Theory of mind and executive function: working-memory capacity and inhibitory control as predictors of false-belief task performance.

PubMed

Mutter, Brigitte; Alcorn, Mark B; Welsh, Marilyn

2006-06-01

This study of the relationship between theory of mind and executive function examined whether on the false-belief task age differences between 3 and 5 ears of age are related to development of working-memory capacity and inhibitory processes. 72 children completed tasks measuring false belief, working memory, and inhibition. Significant age effects were observed for false-belief and working-memory performance, as well as for the false-alarm and perseveration measures of inhibition. A simultaneous multiple linear regression specified the contribution of age, inhibition, and working memory to the prediction of false-belief performance. This model was significant, explaining a total of 36% of the variance. To examine the independent contributions of the working-memory and inhibition variables, after controlling for age, two hierarchical multiple linear regressions were conducted. These multiple regression analyses indicate that working memory and inhibition make small, overlapping contributions to false-belief performance after accounting for age, but that working memory, as measured in this study, is a somewhat better predictor of false-belief understanding than is inhibition.

Parts-Per-Billion Mass Measurement Accuracy Achieved through the Combination of Multiple Linear Regression and Automatic Gain Control in a Fourier Transform Ion Cyclotron Resonance Mass Spectrometer

PubMed Central

Williams, D. Keith; Muddiman, David C.

2008-01-01

Fourier transform ion cyclotron resonance mass spectrometry has the ability to achieve unprecedented mass measurement accuracy (MMA); MMA is one of the most significant attributes of mass spectrometric measurements as it affords extraordinary molecular specificity. However, due to space-charge effects, the achievable MMA significantly depends on the total number of ions trapped in the ICR cell for a particular measurement. Even through the use of automatic gain control (AGC), the total ion population is not constant between spectra. Multiple linear regression calibration in conjunction with AGC is utilized in these experiments to formally account for the differences in total ion population in the ICR cell between the external calibration spectra and experimental spectra. This ability allows for the extension of dynamic range of the instrument while allowing mean MMA values to remain less than 1 ppm. In addition, multiple linear regression calibration is used to account for both differences in total ion population in the ICR cell as well as relative ion abundance of a given species, which also affords mean MMA values at the parts-per-billion level. PMID:17539605

Stature estimation from the lengths of the growing foot-a study on North Indian adolescents.

PubMed

Krishan, Kewal; Kanchan, Tanuj; Passi, Neelam; DiMaggio, John A

2012-12-01

Stature estimation is considered as one of the basic parameters of the investigation process in unknown and commingled human remains in medico-legal case work. Race, age and sex are the other parameters which help in this process. Stature estimation is of the utmost importance as it completes the biological profile of a person along with the other three parameters of identification. The present research is intended to formulate standards for stature estimation from foot dimensions in adolescent males from North India and study the pattern of foot growth during the growing years. 154 male adolescents from the Northern part of India were included in the study. Besides stature, five anthropometric measurements that included the length of the foot from each toe (T1, T2, T3, T4, and T5 respectively) to pternion were measured on each foot. The data was analyzed statistically using Student's t-test, Pearson's correlation, linear and multiple regression analysis for estimation of stature and growth of foot during ages 13-18 years. Correlation coefficients between stature and all the foot measurements were found to be highly significant and positively correlated. Linear regression models and multiple regression models (with age as a co-variable) were derived for estimation of stature from the different measurements of the foot. Multiple regression models (with age as a co-variable) estimate stature with greater accuracy than the regression models for 13-18 years age group. The study shows the growth pattern of feet in North Indian adolescents and indicates that anthropometric measurements of the foot and its segments are valuable in estimation of stature in growing individuals of that population. Copyright © 2012 Elsevier Ltd. All rights reserved.

Temporal Synchronization Analysis for Improving Regression Modeling of Fecal Indicator Bacteria Levels

EPA Science Inventory

Multiple linear regression models are often used to predict levels of fecal indicator bacteria (FIB) in recreational swimming waters based on independent variables (IVs) such as meteorologic, hydrodynamic, and water-quality measures. The IVs used for these analyses are traditiona...

Multiple regression analysis of anthropometric measurements influencing the cephalic index of male Japanese university students.

PubMed

Hossain, Md Golam; Saw, Aik; Alam, Rashidul; Ohtsuki, Fumio; Kamarul, Tunku

2013-09-01

Cephalic index (CI), the ratio of head breadth to head length, is widely used to categorise human populations. The aim of this study was to access the impact of anthropometric measurements on the CI of male Japanese university students. This study included 1,215 male university students from Tokyo and Kyoto, selected using convenient sampling. Multiple regression analysis was used to determine the effect of anthropometric measurements on CI. The variance inflation factor (VIF) showed no evidence of a multicollinearity problem among independent variables. The coefficients of the regression line demonstrated a significant positive relationship between CI and minimum frontal breadth (p < 0.01), bizygomatic breadth (p < 0.01) and head height (p < 0.05), and a negative relationship between CI and morphological facial height (p < 0.01) and head circumference (p < 0.01). Moreover, the coefficient and odds ratio of logistic regression analysis showed a greater likelihood for minimum frontal breadth (p < 0.01) and bizygomatic breadth (p < 0.01) to predict round-headedness, and morphological facial height (p < 0.05) and head circumference (p < 0.01) to predict long-headedness. Stepwise regression analysis revealed bizygomatic breadth, head circumference, minimum frontal breadth, head height and morphological facial height to be the best predictor craniofacial measurements with respect to CI. The results suggest that most of the variables considered in this study appear to influence the CI of adult male Japanese students.

Construction of mathematical model for measuring material concentration by colorimetric method

NASA Astrophysics Data System (ADS)

Liu, Bing; Gao, Lingceng; Yu, Kairong; Tan, Xianghua

2018-06-01

This paper use the method of multiple linear regression to discuss the data of C problem of mathematical modeling in 2017. First, we have established a regression model for the concentration of 5 substances. But only the regression model of the substance concentration of urea in milk can pass through the significance test. The regression model established by the second sets of data can pass the significance test. But this model exists serious multicollinearity. We have improved the model by principal component analysis. The improved model is used to control the system so that it is possible to measure the concentration of material by direct colorimetric method.

Experimental validation of a coupled neutron-photon inverse radiation transport solver

NASA Astrophysics Data System (ADS)

Mattingly, John; Mitchell, Dean J.; Harding, Lee T.

2011-10-01

Sandia National Laboratories has developed an inverse radiation transport solver that applies nonlinear regression to coupled neutron-photon deterministic transport models. The inverse solver uses nonlinear regression to fit a radiation transport model to gamma spectrometry and neutron multiplicity counting measurements. The subject of this paper is the experimental validation of that solver. This paper describes a series of experiments conducted with a 4.5 kg sphere of α-phase, weapons-grade plutonium. The source was measured bare and reflected by high-density polyethylene (HDPE) spherical shells with total thicknesses between 1.27 and 15.24 cm. Neutron and photon emissions from the source were measured using three instruments: a gross neutron counter, a portable neutron multiplicity counter, and a high-resolution gamma spectrometer. These measurements were used as input to the inverse radiation transport solver to evaluate the solver's ability to correctly infer the configuration of the source from its measured radiation signatures.

[Establishment of multiple regression model for virulence factors of Saccharomyces albicans by random amplified polymorphic DNA bands].

PubMed

Liu, Qi; Wu, Youcong; Yuan, Youhua; Bai, Li; Niu, Kun

2011-12-01

To research the relationship between the virulence factors of Saccharomyces albicans (S. albicans) and the random amplified polymorphic DNA (RAPD) bands of them, and establish the regression model by multiple regression analysis. Extracellular phospholipase, secreted proteinase, ability to generate germ tubes and adhere to oral mucosal cells of 92 strains of S. albicans were measured in vitro; RAPD-polymerase chain reaction (RAPD-PCR) was used to get their bands. Multiple regression for virulence factors of S. albicans and RAPD-PCR bands was established. The extracellular phospholipase activity was associated with 4 RAPD bands: 350, 450, 650 and 1 300 bp (P < 0.05); secreted proteinase activity of S. albicans was associated with 2 bands: 350 and 1 200 bp (P < 0.05); the ability of germ tube produce was associated with 2 bands: 400 and 550 bp (P < 0.05). Some RAPD bands will reflect the virulence factors of S. albicans indirectly. These bands would contain some important messages for regulation of S. albicans virulence factors.

Multiple calibrator measurements improve accuracy and stability estimates of automated assays.

PubMed

Akbas, Neval; Budd, Jeffrey R; Klee, George G

2016-01-01

The effects of combining multiple calibrations on assay accuracy (bias) and measurement of calibration stability were investigated for total triiodothyronine (TT3), vitamin B12 and luteinizing hormone (LH) using Beckman Coulter's Access 2 analyzer. Three calibration procedures (CC1, CC2 and CC3) combined 12, 34 and 56 calibrator measurements over 1, 2, and 3 days. Bias was calculated between target values and average measured value over 3 consecutive days after calibration. Using regression analysis of calibrator measurements versus measurement date, calibration stability was determined as the maximum number of days before a calibrator measurement exceeded 5% tolerance limits. Competitive assays (TT3, vitamin B12) had positive time regression slopes, while sandwich assay (LH) had a negative slope. Bias values for TT3 were -2.49%, 1.49%, and -0.50% using CC1, CC2 and CC3 respectively, with calibrator stability of 32, 20, and 30 days. Bias values for vitamin B12 were 2.44%, 0.91%, and -0.50%, with calibrator stability of 4, 9, and 12 days. Bias values for LH were 2.26%, 1.44% and -0.29% with calibrator stability of >43, 39 and 36 days. Measured stability was more consistent across calibration procedures using percent change rather than difference from target: 26 days for TT3, 12 days for B12 and 31 days for LH. Averaging over multiple calibrations produced smaller bias, consistent with improved accuracy. Time regression slopes in percent change were unaffected by number of calibration measurements but calibrator stability measured from the target value was highly affected by the calibrator value at time zero.

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.

Accounting for measurement error in log regression models with applications to accelerated testing.

PubMed

Richardson, Robert; Tolley, H Dennis; Evenson, William E; Lunt, Barry M

2018-01-01

In regression settings, parameter estimates will be biased when the explanatory variables are measured with error. This bias can significantly affect modeling goals. In particular, accelerated lifetime testing involves an extrapolation of the fitted model, and a small amount of bias in parameter estimates may result in a significant increase in the bias of the extrapolated predictions. Additionally, bias may arise when the stochastic component of a log regression model is assumed to be multiplicative when the actual underlying stochastic component is additive. To account for these possible sources of bias, a log regression model with measurement error and additive error is approximated by a weighted regression model which can be estimated using Iteratively Re-weighted Least Squares. Using the reduced Eyring equation in an accelerated testing setting, the model is compared to previously accepted approaches to modeling accelerated testing data with both simulations and real data.

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

PubMed

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

2017-01-01

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

Single and multiple in-season measurements as indicators of at-harvest cotton boll damage caused by verde plant bug (Hemiptera: Miridae).

PubMed

Brewer, Michael J; Armstrong, J Scott; Parker, Roy D

2013-06-01

The ability to monitor verde plant bug, Creontiades signatus Distant (Hemiptera: Miridae), and the progression of cotton, Gossypium hirsutum L., boll responses to feeding and associated cotton boll rot provided opportunity to assess if single in-season measurements had value in evaluating at-harvest damage to bolls and if multiple in-season measurements enhanced their combined use. One in-season verde plant bug density measurement, three in-season plant injury measurements, and two at-harvest damage measurements were taken in 15 cotton fields in South Texas, 2010. Linear regression selected two measurements as potentially useful indicators of at-harvest damage: verde plant bug density (adjusted r2 = 0.68; P = 0.0004) and internal boll injury of the carpel wall (adjusted r2 = 0.72; P = 0.004). Considering use of multiple measurements, a stepwise multiple regression of the four in-season measurements selected a univariate model (verde plant bug density) using a 0.15 selection criterion (adjusted r2 = 0.74; P = 0.0002) and a bivariate model (verde plant bug density-internal boll injury) using a 0.25 selection criterion (adjusted r2 = 0.76; P = 0.0007) as indicators of at-harvest damage. In a validation using cultivar and water regime treatments experiencing low verde plant bug pressure in 2011 and 2012, the bivariate model performed better than models using verde plant bug density or internal boll injury separately. Overall, verde plant bug damaging cotton bolls exemplified the benefits of using multiple in-season measurements in pest monitoring programs, under the challenging situation when at-harvest damage results from a sequence of plant responses initiated by in-season insect feeding.

Confidence Intervals for Squared Semipartial Correlation Coefficients: The Effect of Nonnormality

ERIC Educational Resources Information Center

Algina, James; Keselman, H. J.; Penfield, Randall D.

2010-01-01

The increase in the squared multiple correlation coefficient ([delta]R[superscript 2]) associated with a variable in a regression equation is a commonly used measure of importance in regression analysis. Algina, Keselman, and Penfield found that intervals based on asymptotic principles were typically very inaccurate, even though the sample size…

Generalized and synthetic regression estimators for randomized branch sampling

Treesearch

David L. R. Affleck; Timothy G. Gregoire

2015-01-01

In felled-tree studies, ratio and regression estimators are commonly used to convert more readily measured branch characteristics to dry crown mass estimates. In some cases, data from multiple trees are pooled to form these estimates. This research evaluates the utility of both tactics in the estimation of crown biomass following randomized branch sampling (...

Prediction of anthropometric foot characteristics in children.

PubMed

Morrison, Stewart C; Durward, Brian R; Watt, Gordon F; Donaldson, Malcolm D C

2009-01-01

The establishment of growth reference values is needed in pediatric practice where pathologic conditions can have a detrimental effect on the growth and development of the pediatric foot. This study aims to use multiple regression to evaluate the effects of multiple predictor variables (height, age, body mass, and gender) on anthropometric characteristics of the peripubescent foot. Two hundred children aged 9 to 12 years were recruited, and three anthropometric measurements of the pediatric foot were recorded (foot length, forefoot width, and navicular height). Multiple regression analysis was conducted, and coefficients for gender, height, and body mass all had significant relationships for the prediction of forefoot width and foot length (P < or = .05, r > or = 0.7). The coefficients for gender and body mass were not significant for the prediction of navicular height (P > or = .05), whereas height was (P < or = .05). Normative growth reference values and prognostic regression equations are presented for the peripubescent foot.

Use of factor scores for predicting body weight from linear body measurements in three South African indigenous chicken breeds.

PubMed

Malomane, Dorcus Kholofelo; Norris, David; Banga, Cuthbert B; Ngambi, Jones W

2014-02-01

Body weight and weight of body parts are of economic importance. It is difficult to directly predict body weight from highly correlated morphological traits through multiple regression. Factor analysis was carried out to examine the relationship between body weight and five linear body measurements (body length, body girth, wing length, shank thickness, and shank length) in South African Venda (VN), Naked neck (NN), and Potchefstroom koekoek (PK) indigenous chicken breeds, with a view to identify those factors that define body conformation. Multiple regression was subsequently performed to predict body weight, using orthogonal traits derived from the factor analysis. Measurements were obtained from 210 chickens, 22 weeks of age, 70 chickens per breed. High correlations were obtained between body weight and all body measurements except for wing length in PK. Two factors extracted after varimax rotation explained 91, 95, and 83% of total variation in VN, NN, and PK, respectively. Factor 1 explained 73, 90, and 64% in VN, NN, and PK, respectively, and was loaded on all body measurements except for wing length in VN and PK. In a multiple regression, these two factors accounted for 72% variation in body weight in VN, while only factor 1 accounted for 83 and 74% variation in body weight in NN and PK, respectively. The two factors could be used to define body size and conformation of these breeds. Factor 1 could predict body weight in all three breeds. Body measurements can be better selected jointly to improve body weight in these breeds.

Spatial and visuospatial working memory tests predict performance in classic multiple-object tracking in young adults, but nonspatial measures of the executive do not.

PubMed

Trick, Lana M; Mutreja, Rachna; Hunt, Kelly

2012-02-01

An individual-differences approach was used to investigate the roles of visuospatial working memory and the executive in multiple-object tracking. The Corsi Blocks and Visual Patterns Tests were used to assess visuospatial working memory. Two relatively nonspatial measures of the executive were used: operation span (OSPAN) and reading span (RSPAN). For purposes of comparison, the digit span test was also included (a measure not expected to correlate with tracking). The tests predicted substantial amounts of variance (R (2) = .33), and the visuospatial measures accounted for the majority (R (2) = .30), with each making a significant contribution. Although the executive measures correlated with each other, the RSPAN did not correlate with tracking. The correlation between OSPAN and tracking was similar in magnitude to that between digit span and tracking (p < .05 for both), and when regression was used to partial out shared variance between the two tests, the remaining variance predicted by the OSPAN was minimal (sr ( 2 ) = .029). When measures of spatial memory were included in the regression, the unique variance predicted by the OSPAN became negligible (sr ( 2 ) = .000004). This suggests that the executive, as measured by tests such as the OSPAN, plays little role in explaining individual differences in multiple-object tracking.

Estimation of stature from the foot and its segments in a sub-adult female population of North India

PubMed Central

2011-01-01

Background Establishing personal identity is one of the main concerns in forensic investigations. Estimation of stature forms a basic domain of the investigation process in unknown and co-mingled human remains in forensic anthropology case work. The objective of the present study was to set up standards for estimation of stature from the foot and its segments in a sub-adult female population. Methods The sample for the study constituted 149 young females from the Northern part of India. The participants were aged between 13 and 18 years. Besides stature, seven anthropometric measurements that included length of the foot from each toe (T1, T2, T3, T4, and T5 respectively), foot breadth at ball (BBAL) and foot breadth at heel (BHEL) were measured on both feet in each participant using standard methods and techniques. Results The results indicated that statistically significant differences (p < 0.05) between left and right feet occur in both the foot breadth measurements (BBAL and BHEL). Foot length measurements (T1 to T5 lengths) did not show any statistically significant bilateral asymmetry. The correlation between stature and all the foot measurements was found to be positive and statistically significant (p-value < 0.001). Linear regression models and multiple regression models were derived for estimation of stature from the measurements of the foot. The present study indicates that anthropometric measurements of foot and its segments are valuable in the estimation of stature. Foot length measurements estimate stature with greater accuracy when compared to foot breadth measurements. Conclusions The present study concluded that foot measurements have a strong relationship with stature in the sub-adult female population of North India. Hence, the stature of an individual can be successfully estimated from the foot and its segments using different regression models derived in the study. The regression models derived in the study may be applied successfully for the estimation of stature in sub-adult females, whenever foot remains are brought for forensic examination. Stepwise multiple regression models tend to estimate stature more accurately than linear regression models in female sub-adults. PMID:22104433

Estimation of stature from the foot and its segments in a sub-adult female population of North India.

PubMed

Krishan, Kewal; Kanchan, Tanuj; Passi, Neelam

2011-11-21

Establishing personal identity is one of the main concerns in forensic investigations. Estimation of stature forms a basic domain of the investigation process in unknown and co-mingled human remains in forensic anthropology case work. The objective of the present study was to set up standards for estimation of stature from the foot and its segments in a sub-adult female population. The sample for the study constituted 149 young females from the Northern part of India. The participants were aged between 13 and 18 years. Besides stature, seven anthropometric measurements that included length of the foot from each toe (T1, T2, T3, T4, and T5 respectively), foot breadth at ball (BBAL) and foot breadth at heel (BHEL) were measured on both feet in each participant using standard methods and techniques. The results indicated that statistically significant differences (p < 0.05) between left and right feet occur in both the foot breadth measurements (BBAL and BHEL). Foot length measurements (T1 to T5 lengths) did not show any statistically significant bilateral asymmetry. The correlation between stature and all the foot measurements was found to be positive and statistically significant (p-value < 0.001). Linear regression models and multiple regression models were derived for estimation of stature from the measurements of the foot. The present study indicates that anthropometric measurements of foot and its segments are valuable in the estimation of stature. Foot length measurements estimate stature with greater accuracy when compared to foot breadth measurements. The present study concluded that foot measurements have a strong relationship with stature in the sub-adult female population of North India. Hence, the stature of an individual can be successfully estimated from the foot and its segments using different regression models derived in the study. The regression models derived in the study may be applied successfully for the estimation of stature in sub-adult females, whenever foot remains are brought for forensic examination. Stepwise multiple regression models tend to estimate stature more accurately than linear regression models in female sub-adults.

A Factor Analytic and Regression Approach to Functional Age: Potential Effects of Race.

ERIC Educational Resources Information Center

Colquitt, Alan L.; And Others

Factor analysis and multiple regression are two major approaches used to look at functional age, which takes account of the extensive variation in the rate of physiological and psychological maturation throughout life. To examine the role of racial or cultural influences on the measurement of functional age, a battery of 12 tests concentrating on…

Default Bayes Factors for Model Selection in Regression

ERIC Educational Resources Information Center

Rouder, Jeffrey N.; Morey, Richard D.

2012-01-01

In this article, we present a Bayes factor solution for inference in multiple regression. Bayes factors are principled measures of the relative evidence from data for various models or positions, including models that embed null hypotheses. In this regard, they may be used to state positive evidence for a lack of an effect, which is not possible…

Age- and sex-dependent regression models for predicting the live weight of West African Dwarf goat from body measurements.

PubMed

Sowande, O S; Oyewale, B F; Iyasere, O S

2010-06-01

The relationships between live weight and eight body measurements of West African Dwarf (WAD) goats were studied using 211 animals under farm condition. The animals were categorized based on age and sex. Data obtained on height at withers (HW), heart girth (HG), body length (BL), head length (HL), and length of hindquarter (LHQ) were fitted into simple linear, allometric, and multiple-regression models to predict live weight from the body measurements according to age group and sex. Results showed that live weight, HG, BL, LHQ, HL, and HW increased with the age of the animals. In multiple-regression model, HG and HL best fit the model for goat kids; HG, HW, and HL for goat aged 13-24 months; while HG, LHQ, HW, and HL best fit the model for goats aged 25-36 months. Coefficients of determination (R(2)) values for linear and allometric models for predicting the live weight of WAD goat increased with age in all the body measurements, with HG being the most satisfactory single measurement in predicting the live weight of WAD goat. Sex had significant influence on the model with R(2) values consistently higher in females except the models for LHQ and HW.

Multiple-trait structured antedependence model to study the relationship between litter size and birth weight in pigs and rabbits.

PubMed

David, Ingrid; Garreau, Hervé; Balmisse, Elodie; Billon, Yvon; Canario, Laurianne

2017-01-20

Some genetic studies need to take into account correlations between traits that are repeatedly measured over time. Multiple-trait random regression models are commonly used to analyze repeated traits but suffer from several major drawbacks. In the present study, we developed a multiple-trait extension of the structured antedependence model (SAD) to overcome this issue and validated its usefulness by modeling the association between litter size (LS) and average birth weight (ABW) over parities in pigs and rabbits. The single-trait SAD model assumes that a random effect at time [Formula: see text] can be explained by the previous values of the random effect (i.e. at previous times). The proposed multiple-trait extension of the SAD model consists in adding a cross-antedependence parameter to the single-trait SAD model. This model can be easily fitted using ASReml and the OWN Fortran program that we have developed. In comparison with the random regression model, we used our multiple-trait SAD model to analyze the LS and ABW of 4345 litters from 1817 Large White sows and 8706 litters from 2286 L-1777 does over a maximum of five successive parities. For both species, the multiple-trait SAD fitted the data better than the random regression model. The difference between AIC of the two models (AIC_random regression-AIC_SAD) were equal to 7 and 227 for pigs and rabbits, respectively. A similar pattern of heritability and correlation estimates was obtained for both species. Heritabilities were lower for LS (ranging from 0.09 to 0.29) than for ABW (ranging from 0.23 to 0.39). The general trend was a decrease of the genetic correlation for a given trait between more distant parities. Estimates of genetic correlations between LS and ABW were negative and ranged from -0.03 to -0.52 across parities. No correlation was observed between the permanent environmental effects, except between the permanent environmental effects of LS and ABW of the same parity, for which the estimate of the correlation was strongly negative (ranging from -0.57 to -0.67). We demonstrated that application of our multiple-trait SAD model is feasible for studying several traits with repeated measurements and showed that it provided a better fit to the data than the random regression model.

Validating the absolute reliability of a fat free mass estimate equation in hemodialysis patients using near-infrared spectroscopy.

PubMed

Kono, Kenichi; Nishida, Yusuke; Moriyama, Yoshihumi; Taoka, Masahiro; Sato, Takashi

2015-06-01

The assessment of nutritional states using fat free mass (FFM) measured with near-infrared spectroscopy (NIRS) is clinically useful. This measurement should incorporate the patient's post-dialysis weight ("dry weight"), in order to exclude the effects of any change in water mass. We therefore used NIRS to investigate the regression, independent variables, and absolute reliability of FFM in dry weight. The study included 47 outpatients from the hemodialysis unit. Body weight was measured before dialysis, and FFM was measured using NIRS before and after dialysis treatment. Multiple regression analysis was used to estimate the FFM in dry weight as the dependent variable. The measured FFM before dialysis treatment (Mw-FFM), and the difference between measured and dry weight (Mw-Dw) were independent variables. We performed Bland-Altman analysis to detect errors between the statistically estimated FFM and the measured FFM after dialysis treatment. The multiple regression equation to estimate the FFM in dry weight was: Dw-FFM = 0.038 + (0.984 × Mw-FFM) + (-0.571 × [Mw-Dw]); R(2)  = 0.99). There was no systematic bias between the estimated and the measured values of FFM in dry weight. Using NIRS, FFM in dry weight can be calculated by an equation including FFM in measured weight and the difference between the measured weight and the dry weight. © 2015 The Authors. Therapeutic Apheresis and Dialysis © 2015 International Society for Apheresis.

Emotional Reactions to Stress among Adolescent Boys and Girls: An Examination of the Mediating Mechanisms Proposed by General Strain Theory

ERIC Educational Resources Information Center

Sigfusdottir, Inga-Dora; Silver, Eric

2009-01-01

This study examines the effects of negative life events on anger and depressed mood among a sample of 7,758 Icelandic adolescents, measured as part of the National Survey of Icelandic Adolescents (Thorlindsson, Sigfusdottir, Bernburg, & Halldorsson, 1998). Using multiple linear regression and multinomial logit regression, we find that (a)…

The Relative Contributions of the Cattell-Horn-Carroll Cognitive Abilities in Explaining Writing Achievement during Childhood and Adolescence

ERIC Educational Resources Information Center

Floyd, Randy G.; McGrew, Kevin S.; Evans, Jeffrey J.

2008-01-01

This study examined the relative contributions of measures of Cattell-Horn-Carroll (CHC) cognitive abilities in explaining writing achievement. Drawing from samples that covered the age range of 7 to 18 years, simultaneous multiple regression was used to regress scores from the Woodcock-Johnson III (WJ III; Woodcock, McGrew, & Mather, 2001) that…

Computing mammographic density from a multiple regression model constructed with image-acquisition parameters from a full-field digital mammographic unit

PubMed Central

Lu, Lee-Jane W.; Nishino, Thomas K.; Khamapirad, Tuenchit; Grady, James J; Leonard, Morton H.; Brunder, Donald G.

2009-01-01

Breast density (the percentage of fibroglandular tissue in the breast) has been suggested to be a useful surrogate marker for breast cancer risk. It is conventionally measured using screen-film mammographic images by a labor intensive histogram segmentation method (HSM). We have adapted and modified the HSM for measuring breast density from raw digital mammograms acquired by full-field digital mammography. Multiple regression model analyses showed that many of the instrument parameters for acquiring the screening mammograms (e.g. breast compression thickness, radiological thickness, radiation dose, compression force, etc) and image pixel intensity statistics of the imaged breasts were strong predictors of the observed threshold values (model R2=0.93) and %density (R2=0.84). The intra-class correlation coefficient of the %-density for duplicate images was estimated to be 0.80, using the regression model-derived threshold values, and 0.94 if estimated directly from the parameter estimates of the %-density prediction regression model. Therefore, with additional research, these mathematical models could be used to compute breast density objectively, automatically bypassing the HSM step, and could greatly facilitate breast cancer research studies. PMID:17671343

Multiple regression based imputation for individualizing template human model from a small number of measured dimensions.

PubMed

Nohara, Ryuki; Endo, Yui; Murai, Akihiko; Takemura, Hiroshi; Kouchi, Makiko; Tada, Mitsunori

2016-08-01

Individual human models are usually created by direct 3D scanning or deforming a template model according to the measured dimensions. In this paper, we propose a method to estimate all the necessary dimensions (full set) for the human model individualization from a small number of measured dimensions (subset) and human dimension database. For this purpose, we solved multiple regression equation from the dimension database given full set dimensions as the objective variable and subset dimensions as the explanatory variables. Thus, the full set dimensions are obtained by simply multiplying the subset dimensions to the coefficient matrix of the regression equation. We verified the accuracy of our method by imputing hand, foot, and whole body dimensions from their dimension database. The leave-one-out cross validation is employed in this evaluation. The mean absolute errors (MAE) between the measured and the estimated dimensions computed from 4 dimensions (hand length, breadth, middle finger breadth at proximal, and middle finger depth at proximal) in the hand, 2 dimensions (foot length, breadth, and lateral malleolus height) in the foot, and 1 dimension (height) and weight in the whole body are computed. The average MAE of non-measured dimensions were 4.58% in the hand, 4.42% in the foot, and 3.54% in the whole body, while that of measured dimensions were 0.00%.

Self-reports of trauma and dissociation: An examination of context effects.

PubMed

Lemons, Peter; Lynn, Steven Jay

2016-08-01

To examine context effects in moderating the link between self-reported trauma and dissociation in undergraduate samples, we administered these measures either in the same or different experimental contexts. Trauma History Screen/THS (Carlson et al., 2011)-Dissociative Experiences Scale/DES-II (Bernstein & Putnam, 1986) correlations revealed a context effect (greater correlations in same test context), although multiple regression analyses did not confirm this finding. A context effect was supported in DES-Taxon scores using multiple regression for the THS but not the Modified Posttraumatic Stress Scale (MPSS-SR; Falsetti, Resnick, Resick, & Kilpatrick, 1993), an effect confirmed with correlation comparisons. Ethnicity influenced the association between measures of trauma and dissociation. Overall, the relation between measures of trauma and dissociation was small to medium, although high correlations were observed between the DES depersonalization/derealization subscale and the Multiscale Dissociation Inventory (Briere, Weathers, & Runtz, 2005) depersonalization and derealization subscales, supporting the construct validity of these measures. Copyright © 2016 Elsevier Inc. All rights reserved.

National Culture, Creativity, and Productivity: What's the Relationship with Student Achievement?

ERIC Educational Resources Information Center

Fang, Zheng; Xu, Xianxuan; Grant, Leslie W.; Stronge, James H.; Ward, Thomas J.

2016-01-01

Using Hofstede's culture dimensions and World Values Survey (WVS) dimensions, the study uses a series of multiple regressions to explore the relationship among national culture, creativity as measured by patents, economic productivity as measured by gross domestic product per capita, and student achievement as measured by Trends in International…

Height and Weight Estimation From Anthropometric Measurements Using Machine Learning Regressions

PubMed Central

Fernandes, Bruno J. T.; Roque, Alexandre

2018-01-01

Height and weight are measurements explored to tracking nutritional diseases, energy expenditure, clinical conditions, drug dosages, and infusion rates. Many patients are not ambulant or may be unable to communicate, and a sequence of these factors may not allow accurate estimation or measurements; in those cases, it can be estimated approximately by anthropometric means. Different groups have proposed different linear or non-linear equations which coefficients are obtained by using single or multiple linear regressions. In this paper, we present a complete study of the application of different learning models to estimate height and weight from anthropometric measurements: support vector regression, Gaussian process, and artificial neural networks. The predicted values are significantly more accurate than that obtained with conventional linear regressions. In all the cases, the predictions are non-sensitive to ethnicity, and to gender, if more than two anthropometric parameters are analyzed. The learning model analysis creates new opportunities for anthropometric applications in industry, textile technology, security, and health care. PMID:29651366

Comparative study of outcome measures and analysis methods for traumatic brain injury trials.

PubMed

Alali, Aziz S; Vavrek, Darcy; Barber, Jason; Dikmen, Sureyya; Nathens, Avery B; Temkin, Nancy R

2015-04-15

Batteries of functional and cognitive measures have been proposed as alternatives to the Extended Glasgow Outcome Scale (GOSE) as the primary outcome for traumatic brain injury (TBI) trials. We evaluated several approaches to analyzing GOSE and a battery of four functional and cognitive measures. Using data from a randomized trial, we created a "super" dataset of 16,550 subjects from patients with complete data (n=331) and then simulated multiple treatment effects across multiple outcome measures. Patients were sampled with replacement (bootstrapping) to generate 10,000 samples for each treatment effect (n=400 patients/group). The percentage of samples where the null hypothesis was rejected estimates the power. All analytic techniques had appropriate rates of type I error (≤5%). Accounting for baseline prognosis either by using sliding dichotomy for GOSE or using regression-based methods substantially increased the power over the corresponding analysis without accounting for prognosis. Analyzing GOSE using multivariate proportional odds regression or analyzing the four-outcome battery with regression-based adjustments had the highest power, assuming equal treatment effect across all components. Analyzing GOSE using a fixed dichotomy provided the lowest power for both unadjusted and regression-adjusted analyses. We assumed an equal treatment effect for all measures. This may not be true in an actual clinical trial. Accounting for baseline prognosis is critical to attaining high power in Phase III TBI trials. The choice of primary outcome for future trials should be guided by power, the domain of brain function that an intervention is likely to impact, and the feasibility of collecting outcome data.

Comparative Study of Outcome Measures and Analysis Methods for Traumatic Brain Injury Trials

PubMed Central

Alali, Aziz S.; Vavrek, Darcy; Barber, Jason; Dikmen, Sureyya; Nathens, Avery B.

2015-01-01

Abstract Batteries of functional and cognitive measures have been proposed as alternatives to the Extended Glasgow Outcome Scale (GOSE) as the primary outcome for traumatic brain injury (TBI) trials. We evaluated several approaches to analyzing GOSE and a battery of four functional and cognitive measures. Using data from a randomized trial, we created a “super” dataset of 16,550 subjects from patients with complete data (n=331) and then simulated multiple treatment effects across multiple outcome measures. Patients were sampled with replacement (bootstrapping) to generate 10,000 samples for each treatment effect (n=400 patients/group). The percentage of samples where the null hypothesis was rejected estimates the power. All analytic techniques had appropriate rates of type I error (≤5%). Accounting for baseline prognosis either by using sliding dichotomy for GOSE or using regression-based methods substantially increased the power over the corresponding analysis without accounting for prognosis. Analyzing GOSE using multivariate proportional odds regression or analyzing the four-outcome battery with regression-based adjustments had the highest power, assuming equal treatment effect across all components. Analyzing GOSE using a fixed dichotomy provided the lowest power for both unadjusted and regression-adjusted analyses. We assumed an equal treatment effect for all measures. This may not be true in an actual clinical trial. Accounting for baseline prognosis is critical to attaining high power in Phase III TBI trials. The choice of primary outcome for future trials should be guided by power, the domain of brain function that an intervention is likely to impact, and the feasibility of collecting outcome data. PMID:25317951

White light emitting diode as potential replacement of tungsten-halogen lamp for visible spectroscopy system: a case study in the measurement of mango qualities

NASA Astrophysics Data System (ADS)

Chiong, W. L.; Omar, A. F.

2017-07-01

Non-destructive technique based on visible (VIS) spectroscopy using light emitting diode (LED) as lighting was used for evaluation of the internal quality of mango fruit. The objective of this study was to investigate feasibility of white LED as lighting in spectroscopic instrumentation to predict the acidity and soluble solids content of intact Sala Mango. The reflectance spectra of the mango samples were obtained and measured in the visible range (400-700 nm) using VIS spectroscopy illuminated under different white LEDs and tungsten-halogen lamp (pro lamp). Regression models were developed by multiple linear regression to establish the relationship between spectra and internal quality. Direct calibration transfer procedure was then applied between master and slave lighting to check on the acidity prediction results after transfer. Determination of mango acidity under white LED lighting was successfully performed through VIS spectroscopy using multiple linear regression but otherwise for soluble solids content. Satisfactory results were obtained for calibration transfer between LEDs with different correlated colour temperature indicated this technique was successfully used in spectroscopy measurement between two similar light sources in prediction of internal quality of mango.

Linear viral load increase of a single HPV-type in women with multiple HPV infections predicts progression to cervical cancer.

PubMed

Depuydt, Christophe E; Thys, Sofie; Beert, Johan; Jonckheere, Jef; Salembier, Geert; Bogers, Johannes J

2016-11-01

Persistent high-risk human papillomavirus (HPV) infection is strongly associated with development of high-grade cervical intraepithelial neoplasia or cancer (CIN3+). In single type infections, serial type-specific viral-load measurements predict the natural history of the infection. In infections with multiple HPV-types, the individual type-specific viral-load profile could distinguish progressing HPV-infections from regressing infections. A case-cohort natural history study was established using samples from untreated women with multiple HPV-infections who developed CIN3+ (n = 57) or cleared infections (n = 88). Enriched cell pellet from liquid based cytology samples were subjected to a clinically validated real-time qPCR-assay (18 HPV-types). Using serial type-specific viral-load measurements (≥3) we calculated HPV-specific slopes and coefficient of determination (R(2) ) by linear regression. For each woman slopes and R(2) were used to calculate which HPV-induced processes were ongoing (progression, regression, serial transient, transient). In transient infections with multiple HPV-types, each single HPV-type generated similar increasing (0.27copies/cell/day) and decreasing (-0.27copies/cell/day) viral-load slopes. In CIN3+, at least one of the HPV-types had a clonal progressive course (R(2)  ≥ 0.85; 0.0025copies/cell/day). In selected CIN3+ cases (n = 6), immunostaining detecting type-specific HPV 16, 31, 33, 58 and 67 RNA showed an even staining in clonal populations (CIN3+), whereas in transient virion-producing infections the RNA-staining was less in the basal layer compared to the upper layer where cells were ready to desquamate and release newly-formed virions. RNA-hybridization patterns matched the calculated ongoing processes measured by R(2) and slope in serial type-specific viral-load measurements preceding the biopsy. In women with multiple HPV-types, serial type-specific viral-load measurements predict the natural history of the different HPV-types and elucidates HPV-genotype attribution. © 2016 UICC.

Cancer Patients Enrolled in a Smoking Cessation Clinical Trial: Characteristics and Correlates of Smoking Rate and Nicotine Dependence.

PubMed

Miele, Andrew; Thompson, Morgan; Jao, Nancy C; Kalhan, Ravi; Leone, Frank; Hogarth, Lee; Hitsman, Brian; Schnoll, Robert

2018-01-01

A substantial proportion of cancer patients continue to smoke after their diagnosis but few studies have evaluated correlates of nicotine dependence and smoking rate in this population, which could help guide smoking cessation interventions. This study evaluated correlates of smoking rate and nicotine dependence among 207 cancer patients. A cross-sectional analysis using multiple linear regression evaluated disease, demographic, affective, and tobacco-seeking correlates of smoking rate and nicotine dependence. Smoking rate was assessed using a timeline follow-back method. The Fagerström Test for Nicotine Dependence measured levels of nicotine dependence. A multiple linear regression predicting nicotine dependence showed an association with smoking to alleviate a sense of addiction from the Reasons for Smoking scale and tobacco-seeking behavior from the concurrent choice task ( p < .05), but not with affect measured by the HADS and PANAS ( p > .05). Multiple linear regression predicting prequit showed an association with smoking to alleviate addiction ( p < .05). ANOVA showed that Caucasian participants reported greater rates of smoking compared to other races. The results suggest that behavioral smoking cessation interventions that focus on helping patients to manage tobacco-seeking behavior, rather than mood management interventions, could help cancer patients quit smoking.

Anxiety Levels Are Independently Associated With Cognitive Performance in an Australian Multiple Sclerosis Patient Cohort.

PubMed

Ribbons, Karen; Lea, Rodney; Schofield, Peter W; Lechner-Scott, Jeannette

2017-01-01

Neurological and psychological symptoms in multiple sclerosis can affect cognitive function. The objective of this study was to explore the relationship between psychological measures and cognitive performance in a patient cohort. In 322 multiple sclerosis patients, psychological symptoms were measured using the Depression Anxiety and Stress Scale, and cognitive function was evaluated using Audio Recorded Cognitive Screen. Multifactor linear regression analysis, accounting for all clinical covariates, found that anxiety was the only psychological measure to remain a significant predictor of cognitive performance (p<0.001), particularly memory function (p<0.001). Further prospective studies are required to determine whether treatment of anxiety improves cognitive impairment.

Incremental Validity in the Clinical Assessment of Early Childhood Development

ERIC Educational Resources Information Center

Liu, Xin; Zhou, Xiaobin; Lackaff, Julie

2013-01-01

The authors demonstrate the increment of clinical validity in early childhood assessment of physical impairment (PI), developmental delay (DD), and autism (AUT) using multiple standardized developmental screening measures such as performance measures and parent and teacher rating scales. Hierarchical regression and sensitivity/specificity analyses…

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.

Effects of land cover, topography, and built structure on seasonal water quality at multiple spatial scales.

PubMed

Pratt, Bethany; Chang, Heejun

2012-03-30

The relationship among land cover, topography, built structure and stream water quality in the Portland Metro region of Oregon and Clark County, Washington areas, USA, is analyzed using ordinary least squares (OLS) and geographically weighted (GWR) multiple regression models. Two scales of analysis, a sectional watershed and a buffer, offered a local and a global investigation of the sources of stream pollutants. Model accuracy, measured by R(2) values, fluctuated according to the scale, season, and regression method used. While most wet season water quality parameters are associated with urban land covers, most dry season water quality parameters are related topographic features such as elevation and slope. GWR models, which take into consideration local relations of spatial autocorrelation, had stronger results than OLS regression models. In the multiple regression models, sectioned watershed results were consistently better than the sectioned buffer results, except for dry season pH and stream temperature parameters. This suggests that while riparian land cover does have an effect on water quality, a wider contributing area needs to be included in order to account for distant sources of pollutants. Copyright © 2012 Elsevier B.V. All rights reserved.

NIMEFI: gene regulatory network inference using multiple ensemble feature importance algorithms.

PubMed

Ruyssinck, Joeri; Huynh-Thu, Vân Anh; Geurts, Pierre; Dhaene, Tom; Demeester, Piet; Saeys, Yvan

2014-01-01

One of the long-standing open challenges in computational systems biology is the topology inference of gene regulatory networks from high-throughput omics data. Recently, two community-wide efforts, DREAM4 and DREAM5, have been established to benchmark network inference techniques using gene expression measurements. In these challenges the overall top performer was the GENIE3 algorithm. This method decomposes the network inference task into separate regression problems for each gene in the network in which the expression values of a particular target gene are predicted using all other genes as possible predictors. Next, using tree-based ensemble methods, an importance measure for each predictor gene is calculated with respect to the target gene and a high feature importance is considered as putative evidence of a regulatory link existing between both genes. The contribution of this work is twofold. First, we generalize the regression decomposition strategy of GENIE3 to other feature importance methods. We compare the performance of support vector regression, the elastic net, random forest regression, symbolic regression and their ensemble variants in this setting to the original GENIE3 algorithm. To create the ensemble variants, we propose a subsampling approach which allows us to cast any feature selection algorithm that produces a feature ranking into an ensemble feature importance algorithm. We demonstrate that the ensemble setting is key to the network inference task, as only ensemble variants achieve top performance. As second contribution, we explore the effect of using rankwise averaged predictions of multiple ensemble algorithms as opposed to only one. We name this approach NIMEFI (Network Inference using Multiple Ensemble Feature Importance algorithms) and show that this approach outperforms all individual methods in general, although on a specific network a single method can perform better. An implementation of NIMEFI has been made publicly available.

