improving SCPBs among rural farmers.

Analysis of the Influence of Quantile Regression Model on Mainland Tourists' Service Satisfaction Performance

PubMed Central

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

2014-01-01

It is estimated that mainland Chinese tourists travelling to Taiwan can bring annual revenues of 400 billion NTD to the Taiwan economy. Thus, how the Taiwanese Government formulates relevant measures to satisfy both sides is the focus of most concern. Taiwan must improve the facilities and service quality of its tourism industry so as to attract more mainland tourists. This paper conducted a questionnaire survey of mainland tourists and used grey relational analysis in grey mathematics to analyze the satisfaction performance of all satisfaction question items. The first eight satisfaction items were used as independent variables, and the overall satisfaction performance was used as a dependent variable for quantile regression model analysis to discuss the relationship between the dependent variable under different quantiles and independent variables. Finally, this study further discussed the predictive accuracy of the least mean regression model and each quantile regression model, as a reference for research personnel. The analysis results showed that other variables could also affect the overall satisfaction performance of mainland tourists, in addition to occupation and age. The overall predictive accuracy of quantile regression model Q0.25 was higher than that of the other three models. PMID:24574916

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

PubMed

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

2014-01-01

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

Instrumental variables I: instrumental variables exploit natural variation in nonexperimental data to estimate causal relationships.

PubMed

Rassen, Jeremy A; Brookhart, M Alan; Glynn, Robert J; Mittleman, Murray A; Schneeweiss, Sebastian

2009-12-01

The gold standard of study design for treatment evaluation is widely acknowledged to be the randomized controlled trial (RCT). Trials allow for the estimation of causal effect by randomly assigning participants either to an intervention or comparison group; through the assumption of "exchangeability" between groups, comparing the outcomes will yield an estimate of causal effect. In the many cases where RCTs are impractical or unethical, instrumental variable (IV) analysis offers a nonexperimental alternative based on many of the same principles. IV analysis relies on finding a naturally varying phenomenon, related to treatment but not to outcome except through the effect of treatment itself, and then using this phenomenon as a proxy for the confounded treatment variable. This article demonstrates how IV analysis arises from an analogous but potentially impossible RCT design, and outlines the assumptions necessary for valid estimation. It gives examples of instruments used in clinical epidemiology and concludes with an outline on estimation of effects.

Instrumental variables I: instrumental variables exploit natural variation in nonexperimental data to estimate causal relationships

PubMed Central

Rassen, Jeremy A.; Brookhart, M. Alan; Glynn, Robert J.; Mittleman, Murray A.; Schneeweiss, Sebastian

2010-01-01

The gold standard of study design for treatment evaluation is widely acknowledged to be the randomized controlled trial (RCT). Trials allow for the estimation of causal effect by randomly assigning participants either to an intervention or comparison group; through the assumption of “exchangeability” between groups, comparing the outcomes will yield an estimate of causal effect. In the many cases where RCTs are impractical or unethical, instrumental variable (IV) analysis offers a nonexperimental alternative based on many of the same principles. IV analysis relies on finding a naturally varying phenomenon, related to treatment but not to outcome except through the effect of treatment itself, and then using this phenomenon as a proxy for the confounded treatment variable. This article demonstrates how IV analysis arises from an analogous but potentially impossible RCT design, and outlines the assumptions necessary for valid estimation. It gives examples of instruments used in clinical epidemiology and concludes with an outline on estimation of effects. PMID:19356901

A KPI-based process monitoring and fault detection framework for large-scale processes.

PubMed

Zhang, Kai; Shardt, Yuri A W; Chen, Zhiwen; Yang, Xu; Ding, Steven X; Peng, Kaixiang

2017-05-01

Large-scale processes, consisting of multiple interconnected subprocesses, are commonly encountered in industrial systems, whose performance needs to be determined. A common approach to this problem is to use a key performance indicator (KPI)-based approach. However, the different KPI-based approaches are not developed with a coherent and consistent framework. Thus, this paper proposes a framework for KPI-based process monitoring and fault detection (PM-FD) for large-scale industrial processes, which considers the static and dynamic relationships between process and KPI variables. For the static case, a least squares-based approach is developed that provides an explicit link with least-squares regression, which gives better performance than partial least squares. For the dynamic case, using the kernel representation of each subprocess, an instrument variable is used to reduce the dynamic case to the static case. This framework is applied to the TE benchmark process and the hot strip mill rolling process. The results show that the proposed method can detect faults better than previous methods. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Back pain prevalence and associated factors in children and adolescents: an epidemiological population study

PubMed Central

Noll, Matias; Candotti, Cláudia Tarragô; da Rosa, Bruna Nichele; Loss, Jefferson Fagundes

2016-01-01

ABSTRACT OBJECTIVE To identify the prevalence of back pain among Brazilian school children and the factors associated with this pain. METHODS All 1,720 schoolchildren from the fifth to the eight grade attending schools from the city of Teutonia, RS, Southern Brazil, were invited to participate in the study. From these, 1,597 children participated. We applied the Back Pain and Body Posture Evaluation Instrument. The dependent variable was back pain, while the independent one were demographic, socioeconomic, behavior and heredity data. The prevalence ratio was estimated by multivariate analysis using the Poisson regression model (α = 0.05). RESULTS The prevalence of back pain in the last three months was 55.7% (n = 802). The multivariate analysis showed that back pain is associated with the variables: sex, parents with back pain, weekly frequency of physical activity, daily time spent watching television, studying in bed, sitting posture to write and use the computer, and way of carrying the backpack. CONCLUSIONS The prevalence of back pain in schoolchildren is high and it is associated with demographic, behavior and heredity aspects. PMID:27305406

Frailty measurements and dysphagia in the outpatient setting.

PubMed

Hathaway, Bridget; Vaezi, Alec; Egloff, Ann Marie; Smith, Libby; Wasserman-Wincko, Tamara; Johnson, Jonas T

2014-09-01

Deconditioning and frailty may contribute to dysphagia and aspiration. Early identification of patients at risk of aspiration is important. Aspiration prevention would lead to reduced morbidity and health care costs. We therefore wondered whether objective measurements of frailty could help identify patients at risk for dysphagia and aspiration. Consecutive patients (n = 183) were enrolled. Patient characteristics and objective measures of frailty were recorded prospectively. Variables tested included age, body mass index, grip strength, and 5 meter walk pace. Statistical analysis tested for association between these parameters and dysphagia or aspiration, diagnosed by instrumental swallowing examination. Of variables tested for association with grip strength, only age category (P = .003) and ambulatory status (P < .001) were significantly associated with grip strength in linear regression models. Whereas walk speed was not associated with dysphagia or aspiration, ambulatory status was significantly associated with dysphagia and aspiration in multivariable model building. Nonambulatory status is a predictor of aspiration and should be included in risk assessments for dysphagia. The relationship between frailty and dysphagia deserves further investigation. Frailty assessments may help identify those at risk for complications of dysphagia. © The Author(s) 2014.

Exploration of Individual and Family Factors Related to Community Reintegration in Veterans With Traumatic Brain Injury.

PubMed

Moriarty, Helene; Winter, Laraine; Robinson, Keith; True, Gala; Piersol, Catherine; Vause-Earland, Tracey; Iacovone, Dolores Blazer; Holbert, Laura; Newhart, Brian; Fishman, Deborah; Short, Thomas H

2015-01-01

Community reintegration (CR) poses a major problem for military veterans who have experienced a traumatic brain injury (TBI). Factors contributing to CR after TBI are poorly understood. To address the gap in knowledge, an ecological framework was used to explore individual and family factors related to CR. Baseline data from an intervention study with 83 veterans with primarily mild to moderate TBI were analyzed. Instruments measured CR, depressive symptoms, physical health, quality of the relationship with the family member, and sociodemographics. Posttraumatic stress disorder and TBI characteristics were determined through record review. Five variables that exhibited significant bivariate relationships with CR (veteran rating of quality of relationship, physical functioning, bodily pain, posttraumatic stress disorder diagnosis, and depressive symptoms) were entered into hierarchical regression analysis. In the final analysis, the five variables together accounted for 35% of the variance, but only depression was a significant predictor of CR, with more depressed veterans exhibiting lower CR. Efforts to support CR of Veterans with TBI should carefully assess and target depression, a modifiable factor. © The Author(s) 2015.

Using multilevel modeling to assess case-mix adjusters in consumer experience surveys in health care.

PubMed

Damman, Olga C; Stubbe, Janine H; Hendriks, Michelle; Arah, Onyebuchi A; Spreeuwenberg, Peter; Delnoij, Diana M J; Groenewegen, Peter P

2009-04-01

Ratings on the quality of healthcare from the consumer's perspective need to be adjusted for consumer characteristics to ensure fair and accurate comparisons between healthcare providers or health plans. Although multilevel analysis is already considered an appropriate method for analyzing healthcare performance data, it has rarely been used to assess case-mix adjustment of such data. The purpose of this article is to investigate whether multilevel regression analysis is a useful tool to detect case-mix adjusters in consumer assessment of healthcare. We used data on 11,539 consumers from 27 Dutch health plans, which were collected using the Dutch Consumer Quality Index health plan instrument. We conducted multilevel regression analyses of consumers' responses nested within health plans to assess the effects of consumer characteristics on consumer experience. We compared our findings to the results of another methodology: the impact factor approach, which combines the predictive effect of each case-mix variable with its heterogeneity across health plans. Both multilevel regression and impact factor analyses showed that age and education were the most important case-mix adjusters for consumer experience and ratings of health plans. With the exception of age, case-mix adjustment had little impact on the ranking of health plans. On both theoretical and practical grounds, multilevel modeling is useful for adequate case-mix adjustment and analysis of performance ratings.

The social and community opportunities profile social inclusion measure: Structural equivalence and differential item functioning in community mental health residents in Hong Kong and the United Kingdom.

PubMed

Huxley, Peter John; Chan, Kara; Chiu, Marcus; Ma, Yanni; Gaze, Sarah; Evans, Sherrill

2016-03-01

China's future major health problem will be the management of chronic diseases - of which mental health is a major one. An instrument is needed to measure mental health inclusion outcomes for mental health services in Hong Kong and mainland China as they strive to promote a more inclusive society for their citizens and particular disadvantaged groups. To report on the analysis of structural equivalence and item differentiation in two mentally unhealthy and one healthy sample in the United Kingdom and Hong Kong. The mental health sample in Hong Kong was made up of non-governmental organisation (NGO) referrals meeting the selection/exclusion criteria (being well enough to be interviewed, having a formal psychiatric diagnosis and living in the community). A similar sample in the United Kingdom meeting the same selection criteria was obtained from a community mental health organisation, equivalent to the NGOs in Hong Kong. Exploratory factor analysis and logistic regression were conducted. The single-variable, self-rated 'overall social inclusion' differs significantly between all of the samples, in the way we would expect from previous research, with the healthy population feeling more included than the serious mental illness (SMI) groups. In the exploratory factor analysis, the first two factors explain between a third and half of the variance, and the single variable which enters into all the analyses in the first factor is having friends to visit the home. All the regression models were significant; however, in Hong Kong sample, only one-fifth of the total variance is explained. The structural findings imply that the social and community opportunities profile-Chinese version (SCOPE-C) gives similar results when applied to another culture. As only one-fifth of the variance of 'overall inclusion' was explained in the Hong Kong sample, it may be that the instrument needs to be refined using different or additional items within the structural domains of inclusion. © The Author(s) 2015.

Invited Commentary: Using Financial Credits as Instrumental Variables for Estimating the Causal Relationship Between Income and Health.

PubMed

Pega, Frank

2016-05-01

Social epidemiologists are interested in determining the causal relationship between income and health. Natural experiments in which individuals or groups receive income randomly or quasi-randomly from financial credits (e.g., tax credits or cash transfers) are increasingly being analyzed using instrumental variable analysis. For example, in this issue of the Journal, Hamad and Rehkopf (Am J Epidemiol. 2016;183(9):775-784) used an in-work tax credit called the Earned Income Tax Credit as an instrument to estimate the association between income and child development. However, under certain conditions, the use of financial credits as instruments could violate 2 key instrumental variable analytic assumptions. First, some financial credits may directly influence health, for example, through increasing a psychological sense of welfare security. Second, financial credits and health may have several unmeasured common causes, such as politics, other social policies, and the motivation to maximize the credit. If epidemiologists pursue such instrumental variable analyses, using the amount of an unconditional, universal credit that an individual or group has received as the instrument may produce the most conceptually convincing and generalizable evidence. However, other natural income experiments (e.g., lottery winnings) and other methods that allow better adjustment for confounding might be more promising approaches for estimating the causal relationship between income and health. © The Author 2016. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

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

Treesearch

L.R. Grosenbaugh

1967-01-01

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

Implementations of geographically weighted lasso in spatial data with multicollinearity (Case study: Poverty modeling of Java Island)

NASA Astrophysics Data System (ADS)

Setiyorini, Anis; Suprijadi, Jadi; Handoko, Budhi

2017-03-01

Geographically Weighted Regression (GWR) is a regression model that takes into account the spatial heterogeneity effect. In the application of the GWR, inference on regression coefficients is often of interest, as is estimation and prediction of the response variable. Empirical research and studies have demonstrated that local correlation between explanatory variables can lead to estimated regression coefficients in GWR that are strongly correlated, a condition named multicollinearity. It later results on a large standard error on estimated regression coefficients, and, hence, problematic for inference on relationships between variables. Geographically Weighted Lasso (GWL) is a method which capable to deal with spatial heterogeneity and local multicollinearity in spatial data sets. GWL is a further development of GWR method, which adds a LASSO (Least Absolute Shrinkage and Selection Operator) constraint in parameter estimation. In this study, GWL will be applied by using fixed exponential kernel weights matrix to establish a poverty modeling of Java Island, Indonesia. The results of applying the GWL to poverty datasets show that this method stabilizes regression coefficients in the presence of multicollinearity and produces lower prediction and estimation error of the response variable than GWR does.

Effect of climatic variability on malaria trends in Baringo County, Kenya.

PubMed

Kipruto, Edwin K; Ochieng, Alfred O; Anyona, Douglas N; Mbalanya, Macrae; Mutua, Edna N; Onguru, Daniel; Nyamongo, Isaac K; Estambale, Benson B A

2017-05-25

Malaria transmission in arid and semi-arid regions of Kenya such as Baringo County, is seasonal and often influenced by climatic factors. Unravelling the relationship between climate variables and malaria transmission dynamics is therefore instrumental in developing effective malaria control strategies. The main aim of this study was to describe the effects of variability of rainfall, maximum temperature and vegetation indices on seasonal trends of malaria in selected health facilities within Baringo County, Kenya. Climate variables sourced from the International Research Institute (IRI)/Lamont-Doherty Earth Observatory (LDEO) climate database and malaria cases reported in 10 health facilities spread across four ecological zones (riverine, lowland, mid-altitude and highland) between 2004 and 2014 were subjected to a time series analysis. A negative binomial regression model with lagged climate variables was used to model long-term monthly malaria cases. The seasonal Mann-Kendall trend test was then used to detect overall monotonic trends in malaria cases. Malaria cases increased significantly in the highland and midland zones over the study period. Changes in malaria prevalence corresponded to variations in rainfall and maximum temperature. Rainfall at a time lag of 2 months resulted in an increase in malaria transmission across the four zones while an increase in temperature at time lags of 0 and 1 month resulted in an increase in malaria cases in the riverine and highland zones, respectively. Given the existence of a time lag between climatic variables more so rainfall and peak malaria transmission, appropriate control measures can be initiated at the onset of short and after long rains seasons.

New approach to probability estimate of femoral neck fracture by fall (Slovak regression model).

PubMed

Wendlova, J

2009-01-01

3,216 Slovak women with primary or secondary osteoporosis or osteopenia, aged 20-89 years, were examined with the bone densitometer DXA (dual energy X-ray absorptiometry, GE, Prodigy - Primo), x = 58.9, 95% C.I. (58.42; 59.38). The values of the following variables for each patient were measured: FSI (femur strength index), T-score total hip left, alpha angle - left, theta angle - left, HAL (hip axis length) left, BMI (body mass index) was calculated from the height and weight of the patients. Regression model determined the following order of independent variables according to the intensity of their influence upon the occurrence of values of dependent FSI variable: 1. BMI, 2. theta angle, 3. T-score total hip, 4. alpha angle, 5. HAL. The regression model equation, calculated from the variables monitored in the study, enables a doctor in praxis to determine the probability magnitude (absolute risk) for the occurrence of pathological value of FSI (FSI < 1) in the femoral neck area, i. e., allows for probability estimate of a femoral neck fracture by fall for Slovak women. 1. The Slovak regression model differs from regression models, published until now, in chosen independent variables and a dependent variable, belonging to biomechanical variables, characterising the bone quality. 2. The Slovak regression model excludes the inaccuracies of other models, which are not able to define precisely the current and past clinical condition of tested patients (e.g., to define the length and dose of exposure to risk factors). 3. The Slovak regression model opens the way to a new method of estimating the probability (absolute risk) or the odds for a femoral neck fracture by fall, based upon the bone quality determination. 4. It is assumed that the development will proceed by improving the methods enabling to measure the bone quality, determining the probability of fracture by fall (Tab. 6, Fig. 3, Ref. 22). Full Text (Free, PDF) www.bmj.sk.

Assessing landslide susceptibility by statistical data analysis and GIS: the case of Daunia (Apulian Apennines, Italy)

NASA Astrophysics Data System (ADS)

Ceppi, C.; Mancini, F.; Ritrovato, G.

2009-04-01

This study aim at the landslide susceptibility mapping within an area of the Daunia (Apulian Apennines, Italy) by a multivariate statistical method and data manipulation in a Geographical Information System (GIS) environment. Among the variety of existing statistical data analysis techniques, the logistic regression was chosen to produce a susceptibility map all over an area where small settlements are historically threatened by landslide phenomena. By logistic regression a best fitting between the presence or absence of landslide (dependent variable) and the set of independent variables is performed on the basis of a maximum likelihood criterion, bringing to the estimation of regression coefficients. The reliability of such analysis is therefore due to the ability to quantify the proneness to landslide occurrences by the probability level produced by the analysis. The inventory of dependent and independent variables were managed in a GIS, where geometric properties and attributes have been translated into raster cells in order to proceed with the logistic regression by means of SPSS (Statistical Package for the Social Sciences) package. A landslide inventory was used to produce the bivariate dependent variable whereas the independent set of variable concerned with slope, aspect, elevation, curvature, drained area, lithology and land use after their reductions to dummy variables. The effect of independent parameters on landslide occurrence was assessed by the corresponding coefficient in the logistic regression function, highlighting a major role played by the land use variable in determining occurrence and distribution of phenomena. Once the outcomes of the logistic regression are determined, data are re-introduced in the GIS to produce a map reporting the proneness to landslide as predicted level of probability. As validation of results and regression model a cell-by-cell comparison between the susceptibility map and the initial inventory of landslide events was performed and an agreement at 75% level achieved.

An Instrumental Variable Probit (IVP) analysis on depressed mood in Korea: the impact of gender differences and other socio-economic factors.

PubMed

Gitto, Lara; Noh, Yong-Hwan; Andrés, Antonio Rodríguez

2015-04-16

Depression is a mental health state whose frequency has been increasing in modern societies. It imposes a great burden, because of the strong impact on people's quality of life and happiness. Depression can be reliably diagnosed and treated in primary care: if more people could get effective treatments earlier, the costs related to depression would be reversed. The aim of this study was to examine the influence of socio-economic factors and gender on depressed mood, focusing on Korea. In fact, in spite of the great amount of empirical studies carried out for other countries, few epidemiological studies have examined the socio-economic determinants of depression in Korea and they were either limited to samples of employed women or did not control for individual health status. Moreover, as the likely data endogeneity (i.e. the possibility of correlation between the dependent variable and the error term as a result of autocorrelation or simultaneity, such as, in this case, the depressed mood due to health factors that, in turn might be caused by depression), might bias the results, the present study proposes an empirical approach, based on instrumental variables, to deal with this problem. Data for the year 2008 from the Korea National Health and Nutrition Examination Survey (KNHANES) were employed. About seven thousands of people (N= 6,751, of which 43% were males and 57% females), aged from 19 to 75 years old, were included in the sample considered in the analysis. In order to take into account the possible endogeneity of some explanatory variables, two Instrumental Variables Probit (IVP) regressions were estimated; the variables for which instrumental equations were estimated were related to the participation of women to the workforce and to good health, as reported by people in the sample. Explanatory variables were related to age, gender, family factors (such as the number of family members and marital status) and socio-economic factors (such as education, residence in metropolitan areas, and so on). As the results of the Wald test carried out after the estimations did not allow to reject the null hypothesis of endogeneity, a probit model was run too. Overall, women tend to develop depression more frequently than men. There is an inverse effect of education on depressed mood (probability of -24.6% to report a depressed mood due to high school education, as it emerges from the probit model marginal effects), while marital status and the number of family members may act as protective factors (probability to report a depressed mood of -1.0% for each family member). Depression is significantly associated with socio-economic conditions, such as work and income. Living in metropolitan areas is inversely correlated with depression (probability of -4.1% to report a depressed mood estimated through the probit model): this could be explained considering that, in rural areas, people rarely have immediate access to high-quality health services. This study outlines the factors that are more likely to impact on depression, and applies an IVP model to take into account the potential endogeneity of some of the predictors of depressive mood, such as female participation to workforce and health status. A probit model has been estimated too. Depression is associated with a wide range of socio-economic factors, although the strength and direction of the association can differ by gender. Prevention approaches to contrast depressive symptoms might take into consideration the evidence offered by the present study. © 2015 by Kerman University of Medical Sciences.

An Instrumental Variable Probit (IVP) analysis on depressed mood in Korea: the impact of gender differences and other socio-economic factors

PubMed Central

Gitto, Lara; Noh, Yong-Hwan; Andrés, Antonio Rodríguez

2015-01-01

Background: Depression is a mental health state whose frequency has been increasing in modern societies. It imposes a great burden, because of the strong impact on people’s quality of life and happiness. Depression can be reliably diagnosed and treated in primary care: if more people could get effective treatments earlier, the costs related to depression would be reversed. The aim of this study was to examine the influence of socio-economic factors and gender on depressed mood, focusing on Korea. In fact, in spite of the great amount of empirical studies carried out for other countries, few epidemiological studies have examined the socio-economic determinants of depression in Korea and they were either limited to samples of employed women or did not control for individual health status. Moreover, as the likely data endogeneity (i.e. the possibility of correlation between the dependent variable and the error term as a result of autocorrelation or simultaneity, such as, in this case, the depressed mood due to health factors that, in turn might be caused by depression), might bias the results, the present study proposes an empirical approach, based on instrumental variables, to deal with this problem. Methods: Data for the year 2008 from the Korea National Health and Nutrition Examination Survey (KNHANES) were employed. About seven thousands of people (N= 6,751, of which 43% were males and 57% females), aged from 19 to 75 years old, were included in the sample considered in the analysis. In order to take into account the possible endogeneity of some explanatory variables, two Instrumental Variables Probit (IVP) regressions were estimated; the variables for which instrumental equations were estimated were related to the participation of women to the workforce and to good health, as reported by people in the sample. Explanatory variables were related to age, gender, family factors (such as the number of family members and marital status) and socio-economic factors (such as education, residence in metropolitan areas, and so on). As the results of the Wald test carried out after the estimations did not allow to reject the null hypothesis of endogeneity, a probit model was run too. Results: Overall, women tend to develop depression more frequently than men. There is an inverse effect of education on depressed mood (probability of -24.6% to report a depressed mood due to high school education, as it emerges from the probit model marginal effects), while marital status and the number of family members may act as protective factors (probability to report a depressed mood of -1.0% for each family member). Depression is significantly associated with socio-economic conditions, such as work and income. Living in metropolitan areas is inversely correlated with depression (probability of -4.1% to report a depressed mood estimated through the probit model): this could be explained considering that, in rural areas, people rarely have immediate access to high-quality health services. Conclusion: This study outlines the factors that are more likely to impact on depression, and applies an IVP model to take into account the potential endogeneity of some of the predictors of depressive mood, such as female participation to workforce and health status. A probit model has been estimated too. Depression is associated with a wide range of socio-economic factors, although the strength and direction of the association can differ by gender. Prevention approaches to contrast depressive symptoms might take into consideration the evidence offered by the present study. PMID:26340392

Effectiveness of Psychological and/or Educational Interventions in the Prevention of Anxiety: A Systematic Review, Meta-analysis, and Meta-regression.

PubMed

Moreno-Peral, Patricia; Conejo-Cerón, Sonia; Rubio-Valera, Maria; Fernández, Anna; Navas-Campaña, Desirée; Rodríguez-Morejón, Alberto; Motrico, Emma; Rigabert, Alina; Luna, Juan de Dios; Martín-Pérez, Carlos; Rodríguez-Bayón, Antonina; Ballesta-Rodríguez, María Isabel; Luciano, Juan Vicente; Bellón, Juan Ángel

2017-10-01

To our knowledge, no systematic reviews or meta-analyses have been conducted to assess the effectiveness of preventive psychological and/or educational interventions for anxiety in varied populations. To evaluate the effectiveness of preventive psychological and/or educational interventions for anxiety in varied population types. A systematic review and meta-analysis was conducted based on literature searches of MEDLINE, PsycINFO, Web of Science, EMBASE, OpenGrey, Cochrane Central Register of Controlled Trials, and other sources from inception to March 7, 2017. A search was performed of randomized clinical trials assessing the effectiveness of preventive psychological and/or educational interventions for anxiety in varying populations free of anxiety at baseline as measured using validated instruments. There was no setting or language restriction. Eligibility criteria assessment was conducted by 2 of us. Data extraction and assessment of risk of bias (Cochrane Collaboration's tool) were performed by 2 of us. Pooled standardized mean differences (SMDs) were calculated using random-effect models. Heterogeneity was explored by random-effects meta-regression. Incidence of new cases of anxiety disorders or reduction of anxiety symptoms as measured by validated instruments. Of the 3273 abstracts reviewed, 131 were selected for full-text review, and 29 met the inclusion criteria, representing 10 430 patients from 11 countries on 4 continents. Meta-analysis calculations were based on 36 comparisons. The pooled SMD was -0.31 (95% CI, -0.40 to -0.21; P < .001) and heterogeneity was substantial (I2 = 61.1%; 95% CI, 44% to 73%). There was evidence of publication bias, but the effect size barely varied after adjustment (SMD, -0.27; 95% CI, -0.37 to -0.17; P < .001). Sensitivity analyses confirmed the robustness of effect size results. A meta-regression including 5 variables explained 99.6% of between-study variability, revealing an association between higher SMD, waiting list (comparator) (β = -0.33 [95% CI, -0.55 to -0.11]; P = .005) and a lower sample size (lg) (β = 0.15 [95% CI, 0.06 to 0.23]; P = .001). No association was observed with risk of bias, family physician providing intervention, and use of standardized interviews as outcomes. Psychological and/or educational interventions had a small but statistically significant benefit for anxiety prevention in all populations evaluated. Although more studies with larger samples and active comparators are needed, these findings suggest that anxiety prevention programs should be further developed and implemented.

Data Combination and Instrumental Variables in Linear Models

ERIC Educational Resources Information Center

Khawand, Christopher

2012-01-01

Instrumental variables (IV) methods allow for consistent estimation of causal effects, but suffer from poor finite-sample properties and data availability constraints. IV estimates also tend to have relatively large standard errors, often inhibiting the interpretability of differences between IV and non-IV point estimates. Lastly, instrumental…

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.

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

PubMed

Slinker, B K; Glantz, S A

1985-07-01

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

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.

Generic Feature Selection with Short Fat Data

PubMed Central

Clarke, B.; Chu, J.-H.

2014-01-01

SUMMARY Consider a regression problem in which there are many more explanatory variables than data points, i.e., p ≫ n. Essentially, without reducing the number of variables inference is impossible. So, we group the p explanatory variables into blocks by clustering, evaluate statistics on the blocks and then regress the response on these statistics under a penalized error criterion to obtain estimates of the regression coefficients. We examine the performance of this approach for a variety of choices of n, p, classes of statistics, clustering algorithms, penalty terms, and data types. When n is not large, the discrimination over number of statistics is weak, but computations suggest regressing on approximately [n/K] statistics where K is the number of blocks formed by a clustering algorithm. Small deviations from this are observed when the blocks of variables are of very different sizes. Larger deviations are observed when the penalty term is an Lq norm with high enough q. PMID:25346546

Factor analysis and multiple regression between topography and precipitation on Jeju Island, Korea

NASA Astrophysics Data System (ADS)

Um, Myoung-Jin; Yun, Hyeseon; Jeong, Chang-Sam; Heo, Jun-Haeng

2011-11-01

SummaryIn this study, new factors that influence precipitation were extracted from geographic variables using factor analysis, which allow for an accurate estimation of orographic precipitation. Correlation analysis was also used to examine the relationship between nine topographic variables from digital elevation models (DEMs) and the precipitation in Jeju Island. In addition, a spatial analysis was performed in order to verify the validity of the regression model. From the results of the correlation analysis, it was found that all of the topographic variables had a positive correlation with the precipitation. The relations between the variables also changed in accordance with a change in the precipitation duration. However, upon examining the correlation matrix, no significant relationship between the latitude and the aspect was found. According to the factor analysis, eight topographic variables (latitude being the exception) were found to have a direct influence on the precipitation. Three factors were then extracted from the eight topographic variables. By directly comparing the multiple regression model with the factors (model 1) to the multiple regression model with the topographic variables (model 3), it was found that model 1 did not violate the limits of statistical significance and multicollinearity. As such, model 1 was considered to be appropriate for estimating the precipitation when taking into account the topography. In the study of model 1, the multiple regression model using factor analysis was found to be the best method for estimating the orographic precipitation on Jeju Island.

Critical evaluation of connectivity-based point of care testing systems of glucose in a hospital environment.

PubMed

Floré, Katelijne M J; Fiers, Tom; Delanghe, Joris R

2008-01-01

In recent years a number of point of care testing (POCT) glucometers were introduced on the market. We investigated the analytical variability (lot-to-lot variation, calibration error, inter-instrument and inter-operator variability) of glucose POCT systems in a university hospital environment and compared these results with the analytical needs required for tight glucose monitoring. The reference hexokinase method was compared to different POCT systems based on glucose oxidase (blood gas instruments) or glucose dehydrogenase (handheld glucometers). Based upon daily internal quality control data, total errors were calculated for the various glucose methods and the analytical variability of the glucometers was estimated. The total error of the glucometers exceeded by far the desirable analytical specifications (based on a biological variability model). Lot-to-lot variation, inter-instrument variation and inter-operator variability contributed approximately equally to total variance. As in a hospital environment, distribution of hematocrit values is broad, converting blood glucose into plasma values using a fixed factor further increases variance. The percentage of outliers exceeded the ISO 15197 criteria in a broad glucose concentration range. Total analytical variation of handheld glucometers is larger than expected. Clinicians should be aware that the variability of glucose measurements obtained by blood gas instruments is lower than results obtained with handheld glucometers on capillary blood.

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.

Quantile regression models of animal habitat relationships

USGS Publications Warehouse

Cade, Brian S.

2003-01-01

Typically, all factors that limit an organism are not measured and included in statistical models used to investigate relationships with their environment. If important unmeasured variables interact multiplicatively with the measured variables, the statistical models often will have heterogeneous response distributions with unequal variances. Quantile regression is an approach for estimating the conditional quantiles of a response variable distribution in the linear model, providing a more complete view of possible causal relationships between variables in ecological processes. Chapter 1 introduces quantile regression and discusses the ordering characteristics, interval nature, sampling variation, weighting, and interpretation of estimates for homogeneous and heterogeneous regression models. Chapter 2 evaluates performance of quantile rankscore tests used for hypothesis testing and constructing confidence intervals for linear quantile regression estimates (0 ≤ τ ≤ 1). A permutation F test maintained better Type I errors than the Chi-square T test for models with smaller n, greater number of parameters p, and more extreme quantiles τ. Both versions of the test required weighting to maintain correct Type I errors when there was heterogeneity under the alternative model. An example application related trout densities to stream channel width:depth. Chapter 3 evaluates a drop in dispersion, F-ratio like permutation test for hypothesis testing and constructing confidence intervals for linear quantile regression estimates (0 ≤ τ ≤ 1). Chapter 4 simulates from a large (N = 10,000) finite population representing grid areas on a landscape to demonstrate various forms of hidden bias that might occur when the effect of a measured habitat variable on some animal was confounded with the effect of another unmeasured variable (spatially and not spatially structured). Depending on whether interactions of the measured habitat and unmeasured variable were negative (interference interactions) or positive (facilitation interactions), either upper (τ > 0.5) or lower (τ < 0.5) quantile regression parameters were less biased than mean rate parameters. Sampling (n = 20 - 300) simulations demonstrated that confidence intervals constructed by inverting rankscore tests provided valid coverage of these biased parameters. Quantile regression was used to estimate effects of physical habitat resources on a bivalve mussel (Macomona liliana) in a New Zealand harbor by modeling the spatial trend surface as a cubic polynomial of location coordinates.

Development of cultural belief scales for mammography screening.

PubMed

Russell, Kathleen M; Champion, Victoria L; Perkins, Susan M

2003-01-01

To develop instruments to measure culturally related variables that may influence mammography screening behaviors in African American women. Instrumentation methodology. Community organizations and public housing in the Indianapolis, IN, area. 111 African American women with a mean age of 60.2 years and 64 Caucasian women with a mean age of 60 years. After item development, scales were administered. Data were analyzed by factor analysis, item analysis via internal consistency reliability using Cronbach's alpha, and independent t tests and logistic regression analysis to test theoretical relationships. Personal space preferences, health temporal orientation, and perceived personal control. Space items were factored into interpersonal and physical scales. Temporal orientation items were loaded on one factor, creating a one-dimensional scale. Control items were factored into internal and external control scales. Cronbach's alpha coefficients for the scales ranged from 0.76-0.88. Interpersonal space preference, health temporal orientation, and perceived internal control scales each were predictive of mammography screening adherence. The three tested scales were reliable and valid. Scales, on average, did not differ between African American and Caucasian populations. These scales may be useful in future investigations aimed at increasing mammography screening in African American and Caucasian women.

Risk factors for low birth weight according to the multiple logistic regression model. A retrospective cohort study in José María Morelos municipality, Quintana Roo, Mexico.

PubMed

Franco Monsreal, José; Tun Cobos, Miriam Del Ruby; Hernández Gómez, José Ricardo; Serralta Peraza, Lidia Esther Del Socorro

2018-01-17

Low birth weight has been an enigma for science over time. There have been many researches on its causes and its effects. Low birth weight is an indicator that predicts the probability of a child surviving. In fact, there is an exponential relationship between weight deficit, gestational age, and perinatal mortality. Multiple logistic regression is one of the most expressive and versatile statistical instruments available for the analysis of data in both clinical and epidemiology settings, as well as in public health. To assess in a multivariate fashion the importance of 17 independent variables in low birth weight (dependent variable) of children born in the Mayan municipality of José María Morelos, Quintana Roo, Mexico. Analytical observational epidemiological cohort study with retrospective temporality. Births that met the inclusion criteria occurred in the "Hospital Integral Jose Maria Morelos" of the Ministry of Health corresponding to the Maya municipality of Jose Maria Morelos during the period from August 1, 2014 to July 31, 2015. The total number of newborns recorded was 1,147; 84 of which (7.32%) had low birth weight. To estimate the independent association between the explanatory variables (potential risk factors) and the response variable, a multiple logistic regression analysis was performed using the IBM SPSS Statistics 22 software. In ascending numerical order values of odds ratio > 1 indicated the positive contribution of explanatory variables or possible risk factors: "unmarried" marital status (1.076, 95% confidence interval: 0.550 to 2.104); age at menarche ≤ 12 years (1.08, 95% confidence interval: 0.64 to 1.84); history of abortion(s) (1.14, 95% confidence interval: 0.44 to 2.93); maternal weight < 50 kg (1.51, 95% confidence interval: 0.83 to 2.76); number of prenatal consultations ≤ 5 (1.86, 95% confidence interval: 0.94 to 3.66); maternal age ≥ 36 years (3.5, 95% confidence interval: 0.40 to 30.47); maternal age ≤ 19 years (3.59, 95% confidence interval: 0.43 to 29.87); number of deliveries = 1 (3.86, 95% confidence interval: 0.33 to 44.85); personal pathological history (4.78, 95% confidence interval: 2.16 to 10.59); pathological obstetric history (5.01, 95% confidence interval: 1.66 to 15.18); maternal height < 150 cm (5.16, 95% confidence interval: 3.08 to 8.65); number of births ≥ 5 (5.99, 95% confidence interval: 0.51 to 69.99); and smoking (15.63, 95% confidence interval: 1.07 to 227.97). Four of the independent variables (personal pathological history, obstetric pathological history, maternal stature <150 centimeters and smoking) showed a significant positive contribution, thus they can be considered as clear risk factors for low birth weight. The use of the logistic regression model in the Mayan municipality of José María Morelos, will allow estimating the probability of low birth weight for each pregnant woman in the future, which will be useful for the health authorities of the region.

Panel regressions to estimate low-flow response to rainfall variability in ungaged basins

USGS Publications Warehouse

Bassiouni, Maoya; Vogel, Richard M.; Archfield, Stacey A.

2016-01-01

Multicollinearity and omitted-variable bias are major limitations to developing multiple linear regression models to estimate streamflow characteristics in ungaged areas and varying rainfall conditions. Panel regression is used to overcome limitations of traditional regression methods, and obtain reliable model coefficients, in particular to understand the elasticity of streamflow to rainfall. Using annual rainfall and selected basin characteristics at 86 gaged streams in the Hawaiian Islands, regional regression models for three stream classes were developed to estimate the annual low-flow duration discharges. Three panel-regression structures (random effects, fixed effects, and pooled) were compared to traditional regression methods, in which space is substituted for time. Results indicated that panel regression generally was able to reproduce the temporal behavior of streamflow and reduce the standard errors of model coefficients compared to traditional regression, even for models in which the unobserved heterogeneity between streams is significant and the variance inflation factor for rainfall is much greater than 10. This is because both spatial and temporal variability were better characterized in panel regression. In a case study, regional rainfall elasticities estimated from panel regressions were applied to ungaged basins on Maui, using available rainfall projections to estimate plausible changes in surface-water availability and usable stream habitat for native species. The presented panel-regression framework is shown to offer benefits over existing traditional hydrologic regression methods for developing robust regional relations to investigate streamflow response in a changing climate.

Panel regressions to estimate low-flow response to rainfall variability in ungaged basins

NASA Astrophysics Data System (ADS)

Bassiouni, Maoya; Vogel, Richard M.; Archfield, Stacey A.