Clinical Decision Support Model to Predict Occlusal Force in Bruxism Patients.

PubMed

Thanathornwong, Bhornsawan; Suebnukarn, Siriwan

2017-10-01

The aim of this study was to develop a decision support model for the prediction of occlusal force from the size and color of articulating paper markings in bruxism patients. We used the information from the datasets of 30 bruxism patients in which digital measurements of the size and color of articulating paper markings (12-µm Hanel; Coltene/Whaledent GmbH, Langenau, Germany) on canine protected hard stabilization splints were measured in pixels (P) and in red (R), green (G), and blue (B) values using Adobe Photoshop software (Adobe Systems, San Jose, CA, USA). The occlusal force (F) was measured using T-Scan III (Tekscan Inc., South Boston, MA, USA). The multiple regression equation was applied to predict F from the P and RGB. Model evaluation was performed using the datasets from 10 new patients. The patient's occlusal force measured by T-Scan III was used as a 'gold standard' to compare with the occlusal force predicted by the multiple regression model. The results demonstrate that the correlation between the occlusal force and the pixels and RGB of the articulating paper markings was positive (F = 1.62×P + 0.07×R -0.08×G + 0.08×B + 4.74; R 2 = 0.34). There was a high degree of agreement between the occlusal force of the patient measured using T-Scan III and the occlusal force predicted by the model (kappa value = 0.82). The results obtained demonstrate that the multiple regression model can predict the occlusal force using the digital values for the size and color of the articulating paper markings in bruxism patients.

Clinical Decision Support Model to Predict Occlusal Force in Bruxism Patients

PubMed Central

Thanathornwong, Bhornsawan

2017-01-01

Objectives The aim of this study was to develop a decision support model for the prediction of occlusal force from the size and color of articulating paper markings in bruxism patients. Methods We used the information from the datasets of 30 bruxism patients in which digital measurements of the size and color of articulating paper markings (12-µm Hanel; Coltene/Whaledent GmbH, Langenau, Germany) on canine protected hard stabilization splints were measured in pixels (P) and in red (R), green (G), and blue (B) values using Adobe Photoshop software (Adobe Systems, San Jose, CA, USA). The occlusal force (F) was measured using T-Scan III (Tekscan Inc., South Boston, MA, USA). The multiple regression equation was applied to predict F from the P and RGB. Model evaluation was performed using the datasets from 10 new patients. The patient's occlusal force measured by T-Scan III was used as a ‘gold standard’ to compare with the occlusal force predicted by the multiple regression model. Results The results demonstrate that the correlation between the occlusal force and the pixels and RGB of the articulating paper markings was positive (F = 1.62×P + 0.07×R –0.08×G + 0.08×B + 4.74; R2 = 0.34). There was a high degree of agreement between the occlusal force of the patient measured using T-Scan III and the occlusal force predicted by the model (kappa value = 0.82). Conclusions The results obtained demonstrate that the multiple regression model can predict the occlusal force using the digital values for the size and color of the articulating paper markings in bruxism patients. PMID:29181234

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...

Order Selection for General Expression of Nonlinear Autoregressive Model Based on Multivariate Stepwise Regression

NASA Astrophysics Data System (ADS)

Shi, Jinfei; Zhu, Songqing; Chen, Ruwen

2017-12-01

An order selection method based on multiple stepwise regressions is proposed for General Expression of Nonlinear Autoregressive model which converts the model order problem into the variable selection of multiple linear regression equation. The partial autocorrelation function is adopted to define the linear term in GNAR model. The result is set as the initial model, and then the nonlinear terms are introduced gradually. Statistics are chosen to study the improvements of both the new introduced and originally existed variables for the model characteristics, which are adopted to determine the model variables to retain or eliminate. So the optimal model is obtained through data fitting effect measurement or significance test. The simulation and classic time-series data experiment results show that the method proposed is simple, reliable and can be applied to practical engineering.

A Technique of Fuzzy C-Mean in Multiple Linear Regression Model toward Paddy Yield

NASA Astrophysics Data System (ADS)

Syazwan Wahab, Nur; Saifullah Rusiman, Mohd; Mohamad, Mahathir; Amira Azmi, Nur; Che Him, Norziha; Ghazali Kamardan, M.; Ali, Maselan

2018-04-01

In this paper, we propose a hybrid model which is a combination of multiple linear regression model and fuzzy c-means method. This research involved a relationship between 20 variates of the top soil that are analyzed prior to planting of paddy yields at standard fertilizer rates. Data used were from the multi-location trials for rice carried out by MARDI at major paddy granary in Peninsular Malaysia during the period from 2009 to 2012. Missing observations were estimated using mean estimation techniques. The data were analyzed using multiple linear regression model and a combination of multiple linear regression model and fuzzy c-means method. Analysis of normality and multicollinearity indicate that the data is normally scattered without multicollinearity among independent variables. Analysis of fuzzy c-means cluster the yield of paddy into two clusters before the multiple linear regression model can be used. The comparison between two method indicate that the hybrid of multiple linear regression model and fuzzy c-means method outperform the multiple linear regression model with lower value of mean square error.

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

USGS Publications Warehouse

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

2009-01-01

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

Time-resolved flow reconstruction with indirect measurements using regression models and Kalman-filtered POD ROM

NASA Astrophysics Data System (ADS)

Leroux, Romain; Chatellier, Ludovic; David, Laurent

2018-01-01

This article is devoted to the estimation of time-resolved particle image velocimetry (TR-PIV) flow fields using a time-resolved point measurements of a voltage signal obtained by hot-film anemometry. A multiple linear regression model is first defined to map the TR-PIV flow fields onto the voltage signal. Due to the high temporal resolution of the signal acquired by the hot-film sensor, the estimates of the TR-PIV flow fields are obtained with a multiple linear regression method called orthonormalized partial least squares regression (OPLSR). Subsequently, this model is incorporated as the observation equation in an ensemble Kalman filter (EnKF) applied on a proper orthogonal decomposition reduced-order model to stabilize it while reducing the effects of the hot-film sensor noise. This method is assessed for the reconstruction of the flow around a NACA0012 airfoil at a Reynolds number of 1000 and an angle of attack of {20}°. Comparisons with multi-time delay-modified linear stochastic estimation show that both the OPLSR and EnKF combined with OPLSR are more accurate as they produce a much lower relative estimation error, and provide a faithful reconstruction of the time evolution of the velocity flow fields.

Wheat flour dough Alveograph characteristics predicted by Mixolab regression models.

PubMed

Codină, Georgiana Gabriela; Mironeasa, Silvia; Mironeasa, Costel; Popa, Ciprian N; Tamba-Berehoiu, Radiana

2012-02-01

In Romania, the Alveograph is the most used device to evaluate the rheological properties of wheat flour dough, but lately the Mixolab device has begun to play an important role in the breadmaking industry. These two instruments are based on different principles but there are some correlations that can be found between the parameters determined by the Mixolab and the rheological properties of wheat dough measured with the Alveograph. Statistical analysis on 80 wheat flour samples using the backward stepwise multiple regression method showed that Mixolab values using the ‘Chopin S’ protocol (40 samples) and ‘Chopin + ’ protocol (40 samples) can be used to elaborate predictive models for estimating the value of the rheological properties of wheat dough: baking strength (W), dough tenacity (P) and extensibility (L). The correlation analysis confirmed significant findings (P < 0.05 and P < 0.01) between the parameters of wheat dough studied by the Mixolab and its rheological properties measured with the Alveograph. A number of six predictive linear equations were obtained. Linear regression models gave multiple regression coefficients with R²(adjusted) > 0.70 for P, R²(adjusted) > 0.70 for W and R²(adjusted) > 0.38 for L, at a 95% confidence interval. Copyright © 2011 Society of Chemical Industry.

A Landsat study of water quality in Lake Okeechobee

NASA Technical Reports Server (NTRS)

Gervin, J. C.; Marshall, M. L.

1976-01-01

This paper uses multiple regression techniques to investigate the relationship between Landsat radiance values and water quality measurements. For a period of over one year, the Central and Southern Florida Flood Control District sampled the water of Lake Okeechobee for chlorophyll, carotenoids, turbidity, and various nutrients at the time of Landsat overpasses. Using an overlay map of the sampling stations, Landsat radiance values were measured from computer compatible tapes using a GE image 100 and averaging over a 22-acre area at each station. These radiance values in four bands were used to form a number of functions (powers, logarithms, exponentials, and ratios), which were then compared with the ground measurements using multiple linear regression techniques. Several dates were used to provide generality and to study possible seasonal variations. Individual correlations were presented for the various water quality parameters and best fit equations were examined for chlorophyll and turbidity. The results and their relationship to past hydrological research were discussed.

Estimating carcass fat and protein in northern pintails during the nonbreeding season

USGS Publications Warehouse

Miller, M.R.

1989-01-01

I used northern pintails (Anas acuta) collected from August through March 1979-82 in the Sacramento Valley, California to derive equations to predict ether-extracted carcass fat, carcass protein, and skeletal lean dry weight. Ether-extracted carcass fat was best predicted by total fat depot weight (wet skin, abdominal fat, and intestinal fat) (r2 = 0.94) and estimates based on carcass water content (r2 = 0.93-0.98). Measured carcass protein was best predicted by a multiple regression including total protein depot weight (breast muscles, leg muscles, and gizzard) and tarsus length (R2 = 0.79). I predicted skeletal lean dry weight by a multiple regression incorporating culmen, tarsus, and wing length (R2 = 0.77). Predicted carcass fat agreed well with measured carcass fat in an independent data set of 30 pintails using total fat depot (r2 = 0.92-0.96) and carcass water (r2 = 0.97-0.99), but predicted carcass protein agreed less well with measured protein.

Simultaneous estimation of transcutaneous bilirubin, hemoglobin, and melanin based on diffuse reflectance spectroscopy

NASA Astrophysics Data System (ADS)

Nishidate, Izumi; Abdul, Wares MD.; Ohtsu, Mizuki; Nakano, Kazuya; Haneishi, Hideaki

2018-02-01

We propose a method to estimate transcutaneous bilirubin, hemoglobin, and melanin based on the diffuse reflectance spectroscopy. In the proposed method, the Monte Carlo simulation-based multiple regression analysis for an absorbance spectrum in the visible wavelength region (460-590 nm) is used to specify the concentrations of bilirubin (Cbil), oxygenated hemoglobin (Coh), deoxygenated hemoglobin (Cdh), and melanin (Cm). Using the absorbance spectrum calculated from the measured diffuse reflectance spectrum as a response variable and the extinction coefficients of bilirubin, oxygenated hemoglobin, deoxygenated hemoglobin, and melanin, as predictor variables, multiple regression analysis provides regression coefficients. Concentrations of bilirubin, oxygenated hemoglobin, deoxygenated hemoglobin, and melanin, are then determined from the regression coefficients using conversion vectors that are numerically deduced in advance by the Monte Carlo simulations for light transport in skin. Total hemoglobin concentration (Cth) and tissue oxygen saturation (StO2) are simply calculated from the oxygenated hemoglobin and deoxygenated hemoglobin. In vivo animal experiments with bile duct ligation in rats demonstrated that the estimated Cbil is increased after ligation of bile duct and reaches to around 20 mg/dl at 72 h after the onset of the ligation, which corresponds to the reference value of Cbil measured by a commercially available transcutaneous bilirubin meter. We also performed in vivo experiments with rats while varying the fraction of inspired oxygen (FiO2). Coh and Cdh decreased and increased, respectively, as FiO2 decreased. Consequently, StO2 was dramatically decreased. The results in this study indicate potential of the method for simultaneous evaluation of multiple chromophores in skin tissue.

Comparing cluster-level dynamic treatment regimens using sequential, multiple assignment, randomized trials: Regression estimation and sample size considerations.

PubMed

NeCamp, Timothy; Kilbourne, Amy; Almirall, Daniel

2017-08-01

Cluster-level dynamic treatment regimens can be used to guide sequential treatment decision-making at the cluster level in order to improve outcomes at the individual or patient-level. In a cluster-level dynamic treatment regimen, the treatment is potentially adapted and re-adapted over time based on changes in the cluster that could be impacted by prior intervention, including aggregate measures of the individuals or patients that compose it. Cluster-randomized sequential multiple assignment randomized trials can be used to answer multiple open questions preventing scientists from developing high-quality cluster-level dynamic treatment regimens. In a cluster-randomized sequential multiple assignment randomized trial, sequential randomizations occur at the cluster level and outcomes are observed at the individual level. This manuscript makes two contributions to the design and analysis of cluster-randomized sequential multiple assignment randomized trials. First, a weighted least squares regression approach is proposed for comparing the mean of a patient-level outcome between the cluster-level dynamic treatment regimens embedded in a sequential multiple assignment randomized trial. The regression approach facilitates the use of baseline covariates which is often critical in the analysis of cluster-level trials. Second, sample size calculators are derived for two common cluster-randomized sequential multiple assignment randomized trial designs for use when the primary aim is a between-dynamic treatment regimen comparison of the mean of a continuous patient-level outcome. The methods are motivated by the Adaptive Implementation of Effective Programs Trial which is, to our knowledge, the first-ever cluster-randomized sequential multiple assignment randomized trial in psychiatry.

Evaluation of Relationship between Trunk Muscle Endurance and Static Balance in Male Students

PubMed Central

Barati, Amirhossein; SafarCherati, Afsaneh; Aghayari, Azar; Azizi, Faeze; Abbasi, Hamed

2013-01-01

Purpose Fatigue of trunk muscle contributes to spinal instability over strenuous and prolonged physical tasks and therefore may lead to injury, however from a performance perspective, relation between endurance efficient core muscles and optimal balance control has not been well-known. The purpose of this study was to examine the relationship of trunk muscle endurance and static balance. Methods Fifty male students inhabitant of Tehran university dormitory (age 23.9±2.4, height 173.0±4.5 weight 70.7±6.3) took part in the study. Trunk muscle endurance was assessed using Sørensen test of trunk extensor endurance, trunk flexor endurance test, side bridge endurance test and static balance was measured using single-limb stance test. A multiple linear regression analysis was applied to test if the trunk muscle endurance measures significantly predicted the static balance. Results There were positive correlations between static balance level and trunk flexor, extensor and lateral endurance measures (Pearson correlation test, r=0.80 and P<0.001; r=0.71 and P<0.001; r=0.84 and P<0.001, respectively). According to multiple regression analysis for variables predicting static balance, the linear combination of trunk muscle endurance measures was significantly related to the static balance (F (3,46) = 66.60, P<0.001). Endurance of trunk flexor, extensor and lateral muscles were significantly associated with the static balance level. The regression model which included these factors had the sample multiple correlation coefficient of 0.902, indicating that approximately 81% of the variance of the static balance is explained by the model. Conclusion There is a significant relationship between trunk muscle endurance and static balance. PMID:24800004

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

PubMed

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

2016-01-01

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

Melanin and blood concentration in human skin studied by multiple regression analysis: experiments

NASA Astrophysics Data System (ADS)

Shimada, M.; Yamada, Y.; Itoh, M.; Yatagai, T.

2001-09-01

Knowledge of the mechanism of human skin colour and measurement of melanin and blood concentration in human skin are needed in the medical and cosmetic fields. The absorbance spectrum from reflectance at the visible wavelength of human skin increases under several conditions such as a sunburn or scalding. The change of the absorbance spectrum from reflectance including the scattering effect does not correspond to the molar absorption spectrum of melanin and blood. The modified Beer-Lambert law is applied to the change in the absorbance spectrum from reflectance of human skin as the change in melanin and blood is assumed to be small. The concentration of melanin and blood was estimated from the absorbance spectrum reflectance of human skin using multiple regression analysis. Estimated concentrations were compared with the measured one in a phantom experiment and this method was applied to in vivo skin.

Bayesian correction for covariate measurement error: A frequentist evaluation and comparison with regression calibration.

PubMed

Bartlett, Jonathan W; Keogh, Ruth H

2018-06-01

Bayesian approaches for handling covariate measurement error are well established and yet arguably are still relatively little used by researchers. For some this is likely due to unfamiliarity or disagreement with the Bayesian inferential paradigm. For others a contributory factor is the inability of standard statistical packages to perform such Bayesian analyses. In this paper, we first give an overview of the Bayesian approach to handling covariate measurement error, and contrast it with regression calibration, arguably the most commonly adopted approach. We then argue why the Bayesian approach has a number of statistical advantages compared to regression calibration and demonstrate that implementing the Bayesian approach is usually quite feasible for the analyst. Next, we describe the closely related maximum likelihood and multiple imputation approaches and explain why we believe the Bayesian approach to generally be preferable. We then empirically compare the frequentist properties of regression calibration and the Bayesian approach through simulation studies. The flexibility of the Bayesian approach to handle both measurement error and missing data is then illustrated through an analysis of data from the Third National Health and Nutrition Examination Survey.

Mapping diffuse photosynthetically active radiation from satellite data in Thailand

NASA Astrophysics Data System (ADS)

Choosri, P.; Janjai, S.; Nunez, M.; Buntoung, S.; Charuchittipan, D.

2017-12-01

In this paper, calculation of monthly average hourly diffuse photosynthetically active radiation (PAR) using satellite data is proposed. Diffuse PAR was analyzed at four stations in Thailand. A radiative transfer model was used for calculating the diffuse PAR for cloudless sky conditions. Differences between the diffuse PAR under all sky conditions obtained from the ground-based measurements and those from the model are representative of cloud effects. Two models are developed, one describing diffuse PAR only as a function of solar zenith angle, and the second one as a multiple linear regression with solar zenith angle and satellite reflectivity acting linearly and aerosol optical depth acting in logarithmic functions. When tested with an independent data set, the multiple regression model performed best with a higher coefficient of variance R2 (0.78 vs. 0.70), lower root mean square difference (RMSD) (12.92% vs. 13.05%) and the same mean bias difference (MBD) of -2.20%. Results from the multiple regression model are used to map diffuse PAR throughout the country as monthly averages of hourly data.

Integrating multiple fitting regression and Bayes decision for cancer diagnosis with transcriptomic data from tumor-educated blood platelets.

PubMed

Huang, Guangzao; Yuan, Mingshun; Chen, Moliang; Li, Lei; You, Wenjie; Li, Hanjie; Cai, James J; Ji, Guoli

2017-10-07

The application of machine learning in cancer diagnostics has shown great promise and is of importance in clinic settings. Here we consider applying machine learning methods to transcriptomic data derived from tumor-educated platelets (TEPs) from individuals with different types of cancer. We aim to define a reliability measure for diagnostic purposes to increase the potential for facilitating personalized treatments. To this end, we present a novel classification method called MFRB (for Multiple Fitting Regression and Bayes decision), which integrates the process of multiple fitting regression (MFR) with Bayes decision theory. MFR is first used to map multidimensional features of the transcriptomic data into a one-dimensional feature. The probability density function of each class in the mapped space is then adjusted using the Gaussian probability density function. Finally, the Bayes decision theory is used to build a probabilistic classifier with the estimated probability density functions. The output of MFRB can be used to determine which class a sample belongs to, as well as to assign a reliability measure for a given class. The classical support vector machine (SVM) and probabilistic SVM (PSVM) are used to evaluate the performance of the proposed method with simulated and real TEP datasets. Our results indicate that the proposed MFRB method achieves the best performance compared to SVM and PSVM, mainly due to its strong generalization ability for limited, imbalanced, and noisy data.

Two SPSS programs for interpreting multiple regression results.

PubMed

Lorenzo-Seva, Urbano; Ferrando, Pere J; Chico, Eliseo

2010-02-01

When multiple regression is used in explanation-oriented designs, it is very important to determine both the usefulness of the predictor variables and their relative importance. Standardized regression coefficients are routinely provided by commercial programs. However, they generally function rather poorly as indicators of relative importance, especially in the presence of substantially correlated predictors. We provide two user-friendly SPSS programs that implement currently recommended techniques and recent developments for assessing the relevance of the predictors. The programs also allow the user to take into account the effects of measurement error. The first program, MIMR-Corr.sps, uses a correlation matrix as input, whereas the second program, MIMR-Raw.sps, uses the raw data and computes bootstrap confidence intervals of different statistics. The SPSS syntax, a short manual, and data files related to this article are available as supplemental materials from http://brm.psychonomic-journals.org/content/supplemental.

Efficacy of Social Media Adoption on Client Growth for Independent Management Consultants

DTIC Science & Technology

2017-02-01

design , a linear multiple regression with three predictor variables and one dependent variable per testing were used. Under those circumstances...regression test was used to compare the social media adoption of two groups on a single measure to determine if there was a statistical difference...number and types of social media platforms used and their influence on client growth was examined in this research design that used a descriptive

Retention in Differentiated Care: Multiple Measures Analysis for a Decentralized HIV Care and Treatment Program in North Central Nigeria

PubMed Central

Agaba, Patricia A; Genberg, Becky L; Sagay, Atiene S; Agbaji, Oche O; Meloni, Seema T; Dadem, Nancin Y; Kolawole, Grace O; Okonkwo, Prosper; Kanki, Phyllis J; Ware, Norma C

2018-01-01

Objective Differentiated care refers collectively to flexible service models designed to meet the differing needs of HIV-infected persons in resource-scarce settings. Decentralization is one such service model. Retention is a key indicator for monitoring the success of HIV treatment and care programs. We used multiple measures to compare retention in a cohort of patients receiving HIV care at “hub” (central) and “spoke” (decentralized) sites in a large public HIV treatment program in north central Nigeria. Methods This retrospective cohort study utilized longitudinal program data representing central and decentralized levels of care in the Plateau State Decentralization Initiative, north central Nigeria. We examined retention with patient- level (retention at fixed times, loss-to-follow-up [LTFU]) and visit-level (gaps-in-care, visit constancy) measures. Regression models with generalized estimating equations (GEE) were used to estimate the effect of decentralization on visit-level measures. Patient-level measures were examined using survival methods with Cox regression models, controlling for baseline variables. Results Of 15,650 patients, 43% were enrolled at the hub. Median time in care was 3.1 years. Hub patients were less likely to be LTFU (adjusted hazard ratio (AHR)=0.91, 95% CI: 0.85-0.97), compared to spoke patients. Visit constancy was lower at the hub (−4.5%, 95% CI: −3.5, −5.5), where gaps in care were also more likely to occur (adjusted odds ratio=1.95, 95% CI: 1.83-2.08). Conclusion Decentralized sites demonstrated better retention outcomes using visit-level measures, while the hub achieved better retention outcomes using patient-level measures. Retention estimates produced by incorporating multiple measures showed substantial variation, confirming the influence of measurement strategies on the results of retention research. Future studies of retention in HIV care in sub-Saharan Africa will be well-served by including multiple measures. PMID:29682399

Retention in Differentiated Care: Multiple Measures Analysis for a Decentralized HIV Care and Treatment Program in North Central Nigeria.

PubMed

Agaba, Patricia A; Genberg, Becky L; Sagay, Atiene S; Agbaji, Oche O; Meloni, Seema T; Dadem, Nancin Y; Kolawole, Grace O; Okonkwo, Prosper; Kanki, Phyllis J; Ware, Norma C

2018-01-01

Differentiated care refers collectively to flexible service models designed to meet the differing needs of HIV-infected persons in resource-scarce settings. Decentralization is one such service model. Retention is a key indicator for monitoring the success of HIV treatment and care programs. We used multiple measures to compare retention in a cohort of patients receiving HIV care at "hub" (central) and "spoke" (decentralized) sites in a large public HIV treatment program in north central Nigeria. This retrospective cohort study utilized longitudinal program data representing central and decentralized levels of care in the Plateau State Decentralization Initiative, north central Nigeria. We examined retention with patient- level (retention at fixed times, loss-to-follow-up [LTFU]) and visit-level (gaps-in-care, visit constancy) measures. Regression models with generalized estimating equations (GEE) were used to estimate the effect of decentralization on visit-level measures. Patient-level measures were examined using survival methods with Cox regression models, controlling for baseline variables. Of 15,650 patients, 43% were enrolled at the hub. Median time in care was 3.1 years. Hub patients were less likely to be LTFU (adjusted hazard ratio (AHR)=0.91, 95% CI: 0.85-0.97), compared to spoke patients. Visit constancy was lower at the hub (-4.5%, 95% CI: -3.5, -5.5), where gaps in care were also more likely to occur (adjusted odds ratio=1.95, 95% CI: 1.83-2.08). Decentralized sites demonstrated better retention outcomes using visit-level measures, while the hub achieved better retention outcomes using patient-level measures. Retention estimates produced by incorporating multiple measures showed substantial variation, confirming the influence of measurement strategies on the results of retention research. Future studies of retention in HIV care in sub-Saharan Africa will be well-served by including multiple measures.

Multiple Correlation versus Multiple Regression.

ERIC Educational Resources Information Center

Huberty, Carl J.

2003-01-01

Describes differences between multiple correlation analysis (MCA) and multiple regression analysis (MRA), showing how these approaches involve different research questions and study designs, different inferential approaches, different analysis strategies, and different reported information. (SLD)

Applied immuno-epidemiological research: an approach for integrating existing knowledge into the statistical analysis of multiple immune markers.

PubMed

Genser, Bernd; Fischer, Joachim E; Figueiredo, Camila A; Alcântara-Neves, Neuza; Barreto, Mauricio L; Cooper, Philip J; Amorim, Leila D; Saemann, Marcus D; Weichhart, Thomas; Rodrigues, Laura C

2016-05-20

Immunologists often measure several correlated immunological markers, such as concentrations of different cytokines produced by different immune cells and/or measured under different conditions, to draw insights from complex immunological mechanisms. Although there have been recent methodological efforts to improve the statistical analysis of immunological data, a framework is still needed for the simultaneous analysis of multiple, often correlated, immune markers. This framework would allow the immunologists' hypotheses about the underlying biological mechanisms to be integrated. We present an analytical approach for statistical analysis of correlated immune markers, such as those commonly collected in modern immuno-epidemiological studies. We demonstrate i) how to deal with interdependencies among multiple measurements of the same immune marker, ii) how to analyse association patterns among different markers, iii) how to aggregate different measures and/or markers to immunological summary scores, iv) how to model the inter-relationships among these scores, and v) how to use these scores in epidemiological association analyses. We illustrate the application of our approach to multiple cytokine measurements from 818 children enrolled in a large immuno-epidemiological study (SCAALA Salvador), which aimed to quantify the major immunological mechanisms underlying atopic diseases or asthma. We demonstrate how to aggregate systematically the information captured in multiple cytokine measurements to immunological summary scores aimed at reflecting the presumed underlying immunological mechanisms (Th1/Th2 balance and immune regulatory network). We show how these aggregated immune scores can be used as predictors in regression models with outcomes of immunological studies (e.g. specific IgE) and compare the results to those obtained by a traditional multivariate regression approach. The proposed analytical approach may be especially useful to quantify complex immune responses in immuno-epidemiological studies, where investigators examine the relationship among epidemiological patterns, immune response, and disease outcomes.

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)

Curriculum-Based Measurement of Oral Reading: Quality of Progress Monitoring Outcomes

ERIC Educational Resources Information Center

Christ, Theodore J.; Zopluoglu, Cengiz; Long, Jeffery D.; Monaghen, Barbara D.

2012-01-01

Curriculum-based measurement of oral reading (CBM-R) is frequently used to set student goals and monitor student progress. This study examined the quality of growth estimates derived from CBM-R progress monitoring data. The authors used a linear mixed effects regression (LMER) model to simulate progress monitoring data for multiple levels of…

An improved approach for measuring the impact of multiple CO2 conductances on the apparent photorespiratory CO2 compensation point through slope-intercept regression

USDA-ARS?s Scientific Manuscript database

Biochemical models of leaf photosynthesis, which are essential for understanding the impact of photosynthesis to changing environments, depend on accurate parameterizations. The CO2 photocompensation point can be especially difficult to determine accurately but can be measured from the intersection ...

Complex Intellect vs the IQ Test as a Predictor of Performance.

ERIC Educational Resources Information Center

Dees, James W.

In order to test the ubiquity of the structure of the intellect for predictors of performance, a psychomotor skill (M 16 rifle proficiency test), a measure of perseverance (completion or resignation from OCS Program), and a measure of leadership ability (peer ratings) were selected as criteria on which multiple regressions were conducted with a…

Relations between Measures of Cattell-Horn-Carroll (CHC) Cognitive Abilities and Mathematics Achievement across the School-Age Years

ERIC Educational Resources Information Center

Floyd, Randy G.; Evans, Jeffrey J.; McGrew, Kevin S.

2003-01-01

Cognitive clusters from the Woodcock-Johnson III (WJ III) Tests of Cognitive Abilities that measure select Cattell-Horn-Carroll broad and narrow cognitive abilities were shown to be significantly related to mathematics achievement in a large, nationally representative sample of children and adolescents. Multiple regression analyses were used to…

Comparing Machine Learning Classifiers and Linear/Logistic Regression to Explore the Relationship between Hand Dimensions and Demographic Characteristics

PubMed Central

2016-01-01

Understanding the relationship between physiological measurements from human subjects and their demographic data is important within both the biometric and forensic domains. In this paper we explore the relationship between measurements of the human hand and a range of demographic features. We assess the ability of linear regression and machine learning classifiers to predict demographics from hand features, thereby providing evidence on both the strength of relationship and the key features underpinning this relationship. Our results show that we are able to predict sex, height, weight and foot size accurately within various data-range bin sizes, with machine learning classification algorithms out-performing linear regression in most situations. In addition, we identify the features used to provide these relationships applicable across multiple applications. PMID:27806075

Comparing Machine Learning Classifiers and Linear/Logistic Regression to Explore the Relationship between Hand Dimensions and Demographic Characteristics.

PubMed

Miguel-Hurtado, Oscar; Guest, Richard; Stevenage, Sarah V; Neil, Greg J; Black, Sue

2016-01-01

Understanding the relationship between physiological measurements from human subjects and their demographic data is important within both the biometric and forensic domains. In this paper we explore the relationship between measurements of the human hand and a range of demographic features. We assess the ability of linear regression and machine learning classifiers to predict demographics from hand features, thereby providing evidence on both the strength of relationship and the key features underpinning this relationship. Our results show that we are able to predict sex, height, weight and foot size accurately within various data-range bin sizes, with machine learning classification algorithms out-performing linear regression in most situations. In addition, we identify the features used to provide these relationships applicable across multiple applications.

Beyond Multiple Regression: Using Commonality Analysis to Better Understand R[superscript 2] Results

ERIC Educational Resources Information Center

Warne, Russell T.

2011-01-01

Multiple regression is one of the most common statistical methods used in quantitative educational research. Despite the versatility and easy interpretability of multiple regression, it has some shortcomings in the detection of suppressor variables and for somewhat arbitrarily assigning values to the structure coefficients of correlated…

Test anxiety and academic performance in chiropractic students.

PubMed

Zhang, Niu; Henderson, Charles N R

2014-01-01

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

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.

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data.

PubMed

Spelman, Tim; Gray, Orla; Lucas, Robyn; Butzkueven, Helmut

2015-12-09

This report describes a novel Stata-based application of trigonometric regression modelling to 55 years of multiple sclerosis relapse data from 46 clinical centers across 20 countries located in both hemispheres. Central to the success of this method was the strategic use of plot analysis to guide and corroborate the statistical regression modelling. Initial plot analysis was necessary for establishing realistic hypotheses regarding the presence and structural form of seasonal and latitudinal influences on relapse probability and then testing the performance of the resultant models. Trigonometric regression was then necessary to quantify these relationships, adjust for important confounders and provide a measure of certainty as to how plausible these associations were. Synchronization of graphing techniques with regression modelling permitted a systematic refinement of models until best-fit convergence was achieved, enabling novel inferences to be made regarding the independent influence of both season and latitude in predicting relapse onset timing in MS. These methods have the potential for application across other complex disease and epidemiological phenomena suspected or known to vary systematically with season and/or geographic location.

Accounting for estimated IQ in neuropsychological test performance with regression-based techniques.

PubMed

Testa, S Marc; Winicki, Jessica M; Pearlson, Godfrey D; Gordon, Barry; Schretlen, David J

2009-11-01

Regression-based normative techniques account for variability in test performance associated with multiple predictor variables and generate expected scores based on algebraic equations. Using this approach, we show that estimated IQ, based on oral word reading, accounts for 1-9% of the variability beyond that explained by individual differences in age, sex, race, and years of education for most cognitive measures. These results confirm that adding estimated "premorbid" IQ to demographic predictors in multiple regression models can incrementally improve the accuracy with which regression-based norms (RBNs) benchmark expected neuropsychological test performance in healthy adults. It remains to be seen whether the incremental variance in test performance explained by estimated "premorbid" IQ translates to improved diagnostic accuracy in patient samples. We describe these methods, and illustrate the step-by-step application of RBNs with two cases. We also discuss the rationale, assumptions, and caveats of this approach. More broadly, we note that adjusting test scores for age and other characteristics might actually decrease the accuracy with which test performance predicts absolute criteria, such as the ability to drive or live independently.

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

PubMed Central

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

2016-01-01

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

Predicting story goodness performance from cognitive measures following traumatic brain injury.

PubMed

Lê, Karen; Coelho, Carl; Mozeiko, Jennifer; Krueger, Frank; Grafman, Jordan

2012-05-01

This study examined the prediction of performance on measures of the Story Goodness Index (SGI; Lê, Coelho, Mozeiko, & Grafman, 2011) from executive function (EF) and memory measures following traumatic brain injury (TBI). It was hypothesized that EF and memory measures would significantly predict SGI outcomes. One hundred sixty-seven individuals with TBI participated in the study. Story retellings were analyzed using the SGI protocol. Three cognitive measures--Delis-Kaplan Executive Function System (D-KEFS; Delis, Kaplan, & Kramer, 2001) Sorting Test, Wechsler Memory Scale--Third Edition (WMS-III; Wechsler, 1997) Working Memory Primary Index (WMI), and WMS-III Immediate Memory Primary Index (IMI)--were entered into a multiple linear regression model for each discourse measure. Two sets of regression analyses were performed, the first with the Sorting Test as the first predictor and the second with it as the last. The first set of regression analyses identified the Sorting Test and IMI as the only significant predictors of performance on measures of the SGI. The second set identified all measures as significant predictors when evaluating each step of the regression function. The cognitive variables predicted performance on the SGI measures, although there were differences in the amount of explained variance. The results (a) suggest that storytelling ability draws on a number of underlying skills and (b) underscore the importance of using discrete cognitive tasks rather than broad cognitive indices to investigate the cognitive substrates of discourse.

NIMEFI: Gene Regulatory Network Inference using Multiple Ensemble Feature Importance Algorithms

PubMed Central

Ruyssinck, Joeri; Huynh-Thu, Vân Anh; Geurts, Pierre; Dhaene, Tom; Demeester, Piet; Saeys, Yvan

2014-01-01

One of the long-standing open challenges in computational systems biology is the topology inference of gene regulatory networks from high-throughput omics data. Recently, two community-wide efforts, DREAM4 and DREAM5, have been established to benchmark network inference techniques using gene expression measurements. In these challenges the overall top performer was the GENIE3 algorithm. This method decomposes the network inference task into separate regression problems for each gene in the network in which the expression values of a particular target gene are predicted using all other genes as possible predictors. Next, using tree-based ensemble methods, an importance measure for each predictor gene is calculated with respect to the target gene and a high feature importance is considered as putative evidence of a regulatory link existing between both genes. The contribution of this work is twofold. First, we generalize the regression decomposition strategy of GENIE3 to other feature importance methods. We compare the performance of support vector regression, the elastic net, random forest regression, symbolic regression and their ensemble variants in this setting to the original GENIE3 algorithm. To create the ensemble variants, we propose a subsampling approach which allows us to cast any feature selection algorithm that produces a feature ranking into an ensemble feature importance algorithm. We demonstrate that the ensemble setting is key to the network inference task, as only ensemble variants achieve top performance. As second contribution, we explore the effect of using rankwise averaged predictions of multiple ensemble algorithms as opposed to only one. We name this approach NIMEFI (Network Inference using Multiple Ensemble Feature Importance algorithms) and show that this approach outperforms all individual methods in general, although on a specific network a single method can perform better. An implementation of NIMEFI has been made publicly available. PMID:24667482

Ca analysis: An Excel based program for the analysis of intracellular calcium transients including multiple, simultaneous regression analysis☆

PubMed Central

Greensmith, David J.

2014-01-01

Here I present an Excel based program for the analysis of intracellular Ca transients recorded using fluorescent indicators. The program can perform all the necessary steps which convert recorded raw voltage changes into meaningful physiological information. The program performs two fundamental processes. (1) It can prepare the raw signal by several methods. (2) It can then be used to analyze the prepared data to provide information such as absolute intracellular Ca levels. Also, the rates of change of Ca can be measured using multiple, simultaneous regression analysis. I demonstrate that this program performs equally well as commercially available software, but has numerous advantages, namely creating a simplified, self-contained analysis workflow. PMID:24125908

Effect of Ankle Range of Motion (ROM) and Lower-Extremity Muscle Strength on Static Balance Control Ability in Young Adults: A Regression Analysis

PubMed Central

Kim, Seong-Gil

2018-01-01

Background The purpose of this study was to investigate the effect of ankle ROM and lower-extremity muscle strength on static balance control ability in young adults. Material/Methods This study was conducted with 65 young adults, but 10 young adults dropped out during the measurement, so 55 young adults (male: 19, female: 36) completed the study. Postural sway (length and velocity) was measured with eyes open and closed, and ankle ROM (AROM and PROM of dorsiflexion and plantarflexion) and lower-extremity muscle strength (flexor and extensor of hip, knee, and ankle joint) were measured. Pearson correlation coefficient was used to examine the correlation between variables and static balance ability. Simple linear regression analysis and multiple linear regression analysis were used to examine the effect of variables on static balance ability. Results In correlation analysis, plantarflexion ROM (AROM and PROM) and lower-extremity muscle strength (except hip extensor) were significantly correlated with postural sway (p<0.05). In simple correlation analysis, all variables that passed the correlation analysis procedure had significant influence (p<0.05). In multiple linear regression analysis, plantar flexion PROM with eyes open significantly influenced sway length (B=0.681) and sway velocity (B=0.011). Conclusions Lower-extremity muscle strength and ankle plantarflexion ROM influenced static balance control ability, with ankle plantarflexion PROM showing the greatest influence. Therefore, both contractile structures and non-contractile structures should be of interest when considering static balance control ability improvement. PMID:29760375

Effect of Ankle Range of Motion (ROM) and Lower-Extremity Muscle Strength on Static Balance Control Ability in Young Adults: A Regression Analysis.

PubMed

Kim, Seong-Gil; Kim, Wan-Soo

2018-05-15

BACKGROUND The purpose of this study was to investigate the effect of ankle ROM and lower-extremity muscle strength on static balance control ability in young adults. MATERIAL AND METHODS This study was conducted with 65 young adults, but 10 young adults dropped out during the measurement, so 55 young adults (male: 19, female: 36) completed the study. Postural sway (length and velocity) was measured with eyes open and closed, and ankle ROM (AROM and PROM of dorsiflexion and plantarflexion) and lower-extremity muscle strength (flexor and extensor of hip, knee, and ankle joint) were measured. Pearson correlation coefficient was used to examine the correlation between variables and static balance ability. Simple linear regression analysis and multiple linear regression analysis were used to examine the effect of variables on static balance ability. RESULTS In correlation analysis, plantarflexion ROM (AROM and PROM) and lower-extremity muscle strength (except hip extensor) were significantly correlated with postural sway (p<0.05). In simple correlation analysis, all variables that passed the correlation analysis procedure had significant influence (p<0.05). In multiple linear regression analysis, plantar flexion PROM with eyes open significantly influenced sway length (B=0.681) and sway velocity (B=0.011). CONCLUSIONS Lower-extremity muscle strength and ankle plantarflexion ROM influenced static balance control ability, with ankle plantarflexion PROM showing the greatest influence. Therefore, both contractile structures and non-contractile structures should be of interest when considering static balance control ability improvement.

How does the use of multiple needles/syringes per injecting episode impact on the measurement of individual level needle and syringe program coverage?

PubMed

O'Keefe, Daniel; McCormack, Angus; Cogger, Shelley; Aitken, Campbell; Burns, Lucinda; Bruno, Raimondo; Stafford, Jenny; Butler, Kerryn; Breen, Courtney; Dietze, Paul

2017-08-01

Recent work by McCormack et al. (2016) showed that the inclusion of syringe stockpiling improves the measurement of individual-level syringe coverage. We explored whether including the use of a new parameter, multiple sterile syringes per injecting episode, further improves coverage measures. Data comes from 838 people who inject drugs, interviewed as part of the 2015 Illicit Drug Reporting System. Along with syringe coverage questions, the survey recorded the number of sterile syringes used on average per injecting episode. We constructed three measures of coverage: one adapted from Bluthenthal et al. (2007), the McCormack et al. measure, and a new coverage measure that included use of multiple syringes. Predictors of multiple syringe use and insufficient coverage (<100% of injecting episodes using a sterile syringe) using the new measure, were tested in logistic regression and the ability of the measures to discriminate key risk behaviours was compared using ROC curve analysis. 134 (16%) participants reported needing multiple syringes per injecting episode. Women showed significantly increased odds of multiple syringe use, as did those reporting injection related injuries/diseases and injecting of opioid substitution drugs or pharmaceutical opioids. Levels of insufficient coverage across the three measures were substantial (20%-28%). ROC curve analysis suggested that our new measure was no better at discriminating injecting risk behaviours than the existing measures. Based on our findings, there appears to be little need for adding a multiple syringe use parameter to existing coverage formulae. Hence, we recommend that multiple syringe use is not included in the measurement of individual-level syringe coverage. Copyright © 2017 Elsevier B.V. All rights reserved.

Perceived Parenting Styles on College Students' Optimism

ERIC Educational Resources Information Center

Baldwin, Debora R.; McIntyre, Anne; Hardaway, Elizabeth

2007-01-01

The purpose of this study was to examine the relationship between perceived parenting styles and levels of optimism in undergraduate college students. Sixty-three participants were administered surveys measuring dispositional optimism and perceived parental Authoritative and Authoritarian styles. Multiple regression analysis revealed that both…

Organizational Justice and Physiological Coronary Heart Disease Risk Factors in Japanese Employees: a Cross-Sectional Study.