2016-12-01

Multicollinearity and omitted-variable bias are major limitations to developing multiple linear regression models to estimate streamflow characteristics in ungaged areas and varying rainfall conditions. Panel regression is used to overcome limitations of traditional regression methods, and obtain reliable model coefficients, in particular to understand the elasticity of streamflow to rainfall. Using annual rainfall and selected basin characteristics at 86 gaged streams in the Hawaiian Islands, regional regression models for three stream classes were developed to estimate the annual low-flow duration discharges. Three panel-regression structures (random effects, fixed effects, and pooled) were compared to traditional regression methods, in which space is substituted for time. Results indicated that panel regression generally was able to reproduce the temporal behavior of streamflow and reduce the standard errors of model coefficients compared to traditional regression, even for models in which the unobserved heterogeneity between streams is significant and the variance inflation factor for rainfall is much greater than 10. This is because both spatial and temporal variability were better characterized in panel regression. In a case study, regional rainfall elasticities estimated from panel regressions were applied to ungaged basins on Maui, using available rainfall projections to estimate plausible changes in surface-water availability and usable stream habitat for native species. The presented panel-regression framework is shown to offer benefits over existing traditional hydrologic regression methods for developing robust regional relations to investigate streamflow response in a changing climate.

Quality of life in adults with Gilles de la Tourette Syndrome

PubMed Central

2012-01-01

Background Few studies have used standardized QOL instruments to assess the quality of life (QOL) in Gilles de la Tourette Syndrome (GTS) patients. This work investigates the QOL of adult GTS patients and examines the relationships between physical and psychological variables and QOL. Methods Epidemiological investigation by anonymous national postal survey of QOL of patients of the French Association of Gilles de la Tourette Syndrome (AFGTS) aged 16 years or older. The clinical and QOL measures were collected by four questionnaires: a sociodemographic and GTS-related symptoms questionnaire, the World Health Organization Quality Of Life questionnaire (WHOQOL-26), the Functional Status Questionnaire (FSQ), and a self-rating questionnaire on psychiatric symptoms (SCL-90), all validated in French. We used stepwise regression analysis to explicitly investigate the relationships between physical and psychological variables and QOL domains in GTS. Results Questionnaires were posted to 303 patients, of whom 167 (55%) completed and returned them. Our results, adjusted for age and gender, show that patients with GTS have a worse QOL than the general healthy population. In particular, the “Depression” psychological variable was a significant predictor of impairment in all WHOQOL-26 domains, psychological but also physical and social. Conclusions The present study demonstrates a strong relationship between QOL in GTS and psychiatric symptoms, in particular those of depression. PMID:22888766

Quality of life in adults with Gilles de la Tourette Syndrome.

PubMed

Jalenques, Isabelle; Galland, Fabienne; Malet, Laurent; Morand, Dominique; Legrand, Guillaume; Auclair, Candy; Hartmann, Andreas; Derost, Philippe; Durif, Franck

2012-08-13

Few studies have used standardized QOL instruments to assess the quality of life (QOL) in Gilles de la Tourette Syndrome (GTS) patients. This work investigates the QOL of adult GTS patients and examines the relationships between physical and psychological variables and QOL. Epidemiological investigation by anonymous national postal survey of QOL of patients of the French Association of Gilles de la Tourette Syndrome (AFGTS) aged 16 years or older. The clinical and QOL measures were collected by four questionnaires: a sociodemographic and GTS-related symptoms questionnaire, the World Health Organization Quality Of Life questionnaire (WHOQOL-26), the Functional Status Questionnaire (FSQ), and a self-rating questionnaire on psychiatric symptoms (SCL-90), all validated in French. We used stepwise regression analysis to explicitly investigate the relationships between physical and psychological variables and QOL domains in GTS. Questionnaires were posted to 303 patients, of whom 167 (55%) completed and returned them. Our results, adjusted for age and gender, show that patients with GTS have a worse QOL than the general healthy population. In particular, the "Depression" psychological variable was a significant predictor of impairment in all WHOQOL-26 domains, psychological but also physical and social. The present study demonstrates a strong relationship between QOL in GTS and psychiatric symptoms, in particular those of depression.

Understanding the drivers of interprofessional collaborative practice among HIV primary care providers and case managers in HIV care programmes.

PubMed

Mavronicolas, Heather A; Laraque, Fabienne; Shankar, Arti; Campbell, Claudia

2017-05-01

Care coordination programmes are an important aspect of HIV management whose success depends largely on HIV primary care provider (PCP) and case manager collaboration. Factors influencing collaboration among HIV PCPs and case managers remain to be studied. The study objective was to test an existing theoretical model of interprofessional collaborative practice and determine which factors play the most important role in facilitating collaboration. A self-administered, anonymous mail survey was sent to HIV PCPs and case managers in New York City. An adapted survey instrument elicited information on demographic, contextual, and perceived social exchange (trustworthiness, role specification, and relationship initiation) characteristics. The dependent variable, perceived interprofessional practice, was constructed from a validated scale. A sequential block wise regression model specifying variable entry order examined the relative importance of each group of factors and of individual variables. The analysis showed that social exchange factors were the dominant drivers of collaboration. Relationship initiation was the most important predictor of interprofessional collaboration. Additional influential factors included organisational leadership support of collaboration, practice settings, and frequency of interprofessional meetings. Addressing factors influencing collaboration among providers will help public health programmes optimally design their structural, hiring, and training strategies to foster effective social exchanges and promote collaborative working relationships.

Associations between perceived neighbourhood environmental attributes and self-reported sitting time in patients with schizophrenia: a pilot study.

PubMed

Vancampfort, Davy; De Hert, Marc; De Herdt, Amber; Soundy, Andrew; Stubbs, Brendon; Bernard, Paquito; Probst, Michel

2014-01-30

Sitting behaviours may, independent of physical activity behaviours, be a distinct risk factor for multiple adverse health outcomes in patients with schizophrenia. In order to combat sitting behaviours health care providers and policy makers require further understanding of its determinants in this population group. The aim of the present study was to investigate the variance in sitting time explained by a wide range of community design and recreational environmental variables, above and beyond the variance accounted for by demographic variables. One hundred and twenty-three patients (42♀) with schizophrenia (mean age=41.5 ± 12.6 years) were included in the final analysis. The built environment was rated using the Instruments for Assessing Levels of Physical Activity and Fitness environmental questionnaire and sitting time was assessed using the International Physical Activity Questionnaire-short (IPAQ) version. Regression analysis showed that environmental variables were related to sitting time. The body mass index (BMI) and disease stage explained 8.4% of the variance in sitting, while environmental correlates explained an additional 16.8%. Clinical practice guidelines should incorporate strategies targeting changes in sitting behaviours, from encouraging environmental changes to the availability of exercise equipment. © 2013 Published by Elsevier Ireland Ltd.

Quality of life among Malaysian mothers with a child with Down syndrome.

PubMed

Geok, Chan Kim; Abdullah, Khatijah Lim; Kee, Ling How

2013-08-01

The purpose of this paper is to examine the quality of life (QOL) among mothers with a child with Down syndrome using The World Health Organization Quality of Life scale instrument. A convenience sample of 161 mothers was accessed through the various institutions which provide interventional or educational programmes to children with disabilities within two of the regions of the Borneo State of Malaysia (Sarawak). Nearly half of the group of mothers perceived their QOL as neither poor nor good (n = 73). An overall QOL score of 14.0 ± 1.84 was obtained. The highest and lowest domain scores were found for social relationship domain (Mean = 14.9 ± 2.1) and environmental support domain (Mean = 13.3 ± 2.1) respectively. Correlation analysis of selected background variables (i.e. locality, education, income and marital status) and overall QOL indicated rho (161) = 0.22-0.28 (P < 0.01). Inverse correlation between maternal age and overall QOL score was indicated, with rho (161) = -0.17 (P < 0.05). Linear regression analysis indicated that the combination of these few variables together accounted for 14.5% of the QOL variability in the sample. Findings point to implications for priorities of care provisions by policy-makers and care professionals in their practice. © 2013 Wiley Publishing Asia Pty Ltd.

Clinical utility of the AlphaFIM® instrument in stroke rehabilitation.

PubMed

Lo, Alexander; Tahair, Nicola; Sharp, Shelley; Bayley, Mark T

2012-02-01

The AlphaFIM instrument is an assessment tool designed to facilitate discharge planning of stroke patients from acute care, by extrapolating overall functional status from performance in six key Functional Independence Measure (FIM) instrument items. To determine whether acute care AlphaFIM rating is correlated to stroke rehabilitation outcomes. In this prospective observational study, data were analyzed from 891 patients referred for inpatient stroke rehabilitation through an Internet-based referral system. Simple linear and stepwise regression models determined correlations between rehabilitation-ready AlphaFIM rating and rehabilitation outcomes (admission and discharge FIM ratings, FIM gain, FIM efficiency, and length of stay). Covariates including demographic data, stroke characteristics, medical history, cognitive deficits, and activity tolerance were included in the stepwise regressions. The AlphaFIM instrument was significant in predicting admission and discharge FIM ratings at rehabilitation (adjusted R² 0.40 and 0.28, respectively; P < 0.0001) and was weakly correlated with FIM gain and length of stay (adjusted R² 0.04 and 0.09, respectively; P < 0.0001), but not FIM efficiency. AlphaFIM rating was inversely related to FIM gain. Age, bowel incontinence, left hemiparesis, and previous infarcts were negative predictors of discharge FIM rating on stepwise regression. Intact executive function and physical activity tolerance of 30 to 60 mins were predictors of FIM gain. The AlphaFIM instrument is a valuable tool for triaging stroke patients from acute care to rehabilitation and predicts functional status at discharge from rehabilitation. Patients with low AlphaFIM ratings have the potential to make significant functional gains and should not be denied admission to inpatient rehabilitation programs.

Extreme Ultraviolet Variability Experiment (EVE) Multiple EUV Grating Spectrographs (MEGS): Radiometric Calibrations and Results

NASA Technical Reports Server (NTRS)

Hock, R. A.; Woods, T. N.; Crotser, D.; Eparvier, F. G.; Woodraska, D. L.; Chamberlin, P. C.; Woods, E. C.

2010-01-01

The NASA Solar Dynamics Observatory (SDO), scheduled for launch in early 2010, incorporates a suite of instruments including the Extreme Ultraviolet Variability Experiment (EVE). EVE has multiple instruments including the Multiple Extreme ultraviolet Grating Spectrographs (MEGS) A, B, and P instruments, the Solar Aspect Monitor (SAM), and the Extreme ultraviolet SpectroPhotometer (ESP). The radiometric calibration of EVE, necessary to convert the instrument counts to physical units, was performed at the National Institute of Standards and Technology (NIST) Synchrotron Ultraviolet Radiation Facility (SURF III) located in Gaithersburg, Maryland. This paper presents the results and derived accuracy of this radiometric calibration for the MEGS A, B, P, and SAM instruments, while the calibration of the ESP instrument is addressed by Didkovsky et al. . In addition, solar measurements that were taken on 14 April 2008, during the NASA 36.240 sounding-rocket flight, are shown for the prototype EVE instruments.

CD4 Lymphocyte Enumeration and Hemoglobin Assessment Aid for Priority Decisions: A Multisite Evaluation of the BD FACSPresto™ System

PubMed Central

Thakar, Madhuri; Angira, Francis; Pattanapanyasat, Kovit; Wu, Alan H.B.; O’Gorman, Maurice; Zeng, Hui; Qu, Chenxue; Mahajan, Bharati; Sukapirom, Kasama; Chen, Danying; Hao, Yu; Gong, Yan; Indig, Monika De Arruda; Graminske, Sharon; Orta, Diana; d’Empaire, Nicole; Lu, Beverly; Omana-Zapata, Imelda; Zeh, Clement

2017-01-01

Background: The BD FACSPresto™ system uses capillary and venous blood to measure CD4 absolute counts (CD4), %CD4 in lymphocytes, and hemoglobin (Hb) in approximately 25 minutes. CD4 cell count is used with portable CD4 counters in resource-limited settings to manage HIV/AIDS patients. A method comparison was performed using capillary and venous samples from seven clinical laboratories in five countries. The BD FACSPresto system was assessed for variability between laboratory, instrument/operators, cartridge lots and within-run at four sites. Methods: Samples were collected under approved voluntary consent. EDTA-anticoagulated venous samples were tested for CD4 and %CD4 T cells using the gold-standard BD FACSCalibur™ system, and for Hb, using the Sysmex® KX-21N™ analyzer. Venous and capillary samples were tested on the BD FACSPresto system. Matched data was analyzed for bias (Deming linear regression and Bland-Altman methods), and for concordance around the clinical decision point. The coefficient of variation was estimated per site, instrument/operator, cartridge-lot and between-runs. Results: For method comparison, 93% of the 720 samples were from HIV-positive and 7% from HIV-negative or normal subjects. CD4 and %CD4 T cells venous and capillary results gave slopes within 0.96–1.05 and R2 ≥0.96; Hb slopes were ≥1.00 and R2 ≥0.89. Variability across sites/operators gave %CV <5.8% for CD4 counts, <1.9% for %CD4 and <3.2% for Hb. The total %CV was <7.7% across instrument/cartridge lot. Conclusion: The BD FACSPresto system provides accurate, reliable, precise CD4/%CD4/Hb results compared to gold-standard methods, irrespective of venous or capillary blood sampling. The data showed good agreement between the BD FACSPresto, BD FACSCalibur and Sysmex systems. PMID:29290885

CD4 Lymphocyte Enumeration and Hemoglobin Assessment Aid for Priority Decisions: A Multisite Evaluation of the BD FACSPresto™ System.

PubMed

Thakar, Madhuri; Angira, Francis; Pattanapanyasat, Kovit; Wu, Alan H B; O'Gorman, Maurice; Zeng, Hui; Qu, Chenxue; Mahajan, Bharati; Sukapirom, Kasama; Chen, Danying; Hao, Yu; Gong, Yan; Indig, Monika De Arruda; Graminske, Sharon; Orta, Diana; d'Empaire, Nicole; Lu, Beverly; Omana-Zapata, Imelda; Zeh, Clement

2017-01-01

The BD FACSPresto ™ system uses capillary and venous blood to measure CD4 absolute counts (CD4), %CD4 in lymphocytes, and hemoglobin (Hb) in approximately 25 minutes. CD4 cell count is used with portable CD4 counters in resource-limited settings to manage HIV/AIDS patients. A method comparison was performed using capillary and venous samples from seven clinical laboratories in five countries. The BD FACSPresto system was assessed for variability between laboratory, instrument/operators, cartridge lots and within-run at four sites. Samples were collected under approved voluntary consent. EDTA-anticoagulated venous samples were tested for CD4 and %CD4 T cells using the gold-standard BD FACSCalibur ™ system, and for Hb, using the Sysmex ® KX-21N ™ analyzer. Venous and capillary samples were tested on the BD FACSPresto system. Matched data was analyzed for bias (Deming linear regression and Bland-Altman methods), and for concordance around the clinical decision point. The coefficient of variation was estimated per site, instrument/operator, cartridge-lot and between-runs. For method comparison, 93% of the 720 samples were from HIV-positive and 7% from HIV-negative or normal subjects. CD4 and %CD4 T cells venous and capillary results gave slopes within 0.96-1.05 and R 2 ≥0.96; Hb slopes were ≥1.00 and R 2 ≥0.89. Variability across sites/operators gave %CV <5.8% for CD4 counts, <1.9% for %CD4 and <3.2% for Hb. The total %CV was <7.7% across instrument/cartridge lot. The BD FACSPresto system provides accurate, reliable, precise CD4/%CD4/Hb results compared to gold-standard methods, irrespective of venous or capillary blood sampling. The data showed good agreement between the BD FACSPresto, BD FACSCalibur and Sysmex systems.

Variability in total ozone associated with baroclinic waves

NASA Technical Reports Server (NTRS)

Mote, Philip W.; Holton, James R.; Wallace, John M.

1991-01-01

One-point regression maps of total ozone formed by regressing the time series of bandpass-filtered geopotential height data have been analyzed against Total Ozone Mapping Spectrometer data. Results obtained reveal a strong signature of baroclinic waves in the ozone variability. The regressed patterns are found to be similar in extent and behavior to the relative vorticity patterns reported by Lim and Wallace (1991).

Using Logistic Regression To Predict the Probability of Debris Flows Occurring in Areas Recently Burned By Wildland Fires

USGS Publications Warehouse

Rupert, Michael G.; Cannon, Susan H.; Gartner, Joseph E.

2003-01-01

Logistic regression was used to predict the probability of debris flows occurring in areas recently burned by wildland fires. Multiple logistic regression is conceptually similar to multiple linear regression because statistical relations between one dependent variable and several independent variables are evaluated. In logistic regression, however, the dependent variable is transformed to a binary variable (debris flow did or did not occur), and the actual probability of the debris flow occurring is statistically modeled. Data from 399 basins located within 15 wildland fires that burned during 2000-2002 in Colorado, Idaho, Montana, and New Mexico were evaluated. More than 35 independent variables describing the burn severity, geology, land surface gradient, rainfall, and soil properties were evaluated. The models were developed as follows: (1) Basins that did and did not produce debris flows were delineated from National Elevation Data using a Geographic Information System (GIS). (2) Data describing the burn severity, geology, land surface gradient, rainfall, and soil properties were determined for each basin. These data were then downloaded to a statistics software package for analysis using logistic regression. (3) Relations between the occurrence/non-occurrence of debris flows and burn severity, geology, land surface gradient, rainfall, and soil properties were evaluated and several preliminary multivariate logistic regression models were constructed. All possible combinations of independent variables were evaluated to determine which combination produced the most effective model. The multivariate model that best predicted the occurrence of debris flows was selected. (4) The multivariate logistic regression model was entered into a GIS, and a map showing the probability of debris flows was constructed. The most effective model incorporates the percentage of each basin with slope greater than 30 percent, percentage of land burned at medium and high burn severity in each basin, particle size sorting, average storm intensity (millimeters per hour), soil organic matter content, soil permeability, and soil drainage. The results of this study demonstrate that logistic regression is a valuable tool for predicting the probability of debris flows occurring in recently-burned landscapes.

The effectiveness of manual and mechanical instrumentation for the retreatment of three different root canal filling materials.

PubMed

Somma, Francesco; Cammarota, Giuseppe; Plotino, Gianluca; Grande, Nicola M; Pameijer, Cornelis H

2008-04-01

The aim of this study was to compare the effectiveness of the Mtwo R (Sweden & Martina, Padova, Italy), ProTaper retreatment files (Dentsply-Maillefer, Ballaigues, Switzerland), and a Hedström manual technique in the removal of three different filling materials (gutta-percha, Resilon [Resilon Research LLC, Madison, CT], and EndoRez [Ultradent Products Inc, South Jordan, UT]) during retreatment. Ninety single-rooted straight premolars were instrumented and randomly divided into 9 groups of 10 teeth each (n = 10) with regards to filling material and instrument used. For all roots, the following data were recorded: procedural errors, time of retreatment, apically extruded material, canal wall cleanliness through optical stereomicroscopy (OSM), and scanning electron microscopy (SEM). A linear regression analysis and three logistic regression analyses were performed to assess the level of significance set at p = 0.05. The results indicated that the overall regression models were statistically significant. The Mtwo R, ProTaper retreatment files, and Resilon filling material had a positive impact in reducing the time for retreatment. Both ProTaper retreatment files and Mtwo R showed a greater extrusion of debris. For both OSM and SEM logistic regression models, the root canal apical third had the greatest impact on the score values. EndoRez filling material resulted in cleaner root canal walls using OSM analysis, whereas Resilon filling material and both engine-driven NiTi rotary techniques resulted in less clean root canal walls according to SEM analysis. In conclusion, all instruments left remnants of filling material and debris on the root canal walls irrespective of the root filling material used. Both the engine-driven NiTi rotary systems proved to be safe and fast devices for the removal of endodontic filling material.

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

USGS Publications Warehouse

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

2017-01-01

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

Predictors of unmet needs and psychological distress in adolescent and young adult siblings of people diagnosed with cancer.

PubMed

McDonald, F E J; Patterson, P; White, K J; Butow, P; Bell, M L

2015-03-01

Predictors of psychological distress and unmet needs amongst adolescents and young adults (AYAs) who have a brother or sister diagnosed with cancer were examined. There were 106 AYAs (12-24 years old) who completed questionnaires covering demographics, psychological distress (Kessler 10), unmet needs (Sibling Cancer Needs Instrument) and family relationships (Family Relationship Index; Adult Sibling Relationship Questionnaire; Sibling Perception Questionnaire (SPQ)). Three models were analysed (demographic variables, cancer-specific variables and family functioning variables) using multiple linear regression to determine the role of the variables in predicting psychological distress and unmet needs. Unmet needs were higher for AYA siblings when treatment was current or a relapse had occurred. Higher scores on the SPQ-Interpersonal subscale indicating a perceived decrease in the quality of relationships with parents and others were associated with higher levels of distress and unmet needs. The age and gender of the AYA sibling, whether it was their brother or sister who was diagnosed with cancer, the age difference between them, the number of parents living with the AYA sibling, parental birth country, time since diagnosis, Family Relationship Index, Adult Sibling Relationship Questionnaire and the SPQ-Communication subscale did not significantly impact outcome variables. These results highlight the variables that can assist in identifying AYA siblings of cancer patients who are at risk and have a greater need for psychosocial assistance. Variables that may be associated with increased distress and unmet needs are reported to assist with future research. The results are also useful in informing the development of targeted psychosocial support for AYA siblings of cancer patients. Copyright © 2014 John Wiley & Sons, Ltd.

Drug use, mental health and problems related to crime and violence: cross-sectional study1

PubMed Central

Claro, Heloísa Garcia; de Oliveira, Márcia Aparecida Ferreira; Bourdreaux, Janet Titus; Fernandes, Ivan Filipe de Almeida Lopes; Pinho, Paula Hayasi; Tarifa, Rosana Ribeiro

2015-01-01

Objective: to investigate the correlation between disorders related to the use of alcohol and other drugs and symptoms of mental disorders, problems related to crime and violence and to age and gender. Methods: cross-sectional descriptive study carried out with 128 users of a Psychosocial Care Center for Alcohol and other Drugs, in the city of São Paulo, interviewed by means of the instrument entitled Global Appraisal of Individual Needs - Short Screener. Univariate and multiple linear regression models were used to verify the correlation between the variables. Results: using univariate regression models, internalizing and externalizing symptoms and problems related to crime/violence proved significant and were included in the multiple model, in which only the internalizing symptoms and problems related to crime and violence remained significant. Conclusions: there is a correlation between the severity of problems related to alcohol use and severity of mental health symptoms and crime and violence in the study sample. The results emphasize the need for an interdisciplinary and intersectional character of attention to users of alcohol and other drugs, since they live in a socially vulnerable environment. PMID:26626010

A Linear Dynamical Systems Approach to Streamflow Reconstruction Reveals History of Regime Shifts in Northern Thailand

NASA Astrophysics Data System (ADS)

Nguyen, Hung T. T.; Galelli, Stefano

2018-03-01

Catchment dynamics is not often modeled in streamflow reconstruction studies; yet, the streamflow generation process depends on both catchment state and climatic inputs. To explicitly account for this interaction, we contribute a linear dynamic model, in which streamflow is a function of both catchment state (i.e., wet/dry) and paleoclimatic proxies. The model is learned using a novel variant of the Expectation-Maximization algorithm, and it is used with a paleo drought record—the Monsoon Asia Drought Atlas—to reconstruct 406 years of streamflow for the Ping River (northern Thailand). Results for the instrumental period show that the dynamic model has higher accuracy than conventional linear regression; all performance scores improve by 45-497%. Furthermore, the reconstructed trajectory of the state variable provides valuable insights about the catchment history—e.g., regime-like behavior—thereby complementing the information contained in the reconstructed streamflow time series. The proposed technique can replace linear regression, since it only requires information on streamflow and climatic proxies (e.g., tree-rings, drought indices); furthermore, it is capable of readily generating stochastic streamflow replicates. With a marginal increase in computational requirements, the dynamic model brings more desirable features and value to streamflow reconstructions.

Satisfaction among early and mid-career dentists in a metropolitan dental hospital in China

PubMed Central

Cui, Xiaoxi; Dunning, David G; An, Na

2017-01-01

A growing body of research has examined career satisfaction among dentists using a standardized instrument, dentist satisfaction survey (DSS). This project examined career satisfaction of early to mid-career dentists in China, a population whose career satisfaction, heretofore, has not been studied. This is an especially critical time to examine career satisfaction because of health care reform measures being implemented in China. A culturally sensitive Chinese-language version of the DSS (CDSS) was developed and electronically administered to 367 early and mid-career dentists in a tertiary dental hospital in Beijing, China. One hundred and seventy respondents completed the survey. The average total career score was 123, with a range of 82–157. Data analysis showed some significant differences in total career score and several subscales based on gender, working hours per week, and years in practice. A stepwise regression model revealed that two variables predicted total career score: working hours per week and gender. Stepwise regression also demonstrated that four subscales significantly predicted the overall professional satisfaction subscale score: respect, delivery of care, income and patient relations. Implications of these results are discussed in light of the health care delivery system and dentist career paths in China. PMID:29355243

Satisfaction among early and mid-career dentists in a metropolitan dental hospital in China.

PubMed

Cui, Xiaoxi; Dunning, David G; An, Na

2017-01-01

A growing body of research has examined career satisfaction among dentists using a standardized instrument, dentist satisfaction survey (DSS). This project examined career satisfaction of early to mid-career dentists in China, a population whose career satisfaction, heretofore, has not been studied. This is an especially critical time to examine career satisfaction because of health care reform measures being implemented in China. A culturally sensitive Chinese-language version of the DSS (CDSS) was developed and electronically administered to 367 early and mid-career dentists in a tertiary dental hospital in Beijing, China. One hundred and seventy respondents completed the survey. The average total career score was 123, with a range of 82-157. Data analysis showed some significant differences in total career score and several subscales based on gender, working hours per week, and years in practice. A stepwise regression model revealed that two variables predicted total career score: working hours per week and gender. Stepwise regression also demonstrated that four subscales significantly predicted the overall professional satisfaction subscale score: respect, delivery of care, income and patient relations. Implications of these results are discussed in light of the health care delivery system and dentist career paths in China.

Kinship and nonrelative foster care: the effect of placement type on child well-being.

PubMed

Font, Sarah A

2014-01-01

This study uses a national sample of 1,215 children, ages 6-17, who spent some time in formal kinship or nonrelative foster care to identify the effect of placement type on academic achievement, behavior, and health. Several identification strategies are used to reduce selection bias, including ordinary least squares, change score models, propensity score weighting, and instrumental variables regression. The results consistently estimate a negative effect of kin placements on reading scores, but kin placements appear to have no effect on child health, and findings on children's math and cognitive skills test scores and behavioral problems are mixed. Estimated declines in both academic achievement and behavioral problems are concentrated among children who are lower functioning at baseline. © 2014 The Author. Child Development © 2014 Society for Research in Child Development, Inc.

Compassion fatigue and satisfaction: a cross-sectional survey among US healthcare workers.

PubMed

Smart, Denise; English, Ashley; James, Jennifer; Wilson, Marian; Daratha, Kenn B; Childers, Belinda; Magera, Chris

2014-03-01

Professional quality of life among healthcare providers can impact the quality and safety of patient care. The purpose of this research was to investigate compassion satisfaction and compassion fatigue levels as measured by the Professional Quality of Life Scale self-report instrument in a community hospital in the United States. A cross-sectional survey study examined differences among 139 RNs, physicians, and nursing assistants. Relationships among individual and organizational variables were explored. Caregivers for critical patients scored significantly lower on the Professional Quality of Life subscale of burnout when compared with those working in a noncritical care unit. Linear regression results indicate that high sleep levels and employment in critical care areas are associated with less burnout. Identification of predictors can be used to design interventions that address modifiable risks. © 2013 Wiley Publishing Asia Pty Ltd.

The Relationship between Terrorism and Distress and Drinking: Two Years after September 11, 2001*

PubMed Central

Richman, Judith A.; Shannon, Candice A.; Rospenda, Kathleen M.; Flaherty, Joseph A.; Fendrich, Michael

2014-01-01

This study examined:1) the prevalence of negative beliefs related to terrorism and 2) whether these beliefs were related to distress and drinking. Respondents in a longitudinal cohort study sampled from a United States university workplace were surveyed by mail between 1996 and 2003. Instruments assessed: negative beliefs related to 9/11/01, distress (depression, anxiety, somatization, PTSD-post-traumatic stress disorder), and drinking (frequency, quantity, escapist motives, binge drinking, drinking to intoxication, and problem-related drinking). Regression analyses examined relationships between beliefs and mental health. A sizable percentage of respondents experienced terrorism-related negative beliefs. Higher negative belief scores were related to greater distress and problematic drinking in 2003, controlling for sociodemographic variables and (in most cases) pre-9/11 distress and drinking. Study limitations were noted and future research was recommended. PMID:19895299

USING PARTIAL LEAST SQUARES REGRESSION TO OBTAIN COTTON FIBER LENGTH DISTRIBUTIONS FROM THE BEARD TESTING METHOD

USDA-ARS?s Scientific Manuscript database

The beard testing method for measuring cotton fiber length is based on the fibrogram theory. However, in the instrumental implementations, the engineering complexity alters the original fiber length distribution observed by the instrument. This causes challenges in obtaining the entire original le...

Estimating the concrete compressive strength using hard clustering and fuzzy clustering based regression techniques.

PubMed

Nagwani, Naresh Kumar; Deo, Shirish V

2014-01-01

Understanding of the compressive strength of concrete is important for activities like construction arrangement, prestressing operations, and proportioning new mixtures and for the quality assurance. Regression techniques are most widely used for prediction tasks where relationship between the independent variables and dependent (prediction) variable is identified. The accuracy of the regression techniques for prediction can be improved if clustering can be used along with regression. Clustering along with regression will ensure the more accurate curve fitting between the dependent and independent variables. In this work cluster regression technique is applied for estimating the compressive strength of the concrete and a novel state of the art is proposed for predicting the concrete compressive strength. The objective of this work is to demonstrate that clustering along with regression ensures less prediction errors for estimating the concrete compressive strength. The proposed technique consists of two major stages: in the first stage, clustering is used to group the similar characteristics concrete data and then in the second stage regression techniques are applied over these clusters (groups) to predict the compressive strength from individual clusters. It is found from experiments that clustering along with regression techniques gives minimum errors for predicting compressive strength of concrete; also fuzzy clustering algorithm C-means performs better than K-means algorithm.

Estimating the Concrete Compressive Strength Using Hard Clustering and Fuzzy Clustering Based Regression Techniques

PubMed Central

Nagwani, Naresh Kumar; Deo, Shirish V.

2014-01-01

Understanding of the compressive strength of concrete is important for activities like construction arrangement, prestressing operations, and proportioning new mixtures and for the quality assurance. Regression techniques are most widely used for prediction tasks where relationship between the independent variables and dependent (prediction) variable is identified. The accuracy of the regression techniques for prediction can be improved if clustering can be used along with regression. Clustering along with regression will ensure the more accurate curve fitting between the dependent and independent variables. In this work cluster regression technique is applied for estimating the compressive strength of the concrete and a novel state of the art is proposed for predicting the concrete compressive strength. The objective of this work is to demonstrate that clustering along with regression ensures less prediction errors for estimating the concrete compressive strength. The proposed technique consists of two major stages: in the first stage, clustering is used to group the similar characteristics concrete data and then in the second stage regression techniques are applied over these clusters (groups) to predict the compressive strength from individual clusters. It is found from experiments that clustering along with regression techniques gives minimum errors for predicting compressive strength of concrete; also fuzzy clustering algorithm C-means performs better than K-means algorithm. PMID:25374939

The M Word: Multicollinearity in Multiple Regression.

ERIC Educational Resources Information Center

Morrow-Howell, Nancy

1994-01-01

Notes that existence of substantial correlation between two or more independent variables creates problems of multicollinearity in multiple regression. Discusses multicollinearity problem in social work research in which independent variables are usually intercorrelated. Clarifies problems created by multicollinearity, explains detection of…

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.

Effects of categorization method, regression type, and variable distribution on the inflation of Type-I error rate when categorizing a confounding variable.

PubMed

Barnwell-Ménard, Jean-Louis; Li, Qing; Cohen, Alan A

2015-03-15

The loss of signal associated with categorizing a continuous variable is well known, and previous studies have demonstrated that this can lead to an inflation of Type-I error when the categorized variable is a confounder in a regression analysis estimating the effect of an exposure on an outcome. However, it is not known how the Type-I error may vary under different circumstances, including logistic versus linear regression, different distributions of the confounder, and different categorization methods. Here, we analytically quantified the effect of categorization and then performed a series of 9600 Monte Carlo simulations to estimate the Type-I error inflation associated with categorization of a confounder under different regression scenarios. We show that Type-I error is unacceptably high (>10% in most scenarios and often 100%). The only exception was when the variable categorized was a continuous mixture proxy for a genuinely dichotomous latent variable, where both the continuous proxy and the categorized variable are error-ridden proxies for the dichotomous latent variable. As expected, error inflation was also higher with larger sample size, fewer categories, and stronger associations between the confounder and the exposure or outcome. We provide online tools that can help researchers estimate the potential error inflation and understand how serious a problem this is. Copyright © 2014 John Wiley & Sons, Ltd.

Estimation of streamflow, base flow, and nitrate-nitrogen loads in Iowa using multiple linear regression models

USGS Publications Warehouse

Schilling, K.E.; Wolter, C.F.

2005-01-01

Nineteen variables, including precipitation, soils and geology, land use, and basin morphologic characteristics, were evaluated to develop Iowa regression models to predict total streamflow (Q), base flow (Qb), storm flow (Qs) and base flow percentage (%Qb) in gauged and ungauged watersheds in the state. Discharge records from a set of 33 watersheds across the state for the 1980 to 2000 period were separated into Qb and Qs. Multiple linear regression found that 75.5 percent of long term average Q was explained by rainfall, sand content, and row crop percentage variables, whereas 88.5 percent of Qb was explained by these three variables plus permeability and floodplain area variables. Qs was explained by average rainfall and %Qb was a function of row crop percentage, permeability, and basin slope variables. Regional regression models developed for long term average Q and Qb were adapted to annual rainfall and showed good correlation between measured and predicted values. Combining the regression model for Q with an estimate of mean annual nitrate concentration, a map of potential nitrate loads in the state was produced. Results from this study have important implications for understanding geomorphic and land use controls on streamflow and base flow in Iowa watersheds and similar agriculture dominated watersheds in the glaciated Midwest. (JAWRA) (Copyright ?? 2005).

Medical decision-making capacity in patients with malignant glioma.

PubMed

Triebel, Kristen L; Martin, Roy C; Nabors, Louis B; Marson, Daniel C

2009-12-15

Patients with malignant glioma (MG) must make ongoing medical treatment decisions concerning a progressive disease that erodes cognition. We prospectively assessed medical decision-making capacity (MDC) in patients with MG using a standardized psychometric instrument. Participants were 22 healthy controls and 26 patients with histologically verified MG. Group performance was compared on the Capacity to Consent to Treatment Instrument (CCTI), a psychometric measure of MDC incorporating 4 standards (choice, understanding, reasoning, and appreciation), and on neuropsychological and demographic variables. Capacity outcomes (capable, marginally capable, or incapable) on the CCTI standards were identified for the MG group. Within the MG group, scores on demographic, clinical, and neuropsychological variables were correlated with scores on each CCTI standard, and significant bivariate correlates were subsequently entered into exploratory stepwise regression analyses to identify multivariate cognitive predictors of the CCTI standards. Patients with MG performed significantly below controls on consent standards of understanding and reasoning, and showed a trend on appreciation. Relative to controls, more than 50% of the patients with MG demonstrated capacity compromise (marginally capable or incapable outcomes) in MDC. In the MG group, cognitive measures of verbal acquisition/recall and, to a lesser extent, semantic fluency predicted performance on the appreciation, reasoning, and understanding standards. Karnofsky score was also associated with CCTI performance. Soon after diagnosis, patients with malignant glioma (MG) have impaired capacity to make treatment decisions relative to controls. Medical decision-making capacity (MDC) impairment in MG seems to be primarily related to the effects of short-term verbal memory deficits. Ongoing assessment of MDC in patients with MG is strongly recommended.

Snowpack variations since AD 1150 in the Andes of Chile and Argentina (30°-37°S) inferred from rainfall, tree-ring and documentary records

NASA Astrophysics Data System (ADS)

Masiokas, M. H.; Villalba, R.; Christie, D. A.; Betman, E.; Luckman, B. H.; Le Quesne, C.; Prieto, M. R.; Mauget, S.

2012-03-01

The Andean snowpack is the main source of freshwater and arguably the single most important natural resource for the populated, semi-arid regions of central Chile and central-western Argentina. However, apart from recent analyses of instrumental snowpack data, very little is known about the long term variability of this key natural resource. Here we present two complementary, annually-resolved reconstructions of winter snow accumulation in the southern Andes between 30°-37°S. The reconstructions cover the past 850 years and were developed using simple regression models based on snowpack proxies with different inherent limitations. Rainfall data from central Chile (very strongly correlated with snow accumulation values in the adjacent mountains) were used to extend a regional 1951-2010 snowpack record back to AD 1866. Subsequently, snow accumulation variations since AD 1150 were inferred from precipitation-sensitive tree-ring width series. The reconstructed snowpack values were validated with independent historical and instrumental information. An innovative time series analysis approach allowed the identification of the onset, duration and statistical significance of the main intra- to multi-decadal patterns in the reconstructions and indicates that variations observed in the last 60 years are not particularly anomalous when assessed in a multi-century context. In addition to providing new information on past variations for a highly relevant hydroclimatic variable in the southern Andes, the snowpack reconstructions can also be used to improve the understanding and modeling of related, larger-scale atmospheric features such as ENSO and the PDO.

Factors Influencing Physical Activity Behavior among Iranian Women with Type 2 Diabetes Using the Extended Theory of Reasoned Action.

PubMed

Didarloo, Alireza; Shojaeizadeh, Davoud; Ardebili, Hassan Eftekhar; Niknami, Shamsaddin; Hajizadeh, Ebrahim; Alizadeh, Mohammad

2011-10-01

Findings of most studies indicate that the only way to control diabetes and prevent its debilitating effects is through the continuous performance of self-care behaviors. Physical activity is a non-pharmacological method of diabetes treatment and because of its positive effects on diabetic patients, it is being increasingly considered by researchers and practitioners. This study aimed at determining factors influencing physical activity among diabetic women in Iran, using the extended theory of reasoned action in Iran. A sample of 352 women with type 2 diabetes, referring to a Diabetes Clinic in Khoy, Iran, participated in the study. Appropriate instruments were designed to measure the desired variables (knowledge of diabetes, personal beliefs, subjective norms, perceived self-efficacy, behavioral intention and physical activity behavior). The reliability and validity of the instruments were examined and approved. Statistical analyses of the study were conducted by inferential statistical techniques (independent t-test, correlations and regressions) using the SPSS package. The findings of this investigation indicated that among the constructs of the model, self efficacy was the strongest predictor of intentions among women with type 2 diabetes and both directly and indirectly affected physical activity. In addition to self efficacy, diabetic patients' physical activity also was influenced by other variables of the model and sociodemographic factors. Our findings suggest that the high ability of the theory of reasoned action extended by self-efficacy in forecasting and explaining physical activity can be a base for educational intervention. Educational interventions based on the proposed model are necessary for improving diabetics' physical activity behavior and controlling disease.