PubMed

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

2015-12-01

Growing evidence has shown that lack of organizational justice (i.e., procedural justice and interactional justice) is associated with coronary heart disease (CHD) while biological mechanisms underlying this association have not yet been fully clarified. The purpose of the present study was to investigate the cross-sectional association of organizational justice with physiological CHD risk factors (i.e., blood pressure, high-density lipoprotein [HDL] cholesterol, low-density lipoprotein [LDL] cholesterol, and triglyceride) in Japanese employees. Overall, 3598 male and 901 female employees from two manufacturing companies in Japan completed self-administered questionnaires measuring organizational justice, demographic characteristics, and lifestyle factors. They completed health checkup, which included blood pressure and serum lipid measurements. Multiple logistic regression analyses and trend tests were conducted. Among male employees, multiple logistic regression analyses and trend tests showed significant associations of low procedural justice and low interactional justice with high triglyceride (defined as 150 mg/dL or greater) after adjusting for demographic characteristics and lifestyle factors. Among female employees, trend tests showed significant dose-response relationship between low interactional justice and high LDL cholesterol (defined as 140 mg/dL or greater) while multiple logistic regression analysis showed only marginally significant or insignificant odds ratio of high LDL cholesterol among the low interactional justice group. Neither procedural justice nor interactional justice was associated with blood pressure or HDL cholesterol. Organizational justice may be an important psychosocial factor associated with increased triglyceride at least among Japanese male employees.

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

PubMed

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

2014-12-01

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

Factors affecting match performance in professional Australian football.

PubMed

Sullivan, Courtney; Bilsborough, Johann C; Cianciosi, Michael; Hocking, Joel; Cordy, Justin T; Coutts, Aaron J

2014-05-01

To determine the physical activity measures and skill-performance characteristics that contribute to coaches' perception of performance and player performance rank in professional Australian Football (AF). Prospective, longitudinal. Physical activity profiles were assessed via microtechnology (GPS and accelerometer) from 40 professional AF players from the same team during 15 Australian Football League games. Skill-performance measure and player-rank scores (Champion Data Rank) were provided by a commercial statistical provider. The physical-performance variables, skill involvements, and individual player performance scores were expressed relative to playing time for each quarter. A stepwise multiple regression was used to examine the contribution of physical activity and skill involvements to coaches' perception of performance and player rank in AF. Stepwise multiple-regression analysis revealed that 42.2% of the variance in coaches' perception of a player's performance could be explained by the skill-performance characteristics (player rank/min, effective kicks/min, pressure points/min, handballs/min, and running bounces/ min), with a small contribution from physical activity measures (accelerations/min) (adjusted R2 = .422, F6,282 = 36.054, P < .001). Multiple regression also revealed that 66.4% of the adjusted variance in player rank could be explained by total disposals/min, effective kicks/min, pressure points/min, kick clangers/min, marks/min, speed (m/min), and peak speed (adjusted R2 = .664, F7,281 = 82.289, P < .001). Increased physical activity throughout a match (speed [m/min] β - 0.097 and peak speed β - 0.116) negatively affects player rank in AF. Skill performance rather than increased physical activity is more important to coaches' perception of performance and player rank in professional AF.

The Association between Working Memory and Educational Attainment as Measured in Different Mathematical Subtopics in the Swedish National Assessment: Primary Education

ERIC Educational Resources Information Center

Nyroos, Mikaela; Wiklund-Hornqvist, Carola

2012-01-01

The aim of this study was to examine the relationship between working memory capacity and mathematical performance measured by the national curriculum assessment in third-grade children (n = 40). The national tests concerned six subareas within mathematics. One-way ANOVA, two-tailed Pearson correlation and multiple regression analyses were…

Using Robust Standard Errors to Combine Multiple Regression Estimates with Meta-Analysis

ERIC Educational Resources Information Center

Williams, Ryan T.

2012-01-01

Combining multiple regression estimates with meta-analysis has continued to be a difficult task. A variety of methods have been proposed and used to combine multiple regression slope estimates with meta-analysis, however, most of these methods have serious methodological and practical limitations. The purpose of this study was to explore the use…

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

Treesearch

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

1994-01-01

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

Building Regression Models: The Importance of Graphics.

ERIC Educational Resources Information Center

Dunn, Richard

1989-01-01

Points out reasons for using graphical methods to teach simple and multiple regression analysis. Argues that a graphically oriented approach has considerable pedagogic advantages in the exposition of simple and multiple regression. Shows that graphical methods may play a central role in the process of building regression models. (Author/LS)

Testing Different Model Building Procedures Using Multiple Regression.

ERIC Educational Resources Information Center

Thayer, Jerome D.

The stepwise regression method of selecting predictors for computer assisted multiple regression analysis was compared with forward, backward, and best subsets regression, using 16 data sets. The results indicated the stepwise method was preferred because of its practical nature, when the models chosen by different selection methods were similar…

Noninvasive diagnostics of skin microphysical parameters based on spatially resolved diffuse reflectance spectroscopy

NASA Astrophysics Data System (ADS)

Lisenko, S. A.; Kugeiko, M. M.

2013-01-01

The ability to determine noninvasively microphysical parameters (MPPs) of skin characteristic of malignant melanoma was demonstrated. The MPPs were the melanin content in dermis, saturation of tissue with blood vessels, and concentration and effective size of tissue scatterers. The proposed method was based on spatially resolved spectral measurements of skin diffuse reflectance and multiple regressions between linearly independent measurement components and skin MPPs. The regressions were established by modeling radiation transfer in skin with a wide variation of its MPPs. Errors in the determination of skin MPPs were estimated using fiber-optic measurements of its diffuse reflectance at wavelengths of commercially available semiconductor diode lasers (578, 625, 660, 760, and 806 nm) at source-detector separations of 0.23-1.38 mm.

Decreasing Multicollinearity: A Method for Models with Multiplicative Functions.

ERIC Educational Resources Information Center

Smith, Kent W.; Sasaki, M. S.

1979-01-01

A method is proposed for overcoming the problem of multicollinearity in multiple regression equations where multiplicative independent terms are entered. The method is not a ridge regression solution. (JKS)

The extraction of simple relationships in growth factor-specific multiple-input and multiple-output systems in cell-fate decisions by backward elimination PLS regression.

PubMed

Akimoto, Yuki; Yugi, Katsuyuki; Uda, Shinsuke; Kudo, Takamasa; Komori, Yasunori; Kubota, Hiroyuki; Kuroda, Shinya

2013-01-01

Cells use common signaling molecules for the selective control of downstream gene expression and cell-fate decisions. The relationship between signaling molecules and downstream gene expression and cellular phenotypes is a multiple-input and multiple-output (MIMO) system and is difficult to understand due to its complexity. For example, it has been reported that, in PC12 cells, different types of growth factors activate MAP kinases (MAPKs) including ERK, JNK, and p38, and CREB, for selective protein expression of immediate early genes (IEGs) such as c-FOS, c-JUN, EGR1, JUNB, and FOSB, leading to cell differentiation, proliferation and cell death; however, how multiple-inputs such as MAPKs and CREB regulate multiple-outputs such as expression of the IEGs and cellular phenotypes remains unclear. To address this issue, we employed a statistical method called partial least squares (PLS) regression, which involves a reduction of the dimensionality of the inputs and outputs into latent variables and a linear regression between these latent variables. We measured 1,200 data points for MAPKs and CREB as the inputs and 1,900 data points for IEGs and cellular phenotypes as the outputs, and we constructed the PLS model from these data. The PLS model highlighted the complexity of the MIMO system and growth factor-specific input-output relationships of cell-fate decisions in PC12 cells. Furthermore, to reduce the complexity, we applied a backward elimination method to the PLS regression, in which 60 input variables were reduced to 5 variables, including the phosphorylation of ERK at 10 min, CREB at 5 min and 60 min, AKT at 5 min and JNK at 30 min. The simple PLS model with only 5 input variables demonstrated a predictive ability comparable to that of the full PLS model. The 5 input variables effectively extracted the growth factor-specific simple relationships within the MIMO system in cell-fate decisions in PC12 cells.

Analysis of methods to estimate spring flows in a karst aquifer

USGS Publications Warehouse

Sepulveda, N.

2009-01-01

Hydraulically and statistically based methods were analyzed to identify the most reliable method to predict spring flows in a karst aquifer. Measured water levels at nearby observation wells, measured spring pool altitudes, and the distance between observation wells and the spring pool were the parameters used to match measured spring flows. Measured spring flows at six Upper Floridan aquifer springs in central Florida were used to assess the reliability of these methods to predict spring flows. Hydraulically based methods involved the application of the Theis, Hantush-Jacob, and Darcy-Weisbach equations, whereas the statistically based methods were the multiple linear regressions and the technology of artificial neural networks (ANNs). Root mean square errors between measured and predicted spring flows using the Darcy-Weisbach method ranged between 5% and 15% of the measured flows, lower than the 7% to 27% range for the Theis or Hantush-Jacob methods. Flows at all springs were estimated to be turbulent based on the Reynolds number derived from the Darcy-Weisbach equation for conduit flow. The multiple linear regression and the Darcy-Weisbach methods had similar spring flow prediction capabilities. The ANNs provided the lowest residuals between measured and predicted spring flows, ranging from 1.6% to 5.3% of the measured flows. The model prediction efficiency criteria also indicated that the ANNs were the most accurate method predicting spring flows in a karst aquifer. ?? 2008 National Ground Water Association.

Analysis of methods to estimate spring flows in a karst aquifer.

PubMed

Sepúlveda, Nicasio

2009-01-01

Hydraulically and statistically based methods were analyzed to identify the most reliable method to predict spring flows in a karst aquifer. Measured water levels at nearby observation wells, measured spring pool altitudes, and the distance between observation wells and the spring pool were the parameters used to match measured spring flows. Measured spring flows at six Upper Floridan aquifer springs in central Florida were used to assess the reliability of these methods to predict spring flows. Hydraulically based methods involved the application of the Theis, Hantush-Jacob, and Darcy-Weisbach equations, whereas the statistically based methods were the multiple linear regressions and the technology of artificial neural networks (ANNs). Root mean square errors between measured and predicted spring flows using the Darcy-Weisbach method ranged between 5% and 15% of the measured flows, lower than the 7% to 27% range for the Theis or Hantush-Jacob methods. Flows at all springs were estimated to be turbulent based on the Reynolds number derived from the Darcy-Weisbach equation for conduit flow. The multiple linear regression and the Darcy-Weisbach methods had similar spring flow prediction capabilities. The ANNs provided the lowest residuals between measured and predicted spring flows, ranging from 1.6% to 5.3% of the measured flows. The model prediction efficiency criteria also indicated that the ANNs were the most accurate method predicting spring flows in a karst aquifer.

Application of XGBoost algorithm in hourly PM2.5 concentration prediction

NASA Astrophysics Data System (ADS)

Pan, Bingyue

2018-02-01

In view of prediction techniques of hourly PM2.5 concentration in China, this paper applied the XGBoost(Extreme Gradient Boosting) algorithm to predict hourly PM2.5 concentration. The monitoring data of air quality in Tianjin city was analyzed by using XGBoost algorithm. The prediction performance of the XGBoost method is evaluated by comparing observed and predicted PM2.5 concentration using three measures of forecast accuracy. The XGBoost method is also compared with the random forest algorithm, multiple linear regression, decision tree regression and support vector machines for regression models using computational results. The results demonstrate that the XGBoost algorithm outperforms other data mining methods.

Emotion dysregulation, problem-solving, and hopelessness.

PubMed

Vatan, Sevginar; Lester, David; Gunn, John F

2014-04-01

A sample of 87 Turkish undergraduate students was administered scales to measure hopelessness, problem-solving skills, emotion dysregulation, and psychiatric symptoms. All of the scores from these scales were strongly associated. In a multiple regression, hopelessness scores were predicted by poor problem-solving skills and emotion dysregulation.

GPA, GMAT, and Scale: A Method of Quantification of Admissions Criteria.

ERIC Educational Resources Information Center

Sobol, Marion G.

1984-01-01

Multiple regression analysis was used to establish a scale, measuring college student involvement in campus activities, work experience, technical background, references, and goals. This scale was tested to see whether it improved the prediction of success in graduate school. (Author/MLW)

Energy expenditure estimation during daily military routine with body-fixed sensors.

PubMed

Wyss, Thomas; Mäder, Urs

2011-05-01

The purpose of this study was to develop and validate an algorithm for estimating energy expenditure during the daily military routine on the basis of data collected using body-fixed sensors. First, 8 volunteers completed isolated physical activities according to an established protocol, and the resulting data were used to develop activity-class-specific multiple linear regressions for physical activity energy expenditure on the basis of hip acceleration, heart rate, and body mass as independent variables. Second, the validity of these linear regressions was tested during the daily military routine using indirect calorimetry (n = 12). Volunteers' mean estimated energy expenditure did not significantly differ from the energy expenditure measured with indirect calorimetry (p = 0.898, 95% confidence interval = -1.97 to 1.75 kJ/min). We conclude that the developed activity-class-specific multiple linear regressions applied to the acceleration and heart rate data allow estimation of energy expenditure in 1-minute intervals during daily military routine, with accuracy equal to indirect calorimetry.

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

PubMed

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

2016-02-01

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

Multiple-Instance Regression with Structured Data

NASA Technical Reports Server (NTRS)

Wagstaff, Kiri L.; Lane, Terran; Roper, Alex

2008-01-01

We present a multiple-instance regression algorithm that models internal bag structure to identify the items most relevant to the bag labels. Multiple-instance regression (MIR) operates on a set of bags with real-valued labels, each containing a set of unlabeled items, in which the relevance of each item to its bag label is unknown. The goal is to predict the labels of new bags from their contents. Unlike previous MIR methods, MI-ClusterRegress can operate on bags that are structured in that they contain items drawn from a number of distinct (but unknown) distributions. MI-ClusterRegress simultaneously learns a model of the bag's internal structure, the relevance of each item, and a regression model that accurately predicts labels for new bags. We evaluated this approach on the challenging MIR problem of crop yield prediction from remote sensing data. MI-ClusterRegress provided predictions that were more accurate than those obtained with non-multiple-instance approaches or MIR methods that do not model the bag structure.

Combining semiquantitative measures of fibrosis and qualitative features of parenchymal remodelling to identify fibrosis regression in hepatitis C: a multiple biopsy study.

PubMed

Pattullo, Venessa; Thein, Hla-Hla; Heathcote, Elizabeth Jenny; Guindi, Maha

2012-09-01

A fall in hepatic fibrosis stage may be observed in patients with chronic hepatitis C (CHC); however, parenchymal architectural changes may also signify hepatic remodelling associated with fibrosis regression. The aim of this study was to utilize semiquantitative and qualitative methods to report the prevalence and factors associated with fibrosis regression in CHC. Paired liver biopsies were scored for fibrosis (Ishak), and for the presence of eight qualitative features of parenchymal remodelling, to derive a qualitative regression score (QR score). Combined fibrosis regression was defined as ≥2-stage fall in Ishak stage (Reg-I) or <2-stage fall in Ishak stage with a rise in QR score (Reg-Qual). Among 159 patients (biopsy interval 5.4 ± 3.1 years), Reg-I was observed in 12 (7.5%) and Reg-Qual in 26 (16.4%) patients. The combined diagnostic criteria increased the diagnosis rate for fibrosis regression (38 patients, 23.9%) compared with use of Reg-I alone (P < 0.001). Combined fibrosis regression was observed in nine patients (50%) who achieved sustained virological response (SVR), and in 29 of 141 (21%) patients despite persistent viraemia. SVR was the only clinical factor associated independently with combined fibrosis regression (odds ratio 3.05). The combination of semiquantitative measures and qualitative features aids the identification of fibrosis regression in CHC. © 2012 Blackwell Publishing 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).

Estimating Soil Cation Exchange Capacity from Soil Physical and Chemical Properties

NASA Astrophysics Data System (ADS)

Bateni, S. M.; Emamgholizadeh, S.; Shahsavani, D.

2014-12-01

The soil Cation Exchange Capacity (CEC) is an important soil characteristic that has many applications in soil science and environmental studies. For example, CEC influences soil fertility by controlling the exchange of ions in the soil. Measurement of CEC is costly and difficult. Consequently, several studies attempted to obtain CEC from readily measurable soil physical and chemical properties such as soil pH, organic matter, soil texture, bulk density, and particle size distribution. These studies have often used multiple regression or artificial neural network models. Regression-based models cannot capture the intricate relationship between CEC and soil physical and chemical attributes and provide inaccurate CEC estimates. Although neural network models perform better than regression methods, they act like a black-box and cannot generate an explicit expression for retrieval of CEC from soil properties. In a departure with regression and neural network models, this study uses Genetic Expression Programming (GEP) and Multivariate Adaptive Regression Splines (MARS) to estimate CEC from easily measurable soil variables such as clay, pH, and OM. CEC estimates from GEP and MARS are compared with measurements at two field sites in Iran. Results show that GEP and MARS can estimate CEC accurately. Also, the MARS model performs slightly better than GEP. Finally, a sensitivity test indicates that organic matter and pH have respectively the least and the most significant impact on CEC.

Ca analysis: an Excel based program for the analysis of intracellular calcium transients including multiple, simultaneous regression analysis.

PubMed

Greensmith, David J

2014-01-01

Here I present an Excel based program for the analysis of intracellular Ca transients recorded using fluorescent indicators. The program can perform all the necessary steps which convert recorded raw voltage changes into meaningful physiological information. The program performs two fundamental processes. (1) It can prepare the raw signal by several methods. (2) It can then be used to analyze the prepared data to provide information such as absolute intracellular Ca levels. Also, the rates of change of Ca can be measured using multiple, simultaneous regression analysis. I demonstrate that this program performs equally well as commercially available software, but has numerous advantages, namely creating a simplified, self-contained analysis workflow. Copyright © 2013 The Author. Published by Elsevier Ireland Ltd.. All rights reserved.

Addressing Gender Equity in Nonfaculty Salaries.

ERIC Educational Resources Information Center

Toukoushian, Robert K.

2000-01-01

Discusses methodology of gender equity studies on noninstructional employees of colleges and universities, including variable selection in the multiple regression model and alternative approaches for measuring wage gaps. Analysis of staff data at one institution finds that experience and market differences account for 80 percent of gender pay…

Validation of the Juhnke-Balkin Life Balance Inventory

ERIC Educational Resources Information Center

Davis, R. J.; Balkin, Richard S.; Juhnke, Gerald A.

2014-01-01

Life balance is an important construct within the counseling profession. A validation study utilizing exploratory factor analysis and multiple regression was conducted on the Juhnke-Balkin Life Balance Inventory. Results from the study serve as evidence of validity for an assessment instrument designed to measure life balance.

Impact of Collegiate Recreation on Academic Success

ERIC Educational Resources Information Center

Sanderson, Heather; DeRousie, Jason; Guistwite, Nicole

2018-01-01

This study examined the impact of collegiate recreation participation on academic success as measured by grade point average, course credit completion, and persistence or graduation. Logistic and multiple regressions were run to explore the relationship between total recreation contact hours and outcome variables. Results indicated a positive and…

Family and spacing affect stem profile of loblolly pine at age 19

Treesearch

Joshua P. Adams; Samuel B. Land; Thomas G. Matney

2006-01-01

Profile measurements were taken on a stratified sample of 19-year-old trees from 8 North Carolina families and a commercial Mississippi-Alabama check established at 3 spacings (5 x 5, 8 x 8, and 10 x 10 feet). Measurements were first fitted on a single profile equation using multiple-regression. Data were also segregated by family, spacing, and family-byspacing and...

Serum levels of the immune activation marker neopterin change with age and gender and are modified by race, BMI, and percentage of body fat.

PubMed

Spencer, Monique E; Jain, Alka; Matteini, Amy; Beamer, Brock A; Wang, Nae-Yuh; Leng, Sean X; Punjabi, Naresh M; Walston, Jeremy D; Fedarko, Neal S

2010-08-01

Neopterin, a GTP metabolite expressed by macrophages, is a marker of immune activation. We hypothesize that levels of this serum marker alter with donor age, reflecting increased chronic immune activation in normal aging. In addition to age, we assessed gender, race, body mass index (BMI), and percentage of body fat (%fat) as potential covariates. Serum was obtained from 426 healthy participants whose age ranged from 18 to 87 years. Anthropometric measures included %fat and BMI. Neopterin concentrations were measured by competitive ELISA. The paired associations between neopterin and age, BMI, or %fat were analyzed by Spearman's correlation or by linear regression of log-transformed neopterin, whereas overall associations were modeled by multiple regression of log-transformed neopterin as a function of age, gender, race, BMI, %fat, and interaction terms. Across all participants, neopterin exhibited a positive association with age, BMI, and %fat. Multiple regression modeling of neopterin in women and men as a function of age, BMI, and race revealed that each covariate contributed significantly to neopterin values and that optimal modeling required an interaction term between race and BMI. The covariate %fat was highly correlated with BMI and could be substituted for BMI to yield similar regression coefficients. The association of age and gender with neopterin levels and their modification by race, BMI, or %fat reflect the biology underlying chronic immune activation and perhaps gender differences in disease incidence, morbidity, and mortality.

A Quantile Regression Approach to Understanding the Relations Between Morphological Awareness, Vocabulary, and Reading Comprehension in Adult Basic Education Students

PubMed Central

Tighe, Elizabeth L.; Schatschneider, Christopher

2015-01-01

The purpose of this study was to investigate the joint and unique contributions of morphological awareness and vocabulary knowledge at five reading comprehension levels in Adult Basic Education (ABE) students. We introduce the statistical technique of multiple quantile regression, which enabled us to assess the predictive utility of morphological awareness and vocabulary knowledge at multiple points (quantiles) along the continuous distribution of reading comprehension. To demonstrate the efficacy of our multiple quantile regression analysis, we compared and contrasted our results with a traditional multiple regression analytic approach. Our results indicated that morphological awareness and vocabulary knowledge accounted for a large portion of the variance (82-95%) in reading comprehension skills across all quantiles. Morphological awareness exhibited the greatest unique predictive ability at lower levels of reading comprehension whereas vocabulary knowledge exhibited the greatest unique predictive ability at higher levels of reading comprehension. These results indicate the utility of using multiple quantile regression to assess trajectories of component skills across multiple levels of reading comprehension. The implications of our findings for ABE programs are discussed. PMID:25351773

Measurement of lung volumes from supine portable chest radiographs.

PubMed

Ries, A L; Clausen, J L; Friedman, P J

1979-12-01

Lung volumes in supine nonambulatory patients are physiological parameters often difficult to measure with current techniques (plethysmograph, gas dilution). Existing radiographic methods for measuring lung volumes require standard upright chest radiographs. Accordingly, in 31 normal supine adults, we determined helium-dilution functional residual and total lung capacities and measured planimetric lung field areas (LFA) from corresponding portable anteroposterior and lateral radiographs. Low radiation dose methods, which delivered less than 10% of that from standard portable X-ray technique, were utilized. Correlation between lung volume and radiographic LFA was highly significant (r = 0.96, SEE = 10.6%). Multiple-step regressions using height and chest diameter correction factors reduced variance, but weight and radiographic magnification factors did not. In 17 additional subjects studied for validation, the regression equations accurately predicted radiographic lung volume. Thus, this technique can provide accurate and rapid measurement of lung volume in studies involving supine patients.

Visual Acuity Is Correlated with the Area of the Foveal Avascular Zone in Diabetic Retinopathy and Retinal Vein Occlusion.

PubMed

Balaratnasingam, Chandrakumar; Inoue, Maiko; Ahn, Seungjun; McCann, Jesse; Dhrami-Gavazi, Elona; Yannuzzi, Lawrence A; Freund, K Bailey

2016-11-01

To determine if the area of the foveal avascular zone (FAZ) is correlated with visual acuity (VA) in diabetic retinopathy (DR) and retinal vein occlusion (RVO). Cross-sectional study. Ninety-five eyes of 66 subjects with DR (65 eyes), branch retinal vein occlusion (19 eyes), and central retinal vein occlusion (11 eyes). Structural optical coherence tomography (OCT; Spectralis, Heidelberg Engineering) and OCT angiography (OCTA; Avanti, Optovue RTVue XR) data from a single visit were analyzed. FAZ area, point thickness of central fovea, central 1-mm subfield thickness, the occurrence of intraretinal cysts, ellipsoid zone disruption, and disorganization of retinal inner layers (DRIL) length were measured. VA was also recorded. Correlations between FAZ area and VA were explored using regression models. Main outcome measure was VA. Mean age was 62.9±13.2 years. There was no difference in demographic and OCT-derived anatomic measurements between branch retinal vein occlusion and central retinal vein occlusion groups (all P ≥ 0.058); therefore, data from the 2 groups were pooled together to a single RVO group for further statistical comparisons. Univariate and multiple regression analysis showed that the area of the FAZ was significantly correlated with VA in DR and RVO (all P ≤ 0.003). The relationship between FAZ area and VA varied with age (P = 0.026) such that for a constant FAZ area, an increase in patient age was associated with poorer vision (rise in logarithm of the minimum angle of resolution visual acuity). Disruption of the ellipsoid zone was significantly correlated with VA in univariate and multiple regression analysis (both P < 0.001). Occurrence of intraretinal cysts, DRIL length, and lens status were significantly correlated with VA in the univariate regression analysis (P ≤ 0.018) but not the multiple regression analysis (P ≥ 0.210). Remaining variables evaluated in this study were not predictive of VA (all P ≥ 0.225). The area of the FAZ is significantly correlated with VA in DR and RVO and this relationship is modulated by patient age. Further study about FAZ area and VA correlations during the natural course of retinal vascular diseases and following treatment is warranted. Copyright © 2016 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.

ℓ(p)-Norm multikernel learning approach for stock market price forecasting.

PubMed

Shao, Xigao; Wu, Kun; Liao, Bifeng

2012-01-01

Linear multiple kernel learning model has been used for predicting financial time series. However, ℓ(1)-norm multiple support vector regression is rarely observed to outperform trivial baselines in practical applications. To allow for robust kernel mixtures that generalize well, we adopt ℓ(p)-norm multiple kernel support vector regression (1 ≤ p < ∞) as a stock price prediction model. The optimization problem is decomposed into smaller subproblems, and the interleaved optimization strategy is employed to solve the regression model. The model is evaluated on forecasting the daily stock closing prices of Shanghai Stock Index in China. Experimental results show that our proposed model performs better than ℓ(1)-norm multiple support vector regression model.

Heritability Across the Distribution: An Application of Quantile Regression

PubMed Central

Petrill, Stephen A.; Hart, Sara A.; Schatschneider, Christopher; Thompson, Lee A.; Deater-Deckard, Kirby; DeThorne, Laura S.; Bartlett, Christopher

2016-01-01

We introduce a new method for analyzing twin data called quantile regression. Through the application presented here, quantile regression is able to assess the genetic and environmental etiology of any skill or ability, at multiple points in the distribution of that skill or ability. This method is compared to the Cherny et al. (Behav Genet 22:153–162, 1992) method in an application to four different reading-related outcomes in 304 pairs of first-grade same sex twins enrolled in the Western Reserve Reading Project. Findings across the two methods were similar; both indicated some variation across the distribution of the genetic and shared environmental influences on non-word reading. However, quantile regression provides more details about the location and size of the measured effect. Applications of the technique are discussed. PMID:21877231

Association between MRI structural features and cognitive measures in pediatric multiple sclerosis

NASA Astrophysics Data System (ADS)

Amoroso, N.; Bellotti, R.; Fanizzi, A.; Lombardi, A.; Monaco, A.; Liguori, M.; Margari, L.; Simone, M.; Viterbo, R. G.; Tangaro, S.

2017-09-01

Multiple sclerosis (MS) is an inflammatory and demyelinating disease associated with neurodegenerative processes that lead to brain structural changes. The disease affects mostly young adults, but 3-5% of cases has a pediatric onset (POMS). Magnetic Resonance Imaging (MRI) is generally used for diagnosis and follow-up in MS patients, however the most common MRI measures (e.g. new or enlarging T2-weighted lesions, T1-weighted gadolinium- enhancing lesions) have often failed as surrogate markers of MS disability and progression. MS is clinically heterogenous with symptoms that can include both physical changes (such as visual loss or walking difficulties) and cognitive impairment. 30-50% of POMS experience prominent cognitive dysfunction. In order to investigate the association between cognitive measures and brain morphometry, in this work we present a fully automated pipeline for processing and analyzing MRI brain scans. Relevant anatomical structures are segmented with FreeSurfer; besides, statistical features are computed. Thus, we describe the data referred to 12 patients with early POMS (mean age at MRI: 15.5 +/- 2.7 years) with a set of 181 structural features. The major cognitive abilities measured are verbal and visuo-spatial learning, expressive language and complex attention. Data was collected at the Department of Basic Sciences, Neurosciences and Sense Organs, University of Bari, and exploring different abilities like the verbal and visuo-spatial learning, expressive language and complex attention. Different regression models and parameter configurations are explored to assess the robustness of the results, in particular Generalized Linear Models, Bayes Regression, Random Forests, Support Vector Regression and Artificial Neural Networks are discussed.

Comparing Revictimization in Two Groups of Marginalized Women

ERIC Educational Resources Information Center

Tusher, Chantal Poister; Cook, Sarah L.

2010-01-01

This study examines physical and sexual revictimization in a random sample of incarcerated and poor, urban, nonincarcerated women using multiple measures of physical and sexual child abuse. Researchers used hierarchical logistic regression to compare rates of revictimization and the strength of the association between child abuse and adult…

Using Artificial Neural Networks in Educational Research: Some Comparisons with Linear Statistical Models.

ERIC Educational Resources Information Center

Everson, Howard T.; And Others

This paper explores the feasibility of neural computing methods such as artificial neural networks (ANNs) and abductory induction mechanisms (AIM) for use in educational measurement. ANNs and AIMS methods are contrasted with more traditional statistical techniques, such as multiple regression and discriminant function analyses, for making…

Factors Influencing the Academic Achievement of First-Generation College Students

ERIC Educational Resources Information Center

Strayhorn, Terrell L.

2006-01-01

First-generation college students face a number of unique challenges in college. These obstacles may have a disparate effect on educational outcomes such as academic achievement. This study presents findings from an analysis of the Baccalaureate & Beyond Longitudinal Study using hierarchical multiple regression techniques to measure the influence…

Environmental factors affecting understory diversity in second-growth deciduous forests

Treesearch

Cynthia D. Huebner; J.C. Randolph; G.R. Parker

1995-01-01

The purpose of this study was to determine the most important nonanthropogenic factors affecting understory (herbs, shrubs and low-growing vines) diversity in forested landscapes of southern Indiana. Fourteen environmental variables were measured for 46 sites. Multiple regression analysis showed significant positive correlation between understory diversity and tree...

Optimal Wavelengths Selection Using Hierarchical Evolutionary Algorithm for Prediction of Firmness and Soluble Solids Content in Apples

USDA-ARS?s Scientific Manuscript database

Hyperspectral scattering is a promising technique for rapid and noninvasive measurement of multiple quality attributes of apple fruit. A hierarchical evolutionary algorithm (HEA) approach, in combination with subspace decomposition and partial least squares (PLS) regression, was proposed to select o...

The effect of different distance measures in detecting outliers using clustering-based algorithm for circular regression model

NASA Astrophysics Data System (ADS)

Di, Nur Faraidah Muhammad; Satari, Siti Zanariah

2017-05-01

Outlier detection in linear data sets has been done vigorously but only a small amount of work has been done for outlier detection in circular data. In this study, we proposed multiple outliers detection in circular regression models based on the clustering algorithm. Clustering technique basically utilizes distance measure to define distance between various data points. Here, we introduce the similarity distance based on Euclidean distance for circular model and obtain a cluster tree using the single linkage clustering algorithm. Then, a stopping rule for the cluster tree based on the mean direction and circular standard deviation of the tree height is proposed. We classify the cluster group that exceeds the stopping rule as potential outlier. Our aim is to demonstrate the effectiveness of proposed algorithms with the similarity distances in detecting the outliers. It is found that the proposed methods are performed well and applicable for circular regression model.

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

NASA Astrophysics Data System (ADS)

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

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

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

PubMed

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

2017-09-01

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

Depressive disorder in pregnant Latin women: does intimate partner violence matter?

PubMed

Fonseca-Machado, Mariana de Oliveira; Alves, Lisiane Camargo; Monteiro, Juliana Cristina Dos Santos; Stefanello, Juliana; Nakano, Ana Márcia Spanó; Haas, Vanderlei José; Gomes-Sponholz, Flávia

2015-05-01

To identify the association of antenatal depressive symptoms with intimate partner violence during the current pregnancy in Brazilian women. Intimate partner violence is an important risk factor for antenatal depression. To the authors' knowledge, there has been no study to date that assessed the association between intimate partner violence during pregnancy and antenatal depressive symptoms among Brazilian women. Cross-sectional study. Three hundred and fifty-eight pregnant women were enrolled in the study. The Edinburgh Postnatal Depression Scale and an adapted version of the instrument used in the World Health Organization Multi-country Study on Women's Health and Domestic Violence were used to measure antenatal depressive symptoms and psychological, physical and sexual acts of intimate partner violence during the current pregnancy respectively. Multiple logistic regression and multiple linear regression were used for data analysis. The prevalence of antenatal depressive symptoms, as determined by the cut-off score of 12 in the Edinburgh Postnatal Depression Scale, was 28·2% (101). Of the participants, 63 (17·6%) reported some type of intimate partner violence during pregnancy. Among them, 60 (95·2%) reported suffering psychological violence, 23 (36·5%) physical violence and one (1·6%) sexual violence. Multiple logistic regression and multiple linear regression indicated that antenatal depressive symptoms are extremely associated with intimate partner violence during pregnancy. Among Brazilian women, exposure to intimate partner violence during pregnancy increases the chances of experiencing antenatal depressive symptoms. Clinical nurses and nurses midwifes should pay attention to the particularities of Brazilian women, especially with regard to the occurrence of intimate partner violence, whose impacts on the mental health of this population are extremely significant, both during the gestational period and postpartum. © 2015 John Wiley & Sons Ltd.

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

PubMed

John, Dinesh; Staudenmayer, John; Freedson, Patty

2013-06-01

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

Construction of multiple linear regression models using blood biomarkers for selecting against abdominal fat traits in broilers.

PubMed

Dong, J Q; Zhang, X Y; Wang, S Z; Jiang, X F; Zhang, K; Ma, G W; Wu, M Q; Li, H; Zhang, H

2018-01-01

Plasma very low-density lipoprotein (VLDL) can be used to select for low body fat or abdominal fat (AF) in broilers, but its correlation with AF is limited. We investigated whether any other biochemical indicator can be used in combination with VLDL for a better selective effect. Nineteen plasma biochemical indicators were measured in male chickens from the Northeast Agricultural University broiler lines divergently selected for AF content (NEAUHLF) in the fed state at 46 and 48 d of age. The average concentration of every parameter for the 2 d was used for statistical analysis. Levels of these 19 plasma biochemical parameters were compared between the lean and fat lines. The phenotypic correlations between these plasma biochemical indicators and AF traits were analyzed. Then, multiple linear regression models were constructed to select the best model used for selecting against AF content. and the heritabilities of plasma indicators contained in the best models were estimated. The results showed that 11 plasma biochemical indicators (triglycerides, total bile acid, total protein, globulin, albumin/globulin, aspartate transaminase, alanine transaminase, gamma-glutamyl transpeptidase, uric acid, creatinine, and VLDL) differed significantly between the lean and fat lines (P < 0.01), and correlated significantly with AF traits (P < 0.05). The best multiple linear regression models based on albumin/globulin, VLDL, triglycerides, globulin, total bile acid, and uric acid, had higher R2 (0.73) than the model based only on VLDL (0.21). The plasma parameters included in the best models had moderate heritability estimates (0.21 ≤ h2 ≤ 0.43). These results indicate that these multiple linear regression models can be used to select for lean broiler chickens. © 2017 Poultry Science Association Inc.

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.

Continuous optical measurement system of hemolysis during a photosensitization reaction using absorption spectrum

NASA Astrophysics Data System (ADS)

Hamada, R.; Ogawa, E.; Arai, T.

2018-02-01

To investigate hemolysis phenomena during a photosensitization reaction with the reaction condition continuously and simultaneously for a safety assessment of hemolysis side effect, we constructed an optical system to measure blood sample absorption spectrum during the reaction. Hemolysis degree might be under estimated in general evaluation methods because there is a constant oxygen pressure assumption in spite of oxygen depression take place. By investigating hemoglobin oxidation and oxygen desorption dynamics obtained from the contribution of the visible absorption spectrum and multiple regression analysis, both the hemolysis phenomena and its oxygen environment might be obtained with time. A 664 nm wavelength laser beam for the reaction excitation and 475-650 nm light beam for measuring the absorbance spectrum were arranged perpendicularly crossing. A quartz glass cuvette with 1×10 mm in dimensions for the spectrum measurement was located at this crossing point. A red blood cells suspension medium was arranged with low hematocrit containing 30 μg/ml talaporfin sodium. This medium was irradiated up to 40 J/cm2 . The met-hemoglobin, oxygenatedhemoglobin, and deoxygenated-hemoglobin concentrations were calculated by a multiple regression analysis from the measured spectra. We confirmed the met-hemoglobin concentration increased and oxygen saturation decreased with the irradiation time, which seems to indicate the hemolysis progression and oxygen consumption, respectively. By using our measuring system, the hemolysis progression seems to be obtained with oxygen environment information.

Modeling Polytomous Item Responses Using Simultaneously Estimated Multinomial Logistic Regression Models

ERIC Educational Resources Information Center

Anderson, Carolyn J.; Verkuilen, Jay; Peyton, Buddy L.

2010-01-01

Survey items with multiple response categories and multiple-choice test questions are ubiquitous in psychological and educational research. We illustrate the use of log-multiplicative association (LMA) models that are extensions of the well-known multinomial logistic regression model for multiple dependent outcome variables to reanalyze a set of…

Predicting recreational water quality advisories: A comparison of statistical methods

USGS Publications Warehouse

Brooks, Wesley R.; Corsi, Steven R.; Fienen, Michael N.; Carvin, Rebecca B.

2016-01-01

Epidemiological studies indicate that fecal indicator bacteria (FIB) in beach water are associated with illnesses among people having contact with the water. In order to mitigate public health impacts, many beaches are posted with an advisory when the concentration of FIB exceeds a beach action value. The most commonly used method of measuring FIB concentration takes 18–24 h before returning a result. In order to avoid the 24 h lag, it has become common to ”nowcast” the FIB concentration using statistical regressions on environmental surrogate variables. Most commonly, nowcast models are estimated using ordinary least squares regression, but other regression methods from the statistical and machine learning literature are sometimes used. This study compares 14 regression methods across 7 Wisconsin beaches to identify which consistently produces the most accurate predictions. A random forest model is identified as the most accurate, followed by multiple regression fit using the adaptive LASSO.

Prediction of monthly rainfall in Victoria, Australia: Clusterwise linear regression approach

NASA Astrophysics Data System (ADS)

Bagirov, Adil M.; Mahmood, Arshad; Barton, Andrew

2017-05-01

This paper develops the Clusterwise Linear Regression (CLR) technique for prediction of monthly rainfall. The CLR is a combination of clustering and regression techniques. It is formulated as an optimization problem and an incremental algorithm is designed to solve it. The algorithm is applied to predict monthly rainfall in Victoria, Australia using rainfall data with five input meteorological variables over the period of 1889-2014 from eight geographically diverse weather stations. The prediction performance of the CLR method is evaluated by comparing observed and predicted rainfall values using four measures of forecast accuracy. The proposed method is also compared with the CLR using the maximum likelihood framework by the expectation-maximization algorithm, multiple linear regression, artificial neural networks and the support vector machines for regression models using computational results. The results demonstrate that the proposed algorithm outperforms other methods in most locations.

Which symptoms contribute the most to patients' perception of health in multiple sclerosis?

PubMed

Green, Rivka; Cutter, Gary; Friendly, Michael; Kister, Ilya

2017-01-01

Multiple sclerosis is a polysymptomatic disease. Little is known about relative contributions of the different multiple sclerosis symptoms to self-perception of health. To investigate the relationship between symptom severity in 11 domains affected by multiple sclerosis and self-rated health. Multiple sclerosis patients in two multiple sclerosis centers assessed self-rated health with a validated instrument and symptom burden with symptoMScreen, a validated battery of Likert scales for 11 domains commonly affected by multiple sclerosis. Pearson correlations and multivariate linear regressions were used to investigate the relationship between symptoMScreen scores and self-rated health. Among 1865 multiple sclerosis outpatients (68% women, 78% with relapsing-remitting multiple sclerosis, mean age 46.38 ± 12.47 years, disease duration 13.43 ± 10.04 years), average self-rated health score was 2.30 ('moderate to good'). Symptom burden (composite symptoMScreen score) highly correlated with self-rated health ( r  = 0.68, P  < 0.0001) as did each of the symptoMScreen domain subscores. In regression analysis, pain ( t  = 7.00), ambulation ( t  = 6.91), and fatigue ( t  = 5.85) contributed the highest amount of variance in self-rated health ( P  < 0.001). Pain contributed the most to multiple sclerosis outpatients' perception of health, followed by gait dysfunction and fatigue. These findings suggest that 'invisible disability' may be more important to patients' sense of wellbeing than physical disability, and challenge the notion that physical disability should be the primary outcome measure in multiple sclerosis.

Motor excitability measurements: the influence of gender, body mass index, age and temperature in healthy controls.

PubMed

Casanova, I; Diaz, A; Pinto, S; de Carvalho, M

2014-04-01

The technique of threshold tracking to test axonal excitability gives information about nodal and internodal ion channel function. We aimed to investigate variability of the motor excitability measurements in healthy controls, taking into account age, gender, body mass index (BMI) and small changes in skin temperature. We examined the left median nerve of 47 healthy controls using the automated threshold-tacking program, QTRAC. Statistical multiple regression analysis was applied to test relationship between nerve excitability measurements and subject variables. Comparisons between genders did not find any significant difference (P>0.2 for all comparisons). Multiple regression analysis showed that motor amplitude decreases with age and temperature, stimulus-response slope decreases with age and BMI, and that accommodation half-time decrease with age and temperature. The changes related to demographic features on TRONDE protocol parameters are small and less important than in conventional nerve conduction studies. Nonetheless, our results underscore the relevance of careful temperature control, and indicate that interpretation of stimulus-response slope and accommodation half-time should take into account age and BMI. In contrast, gender is not of major relevance to axonal threshold findings in motor nerves. Copyright © 2014 Elsevier Masson SAS. All rights reserved.

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

PubMed

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

2007-01-01

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

RACE AS LIVED EXPERIENCE

PubMed Central

Garcia, John A.; Sanchez, Gabriel R.; Sanchez-Youngman, Shannon; Vargas, Edward D.; Ybarra, Vickie D.

2015-01-01

A growing body of social science research has sought to conceptualize race as a multidimensional concept in which context, societal relations, and institutional dynamics are key components. Utilizing a specially designed survey, we develop and use multiple measures of race (skin color, ascribed race, and discrimination experiences) to capture race as “lived experience” and assess their impact on Latinos’ self-rated health status. We model these measures of race as a lived experience to test the explanatory power of race, both independently and as an integrated scale with categorical regression, scaling, and dimensional analyses. Our analyses show that our multiple measures of race have significant and negative effects on Latinos’ self-reported health. Skin color is a dominant factor that impacts self-reported health both directly and indirectly. We then advocate for the utilization of multiple measures of race, adding to those used in our analysis, and their application to other health and social outcomes. Our analysis provides important contributions across a wide range of health, illness, social, and political outcomes for communities of color. PMID:26681972

Impulsivity, attention, memory, and decision-making among adolescent marijuana users.

PubMed

Dougherty, Donald M; Mathias, Charles W; Dawes, Michael A; Furr, R Michael; Charles, Nora E; Liguori, Anthony; Shannon, Erin E; Acheson, Ashley

2013-03-01

Marijuana is a popular drug of abuse among adolescents, and they may be uniquely vulnerable to resulting cognitive and behavioral impairments. Previous studies have found impairments among adolescent marijuana users. However, the majority of this research has examined measures individually rather than multiple domains in a single cohesive analysis. This study used a logistic regression model that combines performance on a range of tasks to identify which measures were most altered among adolescent marijuana users. The purpose of this research was to determine unique associations between adolescent marijuana use and performances on multiple cognitive and behavioral domains (attention, memory, decision-making, and impulsivity) in 14- to 17-year-olds while simultaneously controlling for performances across the measures to determine which measures most strongly distinguish marijuana users from nonusers. Marijuana-using adolescents (n = 45) and controls (n = 48) were tested. Logistic regression analyses were conducted to test for: (1) differences between marijuana users and nonusers on each measure, (2) associations between marijuana use and each measure after controlling for the other measures, and (3) the degree to which (1) and (2) together elucidated differences among marijuana users and nonusers. Of all the cognitive and behavioral domains tested, impaired short-term recall memory and consequence sensitivity impulsivity were associated with marijuana use after controlling for performances across all measures. This study extends previous findings by identifying cognitive and behavioral impairments most strongly associated with adolescent marijuana users. These specific deficits are potential targets of intervention for this at-risk population.

Self-identity and the theory of planned behaviour: between- and within-participants analyses.

PubMed

Hagger, Martin S; Chatzisarantis, Nikos L D

2006-12-01

Two studies addressed the hypothesis that a minority of people are more oriented towards their self-identity when forming intentions to act than the traditional antecedents of intentional action; attitudes, subjective norms and perceived behavioural control (PBC). In Study 1, participants (N=241) completed measures of an augmented version of theory of planned behaviour (TPB) that included self-identity for 30 behaviours. Using within-participants multiple regression analyses, the sample was classified into self-identity-oriented (SI-oriented) and TPB-oriented groups. Between-participants multiple regression analyses revealed that self-identity was a significantly stronger predictor of intentions and accounted for significantly more incremental variance in intentions in the SI-oriented sample compared with the TPB-oriented sample across the 30 behaviours. In Study 2, participants (N=250) completed the same TPB and self-identity measures used in Study 1 as well as measures of generalized self-concept and social physique anxiety for dieting behaviour. Results indicated that self-identity was significantly associated with the generalized self-related measures, and self-concept and social physique anxiety moderated the self-identity-intention relationship. This investigation provides some preliminary evidence to support the effect of individual differences in self-identity on the formation of intentions to act.

A novel method to estimate changes in stress-induced salivary α-amylase using heart rate variability and respiratory rate, as measured in a non-contact manner using a single radar attached to the back of a chair.

PubMed

Matsui, Takemi; Katayose, Satoshi

2014-08-01

The authors have developed a non-contact system which estimates changes in salivary α-amylase (sAA ratio) induced by stress. Before and after stressful sound exposure, a single 24 GHz compact radar which is attached to the back of a chair measures the low frequency (LF) component of heart rate variability and respiratory rate, α-amylase in the subjects' buccal secretions was measured by using an α-amylase assay kit. Using multiple regression analysis, sAA ratio was estimated using stress-induced LF change (LF ratio) and stress-induced respiratory rate change (respiratory rate ratio). Twelve healthy subjects were tested (12 males, 22 ± 2 years), who were exposed to audio stimuli with a composite tone of 2120 Hz and 2130 Hz sine waves at a sound pressure level of 95 dB after a silent period through a headphone. The result showed that sAA ratio estimated using multiple regression analysis significantly correlated with measured sAA ratio (R = 0.76, p < 0.01). This indicates that the system may serve for a stress management in the future.

Searching for a neurologic injury's Wechsler Adult Intelligence Scale-Third Edition profile.

PubMed

Gonçalves, Marta A; Moura, Octávio; Castro-Caldas, Alexandre; Simões, Mário R

2017-01-01

This study aimed to investigate the presence of a Wechsler Adult Intelligence Scale-Third Edition (WAIS-III) cognitive profile in a Portuguese neurologic injured sample. The Portuguese WAIS-III was administered to 81 mixed neurologic patients and 81 healthy matched controls selected from the Portuguese standardization sample. Although the mixed neurologic injury group performed significantly lower than the healthy controls for the majority of the WAIS-III scores (i.e., composite measures, discrepancies, and subtests), the mean scores were within the normal range and, therefore, at risk of being unobserved in a clinical evaluation. ROC curves analysis showed poor to acceptable diagnostic accuracy for the WAIS-III composite measures and subtests (Working Memory Index and Digit Span revealed the highest accuracy for discriminating between participants, respectively). Multiple regression analysis showed that both literacy and the presence of brain injury were significant predictors for all of the composite measures. In addition, multiple regression analysis also showed that literacy, age of injury onset, and years of survival predicted all seven composite measures for the mixed neurologic injured group. Despite the failure to find a WAIS-III cognitive profile for mixed neurologic patients, the results showed a significant influence of brain lesion and literacy in the performance of the WAIS-III.

Regression modeling of ground-water flow

USGS Publications Warehouse

Cooley, R.L.; Naff, R.L.

1985-01-01

Nonlinear multiple regression methods are developed to model and analyze groundwater flow systems. Complete descriptions of regression methodology as applied to groundwater flow models allow scientists and engineers engaged in flow modeling to apply the methods to a wide range of problems. Organization of the text proceeds from an introduction that discusses the general topic of groundwater flow modeling, to a review of basic statistics necessary to properly apply regression techniques, and then to the main topic: exposition and use of linear and nonlinear regression to model groundwater flow. Statistical procedures are given to analyze and use the regression models. A number of exercises and answers are included to exercise the student on nearly all the methods that are presented for modeling and statistical analysis. Three computer programs implement the more complex methods. These three are a general two-dimensional, steady-state regression model for flow in an anisotropic, heterogeneous porous medium, a program to calculate a measure of model nonlinearity with respect to the regression parameters, and a program to analyze model errors in computed dependent variables such as hydraulic head. (USGS)

Isolating and Examining Sources of Suppression and Multicollinearity in Multiple Linear Regression

ERIC Educational Resources Information Center

Beckstead, Jason W.

2012-01-01

The presence of suppression (and multicollinearity) in multiple regression analysis complicates interpretation of predictor-criterion relationships. The mathematical conditions that produce suppression in regression analysis have received considerable attention in the methodological literature but until now nothing in the way of an analytic…

General Nature of Multicollinearity in Multiple Regression Analysis.

ERIC Educational Resources Information Center

Liu, Richard

1981-01-01

Discusses multiple regression, a very popular statistical technique in the field of education. One of the basic assumptions in regression analysis requires that independent variables in the equation should not be highly correlated. The problem of multicollinearity and some of the solutions to it are discussed. (Author)

Brain enlargement is associated with regression in preschool-age boys with autism spectrum disorders

PubMed Central

Nordahl, Christine Wu; Lange, Nicholas; Li, Deana D.; Barnett, Lou Ann; Lee, Aaron; Buonocore, Michael H.; Simon, Tony J.; Rogers, Sally; Ozonoff, Sally; Amaral, David G.

2011-01-01

Autism is a heterogeneous disorder with multiple behavioral and biological phenotypes. Accelerated brain growth during early childhood is a well-established biological feature of autism. Onset pattern, i.e., early onset or regressive, is an intensely studied behavioral phenotype of autism. There is currently little known, however, about whether, or how, onset status maps onto the abnormal brain growth. We examined the relationship between total brain volume and onset status in a large sample of 2- to 4-y-old boys and girls with autism spectrum disorder (ASD) [n = 53, no regression (nREG); n = 61, regression (REG)] and a comparison group of age-matched typically developing controls (n = 66). We also examined retrospective head circumference measurements from birth through 18 mo of age. We found that abnormal brain enlargement was most commonly found in boys with regressive autism. Brain size in boys without regression did not differ from controls. Retrospective head circumference measurements indicate that head circumference in boys with regressive autism is normal at birth but diverges from the other groups around 4–6 mo of age. There were no differences in brain size in girls with autism (n = 22, ASD; n = 24, controls). These results suggest that there may be distinct neural phenotypes associated with different onsets of autism. For boys with regressive autism, divergence in brain size occurs well before loss of skills is commonly reported. Thus, rapid head growth may be a risk factor for regressive autism. PMID:22123952

Subject-specific body segment parameter estimation using 3D photogrammetry with multiple cameras

PubMed Central

Morris, Mark; Sellers, William I.

2015-01-01

Inertial properties of body segments, such as mass, centre of mass or moments of inertia, are important parameters when studying movements of the human body. However, these quantities are not directly measurable. Current approaches include using regression models which have limited accuracy: geometric models with lengthy measuring procedures or acquiring and post-processing MRI scans of participants. We propose a geometric methodology based on 3D photogrammetry using multiple cameras to provide subject-specific body segment parameters while minimizing the interaction time with the participants. A low-cost body scanner was built using multiple cameras and 3D point cloud data generated using structure from motion photogrammetric reconstruction algorithms. The point cloud was manually separated into body segments, and convex hulling applied to each segment to produce the required geometric outlines. The accuracy of the method can be adjusted by choosing the number of subdivisions of the body segments. The body segment parameters of six participants (four male and two female) are presented using the proposed method. The multi-camera photogrammetric approach is expected to be particularly suited for studies including populations for which regression models are not available in literature and where other geometric techniques or MRI scanning are not applicable due to time or ethical constraints. PMID:25780778

Subject-specific body segment parameter estimation using 3D photogrammetry with multiple cameras.

PubMed

Peyer, Kathrin E; Morris, Mark; Sellers, William I

2015-01-01

Inertial properties of body segments, such as mass, centre of mass or moments of inertia, are important parameters when studying movements of the human body. However, these quantities are not directly measurable. Current approaches include using regression models which have limited accuracy: geometric models with lengthy measuring procedures or acquiring and post-processing MRI scans of participants. We propose a geometric methodology based on 3D photogrammetry using multiple cameras to provide subject-specific body segment parameters while minimizing the interaction time with the participants. A low-cost body scanner was built using multiple cameras and 3D point cloud data generated using structure from motion photogrammetric reconstruction algorithms. The point cloud was manually separated into body segments, and convex hulling applied to each segment to produce the required geometric outlines. The accuracy of the method can be adjusted by choosing the number of subdivisions of the body segments. The body segment parameters of six participants (four male and two female) are presented using the proposed method. The multi-camera photogrammetric approach is expected to be particularly suited for studies including populations for which regression models are not available in literature and where other geometric techniques or MRI scanning are not applicable due to time or ethical constraints.

Peak oxygen consumption measured during the stair-climbing test in lung resection candidates.

PubMed

Brunelli, Alessandro; Xiumé, Francesco; Refai, Majed; Salati, Michele; Di Nunzio, Luca; Pompili, Cecilia; Sabbatini, Armando

2010-01-01

The stair-climbing test is commonly used in the preoperative evaluation of lung resection candidates, but it is difficult to standardize and provides little physiologic information on the performance. To verify the association between the altitude and the V(O2peak) measured during the stair-climbing test. 109 consecutive candidates for lung resection performed a symptom-limited stair-climbing test with direct breath-by-breath measurement of V(O2peak) by a portable gas analyzer. Stepwise logistic regression and bootstrap analyses were used to verify the association of several perioperative variables with a V(O2peak) <15 ml/kg/min. Subsequently, multiple regression analysis was also performed to develop an equation to estimate V(O2peak) from stair-climbing parameters and other patient-related variables. 56% of patients climbing <14 m had a V(O2peak) <15 ml/kg/min, whereas 98% of those climbing >22 m had a V(O2peak) >15 ml/kg/min. The altitude reached at stair-climbing test resulted in the only significant predictor of a V(O2peak) <15 ml/kg/min after logistic regression analysis. Multiple regression analysis yielded an equation to estimate V(O2peak) factoring altitude (p < 0.0001), speed of ascent (p = 0.005) and body mass index (p = 0.0008). There was an association between altitude and V(O2peak) measured during the stair-climbing test. Most of the patients climbing more than 22 m are able to generate high values of V(O2peak) and can proceed to surgery without any additional tests. All others need to be referred for a formal cardiopulmonary exercise test. In addition, we were able to generate an equation to estimate V(O2peak), which could assist in streamlining the preoperative workup and could be used across different settings to standardize this test. Copyright (c) 2010 S. Karger AG, Basel.

ℓ p-Norm Multikernel Learning Approach for Stock Market Price Forecasting

PubMed Central

Shao, Xigao; Wu, Kun; Liao, Bifeng

2012-01-01

Linear multiple kernel learning model has been used for predicting financial time series. However, ℓ 1-norm multiple support vector regression is rarely observed to outperform trivial baselines in practical applications. To allow for robust kernel mixtures that generalize well, we adopt ℓ p-norm multiple kernel support vector regression (1 ≤ p < ∞) as a stock price prediction model. The optimization problem is decomposed into smaller subproblems, and the interleaved optimization strategy is employed to solve the regression model. The model is evaluated on forecasting the daily stock closing prices of Shanghai Stock Index in China. Experimental results show that our proposed model performs better than ℓ 1-norm multiple support vector regression model. PMID:23365561

Sources of variability in satellite-derived estimates of phytoplankton production in the eastern tropical Pacific

NASA Technical Reports Server (NTRS)

Banse, Karl; Yong, Marina

1990-01-01

As a proxy for satellite CZCS observations and concurrent measurements of primary production rates, data from 138 stations occupied seasonally during 1967-1968 in the offshore eastern tropical Pacific were analyzed in terms of six temporal groups and our current regimes. Multiple linear regressions on column production Pt show that simulated satellite pigment is generally weakly correlated, but sometimes not correlated with Pt, and that incident irradiance, sea surface temperature, nitrate, transparency, and depths of mixed layer or nitracline assume little or no importance. After a proxy for the light-saturated chlorophyll-specific photosynthetic rate P(max) is added, the coefficient of determination ranges from 0.55 to 0.91 (median of 0.85) for the 10 cases. In stepwise multiple linear regressions the P(max) proxy is the best predictor for Pt.

Multiple regression analysis of factors influencing dominant hand grip strength in an adult Malaysian population.

PubMed

Hossain, M G; Zyroul, R; Pereira, B P; Kamarul, T

2012-01-01

Grip strength is an important measure used to monitor the progression of a condition, and to evaluate outcomes of treatment. We assessed how various physical and social factors predict normal grip strength in an adult Malaysian population of mixed Asian ethnicity (254 men, 246 women). Grip strength was recorded using the Jamar dynamometer. The mean grip strength for the dominant hand was 29.8 kg for men and 17.6 kg for women. Multiple regression analysis demonstrated that the dominant hand grip strength was positively associated with height and body mass index, and negatively associated with age for both sexes. Dominant hand grip strength was related to work status for men (p < 0.05) but not for women. However, there was no difference in grip strength among ethnic groups.

Fundamental Analysis of the Linear Multiple Regression Technique for Quantification of Water Quality Parameters from Remote Sensing Data. Ph.D. Thesis - Old Dominion Univ.

NASA Technical Reports Server (NTRS)

Whitlock, C. H., III

1977-01-01

Constituents with linear radiance gradients with concentration may be quantified from signals which contain nonlinear atmospheric and surface reflection effects for both homogeneous and non-homogeneous water bodies provided accurate data can be obtained and nonlinearities are constant with wavelength. Statistical parameters must be used which give an indication of bias as well as total squared error to insure that an equation with an optimum combination of bands is selected. It is concluded that the effect of error in upwelled radiance measurements is to reduce the accuracy of the least square fitting process and to increase the number of points required to obtain a satisfactory fit. The problem of obtaining a multiple regression equation that is extremely sensitive to error is discussed.

Evaluating the relationship between wildfire extent and nitrogen dry deposition in a boreal forest in interior Alaska

NASA Astrophysics Data System (ADS)

Nagano, Hirohiko; Iwata, Hiroki

2017-03-01

Alaska wildfires may play an important role in nitrogen (N) dry deposition in Alaskan boreal forests. Here we used annual N dry deposition data measured by CASTNET at Denali National Park (DEN417) during 1999-2013, to evaluate the relationships between wildfire extent and N dry deposition in Alaska. We established six potential factors for multiple regression analysis, including burned area within 100 km of DEN417 (BA100km) and in other distant parts of Alaska (BAAK), the sum of indexes of North Atlantic Oscillation and Arctic Oscillation (OI), number of days with negative OI (OIday), precipitation (PRCP), and number of days with PRCP (PRCPday). Multiple regression analysis was conducted for both time scales, annual (using only annual values of factors) and six-month (using annual values of BAAK and BA100km, and fire and non-fire seasons' values of other four factors) time scales. Together, BAAK, BA100km, and OIday, along with PRCPday in the case of the six-month scale, explained more than 92% of the interannual variation in N dry deposition. The influence of BA100km on N dry deposition was ten-fold greater than from BAAK; the qualitative contribution was almost zero, however, due to the small BA100km. BAAK was the leading explanatory factor, with a 15 ± 14% contribution. We further calculated N dry deposition during 1950-2013 using the obtained regression equation and long-term records for the factors. The N dry deposition calculated for 1950-2013 revealed that an increased occurrence of wildfires during the 2000s led to the maximum N dry deposition exhibited during this decade. As a result, the effect of BAAK on N dry deposition remains sufficiently large, even when large possible uncertainties (>40%) in the measurement of N dry deposition are taken into account for the multiple regression analysis.

A Quantile Regression Approach to Understanding the Relations Among Morphological Awareness, Vocabulary, and Reading Comprehension in Adult Basic Education Students.

PubMed

Tighe, Elizabeth L; Schatschneider, Christopher

2016-07-01

The purpose of this study was to investigate the joint and unique contributions of morphological awareness and vocabulary knowledge at five reading comprehension levels in adult basic education (ABE) students. We introduce the statistical technique of multiple quantile regression, which enabled us to assess the predictive utility of morphological awareness and vocabulary knowledge at multiple points (quantiles) along the continuous distribution of reading comprehension. To demonstrate the efficacy of our multiple quantile regression analysis, we compared and contrasted our results with a traditional multiple regression analytic approach. Our results indicated that morphological awareness and vocabulary knowledge accounted for a large portion of the variance (82%-95%) in reading comprehension skills across all quantiles. Morphological awareness exhibited the greatest unique predictive ability at lower levels of reading comprehension whereas vocabulary knowledge exhibited the greatest unique predictive ability at higher levels of reading comprehension. These results indicate the utility of using multiple quantile regression to assess trajectories of component skills across multiple levels of reading comprehension. The implications of our findings for ABE programs are discussed. © Hammill Institute on Disabilities 2014.

Stepwise versus Hierarchical Regression: Pros and Cons

ERIC Educational Resources Information Center

Lewis, Mitzi

2007-01-01

Multiple regression is commonly used in social and behavioral data analysis. In multiple regression contexts, researchers are very often interested in determining the "best" predictors in the analysis. This focus may stem from a need to identify those predictors that are supportive of theory. Alternatively, the researcher may simply be interested…

Attachment Style, Social Support, and Coping as Psychosocial Correlates of Happiness in Persons with Spinal Cord Injuries

ERIC Educational Resources Information Center

Wilson, Lisa; Catalano, Denise; Sung, Connie; Phillips, Brian; Chou, Chih-Chin; Chan, Jacob Yui Chung; Chan, Fong

2013-01-01

Objective: To examine the roles of attachment, social support, and coping as psychosocial correlates in predicting happiness in people with spinal cord injuries. Design: Quantitative descriptive research design using multiple regression and correlation techniques. Participants: 274 individuals with spinal cord injuries. Outcome Measures: Happiness…

Dietary Patterns and Intelligence in Early and Middle Childhood

ERIC Educational Resources Information Center

Theodore, Reremoana F.; Thompson, John M. D.; Waldie, Karen E.; Wall, Clare; Becroft, David M. O.; Robinson, Elizabeth; Wild, Chris J.; Clark, Philippa M.; Mitchell, Ed A.

2009-01-01

The association between intelligence and diet at 3.5 and 7 years was examined in 591 children of European descent. Approximately half of the children were born small-for-gestational age (birth weight @?10th percentile). The relationship between IQ and diet (measured by food frequency) was investigated using multiple regression analyses. Eating…

A Course Specific Perspective in the Prediction of Academic Success.

ERIC Educational Resources Information Center

Beaulieu, R. P.

1990-01-01

Students (N=94) enrolled in a senior-level management course over six semesters were used to investigate the ability of four measures from two industrial tests to predict course performance. The resulting multiple regression equation with four predictors could accurately predict achievement of males, but not of females. (Author/TE)

African American Career Aspirations: Examining the Relative Influence of Internalized Racism

ERIC Educational Resources Information Center

Brown, Danice L.; Segrist, Daniel

2016-01-01

The present study examined the relative influence of aspects of internalized racism on the career aspirations of a sample of African American adults. Participants (N = 315), ranging in age from 18 to 62 years, completed measures of internalized racism and career aspirations online. A hierarchical multiple regression analysis was conducted to…

Crude oil price forecasting based on hybridizing wavelet multiple linear regression model, particle swarm optimization techniques, and principal component analysis.

PubMed

Shabri, Ani; Samsudin, Ruhaidah

2014-01-01

Crude oil prices do play significant role in the global economy and are a key input into option pricing formulas, portfolio allocation, and risk measurement. In this paper, a hybrid model integrating wavelet and multiple linear regressions (MLR) is proposed for crude oil price forecasting. In this model, Mallat wavelet transform is first selected to decompose an original time series into several subseries with different scale. Then, the principal component analysis (PCA) is used in processing subseries data in MLR for crude oil price forecasting. The particle swarm optimization (PSO) is used to adopt the optimal parameters of the MLR model. To assess the effectiveness of this model, daily crude oil market, West Texas Intermediate (WTI), has been used as the case study. Time series prediction capability performance of the WMLR model is compared with the MLR, ARIMA, and GARCH models using various statistics measures. The experimental results show that the proposed model outperforms the individual models in forecasting of the crude oil prices series.

Artificial neural networks environmental forecasting in comparison with multiple linear regression technique: From heavy metals to organic micropollutants screening in agricultural soils

NASA Astrophysics Data System (ADS)

Bonelli, Maria Grazia; Ferrini, Mauro; Manni, Andrea

2016-12-01

The assessment of metals and organic micropollutants contamination in agricultural soils is a difficult challenge due to the extensive area used to collect and analyze a very large number of samples. With Dioxins and dioxin-like PCBs measurement methods and subsequent the treatment of data, the European Community advises the develop low-cost and fast methods allowing routing analysis of a great number of samples, providing rapid measurement of these compounds in the environment, feeds and food. The aim of the present work has been to find a method suitable to describe the relations occurring between organic and inorganic contaminants and use the value of the latter in order to forecast the former. In practice, the use of a metal portable soil analyzer coupled with an efficient statistical procedure enables the required objective to be achieved. Compared to Multiple Linear Regression, the Artificial Neural Networks technique has shown to be an excellent forecasting method, though there is no linear correlation between the variables to be analyzed.

Crude Oil Price Forecasting Based on Hybridizing Wavelet Multiple Linear Regression Model, Particle Swarm Optimization Techniques, and Principal Component Analysis

PubMed Central

Shabri, Ani; Samsudin, Ruhaidah

2014-01-01

Crude oil prices do play significant role in the global economy and are a key input into option pricing formulas, portfolio allocation, and risk measurement. In this paper, a hybrid model integrating wavelet and multiple linear regressions (MLR) is proposed for crude oil price forecasting. In this model, Mallat wavelet transform is first selected to decompose an original time series into several subseries with different scale. Then, the principal component analysis (PCA) is used in processing subseries data in MLR for crude oil price forecasting. The particle swarm optimization (PSO) is used to adopt the optimal parameters of the MLR model. To assess the effectiveness of this model, daily crude oil market, West Texas Intermediate (WTI), has been used as the case study. Time series prediction capability performance of the WMLR model is compared with the MLR, ARIMA, and GARCH models using various statistics measures. The experimental results show that the proposed model outperforms the individual models in forecasting of the crude oil prices series. PMID:24895666

Use of Empirical Estimates of Shrinkage in Multiple Regression: A Caution.

ERIC Educational Resources Information Center

Kromrey, Jeffrey D.; Hines, Constance V.

1995-01-01

The accuracy of four empirical techniques to estimate shrinkage in multiple regression was studied through Monte Carlo simulation. None of the techniques provided unbiased estimates of the population squared multiple correlation coefficient, but the normalized jackknife and bootstrap techniques demonstrated marginally acceptable performance with…

Enhance-Synergism and Suppression Effects in Multiple Regression

ERIC Educational Resources Information Center

Lipovetsky, Stan; Conklin, W. Michael

2004-01-01

Relations between pairwise correlations and the coefficient of multiple determination in regression analysis are considered. The conditions for the occurrence of enhance-synergism and suppression effects when multiple determination becomes bigger than the total of squared correlations of the dependent variable with the regressors are discussed. It…

Mean annual runoff and peak flow estimates based on channel geometry of streams in northeastern and western Montana

USGS Publications Warehouse

Parrett, Charles; Omang, R.J.; Hull, J.A.

1983-01-01

Equations for estimating mean annual runoff and peak discharge from measurements of channel geometry were developed for western and northeastern Montana. The study area was divided into two regions for the mean annual runoff analysis, and separate multiple-regression equations were developed for each region. The active-channel width was determined to be the most important independent variable in each region. The standard error of estimate for the estimating equation using active-channel width was 61 percent in the Northeast Region and 38 percent in the West region. The study area was divided into six regions for the peak discharge analysis, and multiple regression equations relating channel geometry and basin characteristics to peak discharges having recurrence intervals of 2, 5, 10, 25, 50 and 100 years were developed for each region. The standard errors of estimate for the regression equations using only channel width as an independent variable ranged from 35 to 105 percent. The standard errors improved in four regions as basin characteristics were added to the estimating equations. (USGS)

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

PubMed

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

2014-03-01

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

Change in bone mineral density and its determinants in pre- and perimenopausal Chinese women: the Hong Kong Perimenopausal Women Osteoporosis Study.

PubMed

Ho, S C; Chan, S G; Yip, Y B; Chan, C S Y; Woo, J L F; Sham, A

2008-12-01

This 30-month study investigating bone change and its determinants in 438 perimenopausal Chinese women revealed that the fastest bone loss occurred in women undergoing menopausal transition but maintenance of body weight and physical fitness were beneficial for bone health. Soy protein intake also seemed to exert a protective effect. This 30-month follow-up study aims to investigate change in bone mineral density and its determinants in Hong Kong Chinese perimenopausal women. Four hundred and thirty-eight women aged 45 to 55 years were recruited through random telephone dialing and primary care clinic. Bone mass, body composition, lifestyle measurements were obtained at baseline and at 9-, 18- and 30-month follow-ups. Univariate and stepwise multiple regression analyses were performed with the regression coefficients of BMD/C (derived from baseline and follow-up measurements) as the outcome variables. Menopausal status was classified as pre- or postmenopausal or transitional. Menopausal status was the strongest determinant of bone changes. An annual bone loss of about 0.5% was observed among premenopausal, 2% to 2.5% among transitional, and about 1.5% in postmenopausal women. Multiple regression analyses, revealed that a positive regression slope of body weight was protective for follow-up bone loss at all sites. Number of pregnancy, soy protein intake and walking were protective for total body BMC. Higher baseline LM was also protective for neck of femur BMD. Maintenance of body weight and physical fitness were observed to have a protective effect on for bone loss in Chinese perimenopausal women.

Measuring the influence of professional nursing practice on global hospital performance in an organizational context.

PubMed

Fasoli, Dijon R

2008-01-01

The purpose of this study was to measure the influence of professional nursing practice (PNP) on global hospital performance (GHP). Evidence links PNP and positive outcomes for patients and nurses, however, little is known about PNP influence on GHP measures used for patient decision-making and hospital management resource allocation decisions. A quantitative study using multiple regression analysis to predict a composite measure of GHP was conducted. Two survey instruments measuring perspectives of the PNP environment were completed by 1815 (31.3%) Registered Nurses (RN) and 28 (100%) Senior Nurse Executives (SNE) at 28 northeastern US hospitals. Secondary data provided organizational attributes. The degree of PNP was consistently reported by RNs and SNEs. When regressed with organizational factors, PNP was not a significant predictor of GHP. Better GHP was associated with lower lengths of stay, lower profitability, less admission growth, and non-health system affiliation. Further research is needed to define a nursing-sensitive GHP measure.

Cox Regression Models with Functional Covariates for Survival Data.

PubMed

Gellar, Jonathan E; Colantuoni, Elizabeth; Needham, Dale M; Crainiceanu, Ciprian M

2015-06-01

We extend the Cox proportional hazards model to cases when the exposure is a densely sampled functional process, measured at baseline. The fundamental idea is to combine penalized signal regression with methods developed for mixed effects proportional hazards models. The model is fit by maximizing the penalized partial likelihood, with smoothing parameters estimated by a likelihood-based criterion such as AIC or EPIC. The model may be extended to allow for multiple functional predictors, time varying coefficients, and missing or unequally-spaced data. Methods were inspired by and applied to a study of the association between time to death after hospital discharge and daily measures of disease severity collected in the intensive care unit, among survivors of acute respiratory distress syndrome.

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

PubMed Central

Dipnall, Joanna F.

2016-01-01

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

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

PubMed

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

2016-01-01

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

Near infrared spectral linearisation in quantifying soluble solids content of intact carambola.

PubMed

Omar, Ahmad Fairuz; MatJafri, Mohd Zubir

2013-04-12

This study presents a novel application of near infrared (NIR) spectral linearisation for measuring the soluble solids content (SSC) of carambola fruits. NIR spectra were measured using reflectance and interactance methods. In this study, only the interactance measurement technique successfully generated a reliable measurement result with a coefficient of determination of (R2) = 0.724 and a root mean square error of prediction for (RMSEP) = 0.461° Brix. The results from this technique produced a highly accurate and stable prediction model compared with multiple linear regression techniques.

Near Infrared Spectral Linearisation in Quantifying Soluble Solids Content of Intact Carambola

PubMed Central

Omar, Ahmad Fairuz; MatJafri, Mohd Zubir

2013-01-01

This study presents a novel application of near infrared (NIR) spectral linearisation for measuring the soluble solids content (SSC) of carambola fruits. NIR spectra were measured using reflectance and interactance methods. In this study, only the interactance measurement technique successfully generated a reliable measurement result with a coefficient of determination of (R2) = 0.724 and a root mean square error of prediction for (RMSEP) = 0.461° Brix. The results from this technique produced a highly accurate and stable prediction model compared with multiple linear regression techniques. PMID:23584118

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…

Regression Analysis: Legal Applications in Institutional Research

ERIC Educational Resources Information Center

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

2008-01-01

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

RAWS II: A MULTIPLE REGRESSION ANALYSIS PROGRAM,

DTIC Science & Technology

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

Incremental Net Effects in Multiple Regression

ERIC Educational Resources Information Center

Lipovetsky, Stan; Conklin, Michael

2005-01-01

A regular problem in regression analysis is estimating the comparative importance of the predictors in the model. This work considers the 'net effects', or shares of the predictors in the coefficient of the multiple determination, which is a widely used characteristic of the quality of a regression model. Estimation of the net effects can be a…

Floating Data and the Problem with Illustrating Multiple Regression.

ERIC Educational Resources Information Center

Sachau, Daniel A.

2000-01-01

Discusses how to introduce basic concepts of multiple regression by creating a large-scale, three-dimensional regression model using the classroom walls and floor. Addresses teaching points that should be covered and reveals student reaction to the model. Finds that the greatest benefit of the model is the low fear, walk-through, nonmathematical…

Enhancing the estimation of blood pressure using pulse arrival time and two confounding factors.

PubMed

Baek, Hyun Jae; Kim, Ko Keun; Kim, Jung Soo; Lee, Boreom; Park, Kwang Suk

2010-02-01

A new method of blood pressure (BP) estimation using multiple regression with pulse arrival time (PAT) and two confounding factors was evaluated in clinical and unconstrained monitoring situations. For the first analysis with clinical data, electrocardiogram (ECG), photoplethysmogram (PPG) and invasive BP signals were obtained by a conventional patient monitoring device during surgery. In the second analysis, ECG, PPG and non-invasive BP were measured using systems developed to obtain data under conditions in which the subject was not constrained. To enhance the performance of BP estimation methods, heart rate (HR) and arterial stiffness were considered as confounding factors in regression analysis. The PAT and HR were easily extracted from ECG and PPG signals. For arterial stiffness, the duration from the maximum derivative point to the maximum of the dicrotic notch in the PPG signal, a parameter called TDB, was employed. In two experiments that normally cause BP variation, the correlation between measured BP and the estimated BP was investigated. Multiple-regression analysis with the two confounding factors improved correlation coefficients for diastolic blood pressure and systolic blood pressure to acceptable confidence levels, compared to existing methods that consider PAT only. In addition, reproducibility for the proposed method was determined using constructed test sets. Our results demonstrate that non-invasive, non-intrusive BP estimation can be obtained using methods that can be applied in both clinical and daily healthcare situations.

Using Multiple and Logistic Regression to Estimate the Median WillCost and Probability of Cost and Schedule Overrun for Program Managers

DTIC Science & Technology

2017-03-23

PUBLIC RELEASE; DISTRIBUTION UNLIMITED Using Multiple and Logistic Regression to Estimate the Median Will- Cost and Probability of Cost and... Cost and Probability of Cost and Schedule Overrun for Program Managers Ryan C. Trudelle Follow this and additional works at: https://scholar.afit.edu...afit.edu. Recommended Citation Trudelle, Ryan C., "Using Multiple and Logistic Regression to Estimate the Median Will- Cost and Probability of Cost and

High doses of folic acid in the periconceptional period and risk of low weight for gestational age at birth in a population based cohort study.

PubMed

Navarrete-Muñoz, Eva María; Valera-Gran, Desirée; Garcia-de-la-Hera, Manuela; Gonzalez-Palacios, Sandra; Riaño, Isolina; Murcia, Mario; Lertxundi, Aitana; Guxens, Mònica; Tardón, Adonina; Amiano, Pilar; Vrijheid, Martine; Rebagliato, Marisa; Vioque, Jesus

2017-11-27

We investigated the association between maternal use of folic acid (FA) during pregnancy and child anthropometric measures at birth. We included 2302 mother-child pairs from a population-based birth cohort in Spain (INMA Project). FA dosages at first and third trimester of pregnancy were assessed using a specific battery questionnaire and were categorized in non-user, < 1000, 1000-4999, and ≥ 5000 µg/day. Anthropometric measures at birth (weight in grams, length and head circumference in centimetres) were obtained from medical records. Small for gestational age according to weight (SGA-w), length (SGA-l) and head circumference (SGA-hc) were defined using the 10th percentile based on Spanish standardized growth reference charts. Multiple linear and logistic regression analyses were used to explore the association between FA dosages in different stages of pregnancy and child anthropometric measures at birth. In the multiple linear regression analysis, we found a tendency for a negative association between the use of high dosages of FA (≥ 5000 µg/day) in the periconceptional period of pregnancy and weight at birth compared to mothers who were non-users of FA (β = - 73.83; 95% CI - 151.71, 4.06). In the multiple logistic regression, a greater risk of SGA-w was also evident among children whose mothers took FA dosages of 1000-4999 (OR = 2.21; 95% CI 1.17, 4.19) and of ≥ 5000 µg/day (OR = 2.32; 95% CI 1.06, 5.08) compared to mothers non-users of FA in the periconceptional period of pregnancy. Our findings suggest that a high dosage of FA (≥ 1000 µg/day) may be associated with an increased risk of SGA-w at birth.

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

PubMed

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

2018-05-01

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

Abnormal dynamics of language in schizophrenia.

PubMed

Stephane, Massoud; Kuskowski, Michael; Gundel, Jeanette

2014-05-30

Language could be conceptualized as a dynamic system that includes multiple interactive levels (sub-lexical, lexical, sentence, and discourse) and components (phonology, semantics, and syntax). In schizophrenia, abnormalities are observed at all language elements (levels and components) but the dynamic between these elements remains unclear. We hypothesize that the dynamics between language elements in schizophrenia is abnormal and explore how this dynamic is altered. We, first, investigated language elements with comparable procedures in patients and healthy controls. Second, using measures of reaction time, we performed multiple linear regression analyses to evaluate the inter-relationships among language elements and the effect of group on these relationships. Patients significantly differed from controls with respect to sub-lexical/lexical, lexical/sentence, and sentence/discourse regression coefficients. The intercepts of the regression slopes increased in the same order above (from lower to higher levels) in patients but not in controls. Regression coefficients between syntax and both sentence level and discourse level semantics did not differentiate patients from controls. This study indicates that the dynamics between language elements is abnormal in schizophrenia. In patients, top-down flow of linguistic information might be reduced, and the relationship between phonology and semantics but not between syntax and semantics appears to be altered. Published by Elsevier Ireland Ltd.

Predictive Utility of Marketed Volumetric Software Tools in Subjects at Risk for Alzheimer's: Do Regions Outside the Hippocampus Matter?

PubMed Central

Tanpitukpongse, Teerath P.; Mazurowski, Maciej A.; Ikhena, John; Petrella, Jeffrey R.

2016-01-01

Background and Purpose To assess prognostic efficacy of individual versus combined regional volumetrics in two commercially-available brain volumetric software packages for predicting conversion of patients with mild cognitive impairment to Alzheimer's disease. Materials and Methods Data was obtained through the Alzheimer's Disease Neuroimaging Initiative. 192 subjects (mean age 74.8 years, 39% female) diagnosed with mild cognitive impairment at baseline were studied. All had T1WI MRI sequences at baseline and 3-year clinical follow-up. Analysis was performed with NeuroQuant® and Neuroreader™. Receiver operating characteristic curves assessing the prognostic efficacy of each software package were generated using a univariable approach employing individual regional brain volumes, as well as two multivariable approaches (multiple regression and random forest), combining multiple volumes. Results On univariable analysis of 11 NeuroQuant® and 11 Neuroreader™ regional volumes, hippocampal volume had the highest area under the curve for both software packages (0.69 NeuroQuant®, 0.68 Neuroreader™), and was not significantly different (p > 0.05) between packages. Multivariable analysis did not increase the area under the curve for either package (0.63 logistic regression, 0.60 random forest NeuroQuant®; 0.65 logistic regression, 0.62 random forest Neuroreader™). Conclusion Of the multiple regional volume measures available in FDA-cleared brain volumetric software packages, hippocampal volume remains the best single predictor of conversion of mild cognitive impairment to Alzheimer's disease at 3-year follow-up. Combining volumetrics did not add additional prognostic efficacy. Therefore, future prognostic studies in MCI, combining such tools with demographic and other biomarker measures, are justified in using hippocampal volume as the only volumetric biomarker. PMID:28057634

[Stature estimation for Sichuan Han nationality female based on X-ray technology with measurement of lumbar vertebrae].

PubMed

Qing, Si-han; Chang, Yun-feng; Dong, Xiao-ai; Li, Yuan; Chen, Xiao-gang; Shu, Yong-kang; Deng, Zhen-hua

2013-10-01

To establish the mathematical models of stature estimation for Sichuan Han female with measurement of lumbar vertebrae by X-ray to provide essential data for forensic anthropology research. The samples, 206 Sichuan Han females, were divided into three groups including group A, B and C according to the ages. Group A (206 samples) consisted of all ages, group B (116 samples) were 20-45 years old and 90 samples over 45 years old were group C. All the samples were examined lumbar vertebrae through CR technology, including the parameters of five centrums (L1-L5) as anterior border, posterior border and central heights (x1-x15), total central height of lumbar spine (x16), and the real height of every sample. The linear regression analysis was produced using the parameters to establish the mathematical models of stature estimation. Sixty-two trained subjects were tested to verify the accuracy of the mathematical models. The established mathematical models by hypothesis test of linear regression equation model were statistically significant (P<0.05). The standard errors of the equation were 2.982-5.004 cm, while correlation coefficients were 0.370-0.779 and multiple correlation coefficients were 0.533-0.834. The return tests of the highest correlation coefficient and multiple correlation coefficient of each group showed that the highest accuracy of the multiple regression equation, y = 100.33 + 1.489 x3 - 0.548 x6 + 0.772 x9 + 0.058 x12 + 0.645 x15, in group A were 80.6% (+/- lSE) and 100% (+/- 2SE). The established mathematical models in this study could be applied for the stature estimation for Sichuan Han females.

Memory complaints in epilepsy: An examination of the role of mood and illness perceptions.

PubMed

Tinson, Deborah; Crockford, Christopher; Gharooni, Sara; Russell, Helen; Zoeller, Sophie; Leavy, Yvonne; Lloyd, Rachel; Duncan, Susan

2018-03-01

The study examined the role of mood and illness perceptions in explaining the variance in the memory complaints of patients with epilepsy. Forty-four patients from an outpatient tertiary care center and 43 volunteer controls completed a formal assessment of memory and a verbal fluency test, as well as validated self-report questionnaires on memory complaints, mood, and illness perceptions. In hierarchical multiple regression analyses, objective memory test performance and verbal fluency did not contribute significantly to the variance in memory complaints for either patients or controls. In patients, illness perceptions and mood were highly correlated. Illness perceptions correlated more highly with memory complaints than mood and were therefore added to the multiple regression analysis. This accounted for an additional 25% of the variance, after controlling for objective memory test performance and verbal fluency, and the model was significant (model B). In order to compare with other studies, mood was added to a second model, instead of illness perceptions. This accounted for an additional 24% of the variance, which was again significant (model C). In controls, low mood accounted for 11% of the variance in memory complaints (model C2). A measure of illness perceptions was more highly correlated with the memory complaints of patients with epilepsy than with a measure of mood. In a hierarchical multiple regression model, illness perceptions accounted for 25% of the variance in memory complaints. Illness perceptions could provide useful information in a clinical investigation into the self-reported memory complaints of patients with epilepsy, alongside the assessment of mood and formal memory testing. Copyright © 2017 Elsevier Inc. All rights reserved.

Do climate variables and human density affect Achatina fulica (Bowditch) (Gastropoda: Pulmonata) shell length, total weight and condition factor?

PubMed

Albuquerque, F S; Peso-Aguiar, M C; Assunção-Albuquerque, M J T; Gálvez, L

2009-08-01

The length-weight relationship and condition factor have been broadly investigated in snails to obtain the index of physical condition of populations and evaluate habitat quality. Herein, our goal was to describe the best predictors that explain Achatina fulica biometrical parameters and well being in a recently introduced population. From November 2001 to November 2002, monthly snail samples were collected in Lauro de Freitas City, Bahia, Brazil. Shell length and total weight were measured in the laboratory and the potential curve and condition factor were calculated. Five environmental variables were considered: temperature range, mean temperature, humidity, precipitation and human density. Multiple regressions were used to generate models including multiple predictors, via model selection approach, and then ranked with AIC criteria. Partial regressions were used to obtain the separated coefficients of determination of climate and human density models. A total of 1.460 individuals were collected, presenting a shell length range between 4.8 to 102.5 mm (mean: 42.18 mm). The relationship between total length and total weight revealed that Achatina fulica presented a negative allometric growth. Simple regression indicated that humidity has a significant influence on A. fulica total length and weight. Temperature range was the main variable that influenced the condition factor. Multiple regressions showed that climatic and human variables explain a small proportion of the variance in shell length and total weight, but may explain up to 55.7% of the condition factor variance. Consequently, we believe that the well being and biometric parameters of A. fulica can be influenced by climatic and human density factors.