Correlation of hospital magnet status with the quality of physicians performing neurosurgical procedures in New York State.

PubMed

Bekelis, Kimon; Missios, Symeon; MacKenzie, Todd A

2018-01-24

The quality of physicians practicing in hospitals recognized for nursing excellence by the American Nurses Credentialing Center has not been studied before. We investigated whether Magnet hospital recognition is associated with higher quality of physicians performing neurosurgical procedures. We performed a cohort study of patients undergoing neurosurgical procedures from 2009-2013, who were registered in the New York Statewide Planning and Research Cooperative System (SPARCS) database. Propensity score adjusted multivariable regression models were used to adjust for known confounders, with mixed effects methods to control for clustering at the facility level. An instrumental variable analysis was used to control for unmeasured confounding and simulate the effect of a randomized trial. During the study period, 185,277 patients underwent neurosurgical procedures, and met the inclusion criteria. Of these, 66,607 (35.6%) were hospitalized in Magnet hospitals, and 118,670 (64.4%) in non-Magnet institutions. Instrumental variable analysis demonstrated that undergoing neurosurgical operations in Magnet hospitals was associated with a 13.6% higher chance of being treated by a physician with superior performance in terms of mortality (95% CI, 13.2% to 14.1%), and a 4.3% higher chance of being treated by a physician with superior performance in terms of length-of-stay (LOS) (95% CI, 3.8% to 4.7%) in comparison to non-Magnet institutions. The same associations were present in propensity score adjusted mixed effects models. Using a comprehensive all-payer cohort of neurosurgical patients in New York State we identified an association of Magnet hospital recognition with superior physician performance.

Determinants of helmet use behaviour among employed motorcycle riders in Yazd, Iran based on theory of planned behaviour.

PubMed

Ali, Mehri; Saeed, Mazloomy Mahmoodabad Seyed; Ali, Morowatisharifabad Mohammad; Haidar, Nadrian

2011-09-01

This paper reports on predictors of helmet use behaviour, using variables based on the theory of planned behaviour model among the employed motorcycle riders in Yazd-Iran, in an attempt to identify influential factors that may be addressed through intervention efforts. In 2007, a cluster random sample of 130 employed motorcycle riders in the city of Yazd in central Iran, participated in the study. Appropriate instruments were designed to measure the variables of interest (attitude, subjective norms, perceived behaviour control, intention along with helmet use behaviour). Reliability and validity of the instruments were examined and approved. The statistical analysis of the data included descriptive statistics, bivariate correlations, and multiple regression. Based on the results, 56 out of all the respondents (43.1%) had history of accident by motorcycle. Of these motorcycle riders only 10.7% were wearing their helmet at the time of their accident. Intention and perceived behavioural control showed a significant relationship with helmet use behaviour and perceived behaviour control was the strongest predictor of helmet use intention, followed by subjective norms, and attitude. It was found that that helmet use rate among motorcycle riders was very low. The findings of present study provide a preliminary support for the TPB model as an effective framework for examining helmet use in motorcycle riders. Understanding motorcycle rider's thoughts, feelings and beliefs about helmet use behaviour can assist intervention specialists to develop and implement effective programs in order to promote helmet use among motorcycle riders. Copyright © 2010 Elsevier Ltd. All rights reserved.

Measurement of Impact Acceleration: Mouthpiece Accelerometer Versus Helmet Accelerometer

PubMed Central

Higgins, Michael; Halstead, P. David; Snyder-Mackler, Lynn; Barlow, David

2007-01-01

Context: Instrumented helmets have been used to estimate impact acceleration imparted to the head during helmet impacts. These instrumented helmets may not accurately measure the actual amount of acceleration experienced by the head due to factors such as helmet-to-head fit. Objective: To determine if an accelerometer attached to a mouthpiece (MP) provides a more accurate representation of headform center of gravity (HFCOG) acceleration during impact than does an accelerometer attached to a helmet fitted on the headform. Design: Single-factor research design in which the independent variable was accelerometer position (HFCOG, helmet, MP) and the dependent variables were g and Severity Index (SI). Setting: Independent impact research laboratory. Intervention(s): The helmeted headform was dropped (n = 168) using a National Operating Committee on Standards for Athletic Equipment (NOCSAE) drop system from the standard heights and impact sites according to NOCSAE test standards. Peak g and SI were measured for each accelerometer position during impact. Main Outcome Measures: Upon impact, the peak g and SI were recorded for each accelerometer location. Results: Strong relationships were noted for HFCOG and MP measures, and significant differences were seen between HFCOG and helmet g measures and HFCOG and helmet SI measures. No statistically significant differences were noted between HFCOG and MP g and SI measures. Regression analyses showed a significant relationship between HFCOG and MP measures but not between HFCOG and helmet measures. Conclusions: Upon impact, MP acceleration (g) and SI measurements were closely related to and more accurate in measuring HFCOG g and SI than helmet measurements. The MP accelerometer is a valid method for measuring head acceleration. PMID:17597937

Does social trust increase willingness to pay taxes to improve public healthcare? Cross-sectional cross-country instrumental variable analysis.

PubMed

Habibov, Nazim; Cheung, Alex; Auchynnikava, Alena

2017-09-01

The purpose of this paper is to investigate the effect of social trust on the willingness to pay more taxes to improve public healthcare in post-communist countries. The well-documented association between higher levels of social trust and better health has traditionally been assumed to reflect the notion that social trust is positively associated with support for public healthcare system through its encouragement of cooperative behaviour, social cohesion, social solidarity, and collective action. Hence, in this paper, we have explicitly tested the notion that social trust contributes to an increase in willingness to financially support public healthcare. We use micro data from the 2010 Life-in-Transition survey (N = 29,526). Classic binomial probit and instrumental variables ivprobit regressions are estimated to model the relationship between social trust and paying more taxes to improve public healthcare. We found that an increase in social trust is associated with a greater willingness to pay more taxes to improve public healthcare. From the perspective of policy-making, healthcare administrators, policy-makers, and international donors should be aware that social trust is an important factor in determining the willingness of the population to provide much-needed financial resources to supporting public healthcare. From a theoretical perspective, we found that estimating the effect of trust on support for healthcare without taking confounding and measurement error problems into consideration will likely lead to an underestimation of the true effect of trust. Copyright © 2017 Elsevier Ltd. All rights reserved.

Do clinical and translational science graduate students understand linear regression? Development and early validation of the REGRESS quiz.

PubMed

Enders, Felicity

2013-12-01

Although regression is widely used for reading and publishing in the medical literature, no instruments were previously available to assess students' understanding. The goal of this study was to design and assess such an instrument for graduate students in Clinical and Translational Science and Public Health. A 27-item REsearch on Global Regression Expectations in StatisticS (REGRESS) quiz was developed through an iterative process. Consenting students taking a course on linear regression in a Clinical and Translational Science program completed the quiz pre- and postcourse. Student results were compared to practicing statisticians with a master's or doctoral degree in statistics or a closely related field. Fifty-two students responded precourse, 59 postcourse , and 22 practicing statisticians completed the quiz. The mean (SD) score was 9.3 (4.3) for students precourse and 19.0 (3.5) postcourse (P < 0.001). Postcourse students had similar results to practicing statisticians (mean (SD) of 20.1(3.5); P = 0.21). Students also showed significant improvement pre/postcourse in each of six domain areas (P < 0.001). The REGRESS quiz was internally reliable (Cronbach's alpha 0.89). The initial validation is quite promising with statistically significant and meaningful differences across time and study populations. Further work is needed to validate the quiz across multiple institutions. © 2013 Wiley Periodicals, Inc.

Mixed geographically weighted regression (MGWR) model with weighted adaptive bi-square for case of dengue hemorrhagic fever (DHF) in Surakarta

NASA Astrophysics Data System (ADS)

Astuti, H. N.; Saputro, D. R. S.; Susanti, Y.

2017-06-01

MGWR model is combination of linear regression model and geographically weighted regression (GWR) model, therefore, MGWR model could produce parameter estimation that had global parameter estimation, and other parameter that had local parameter in accordance with its observation location. The linkage between locations of the observations expressed in specific weighting that is adaptive bi-square. In this research, we applied MGWR model with weighted adaptive bi-square for case of DHF in Surakarta based on 10 factors (variables) that is supposed to influence the number of people with DHF. The observation unit in the research is 51 urban villages and the variables are number of inhabitants, number of houses, house index, many public places, number of healthy homes, number of Posyandu, area width, level population density, welfare of the family, and high-region. Based on this research, we obtained 51 MGWR models. The MGWR model were divided into 4 groups with significant variable is house index as a global variable, an area width as a local variable and the remaining variables vary in each. Global variables are variables that significantly affect all locations, while local variables are variables that significantly affect a specific location.

[Culture and quality of life assessment in Chinese populations].

PubMed

Xia, Ping; Li, Ning-Xiu; Liu, Chao-Jie; Lü, Yu-Bo; Zhang, Qiang; Ou, Ai-Hua

2010-07-01

To investigate the impact of cultural factors on quality of life (QOL) and to identify appropriate ways of dividing sub-populations for population norm-based quality of life assessment. The WHOQOL-BREF was used as a QOL instrument. Another questionnaire was developed to assess cultural values. A cross-sectional survey was undertaken in 1090 Guangzhou residents, which included 635 respondents from communities and 455 patients who visited outpatient departments of hospitals. Cronbach's a coefficients and item-domain correlation coefficients were calculated to test the reliability and validity of the WHOQOL-BREF, respectively. Student t test, ANOVA and stepwise multiple linear regression analysis were performed to identify the variables that might have an impact on the QOL. Two regression models with and without including cultural variables were constructed, and the extent of impact exerted by the cultural factors was assessed through a comparison of the change of adjusted R square values. A total of 1052 (96%) valid questionnaire were returned. The Cronbach's alpha coefficients of the WHOQOL-BREF ranged from 0.67 to 0.78. Age, education, occupation and family income were correlated with all of the domains of the WHOQOL-BREF. Chronic condition was correlated with physical, psychological, and social relationship domains of the WHOQOL-BREF. Gender was correlated with physical and psychological domains of the WHOQOL-BREF. The multiple regression analysis showed that social and demographic factors contributed to 6.3%, 13.6%, 10.4% and 8.7% of the predicted variances for the physical, psychological, social relationship, and environment domains, respectively. Social support, horizontal collectivism, vertical individualism, escape acceptance, fear of death, health value, supernatural belief had a significant impact on QOL. However, social support was the only one factor that had an impact on all of the four QOL domains. It is necessary to divide sub-cultural populations for population norm-based QOL assessment. Further research is needed to develop a practical approach to the sub-cultural population division.

On the Bias-Amplifying Effect of Near Instruments in Observational Studies

ERIC Educational Resources Information Center

Steiner, Peter M.; Kim, Yongnam

2014-01-01

In contrast to randomized experiments, the estimation of unbiased treatment effects from observational data requires an analysis that conditions on all confounding covariates. Conditioning on covariates can be done via standard parametric regression techniques or nonparametric matching like propensity score (PS) matching. The regression or…

Army College Fund Cost-Effectiveness Study

DTIC Science & Technology

1990-11-01

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

Genetic markers as instrumental variables.

PubMed

von Hinke, Stephanie; Davey Smith, George; Lawlor, Debbie A; Propper, Carol; Windmeijer, Frank

2016-01-01

The use of genetic markers as instrumental variables (IV) is receiving increasing attention from economists, statisticians, epidemiologists and social scientists. Although IV is commonly used in economics, the appropriate conditions for the use of genetic variants as instruments have not been well defined. The increasing availability of biomedical data, however, makes understanding of these conditions crucial to the successful use of genotypes as instruments. We combine the econometric IV literature with that from genetic epidemiology, and discuss the biological conditions and IV assumptions within the statistical potential outcomes framework. We review this in the context of two illustrative applications. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

Determining Directional Dependency in Causal Associations

PubMed Central

Pornprasertmanit, Sunthud; Little, Todd D.

2014-01-01

Directional dependency is a method to determine the likely causal direction of effect between two variables. This article aims to critique and improve upon the use of directional dependency as a technique to infer causal associations. We comment on several issues raised by von Eye and DeShon (2012), including: encouraging the use of the signs of skewness and excessive kurtosis of both variables, discouraging the use of D’Agostino’s K2, and encouraging the use of directional dependency to compare variables only within time points. We offer improved steps for determining directional dependency that fix the problems we note. Next, we discuss how to integrate directional dependency into longitudinal data analysis with two variables. We also examine the accuracy of directional dependency evaluations when several regression assumptions are violated. Directional dependency can suggest the direction of a relation if (a) the regression error in population is normal, (b) an unobserved explanatory variable correlates with any variables equal to or less than .2, (c) a curvilinear relation between both variables is not strong (standardized regression coefficient ≤ .2), (d) there are no bivariate outliers, and (e) both variables are continuous. PMID:24683282

The Paired Availability Design and Related Instrumental Variable Meta-analyses | Division of Cancer Prevention

Cancer.gov

Stuart G. Baker, 2017 Introduction This software computes meta-analysis and extrapolation estimates for an instrumental variable meta-analysis of randomized trial or before-and-after studies (the latter also known as the paired availability design). The software also checks on the assumptions if sufficient data are available. |

Statistical Analysis for Multisite Trials Using Instrumental Variables with Random Coefficients

ERIC Educational Resources Information Center

Raudenbush, Stephen W.; Reardon, Sean F.; Nomi, Takako

2012-01-01

Multisite trials can clarify the average impact of a new program and the heterogeneity of impacts across sites. Unfortunately, in many applications, compliance with treatment assignment is imperfect. For these applications, we propose an instrumental variable (IV) model with person-specific and site-specific random coefficients. Site-specific IV…

An investigation of correlation between pilot scanning behavior and workload using stepwise regression analysis

NASA Technical Reports Server (NTRS)

Waller, M. C.

1976-01-01

An electro-optical device called an oculometer which tracks a subject's lookpoint as a time function has been used to collect data in a real-time simulation study of instrument landing system (ILS) approaches. The data describing the scanning behavior of a pilot during the instrument approaches have been analyzed by use of a stepwise regression analysis technique. A statistically significant correlation between pilot workload, as indicated by pilot ratings, and scanning behavior has been established. In addition, it was demonstrated that parameters derived from the scanning behavior data can be combined in a mathematical equation to provide a good representation of pilot workload.

When Significant Others Suffer: German Validation of the Burden Assessment Scale (BAS)

PubMed Central

Hunger, Christina; Krause, Lena; Hilzinger, Rebecca; Ditzen, Beate; Schweitzer, Jochen

2016-01-01

There is a need of an economical, reliable, and valid instrument in the German-speaking countries to measure the burden of relatives who care for mentally ill persons. We translated the Burden Assessment Scale (BAS) and conducted a study investigating factor structure, psychometric quality and predictive validity. We used confirmative factor analyses (CFA, maximum-likelihood method) to examine the dimensionality of the German BAS in a sample of 215 relatives (72% women; M = 32 years, SD = 14, range: 18 to 77; 39% employed) of mentally ill persons (50% (ex-)partner or (best) friend; M = 32 years, SD = 13, range 8 to 64; main complaints were depression and/or anxiety). Cronbach’s α determined the internal consistency. We examined predictive validity using regression analyses including the BAS and validated scales of social systems functioning (Experience In Social Systems Questionnaire, EXIS.pers, EXIS.org) and psychopathology (Brief Symptom Inventory, BSI). Variables that might have influenced the dependent variables (e.g. age, gender, education, employment and civil status) were controlled by their introduction in the first step, and the BAS in the second step of the regression analyses. A model with four correlated factors (Disrupted Activities, Personal Distress, Time Perspective, Guilt) showed the best fit. With respect to the number of items included, the internal consistency was very good. The modified German BAS predicted relatives’ social systems functioning and psychopathology. The economical design makes the 19-item BAS promising for practice-oriented research, and for studies under time constraints. Strength, limitations and future directions are discussed. PMID:27764109

The role of social support in anxiety for persons with COPD.

PubMed

Dinicola, Gia; Julian, Laura; Gregorich, Steven E; Blanc, Paul D; Katz, Patricia P

2013-02-01

This study examined the contribution of perceived social support to the presence of anxiety in persons with chronic obstructive pulmonary disease (COPD). A cross-sectional survey sample of 452 persons with COPD (61.3% female; 53.5% older than 65; 70.8% without a college degree or higher educational achievement, and 54.8% with household income of $40,000 or less) completed a telephone survey. Measures included the anxiety subscale of the Hospital Anxiety and Depression Scale (HADS-A), 5 social support subscales from the Positive and Negative Social Exchanges (PANSE) Scale, a COPD Severity Score (CSS; a weighted algorithmic combination of symptoms and the need for various COPD medical interventions), and the Geriatric Depression Scale, Short Form (GDS-SF). Zero order correlations and a series of multiple regression analyses were calculated. Multiple regression analysis revealed that the receipt of instrumental support, feeling let down by the failure of others to provide needed help, and unsympathetic or insensitive behavior from others each positively predicted a higher level of patient anxiety in COPD patients, after controlling for demographic variables, smoking status, comorbid depression (GDS) and severity of illness (CSS). Additionally, the control variable of depression was the strongest predictor of anxiety, suggesting a high degree of co-morbidity in this sample. Anxiety and depression are serious co-morbid mental health concerns for persons with COPD. It is important to examine both positive and negative aspects of perceived social support for COPD patients and how they may impact or interact with these mental health concerns. Copyright © 2012 Elsevier Inc. All rights reserved.

Is malaria illness among young children a cause or a consequence of low socioeconomic status? evidence from the united Republic of Tanzania

PubMed Central

2012-01-01

Background Malaria is commonly considered a disease of the poor, but there is very little evidence of a possible two-way causality in the association between malaria and poverty. Until now, limitations to examine that dual relationship were the availability of representative data on confirmed malaria cases, the use of a good proxy for poverty, and accounting for endogeneity in regression models. Methods A simultaneous equation model was estimated with nationally representative data for Tanzania that included malaria parasite testing with RDTs for young children (six-59 months), and accounted for environmental variables assembled with the aid of GIS. A wealth index based on assets, access to utilities/infrastructure, and housing characteristics was used as a proxy for socioeconomic status. Model estimation was done with instrumental variables regression. Results Results show that households with a child who tested positive for malaria at the time of the survey had a wealth index that was, on average, 1.9 units lower (p-value < 0.001), and that an increase in the wealth index did not reveal significant effects on malaria. Conclusion If malaria is indeed a cause of poverty, as the findings of this study suggest, then malaria control activities, and particularly the current efforts to eliminate/eradicate malaria, are much more than just a public health policy, but also a poverty alleviation strategy. However, if poverty has no causal effect on malaria, then poverty alleviation policies should not be advertised as having the potential additional effect of reducing the prevalence of malaria. PMID:22571516

Estimation of 1RM for knee extension based on the maximal isometric muscle strength and body composition.