Tools to Support Interpreting Multiple Regression in the Face of Multicollinearity

PubMed Central

Kraha, Amanda; Turner, Heather; Nimon, Kim; Zientek, Linda Reichwein; Henson, Robin K.

2012-01-01

While multicollinearity may increase the difficulty of interpreting multiple regression (MR) results, it should not cause undue problems for the knowledgeable researcher. In the current paper, we argue that rather than using one technique to investigate regression results, researchers should consider multiple indices to understand the contributions that predictors make not only to a regression model, but to each other as well. Some of the techniques to interpret MR effects include, but are not limited to, correlation coefficients, beta weights, structure coefficients, all possible subsets regression, commonality coefficients, dominance weights, and relative importance weights. This article will review a set of techniques to interpret MR effects, identify the elements of the data on which the methods focus, and identify statistical software to support such analyses. PMID:22457655

Tools to support interpreting multiple regression in the face of multicollinearity.

PubMed

Kraha, Amanda; Turner, Heather; Nimon, Kim; Zientek, Linda Reichwein; Henson, Robin K

2012-01-01

While multicollinearity may increase the difficulty of interpreting multiple regression (MR) results, it should not cause undue problems for the knowledgeable researcher. In the current paper, we argue that rather than using one technique to investigate regression results, researchers should consider multiple indices to understand the contributions that predictors make not only to a regression model, but to each other as well. Some of the techniques to interpret MR effects include, but are not limited to, correlation coefficients, beta weights, structure coefficients, all possible subsets regression, commonality coefficients, dominance weights, and relative importance weights. This article will review a set of techniques to interpret MR effects, identify the elements of the data on which the methods focus, and identify statistical software to support such analyses.

The comparison between several robust ridge regression estimators in the presence of multicollinearity and multiple outliers

NASA Astrophysics Data System (ADS)

Zahari, Siti Meriam; Ramli, Norazan Mohamed; Moktar, Balkiah; Zainol, Mohammad Said

2014-09-01

In the presence of multicollinearity and multiple outliers, statistical inference of linear regression model using ordinary least squares (OLS) estimators would be severely affected and produces misleading results. To overcome this, many approaches have been investigated. These include robust methods which were reported to be less sensitive to the presence of outliers. In addition, ridge regression technique was employed to tackle multicollinearity problem. In order to mitigate both problems, a combination of ridge regression and robust methods was discussed in this study. The superiority of this approach was examined when simultaneous presence of multicollinearity and multiple outliers occurred in multiple linear regression. This study aimed to look at the performance of several well-known robust estimators; M, MM, RIDGE and robust ridge regression estimators, namely Weighted Ridge M-estimator (WRM), Weighted Ridge MM (WRMM), Ridge MM (RMM), in such a situation. Results of the study showed that in the presence of simultaneous multicollinearity and multiple outliers (in both x and y-direction), the RMM and RIDGE are more or less similar in terms of superiority over the other estimators, regardless of the number of observation, level of collinearity and percentage of outliers used. However, when outliers occurred in only single direction (y-direction), the WRMM estimator is the most superior among the robust ridge regression estimators, by producing the least variance. In conclusion, the robust ridge regression is the best alternative as compared to robust and conventional least squares estimators when dealing with simultaneous presence of multicollinearity and outliers.

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.

Deadlines at work and sleep quality. Cross-sectional and longitudinal findings among Danish knowledge workers.

PubMed

Rugulies, Reiner; Martin, Marie H T; Garde, Anne Helene; Persson, Roger; Albertsen, Karen

2012-03-01

Exposure to deadlines at work is increasing in several countries and may affect health. We aimed to investigate cross-sectional and longitudinal associations between frequency of difficult deadlines at work and sleep quality. Study participants were knowledge workers, drawn from a representative sample of Danish employees who responded to a baseline questionnaire in 2006 (n = 363) and a follow-up questionnaire in 2007 (n = 302). Frequency of difficult deadlines was measured by self-report and categorized into low, intermediate, and high. Sleep quality was measured with a Total Sleep Quality Score and two indexes (Awakening Index and Disturbed Sleep Index) derived from the Karolinska Sleep Questionnaire. Analyses on the association between frequency of deadlines and sleep quality scores were conducted with multiple linear regression models, adjusted for potential confounders. In addition, we used multiple logistic regression models to analyze whether frequency of deadlines at baseline predicted caseness of sleep problems at follow-up among participants free of sleep problems at baseline. Frequent deadlines were cross-sectionally and longitudinally associated with poorer sleep quality on all three sleep quality measures. Associations in the longitudinal analyses were greatly attenuated when we adjusted for baseline sleep quality. The logistic regression analyses showed that frequent deadlines at baseline were associated with elevated odds ratios for caseness of sleep problems at follow-up, however, confidence intervals were wide in these analyses. Frequent deadlines at work were associated with poorer sleep quality among Danish knowledge workers. We recommend investigating the relation between deadlines and health endpoints in large-scale epidemiologic studies. Copyright © 2011 Wiley Periodicals, Inc.

Estimates of self, parental, and partner multiple intelligence and their relationship with personality, values, and demographic variables: a study in Britain and France.

PubMed

Swami, Viren; Furnham, Adrian; Zilkha, Susan

2009-11-01

In the present study, 151 British and 151 French participants estimated their own, their parents' and their partner's overall intelligence and 13 'multiple intelligences.' In accordance with previous studies, men rated themselves as higher on almost all measures of intelligence, but there were few cross-national differences. There were also important sex differences in ratings of parental and partner intelligence. Participants generally believed they were more intelligent than their parents but not their partners. Regressions indicated that participants believed verbal, logical-mathematical, and spatial intelligence to be the main predictors of intelligence. Regressions also showed that participants' Big Five personality scores (in particular, Extraversion and Openness), but not values or beliefs about intelligence and intelligences tests, were good predictors of intelligence. Results were discussed in terms of the influence of gender-role stereotypes.

Bayesian function-on-function regression for multilevel functional data.

PubMed

Meyer, Mark J; Coull, Brent A; Versace, Francesco; Cinciripini, Paul; Morris, Jeffrey S

2015-09-01

Medical and public health research increasingly involves the collection of complex and high dimensional data. In particular, functional data-where the unit of observation is a curve or set of curves that are finely sampled over a grid-is frequently obtained. Moreover, researchers often sample multiple curves per person resulting in repeated functional measures. A common question is how to analyze the relationship between two functional variables. We propose a general function-on-function regression model for repeatedly sampled functional data on a fine grid, presenting a simple model as well as a more extensive mixed model framework, and introducing various functional Bayesian inferential procedures that account for multiple testing. We examine these models via simulation and a data analysis with data from a study that used event-related potentials to examine how the brain processes various types of images. © 2015, The International Biometric Society.

Time Series Analysis of Soil Radon Data Using Multiple Linear Regression and Artificial Neural Network in Seismic Precursory Studies

NASA Astrophysics Data System (ADS)

Singh, S.; Jaishi, H. P.; Tiwari, R. P.; Tiwari, R. C.

2017-07-01

This paper reports the analysis of soil radon data recorded in the seismic zone-V, located in the northeastern part of India (latitude 23.73N, longitude 92.73E). Continuous measurements of soil-gas emission along Chite fault in Mizoram (India) were carried out with the replacement of solid-state nuclear track detectors at weekly interval. The present study was done for the period from March 2013 to May 2015 using LR-115 Type II detectors, manufactured by Kodak Pathe, France. In order to reduce the influence of meteorological parameters, statistical analysis tools such as multiple linear regression and artificial neural network have been used. Decrease in radon concentration was recorded prior to some earthquakes that occurred during the observation period. Some false anomalies were also recorded which may be attributed to the ongoing crustal deformation which was not major enough to produce an earthquake.

Variables Associated with Communicative Participation in People with Multiple Sclerosis: A Regression Analysis

ERIC Educational Resources Information Center

Baylor, Carolyn; Yorkston, Kathryn; Bamer, Alyssa; Britton, Deanna; Amtmann, Dagmar

2010-01-01

Purpose: To explore variables associated with self-reported communicative participation in a sample (n = 498) of community-dwelling adults with multiple sclerosis (MS). Method: A battery of questionnaires was administered online or on paper per participant preference. Data were analyzed using multiple linear backward stepwise regression. The…

Statistical analysis of water-quality data containing multiple detection limits: S-language software for regression on order statistics

USGS Publications Warehouse

Lee, L.; Helsel, D.

2005-01-01

Trace contaminants in water, including metals and organics, often are measured at sufficiently low concentrations to be reported only as values below the instrument detection limit. Interpretation of these "less thans" is complicated when multiple detection limits occur. Statistical methods for multiply censored, or multiple-detection limit, datasets have been developed for medical and industrial statistics, and can be employed to estimate summary statistics or model the distributions of trace-level environmental data. We describe S-language-based software tools that perform robust linear regression on order statistics (ROS). The ROS method has been evaluated as one of the most reliable procedures for developing summary statistics of multiply censored data. It is applicable to any dataset that has 0 to 80% of its values censored. These tools are a part of a software library, or add-on package, for the R environment for statistical computing. This library can be used to generate ROS models and associated summary statistics, plot modeled distributions, and predict exceedance probabilities of water-quality standards. ?? 2005 Elsevier Ltd. All rights reserved.

Impulsivity, Attention, Memory, and Decision-Making among Adolescent Marijuana Users

PubMed Central

Dougherty, Donald M.; Mathias, Charles W.; Dawes, Michael A.; Furr, R. Michael; Charles, Nora E.; Liguori, Anthony; Shannon, Erin E.; Acheson, Ashley

2012-01-01

Rationale Marijuana is a popular drug of abuse among adolescents, and they may be uniquely vulnerable to resulting cognitive and behavioral impairments. Previous studies have found impairments among adolescent marijuana users. However, the majority of this research has examined measures individually rather than multiple domains in a single cohesive analysis. This study used a logistic regression model that combines performance on a range of tasks to identify which measures were most altered among adolescent marijuana users. Objectives The purpose of this research was to determine unique associations between adolescent marijuana user and performances on multiple cognitive and behavioral domains (attention, memory, decision-making, and impulsivity) in 14- to 17-year-olds while simultaneously controlling for performances across the measures to determine which measures most strongly distinguish marijuana users from non-users. Methods Marijuana-using adolescents (n=45) and controls (n=48) were tested. Logistic regression analyses were conducted to test for: (a) differences between marijuana users and non-users on each measure, (b) associations between marijuana use and each measure after controlling for the other measures, and (c) the degree to which (a) and (b) together elucidated differences among marijuana users and non-users. Results Of all the cognitive and behavioral domains tested, impaired short-term recall memory and consequence sensitivity impulsivity were associated with marijuana use after controlling for performances across all measures. Conclusions This study extends previous findings by identifying cognitive and behavioral impairments most strongly associated with adolescent marijuana users. These specific deficits are potential targets of intervention for this at-risk population. PMID:23138434

Validity of bioelectrical impedance measurement in predicting fat-free mass of Chinese children and adolescents.

PubMed

Wang, Lin; Hui, Stanley Sai-chuen; Wong, Stephen Heung-sang

2014-11-15

The current study aimed to examine the validity of various published bioelectrical impedance analysis (BIA) equations in estimating FFM among Chinese children and adolescents and to develop BIA equations for the estimation of fat-free mass (FFM) appropriate for Chinese children and adolescents. A total of 255 healthy Chinese children and adolescents aged 9 to 19 years old (127 males and 128 females) from Tianjin, China, participated in the BIA measurement at 50 kHz between the hand and the foot. The criterion measure of FFM was also employed using dual-energy X-ray absorptiometry (DEXA). FFM estimated from 24 published BIA equations was cross-validated against the criterion measure from DEXA. Multiple linear regression was conducted to examine alternative BIA equation for the studied population. FFM estimated from the 24 published BIA equations yielded high correlations with the directly measured FFM from DEXA. However, none of the 24 equations was statistically equivalent with the DEXA-measured FFM. Using multiple linear regression and cross-validation against DEXA measurement, an alternative prediction equation was determined as follows: FFM (kg)=1.613+0.742×height (cm)2/impedance (Ω)+0.151×body weight (kg); R2=0.95; SEE=2.45 kg; CV=6.5, 93.7% of the residuals of all the participants fell within the 95% limits of agreement. BIA was highly correlated with FFM in Chinese children and adolescents. When the new developed BIA equations are applied, BIA can provide a practical and valid measurement of body composition in Chinese children and adolescents.

Validity of Bioelectrical Impedance Measurement in Predicting Fat-Free Mass of Chinese Children and Adolescents

PubMed Central

Wang, Lin; Hui, Stanley Sai-chuen; Wong, Stephen Heung-sang

2014-01-01

Background The current study aimed to examine the validity of various published bioelectrical impedance analysis (BIA) equations in estimating FFM among Chinese children and adolescents and to develop BIA equations for the estimation of fat-free mass (FFM) appropriate for Chinese children and adolescents. Material/Methods A total of 255 healthy Chinese children and adolescents aged 9 to 19 years old (127 males and 128 females) from Tianjin, China, participated in the BIA measurement at 50 kHz between the hand and the foot. The criterion measure of FFM was also employed using dual-energy X-ray absorptiometry (DEXA). FFM estimated from 24 published BIA equations was cross-validated against the criterion measure from DEXA. Multiple linear regression was conducted to examine alternative BIA equation for the studied population. Results FFM estimated from the 24 published BIA equations yielded high correlations with the directly measured FFM from DEXA. However, none of the 24 equations was statistically equivalent with the DEXA-measured FFM. Using multiple linear regression and cross-validation against DEXA measurement, an alternative prediction equation was determined as follows: FFM (kg)=1.613+0.742×height (cm)2/impedance (Ω)+0.151×body weight (kg); R2=0.95; SEE=2.45kg; CV=6.5, 93.7% of the residuals of all the participants fell within the 95% limits of agreement. Conclusions BIA was highly correlated with FFM in Chinese children and adolescents. When the new developed BIA equations are applied, BIA can provide a practical and valid measurement of body composition in Chinese children and adolescents. PMID:25398209

LAI inversion from optical reflectance using a neural network trained with a multiple scattering model

NASA Technical Reports Server (NTRS)

Smith, James A.

1992-01-01

The inversion of the leaf area index (LAI) canopy parameter from optical spectral reflectance measurements is obtained using a backpropagation artificial neural network trained using input-output pairs generated by a multiple scattering reflectance model. The problem of LAI estimation over sparse canopies (LAI < 1.0) with varying soil reflectance backgrounds is particularly difficult. Standard multiple regression methods applied to canopies within a single homogeneous soil type yield good results but perform unacceptably when applied across soil boundaries, resulting in absolute percentage errors of >1000 percent for low LAI. Minimization methods applied to merit functions constructed from differences between measured reflectances and predicted reflectances using multiple-scattering models are unacceptably sensitive to a good initial guess for the desired parameter. In contrast, the neural network reported generally yields absolute percentage errors of <30 percent when weighting coefficients trained on one soil type were applied to predicted canopy reflectance at a different soil background.

Serum alpha-fetoprotein in the three trimesters of pregnancy: effects of maternal characteristics and medical history.

PubMed

Bredaki, F E; Sciorio, C; Wright, A; Wright, D; Nicolaides, K H

2015-07-01

To define the contribution of maternal variables which influence the measured level of maternal serum alpha-fetoprotein (AFP) in screening for pregnancy complications. Maternal characteristics and medical history were recorded and serum AFP was measured in women with a singleton pregnancy attending for three routine hospital visits at 11 + 0 to 13 + 6, 19 + 0 to 24 + 6 and 30 + 0 to 34 + 6 weeks' gestation. For pregnancies delivering phenotypically normal live births or stillbirths ≥ 24 weeks' gestation, variables from maternal demographic characteristics and medical history that are important in the prediction of AFP were determined from a linear mixed-effects multiple regression. Serum AFP was measured in 17 071 cases in the first trimester, 8583 in the second trimester and 8607 in the third trimester. Significant independent contributions to serum AFP were provided by gestational age, maternal weight, racial origin, gestational age at delivery and birth-weight Z-score of the neonate of the previous pregnancy and interpregnancy interval. Cigarette smoking was found to significantly affect serum AFP in the first trimester only. The machine used to measure serum AFP was also found to have a significant effect. Random-effects multiple regression analysis was used to define the contribution of maternal variables that influence the measured level of serum AFP and express the values as multiples of the median (MoMs). The model was shown to provide an adequate fit of MoM values for all covariates, both in pregnancies that developed pre-eclampsia and in those without this pregnancy complication. A model was fitted to express measured serum AFP across the three trimesters of pregnancy as MoMs, after adjusting for variables from maternal characteristics and medical history that affect this measurement. Copyright © 2015 ISUOG. Published by John Wiley & Sons Ltd.

Diameter and height growth of suppressed grand fir saplings after overstory removal.

Treesearch

K.W. Seidel

1980-01-01

The 2- and 5-year diameter and height growth of suppressed grand fir (Abies grandis (Dougl. ex D. Don) Lindl.) advance reproduction was measured in central Oregon after the overstory was removed. Multiple regression analyses were used to predict growth response as a function of individual tree variables. The resulting equations, although highly...

Four Dimensions of Student Leadership: What Predicts Students' Attitudes toward Leadership Development?

ERIC Educational Resources Information Center

Shertzer, John; Wall, Vernon; Frandsen, Alisa; Guo, Yan; Whalen, Donald F.; Shelley, Mack C., II

2005-01-01

Multiple regression was performed on four dependent variables derived from the results of a student survey measuring attitudes about student leadership: (a) leadership is important to the student, (b) the student considers himself or herself to be a leader, (c) leadership will be important to the student after college, and (d) leaders need to be…

Two Readiness Measures As Predictors Of First- And Third-Grade Reading Achievement

ERIC Educational Resources Information Center

Randel, Mildred A.; And Others

1977-01-01

Multiple-regression procedures were used to assess effectiveness of the ABC Inventory and the Metropolitan Readiness Test (MRT) in predicting first- and third-grade reading achievement. MRT performance accounted for 11 percent of the variance in first-grade SRA reading scores. In predicting third-grade reading, the MRT accounted for 26 percent of…

Analyzing the Gender Gap in Math Achievement: Evidence from a Large-Scale US Sample

ERIC Educational Resources Information Center

Cheema, Jehanzeb R.; Galluzzo, Gary

2013-01-01

The US portion of the Program for International Student Assessment (PISA) 2003 student questionnaire comprising of 4,733 observations was used in a multiple regression framework to predict math achievement from demographic variables, such as gender, race, and socioeconomic status, and two student-specific measures of perception, math anxiety and…

[The importance of handprint morphometry for determining the human body length].

PubMed

Grigor'eva, M A

2018-01-01

Handprint morphometry for the purpose of personality identification still remains a relatively novel approach. The methods employed for the measurements are not infrequently difficult to reproduce and therefore cause controversy. The objective of the present study was to introduce the system of methods for the measurement of handprints suitable for the reliable determination of the human body length. The study included the measurement of the size of 40 handprints left by124 adult subjects (52 men and 72 women). Two methods of the regression analysis, stepwise and forced inclusion, were applied to the combined group of handprints to select the equations with the high (R>0.800) coefficients of multiple correlation with the body length. 13 equations of multiple regression were obtained and analyzed. The standard error of estimating (SEE) varied from 4.30 to 5.19 cm. The best results were obtained with the equations constructed from the sizes I, II, and III of the rays without their distal phalanges. It was shown that the body length can be successfully reconstructed within the height range from 168 to 183 cm for men and from 157 to 176 cm for women. The examples of the use of the equations for the purpose of expertise of illegible and incomplete handprints are presented.

Trend analysis of the long-term Swiss ozone measurements

NASA Technical Reports Server (NTRS)

Staehelin, Johannes; Bader, Juerg; Gelpke, Verena

1994-01-01

Trend analyses, assuming a linear trend which started at 1970, were performed from total ozone measurements from Arosa (Switzerland, 1926-1991). Decreases in monthly mean values were statistically significant for October through April showing decreases of about 2.0-4 percent per decade. For the period 1947-91, total ozone trends were further investigated using a multiple regression model. Temperature of a mountain peak in Switzerland (Mt. Santis), the F10.7 solar flux series, the QBO series (quasi biennial oscillation), and the southern oscillation index (SOI) were included as explanatory variables. Trends in the monthly mean values were statistically significant for December through April. The same multiple regression model was applied to investigate the ozone trends at various altitudes using the ozone balloon soundings from Payerne (1967-1989) and the Umkehr measurements from Arosa (1947-1989). The results show four different vertical trend regimes: On a relative scale changes were largest in the troposphere (increase of about 10 percent per decade). On an absolute scale the largest trends were obtained in the lower stratosphere (decrease of approximately 6 per decade at an altitude of about 18 to 22 km). No significant trends were observed at approximately 30 km, whereas stratospheric ozone decreased in the upper stratosphere.

Relationship between postoperative refractive outcomes and cataract density: multiple regression analysis.

PubMed

Ueda, Tetsuo; Ikeda, Hitoe; Ota, Takeo; Matsuura, Toyoaki; Hara, Yoshiaki

2010-05-01

To evaluate the relationship between cataract density and the deviation from the predicted refraction. Department of Ophthalmology, Nara Medical University, Kashihara, Japan. Axial length (AL) was measured in eyes with mainly nuclear cataract using partial coherence interferometry (IOLMaster). The postoperative AL was measured in pseudophakic mode. The AL difference was calculated by subtracting the postoperative AL from the preoperative AL. Cataract density was measured with the pupil dilated using anterior segment Scheimpflug imaging (EAS-1000). The predicted postoperative refraction was calculated using the SRK/T formula. The subjective refraction 3 months postoperatively was also measured. The mean absolute prediction error (MAE) (mean of absolute difference between predicted postoperative refraction and spherical equivalent of postoperative subjective refraction) was calculated. The relationship between the MAE and cataract density, age, preoperative visual acuity, anterior chamber depth, corneal radius of curvature, and AL difference was evaluated using multiple regression analysis. In the 96 eyes evaluated, the MAE was correlated with cataract density (r = 0.37, P = .001) and the AL difference (r = 0.34, P = .003) but not with the other parameters. The AL difference was correlated with cataract density (r = 0.53, P<.0001). The postoperative refractive outcome was affected by cataract density. This should be taken into consideration in eyes with a higher density cataract. (c) 2010 ASCRS and ESCRS. Published by Elsevier Inc. All rights reserved.

Improved model of the retardance in citric acid coated ferrofluids using stepwise regression

NASA Astrophysics Data System (ADS)

Lin, J. F.; Qiu, X. R.

2017-06-01

Citric acid (CA) coated Fe3O4 ferrofluids (FFs) have been conducted for biomedical application. The magneto-optical retardance of CA coated FFs was measured by a Stokes polarimeter. Optimization and multiple regression of retardance in FFs were executed by Taguchi method and Microsoft Excel previously, and the F value of regression model was large enough. However, the model executed by Excel was not systematic. Instead we adopted the stepwise regression to model the retardance of CA coated FFs. From the results of stepwise regression by MATLAB, the developed model had highly predictable ability owing to F of 2.55897e+7 and correlation coefficient of one. The average absolute error of predicted retardances to measured retardances was just 0.0044%. Using the genetic algorithm (GA) in MATLAB, the optimized parametric combination was determined as [4.709 0.12 39.998 70.006] corresponding to the pH of suspension, molar ratio of CA to Fe3O4, CA volume, and coating temperature. The maximum retardance was found as 31.712°, close to that obtained by evolutionary solver in Excel and a relative error of -0.013%. Above all, the stepwise regression method was successfully used to model the retardance of CA coated FFs, and the maximum global retardance was determined by the use of GA.

The Geometry of Enhancement in Multiple Regression

ERIC Educational Resources Information Center

Waller, Niels G.

2011-01-01

In linear multiple regression, "enhancement" is said to occur when R[superscript 2] = b[prime]r greater than r[prime]r, where b is a p x 1 vector of standardized regression coefficients and r is a p x 1 vector of correlations between a criterion y and a set of standardized regressors, x. When p = 1 then b [is congruent to] r and…

Use of magnetic resonance imaging to predict the body composition of pigs in vivo.

PubMed

Kremer, P V; Förster, M; Scholz, A M

2013-06-01

The objective of the study was to evaluate whether magnetic resonance imaging (MRI) offers the opportunity to reliably analyze body composition of pigs in vivo. Therefore, the relation between areas of loin eye muscle and its back fat based on MRI images were used to predict body composition values measured by dual energy X-ray absorptiometry (DXA). During the study, a total of 77 pigs were studied by MRI and DXA, with a BW ranging between 42 and 102 kg. The pigs originated from different extensive or conventional breeds or crossbreds such as Cerdo Iberico, Duroc, German Landrace, German Large White, Hampshire and Pietrain. A Siemens Magnetom Open was used for MRI in the thorax region between 13th and 14th vertebrae in order to measure the loin eye area (MRI-LA) and the above back fat area (MRI-FA) of both body sides, whereas a whole body scan was performed by DXA with a GE Lunar DPX-IQ in order to measure the amount and percentage of fat tissue (DXA-FM; DXA-%FM) and lean tissue mass (DXA-LM; DXA-%LM). A linear single regression analysis was performed to quantify the linear relationships between MRI- and DXA-derived traits. In addition, a stepwise regression procedure was carried out to calculate (multiple) regression equations between MRI and DXA variables (including BW). Single regression analyses showed high relationships between DXA-%FM and MRI-FA (R 2 = 0.89, √MSE = 2.39%), DXA-FM and MRI-FA (R 2 = 0.82, √MSE = 2757 g) and DXA-LM and MRI-LA (R 2 = 0.82, √MSE = 4018 g). Only DXA-%LM and MRI-LA did not show any relationship (R 2 = 0). As a result of the multiple regression analysis, DXA-LM and DXA-FM were both highly related to MRI-LA, MRI-FA and BW (R 2 = 0.96; √MSE = 1784 g, and R 2 = 0.95, √MSE = 1496 g). Therefore, it can be concluded that the use of MRI-derived images provides exact information about important 'carcass-traits' in pigs and may be used to reliably predict the body composition in vivo.

Evaluating the performance of different predictor strategies in regression-based downscaling with a focus on glacierized mountain environments

NASA Astrophysics Data System (ADS)

Hofer, Marlis; Nemec, Johanna

2016-04-01

This study presents first steps towards verifying the hypothesis that uncertainty in global and regional glacier mass simulations can be reduced considerably by reducing the uncertainty in the high-resolution atmospheric input data. To this aim, we systematically explore the potential of different predictor strategies for improving the performance of regression-based downscaling approaches. The investigated local-scale target variables are precipitation, air temperature, wind speed, relative humidity and global radiation, all at a daily time scale. Observations of these target variables are assessed from three sites in geo-environmentally and climatologically very distinct settings, all within highly complex topography and in the close proximity to mountain glaciers: (1) the Vernagtbach station in the Northern European Alps (VERNAGT), (2) the Artesonraju measuring site in the tropical South American Andes (ARTESON), and (3) the Brewster measuring site in the Southern Alps of New Zealand (BREWSTER). As the large-scale predictors, ERA interim reanalysis data are used. In the applied downscaling model training and evaluation procedures, particular emphasis is put on appropriately accounting for the pitfalls of limited and/or patchy observation records that are usually the only (if at all) available data from the glacierized mountain sites. Generalized linear models and beta regression are investigated as alternatives to ordinary least squares regression for the non-Gaussian target variables. By analyzing results for the three different sites, five predictands and for different times of the year, we look for systematic improvements in the downscaling models' skill specifically obtained by (i) using predictor data at the optimum scale rather than the minimum scale of the reanalysis data, (ii) identifying the optimum predictor allocation in the vertical, and (iii) considering multiple (variable, level and/or grid point) predictor options combined with state-of-art empirical feature selection tools. First results show that in particular for air temperature, those downscaling models based on direct predictor selection show comparative skill like those models based on multiple predictors. For all other target variables, however, multiple predictor approaches can considerably outperform those models based on single predictors. Including multiple variable types emerges as the most promising predictor option (in particular for wind speed at all sites), even if the same predictor set is used across the different cases.

A study of the effect of selected material properties on the ablation performance of artificial graphite

NASA Technical Reports Server (NTRS)

Maahs, H. G.

1972-01-01

Eighteen material properties were measured on 45 different, commercially available, artificial graphites. Ablation performance of these same graphites were also measured in a Mach 2 airstream at a stagnation pressure of 5.6 atm. Correlations were developed, where possible, between pairs of the material properties. Multiple regression equations were then formulated relating ablation performance to the various material properties, thus identifying those material properties having the strongest effect on ablation performance. These regression equations reveal that ablation performance in the present test environment depends primarily on maximum grain size, density, ash content, thermal conductivity, and mean pore radius. For optimization of ablation performance, grain size should be small, ash content low, density and thermal conductivity high, and mean pore radius large.

A frequency domain global parameter estimation method for multiple reference frequency response measurements

NASA Astrophysics Data System (ADS)

Shih, C. Y.; Tsuei, Y. G.; Allemang, R. J.; Brown, D. L.

1988-10-01

A method of using the matrix Auto-Regressive Moving Average (ARMA) model in the Laplace domain for multiple-reference global parameter identification is presented. This method is particularly applicable to the area of modal analysis where high modal density exists. The method is also applicable when multiple reference frequency response functions are used to characterise linear systems. In order to facilitate the mathematical solution, the Forsythe orthogonal polynomial is used to reduce the ill-conditioning of the formulated equations and to decouple the normal matrix into two reduced matrix blocks. A Complex Mode Indicator Function (CMIF) is introduced, which can be used to determine the proper order of the rational polynomials.

Evaluation of strength and conditioning measures with game success in Division I collegiate volleyball: A retrospective study.

PubMed

Bunn, Jennifer A; Ryan, Greg A; Button, Gabriel R; Zhang, S

2017-08-04

The purpose of this study was to retrospectively assess relationships between strength and conditioning (SC) measures and game performance in Division I volleyball. Five years of SC and game data were collected from one women's Division I collegiate team, n = 76. SC measures included: T-drill, 18.3 m sprint, back squat, hang clean, vertical jump, and broad jump. All game and SC stats were normalized to Z-scores. Analyses included assessing SC differences by position, and multiple stepwise regression to assess relationships between game and SC stats. There was a significant difference by position for broad jump (p =.002), 18.3 m sprint (p =.036), vertical (p <.001), and total strength (p =.019). Overall, game performance and SC measures were significantly correlated (r = .439, p <.001). Multiple regression analyses indicated significant relationships (p < .05) between SC measures and game success by position as follows: defensive specialist stats with squat and total strength; setters game stats with hang cleans, T-drill, and broad jump; pin hitter game stats with vertical, squat, and total strength; middle blockers game stats with broad jump. These data indicate that SC measures correlate well with game performance and are specific by position. These data could help SC coaches create a more precise training approach to focus on improving specific measures by position, which could then translate to improved game performance. These data could also help coaches with talent identification to determine playing time and rotations to maximize player ability and achieve success.

Prediction of human core body temperature using non-invasive measurement methods.

PubMed

Niedermann, Reto; Wyss, Eva; Annaheim, Simon; Psikuta, Agnes; Davey, Sarah; Rossi, René Michel

2014-01-01

The measurement of core body temperature is an efficient method for monitoring heat stress amongst workers in hot conditions. However, invasive measurement of core body temperature (e.g. rectal, intestinal, oesophageal temperature) is impractical for such applications. Therefore, the aim of this study was to define relevant non-invasive measures to predict core body temperature under various conditions. We conducted two human subject studies with different experimental protocols, different environmental temperatures (10 °C, 30 °C) and different subjects. In both studies the same non-invasive measurement methods (skin temperature, skin heat flux, heart rate) were applied. A principle component analysis was conducted to extract independent factors, which were then used in a linear regression model. We identified six parameters (three skin temperatures, two skin heat fluxes and heart rate), which were included for the calculation of two factors. The predictive value of these factors for core body temperature was evaluated by a multiple regression analysis. The calculated root mean square deviation (rmsd) was in the range from 0.28 °C to 0.34 °C for all environmental conditions. These errors are similar to previous models using non-invasive measures to predict core body temperature. The results from this study illustrate that multiple physiological parameters (e.g. skin temperature and skin heat fluxes) are needed to predict core body temperature. In addition, the physiological measurements chosen in this study and the algorithm defined in this work are potentially applicable as real-time core body temperature monitoring to assess health risk in broad range of working conditions.

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

PubMed

Marill, Keith A

2004-01-01

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

A Comparison between Multiple Regression Models and CUN-BAE Equation to Predict Body Fat in Adults

PubMed Central

Fuster-Parra, Pilar; Bennasar-Veny, Miquel; Tauler, Pedro; Yañez, Aina; López-González, Angel A.; Aguiló, Antoni

2015-01-01

Background Because the accurate measure of body fat (BF) is difficult, several prediction equations have been proposed. The aim of this study was to compare different multiple regression models to predict BF, including the recently reported CUN-BAE equation. Methods Multi regression models using body mass index (BMI) and body adiposity index (BAI) as predictors of BF will be compared. These models will be also compared with the CUN-BAE equation. For all the analysis a sample including all the participants and another one including only the overweight and obese subjects will be considered. The BF reference measure was made using Bioelectrical Impedance Analysis. Results The simplest models including only BMI or BAI as independent variables showed that BAI is a better predictor of BF. However, adding the variable sex to both models made BMI a better predictor than the BAI. For both the whole group of participants and the group of overweight and obese participants, using simple models (BMI, age and sex as variables) allowed obtaining similar correlations with BF as when the more complex CUN-BAE was used (ρ = 0:87 vs. ρ = 0:86 for the whole sample and ρ = 0:88 vs. ρ = 0:89 for overweight and obese subjects, being the second value the one for CUN-BAE). Conclusions There are simpler models than CUN-BAE equation that fits BF as well as CUN-BAE does. Therefore, it could be considered that CUN-BAE overfits. Using a simple linear regression model, the BAI, as the only variable, predicts BF better than BMI. However, when the sex variable is introduced, BMI becomes the indicator of choice to predict BF. PMID:25821960

A comparison between multiple regression models and CUN-BAE equation to predict body fat in adults.

PubMed

Fuster-Parra, Pilar; Bennasar-Veny, Miquel; Tauler, Pedro; Yañez, Aina; López-González, Angel A; Aguiló, Antoni

2015-01-01

Because the accurate measure of body fat (BF) is difficult, several prediction equations have been proposed. The aim of this study was to compare different multiple regression models to predict BF, including the recently reported CUN-BAE equation. Multi regression models using body mass index (BMI) and body adiposity index (BAI) as predictors of BF will be compared. These models will be also compared with the CUN-BAE equation. For all the analysis a sample including all the participants and another one including only the overweight and obese subjects will be considered. The BF reference measure was made using Bioelectrical Impedance Analysis. The simplest models including only BMI or BAI as independent variables showed that BAI is a better predictor of BF. However, adding the variable sex to both models made BMI a better predictor than the BAI. For both the whole group of participants and the group of overweight and obese participants, using simple models (BMI, age and sex as variables) allowed obtaining similar correlations with BF as when the more complex CUN-BAE was used (ρ = 0:87 vs. ρ = 0:86 for the whole sample and ρ = 0:88 vs. ρ = 0:89 for overweight and obese subjects, being the second value the one for CUN-BAE). There are simpler models than CUN-BAE equation that fits BF as well as CUN-BAE does. Therefore, it could be considered that CUN-BAE overfits. Using a simple linear regression model, the BAI, as the only variable, predicts BF better than BMI. However, when the sex variable is introduced, BMI becomes the indicator of choice to predict BF.

Locomotive syndrome is associated not only with physical capacity but also degree of depression.

PubMed

Ikemoto, Tatsunori; Inoue, Masayuki; Nakata, Masatoshi; Miyagawa, Hirofumi; Shimo, Kazuhiro; Wakabayashi, Toshiko; Arai, Young-Chang P; Ushida, Takahiro

2016-05-01

Reports of locomotive syndrome (LS) have recently been increasing. Although physical performance measures for LS have been well investigated to date, studies including psychiatric assessment are still scarce. Hence, the aim of this study was to investigate both physical and mental parameters in relation to presence and severity of LS using a 25-question geriatric locomotive function scale (GLFS-25) questionnaire. 150 elderly people aged over 60 years who were members of our physical-fitness center and displayed well-being were enrolled in this study. Firstly, using the previously determined GLFS-25 cutoff value (=16 points), subjects were divided into two groups accordingly: an LS and non-LS group in order to compare each parameter (age, grip strength, timed-up-and-go test (TUG), one-leg standing with eye open, back muscle and leg muscle strength, degree of depression and cognitive impairment) between the groups using the Mann-Whitney U-test followed by multiple logistic regression analysis. Secondly, a multiple linear regression was conducted to determine which variables showed the strongest correlation with severity of LS. We confirmed 110 people for non-LS (73%) and 40 people for LS using the GLFS-25 cutoff value. Comparative analysis between LS and non-LS revealed significant differences in parameters in age, grip strength, TUG, one-leg standing, back muscle strength and degree of depression (p < 0.006, after Bonferroni correction). Multiple logistic regression revealed that functional decline in grip strength, TUG and one-leg standing and degree of depression were significantly associated with LS. On the other hand, we observed that the significant contributors towards the GLFS-25 score were TUG and degree of depression in multiple linear regression analysis. The results indicate that LS is associated with not only the capacity of physical performance but also the degree of depression although most participants fell under the criteria of LS. Copyright © 2016 The Japanese Orthopaedic Association. Published by Elsevier B.V. All rights reserved.

Predicting individualized clinical measures by a generalized prediction framework and multimodal fusion of MRI data

PubMed Central

Meng, Xing; Jiang, Rongtao; Lin, Dongdong; Bustillo, Juan; Jones, Thomas; Chen, Jiayu; Yu, Qingbao; Du, Yuhui; Zhang, Yu; Jiang, Tianzi; Sui, Jing; Calhoun, Vince D.

2016-01-01

Neuroimaging techniques have greatly enhanced the understanding of neurodiversity (human brain variation across individuals) in both health and disease. The ultimate goal of using brain imaging biomarkers is to perform individualized predictions. Here we proposed a generalized framework that can predict explicit values of the targeted measures by taking advantage of joint information from multiple modalities. This framework also enables whole brain voxel-wise searching by combining multivariate techniques such as ReliefF, clustering, correlation-based feature selection and multiple regression models, which is more flexible and can achieve better prediction performance than alternative atlas-based methods. For 50 healthy controls and 47 schizophrenia patients, three kinds of features derived from resting-state fMRI (fALFF), sMRI (gray matter) and DTI (fractional anisotropy) were extracted and fed into a regression model, achieving high prediction for both cognitive scores (MCCB composite r = 0.7033, MCCB social cognition r = 0.7084) and symptomatic scores (positive and negative syndrome scale [PANSS] positive r = 0.7785, PANSS negative r = 0.7804). Moreover, the brain areas likely responsible for cognitive deficits of schizophrenia, including middle temporal gyrus, dorsolateral prefrontal cortex, striatum, cuneus and cerebellum, were located with different weights, as well as regions predicting PANSS symptoms, including thalamus, striatum and inferior parietal lobule, pinpointing the potential neuromarkers. Finally, compared to a single modality, multimodal combination achieves higher prediction accuracy and enables individualized prediction on multiple clinical measures. There is more work to be done, but the current results highlight the potential utility of multimodal brain imaging biomarkers to eventually inform clinical decision-making. PMID:27177764

Interaction Models for Functional Regression.

PubMed

Usset, Joseph; Staicu, Ana-Maria; Maity, Arnab

2016-02-01

A functional regression model with a scalar response and multiple functional predictors is proposed that accommodates two-way interactions in addition to their main effects. The proposed estimation procedure models the main effects using penalized regression splines, and the interaction effect by a tensor product basis. Extensions to generalized linear models and data observed on sparse grids or with measurement error are presented. A hypothesis testing procedure for the functional interaction effect is described. The proposed method can be easily implemented through existing software. Numerical studies show that fitting an additive model in the presence of interaction leads to both poor estimation performance and lost prediction power, while fitting an interaction model where there is in fact no interaction leads to negligible losses. The methodology is illustrated on the AneuRisk65 study data.

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

NASA Astrophysics Data System (ADS)

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

2011-08-01

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

Using the Coefficient of Determination "R"[superscript 2] to Test the Significance of Multiple Linear Regression

ERIC Educational Resources Information Center

Quinino, Roberto C.; Reis, Edna A.; Bessegato, Lupercio F.

2013-01-01

This article proposes the use of the coefficient of determination as a statistic for hypothesis testing in multiple linear regression based on distributions acquired by beta sampling. (Contains 3 figures.)

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

PubMed

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

2014-01-01

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

Younger age, female sex, and high number of awakenings and arousals predict fatigue in patients with sleep disorders: a retrospective polysomnographic observational study

PubMed Central

Veauthier, Christian

2013-01-01

Background The Fatigue Severity Scale (FSS) is widely used to assess fatigue, not only in the context of multiple sclerosis-related fatigue, but also in many other medical conditions. Some polysomnographic studies have shown high FSS values in sleep-disordered patients without multiple sclerosis. The Modified Fatigue Impact Scale (MFIS) has increasingly been used in order to assess fatigue, but polysomnographic data investigating sleep-disordered patients are thus far unavailable. Moreover, the pathophysiological link between sleep architecture and fatigue measured with the MFIS and the FSS has not been previously investigated. Methods This was a retrospective observational study (n = 410) with subgroups classified according to sleep diagnosis. The statistical analysis included nonparametric correlation between questionnaire results and polysomnographic data, age and sex, and univariate and multiple logistic regression. Results The multiple logistic regression showed a significant relationship between FSS/MFIS values and younger age and female sex. Moreover, there was a significant relationship between FSS values and number of arousals and between MFIS values and number of awakenings. Conclusion Younger age, female sex, and high number of awakenings and arousals are predictive of fatigue in sleep-disordered patients. Further investigations are needed to find the pathophysiological explanation for these relationships. PMID:24109185

Bayesian quantile regression-based partially linear mixed-effects joint models for longitudinal data with multiple features.

PubMed

Zhang, Hanze; Huang, Yangxin; Wang, Wei; Chen, Henian; Langland-Orban, Barbara

2017-01-01

In longitudinal AIDS studies, it is of interest to investigate the relationship between HIV viral load and CD4 cell counts, as well as the complicated time effect. Most of common models to analyze such complex longitudinal data are based on mean-regression, which fails to provide efficient estimates due to outliers and/or heavy tails. Quantile regression-based partially linear mixed-effects models, a special case of semiparametric models enjoying benefits of both parametric and nonparametric models, have the flexibility to monitor the viral dynamics nonparametrically and detect the varying CD4 effects parametrically at different quantiles of viral load. Meanwhile, it is critical to consider various data features of repeated measurements, including left-censoring due to a limit of detection, covariate measurement error, and asymmetric distribution. In this research, we first establish a Bayesian joint models that accounts for all these data features simultaneously in the framework of quantile regression-based partially linear mixed-effects models. The proposed models are applied to analyze the Multicenter AIDS Cohort Study (MACS) data. Simulation studies are also conducted to assess the performance of the proposed methods under different scenarios.