PubMed

Kanada, Yoshikiyo; Sakurai, Hiroaki; Sugiura, Yoshito; Arai, Tomoaki; Koyama, Soichiro; Tanabe, Shigeo

2017-11-01

[Purpose] To create a regression formula in order to estimate 1RM for knee extensors, based on the maximal isometric muscle strength measured using a hand-held dynamometer and data regarding the body composition. [Subjects and Methods] Measurement was performed in 21 healthy males in their twenties to thirties. Single regression analysis was performed, with measurement values representing 1RM and the maximal isometric muscle strength as dependent and independent variables, respectively. Furthermore, multiple regression analysis was performed, with data regarding the body composition incorporated as another independent variable, in addition to the maximal isometric muscle strength. [Results] Through single regression analysis with the maximal isometric muscle strength as an independent variable, the following regression formula was created: 1RM (kg)=0.714 + 0.783 × maximal isometric muscle strength (kgf). On multiple regression analysis, only the total muscle mass was extracted. [Conclusion] A highly accurate regression formula to estimate 1RM was created based on both the maximal isometric muscle strength and body composition. Using a hand-held dynamometer and body composition analyzer, it was possible to measure these items in a short time, and obtain clinically useful results.

On Using the Average Intercorrelation Among Predictor Variables and Eigenvector Orientation to Choose a Regression Solution.

ERIC Educational Resources Information Center

Mugrage, Beverly; And Others

Three ridge regression solutions are compared with ordinary least squares regression and with principal components regression using all components. Ridge regression, particularly the Lawless-Wang solution, out-performed ordinary least squares regression and the principal components solution on the criteria of stability of coefficient and closeness…

Continuous water-quality monitoring and regression analysis to estimate constituent concentrations and loads in the Red River of the North, Fargo, North Dakota, 2003-05

USGS Publications Warehouse

Ryberg, Karen R.

2006-01-01

This report presents the results of a study by the U.S. Geological Survey, done in cooperation with the Bureau of Reclamation, U.S. Department of the Interior, to estimate water-quality constituent concentrations in the Red River of the North at Fargo, North Dakota. Regression analysis of water-quality data collected in 2003-05 was used to estimate concentrations and loads for alkalinity, dissolved solids, sulfate, chloride, total nitrite plus nitrate, total nitrogen, total phosphorus, and suspended sediment. The explanatory variables examined for regression relation were continuously monitored physical properties of water-streamflow, specific conductance, pH, water temperature, turbidity, and dissolved oxygen. For the conditions observed in 2003-05, streamflow was a significant explanatory variable for all estimated constituents except dissolved solids. pH, water temperature, and dissolved oxygen were not statistically significant explanatory variables for any of the constituents in this study. Specific conductance was a significant explanatory variable for alkalinity, dissolved solids, sulfate, and chloride. Turbidity was a significant explanatory variable for total phosphorus and suspended sediment. For the nutrients, total nitrite plus nitrate, total nitrogen, and total phosphorus, cosine and sine functions of time also were used to explain the seasonality in constituent concentrations. The regression equations were evaluated using common measures of variability, including R2, or the proportion of variability in the estimated constituent explained by the regression equation. R2 values ranged from 0.703 for total nitrogen concentration to 0.990 for dissolved-solids concentration. The regression equations also were evaluated by calculating the median relative percentage difference (RPD) between measured constituent concentration and the constituent concentration estimated by the regression equations. Median RPDs ranged from 1.1 for dissolved solids to 35.2 for total nitrite plus nitrate. Regression equations also were used to estimate daily constituent loads. Load estimates can be used by water-quality managers for comparison of current water-quality conditions to water-quality standards expressed as total maximum daily loads (TMDLs). TMDLs are a measure of the maximum amount of chemical constituents that a water body can receive and still meet established water-quality standards. The peak loads generally occurred in June and July when streamflow also peaked.

Intraindividual variability in executive functions but not speed of processing or conflict resolution predicts performance differences in gait speed in older adults.

PubMed

Holtzer, Roee; Mahoney, Jeannette; Verghese, Joe

2014-08-01

The relationship between executive functions (EF) and gait speed is well established. However, with the exception of dual tasking, the key components of EF that predict differences in gait performance have not been determined. Therefore, the current study was designed to determine whether processing speed, conflict resolution, and intraindividual variability in EF predicted variance in gait performance in single- and dual-task conditions. Participants were 234 nondemented older adults (mean age 76.48 years; 55% women) enrolled in a community-based cohort study. Gait speed was assessed using an instrumented walkway during single- and dual-task conditions. The flanker task was used to assess EF. Results from the linear mixed effects model showed that (a) dual-task interference caused a significant dual-task cost in gait speed (estimate = 35.99; 95% CI = 33.19-38.80) and (b) of the cognitive predictors, only intraindividual variability was associated with gait speed (estimate = -.606; 95% CI = -1.11 to -.10). In unadjusted analyses, the three EF measures were related to gait speed in single- and dual-task conditions. However, in fully adjusted linear regression analysis, only intraindividual variability predicted performance differences in gait speed during dual tasking (B = -.901; 95% CI = -1.557 to -.245). Among the three EF measures assessed, intraindividual variability but not speed of processing or conflict resolution predicted performance differences in gait speed. © The Author 2013. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Variability of OH(3-1) and OH(6-2) emission altitude and volume emission rate from 2003 to 2011

NASA Astrophysics Data System (ADS)

Teiser, Georg; von Savigny, Christian

2017-08-01

In this study we report on variability in emission rate and centroid emission altitude of the OH(3-1) and OH(6-2) Meinel bands in the terrestrial nightglow based on spaceborne nightglow measurements with the SCIAMACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) instrument on the Envisat satellite. The SCIAMACHY observations cover the time period from August 2002 to April 2012 and the nighttime observations used in this study are performed at 10:00 p.m. local solar time. Characterizing variability in OH emission altitude - particularly potential long-term variations - is important for an appropriate interpretation of ground-based OH rotational temperature measurements, because simultaneous observations of the vertical OH volume emission rate profile are usually not available for these measurements. OH emission altitude and vertically integrated emission rate time series with daily resolution for the OH(3-1) band and monthly resolution for the OH(6-2) band were analyzed using a standard multilinear regression approach allowing for seasonal variations, QBO-effects (Quasi-Biennial Oscillation), solar cycle (SC) variability and a linear long-term trend. The analysis focuses on low latitudes, where SCIAMACHY nighttime observations are available all year. The dominant sources of variability for both OH emission rate and altitude are the semi-annual and annual variations, with emission rate and altitude being highly anti-correlated. There is some evidence for a 11-year solar cycle signature in the vertically integrated emission rate and in the centroid emission altitude of both the OH(3-1) and OH(6-2) bands.

Cisplatin and Etoposide Versus Carboplatin and Paclitaxel With Concurrent Radiotherapy for Stage III Non–Small-Cell Lung Cancer: An Analysis of Veterans Health Administration Data

PubMed Central

Santana-Davila, Rafael; Devisetty, Kiran; Szabo, Aniko; Sparapani, Rodney; Arce-Lara, Carlos; Gore, Elizabeth M.; Moran, Amy; Williams, Christina D.; Kelley, Michael J.; Whittle, Jeffrey

2015-01-01

Purpose The optimal chemotherapy regimen to use with radiotherapy in stage III non–small-cell lung cancer is unknown. Here, we compare the outcome of patents treated within the Veterans Health Administration with either etoposide-cisplatin (EP) or carboplatin-paclitaxel (CP). Methods We identified patients treated with EP and CP with concurrent radiotherapy from 2001 to 2010. Survival rates were compared using Cox proportional hazards regression models with adjustments for confounding provided by propensity score methods and an instrumental variables analysis. Comorbidities and treatment complications were identified through administrative data. Results A total of 1,842 patients were included; EP was used in 27% (n = 499). Treatment with EP was not associated with a survival advantage in a Cox proportional hazards model (hazard ratio [HR], 0.97; 95% CI, 0.85 to 1.10), a propensity score matched cohort (HR, 1.07; 95% CI, 0.91 to 1.24), or a propensity score adjusted model (HR, 0.97; 95% CI, 0.85 to 1.10). In an instrumental variables analysis, there was no survival advantage for patients treated in centers where EP was used more than 50% of the time as compared with centers where EP was used in less than 10% of the patients (HR, 1.07; 95% CI, 0.90 to 1.26). Patients treated with EP, compared with patients treated with CP, had more hospitalizations (2.4 v 1.7 hospitalizations, respectively; P < .001), outpatient visits (17.6 v 12.6 visits, respectively; P < .001), infectious complications (47.3% v 39.4%, respectively; P = .0022), acute kidney disease/dehydration (30.5% v 21.2%, respectively; P < .001), and mucositis/esophagitis (18.6% v 14.4%, respectively; P = .0246). Conclusion After accounting for prognostic variables, patients treated with EP versus CP had similar overall survival, but EP was associated with increased morbidity. PMID:25422491

Dynamic modal estimation using instrumental variables

NASA Technical Reports Server (NTRS)

Salzwedel, H.

1980-01-01

A method to determine the modes of dynamical systems is described. The inputs and outputs of a system are Fourier transformed and averaged to reduce the error level. An instrumental variable method that estimates modal parameters from multiple correlations between responses of single input, multiple output systems is applied to estimate aircraft, spacecraft, and off-shore platform modal parameters.

Teachers' Pedagogical Management and Instrumental Performance in Students of an Artistic Higher Education School

ERIC Educational Resources Information Center

De La Cruz Bautista, Edwin

2017-01-01

This research aims to know the relationship between the variables teachers' pedagogical management and instrumental performance in students from an Artistic Higher Education School. It is a descriptive and correlational research that seeks to find the relationship between both variables. The sample of the study consisted of 30 students of the…

Polychronometry: The Study of Time Variables in Behavior.

ERIC Educational Resources Information Center

Mackey, William Francis

There is a growing need for instrumentation which can enable us to observe and compute phenomena that take place in time. Although problems of observation, computation, interpretation and categorization vary from field to field and from problem to problem, it is possible to design an instrument for use in any situation where time-variables have to…

Predicting Preservice Music Teachers' Performance Success in Instrumental Courses Using Self-Regulated Study Strategies and Predictor Variables

ERIC Educational Resources Information Center

Ersozlu, Zehra N.; Nietfeld, John L.; Huseynova, Lale

2017-01-01

The purpose of this study was to examine the extent to which self-regulated study strategies and predictor variables predict performance success in instrumental performance college courses. Preservice music teachers (N = 123) from a music education department in two state universities in Turkey completed the Music Self-Regulated Studying…

Can Two Psychotherapy Process Measures Be Dependably Rated Simultaneously? A Generalizability Study

ERIC Educational Resources Information Center

Ulvenes, Pal G.; Berggraf, Lene; Hoffart, Asle; Levy, Raymon A.; Ablon, J. Stuart; McCullough, Leigh; Wampold, Bruce E.

2012-01-01

Observer ratings in psychotherapy are a common way of collecting information in psychotherapy research. However, human observers are imperfect instruments, and their ratings may be subject to variability from several sources. One source of variability can be raters' assessing more than 1 instrument at a time. The purpose of this research is to…

Categorical Variables in Multiple Regression: Some Cautions.

ERIC Educational Resources Information Center

O'Grady, Kevin E.; Medoff, Deborah R.

1988-01-01

Limitations of dummy coding and nonsense coding as methods of coding categorical variables for use as predictors in multiple regression analysis are discussed. The combination of these approaches often yields estimates and tests of significance that are not intended by researchers for inclusion in their models. (SLD)

On the Asymptotic Relative Efficiency of Planned Missingness Designs.

PubMed

Rhemtulla, Mijke; Savalei, Victoria; Little, Todd D

2016-03-01

In planned missingness (PM) designs, certain data are set a priori to be missing. PM designs can increase validity and reduce cost; however, little is known about the loss of efficiency that accompanies these designs. The present paper compares PM designs to reduced sample (RN) designs that have the same total number of data points concentrated in fewer participants. In 4 studies, we consider models for both observed and latent variables, designs that do or do not include an "X set" of variables with complete data, and a full range of between- and within-set correlation values. All results are obtained using asymptotic relative efficiency formulas, and thus no data are generated; this novel approach allows us to examine whether PM designs have theoretical advantages over RN designs removing the impact of sampling error. Our primary findings are that (a) in manifest variable regression models, estimates of regression coefficients have much lower relative efficiency in PM designs as compared to RN designs, (b) relative efficiency of factor correlation or latent regression coefficient estimates is maximized when the indicators of each latent variable come from different sets, and (c) the addition of an X set improves efficiency in manifest variable regression models only for the parameters that directly involve the X-set variables, but it substantially improves efficiency of most parameters in latent variable models. We conclude that PM designs can be beneficial when the model of interest is a latent variable model; recommendations are made for how to optimize such a design.

Derivation and validation of the Personal Support Algorithm: an evidence-based framework to inform allocation of personal support services in home and community care.

PubMed

Sinn, Chi-Ling Joanna; Jones, Aaron; McMullan, Janet Legge; Ackerman, Nancy; Curtin-Telegdi, Nancy; Eckel, Leslie; Hirdes, John P

2017-11-25

Personal support services enable many individuals to stay in their homes, but there are no standard ways to classify need for functional support in home and community care settings. The goal of this project was to develop an evidence-based clinical tool to inform service planning while allowing for flexibility in care coordinator judgment in response to patient and family circumstances. The sample included 128,169 Ontario home care patients assessed in 2013 and 25,800 Ontario community support clients assessed between 2014 and 2016. Independent variables were drawn from the Resident Assessment Instrument-Home Care and interRAI Community Health Assessment that are standardised, comprehensive, and fully compatible clinical assessments. Clinical expertise and regression analyses identified candidate variables that were entered into decision tree models. The primary dependent variable was the weekly hours of personal support calculated based on the record of billed services. The Personal Support Algorithm classified need for personal support into six groups with a 32-fold difference in average billed hours of personal support services between the highest and lowest group. The algorithm explained 30.8% of the variability in billed personal support services. Care coordinators and managers reported that the guidelines based on the algorithm classification were consistent with their clinical judgment and current practice. The Personal Support Algorithm provides a structured yet flexible decision-support framework that may facilitate a more transparent and equitable approach to the allocation of personal support services.

A Database Approach for Predicting and Monitoring Baked Anode Properties

NASA Astrophysics Data System (ADS)

Lauzon-Gauthier, Julien; Duchesne, Carl; Tessier, Jayson

2012-11-01

The baked anode quality control strategy currently used by most carbon plants based on testing anode core samples in the laboratory is inadequate for facing increased raw material variability. The low core sampling rate limited by lab capacity and the common practice of reporting averaged properties based on some anode population mask a significant amount of individual anode variability. In addition, lab results are typically available a few weeks after production and the anodes are often already set in the reduction cells preventing early remedial actions when necessary. A database approach is proposed in this work to develop a soft-sensor for predicting individual baked anode properties at the end of baking cycle. A large historical database including raw material properties, process operating parameters and anode core data was collected from a modern Alcoa plant. A multivariate latent variable PLS regression method was used for analyzing the large database and building the soft-sensor model. It is shown that the general low frequency trends in most anode physical and mechanical properties driven by raw material changes are very well captured by the model. Improvements in the data infrastructure (instrumentation, sampling frequency and location) will be necessary for predicting higher frequency variations in individual baked anode properties. This paper also demonstrates how multivariate latent variable models can be interpreted against process knowledge and used for real-time process monitoring of carbon plants, and detection of faults and abnormal operation.

Fear of Public Speaking: Perception of College Students and Correlates.

PubMed

Ferreira Marinho, Anna Carolina; Mesquita de Medeiros, Adriane; Côrtes Gama, Ana Cristina; Caldas Teixeira, Letícia

2017-01-01

The aims of the study were to determine the prevalence of fear of public speaking among college students and to assess its association with sociodemographic variables and those related to the voice and oral communication. A cross-sectional descriptive and analytic study was conducted with 1135 undergraduates aged 17-58 years. The assessment instruments were (1) a questionnaire addressing the variables sex, age, field of undergraduate study, voice, and frequency of exposure to public speaking, and (2) the Self-statements During Public Speaking Scale (SSPS), which includes variables implicated in specific domains of public speaking. A descriptive analysis was performed of the variables as well as uni- and multivariate logistic regressions to examine their association with fear of public speaking. The level of significance was set at 5%. In all, 63.9% of the college students reported fear of public speaking. As many as 89.3% of the students would like their undergraduate program to include classes to improve public speaking. Being female, having infrequent participation as speakers in groups, and perceiving their voice as high-pitched or too soft increase the odds of exhibiting fear of public speaking compared with students without those features. A great number of undergraduates report fear of public speaking. This fear is more prevalent among women, students who participate in few activities involving speaking to groups of people, and those who have a self-perception of their voice as high-pitched or too soft. Copyright © 2017 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

Individual and organizational factors related to work engagement among home-visiting nurses in Japan.

PubMed

Naruse, Takashi; Sakai, Mahiro; Watai, Izumi; Taguchi, Atsuko; Kuwahara, Yuki; Nagata, Satoko; Murashima, Sachiyo

2013-12-01

The increasing number of elderly people has caused increased demand for home-visiting nurses. Nursing managers should develop healthy workplaces in order to grow their workforce. This study investigated the work engagement of home-visiting nurses as an index of workplace health. The aim of the present study was to reveal factors contributing to work engagement among Japanese home-visiting nurses. An anonymous, self-administered questionnaire was sent to 208 home-visiting nurses from 28 nursing agencies in three districts; 177 (85.1%) returned the questionnaires. The Job Demands-Resources model, which explains the relationship between work environment and employee well-being, was used as a conceptual guide. The authors employed three survey instruments: (i) questions on individual variables; (ii) questions on organizational variables; and (iii) the Utrecht Work Engagement Scale (Japanese version). Multiple regression analyses were performed in order to examine the relationships between individual variables, organizational variables, and work engagement. Nurse managers and nurses who felt that there was a positive relationship between work and family had significantly higher work engagement levels than others. The support of a supervisor was significantly associated with work engagement. Nurses in middle-sized but not large agencies had significantly higher work engagement than nurses in small agencies. Supervisor support and an appropriate number of people reporting to each supervisor are important factors in fostering work engagement among home-visiting nurses. © 2013 The Authors. Japan Journal of Nursing Science © 2013 Japan Academy of Nursing Science.

Causal relationship model between variables using linear regression to improve professional commitment of lecturer

NASA Astrophysics Data System (ADS)

Setyaningsih, S.

2017-01-01

The main element to build a leading university requires lecturer commitment in a professional manner. Commitment is measured through willpower, loyalty, pride, loyalty, and integrity as a professional lecturer. A total of 135 from 337 university lecturers were sampled to collect data. Data were analyzed using validity and reliability test and multiple linear regression. Many studies have found a link on the commitment of lecturers, but the basic cause of the causal relationship is generally neglected. These results indicate that the professional commitment of lecturers affected by variables empowerment, academic culture, and trust. The relationship model between variables is composed of three substructures. The first substructure consists of endogenous variables professional commitment and exogenous three variables, namely the academic culture, empowerment and trust, as well as residue variable ɛ y . The second substructure consists of one endogenous variable that is trust and two exogenous variables, namely empowerment and academic culture and the residue variable ɛ 3. The third substructure consists of one endogenous variable, namely the academic culture and exogenous variables, namely empowerment as well as residue variable ɛ 2. Multiple linear regression was used in the path model for each substructure. The results showed that the hypothesis has been proved and these findings provide empirical evidence that increasing the variables will have an impact on increasing the professional commitment of the lecturers.