Effects of Barometric Fluctuations on Well Water-Level Measurements and Aquifer Test Data

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

Spane, Frank A.

1999-12-16

This report examines the effects of barometric fluctuations on well water-level measurements and evaluates adjustment and removal methods for determining areal aquifer head conditions and aquifer test analysis. Two examples of Hanford Site unconfined aquifer tests are examined that demonstrate baro-metric response analysis and illustrate the predictive/removal capabilities of various methods for well water-level and aquifer total head values. Good predictive/removal characteristics were demonstrated with best corrective results provided by multiple-regression deconvolution methods.

Factor Structure of the Primary Scales of the Inventory of Personality Organization in a Nonclinical Sample Using Exploratory Structural Equation Modeling

ERIC Educational Resources Information Center

Ellison, William D.; Levy, Kenneth N.

2012-01-01

Using exploratory structural equation modeling and multiple regression, we examined the factor structure and criterion relations of the primary scales of the Inventory of Personality Organization (IPO; Kernberg & Clarkin, 1995) in a nonclinical sample. Participants (N = 1,260) completed the IPO and measures of self-concept clarity, defenses,…

Making Curriculum Decisions in K-8 Science: The Relationship between Teacher Dispositions and Curriculum Content

ERIC Educational Resources Information Center

Eidietis, L.; Jewkes, A. M.

2011-01-01

This study examined teachers' dispositions toward and choices to teach ocean science using a survey design. A sample of 89 in-service K-8 teachers in the United States reported their (1) feelings of preparedness to teach about ocean literacy and (2) attitudes toward ocean science on three measures. Results of multiple linear regression showed that…

The Role of Core Self-Evaluations in the Relationship between Stress and Depression in Persons with Spinal Cord Injury

ERIC Educational Resources Information Center

DeAngelis, Jesse B.; Yaghmaian, Rana; Smedema, Susan Miller

2016-01-01

Purpose: To investigate the role of core self-evaluations (CSE) in the relationship between perceived stress and depression in persons with spinal cord injury. Method: Two hundred forty-seven adults with spinal cord injury completed an online survey measuring perceived stress, CSE, and depressive symptoms. Results: A multiple regression analysis…

"Hits" (Not "Discussion Posts") Predict Student Success in Online Courses: A Double Cross-Validation Study

ERIC Educational Resources Information Center

Ramos, Cheryl; Yudko, Errol

2008-01-01

The efficacy of individual components of an online course on positive course outcome was examined via stepwise multiple regression analysis. Outcome was measured as the student's total score on all exams given during the course. The predictors were page hits, discussion posts, and discussion reads. The vast majority of the variance of outcome was…

Photosynthesis, water relations, and growth of planted Pinus strobus L. on burned sites in the southern Appalachians

Treesearch

Katherine J. Elliott; James M. Vose

1994-01-01

We measured net photosynthesis,leaf conductance, xylem water potential, and growth of Pinus strbus L. seedlings two years after planting on two clear-cut and burned sites in the southern Appalachians. Multiple regression analysis was used to relate seedling net pholosynthesis to vapor pressure deficit, seedling crown temperature, photosynthetically active radiation (...

Implications of Interactions among Society, Education and Technology: A Comparison of Multiple Linear Regression and Multilevel Modeling in Mathematics Achievement Analyses

ERIC Educational Resources Information Center

Deering, Pamela Rose

2014-01-01

This research compares and contrasts two approaches to predictive analysis of three years' of school district data to investigate relationships between student and teacher characteristics and math achievement as measured by the state-mandated Maryland School Assessment mathematics exam. The sample for the study consisted of 3,514 students taught…

Can we "predict" long-term outcome for ambulatory transcutaneous electrical nerve stimulation in patients with chronic pain?

PubMed

Köke, Albère J; Smeets, Rob J E M; Perez, Roberto S; Kessels, Alphons; Winkens, Bjorn; van Kleef, Maarten; Patijn, Jacob

2015-03-01

Evidence for effectiveness of transcutaneous electrical nerve stimulation (TENS) is still inconclusive. As heterogeneity of chronic pain patients might be an important factor for this lack of efficacy, identifying factors for a successful long-term outcome is of great importance. A prospective study was performed to identify variables with potential predictive value for 2 outcome measures on long term (6 months); (1) continuation of TENS, and (2) a minimally clinical important pain reduction of ≥ 33%. At baseline, a set of risk factors including pain-related variables, psychological factors, and disability was measured. In a multiple logistic regression analysis, higher patient's expectations, neuropathic pain, no severe pain (< 80 mm visual analogue scale [VAS]) were independently related to long-term continuation of TENS. For the outcome "minimally clinical important pain reduction," the multiple logistic regression analysis indicated that no multisited pain (> 2 pain locations) and intermittent pain were positively and independently associated with a minimally clinical important pain reduction of ≥ 33%. The results showed that factors associated with a successful outcome in the long term are dependent on definition of successful outcome. © 2014 World Institute of Pain.

The impact of menopausal symptoms on work ability.

PubMed

Geukes, Marije; van Aalst, Mariëlle P; Nauta, Mary C E; Oosterhof, Henk

2012-03-01

Menopause is an important life event that may have a negative influence on quality of life. Work ability, a concept widely used in occupational health, can predict both future impairment and duration of sickness absence. The aim of this study was to examine the impact of menopausal symptoms on work ability. This was a cross-sectional study that used a sample of healthy working Dutch women aged 44 to 60 years. Work ability was measured using the Work Ability Index, and menopausal symptoms were measured using the Greene Climacteric Scale. Stepwise multiple linear regression models were used to examine the relationship between menopausal symptoms and work ability. A total of 208 women were included in this study. There was a significant negative correlation between total Greene Climacteric Scale score and Work Ability Index score. Total Greene Climacteric Scale score predicted 33.8% of the total variance in the Work Ability Index score. Only the psychological and somatic subscales of the Greene Climacteric Scale were significant predictors in multiple linear regression analysis. Together, they accounted for 36.5% of total variance in Work Ability Index score. Menopausal symptoms are negatively associated with work ability and may increase the risk of sickness absence.

Age, Body Mass Index, and Frequency of Sexual Activity are Independent Predictors of Testosterone Deficiency in Men With Erectile Dysfunction.

PubMed

Pagano, Matthew J; De Fazio, Adam; Levy, Alison; RoyChoudhury, Arindam; Stahl, Peter J

2016-04-01

To identify clinical predictors of testosterone deficiency (TD) in men with erectile dysfunction (ED), thereby identifying subgroups that are most likely to benefit from targeted testosterone screening. Retrospective review was conducted on 498 men evaluated for ED between January 2013 and July 2014. Testing for TD by early morning serum measurement was offered to all eligible men. Patients with history of prostate cancer or testosterone replacement were excluded. Univariable linear regression was conducted to analyze 19 clinical variables for associations with serum total testosterone (TT), calculated free testosterone (cFT), and TD (T <300 ng/dL or cFT <6.5 ng/dL). Variables significant on univariable analysis were included in multiple regression models. A total of 225 men met inclusion criteria. Lower TT levels were associated with greater body mass index (BMI), less frequent sexual activity, and absence of clinical depression on multiple regression analysis. TT decreased by 49.5 ng/dL for each 5-point increase in BMI. BMI and age were the only independent predictors of cFT levels on multivariable analysis. Overall, 62 subjects (27.6%) met criteria for TD. Older age, greater BMI, and less frequent sexual activity were the only independent predictors of TD on multiple regression. We observed a 2.2-fold increase in the odds of TD for every 5-point increase in BMI, and a 1.8-fold increase for every 10 year increase in age. Men with ED and elevated BMI, advanced age, or infrequent sexual activity appear to be at high risk of TD, and such patients represent excellent potential candidates for targeted testosterone screening. Copyright © 2016 Elsevier Inc. All rights reserved.

A difference in systolic blood pressure between arms is a novel predictor of the development and progression of diabetic nephropathy in patients with type 2 diabetes.

PubMed

Okada, Hiroshi; Fukui, Michiaki; Tanaka, Muhei; Matsumoto, Shinobu; Iwase, Hiroya; Kobayashi, Kanae; Asano, Mai; Yamazaki, Masahiro; Hasegawa, Goji; Nakamura, Naoto

2013-10-01

Recent studies have suggested that a difference in systolic blood pressure (SBP) between arms is associated with both vascular disease and mortality. The aim of this study was to investigate the relationship between a difference in SBP between arms and change in urinary albumin excretion or development of albuminuria in patients with type 2 diabetes. We measured SBP in 408 consecutive patients with type 2 diabetes, and calculated a difference in SBP between arms. We performed follow-up study to assess change in urinary albumin excretion or development of albuminuria, mean interval of which was 4.6 ± 1.7 years. We then evaluated the relationship of a difference in SBP between arms to diabetic nephropathy using multiple regression analysis and multiple Cox regression model. Multiple regression analyses demonstrated that a difference in SBP between arms was independently associated with change in urinary albumin excretion (β = 0.1869, P = 0.0010). Adjusted Cox regression analyses demonstrated that a difference in SBP between arms was associated with an increased hazard of development of albuminuria; hazard ratio was 1.215 (95% confidence interval 1.077-1.376). Moreover, the risk of development of albuminuria was increased in patients with a difference in SBP of equal to or more than 10 mmHg between arms; hazard ratio was 4.168 (95% confidence interval 1.478-11.70). A difference in SBP between arms could be a novel predictor of the development and progression of diabetic nephropathy in patients with type 2 diabetes. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Predicting Reactive Intermediate Quantum Yields from Dissolved Organic Matter Photolysis Using Optical Properties and Antioxidant Capacity.

PubMed

Mckay, Garrett; Huang, Wenxi; Romera-Castillo, Cristina; Crouch, Jenna E; Rosario-Ortiz, Fernando L; Jaffé, Rudolf

2017-05-16

The antioxidant capacity and formation of photochemically produced reactive intermediates (RI) was studied for water samples collected from the Florida Everglades with different spatial (marsh versus estuarine) and temporal (wet versus dry season) characteristics. Measured RI included triplet excited states of dissolved organic matter ( 3 DOM*), singlet oxygen ( 1 O 2 ), and the hydroxyl radical ( • OH). Single and multiple linear regression modeling were performed using a broad range of extrinsic (to predict RI formation rates, R RI ) and intrinsic (to predict RI quantum yields, Φ RI ) parameters. Multiple linear regression models consistently led to better predictions of R RI and Φ RI for our data set but poor prediction of Φ RI for a previously published data set,1 probably because the predictors are intercorrelated (Pearson's r > 0.5). Single linear regression models were built with data compiled from previously published studies (n ≈ 120) in which E2:E3, S, and Φ RI values were measured, which revealed a high degree of similarity between RI-optical property relationships across DOM samples of diverse sources. This study reveals that • OH formation is, in general, decoupled from 3 DOM* and 1 O 2 formation, providing supporting evidence that 3 DOM* is not a • OH precursor. Finally, Φ RI for 1 O 2 and 3 DOM* correlated negatively with antioxidant activity (a surrogate for electron donating capacity) for the collected samples, which is consistent with intramolecular oxidation of DOM moieties by 3 DOM*.

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…

[Prediction model of health workforce and beds in county hospitals of Hunan by multiple linear regression].

PubMed

Ling, Ru; Liu, Jiawang

2011-12-01

To construct prediction model for health workforce and hospital beds in county hospitals of Hunan by multiple linear regression. We surveyed 16 counties in Hunan with stratified random sampling according to uniform questionnaires,and multiple linear regression analysis with 20 quotas selected by literature view was done. Independent variables in the multiple linear regression model on medical personnels in county hospitals included the counties' urban residents' income, crude death rate, medical beds, business occupancy, professional equipment value, the number of devices valued above 10 000 yuan, fixed assets, long-term debt, medical income, medical expenses, outpatient and emergency visits, hospital visits, actual available bed days, and utilization rate of hospital beds. Independent variables in the multiple linear regression model on county hospital beds included the the population of aged 65 and above in the counties, disposable income of urban residents, medical personnel of medical institutions in county area, business occupancy, the total value of professional equipment, fixed assets, long-term debt, medical income, medical expenses, outpatient and emergency visits, hospital visits, actual available bed days, utilization rate of hospital beds, and length of hospitalization. The prediction model shows good explanatory and fitting, and may be used for short- and mid-term forecasting.

Multivariate random regression analysis for body weight and main morphological traits in genetically improved farmed tilapia (Oreochromis niloticus).

PubMed

He, Jie; Zhao, Yunfeng; Zhao, Jingli; Gao, Jin; Han, Dandan; Xu, Pao; Yang, Runqing

2017-11-02

Because of their high economic importance, growth traits in fish are under continuous improvement. For growth traits that are recorded at multiple time-points in life, the use of univariate and multivariate animal models is limited because of the variable and irregular timing of these measures. Thus, the univariate random regression model (RRM) was introduced for the genetic analysis of dynamic growth traits in fish breeding. We used a multivariate random regression model (MRRM) to analyze genetic changes in growth traits recorded at multiple time-point of genetically-improved farmed tilapia. Legendre polynomials of different orders were applied to characterize the influences of fixed and random effects on growth trajectories. The final MRRM was determined by optimizing the univariate RRM for the analyzed traits separately via penalizing adaptively the likelihood statistical criterion, which is superior to both the Akaike information criterion and the Bayesian information criterion. In the selected MRRM, the additive genetic effects were modeled by Legendre polynomials of three orders for body weight (BWE) and body length (BL) and of two orders for body depth (BD). By using the covariance functions of the MRRM, estimated heritabilities were between 0.086 and 0.628 for BWE, 0.155 and 0.556 for BL, and 0.056 and 0.607 for BD. Only heritabilities for BD measured from 60 to 140 days of age were consistently higher than those estimated by the univariate RRM. All genetic correlations between growth time-points exceeded 0.5 for either single or pairwise time-points. Moreover, correlations between early and late growth time-points were lower. Thus, for phenotypes that are measured repeatedly in aquaculture, an MRRM can enhance the efficiency of the comprehensive selection for BWE and the main morphological traits.

Nitrate removal in stream ecosystems measured by 15N addition experiments: Total uptake

USGS Publications Warehouse

Hall, R.O.; Tank, J.L.; Sobota, D.J.; Mulholland, P.J.; O'Brien, J. M.; Dodds, W.K.; Webster, J.R.; Valett, H.M.; Poole, G.C.; Peterson, B.J.; Meyer, J.L.; McDowell, W.H.; Johnson, S.L.; Hamilton, S.K.; Grimm, N. B.; Gregory, S.V.; Dahm, Clifford N.; Cooper, L.W.; Ashkenas, L.R.; Thomas, S.M.; Sheibley, R.W.; Potter, J.D.; Niederlehner, B.R.; Johnson, L.T.; Helton, A.M.; Crenshaw, C.M.; Burgin, A.J.; Bernot, M.J.; Beaulieu, J.J.; Arangob, C.P.

2009-01-01

We measured uptake length of 15NO-3 in 72 streams in eight regions across the United States and Puerto Rico to develop quantitative predictive models on controls of NO-3 uptake length. As part of the Lotic Intersite Nitrogen eXperiment II project, we chose nine streams in each region corresponding to natural (reference), suburban-urban, and agricultural land uses. Study streams spanned a range of human land use to maximize variation in NO-3 concentration, geomorphology, and metabolism. We tested a causal model predicting controls on NO-3 uptake length using structural equation modeling. The model included concomitant measurements of ecosystem metabolism, hydraulic parameters, and nitrogen concentration. We compared this structural equation model to multiple regression models which included additional biotic, catchment, and riparian variables. The structural equation model explained 79% of the variation in log uptake length (S Wtot). Uptake length increased with specific discharge (Q/w) and increasing NO-3 concentrations, showing a loss in removal efficiency in streams with high NO-3 concentration. Uptake lengths shortened with increasing gross primary production, suggesting autotrophic assimilation dominated NO-3 removal. The fraction of catchment area as agriculture and suburban-urban land use weakly predicted NO-3 uptake in bivariate regression, and did improve prediction in a set of multiple regression models. Adding land use to the structural equation model showed that land use indirectly affected NO-3 uptake lengths via directly increasing both gross primary production and NO-3 concentration. Gross primary production shortened SWtot, while increasing NO-3 lengthened SWtot resulting in no net effect of land use on NO- 3 removal. ?? 2009.

War-related trauma exposure and multiple risk behaviors among school-going adolescents in Northern Uganda: the mediating role of depression symptoms.

PubMed

Okello, James; Nakimuli-Mpungu, Etheldreda; Musisi, Seggane; Broekaert, Eric; Derluyn, Ilse

2013-11-01

The relationship between war-related trauma exposure, depressive symptoms and multiple risk behaviors among adolescents is less clear in sub-Saharan Africa. We analyzed data collected from a sample of school-going adolescents four years postwar. Participants completed interviews assessing various risk behaviors defined by the Youth Self Report (YSR) and a sexual risk behavior survey, and were screened for post-traumatic stress, anxiety and depression symptoms based on the Impact of Events Scale Revised (IESR) and Hopkins Symptom Checklist for Adolescents (HSCL-37A) respectively. Multivariate logistic regression was used to assess factors independently associated with multiple risk behaviors. The logistic regression model of Baron and Kenny (1986) was used to evaluate the mediating role of depression in the relationship between stressful war events and multiple risk behaviors. Of 551 participants, 139 (25%) reported multiple (three or more) risk behaviors in the past year. In the multivariate analyses, depression symptoms remained uniquely associated with multiple risk behavior after adjusting for potential confounders including socio-demographic characteristics, war-related trauma exposure variables, anxiety and post-traumatic stress symptoms. In mediation analysis, depression symptoms mediated the associations between stressful war events and multiple risk behaviors. The psychometric properties of the questionnaires used in this study are not well established in war affected African samples thus ethno cultural variation may decrease the validity of our measures. Adolescents with depression may be at a greater risk of increased engagement in multiple risk behaviors. Culturally sensitive and integrated interventions to treat and prevent depression among adolescents in post-conflict settings are urgently needed. © 2013 Elsevier B.V. All rights reserved.

The use of regression analysis in determining reference intervals for low hematocrit and thrombocyte count in multiple electrode aggregometry and platelet function analyzer 100 testing of platelet function.

PubMed

Kuiper, Gerhardus J A J M; Houben, Rik; Wetzels, Rick J H; Verhezen, Paul W M; Oerle, Rene van; Ten Cate, Hugo; Henskens, Yvonne M C; Lancé, Marcus D

2017-11-01

Low platelet counts and hematocrit levels hinder whole blood point-of-care testing of platelet function. Thus far, no reference ranges for MEA (multiple electrode aggregometry) and PFA-100 (platelet function analyzer 100) devices exist for low ranges. Through dilution methods of volunteer whole blood, platelet function at low ranges of platelet count and hematocrit levels was assessed on MEA for four agonists and for PFA-100 in two cartridges. Using (multiple) regression analysis, 95% reference intervals were computed for these low ranges. Low platelet counts affected MEA in a positive correlation (all agonists showed r 2 ≥ 0.75) and PFA-100 in an inverse correlation (closure times were prolonged with lower platelet counts). Lowered hematocrit did not affect MEA testing, except for arachidonic acid activation (ASPI), which showed a weak positive correlation (r 2 = 0.14). Closure time on PFA-100 testing was inversely correlated with hematocrit for both cartridges. Regression analysis revealed different 95% reference intervals in comparison with originally established intervals for both MEA and PFA-100 in low platelet or hematocrit conditions. Multiple regression analysis of ASPI and both tests on the PFA-100 for combined low platelet and hematocrit conditions revealed that only PFA-100 testing should be adjusted for both thrombocytopenia and anemia. 95% reference intervals were calculated using multiple regression analysis. However, coefficients of determination of PFA-100 were poor, and some variance remained unexplained. Thus, in this pilot study using (multiple) regression analysis, we could establish reference intervals of platelet function in anemia and thrombocytopenia conditions on PFA-100 and in thrombocytopenia conditions on MEA.

Reduction of interferences in graphite furnace atomic absorption spectrometry by multiple linear regression modelling

NASA Astrophysics Data System (ADS)

Grotti, Marco; Abelmoschi, Maria Luisa; Soggia, Francesco; Tiberiade, Christian; Frache, Roberto

2000-12-01

The multivariate effects of Na, K, Mg and Ca as nitrates on the electrothermal atomisation of manganese, cadmium and iron were studied by multiple linear regression modelling. Since the models proved to efficiently predict the effects of the considered matrix elements in a wide range of concentrations, they were applied to correct the interferences occurring in the determination of trace elements in seawater after pre-concentration of the analytes. In order to obtain a statistically significant number of samples, a large volume of the certified seawater reference materials CASS-3 and NASS-3 was treated with Chelex-100 resin; then, the chelating resin was separated from the solution, divided into several sub-samples, each of them was eluted with nitric acid and analysed by electrothermal atomic absorption spectrometry (for trace element determinations) and inductively coupled plasma optical emission spectrometry (for matrix element determinations). To minimise any other systematic error besides that due to matrix effects, accuracy of the pre-concentration step and contamination levels of the procedure were checked by inductively coupled plasma mass spectrometric measurements. Analytical results obtained by applying the multiple linear regression models were compared with those obtained with other calibration methods, such as external calibration using acid-based standards, external calibration using matrix-matched standards and the analyte addition technique. Empirical models proved to efficiently reduce interferences occurring in the analysis of real samples, allowing an improvement of accuracy better than for other calibration methods.

Screening for ketosis using multiple logistic regression based on milk yield and composition.

PubMed

Kayano, Mitsunori; Kataoka, Tomoko

2015-11-01

Multiple logistic regression was applied to milk yield and composition data for 632 records of healthy cows and 61 records of ketotic cows in Hokkaido, Japan. The purpose was to diagnose ketosis based on milk yield and composition, simultaneously. The cows were divided into two groups: (1) multiparous, including 314 healthy cows and 45 ketotic cows and (2) primiparous, including 318 healthy cows and 16 ketotic cows, since nutritional status, milk yield and composition are affected by parity. Multiple logistic regression was applied to these groups separately. For multiparous cows, milk yield (kg/day/cow) and protein-to-fat (P/F) ratio in milk were significant factors (P<0.05) for the diagnosis of ketosis. For primiparous cows, lactose content (%), solid not fat (SNF) content (%) and milk urea nitrogen (MUN) content (mg/dl) were significantly associated with ketosis (P<0.01). A diagnostic rule was constructed for each group of cows: (1) 9.978 × P/F ratio + 0.085 × milk yield <10 and (2) 2.327 × SNF - 2.703 × lactose + 0.225 × MUN <10. The sensitivity, specificity and the area under the curve (AUC) of the diagnostic rules were (1) 0.800, 0.729 and 0.811; (2) 0.813, 0.730 and 0.787, respectively. The P/F ratio, which is a widely used measure of ketosis, provided the sensitivity, specificity and AUC values of (1) 0.711, 0.726 and 0.781; and (2) 0.678, 0.767 and 0.738, respectively.

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.

SOME STATISTICAL ISSUES RELATED TO MULTIPLE LINEAR REGRESSION MODELING OF BEACH BACTERIA CONCENTRATIONS

EPA Science Inventory

As a fast and effective technique, the multiple linear regression (MLR) method has been widely used in modeling and prediction of beach bacteria concentrations. Among previous works on this subject, however, several issues were insufficiently or inconsistently addressed. Those is...

MULTIPLE REGRESSION MODELS FOR HINDCASTING AND FORECASTING MIDSUMMER HYPOXIA IN THE GULF OF MEXICO

EPA Science Inventory

A new suite of multiple regression models were developed that describe the relationship between the area of bottom water hypoxia along the northern Gulf of Mexico and Mississippi-Atchafalaya River nitrate concentration, total phosphorus (TP) concentration, and discharge. Variabil...

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

PubMed Central

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

2016-01-01

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

Evaluation of regression-based 3-D shoulder rhythms.

PubMed

Xu, Xu; Dickerson, Clark R; Lin, Jia-Hua; McGorry, Raymond W

2016-08-01

The movements of the humerus, the clavicle, and the scapula are not completely independent. The coupled pattern of movement of these bones is called the shoulder rhythm. To date, multiple studies have focused on providing regression-based 3-D shoulder rhythms, in which the orientations of the clavicle and the scapula are estimated by the orientation of the humerus. In this study, six existing regression-based shoulder rhythms were evaluated by an independent dataset in terms of their predictability. The datasets include the measured orientations of the humerus, the clavicle, and the scapula of 14 participants over 118 different upper arm postures. The predicted orientations of the clavicle and the scapula were derived from applying those regression-based shoulder rhythms to the humerus orientation. The results indicated that none of those regression-based shoulder rhythms provides consistently more accurate results than the others. For all the joint angles and all the shoulder rhythms, the RMSE are all greater than 5°. Among those shoulder rhythms, the scapula lateral/medial rotation has the strongest correlation between the predicted and the measured angles, while the other thoracoclavicular and thoracoscapular bone orientation angles only showed a weak to moderate correlation. Since the regression-based shoulder rhythm has been adopted for shoulder biomechanical models to estimate shoulder muscle activities and structure loads, there needs to be further investigation on how the predicted error from the shoulder rhythm affects the output of the biomechanical model. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Serial measurement of type-specific human papillomavirus load enables classification of cervical intraepithelial neoplasia lesions according to occurring human papillomavirus-induced pathway.

PubMed

Verhelst, Stefanie; Poppe, Willy A J; Bogers, Johannes J; Depuydt, Christophe E

2017-03-01

This retrospective study examined whether human papillomavirus (HPV) type-specific viral load changes measured in two or three serial cervical smears are predictive for the natural evolution of HPV infections and correlate with histological grades of cervical intraepithelial neoplasia (CIN), allowing triage of HPV-positive women. A cervical histology database was used to select consecutive women with biopsy-proven CIN in 2012 who had at least two liquid-based cytology samples before the diagnosis of CIN. Before performing cytology, 18 different quantitative PCRs allowed HPV type-specific viral load measurement. Changes in HPV-specific load between measurements were assessed by linear regression, with calculation of coefficient of determination (R) and slope. All infections could be classified into one of five categories: (i) clonal progressing process (R≥0.85; positive slope), (ii) simultaneously occurring clonal progressive and transient infection, (iii) clonal regressing process (R≥0.85; negative slope), (iv) serial transient infection with latency [R<0.85; slopes (two points) between 0.0010 and -0.0010 HPV copies/cell/day], and (v) transient productive infection (R<0.85; slope: ±0.0099 HPV copies/cell/day). Three hundred and seven women with CIN were included; 124 had single-type infections and 183 had multiple HPV types. Only with three consecutive measurements could a clonal process be identified in all CIN3 cases. We could clearly demonstrate clonal regressing lesions with a persistent linear decrease in viral load (R≥0.85; -0.003 HPV copies/cell/day) in all CIN categories. Type-specific viral load increase/decrease in three consecutive measurements enabled classification of CIN lesions in clonal HPV-driven transformation (progression/regression) and nonclonal virion-productive (serial transient/transient) processes.

Mean centering, multicollinearity, and moderators in multiple regression: The reconciliation redux.

PubMed

Iacobucci, Dawn; Schneider, Matthew J; Popovich, Deidre L; Bakamitsos, Georgios A

2017-02-01

In this article, we attempt to clarify our statements regarding the effects of mean centering. In a multiple regression with predictors A, B, and A × B (where A × B serves as an interaction term), mean centering A and B prior to computing the product term can clarify the regression coefficients (which is good) and the overall model fit R 2 will remain undisturbed (which is also good).

A Common Mechanism for Resistance to Oxime Reactivation of Acetylcholinesterase Inhibited by Organophosphorus Compounds

DTIC Science & Technology

2013-01-01

application of the Hammett equation with the constants rph in the chemistry of organophosphorus compounds, Russ. Chem. Rev. 38 (1969) 795–811. [13...of oximes and OP compounds and the ability of oximes to reactivate OP- inhibited AChE. Multiple linear regression equations were analyzed using...phosphonate pairs, 21 oxime/ phosphoramidate pairs and 12 oxime/phosphate pairs. The best linear regression equation resulting from multiple regression anal

Novel applications of multitask learning and multiple output regression to multiple genetic trait prediction.

PubMed

He, Dan; Kuhn, David; Parida, Laxmi

2016-06-15

Given a set of biallelic molecular markers, such as SNPs, with genotype values encoded numerically on a collection of plant, animal or human samples, the goal of genetic trait prediction is to predict the quantitative trait values by simultaneously modeling all marker effects. Genetic trait prediction is usually represented as linear regression models. In many cases, for the same set of samples and markers, multiple traits are observed. Some of these traits might be correlated with each other. Therefore, modeling all the multiple traits together may improve the prediction accuracy. In this work, we view the multitrait prediction problem from a machine learning angle: as either a multitask learning problem or a multiple output regression problem, depending on whether different traits share the same genotype matrix or not. We then adapted multitask learning algorithms and multiple output regression algorithms to solve the multitrait prediction problem. We proposed a few strategies to improve the least square error of the prediction from these algorithms. Our experiments show that modeling multiple traits together could improve the prediction accuracy for correlated traits. The programs we used are either public or directly from the referred authors, such as MALSAR (http://www.public.asu.edu/~jye02/Software/MALSAR/) package. The Avocado data set has not been published yet and is available upon request. dhe@us.ibm.com. © The Author 2016. Published by Oxford University Press.

Simple and multiple linear regression: sample size considerations.

PubMed

Hanley, James A

2016-11-01

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

Multiple imputation for cure rate quantile regression with censored data.

PubMed

Wu, Yuanshan; Yin, Guosheng

2017-03-01

The main challenge in the context of cure rate analysis is that one never knows whether censored subjects are cured or uncured, or whether they are susceptible or insusceptible to the event of interest. Considering the susceptible indicator as missing data, we propose a multiple imputation approach to cure rate quantile regression for censored data with a survival fraction. We develop an iterative algorithm to estimate the conditionally uncured probability for each subject. By utilizing this estimated probability and Bernoulli sample imputation, we can classify each subject as cured or uncured, and then employ the locally weighted method to estimate the quantile regression coefficients with only the uncured subjects. Repeating the imputation procedure multiple times and taking an average over the resultant estimators, we obtain consistent estimators for the quantile regression coefficients. Our approach relaxes the usual global linearity assumption, so that we can apply quantile regression to any particular quantile of interest. We establish asymptotic properties for the proposed estimators, including both consistency and asymptotic normality. We conduct simulation studies to assess the finite-sample performance of the proposed multiple imputation method and apply it to a lung cancer study as an illustration. © 2016, The International Biometric Society.

The role of speed versus working memory in predicting learning new information in multiple sclerosis.

PubMed

Chiaravalloti, Nancy D; Stojanovic-Radic, Jelena; DeLuca, John

2013-01-01

The most common cognitive impairments in multiple sclerosis (MS) have been documented in specific domains, including new learning and memory, working memory, and information processing speed. However, little attempt has been made to increase our understanding of their relationship to one another. While recent studies have shown that processing speed impacts new learning and memory abilities in MS, the role of working memory in this relationship has received less attention. The present study examines the relative contribution of impaired working memory versus processing speed in new learning and memory functions in MS. Participants consisted of 51 individuals with clinically definite MS. Participants completed two measures of processing speed, two measures of working memory, and two measures of episodic memory. Data were analyzed via correlational and multiple regression analysis. Results indicate that the variance in new learning abilities in this sample was primarily associated with processing speed, with working memory exerting much less of an influence. Results are discussed in terms of the role of cognitive rehabilitation of new learning and memory abilities in persons with MS.

Neurocognition and community outcome in schizophrenia: long-term predictive validity.

PubMed

Fujii, Daryl E; Wylie, A Michael

2003-02-01

The present study examined the predictive validity of neuropsychological measures to functional outcome in 26 schizophrenic patients 15-plus year post-testing. Outcome measures included score on the Resource Associated Functional Level Scale (RAFLS), number of state hospital admissions, and total duration of state hospital inpatient stay. Results of several stepwise multiple regressions revealed that verbal memory significantly predicted RAFLS score, accounting for nearly half of the variance. Trails B significantly predicted duration of state hospital inpatient status. Discussion focused on the utility of these measures for clinicians and system planners. Copyright 2002 Elsevier Science B.V.

The Relationship of Item-Level Response Times with Test-Taker and Item Variables in an Operational CAT Environment. LSAC Research Report Series.

ERIC Educational Resources Information Center

Swygert, Kimberly A.

In this study, data from an operational computerized adaptive test (CAT) were examined in order to gather information concerning item response times in a CAT environment. The CAT under study included multiple-choice items measuring verbal, quantitative, and analytical reasoning. The analyses included the fitting of regression models describing the…

Rape Myth Acceptance, Hypermasculinity, and SAT Scores as Correlates of Moral Development: Understanding Sexually Aggressive Attitudes in First-Year College Men

ERIC Educational Resources Information Center

Tatum, Jerry L.; Foubert, John D.

2009-01-01

Male perpetrated sexual aggression has long been recognized as a serious problem on college campuses. The purpose of this multiple regression correlation study was to assess the relationship between levels of moral development (measured by the Defining Issues Test) and the degree to which first-year college men (N = 161) ascribed to rape…

Undergraduate Student Motivation in Modularized Developmental Mathematics Courses

ERIC Educational Resources Information Center

Pachlhofer, Keith A.

2017-01-01

This study used the Motivated Strategies for Learning Questionnaire in modularized courses at three institutions across the nation (N = 189), and multiple regression was completed to investigate five categories of student motivation that predicted academic success and course completion. The overall multiple regression analysis was significant and…

MULGRES: a computer program for stepwise multiple regression analysis

Treesearch

A. Jeff Martin

1971-01-01

MULGRES is a computer program source deck that is designed for multiple regression analysis employing the technique of stepwise deletion in the search for most significant variables. The features of the program, along with inputs and outputs, are briefly described, with a note on machine compatibility.

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)

Relationship between menopause and health-related quality of life in middle-aged Chinese women: a cross-sectional study.

PubMed

Liu, Kuo; He, Liu; Tang, Xun; Wang, Jinwei; Li, Na; Wu, Yiqun; Marshall, Roger; Li, Jingrong; Zhang, Zongxin; Liu, Jianjiang; Xu, Haitao; Yu, Liping; Hu, Yonghua

2014-01-10

Chinese menopausal women comprise a large population and the women in it experience menopausal symptoms in many different ways. Their health related quality of life (HRQOL) is not particularly well studied. Our study intends to evaluate the influence of menopause on HRQOL and explore other risk factors for HRQOL in rural China. An interview study was conducted from June to August 2010 in Beijing based on cross-sectional design. 1,351 women aged 40-59 were included in the study. HRQOL was measured using the EuroQol Group's 5-domain (EQ5D) questionnaire. Comparison of HRQOL measures (EQ5D index and EQ5D-VAS scores) was done between different menopausal groups. Logistic regression and multiple regression analysis were performed to adjust potential confounders and explore other risk factors for health problems and HRQOL measures. Postmenopausal women who had menopause for 2-5 years (+1b stage) were more likely to suffer mobility problems (OR = 1.835, p = 0.008) after multiple adjustment. Menopause was also related to impaired EQ5D index and EQ5D-VAS scores after adjustment for age. Among menopausal groups categorized by menopausal duration, a consistent decrement in EQ5D index and EQ5D-VAS scores, that is, worsening HRQOL, was observed (p < 0.05). Multiple regression analysis revealed low education level and physical activity were associated with EQ5D index (β = -0.080, p = 0.003, and β = 0.056, p = 0.040, respectively). Cigarette smoking and chronic disease were associated with EQ5D index (β = -0.135, p < 0.001 and β = -0.104, p < 0.001, respectively) and EQ5D-VAS (β = -0.057, P = 0.034 and β = -0.214, p < 0.001, respectively). Reduction in physical function was found within the first five years after menopause. Worsening EQ5D index and EQ5D-VAS scores were related to menopause. Education level, physical activity, cigarette smoking, and chronic disease history were associated with HRQOL in middle aged Chinese rural women.

SOCIAL STABILITY AND HIV RISK BEHAVIOR: EVALUATING THE ROLE OF ACCUMULATED VULNERABILITY

PubMed Central

German, Danielle; Latkin, Carl A.

2011-01-01

This study evaluated a cumulative and syndromic relationship among commonly co-occurring vulnerabilites (homelessness, incarceration, low-income, residential transition) in association with HIV-related risk behaviors among 635 low-income women in Baltimore. Analysis included descriptive statistics, logistic regression, latent class analysis and latent class regression. Both methods of assessing multidimensional instability showed significant associations with risk indicators. Risk of multiple partners, sex exchange, and drug use decreased significantly with each additional domain. Higher stability class membership (77%) was associated with decreased likelihood of multiple partners, exchange partners, recent drug use, and recent STI. Multidimensional social vulnerabilities were cumulatively and synergistically linked to HIV risk behavior. Independent instability measures may miss important contextual determinants of risk. Social stability offers a useful framework to understand the synergy of social vulnerabilities that shape sexual risk behavior. Social policies and programs aiming to enhance housing and overall social stability are likely to be beneficial for HIV prevention. PMID:21259043

Modeling Pan Evaporation for Kuwait by Multiple Linear Regression

PubMed Central

Almedeij, Jaber

2012-01-01

Evaporation is an important parameter for many projects related to hydrology and water resources systems. This paper constitutes the first study conducted in Kuwait to obtain empirical relations for the estimation of daily and monthly pan evaporation as functions of available meteorological data of temperature, relative humidity, and wind speed. The data used here for the modeling are daily measurements of substantial continuity coverage, within a period of 17 years between January 1993 and December 2009, which can be considered representative of the desert climate of the urban zone of the country. Multiple linear regression technique is used with a procedure of variable selection for fitting the best model forms. The correlations of evaporation with temperature and relative humidity are also transformed in order to linearize the existing curvilinear patterns of the data by using power and exponential functions, respectively. The evaporation models suggested with the best variable combinations were shown to produce results that are in a reasonable agreement with observation values. PMID:23226984

Relationship of physical activity to fundamental movement skills among adolescents.

PubMed

Okely, A D; Booth, M L; Patterson, J W

2001-11-01

To determine the relationship of participation in organized and nonorganized physical activity with fundamental movement skills among adolescents. Male and female children in Grade 8 (mean age, 13.3 yr) and Grade 10 (mean age, 15.3 yr) were assessed on six fundamental movement skills (run, vertical jump, catch, overhand throw, forehand strike, and kick). Physical activity was assessed using a self-report recall measure where students reported the type, duration, and frequency of participation in organized physical activity and nonorganized physical activity during a usual week. Multiple regression analysis indicated that fundamental movement skills significantly predicted time in organized physical activity, although the percentage of variance it could explain was small. This prediction was stronger for girls than for boys. Multiple regression analysis showed no relationship between time in nonorganized physical activity and fundamental movement skills. Fundamental movement skills are significantly associated with adolescents' participation in organized physical activity, but predict only a small portion of it.

[Childbirth pain, perinatal dissociation and perinatal distress as predictors of posttraumatic stress symptoms].

PubMed

Boudou, M; Séjourné, N; Chabrol, H

2007-11-01

This prospective, longitudinal study investigated the contributive role of childbirth pain, perinatal distress and perinatal dissociation to the development of PTSD symptoms following childbirth. One hundred and seventeen women participated at the study. The first day after delivery they completed a questionnaire to evaluate pain, the peritraumatic distress inventory (PDI) and the peritraumatic dissociative experience questionnaire (PDEQ). Six weeks after birth, they completed the impact of event scale-revised (IES-R) to measure posttraumatic stress symptoms and the Edinburgh Postnatal Depression Scale (EPDS) to assess maternal depression. A multiple regression analysis revealed that only both components of perinatal distress, life-threat perception and dysphoric emotions were significant predictors of posttraumatic stress symptoms. In another multiple regression analysis predicting dysphoric emotions, affective dimension of pain was the only significant predictor. Perinatal distress was the best predictor of posttraumatic stress symptoms. Dysphoric emotions were associated with affective dimension of pain, suggesting that women distressed by the childbirth pain would have higher risk to develop posttraumatic stress symptoms.

Systematic review of statistical approaches to quantify, or correct for, measurement error in a continuous exposure in nutritional epidemiology.

PubMed

Bennett, Derrick A; Landry, Denise; Little, Julian; Minelli, Cosetta

2017-09-19

Several statistical approaches have been proposed to assess and correct for exposure measurement error. We aimed to provide a critical overview of the most common approaches used in nutritional epidemiology. MEDLINE, EMBASE, BIOSIS and CINAHL were searched for reports published in English up to May 2016 in order to ascertain studies that described methods aimed to quantify and/or correct for measurement error for a continuous exposure in nutritional epidemiology using a calibration study. We identified 126 studies, 43 of which described statistical methods and 83 that applied any of these methods to a real dataset. The statistical approaches in the eligible studies were grouped into: a) approaches to quantify the relationship between different dietary assessment instruments and "true intake", which were mostly based on correlation analysis and the method of triads; b) approaches to adjust point and interval estimates of diet-disease associations for measurement error, mostly based on regression calibration analysis and its extensions. Two approaches (multiple imputation and moment reconstruction) were identified that can deal with differential measurement error. For regression calibration, the most common approach to correct for measurement error used in nutritional epidemiology, it is crucial to ensure that its assumptions and requirements are fully met. Analyses that investigate the impact of departures from the classical measurement error model on regression calibration estimates can be helpful to researchers in interpreting their findings. With regard to the possible use of alternative methods when regression calibration is not appropriate, the choice of method should depend on the measurement error model assumed, the availability of suitable calibration study data and the potential for bias due to violation of the classical measurement error model assumptions. On the basis of this review, we provide some practical advice for the use of methods to assess and adjust for measurement error in nutritional epidemiology.

Quantitative laser-induced breakdown spectroscopy data using peak area step-wise regression analysis: an alternative method for interpretation of Mars science laboratory results

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

Clegg, Samuel M; Barefield, James E; Wiens, Roger C

2008-01-01

The ChemCam instrument on the Mars Science Laboratory (MSL) will include a laser-induced breakdown spectrometer (LIBS) to quantify major and minor elemental compositions. The traditional analytical chemistry approach to calibration curves for these data regresses a single diagnostic peak area against concentration for each element. This approach contrasts with a new multivariate method in which elemental concentrations are predicted by step-wise multiple regression analysis based on areas of a specific set of diagnostic peaks for each element. The method is tested on LIBS data from igneous and metamorphosed rocks. Between 4 and 13 partial regression coefficients are needed to describemore » each elemental abundance accurately (i.e., with a regression line of R{sup 2} > 0.9995 for the relationship between predicted and measured elemental concentration) for all major and minor elements studied. Validation plots suggest that the method is limited at present by the small data set, and will work best for prediction of concentration when a wide variety of compositions and rock types has been analyzed.« less

Assessing risk factors for periodontitis using regression

NASA Astrophysics Data System (ADS)

Lobo Pereira, J. A.; Ferreira, Maria Cristina; Oliveira, Teresa

2013-10-01

Multivariate statistical analysis is indispensable to assess the associations and interactions between different factors and the risk of periodontitis. Among others, regression analysis is a statistical technique widely used in healthcare to investigate and model the relationship between variables. In our work we study the impact of socio-demographic, medical and behavioral factors on periodontal health. Using regression, linear and logistic models, we can assess the relevance, as risk factors for periodontitis disease, of the following independent variables (IVs): Age, Gender, Diabetic Status, Education, Smoking status and Plaque Index. The multiple linear regression analysis model was built to evaluate the influence of IVs on mean Attachment Loss (AL). Thus, the regression coefficients along with respective p-values will be obtained as well as the respective p-values from the significance tests. The classification of a case (individual) adopted in the logistic model was the extent of the destruction of periodontal tissues defined by an Attachment Loss greater than or equal to 4 mm in 25% (AL≥4mm/≥25%) of sites surveyed. The association measures include the Odds Ratios together with the correspondent 95% confidence intervals.