Geodesic least squares regression on information manifolds

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

Verdoolaege, Geert, E-mail: geert.verdoolaege@ugent.be

We present a novel regression method targeted at situations with significant uncertainty on both the dependent and independent variables or with non-Gaussian distribution models. Unlike the classic regression model, the conditional distribution of the response variable suggested by the data need not be the same as the modeled distribution. Instead they are matched by minimizing the Rao geodesic distance between them. This yields a more flexible regression method that is less constrained by the assumptions imposed through the regression model. As an example, we demonstrate the improved resistance of our method against some flawed model assumptions and we apply thismore » to scaling laws in magnetic confinement fusion.« less

Multiple Linear Regression Analysis of Factors Affecting Real Property Price Index From Case Study Research In Istanbul/Turkey

NASA Astrophysics Data System (ADS)

Denli, H. H.; Koc, Z.

2015-12-01

Estimation of real properties depending on standards is difficult to apply in time and location. Regression analysis construct mathematical models which describe or explain relationships that may exist between variables. The problem of identifying price differences of properties to obtain a price index can be converted into a regression problem, and standard techniques of regression analysis can be used to estimate the index. Considering regression analysis for real estate valuation, which are presented in real marketing process with its current characteristics and quantifiers, the method will help us to find the effective factors or variables in the formation of the value. In this study, prices of housing for sale in Zeytinburnu, a district in Istanbul, are associated with its characteristics to find a price index, based on information received from a real estate web page. The associated variables used for the analysis are age, size in m2, number of floors having the house, floor number of the estate and number of rooms. The price of the estate represents the dependent variable, whereas the rest are independent variables. Prices from 60 real estates have been used for the analysis. Same price valued locations have been found and plotted on the map and equivalence curves have been drawn identifying the same valued zones as lines.

Data mining of tree-based models to analyze freeway accident frequency.

PubMed

Chang, Li-Yen; Chen, Wen-Chieh

2005-01-01

Statistical models, such as Poisson or negative binomial regression models, have been employed to analyze vehicle accident frequency for many years. However, these models have their own model assumptions and pre-defined underlying relationship between dependent and independent variables. If these assumptions are violated, the model could lead to erroneous estimation of accident likelihood. Classification and Regression Tree (CART), one of the most widely applied data mining techniques, has been commonly employed in business administration, industry, and engineering. CART does not require any pre-defined underlying relationship between target (dependent) variable and predictors (independent variables) and has been shown to be a powerful tool, particularly for dealing with prediction and classification problems. This study collected the 2001-2002 accident data of National Freeway 1 in Taiwan. A CART model and a negative binomial regression model were developed to establish the empirical relationship between traffic accidents and highway geometric variables, traffic characteristics, and environmental factors. The CART findings indicated that the average daily traffic volume and precipitation variables were the key determinants for freeway accident frequencies. By comparing the prediction performance between the CART and the negative binomial regression models, this study demonstrates that CART is a good alternative method for analyzing freeway accident frequencies. By comparing the prediction performance between the CART and the negative binomial regression models, this study demonstrates that CART is a good alternative method for analyzing freeway accident frequencies.

Progressive and Regressive Aspects of Information Technology in Society: A Third Sector Perspective

ERIC Educational Resources Information Center

Miller, Kandace R.

2009-01-01

This dissertation explores the impact of information technology on progressive and regressive values in society from the perspective of one international foundation and four of its technology-related programs. Through a critical interpretive approach employing an instrumental multiple-case method, a framework to help explain the influence of…

Criteria for the use of regression analysis for remote sensing of sediment and pollutants

NASA Technical Reports Server (NTRS)

Whitlock, C. H.; Kuo, C. Y.; Lecroy, S. R. (Principal Investigator)

1982-01-01

Data analysis procedures for quantification of water quality parameters that are already identified and are known to exist within the water body are considered. The liner multiple-regression technique was examined as a procedure for defining and calibrating data analysis algorithms for such instruments as spectrometers and multispectral scanners.

Socio-demographic, clinical characteristics and utilization of mental health care services associated with SF-6D utility scores in patients with mental disorders: contributions of the quantile regression.

PubMed

Prigent, Amélie; Kamendje-Tchokobou, Blaise; Chevreul, Karine

2017-11-01

Health-related quality of life (HRQoL) is a widely used concept in the assessment of health care. Some generic HRQoL instruments, based on specific algorithms, can generate utility scores which reflect the preferences of the general population for the different health states described by the instrument. This study aimed to investigate the relationships between utility scores and potentially associated factors in patients with mental disorders followed in inpatient and/or outpatient care settings using two statistical methods. Patients were recruited in four psychiatric sectors in France. Patient responses to the SF-36 generic HRQoL instrument were used to calculate SF-6D utility scores. The relationships between utility scores and patient socio-demographic, clinical characteristics, and mental health care utilization, considered as potentially associated factors, were studied using OLS and quantile regressions. One hundred and seventy six patients were included. Women, severely ill patients and those hospitalized full-time tended to report lower utility scores, whereas psychotic disorders (as opposed to mood disorders) and part-time care were associated with higher scores. The quantile regression highlighted that the size of the associations between the utility scores and some patient characteristics varied along with the utility score distribution, and provided more accurate estimated values than OLS regression. The quantile regression may constitute a relevant complement for the analysis of factors associated with utility scores. For policy decision-making, the association of full-time hospitalization with lower utility scores while part-time care was associated with higher scores supports the further development of alternatives to full-time hospitalizations.

Kendall-Theil Robust Line (KTRLine--version 1.0)-A Visual Basic Program for Calculating and Graphing Robust Nonparametric Estimates of Linear-Regression Coefficients Between Two Continuous Variables

USGS Publications Warehouse

Granato, Gregory E.

2006-01-01

The Kendall-Theil Robust Line software (KTRLine-version 1.0) is a Visual Basic program that may be used with the Microsoft Windows operating system to calculate parameters for robust, nonparametric estimates of linear-regression coefficients between two continuous variables. The KTRLine software was developed by the U.S. Geological Survey, in cooperation with the Federal Highway Administration, for use in stochastic data modeling with local, regional, and national hydrologic data sets to develop planning-level estimates of potential effects of highway runoff on the quality of receiving waters. The Kendall-Theil robust line was selected because this robust nonparametric method is resistant to the effects of outliers and nonnormality in residuals that commonly characterize hydrologic data sets. The slope of the line is calculated as the median of all possible pairwise slopes between points. The intercept is calculated so that the line will run through the median of input data. A single-line model or a multisegment model may be specified. The program was developed to provide regression equations with an error component for stochastic data generation because nonparametric multisegment regression tools are not available with the software that is commonly used to develop regression models. The Kendall-Theil robust line is a median line and, therefore, may underestimate total mass, volume, or loads unless the error component or a bias correction factor is incorporated into the estimate. Regression statistics such as the median error, the median absolute deviation, the prediction error sum of squares, the root mean square error, the confidence interval for the slope, and the bias correction factor for median estimates are calculated by use of nonparametric methods. These statistics, however, may be used to formulate estimates of mass, volume, or total loads. The program is used to read a two- or three-column tab-delimited input file with variable names in the first row and data in subsequent rows. The user may choose the columns that contain the independent (X) and dependent (Y) variable. A third column, if present, may contain metadata such as the sample-collection location and date. The program screens the input files and plots the data. The KTRLine software is a graphical tool that facilitates development of regression models by use of graphs of the regression line with data, the regression residuals (with X or Y), and percentile plots of the cumulative frequency of the X variable, Y variable, and the regression residuals. The user may individually transform the independent and dependent variables to reduce heteroscedasticity and to linearize data. The program plots the data and the regression line. The program also prints model specifications and regression statistics to the screen. The user may save and print the regression results. The program can accept data sets that contain up to about 15,000 XY data points, but because the program must sort the array of all pairwise slopes, the program may be perceptibly slow with data sets that contain more than about 1,000 points.

Selection of a Geostatistical Method to Interpolate Soil Properties of the State Crop Testing Fields using Attributes of a Digital Terrain Model

NASA Astrophysics Data System (ADS)

Sahabiev, I. A.; Ryazanov, S. S.; Kolcova, T. G.; Grigoryan, B. R.

2018-03-01

The three most common techniques to interpolate soil properties at a field scale—ordinary kriging (OK), regression kriging with multiple linear regression drift model (RK + MLR), and regression kriging with principal component regression drift model (RK + PCR)—were examined. The results of the performed study were compiled into an algorithm of choosing the most appropriate soil mapping technique. Relief attributes were used as the auxiliary variables. When spatial dependence of a target variable was strong, the OK method showed more accurate interpolation results, and the inclusion of the auxiliary data resulted in an insignificant improvement in prediction accuracy. According to the algorithm, the RK + PCR method effectively eliminates multicollinearity of explanatory variables. However, if the number of predictors is less than ten, the probability of multicollinearity is reduced, and application of the PCR becomes irrational. In that case, the multiple linear regression should be used instead.

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

PubMed Central

Chun, Hyonho; Keleş, Sündüz

2010-01-01

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

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

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

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

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

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

DOE PAGES

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

2016-12-15

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

Characterizing nonconstant instrumental variance in emerging miniaturized analytical techniques.

PubMed

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

2016-04-07

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

Poisson Mixture Regression Models for Heart Disease Prediction.

PubMed

Mufudza, Chipo; Erol, Hamza

2016-01-01

Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model.

Poisson Mixture Regression Models for Heart Disease Prediction

PubMed Central

Erol, Hamza

2016-01-01

Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model. PMID:27999611

Continuous water-quality monitoring and regression analysis to estimate constituent concentrations and loads in the Red River of the North at Fargo and Grand Forks, North Dakota, 2003-12

USGS Publications Warehouse

Galloway, Joel M.

2014-01-01

The Red River of the North (hereafter referred to as “Red River”) Basin is an important hydrologic region where water is a valuable resource for the region’s economy. Continuous water-quality monitors have been operated by the U.S. Geological Survey, in cooperation with the North Dakota Department of Health, Minnesota Pollution Control Agency, City of Fargo, City of Moorhead, City of Grand Forks, and City of East Grand Forks at the Red River at Fargo, North Dakota, from 2003 through 2012 and at Grand Forks, N.Dak., from 2007 through 2012. The purpose of the monitoring was to provide a better understanding of the water-quality dynamics of the Red River and provide a way to track changes in water quality. Regression equations were developed that can be used to estimate concentrations and loads for dissolved solids, sulfate, chloride, nitrate plus nitrite, total phosphorus, and suspended sediment using explanatory variables such as streamflow, specific conductance, and turbidity. Specific conductance was determined to be a significant explanatory variable for estimating dissolved solids concentrations at the Red River at Fargo and Grand Forks. The regression equations provided good relations between dissolved solid concentrations and specific conductance for the Red River at Fargo and at Grand Forks, with adjusted coefficients of determination of 0.99 and 0.98, respectively. Specific conductance, log-transformed streamflow, and a seasonal component were statistically significant explanatory variables for estimating sulfate in the Red River at Fargo and Grand Forks. Regression equations provided good relations between sulfate concentrations and the explanatory variables, with adjusted coefficients of determination of 0.94 and 0.89, respectively. For the Red River at Fargo and Grand Forks, specific conductance, streamflow, and a seasonal component were statistically significant explanatory variables for estimating chloride. For the Red River at Grand Forks, a time component also was a statistically significant explanatory variable for estimating chloride. The regression equations for chloride at the Red River at Fargo provided a fair relation between chloride concentrations and the explanatory variables, with an adjusted coefficient of determination of 0.66 and the equation for the Red River at Grand Forks provided a relatively good relation between chloride concentrations and the explanatory variables, with an adjusted coefficient of determination of 0.77. Turbidity and streamflow were statistically significant explanatory variables for estimating nitrate plus nitrite concentrations at the Red River at Fargo and turbidity was the only statistically significant explanatory variable for estimating nitrate plus nitrite concentrations at Grand Forks. The regression equation for the Red River at Fargo provided a relatively poor relation between nitrate plus nitrite concentrations, turbidity, and streamflow, with an adjusted coefficient of determination of 0.46. The regression equation for the Red River at Grand Forks provided a fair relation between nitrate plus nitrite concentrations and turbidity, with an adjusted coefficient of determination of 0.73. Some of the variability that was not explained by the equations might be attributed to different sources contributing nitrates to the stream at different times. Turbidity, streamflow, and a seasonal component were statistically significant explanatory variables for estimating total phosphorus at the Red River at Fargo and Grand Forks. The regression equation for the Red River at Fargo provided a relatively fair relation between total phosphorus concentrations, turbidity, streamflow, and season, with an adjusted coefficient of determination of 0.74. The regression equation for the Red River at Grand Forks provided a good relation between total phosphorus concentrations, turbidity, streamflow, and season, with an adjusted coefficient of determination of 0.87. For the Red River at Fargo, turbidity and streamflow were statistically significant explanatory variables for estimating suspended-sediment concentrations. For the Red River at Grand Forks, turbidity was the only statistically significant explanatory variable for estimating suspended-sediment concentration. The regression equation at the Red River at Fargo provided a good relation between suspended-sediment concentration, turbidity, and streamflow, with an adjusted coefficient of determination of 0.95. The regression equation for the Red River at Grand Forks provided a good relation between suspended-sediment concentration and turbidity, with an adjusted coefficient of determination of 0.96.

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

NASA Astrophysics Data System (ADS)

Naguib, Ibrahim A.; Darwish, Hany W.

2012-02-01

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

[Contents of vitreous humor of dead body with different postmortem intervals].

PubMed

Tao, Tao; Xu, Jing; Luo, Tong-Xing; Liao, Zhi-Gang; Pan, Hong-Fu

2006-11-01

To establish regression correlations between postmortem interval (PMI) and contents of human vitreous humor of dead bodies for forensic purposes. The human vitreous humor were taken from 126 dead bodies between 0.5 to 216 hours after death, and 11 chemical elements were detected by the OLYMPUS AU400 auto-biochemistry instrument. (1) The glucose, natrium and chlorine in human vitreous humor decreased, while the urea, creatinine, uric acid, potassium, calcium, magnesium, phosphorus, and micro-protein increased after death. The change of glucose, potassium and phosphorus were well correlated with the PMI (r = 0.824, 0.967, 0.880). But the uric acid and micro-protein did not have a good correlation with the PMI(r = 0.350, 0.153). (2) The stepwise regression analysis established the following equations for the PMI (Y): Y = -35. 15+6.05X, R2 = 0.957 (X = potassium); Y = -27.83+ 5.49X(1) - 1.35X(2), R2 = 0.960 (X(1) = potassium, X(2) = glucose); Y = -6.37+3.93X(1) -2.29X(2) + 5.36X(3), R2 = 0.966 (X(1) = potassium, X(2) = glucose, X(3) = phosphorus). (1) Eleven chemical components in human vitreous humor change after death, among which postassium has the best linear correlation with the PMI within 72 hours after death. (2) The accuracy of the estimation of PMI could be improved by establishing a multi-variable equation through stepwise regression.

Do alcohol excise taxes affect traffic accidents? Evidence from Estonia.

PubMed

Saar, Indrek

2015-01-01

This article examines the association between alcohol excise tax rates and alcohol-related traffic accidents in Estonia. Monthly time series of traffic accidents involving drunken motor vehicle drivers from 1998 through 2013 were regressed on real average alcohol excise tax rates while controlling for changes in economic conditions and the traffic environment. Specifically, regression models with autoregressive integrated moving average (ARIMA) errors were estimated in order to deal with serial correlation in residuals. Counterfactual models were also estimated in order to check the robustness of the results, using the level of non-alcohol-related traffic accidents as a dependent variable. A statistically significant (P <.01) strong negative relationship between the real average alcohol excise tax rate and alcohol-related traffic accidents was disclosed under alternative model specifications. For instance, the regression model with ARIMA (0, 1, 1)(0, 1, 1) errors revealed that a 1-unit increase in the tax rate is associated with a 1.6% decrease in the level of accidents per 100,000 population involving drunk motor vehicle drivers. No similar association was found in the cases of counterfactual models for non-alcohol-related traffic accidents. This article indicates that the level of alcohol-related traffic accidents in Estonia has been affected by changes in real average alcohol excise taxes during the period 1998-2013. Therefore, in addition to other measures, the use of alcohol taxation is warranted as a policy instrument in tackling alcohol-related traffic accidents.

Exploratory Study of Dimensions of Health-related Quality of Life in the General Population of South Korea

PubMed Central

2017-01-01

Objectives This study aimed to explore dimensions in addition to the 5 dimensions of the 5-level EQ-5D version (EQ-5D-5L) that could satisfactorily explain variation in health-related quality of life (HRQoL) in the general population of South Korea. Methods Domains related to HRQoL were searched through a review of existing HRQoL instruments. Among the 28 potential dimensions, the 5 dimensions of the EQ-5D-5L and 7 additional dimensions (vision, hearing, communication, cognitive function, social relationships, vitality, and sleep) were included. A representative sample of 600 subjects was selected for the survey, which was administered through face-to-face interviews. Subjects were asked to report problems in 12 health dimensions at 5 levels, as well as their self-rated health status using the EuroQol visual analogue scale (EQ-VAS) and a 5-point Likert scale. Among subjects who reported no problems for any of the parameters in the EQ-5D-5L, we analyzed the frequencies of problems in the additional dimensions. A linear regression model with the EQ-VAS as the dependent variable was performed to identify additional significant dimensions. Results Among respondents who reported full health on the EQ-5D-5L (n=365), 32% reported a problem for at least 1 additional dimension, and 14% reported worse than moderate self-rated health. Regression analysis revealed a R2 of 0.228 for the original EQ-5D-5L dimensions, 0.200 for the new dimensions, and 0.263 for the 12 dimensions together. Among the added dimensions, vitality and sleep were significantly associated with EQ-VAS scores. Conclusions This study identified significant dimensions for assessing self-rated health among members of the general public, in addition to the 5 dimensions of the EQ-5D-5L. These dimensions could be considered for inclusion in a new preference-based instrument or for developing a country-specific HRQoL instrument. PMID:29207449

Comorbidity and health-related quality of life in patients with severe chronic obstructive pulmonary disease attending Swedish secondary care units.

PubMed

Sundh, Josefin; Johansson, Gunnar; Larsson, Kjell; Lindén, Anders; Löfdahl, Claes-Göran; Janson, Christer; Sandström, Thomas

2015-01-01

Our understanding of how comorbid diseases influence health-related quality of life (HRQL) in patients with chronic obstructive pulmonary disease (COPD) is limited and in need of improvement. The aim of this study was to examine the associations between comorbidities and HRQL as measured by the instruments EuroQol-5 dimension (EQ-5D) and the COPD Assessment Test (CAT). Information on patient characteristics, chronic bronchitis, cardiovascular disease, diabetes, renal impairment, musculoskeletal symptoms, osteoporosis, depression, and EQ-5D and CAT questionnaire results was collected from 373 patients with Forced Expiratory Volume in one second (FEV1) <50% of predicted value from 27 secondary care respiratory units in Sweden. Correlation analyses and multiple linear regression models were performed using EQ-5D index, EQ-5D visual analog scale (VAS), and CAT scores as response variables. Having more comorbid conditions was associated with a worse HRQL as assessed by all instruments. Chronic bronchitis was significantly associated with a worse HRQL as assessed by EQ-5D index (adjusted regression coefficient [95% confidence interval] -0.07 [-0.13 to -0.02]), EQ-5D VAS (-5.17 [-9.42 to -0.92]), and CAT (3.78 [2.35 to 5.20]). Musculoskeletal symptoms were significantly associated with worse EQ-5D index (-0.08 [-0.14 to -0.02]), osteoporosis with worse EQ-5D VAS (-4.65 [-9.27 to -0.03]), and depression with worse EQ-5D index (-0.10 [-0.17 to -0.04]). In stratification analyses, the associations of musculoskeletal symptoms, osteoporosis, and depression with HRQL were limited to female patients. The instruments EQ-5D and CAT complement each other and emerge as useful for assessing HRQL in patients with COPD. Chronic bronchitis, musculoskeletal symptoms, osteoporosis, and depression were associated with worse HRQL. We conclude that comorbid conditions, in particular chronic bronchitis, depression, osteoporosis, and musculoskeletal symptoms, should be taken into account in the clinical management of patients with severe COPD.