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

NASA Astrophysics Data System (ADS)

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

2015-04-01

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

Depression is a predictor for balance in people with multiple sclerosis.

PubMed

Alghwiri, Alia A; Khalil, Hanan; Al-Sharman, Alham; El-Salem, Khalid

2018-05-26

Balance impairments are common and multifactorial among people with multiple sclerosis (MS). Depression is the most common psychological disorder in MS population and is strongly correlated with MS disease. Depression might be one of the factors that contribute to balance deficits in this population. However, the relationship between depression and balance impairments has not been explored in people with MS. To investigate the association between depression and balance impairments in people with MS. Cross sectional design was used in patients with MS. The Activities-specific Balance Confidence scale (ABC) and Berg Balance Scale (BBS) was used to assess balance. Beck Depression Inventory (BDI-II) was used to quantify depression and Kurtizki Expanded Disability Status Scale (EDSS) was utilized for the evaluation of MS disability severity. Pearson correlation coefficient was used to examine the association between depression and balance measurements. Multiple linear stepwise regressions were also conducted to find out if depression is a potential predictor for balance deficits. Seventy-five individuals with MS (Female = 69%) with a mean age (SD) of 38.8 (10) and a mean (SD) EDSS score of 3.0 (1.4) were recruited in this study. Depression was present in 53% of the patients. Depression was significantly correlated with balance measurements and EDSS. However, multiple linear stepwise regressions found that only depression and age significantly predict balance. Depression and balance were found frequent and associated in people with MS. Importantly depression was a significant predictor for balance impairments in individuals with MS. Balance rehabilitation may be hindered by depression. Therefore, depression should be evaluated and treated properly in individuals with MS. Copyright © 2018 Elsevier B.V. All rights reserved.

Multiple Imputation of a Randomly Censored Covariate Improves Logistic Regression Analysis.

PubMed

Atem, Folefac D; Qian, Jing; Maye, Jacqueline E; Johnson, Keith A; Betensky, Rebecca A

2016-01-01

Randomly censored covariates arise frequently in epidemiologic studies. The most commonly used methods, including complete case and single imputation or substitution, suffer from inefficiency and bias. They make strong parametric assumptions or they consider limit of detection censoring only. We employ multiple imputation, in conjunction with semi-parametric modeling of the censored covariate, to overcome these shortcomings and to facilitate robust estimation. We develop a multiple imputation approach for randomly censored covariates within the framework of a logistic regression model. We use the non-parametric estimate of the covariate distribution or the semiparametric Cox model estimate in the presence of additional covariates in the model. We evaluate this procedure in simulations, and compare its operating characteristics to those from the complete case analysis and a survival regression approach. We apply the procedures to an Alzheimer's study of the association between amyloid positivity and maternal age of onset of dementia. Multiple imputation achieves lower standard errors and higher power than the complete case approach under heavy and moderate censoring and is comparable under light censoring. The survival regression approach achieves the highest power among all procedures, but does not produce interpretable estimates of association. Multiple imputation offers a favorable alternative to complete case analysis and ad hoc substitution methods in the presence of randomly censored covariates within the framework of logistic regression.

Cyst-based measurements for assessing lymphangioleiomyomatosis in computed tomography

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

Lo, P., E-mail: pechinlo@mednet.edu.ucla; Brown, M. S.; Kim, H.

Purpose: To investigate the efficacy of a new family of measurements made on individual pulmonary cysts extracted from computed tomography (CT) for assessing the severity of lymphangioleiomyomatosis (LAM). Methods: CT images were analyzed using thresholding to identify a cystic region of interest from chest CT of LAM patients. Individual cysts were then extracted from the cystic region by the watershed algorithm, which separates individual cysts based on subtle edges within the cystic regions. A family of measurements were then computed, which quantify the amount, distribution, and boundary appearance of the cysts. Sequential floating feature selection was used to select amore » small subset of features for quantification of the severity of LAM. Adjusted R{sup 2} from multiple linear regression and R{sup 2} from linear regression against measurements from spirometry were used to compare the performance of our proposed measurements with currently used density based CT measurements in the literature, namely, the relative area measure and the D measure. Results: Volumetric CT data, performed at total lung capacity and residual volume, from a total of 49 subjects enrolled in the MILES trial were used in our study. Our proposed measures had adjusted R{sup 2} ranging from 0.42 to 0.59 when regressing against the spirometry measures, with p < 0.05. For previously used density based CT measurements in the literature, the best R{sup 2} was 0.46 (for only one instance), with the majority being lower than 0.3 or p > 0.05. Conclusions: The proposed family of CT-based cyst measurements have better correlation with spirometric measures than previously used density based CT measurements. They show potential as a sensitive tool for quantitatively assessing the severity of LAM.« less

[Establishment of cervical vertebral skeletal maturation of female children in Shanghai].

PubMed

Sun, Yan; Chen, Rong-jing; Yu, Quan; Fan, Li; Chen, Wei; Shen, Gang

2009-06-01

To establish a method for quantitatively evaluating skeletal maturation of cervical vertebrae of female children in Shanghai. The samples were selected from lateral cephalometric radiographs of 240 Shanghai girls, aged 8 to 15 years. The parameters were measured to indicate the morphological changes of the third (C3) and fourth (C4) vertebrae in width, height and the depth of the inferior curvature. The independent-sample t test and stepwise multiple regression analysis were used to estimate the growth status and the ratios of C3, C4 cervical vertebrae by SPSS 15.0 software package. The physical and morphological contour of C3, C4 cervical vertebrae increased proportionately with the increment of age. The regression formula for indicating cervical vertebral skeletal age of female children in Shanghai was expressed by the equation Y= -5.696+8.010 AH3/AP3+6.654 AH3/H3+6.045AH4/PH4 (r=0.912). The regression formula resulted from morphological measurements quantitatively indicates the skeletal maturation of cervical vertebrae of female children in Shanghai.

Estimation of PM2.5 and PM10 using ground-based AOD measurements during KORUS-AQ campaign

NASA Astrophysics Data System (ADS)

Koo, J. H.; Kim, J.; Kim, S.; Go, S.; Lee, S.; Lee, H.; Mok, J.; Hong, J.; Lee, J.; Eck, T. F.; Holben, B. N.

2017-12-01

During the KORUS-AQ campaign (2 May - 12 June, 2016), aerosol optical depth (AOD) was obtained at multiple channels using various ground-based instruments at Yonsei University, Seoul: AERONET sunphotometer, SKYNET skyradiometer, Brewer spectrophotometer, and multi-filter rotating shadowband radiometer (MFRSR). At the same location, planetary boundary layer (PBL) height and vertical profile of backscattering coefficients also can be obtained based on the celiometer measurements. Using celiometer products and various AODs, we try to estimate the amount of particular matter (PM2.5 and PM10) and validate with in-situ surface PM2.5 and PM10 measurements from AIRKOREA network. Direct comparison between PM2.5 and AOD reveals that the ultraviolet(UV) channel AOD has better correlations, due to the higher sensitivity of short wavelength to the fine-mode particle. In contrast, PM10 shows the highest correlation with the near-infrared(NIR) AOD. Next, we extract the boundary-layer portion of AOD using either PBL height or vertical profile of backscattering coefficients to compare with PM2.5 and PM10. Both results enhance the correlation, but consideration of weighting factor calculated from backscattering coefficients shows larger contribution to the correlation increase. Finally, we performed the multiple linear regression to estimate PM2.5 and PM10 using AODs. Consideration of meteorology (temperature, wind speed, and relative humidity) can enhance the correlation and also O3 and NO2 consideration highly contributes to the high correlation. This finding implies the importance to consider the ambient condition of secondary aerosol formation related to the PM2.5 variation. Multiple regression model finally finds the correlation 0.7-0.8, and diminishes the wavelength-dependent correlation patterns.

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.

Use of Thematic Mapper for water quality assessment

NASA Technical Reports Server (NTRS)

Horn, E. M.; Morrissey, L. A.

1984-01-01

The evaluation of simulated TM data obtained on an ER-2 aircraft at twenty-five predesignated sample sites for mapping water quality factors such as conductivity, pH, suspended solids, turbidity, temperature, and depth, is discussed. Using a multiple regression for the seven TM bands, an equation is developed for the suspended solids. TM bands 1, 2, 3, 4, and 6 are used with logarithm conductivity in a multiple regression. The assessment of regression equations for a high coefficient of determination (R-squared) and statistical significance is considered. Confidence intervals about the mean regression point are calculated in order to assess the robustness of the regressions used for mapping conductivity, turbidity, and suspended solids, and by regressing random subsamples of sites and comparing the resultant range of R-squared, cross validation is conducted.

INTRODUCTION TO A COMBINED MULTIPLE LINEAR REGRESSION AND ARMA MODELING APPROACH FOR BEACH BACTERIA PREDICTION

EPA Science Inventory

Due to the complexity of the processes contributing to beach bacteria concentrations, many researchers rely on statistical modeling, among which multiple linear regression (MLR) modeling is most widely used. Despite its ease of use and interpretation, there may be time dependence...

Determining the Spatial and Seasonal Variability in OM/OC Ratios across the U.S. Using Multiple Regression

EPA Science Inventory

Data from the Interagency Monitoring of Protected Visual Environments (IMPROVE) network are used to estimate organic mass to organic carbon (OM/OC) ratios across the United States by extending previously published multiple regression techniques. Our new methodology addresses com...

Analysis and Interpretation of Findings Using Multiple Regression Techniques

ERIC Educational Resources Information Center

Hoyt, William T.; Leierer, Stephen; Millington, Michael J.

2006-01-01

Multiple regression and correlation (MRC) methods form a flexible family of statistical techniques that can address a wide variety of different types of research questions of interest to rehabilitation professionals. In this article, we review basic concepts and terms, with an emphasis on interpretation of findings relevant to research questions…

Tracking the Gender Pay Gap: A Case Study

ERIC Educational Resources Information Center

Travis, Cheryl B.; Gross, Louis J.; Johnson, Bruce A.

2009-01-01

This article provides a short introduction to standard considerations in the formal study of wages and illustrates the use of multiple regression and resampling simulation approaches in a case study of faculty salaries at one university. Multiple regression is especially beneficial where it provides information on strength of association, specific…

Estimating air drying times of lumber with multiple regression

Treesearch

William T. Simpson

2004-01-01

In this study, the applicability of a multiple regression equation for estimating air drying times of red oak, sugar maple, and ponderosa pine lumber was evaluated. The equation allows prediction of estimated air drying times from historic weather records of temperature and relative humidity at any desired location.

Using Robust Variance Estimation to Combine Multiple Regression Estimates with Meta-Analysis

ERIC Educational Resources Information Center

Williams, Ryan

2013-01-01

The purpose of this study was to explore the use of robust variance estimation for combining commonly specified multiple regression models and for combining sample-dependent focal slope estimates from diversely specified models. The proposed estimator obviates traditionally required information about the covariance structure of the dependent…

Multiple Regression: A Leisurely Primer.

ERIC Educational Resources Information Center

Daniel, Larry G.; Onwuegbuzie, Anthony J.

Multiple regression is a useful statistical technique when the researcher is considering situations in which variables of interest are theorized to be multiply caused. It may also be useful in those situations in which the researchers is interested in studies of predictability of phenomena of interest. This paper provides an introduction to…

Using Monte Carlo Techniques to Demonstrate the Meaning and Implications of Multicollinearity

ERIC Educational Resources Information Center

Vaughan, Timothy S.; Berry, Kelly E.

2005-01-01

This article presents an in-class Monte Carlo demonstration, designed to demonstrate to students the implications of multicollinearity in a multiple regression study. In the demonstration, students already familiar with multiple regression concepts are presented with a scenario in which the "true" relationship between the response and…

Assessing the Impact of Influential Observations on Multiple Regression Analysis on Human Resource Research.

ERIC Educational Resources Information Center

Bates, Reid A.; Holton, Elwood F., III; Burnett, Michael F.

1999-01-01

A case study of learning transfer demonstrates the possible effect of influential observation on linear regression analysis. A diagnostic method that tests for violation of assumptions, multicollinearity, and individual and multiple influential observations helps determine which observation to delete to eliminate bias. (SK)

Predictive Utility of Marketed Volumetric Software Tools in Subjects at Risk for Alzheimer Disease: Do Regions Outside the Hippocampus Matter?

PubMed

Tanpitukpongse, T P; Mazurowski, M A; Ikhena, J; Petrella, J R

2017-03-01

Alzheimer disease is a prevalent neurodegenerative disease. Computer assessment of brain atrophy patterns can help predict conversion to Alzheimer disease. Our aim was to assess the prognostic efficacy of individual-versus-combined regional volumetrics in 2 commercially available brain volumetric software packages for predicting conversion of patients with mild cognitive impairment to Alzheimer disease. Data were obtained through the Alzheimer's Disease Neuroimaging Initiative. One hundred ninety-two subjects (mean age, 74.8 years; 39% female) diagnosed with mild cognitive impairment at baseline were studied. All had T1-weighted MR imaging sequences at baseline and 3-year clinical follow-up. Analysis was performed with NeuroQuant and Neuroreader. Receiver operating characteristic curves assessing the prognostic efficacy of each software package were generated by using a univariable approach using individual regional brain volumes and 2 multivariable approaches (multiple regression and random forest), combining multiple volumes. On univariable analysis of 11 NeuroQuant and 11 Neuroreader regional volumes, hippocampal volume had the highest area under the curve for both software packages (0.69, NeuroQuant; 0.68, Neuroreader) and was not significantly different ( P > .05) between packages. Multivariable analysis did not increase the area under the curve for either package (0.63, logistic regression; 0.60, random forest NeuroQuant; 0.65, logistic regression; 0.62, random forest Neuroreader). Of the multiple regional volume measures available in FDA-cleared brain volumetric software packages, hippocampal volume remains the best single predictor of conversion of mild cognitive impairment to Alzheimer disease at 3-year follow-up. Combining volumetrics did not add additional prognostic efficacy. Therefore, future prognostic studies in mild cognitive impairment, combining such tools with demographic and other biomarker measures, are justified in using hippocampal volume as the only volumetric biomarker. © 2017 by American Journal of Neuroradiology.

Characterizing stand-level forest canopy cover and height using Landsat time series, samples of airborne LiDAR, and the Random Forest algorithm

NASA Astrophysics Data System (ADS)

Ahmed, Oumer S.; Franklin, Steven E.; Wulder, Michael A.; White, Joanne C.

2015-03-01

Many forest management activities, including the development of forest inventories, require spatially detailed forest canopy cover and height data. Among the various remote sensing technologies, LiDAR (Light Detection and Ranging) offers the most accurate and consistent means for obtaining reliable canopy structure measurements. A potential solution to reduce the cost of LiDAR data, is to integrate transects (samples) of LiDAR data with frequently acquired and spatially comprehensive optical remotely sensed data. Although multiple regression is commonly used for such modeling, often it does not fully capture the complex relationships between forest structure variables. This study investigates the potential of Random Forest (RF), a machine learning technique, to estimate LiDAR measured canopy structure using a time series of Landsat imagery. The study is implemented over a 2600 ha area of industrially managed coastal temperate forests on Vancouver Island, British Columbia, Canada. We implemented a trajectory-based approach to time series analysis that generates time since disturbance (TSD) and disturbance intensity information for each pixel and we used this information to stratify the forest land base into two strata: mature forests and young forests. Canopy cover and height for three forest classes (i.e. mature, young and mature and young (combined)) were modeled separately using multiple regression and Random Forest (RF) techniques. For all forest classes, the RF models provided improved estimates relative to the multiple regression models. The lowest validation error was obtained for the mature forest strata in a RF model (R2 = 0.88, RMSE = 2.39 m and bias = -0.16 for canopy height; R2 = 0.72, RMSE = 0.068% and bias = -0.0049 for canopy cover). This study demonstrates the value of using disturbance and successional history to inform estimates of canopy structure and obtain improved estimates of forest canopy cover and height using the RF algorithm.

Relationship among several measurements of slipperiness obtained in a laboratory environment.

PubMed

Chang, Wen-Ruey; Chang, Chien-Chi

2018-04-01

Multiple sensing mechanisms could be used in forming responses to avoid slips, but previous studies, correlating only two parameters, revealed a limited picture of this complex system. In this study, the participants walked as fast as possible without a slip under 15 conditions of different degrees of slipperiness. The relationships among various response parameters, including perceived slipperiness rating, utilized coefficient of friction (UCOF), slipmeter measurement and kinematic parameters, were evaluated. The results showed that the UCOF, perceived rating and heel angle had higher adjusted R 2 values as dependent variables in the multiple linear regressions with the remaining variables in the final pool as independent variables. Although each variable in the final data pool could reflect some measurement of slipperiness, these three variables are more inclusive than others in representing the other variables and were bigger predictors of other variables, so they could be better candidates for measurements of slipperiness. Copyright © 2017 Elsevier Ltd. All rights reserved.

Estimating a child's age from an image using whole body proportions.

PubMed

Lucas, Teghan; Henneberg, Maciej

2017-09-01

The use and distribution of child pornography is an increasing problem. Forensic anthropologists are often asked to estimate a child's age from a photograph. Previous studies have attempted to estimate the age of children from photographs using ratios of the face. Here, we propose to include body measurement ratios into age estimates. A total of 1603 boys and 1833 girls aged 5-16 years were measured over a 10-year period. They are 'Cape Coloured' children from South Africa. Their age was regressed on ratios derived from anthropometric measurements of the head as well as the body. Multiple regression equations including four ratios for each sex (head height to shoulder and hip width, knee width, leg length and trunk length) have a standard error of 1.6-1.7 years. The error is of the same order as variation of differences between biological and chronological ages of the children. Thus, the error cannot be minimised any further as it is a direct reflection of a naturally occurring phenomenon.

Improved determination of particulate absorption from combined filter pad and PSICAM measurements.

PubMed

Lefering, Ina; Röttgers, Rüdiger; Weeks, Rebecca; Connor, Derek; Utschig, Christian; Heymann, Kerstin; McKee, David

2016-10-31

Filter pad light absorption measurements are subject to two major sources of experimental uncertainty: the so-called pathlength amplification factor, β, and scattering offsets, o, for which previous null-correction approaches are limited by recent observations of non-zero absorption in the near infrared (NIR). A new filter pad absorption correction method is presented here which uses linear regression against point-source integrating cavity absorption meter (PSICAM) absorption data to simultaneously resolve both β and the scattering offset. The PSICAM has previously been shown to provide accurate absorption data, even in highly scattering waters. Comparisons of PSICAM and filter pad particulate absorption data reveal linear relationships that vary on a sample by sample basis. This regression approach provides significantly improved agreement with PSICAM data (3.2% RMS%E) than previously published filter pad absorption corrections. Results show that direct transmittance (T-method) filter pad absorption measurements perform effectively at the same level as more complex geometrical configurations based on integrating cavity measurements (IS-method and QFT-ICAM) because the linear regression correction compensates for the sensitivity to scattering errors in the T-method. This approach produces accurate filter pad particulate absorption data for wavelengths in the blue/UV and in the NIR where sensitivity issues with PSICAM measurements limit performance. The combination of the filter pad absorption and PSICAM is therefore recommended for generating full spectral, best quality particulate absorption data as it enables correction of multiple errors sources across both measurements.

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

PubMed

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

2015-10-01

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

Age estimation standards for a Western Australian population using the coronal pulp cavity index.

PubMed

Karkhanis, Shalmira; Mack, Peter; Franklin, Daniel

2013-09-10

Age estimation is a vital aspect in creating a biological profile and aids investigators by narrowing down potentially matching identities from the available pool. In addition to routine casework, in the present global political scenario, age estimation in living individuals is required in cases of refugees, asylum seekers, human trafficking and to ascertain age of criminal responsibility. Thus robust methods that are simple, non-invasive and ethically viable are required. The aim of the present study is, therefore, to test the reliability and applicability of the coronal pulp cavity index method, for the purpose of developing age estimation standards for an adult Western Australian population. A total of 450 orthopantomograms (220 females and 230 males) of Australian individuals were analyzed. Crown and coronal pulp chamber heights were measured in the mandibular left and right premolars, and the first and second molars. These measurements were then used to calculate the tooth coronal index. Data was analyzed using paired sample t-tests to assess bilateral asymmetry followed by simple linear and multiple regressions to develop age estimation models. The most accurate age estimation based on simple linear regression model was with mandibular right first molar (SEE ±8.271 years). Multiple regression models improved age prediction accuracy considerably and the most accurate model was with bilateral first and second molars (SEE ±6.692 years). This study represents the first investigation of this method in a Western Australian population and our results indicate that the method is suitable for forensic application. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Empirical predictive models of daily relativistic electron flux at geostationary orbit: Multiple regression analysis

DOE PAGES

Simms, Laura E.; Engebretson, Mark J.; Pilipenko, Viacheslav; ...

2016-04-07

The daily maximum relativistic electron flux at geostationary orbit can be predicted well with a set of daily averaged predictor variables including previous day's flux, seed electron flux, solar wind velocity and number density, AE index, IMF Bz, Dst, and ULF and VLF wave power. As predictor variables are intercorrelated, we used multiple regression analyses to determine which are the most predictive of flux when other variables are controlled. Empirical models produced from regressions of flux on measured predictors from 1 day previous were reasonably effective at predicting novel observations. Adding previous flux to the parameter set improves the predictionmore » of the peak of the increases but delays its anticipation of an event. Previous day's solar wind number density and velocity, AE index, and ULF wave activity are the most significant explanatory variables; however, the AE index, measuring substorm processes, shows a negative correlation with flux when other parameters are controlled. This may be due to the triggering of electromagnetic ion cyclotron waves by substorms that cause electron precipitation. VLF waves show lower, but significant, influence. The combined effect of ULF and VLF waves shows a synergistic interaction, where each increases the influence of the other on flux enhancement. Correlations between observations and predictions for this 1 day lag model ranged from 0.71 to 0.89 (average: 0.78). Furthermore, a path analysis of correlations between predictors suggests that solar wind and IMF parameters affect flux through intermediate processes such as ring current ( Dst), AE, and wave activity.« less

Empirical predictive models of daily relativistic electron flux at geostationary orbit: Multiple regression analysis

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

Simms, Laura E.; Engebretson, Mark J.; Pilipenko, Viacheslav

The daily maximum relativistic electron flux at geostationary orbit can be predicted well with a set of daily averaged predictor variables including previous day's flux, seed electron flux, solar wind velocity and number density, AE index, IMF Bz, Dst, and ULF and VLF wave power. As predictor variables are intercorrelated, we used multiple regression analyses to determine which are the most predictive of flux when other variables are controlled. Empirical models produced from regressions of flux on measured predictors from 1 day previous were reasonably effective at predicting novel observations. Adding previous flux to the parameter set improves the predictionmore » of the peak of the increases but delays its anticipation of an event. Previous day's solar wind number density and velocity, AE index, and ULF wave activity are the most significant explanatory variables; however, the AE index, measuring substorm processes, shows a negative correlation with flux when other parameters are controlled. This may be due to the triggering of electromagnetic ion cyclotron waves by substorms that cause electron precipitation. VLF waves show lower, but significant, influence. The combined effect of ULF and VLF waves shows a synergistic interaction, where each increases the influence of the other on flux enhancement. Correlations between observations and predictions for this 1 day lag model ranged from 0.71 to 0.89 (average: 0.78). Furthermore, a path analysis of correlations between predictors suggests that solar wind and IMF parameters affect flux through intermediate processes such as ring current ( Dst), AE, and wave activity.« less

Color vision impairment in multiple sclerosis points to retinal ganglion cell damage.

PubMed

Lampert, E J; Andorra, M; Torres-Torres, R; Ortiz-Pérez, S; Llufriu, S; Sepúlveda, M; Sola, N; Saiz, A; Sánchez-Dalmau, B; Villoslada, P; Martínez-Lapiscina, Elena H

2015-11-01

Multiple Sclerosis (MS) results in color vision impairment regardless of optic neuritis (ON). The exact location of injury remains undefined. The objective of this study is to identify the region leading to dyschromatopsia in MS patients' NON-eyes. We evaluated Spearman correlations between color vision and measures of different regions in the afferent visual pathway in 106 MS patients. Regions with significant correlations were included in logistic regression models to assess their independent role in dyschromatopsia. We evaluated color vision with Hardy-Rand-Rittler plates and retinal damage using Optical Coherence Tomography. We ran SIENAX to measure Normalized Brain Parenchymal Volume (NBPV), FIRST for thalamus volume and Freesurfer for visual cortex areas. We found moderate, significant correlations between color vision and macular retinal nerve fiber layer (rho = 0.289, p = 0.003), ganglion cell complex (GCC = GCIP) (rho = 0.353, p < 0.001), thalamus (rho = 0.361, p < 0.001), and lesion volume within the optic radiations (rho = -0.230, p = 0.030). Only GCC thickness remained significant (p = 0.023) in the logistic regression model. In the final model including lesion load and NBPV as markers of diffuse neuroaxonal damage, GCC remained associated with dyschromatopsia [OR = 0.88 95 % CI (0.80-0.97) p = 0.016]. This association remained significant when we also added sex, age, and disease duration as covariates in the regression model. Dyschromatopsia in NON-eyes is due to damage of retinal ganglion cells (RGC) in MS. Color vision can serve as a marker of RGC damage in MS.

Age differences in cognitive performance in later life: relationships to self-reported health and activity life style.

PubMed

Hultsch, D F; Hammer, M; Small, B J

1993-01-01

The predictive relationships among individual differences in self-reported physical health and activity life style and performance on an array of information processing and intellectual ability measures were examined. A sample of 484 men and women aged 55 to 86 years completed a battery of cognitive tasks measuring verbal processing time, working memory, vocabulary, verbal fluency, world knowledge, word recall, and text recall. Hierarchical regression was used to predict performance on these tasks from measures of self-reported physical health, alcohol and tobacco use, and level of participation in everyday activities. The results indicated: (a) individual differences in self-reported health and activity predicted performance on multiple cognitive measures; (b) self-reported health was more predictive of processing resource variables than knowledge-based abilities; (c) interaction effects indicated that participation in cognitively demanding activities was more highly related to performance on some measures for older adults than for middle-aged adults; and (d) age-related differences in performance on multiple measures were attenuated by partialing individual differences in self-reported health and activity.

Application of Linear Mixed-Effects Models in Human Neuroscience Research: A Comparison with Pearson Correlation in Two Auditory Electrophysiology Studies.

PubMed

Koerner, Tess K; Zhang, Yang

2017-02-27

Neurophysiological studies are often designed to examine relationships between measures from different testing conditions, time points, or analysis techniques within the same group of participants. Appropriate statistical techniques that can take into account repeated measures and multivariate predictor variables are integral and essential to successful data analysis and interpretation. This work implements and compares conventional Pearson correlations and linear mixed-effects (LME) regression models using data from two recently published auditory electrophysiology studies. For the specific research questions in both studies, the Pearson correlation test is inappropriate for determining strengths between the behavioral responses for speech-in-noise recognition and the multiple neurophysiological measures as the neural responses across listening conditions were simply treated as independent measures. In contrast, the LME models allow a systematic approach to incorporate both fixed-effect and random-effect terms to deal with the categorical grouping factor of listening conditions, between-subject baseline differences in the multiple measures, and the correlational structure among the predictor variables. Together, the comparative data demonstrate the advantages as well as the necessity to apply mixed-effects models to properly account for the built-in relationships among the multiple predictor variables, which has important implications for proper statistical modeling and interpretation of human behavior in terms of neural correlates and biomarkers.

Impact of cataract surgery on vision-related life performances: the usefulness of Real-Life Vision Test for cataract surgery outcomes evaluation

PubMed Central

Ni, W; Li, X; Hou, Z; Zhang, H; Qiu, W; Wang, W

2015-01-01

Purpose Real-Life Vision Test (RLVT) is a newly developed performance-based measures of functional vision. This present study is designed to determine whether it could be a meaningful assessment for cataract surgery outcomes evaluation. Patients and methods Age-related cataract patients (56) who scheduled for bilateral cataract surgery and 44 age-matched controls were evaluated by four types of measurements: (1) demographic, medical, cognitive and depressive evaluation, and the reaction time testing; (2) clinical measures (visual acuity, contrast sensitivity, stereopsis, and color perception); (3) the 25-item National Eye Institute's Visual Functioning Questionnaire (NEI-VFQ); (4) the RLVT. Spearman's coefficients and multiple regression analysis were conducted to investigate the relationship among RLVT, clinical measures, and self-report assessment of visual function. Results The results of RLVT, clinical measures, and NEI-VFQ total scores were improved significantly after cataract surgery. There were no differences between control subjects and post-surgery patients with respect to NEI-VFQ-25 total scores, self-rating depression scale scores and three tasks of RLVT. Change of RLVT was significantly associated with the change of clinical measures in the cataract group. Multiple regression analysis demonstrated that change of distance, intermediate, and near visual acuity, and binocular contrast sensitivity were significant predictors of improvements of RLVT. Conclusions Cataract surgery could improve real-world visual ability effectively for cataract patients. Our study highlights the potential usefulness of RLVT as an adjunct to the current outcomes evaluation system for cataract surgery. The use of RLVT combined with clinical and self-survey methods may be the comprehensive strategy to manifest the impact of cataract surgery on patients' overall vision-related quality of life. PMID:26272444

Parsimonious model for blood glucose level monitoring in type 2 diabetes patients.

PubMed

Zhao, Fang; Ma, Yan Fen; Wen, Jing Xiao; DU, Yan Fang; Li, Chun Lin; Li, Guang Wei

2014-07-01

To establish the parsimonious model for blood glucose monitoring in patients with type 2 diabetes receiving oral hypoglycemic agent treatment. One hundred and fifty-nine adult Chinese type 2 diabetes patients were randomized to receive rapid-acting or sustained-release gliclazide therapy for 12 weeks. Their blood glucose levels were measured at 10 time points in a 24 h period before and after treatment, and the 24 h mean blood glucose levels were measured. Contribution of blood glucose levels to the mean blood glucose level and HbA1c was assessed by multiple regression analysis. The correlation coefficients of blood glucose level measured at 10 time points to the daily MBG were 0.58-0.74 and 0.59-0.79, respectively, before and after treatment (P<0.0001). The multiple stepwise regression analysis showed that the blood glucose levels measured at 6 of the 10 time points could explain 95% and 97% of the changes in MBG before and after treatment. The three blood glucose levels, which were measured at fasting, 2 h after breakfast and before dinner, of the 10 time points could explain 84% and 86% of the changes in MBG before and after treatment, but could only explain 36% and 26% of the changes in HbA1c before and after treatment, and they had a poorer correlation with the HbA1c than with the 24 h MBG. The blood glucose levels measured at fasting, 2 h after breakfast and before dinner truly reflected the change 24 h blood glucose level, suggesting that they are appropriate for the self-monitoring of blood glucose levels in diabetes patients receiving oral anti-diabetes therapy. Copyright © 2014 The Editorial Board of Biomedical and Environmental Sciences. Published by China CDC. All rights reserved.

Assessing Risk Prediction Models Using Individual Participant Data From Multiple Studies

PubMed Central

Pennells, Lisa; Kaptoge, Stephen; White, Ian R.; Thompson, Simon G.; Wood, Angela M.; Tipping, Robert W.; Folsom, Aaron R.; Couper, David J.; Ballantyne, Christie M.; Coresh, Josef; Goya Wannamethee, S.; Morris, Richard W.; Kiechl, Stefan; Willeit, Johann; Willeit, Peter; Schett, Georg; Ebrahim, Shah; Lawlor, Debbie A.; Yarnell, John W.; Gallacher, John; Cushman, Mary; Psaty, Bruce M.; Tracy, Russ; Tybjærg-Hansen, Anne; Price, Jackie F.; Lee, Amanda J.; McLachlan, Stela; Khaw, Kay-Tee; Wareham, Nicholas J.; Brenner, Hermann; Schöttker, Ben; Müller, Heiko; Jansson, Jan-Håkan; Wennberg, Patrik; Salomaa, Veikko; Harald, Kennet; Jousilahti, Pekka; Vartiainen, Erkki; Woodward, Mark; D'Agostino, Ralph B.; Bladbjerg, Else-Marie; Jørgensen, Torben; Kiyohara, Yutaka; Arima, Hisatomi; Doi, Yasufumi; Ninomiya, Toshiharu; Dekker, Jacqueline M.; Nijpels, Giel; Stehouwer, Coen D. A.; Kauhanen, Jussi; Salonen, Jukka T.; Meade, Tom W.; Cooper, Jackie A.; Cushman, Mary; Folsom, Aaron R.; Psaty, Bruce M.; Shea, Steven; Döring, Angela; Kuller, Lewis H.; Grandits, Greg; Gillum, Richard F.; Mussolino, Michael; Rimm, Eric B.; Hankinson, Sue E.; Manson, JoAnn E.; Pai, Jennifer K.; Kirkland, Susan; Shaffer, Jonathan A.; Shimbo, Daichi; Bakker, Stephan J. L.; Gansevoort, Ron T.; Hillege, Hans L.; Amouyel, Philippe; Arveiler, Dominique; Evans, Alun; Ferrières, Jean; Sattar, Naveed; Westendorp, Rudi G.; Buckley, Brendan M.; Cantin, Bernard; Lamarche, Benoît; Barrett-Connor, Elizabeth; Wingard, Deborah L.; Bettencourt, Richele; Gudnason, Vilmundur; Aspelund, Thor; Sigurdsson, Gunnar; Thorsson, Bolli; Kavousi, Maryam; Witteman, Jacqueline C.; Hofman, Albert; Franco, Oscar H.; Howard, Barbara V.; Zhang, Ying; Best, Lyle; Umans, Jason G.; Onat, Altan; Sundström, Johan; Michael Gaziano, J.; Stampfer, Meir; Ridker, Paul M.; Michael Gaziano, J.; Ridker, Paul M.; Marmot, Michael; Clarke, Robert; Collins, Rory; Fletcher, Astrid; Brunner, Eric; Shipley, Martin; Kivimäki, Mika; Ridker, Paul M.; Buring, Julie; Cook, Nancy; Ford, Ian; Shepherd, James; Cobbe, Stuart M.; Robertson, Michele; Walker, Matthew; Watson, Sarah; Alexander, Myriam; Butterworth, Adam S.; Angelantonio, Emanuele Di; Gao, Pei; Haycock, Philip; Kaptoge, Stephen; Pennells, Lisa; Thompson, Simon G.; Walker, Matthew; Watson, Sarah; White, Ian R.; Wood, Angela M.; Wormser, David; Danesh, John

2014-01-01

Individual participant time-to-event data from multiple prospective epidemiologic studies enable detailed investigation into the predictive ability of risk models. Here we address the challenges in appropriately combining such information across studies. Methods are exemplified by analyses of log C-reactive protein and conventional risk factors for coronary heart disease in the Emerging Risk Factors Collaboration, a collation of individual data from multiple prospective studies with an average follow-up duration of 9.8 years (dates varied). We derive risk prediction models using Cox proportional hazards regression analysis stratified by study and obtain estimates of risk discrimination, Harrell's concordance index, and Royston's discrimination measure within each study; we then combine the estimates across studies using a weighted meta-analysis. Various weighting approaches are compared and lead us to recommend using the number of events in each study. We also discuss the calculation of measures of reclassification for multiple studies. We further show that comparison of differences in predictive ability across subgroups should be based only on within-study information and that combining measures of risk discrimination from case-control studies and prospective studies is problematic. The concordance index and discrimination measure gave qualitatively similar results throughout. While the concordance index was very heterogeneous between studies, principally because of differing age ranges, the increments in the concordance index from adding log C-reactive protein to conventional risk factors were more homogeneous. PMID:24366051

Assessing risk prediction models using individual participant data from multiple studies.

PubMed

Pennells, Lisa; Kaptoge, Stephen; White, Ian R; Thompson, Simon G; Wood, Angela M

2014-03-01

Individual participant time-to-event data from multiple prospective epidemiologic studies enable detailed investigation into the predictive ability of risk models. Here we address the challenges in appropriately combining such information across studies. Methods are exemplified by analyses of log C-reactive protein and conventional risk factors for coronary heart disease in the Emerging Risk Factors Collaboration, a collation of individual data from multiple prospective studies with an average follow-up duration of 9.8 years (dates varied). We derive risk prediction models using Cox proportional hazards regression analysis stratified by study and obtain estimates of risk discrimination, Harrell's concordance index, and Royston's discrimination measure within each study; we then combine the estimates across studies using a weighted meta-analysis. Various weighting approaches are compared and lead us to recommend using the number of events in each study. We also discuss the calculation of measures of reclassification for multiple studies. We further show that comparison of differences in predictive ability across subgroups should be based only on within-study information and that combining measures of risk discrimination from case-control studies and prospective studies is problematic. The concordance index and discrimination measure gave qualitatively similar results throughout. While the concordance index was very heterogeneous between studies, principally because of differing age ranges, the increments in the concordance index from adding log C-reactive protein to conventional risk factors were more homogeneous.

The 11-year solar cycle in current reanalyses: a (non)linear attribution study of the middle atmosphere

NASA Astrophysics Data System (ADS)

Kuchar, A.; Sacha, P.; Miksovsky, J.; Pisoft, P.

2015-06-01

This study focusses on the variability of temperature, ozone and circulation characteristics in the stratosphere and lower mesosphere with regard to the influence of the 11-year solar cycle. It is based on attribution analysis using multiple nonlinear techniques (support vector regression, neural networks) besides the multiple linear regression approach. The analysis was applied to several current reanalysis data sets for the 1979-2013 period, including MERRA, ERA-Interim and JRA-55, with the aim to compare how these types of data resolve especially the double-peaked solar response in temperature and ozone variables and the consequent changes induced by these anomalies. Equatorial temperature signals in the tropical stratosphere were found to be in qualitative agreement with previous attribution studies, although the agreement with observational results was incomplete, especially for JRA-55. The analysis also pointed to the solar signal in the ozone data sets (i.e. MERRA and ERA-Interim) not being consistent with the observed double-peaked ozone anomaly extracted from satellite measurements. The results obtained by linear regression were confirmed by the nonlinear approach through all data sets, suggesting that linear regression is a relevant tool to sufficiently resolve the solar signal in the middle atmosphere. The seasonal evolution of the solar response was also discussed in terms of dynamical causalities in the winter hemispheres. The hypothetical mechanism of a weaker Brewer-Dobson circulation at solar maxima was reviewed together with a discussion of polar vortex behaviour.

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

PubMed

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

2006-08-01

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

Modeling the language learning strategies and English language proficiency of pre-university students in UMS: A case study

NASA Astrophysics Data System (ADS)

Kiram, J. J.; Sulaiman, J.; Swanto, S.; Din, W. A.

2015-10-01

This study aims to construct a mathematical model of the relationship between a student's Language Learning Strategy usage and English Language proficiency. Fifty-six pre-university students of University Malaysia Sabah participated in this study. A self-report questionnaire called the Strategy Inventory for Language Learning was administered to them to measure their language learning strategy preferences before they sat for the Malaysian University English Test (MUET), the results of which were utilised to measure their English language proficiency. We attempted the model assessment specific to Multiple Linear Regression Analysis subject to variable selection using Stepwise regression. We conducted various assessments to the model obtained, including the Global F-test, Root Mean Square Error and R-squared. The model obtained suggests that not all language learning strategies should be included in the model in an attempt to predict Language Proficiency.

Odontological approach to sexual dimorphism in southeastern France.

PubMed

Lladeres, Emilie; Saliba-Serre, Bérengère; Sastre, Julien; Foti, Bruno; Tardivo, Delphine; Adalian, Pascal

2013-01-01

The aim of this study was to establish a prediction formula to allow for the determination of sex among the southeastern French population using dental measurements. The sample consisted of 105 individuals (57 males and 48 females, aged between 18 and 25 years). Dental measurements were calculated using Euclidean distances, in three-dimensional space, from point coordinates obtained by a Microscribe. A multiple logistic regression analysis was performed to establish the prediction formula. Among 12 selected dental distances, a stepwise logistic regression analysis highlighted the two most significant discriminate predictors of sex: one located at the mandible and the other at the maxilla. A cutpoint was proposed to prediction of true sex. The prediction formula was then tested on a validation sample (20 males and 34 females, aged between 18 and 62 years and with a history of orthodontics or restorative care) to evaluate the accuracy of the method. © 2012 American Academy of Forensic Sciences.

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

PubMed

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

2016-05-01

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

Wavelet regression model in forecasting crude oil price

NASA Astrophysics Data System (ADS)

Hamid, Mohd Helmie; Shabri, Ani

2017-05-01

This study presents the performance of wavelet multiple linear regression (WMLR) technique in daily crude oil forecasting. WMLR model was developed by integrating the discrete wavelet transform (DWT) and multiple linear regression (MLR) model. The original time series was decomposed to sub-time series with different scales by wavelet theory. Correlation analysis was conducted to assist in the selection of optimal decomposed components as inputs for the WMLR model. The daily WTI crude oil price series has been used in this study to test the prediction capability of the proposed model. The forecasting performance of WMLR model were also compared with regular multiple linear regression (MLR), Autoregressive Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) using root mean square errors (RMSE) and mean absolute errors (MAE). Based on the experimental results, it appears that the WMLR model performs better than the other forecasting technique tested in this study.

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.

Testing Mediation Using Multiple Regression and Structural Equation Modeling Analyses in Secondary Data

ERIC Educational Resources Information Center

Li, Spencer D.

2011-01-01

Mediation analysis in child and adolescent development research is possible using large secondary data sets. This article provides an overview of two statistical methods commonly used to test mediated effects in secondary analysis: multiple regression and structural equation modeling (SEM). Two empirical studies are presented to illustrate the…

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

ERIC Educational Resources Information Center

Cooper, Paul D.

2010-01-01

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

Conjoint Analysis: A Study of the Effects of Using Person Variables.

ERIC Educational Resources Information Center

Fraas, John W.; Newman, Isadore

Three statistical techniques--conjoint analysis, a multiple linear regression model, and a multiple linear regression model with a surrogate person variable--were used to estimate the relative importance of five university attributes for students in the process of selecting a college. The five attributes include: availability and variety of…

An Exploratory Study of Face-to-Face and Cyberbullying in Sixth Grade Students

ERIC Educational Resources Information Center

Accordino, Denise B.; Accordino, Michael P.

2011-01-01

In a pilot study, sixth grade students (N = 124) completed a questionnaire assessing students' experience with bullying and cyberbullying, demographic information, quality of parent-child relationship, and ways they have dealt with bullying/cyberbullying in the past. Two multiple regression analyses were conducted. The multiple regression analysis…

The Use of Multiple Regression and Trend Analysis to Understand Enrollment Fluctuations. AIR Forum 1979 Paper.