The effect of intimate exposure to alcohol abuse on the acquisition of knowledge about drinking.

PubMed

Rainer, J P

1994-01-01

This study explored how an alcohol education program might be structured to effectively educate college students about the consequences of alcohol use. The primary hypothesis tested stated that individuals would vary significantly in the amount of knowledge learned from a structured alcohol education workshop, based on the degree of familial or social exposure s/he has had to alcohol abuse. Social learning variables of locus of control, dogmatism, and expectancy for risk were tested for interaction with degree of exposure, to determine their influence on learning. A pretest-posttest control group was employed with a sample of 66 undergraduate college students. A four hour alcohol education program was administered to teach cognitive information and fact about alcohol, with a goal of facilitating responsible use/nonuse of alcohol. The Student Drinking Questionnaire measured acquisition of knowledge. The Adult Nowicki-Strickland Internal/External Scale measured locus of control, and Schultze's Short Dogmatism Scale measured dogmatism. The researcher developed an instrument for expectancy for risk. Multiple regression analyses yielded prediction equations for the variables under study. For the sample group, results demonstrated that a significant portion of the variance in the residualized posttest scores was accounted for by level of exposure and dogmatism. When the sample was blocked according to intimate or social exposure, dogmatism was the only construct entering the regression equation at a significant level for the intimate exposure group. None of the constructs were able to predict any of the residualized posttest scores for the social exposure group. It was concluded that: (1) Students in the sample learned differentially based on the degree of intimate exposure of alcohol; (2) Dogmatism is a moderating variable with acquisition of knowledge for those intimately exposed to alcohol abuse, but locus of control and expectancy for risk are not; and (3) Further research is needed to study the effects of differential learning goals set for different populations.

Factors influencing the quality of life of haemodialysis patients according to symptom cluster.

PubMed

Shim, Hye Yeung; Cho, Mi-Kyoung

2018-05-01

To identify the characteristics in each symptom cluster and factors influencing the quality of life of haemodialysis patients in Korea according to cluster. Despite developments in renal replacement therapy, haemodialysis still restricts the activities of daily living due to pain and impairs physical functioning induced by the disease and its complications. Descriptive survey. Two hundred and thirty dialysis patients aged >18 years. They completed self-administered questionnaires of Dialysis Symptom Index and Kidney Disease Quality of Life instrument-Short Form 1.3. To determine the optimal number of clusters, the collected data were analysed using polytomous variable latent class analysis in R software (poLCA) to estimate the latent class models and the latent class regression models for polytomous outcome variables. Differences in characteristics, symptoms and QOL according to the symptom cluster of haemodialysis patients were analysed using the independent t test and chi-square test. The factors influencing the QOL according to symptom cluster were identified using hierarchical multiple regression analysis. Physical and emotional symptoms were significantly more severe, and the QOL was significantly worse in Cluster 1 than in Cluster 2. The factors influencing the QOL were spouse, job, insurance type and physical and emotional symptoms in Cluster 1, with these variables having an explanatory power of 60.9%. Physical and emotional symptoms were the only influencing factors in Cluster 2, and they had an explanatory power of 37.4%. Mitigating the symptoms experienced by haemodialysis patients and improving their QOL require educational and therapeutic symptom management interventions that are tailored according to the characteristics and symptoms in each cluster. The findings of this study are expected to lead to practical guidelines for addressing the symptoms experienced by haemodialysis patients, and they provide basic information for developing nursing interventions to manage these symptoms and improve the QOL of these patients. © 2017 John Wiley & Sons Ltd.

Health Literacy is associated with Healthy Eating Index Scores and Sugar-Sweetened Beverage Intake: Findings from the Rural Lower Mississippi Delta

PubMed Central

Zoellner, Jamie; You, Wen; Connell, Carol; Smith-Ray, Renae L.; Allen, Kacie; Tucker, Katherine L; Davy, Brenda M.; Estabrooks, Paul A.

2011-01-01

Background Although health literacy has been a public health priority area for over a decade, the relationship between health literacy and dietary quality has not been thoroughly explored. Objective To evaluate health literacy skills in relation to Healthy Eating Index scores (HEI) and Sugar-Sweetened Beverage (SSB) consumption, while accounting for demographic variables. Design Cross-sectional survey. Participants/setting A community-based proportional sample of adults residing in the rural Lower Mississippi Delta. Methods Instruments included a validated 158-item regional food frequency questionnaire and the Newest Vital Sign (scores range 0–6) to assess health literacy. Statistical analyses performed Descriptive statistics, ANOVA, and multivariate linear regression. Results Of 376 participants, the majority were African American (67.6%), without a college degree (71.5%), and household income level <$20,000/year (55.0%). Most participants (73.9%) scored in the two lowest health literacy categories. The multivariate linear regression model to predict total HEI scores was significant (R2=0.24; F=18.8; p<0.01), such that every 1 point increase in health literacy was associated with a 1.21 point increase in healthy eating index scores, while controlling for all other variables. Other significant predictors of HEI scores included age, gender, and SNAP participation. Health literacy also significantly predicted sugar-sweetened beverages consumption (R2=0.15; F=6.3; p<0.01), while accounting for demographic variables. Every 1 point in health literacy scores was associated with 34 fewer SSB kilocalories/day. Age was the only significant covariate in the SSB model. Conclusion While health literacy has been linked to numerous poor health outcomes, to our knowledge this is the first investigation to establish a relationship between health literacy and HEI scores and SSB consumption. Our study suggests that understanding the causes and consequences of limited health literacy is an important factor in promoting compliance to the Dietary Guidelines for Americans. PMID:21703379

USAF (United States Air Force) Stability and Control DATCOM (Data Compendium)

DTIC Science & Technology

1978-04-01

regression analysis involves the study of a group of variables to determine their effect on a given parameter. Because of the empirical nature of this...regression analysis of mathematical statistics. In general, a regression analysis involves the study of a group of variables to determine their effect on a...Excperiment, OSR TN 58-114, MIT Fluid Dynamics Research Group Rapt. 57-5, 1957. (U) 90. Kennet, H., and Ashley, H.: Review of Unsteady Aerodynamic Studies in

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.

Modelling of binary logistic regression for obesity among secondary students in a rural area of Kedah

NASA Astrophysics Data System (ADS)

Kamaruddin, Ainur Amira; Ali, Zalila; Noor, Norlida Mohd.; Baharum, Adam; Ahmad, Wan Muhamad Amir W.

2014-07-01

Logistic regression analysis examines the influence of various factors on a dichotomous outcome by estimating the probability of the event's occurrence. Logistic regression, also called a logit model, is a statistical procedure used to model dichotomous outcomes. In the logit model the log odds of the dichotomous outcome is modeled as a linear combination of the predictor variables. The log odds ratio in logistic regression provides a description of the probabilistic relationship of the variables and the outcome. In conducting logistic regression, selection procedures are used in selecting important predictor variables, diagnostics are used to check that assumptions are valid which include independence of errors, linearity in the logit for continuous variables, absence of multicollinearity, and lack of strongly influential outliers and a test statistic is calculated to determine the aptness of the model. This study used the binary logistic regression model to investigate overweight and obesity among rural secondary school students on the basis of their demographics profile, medical history, diet and lifestyle. The results indicate that overweight and obesity of students are influenced by obesity in family and the interaction between a student's ethnicity and routine meals intake. The odds of a student being overweight and obese are higher for a student having a family history of obesity and for a non-Malay student who frequently takes routine meals as compared to a Malay student.

Predicting Social Trust with Binary Logistic Regression

ERIC Educational Resources Information Center

Adwere-Boamah, Joseph; Hufstedler, Shirley

2015-01-01

This study used binary logistic regression to predict social trust with five demographic variables from a national sample of adult individuals who participated in The General Social Survey (GSS) in 2012. The five predictor variables were respondents' highest degree earned, race, sex, general happiness and the importance of personally assisting…

A Latent-Variable Causal Model of Faculty Reputational Ratings.

ERIC Educational Resources Information Center

King, Suzanne; Wolfle, Lee M.

A reanalysis was conducted of Saunier's research (1985) on sources of variation in the National Research Council (NRC) reputational ratings of university faculty. Saunier conducted a stepwise regression analysis using 12 predictor variables. Due to problems with multicollinearity and because of the atheoretical nature of stepwise regression,…

Violent Recidivism: A Long-Time Follow-Up Study of Mentally Disordered Offenders

PubMed Central

Nilsson, Thomas; Wallinius, Märta; Gustavson, Christina; Anckarsäter, Henrik; Kerekes, Nóra

2011-01-01

Background In this prospective study, mentally disordered perpetrators of severe violent and/or sexual crimes were followed through official registers for 59 (range 8 to 73) months. The relapse rate in criminality was assessed, compared between offenders sentenced to prison versus forensic psychiatric care, and the predictive ability of various risk factors (criminological, clinical, and of structured assessment instruments) was investigated. Method One hundred perpetrators were consecutively assessed between 1998 and 2001 by a clinical battery of established instruments covering DSM-IV diagnoses, psychosocial background factors, and structured assessment instruments (HCR-20, PCL-R, and life-time aggression (LHA)). Follow-up data was collected from official registers for: (i) recidivistic crimes, (ii) crimes during ongoing sanction. Results Twenty subjects relapsed in violent criminality during ongoing sanctions (n = 6) or after discharge/parole (n = 14). Individuals in forensic psychiatric care spent significantly more time at liberty after discharge compared to those in prison, but showed significantly fewer relapses. Criminological (age at first conviction), and clinical (conduct disorder and substance abuse/dependence) risk factors, as well as scores on structured assessment instruments, were moderately associated with violent recidivism. Logistic regression analyses showed that the predictive ability of criminological risk factors versus clinical risk factors combined with scores from assessment instruments was comparable, with each set of variables managing to correctly classify about 80% of all individuals, but the only predictors that remained significant in multiple models were criminological (age at first conviction, and a history of substance abuse among primary relatives). Conclusions Only one in five relapsed into serious criminality, with significantly more relapses among subjects sentenced to prison as compared to forensic psychiatric care. Criminological risk factors tended to be the best predictors of violent relapses, while few synergies were seen when the risk factors were combined. Overall, the predictive validity of common risk factors for violent criminality was rather weak. PMID:22022445

Synthesizing US Colonial Climate: Available Data and a "Proxy Adjustment" Method

NASA Astrophysics Data System (ADS)

Zalzal, K. S.; Munoz-Hernandez, A.; Arrigo, J. S.

2008-12-01

Climate and its variability is a primary driver of hydrologic systems. A paucity of instrumental data makes reconstructing seventeenth- and eighteenth-century climatic conditions along the Northeast corridor difficult, yet this information is necessary if we are to understand the conditions, changes and interactions society had with hydrosystems during this first period of permanent European settlement. For this period (approx. 1600- 1800) there are instrumental records for some regions such as annual temperature and precipitation data for Philadelphia beginning in 1738; Cambridge, Mass., from 1747-1776; and temperature for New Haven, Conn., from 1780 to 1800. There are also paleorecords, including tree-rings analyses and sediment core examinations of pollen and overwash deposits, and historical accounts of extreme weather events. Our analyses of these data show that correlating even the available data is less than straightforward. To produce a "best track" climate record, we introduce a new method of "paleoadjustment" as a means to characterize climate statistical properties as opposed to a strict reconstruction. Combining the instrumented record with the paleorecord, we estimated two sets of climate forcings to use in colonial hydrology study. The first utilized a recent instrumented record (1817-1917) from Baltimore, Md, statistically adjusted in 20-year windows to match trends in the paleorecords and anecdotal evidence from the Middle Colonies and Chesapeake Bay region. The second was a regression reconstruction for New England using climate indices developed from journal records and the Cambridge, Mass., instrumental record. The two climate reconstructions were used to compute the annual potential water yield over the 200-year period of interest. A comparison of these results allowed us to make preliminary conclusions regarding the effect of climate on hydrology during the colonial period. We contend that an understanding of historical hydrology will improve our ability to predict and react to changes in global water resources.

Comparing near-infrared conventional diffuse reflectance spectroscopy and hyperspectral imaging for determination of the bulk properties of solid samples by multivariate regression: determination of Mooney viscosity and plasticity indices of natural rubber.

PubMed

Juliano da Silva, Carlos; Pasquini, Celio

2015-01-21

Conventional reflectance spectroscopy (NIRS) and hyperspectral imaging (HI) in the near-infrared region (1000-2500 nm) are evaluated and compared, using, as the case study, the determination of relevant properties related to the quality of natural rubber. Mooney viscosity (MV) and plasticity indices (PI) (PI0 - original plasticity, PI30 - plasticity after accelerated aging, and PRI - the plasticity retention index after accelerated aging) of rubber were determined using multivariate regression models. Two hundred and eighty six samples of rubber were measured using conventional and hyperspectral near-infrared imaging reflectance instruments in the range of 1000-2500 nm. The sample set was split into regression (n = 191) and external validation (n = 95) sub-sets. Three instruments were employed for data acquisition: a line scanning hyperspectral camera and two conventional FT-NIR spectrometers. Sample heterogeneity was evaluated using hyperspectral images obtained with a resolution of 150 × 150 μm and principal component analysis. The probed sample area (5 cm(2); 24,000 pixels) to achieve representativeness was found to be equivalent to the average of 6 spectra for a 1 cm diameter probing circular window of one FT-NIR instrument. The other spectrophotometer can probe the whole sample in only one measurement. The results show that the rubber properties can be determined with very similar accuracy and precision by Partial Least Square (PLS) regression models regardless of whether HI-NIR or conventional FT-NIR produce the spectral datasets. The best Root Mean Square Errors of Prediction (RMSEPs) of external validation for MV, PI0, PI30, and PRI were 4.3, 1.8, 3.4, and 5.3%, respectively. Though the quantitative results provided by the three instruments can be considered equivalent, the hyperspectral imaging instrument presents a number of advantages, being about 6 times faster than conventional bulk spectrometers, producing robust spectral data by ensuring sample representativeness, and minimizing the effect of the presence of contaminants.

Validation of the conceptual research utilization scale: an application of the standards for educational and psychological testing in healthcare.

PubMed

Squires, Janet E; Estabrooks, Carole A; Newburn-Cook, Christine V; Gierl, Mark

2011-05-19

There is a lack of acceptable, reliable, and valid survey instruments to measure conceptual research utilization (CRU). In this study, we investigated the psychometric properties of a newly developed scale (the CRU Scale). We used the Standards for Educational and Psychological Testing as a validation framework to assess four sources of validity evidence: content, response processes, internal structure, and relations to other variables. A panel of nine international research utilization experts performed a formal content validity assessment. To determine response process validity, we conducted a series of one-on-one scale administration sessions with 10 healthcare aides. Internal structure and relations to other variables validity was examined using CRU Scale response data from a sample of 707 healthcare aides working in 30 urban Canadian nursing homes. Principal components analysis and confirmatory factor analyses were conducted to determine internal structure. Relations to other variables were examined using: (1) bivariate correlations; (2) change in mean values of CRU with increasing levels of other kinds of research utilization; and (3) multivariate linear regression. Content validity index scores for the five items ranged from 0.55 to 1.00. The principal components analysis predicted a 5-item 1-factor model. This was inconsistent with the findings from the confirmatory factor analysis, which showed best fit for a 4-item 1-factor model. Bivariate associations between CRU and other kinds of research utilization were statistically significant (p < 0.01) for the latent CRU scale score and all five CRU items. The CRU scale score was also shown to be significant predictor of overall research utilization in multivariate linear regression. The CRU scale showed acceptable initial psychometric properties with respect to responses from healthcare aides in nursing homes. Based on our validity, reliability, and acceptability analyses, we recommend using a reduced (four-item) version of the CRU scale to yield sound assessments of CRU by healthcare aides. Refinement to the wording of one item is also needed. Planned future research will include: latent scale scoring, identification of variables that predict and are outcomes to conceptual research use, and longitudinal work to determine CRU Scale sensitivity to change.

An Assessment of the Need for Standard Variable Names for Airborne Field Campaigns

NASA Astrophysics Data System (ADS)

Beach, A. L., III; Chen, G.; Northup, E. A.; Kusterer, J.; Quam, B. M.

2017-12-01

The NASA Earth Venture Program has led to a dramatic increase in airborne observations, requiring updated data management practices with clearly defined data standards and protocols for metadata. An airborne field campaign can involve multiple aircraft and a variety of instruments. It is quite common to have different instruments/techniques measure the same parameter on one or more aircraft platforms. This creates a need to allow instrument Principal Investigators (PIs) to name their variables in a way that would distinguish them across various data sets. A lack of standardization of variables names presents a challenge for data search tools in enabling discovery of similar data across airborne studies, aircraft platforms, and instruments. This was also identified by data users as one of the top issues in data use. One effective approach for mitigating this problem is to enforce variable name standardization, which can effectively map the unique PI variable names to fixed standard names. In order to ensure consistency amongst the standard names, it will be necessary to choose them from a controlled list. However, no such list currently exists despite a number of previous efforts to establish a sufficient list of atmospheric variable names. The Atmospheric Composition Variable Standard Name Working Group was established under the auspices of NASA's Earth Science Data Systems Working Group (ESDSWG) to solicit research community feedback to create a list of standard names that are acceptable to data providers and data users This presentation will discuss the challenges and recommendations of standard variable names in an effort to demonstrate how airborne metadata curation/management can be improved to streamline data ingest, improve interoperability, and discoverability to a broader user community.

Use of allele scores as instrumental variables for Mendelian randomization

PubMed Central

Burgess, Stephen; Thompson, Simon G

2013-01-01

Background An allele score is a single variable summarizing multiple genetic variants associated with a risk factor. It is calculated as the total number of risk factor-increasing alleles for an individual (unweighted score), or the sum of weights for each allele corresponding to estimated genetic effect sizes (weighted score). An allele score can be used in a Mendelian randomization analysis to estimate the causal effect of the risk factor on an outcome. Methods Data were simulated to investigate the use of allele scores in Mendelian randomization where conventional instrumental variable techniques using multiple genetic variants demonstrate ‘weak instrument’ bias. The robustness of estimates using the allele score to misspecification (for example non-linearity, effect modification) and to violations of the instrumental variable assumptions was assessed. Results Causal estimates using a correctly specified allele score were unbiased with appropriate coverage levels. The estimates were generally robust to misspecification of the allele score, but not to instrumental variable violations, even if the majority of variants in the allele score were valid instruments. Using a weighted rather than an unweighted allele score increased power, but the increase was small when genetic variants had similar effect sizes. Naive use of the data under analysis to choose which variants to include in an allele score, or for deriving weights, resulted in substantial biases. Conclusions Allele scores enable valid causal estimates with large numbers of genetic variants. The stringency of criteria for genetic variants in Mendelian randomization should be maintained for all variants in an allele score. PMID:24062299