ERIC Educational Resources Information Center

Campbell, S. Duke; Greenberg, Barry

The development of a predictive equation capable of explaining a significant percentage of enrollment variability at Florida International University is described. A model utilizing trend analysis and a multiple regression approach to enrollment forecasting was adapted to investigate enrollment dynamics at the university. Four independent…

Double Cross-Validation in Multiple Regression: A Method of Estimating the Stability of Results.

ERIC Educational Resources Information Center

Rowell, R. Kevin

In multiple regression analysis, where resulting predictive equation effectiveness is subject to shrinkage, it is especially important to evaluate result replicability. Double cross-validation is an empirical method by which an estimate of invariance or stability can be obtained from research data. A procedure for double cross-validation is…

Influence of overstory on snow depth and density in hemlock-spruce stands: implications for management of deer habitat in Southeastern Alaska.

Treesearch

Thomas A. Hanley; Cathy L. Rose

1987-01-01

Snow depth and density were measured in 33 stands of western hemlock-Sitka spruce (Tsuga heterophylla [Rat] Sarg.-Picea sitchensis [Bong.] Carr.) over a 3-year period. The stands, near Juneau, Alaska, provided broad ranges of species composition, age, over-story canopy coverage, tree density, and wood volume. Stepwise multiple regression analyses indicated that both...

Measurement of pyridoxal 5'-phosphate, pyridoxal, and 4-pyridoxic acid in the cerebrospinal fluid of children.

PubMed

Akiyama, Tomoyuki; Akiyama, Mari; Hayashi, Yumiko; Shibata, Takashi; Hanaoka, Yoshiyuki; Toda, Soichiro; Imai, Katsumi; Hamano, Shin-Ichiro; Okanishi, Tohru; Yoshinaga, Harumi; Kobayashi, Katsuhiro

2017-03-01

We quantified pyridoxal 5'-phosphate (PLP), pyridoxal (PL), and 4-pyridoxic acid (PA) in the cerebrospinal fluid (CSF) of children and to investigate the effect of age, sex, epilepsy, and anti-epileptic drug (AED) therapy on these vitamers. CSF samples prospectively collected from 116 pediatric patients were analyzed. PLP, PL, and PA were measured using high-performance liquid chromatography with fluorescence detection, using pre-column derivatization by semicarbazide. Effects of age, sex, epilepsy, and AEDs on these vitamers and the PLP/PL ratio were evaluated using multiple linear regression models. The PLP, PL, and PA concentrations were correlated negatively with age and the PLP/PL ratio was correlated positively with age. Multiple regression analysis revealed that the presence of epilepsy was associated with lower PLP concentrations and PLP/PL ratios but sex and AED therapy had no influence on these values. The observed ranges of these vitamers in epileptic and non-epileptic patients were demonstrated. We showed the age dependence of PLP and PL in CSF from pediatric patients. Epileptic patients had lower PLP concentrations and PLP/PL ratios than non-epileptic patients, but it is unknown whether this is the cause, or a result, of epilepsy. Copyright © 2016 Elsevier B.V. All rights reserved.

Integrative Analysis of High-throughput Cancer Studies with Contrasted Penalization

PubMed Central

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

2015-01-01

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

Estimation of the quantification uncertainty from flow injection and liquid chromatography transient signals in inductively coupled plasma mass spectrometry

NASA Astrophysics Data System (ADS)

Laborda, Francisco; Medrano, Jesús; Castillo, Juan R.

2004-06-01

The quality of the quantitative results obtained from transient signals in high-performance liquid chromatography-inductively coupled plasma mass spectrometry (HPLC-ICPMS) and flow injection-inductively coupled plasma mass spectrometry (FI-ICPMS) was investigated under multielement conditions. Quantification methods were based on multiple-point calibration by simple and weighted linear regression, and double-point calibration (measurement of the baseline and one standard). An uncertainty model, which includes the main sources of uncertainty from FI-ICPMS and HPLC-ICPMS (signal measurement, sample flow rate and injection volume), was developed to estimate peak area uncertainties and statistical weights used in weighted linear regression. The behaviour of the ICPMS instrument was characterized in order to be considered in the model, concluding that the instrument works as a concentration detector when it is used to monitorize transient signals from flow injection or chromatographic separations. Proper quantification by the three calibration methods was achieved when compared to reference materials, although the double-point calibration allowed to obtain results of the same quality as the multiple-point calibration, shortening the calibration time. Relative expanded uncertainties ranged from 10-20% for concentrations around the LOQ to 5% for concentrations higher than 100 times the LOQ.

Relationship of cariogenic bacteria levels with periodontal status and root surface caries in elderly Japanese.

PubMed

Saotome, Yasuhiko; Tada, Akio; Hanada, Nobuhiro; Yoshihara, Akihiro; Uematsu, Hiroshi; Miyazaki, Hideo; Senpuku, Hidenobu

2006-12-01

The relationship of the levels of cariogenic bacterial species with periodontal status and decayed root surfaces was investigated in elderly Japanese subjects. Three hundred and sixty-eight individuals (each 75 years old) were examined for periodontal status (pocket depth, attachment loss), root surface caries and salivary levels of mutans streptococci (MS) and lactobacilli (LB). Values >4 mm of attachment loss (rAL4) and for average attachment loss (aAL) of sites measured were significantly higher in subjects with LB than those without. Multiple regression analysis also showed a correlation between aAL and rAL4 values with the presence of LB (aAL p = 0.003; rAL4 p = 0.002). Further, multiple regression analysis of interacting factors regarding decayed root surfaces showed that LB carriers had a greater incidence of decayed root surface caries (p = 0.003), while MS and LB levels were correlated to the number of decayed root surfaces (LB p = 0.010; MS p = 0.026). Our results indicate that considerable attachment loss elevates the possibility of having LB, thus increasing the risk of root surface caries. It was also found that LB and MS measurements may be useful indicators of decayed root surfaces in elderly individuals with attachment loss.

Prediction of Maximal Aerobic Capacity in Severely Burned Children

PubMed Central

Porro, Laura; Rivero, Haidy G.; Gonzalez, Dante; Tan, Alai; Herndon, David N.; Suman, Oscar E.

2011-01-01

Introduction Maximal oxygen uptake (VO2 peak) is an indicator of cardiorespiratory fitness, but requires expensive equipment and a relatively high technical skill level. Purpose The aim of this study is to provide a formula for estimating VO2 peak in burned children, using information obtained without expensive equipment. Methods Children, with ≥40% total surface area burned (TBSA), underwent a modified Bruce treadmill test to asses VO2 peak at 6 months after injury. We recorded gender, age, %TBSA, %3rd degree burn, height, weight, treadmill time, maximal speed, maximal grade, and peak heart rate, and applied McHenry’s select algorithm to extract important independent variables and Robust multiple regression to establish prediction equations. Results 42 children; 7 to 17 years old were tested. Robust multiple regression model provided the equation: VO2=10.33 – 0.62 *Age (years) + 1.88 * Treadmill Time (min) + 2.3 (gender; Females = 0, Males = 1). The correlation between measured and estimated VO2 peak was R=0.80. We then validated the equation with a group of 33 burned children, which yielded a correlation between measured and estimated VO2 peak of R=0.79. Conclusions Using only a treadmill and easily gathered information, VO2 peak can be estimated in children with burns. PMID:21316155

Use of Two-Part Regression Calibration Model to Correct for Measurement Error in Episodically Consumed Foods in a Single-Replicate Study Design: EPIC Case Study

PubMed Central

Agogo, George O.; van der Voet, Hilko; Veer, Pieter van’t; Ferrari, Pietro; Leenders, Max; Muller, David C.; Sánchez-Cantalejo, Emilio; Bamia, Christina; Braaten, Tonje; Knüppel, Sven; Johansson, Ingegerd; van Eeuwijk, Fred A.; Boshuizen, Hendriek

2014-01-01

In epidemiologic studies, measurement error in dietary variables often attenuates association between dietary intake and disease occurrence. To adjust for the attenuation caused by error in dietary intake, regression calibration is commonly used. To apply regression calibration, unbiased reference measurements are required. Short-term reference measurements for foods that are not consumed daily contain excess zeroes that pose challenges in the calibration model. We adapted two-part regression calibration model, initially developed for multiple replicates of reference measurements per individual to a single-replicate setting. We showed how to handle excess zero reference measurements by two-step modeling approach, how to explore heteroscedasticity in the consumed amount with variance-mean graph, how to explore nonlinearity with the generalized additive modeling (GAM) and the empirical logit approaches, and how to select covariates in the calibration model. The performance of two-part calibration model was compared with the one-part counterpart. We used vegetable intake and mortality data from European Prospective Investigation on Cancer and Nutrition (EPIC) study. In the EPIC, reference measurements were taken with 24-hour recalls. For each of the three vegetable subgroups assessed separately, correcting for error with an appropriately specified two-part calibration model resulted in about three fold increase in the strength of association with all-cause mortality, as measured by the log hazard ratio. Further found is that the standard way of including covariates in the calibration model can lead to over fitting the two-part calibration model. Moreover, the extent of adjusting for error is influenced by the number and forms of covariates in the calibration model. For episodically consumed foods, we advise researchers to pay special attention to response distribution, nonlinearity, and covariate inclusion in specifying the calibration model. PMID:25402487

Total energy expenditure in adults with cerebral palsy as assessed by doubly labeled water.

PubMed

Johnson, R K; Hildreth, H G; Contompasis, S H; Goran, M I

1997-09-01

To characterize total energy expenditure (TEE) in free-living adults with cerebral palsy (CP) using the doubly labeled water technique, and to determine those physiologic variables and characteristics of CP that were markers of TEE in adults with CP. TEE was measured using the doubly labeled water technique in 30 free-living adults with CP (12 women, 18 men). To determine the best markers of TEE, the following factors were examined: CP status, resting metabolic rate (RMR), anthropometric characteristics and body composition by means of dual-energy x-ray absorptiometry (DXA) and skinfold thickness measurements, energy cost of leisure-time activities, and oral-motor impairment. Means +/- standard deviations, t tests, Pearson product-moment correlation coefficients, Spearman rank correlation coefficients, chi 2, stepwise multiple-correlation regression analysis, and analysis of covariance were used to examine the relationships among variables of interest. TEE was highly variable in the sample (mean = 2,455 +/- 622 kcal/day for men and 1,986 +/- 363 kcal/day for women). Stepwise regression analysis showed that TEE was best predicted in the sample by RMR, percentage body fat determined by DXA, ambulation status, and sex (multiple R = .68, P = .003). When practical, easily measured variables were used, TEE was best predicted by height, ambulation status, percentage body fat by skinfold thickness measurements, and sex (multiple R = .61, P. = 018). The contribution of energy expended in physical activity to TEE was significantly higher in the ambulatory subjects than the nonambulatory subjects (25% vs 16%, respectively; P = .009). The high degree of variability in TEE, largely attributable to high interindividual variation in energy expended in physical activity, makes it difficult to provide general guidelines for energy requirements for adults with CP. Because ambulation status was an important predictor of TEE, it must be accounted for in estimating energy requirements in this population.

Confounder Detection in High-Dimensional Linear Models Using First Moments of Spectral Measures.

PubMed

Liu, Furui; Chan, Laiwan

2018-06-12

In this letter, we study the confounder detection problem in the linear model, where the target variable [Formula: see text] is predicted using its [Formula: see text] potential causes [Formula: see text]. Based on an assumption of a rotation-invariant generating process of the model, recent study shows that the spectral measure induced by the regression coefficient vector with respect to the covariance matrix of [Formula: see text] is close to a uniform measure in purely causal cases, but it differs from a uniform measure characteristically in the presence of a scalar confounder. Analyzing spectral measure patterns could help to detect confounding. In this letter, we propose to use the first moment of the spectral measure for confounder detection. We calculate the first moment of the regression vector-induced spectral measure and compare it with the first moment of a uniform spectral measure, both defined with respect to the covariance matrix of [Formula: see text]. The two moments coincide in nonconfounding cases and differ from each other in the presence of confounding. This statistical causal-confounding asymmetry can be used for confounder detection. Without the need to analyze the spectral measure pattern, our method avoids the difficulty of metric choice and multiple parameter optimization. Experiments on synthetic and real data show the performance of this method.

Method and Excel VBA Algorithm for Modeling Master Recession Curve Using Trigonometry Approach.

PubMed

Posavec, Kristijan; Giacopetti, Marco; Materazzi, Marco; Birk, Steffen

2017-11-01

A new method was developed and implemented into an Excel Visual Basic for Applications (VBAs) algorithm utilizing trigonometry laws in an innovative way to overlap recession segments of time series and create master recession curves (MRCs). Based on a trigonometry approach, the algorithm horizontally translates succeeding recession segments of time series, placing their vertex, that is, the highest recorded value of each recession segment, directly onto the appropriate connection line defined by measurement points of a preceding recession segment. The new method and algorithm continues the development of methods and algorithms for the generation of MRC, where the first published method was based on a multiple linear/nonlinear regression model approach (Posavec et al. 2006). The newly developed trigonometry-based method was tested on real case study examples and compared with the previously published multiple linear/nonlinear regression model-based method. The results show that in some cases, that is, for some time series, the trigonometry-based method creates narrower overlaps of the recession segments, resulting in higher coefficients of determination R 2 , while in other cases the multiple linear/nonlinear regression model-based method remains superior. The Excel VBA algorithm for modeling MRC using the trigonometry approach is implemented into a spreadsheet tool (MRCTools v3.0 written by and available from Kristijan Posavec, Zagreb, Croatia) containing the previously published VBA algorithms for MRC generation and separation. All algorithms within the MRCTools v3.0 are open access and available free of charge, supporting the idea of running science on available, open, and free of charge software. © 2017, National Ground Water Association.

Inhibitory saccadic dysfunction is associated with cerebellar injury in multiple sclerosis.

PubMed

Kolbe, Scott C; Kilpatrick, Trevor J; Mitchell, Peter J; White, Owen; Egan, Gary F; Fielding, Joanne

2014-05-01

Cognitive dysfunction is common in patients with multiple sclerosis (MS). Saccadic eye movement paradigms such as antisaccades (AS) can sensitively interrogate cognitive function, in particular, the executive and attentional processes of response selection and inhibition. Although we have previously demonstrated significant deficits in the generation of AS in MS patients, the neuropathological changes underlying these deficits were not elucidated. In this study, 24 patients with relapsing-remitting MS underwent testing using an AS paradigm. Rank correlation and multiple regression analyses were subsequently used to determine whether AS errors in these patients were associated with: (i) neurological and radiological abnormalities, as measured by standard clinical techniques, (ii) cognitive dysfunction, and (iii) regionally specific cerebral white and gray-matter damage. Although AS error rates in MS patients did not correlate with clinical disability (using the Expanded Disability Status Score), T2 lesion load or brain parenchymal fraction, AS error rate did correlate with performance on the Paced Auditory Serial Addition Task and the Symbol Digit Modalities Test, neuropsychological tests commonly used in MS. Further, voxel-wise regression analyses revealed associations between AS errors and reduced fractional anisotropy throughout most of the cerebellum, and increased mean diffusivity in the cerebellar vermis. Region-wise regression analyses confirmed that AS errors also correlated with gray-matter atrophy in the cerebellum right VI subregion. These results support the use of the AS paradigm as a marker for cognitive dysfunction in MS and implicate structural and microstructural changes to the cerebellum as a contributing mechanism for AS deficits in these patients. Copyright © 2013 Wiley Periodicals, Inc.

Screening for ketosis using multiple logistic regression based on milk yield and composition

PubMed Central

KAYANO, Mitsunori; KATAOKA, Tomoko

2015-01-01

Multiple logistic regression was applied to milk yield and composition data for 632 records of healthy cows and 61 records of ketotic cows in Hokkaido, Japan. The purpose was to diagnose ketosis based on milk yield and composition, simultaneously. The cows were divided into two groups: (1) multiparous, including 314 healthy cows and 45 ketotic cows and (2) primiparous, including 318 healthy cows and 16 ketotic cows, since nutritional status, milk yield and composition are affected by parity. Multiple logistic regression was applied to these groups separately. For multiparous cows, milk yield (kg/day/cow) and protein-to-fat (P/F) ratio in milk were significant factors (P<0.05) for the diagnosis of ketosis. For primiparous cows, lactose content (%), solid not fat (SNF) content (%) and milk urea nitrogen (MUN) content (mg/dl) were significantly associated with ketosis (P<0.01). A diagnostic rule was constructed for each group of cows: (1) 9.978 × P/F ratio + 0.085 × milk yield <10 and (2) 2.327 × SNF − 2.703 × lactose + 0.225 × MUN <10. The sensitivity, specificity and the area under the curve (AUC) of the diagnostic rules were (1) 0.800, 0.729 and 0.811; (2) 0.813, 0.730 and 0.787, respectively. The P/F ratio, which is a widely used measure of ketosis, provided the sensitivity, specificity and AUC values of (1) 0.711, 0.726 and 0.781; and (2) 0.678, 0.767 and 0.738, respectively. PMID:26074408

Knowledge, attitudes and practices survey on organ donation among a selected adult population of Pakistan

PubMed Central

Saleem, Taimur; Ishaque, Sidra; Habib, Nida; Hussain, Syedda Saadia; Jawed, Areeba; Khan, Aamir Ali; Ahmad, Muhammad Imran; Iftikhar, Mian Omer; Mughal, Hamza Pervez; Jehan, Imtiaz

2009-01-01

Background To determine the knowledge, attitudes and practices regarding organ donation in a selected adult population in Pakistan. Methods Convenience sampling was used to generate a sample of 440; 408 interviews were successfully completed and used for analysis. Data collection was carried out via a face to face interview based on a pre-tested questionnaire in selected public areas of Karachi, Pakistan. Data was analyzed using SPSS v.15 and associations were tested using the Pearson's Chi square test. Multiple logistic regression was used to find independent predictors of knowledge status and motivation of organ donation. Results Knowledge about organ donation was significantly associated with education (p = 0.000) and socioeconomic status (p = 0.038). 70/198 (35.3%) people expressed a high motivation to donate. Allowance of organ donation in religion was significantly associated with the motivation to donate (p = 0.000). Multiple logistic regression analysis revealed that higher level of education and higher socioeconomic status were significant (p < 0.05) independent predictors of knowledge status of organ donation. For motivation, multiple logistic regression revealed that higher socioeconomic status, adequate knowledge score and belief that organ donation is allowed in religion were significant (p < 0.05) independent predictors. Television emerged as the major source of information. Only 3.5% had themselves donated an organ; with only one person being an actual kidney donor. Conclusion Better knowledge may ultimately translate into the act of donation. Effective measures should be taken to educate people with relevant information with the involvement of media, doctors and religious scholars. PMID:19534793

Flourishing: exploring predictors of mental health within the college environment.

PubMed

Fink, John E

2014-01-01

To explore the predictive factors of student mental health within the college environment. Students enrolled at 7 unique universities during years 2008 (n=1,161) and 2009 (n=1,459). Participants completed survey measures of mental health, consequences of alcohol use, and engagement in the college environment. In addition to replicating previous findings related to Keyes' Mental Health Continuum, multiple regression analysis revealed several predictors of college student mental health, including supportive college environments, students' sense of belonging, professional confidence, and civic engagement. However, multiple measures of engaged learning were not found to predict mental health. Results suggest that supportive college environments foster student flourishing. Implications for promoting mental health across campus are discussed. Future research should build on exploratory findings and test confirmatory models to better understand relationships between the college environment and student flourishing.

Comparison of the color of natural teeth measured by a colorimeter and Shade Vision System.

PubMed

Cho, Byeong-Hoon; Lim, Yong-Kyu; Lee, Yong-Keun

2007-10-01

The objectives were to measure the difference in the color and color parameters of natural teeth measured by a tristimulus colorimeter (CM, used as a reference) and Shade Vision System (SV), and to determine the influence of color parameters on the color difference between the values measured by two instruments. Color of 12 maxillary and mandibular anterior teeth was measured by CM and SV for 47 volunteers (number of teeth=564). Color parameters such as CIE L*, a* and b* values, chroma and hue angle measured by two instruments were compared. Chroma was calculated as C*ab=(a*2 = b*2)1/2, and hue angle was calculated as h degrees =arctan(b*/a*). The influence of color parameters measured by CM on the color difference (DeltaE*(ab)) between the values measured by two instruments was analyzed with multiple regression analysis (alpha=0.01). Mean DeltaE*(ab) value between the values measured by two instruments was 21.7 (+/-3.7), and the mean difference in lightness (CIE L*) and chroma was 16.2 (+/-3.9) and 13.2 (+/-3.0), respectively. Difference in hue angle was high as 132.7 (+/-53.3) degrees . Except for the hue angle, all the color parameters showed significant correlations and the coefficient of determination (r(2)) was in the range of 0.089-0.478. Based on multiple regression analysis, the standardized partial correlation coefficient (beta) of the included predictors for the color difference was -0.710 for CIE L* and -0.300 for C*(ab) (p<0.01). All the color parameters showed significant but weak correlations except for hue angle. When lightness and chroma of teeth were high, color difference between the values measured by two instruments was small. Clinical accuracy of two instruments should be investigated further.

Ridge: a computer program for calculating ridge regression estimates

Treesearch

Donald E. Hilt; Donald W. Seegrist

1977-01-01

Least-squares coefficients for multiple-regression models may be unstable when the independent variables are highly correlated. Ridge regression is a biased estimation procedure that produces stable estimates of the coefficients. Ridge regression is discussed, and a computer program for calculating the ridge coefficients is presented.

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

PubMed Central

Zhu, Xiang; Stephens, Matthew

2017-01-01

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

Standardizing the intrinsic brain: Towards robust measurement of inter-individual variation in 1000 functional connectomes

PubMed Central

Yan, Chao-Gan; Craddock, R. Cameron; Zuo, Xi-Nian; Zang, Yu-Feng; Milham, Michael P.

2014-01-01

As researchers increase their efforts to characterize variations in the functional connectome across studies and individuals, concerns about the many sources of nuisance variation present and their impact on resting state fMRI (R-fMRI) measures continue to grow. Although substantial within-site variation can exist, efforts to aggregate data across multiple sites such as the 1000 Functional Connectomes Project (FCP) and International Neuroimaging Data-sharing Initiative (INDI) datasets amplify these concerns. The present work draws upon standardization approaches commonly used in the microarray gene expression literature, and to a lesser extent recent imaging studies, and compares them with respect to their impact on relationships between common R-fMRI measures and nuisance variables (e.g., imaging site, motion), as well as phenotypic variables of interest (age, sex). Standardization approaches differed with regard to whether they were applied post-hoc vs. during pre-processing, and at the individual vs. group level; additionally they varied in whether they addressed additive effects vs. additive + multiplicative effects, and were parametric vs. non-parametric. While all standardization approaches were effective at reducing undesirable relationships with nuisance variables, post-hoc approaches were generally more effective than global signal regression (GSR). Across approaches, correction for additive effects (global mean) appeared to be more important than for multiplicative effects (global SD) for all R-fMRI measures, with the exception of amplitude of low frequency fluctuations (ALFF). Group-level post-hoc standardizations for mean-centering and variance-standardization were found to be advantageous in their ability to avoid the introduction of artifactual relationships with standardization parameters; though results between individual and group-level post-hoc approaches were highly similar overall. While post-hoc standardization procedures drastically increased test–retest (TRT) reliability for ALFF, modest reductions were observed for other measures after post-hoc standardizations—a phenomena likely attributable to the separation of voxel-wise from global differences among subjects (global mean and SD demonstrated moderate TRT reliability for these measures). Finally, the present work calls into question previous observations of increased anatomical specificity for GSR over mean centering, and draws attention to the near equivalence of global and gray matter signal regression. PMID:23631983

Statistical experiments using the multiple regression research for prediction of proper hardness in areas of phosphorus cast-iron brake shoes manufacturing

NASA Astrophysics Data System (ADS)

Kiss, I.; Cioată, V. G.; Ratiu, S. A.; Rackov, M.; Penčić, M.

2018-01-01

Multivariate research is important in areas of cast-iron brake shoes manufacturing, because many variables interact with each other simultaneously. This article focuses on expressing the multiple linear regression model related to the hardness assurance by the chemical composition of the phosphorous cast irons destined to the brake shoes, having in view that the regression coefficients will illustrate the unrelated contributions of each independent variable towards predicting the dependent variable. In order to settle the multiple correlations between the hardness of the cast-iron brake shoes, and their chemical compositions several regression equations has been proposed. Is searched a mathematical solution which can determine the optimum chemical composition for the hardness desirable values. Starting from the above-mentioned affirmations two new statistical experiments are effectuated related to the values of Phosphorus [P], Manganese [Mn] and Silicon [Si]. Therefore, the regression equations, which describe the mathematical dependency between the above-mentioned elements and the hardness, are determined. As result, several correlation charts will be revealed.

Determination of the spectral values of the real part of the relative refractive index of human blood erythrocytes from the measured directional scattering coefficients

NASA Astrophysics Data System (ADS)

Kugeiko, M. M.; Lisenko, S. A.

2008-07-01

An easily automated method for determining the real part of the refractive index of human blood erythrocytes in the range 0.3 1.2 μm is proposed. The method is operationally and metrologically reliable and is based on the measurement of the coefficients of light scattering from forward and backward hemisphere by two pairs of angles and on the use of multiple regression equations. An engineering solution for constructing a measurement system according to this method is proposed, which makes it possible to maximally reduce the calibration errors and effects of destabilizing factors.

Analysis of the thermal comfort model in an environment of metal mechanical branch.

PubMed

Pinto, N M; Xavier, A A P; do Amaral, Regiane T

2012-01-01

This study aims to identify the correlation between the Predicted Mean Vote (PMV) with the thermal sensation (S) of 55 employees, establishing a linear multiple regression equation. The measurement of environmental variables followed established standards. The survey was conducted in a metal industry located in Ponta Grossa of the State of Parana in Brazil. It was applied the physical model of thermal comfort to the environmental variables and also to the subjective data on the thermal sensations of employees. The survey was conducted from May to November, 2010, with 48 measurements. This study will serve as the basis for a dissertation consisting of 72 measurements.

Pediatric Irritable Bowel Syndrome Patient and Parental Characteristics Differ by Care Management Type.

PubMed

Hollier, John M; Czyzewski, Danita I; Self, Mariella M; Weidler, Erica M; Smith, E O'Brian; Shulman, Robert J

2017-03-01

This study evaluates whether certain patient or parental characteristics are associated with gastroenterology (GI) referral versus primary pediatrics care for pediatric irritable bowel syndrome (IBS). A retrospective clinical trial sample of patients meeting pediatric Rome III IBS criteria was assembled from a single metropolitan health care system. Baseline socioeconomic status (SES) and clinical symptom measures were gathered. Various instruments measured participant and parental psychosocial traits. Study outcomes were stratified by GI referral versus primary pediatrics care. Two separate analyses of SES measures and GI clinical symptoms and psychosocial measures identified key factors by univariate and multiple logistic regression analyses. For each analysis, identified factors were placed in unadjusted and adjusted multivariate logistic regression models to assess their impact in predicting GI referral. Of the 239 participants, 152 were referred to pediatric GI, and 87 were managed in primary pediatrics care. Of the SES and clinical symptom factors, child self-assessment of abdominal pain duration and lower percentage of people living in poverty were the strongest predictors of GI referral. Among the psychosocial measures, parental assessment of their child's functional disability was the sole predictor of GI referral. In multivariate logistic regression models, all selected factors continued to predict GI referral in each model. Socioeconomic environment, clinical symptoms, and functional disability are associated with GI referral. Future interventions designed to ameliorate the effect of these identified factors could reduce unnecessary specialty consultations and health care overutilization for IBS.

Simulating the effects of climatic variation on stem carbon accumulation of a ponderosa pine stand: comparison with annual growth increment data.

PubMed

Hunt, E R; Martin, F C; Running, S W

1991-01-01

Simulation models of ecosystem processes may be necessary to separate the long-term effects of climate change on forest productivity from the effects of year-to-year variations in climate. The objective of this study was to compare simulated annual stem growth with measured annual stem growth from 1930 to 1982 for a uniform stand of ponderosa pine (Pinus ponderosa Dougl.) in Montana, USA. The model, FOREST-BGC, was used to simulate growth assuming leaf area index (LAI) was either constant or increasing. The measured stem annual growth increased exponentially over time; the differences between the simulated and measured stem carbon accumulations were not large. Growth trends were removed from both the measured and simulated annual increments of stem carbon to enhance the year-to-year variations in growth resulting from climate. The detrended increments from the increasing LAI simulation fit the detrended increments of the stand data over time with an R(2) of 0.47; the R(2) increased to 0.65 when the previous year's simulated detrended increment was included with the current year's simulated increment to account for autocorrelation. Stepwise multiple linear regression of the detrended increments of the stand data versus monthly meteorological variables had an R(2) of 0.37, and the R(2) increased to 0.47 when the previous year's meteorological data were included to account for autocorrelation. Thus, FOREST-BGC was more sensitive to the effects of year-to-year climate variation on annual stem growth than were multiple linear regression models.

Estimating Causal Effects of Education Interventions Using a Two-Rating Regression Discontinuity Design: Lessons from a Simulation Study

ERIC Educational Resources Information Center

Porter, Kristin E.; Reardon, Sean F.; Unlu, Fatih; Bloom, Howard S.; Robinson-Cimpian, Joseph P.

2014-01-01

A valuable extension of the single-rating regression discontinuity design (RDD) is a multiple-rating RDD (MRRDD). To date, four main methods have been used to estimate average treatment effects at the multiple treatment frontiers of an MRRDD: the "surface" method, the "frontier" method, the "binding-score" method, and…

Development of Multiple Regression Equations To Predict Fourth Graders' Achievement in Reading and Selected Content Areas.

ERIC Educational Resources Information Center

Hafner, Lawrence E.

A study developed a multiple regression prediction equation for each of six selected achievement variables in a popular standardized test of achievement. Subjects, 42 fourth-grade pupils randomly selected across several classes in a large elementary school in a north Florida city, were administered several standardized tests to determine predictor…

Physical and Cognitive-Affective Factors Associated with Fatigue in Individuals with Fibromyalgia: A Multiple Regression Analysis

ERIC Educational Resources Information Center

Muller, Veronica; Brooks, Jessica; Tu, Wei-Mo; Moser, Erin; Lo, Chu-Ling; Chan, Fong

2015-01-01

Purpose: The main objective of this study was to determine the extent to which physical and cognitive-affective factors are associated with fibromyalgia (FM) fatigue. Method: A quantitative descriptive design using correlation techniques and multiple regression analysis. The participants consisted of 302 members of the National Fibromyalgia &…

Latent Variable Regression 4-Level Hierarchical Model Using Multisite Multiple-Cohorts Longitudinal Data. CRESST Report 801

ERIC Educational Resources Information Center

Choi, Kilchan

2011-01-01

This report explores a new latent variable regression 4-level hierarchical model for monitoring school performance over time using multisite multiple-cohorts longitudinal data. This kind of data set has a 4-level hierarchical structure: time-series observation nested within students who are nested within different cohorts of students. These…

What Is Wrong with ANOVA and Multiple Regression? Analyzing Sentence Reading Times with Hierarchical Linear Models

ERIC Educational Resources Information Center

Richter, Tobias

2006-01-01

Most reading time studies using naturalistic texts yield data sets characterized by a multilevel structure: Sentences (sentence level) are nested within persons (person level). In contrast to analysis of variance and multiple regression techniques, hierarchical linear models take the multilevel structure of reading time data into account. They…

Some Applied Research Concerns Using Multiple Linear Regression Analysis.

ERIC Educational Resources Information Center

Newman, Isadore; Fraas, John W.

The intention of this paper is to provide an overall reference on how a researcher can apply multiple linear regression in order to utilize the advantages that it has to offer. The advantages and some concerns expressed about the technique are examined. A number of practical ways by which researchers can deal with such concerns as…

A Spreadsheet Tool for Learning the Multiple Regression F-Test, T-Tests, and Multicollinearity

ERIC Educational Resources Information Center

Martin, David

2008-01-01

This note presents a spreadsheet tool that allows teachers the opportunity to guide students towards answering on their own questions related to the multiple regression F-test, the t-tests, and multicollinearity. The note demonstrates approaches for using the spreadsheet that might be appropriate for three different levels of statistics classes,…

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

ERIC Educational Resources Information Center

Anderson, Joan L.

2006-01-01

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

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

ERIC Educational Resources Information Center

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

2006-01-01

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

Functional Capacity Evaluation in Different Societal Contexts: Results of a Multicountry Study.

PubMed

Ansuategui Echeita, Jone; Bethge, Matthias; van Holland, Berry J; Gross, Douglas P; Kool, Jan; Oesch, Peter; Trippolini, Maurizio A; Chapman, Elizabeth; Cheng, Andy S K; Sellars, Robert; Spavins, Megan; Streibelt, Marco; van der Wurff, Peter; Reneman, Michiel F

2018-05-25

Purpose To examine factors associated with Functional Capacity Evaluation (FCE) results in patients with painful musculoskeletal conditions, with focus on social factors across multiple countries. Methods International cross-sectional study was performed within care as usual. Simple and multiple multilevel linear regression analyses which considered measurement's dependency within clinicians and country were conducted: FCE characteristics and biopsychosocial variables from patients and clinicians as independent variables; and FCE results (floor-to-waist lift, six-minute walk, and handgrip strength) as dependent variables. Results Data were collected for 372 patients, 54 clinicians, 18 facilities and 8 countries. Patients' height and reported pain intensity were consistently associated with every FCE result. Patients' sex, height, reported pain intensity, effort during FCE, social isolation, and disability, clinician's observed physical effort, and whether FCE test was prematurely ended were associated with lift. Patient's height, Body Mass Index, post-test heart-rate, reported pain intensity and effort during FCE, days off work, and whether FCE test was prematurely ended were associated with walk. Patient's age, sex, height, affected body area, reported pain intensity and catastrophizing, and physical work demands were associated with handgrip. Final regression models explained 38‒65% of total variance. Clinician and country random effects composed 1-39% of total residual variance in these models. Conclusion Biopsychosocial factors were associated with every FCE result across multiple countries; specifically, patients' height, reported pain intensity, clinician, and measurement country. Social factors, which had been under-researched, were consistently associated with FCE performances. Patients' FCE results should be considered from a biopsychosocial perspective, including different social contexts.

Stress and health behaviors as potential mediators of the relationship between neighborhood quality and allostatic load.

PubMed

Buschmann, Robert N; Prochaska, John D; Cutchin, Malcolm P; Peek, M Kristen

2018-03-29

Neighborhood quality is associated with health. Increasingly, researchers are focusing on the mechanisms underlying that association, including the role of stress, risky health behaviors, and subclinical measures such as allostatic load (AL). This study uses mixed-effects regression modeling to examine the association between two objective measures and one subjective measure of neighborhood quality and AL in an ethnically diverse population-based sample (N = 2706) from a medium-sized Texas city. We also examine whether several measures of psychological stress and health behaviors mediate any relationship between neighborhood quality and AL. In this sample, all three separate measures of neighborhood quality were associated with individual AL (P < .01). However, only the subjective measure, perceived neighborhood quality, was associated with AL after adjusting for covariates. In mixed-effects multiple regression models there was no evidence of mediation by either stress or health behaviors. In this study, only one measure of neighborhood quality was related to a measure of health, which contrasts with considerable previous research in this area. In this sample, neighborhood quality may affect AL through other mechanisms, or there may be other health-affecting factors is this area that share that overshadow local neighborhood variation. Copyright © 2018 Elsevier Inc. All rights reserved.

[The effect of losartan on the intima-media thickness of carotid artery].

PubMed

Sönmez, Hulki Meltem; Turan, Filiz Canli; Köseoğlu, Kutsi

2008-06-01

There are findings about negative effects of angiotensin 1 (AT1) receptor stimulation at every stage of atherosclerosis formation. Recently, AT1 receptors, especially the effects of AT1 receptor antagonists on the regression of atherosclerosis, are being researched intensively. Measurement of carotid artery thickness has been accepted as a marker of atherosclerosis. In our study, we investigated the effect of AT1 receptor antagonist, losartan, on the carotid artery intima-media thickness of newly diagnosed hypertensive patients. We reached to 450 individuals by the stratified and random sampling method and measured their blood pressure to find out undiagnosed hypertensive patients. Fifty-one patients (mean age 54+/-9 years) were accepted to participate in our study. Forty-nine of them (33 women and 16 men) completed the study. After the measurements of the carotid artery intima-media thicknesses by B-mode Doppler ultrasonography, their blood tests were performed and arterial blood pressures were measured. Soon after, treatment with losartan as an antihypertensive agent was begun. All measurements were repeated on the eighth month of this therapy. The mean systolic and diastolic blood pressure of the cases were 167+/-14 mmHg and 102+/-8 mmHg, respectively. At the end of the eighth month these measurements regressed to 139+/-11 mmHg and 84+/-8 mmHg, respectively (p<0.05). Meaningful regression of carotid artery intima-media thickness was established. The mean regression was 0.10+/-0.19 mm (p=0.004) for women, 0.18+/-0.29 mm (p=0.007) for men and 0.13+/-0.23 mm (p<0.001) for the study population. No relation was seen between the carotid artery intima-media thickness and first systolic and diastolic blood pressure measurements of the patients (r=0.122, p=0.403 and r=0.032, p=0.828, respectively). We think that losartan should be recommended to use for protection against atherosclerosis at the young aged individuals that have multiple risks for atherosclerosis, other than hypertension.

Interpretation of commonly used statistical regression models.

PubMed

Kasza, Jessica; Wolfe, Rory

2014-01-01

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

Applied Multiple Linear Regression: A General Research Strategy

ERIC Educational Resources Information Center

Smith, Brandon B.

1969-01-01

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

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

PubMed Central

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

2015-01-01

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

Job characteristics in relation to the prevalence of myocardial infarction in the US Health Examination Survey (HES) and the Health and Nutrition Examination Survey (HANES).

PubMed

Karasek, R A; Theorell, T; Schwartz, J E; Schnall, P L; Pieper, C F; Michela, J L

1988-08-01

Associations between psychosocial job characteristics and past myocardial infarction (MI) prevalence for employed males were tested with the Health Examination Survey (HES) 1960-61, N = 2,409, and the Health and Nutrition Examination Survey (HANES) 1971-75, N = 2,424. A new estimation method is used which imputes to census occupation codes, job characteristic information from national surveys of job characteristics (US Department of Labor, Quality of Employment Surveys). Controlling for age, we find that employed males with jobs which are simultaneously low in decision latitude and high in psychological work load (a multiplicative product term isolating 20 per cent of the population) have a higher prevalence of myocardial infarction in both data bases. In a logistic regression analysis, using job measures adjusted for demographic factors and controlling for age, race, education, systolic blood pressure, serum cholesterol, smoking (HANES only), and physical exertion, we find a low decision latitude/high psychological demand multiplicative product term associated with MI in both data bases. Additional multiple logistic regressions show that low decision latitude is associated with increased prevalence of MI in both the HES and the HANES. Psychological workload and physical exertion are significant only in the HANES.

Job characteristics in relation to the prevalence of myocardial infarction in the US Health Examination Survey (HES) and the Health and Nutrition Examination Survey (HANES).

PubMed Central

Karasek, R A; Theorell, T; Schwartz, J E; Schnall, P L; Pieper, C F; Michela, J L

1988-01-01

Associations between psychosocial job characteristics and past myocardial infarction (MI) prevalence for employed males were tested with the Health Examination Survey (HES) 1960-61, N = 2,409, and the Health and Nutrition Examination Survey (HANES) 1971-75, N = 2,424. A new estimation method is used which imputes to census occupation codes, job characteristic information from national surveys of job characteristics (US Department of Labor, Quality of Employment Surveys). Controlling for age, we find that employed males with jobs which are simultaneously low in decision latitude and high in psychological work load (a multiplicative product term isolating 20 per cent of the population) have a higher prevalence of myocardial infarction in both data bases. In a logistic regression analysis, using job measures adjusted for demographic factors and controlling for age, race, education, systolic blood pressure, serum cholesterol, smoking (HANES only), and physical exertion, we find a low decision latitude/high psychological demand multiplicative product term associated with MI in both data bases. Additional multiple logistic regressions show that low decision latitude is associated with increased prevalence of MI in both the HES and the HANES. Psychological workload and physical exertion are significant only in the HANES. PMID:3389427

White matter tract abnormalities are associated with cognitive dysfunction in secondary progressive multiple sclerosis.

PubMed

Meijer, Kim A; Muhlert, Nils; Cercignani, Mara; Sethi, Varun; Ron, Maria A; Thompson, Alan J; Miller, David H; Chard, Declan; Geurts, Jeroen Jg; Ciccarelli, Olga

2016-10-01

While our knowledge of white matter (WM) pathology underlying cognitive impairment in relapsing remitting multiple sclerosis (MS) is increasing, equivalent understanding in those with secondary progressive (SP) MS lags behind. The aim of this study is to examine whether the extent and severity of WM tract damage differ between cognitively impaired (CI) and cognitively preserved (CP) secondary progressive multiple sclerosis (SPMS) patients. Conventional magnetic resonance imaging (MRI) and diffusion MRI were acquired from 30 SPMS patients and 32 healthy controls (HC). Cognitive domains commonly affected in MS patients were assessed. Linear regression was used to predict cognition. Diffusion measures were compared between groups using tract-based spatial statistics (TBSS). A total of 12 patients were classified as CI, and processing speed was the most commonly affected domain. The final regression model including demographic variables and radial diffusivity explained the greatest variance of cognitive performance (R 2  = 0.48, p = 0.002). SPMS patients showed widespread loss of WM integrity throughout the WM skeleton when compared with HC. When compared with CP patients, CI patients showed more extensive and severe damage of several WM tracts, including the fornix, superior longitudinal fasciculus and forceps major. Loss of WM integrity assessed using TBSS helps to explain cognitive decline in SPMS patients. © The Author(s), 2016.

The use of artificial neural networks and multiple linear regression to predict rate of medical waste generation

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

Jahandideh, Sepideh; Jahandideh, Samad; Asadabadi, Ebrahim Barzegari

2009-11-15

Prediction of the amount of hospital waste production will be helpful in the storage, transportation and disposal of hospital waste management. Based on this fact, two predictor models including artificial neural networks (ANNs) and multiple linear regression (MLR) were applied to predict the rate of medical waste generation totally and in different types of sharp, infectious and general. In this study, a 5-fold cross-validation procedure on a database containing total of 50 hospitals of Fars province (Iran) were used to verify the performance of the models. Three performance measures including MAR, RMSE and R{sup 2} were used to evaluate performancemore » of models. The MLR as a conventional model obtained poor prediction performance measure values. However, MLR distinguished hospital capacity and bed occupancy as more significant parameters. On the other hand, ANNs as a more powerful model, which has not been introduced in predicting rate of medical waste generation, showed high performance measure values, especially 0.99 value of R{sup 2} confirming the good fit of the data. Such satisfactory results could be attributed to the non-linear nature of ANNs in problem solving which provides the opportunity for relating independent variables to dependent ones non-linearly. In conclusion, the obtained results showed that our ANN-based model approach is very promising and may play a useful role in developing a better cost-effective strategy for waste management in future.« less

Design and baseline data from the Gratitude Research in Acute Coronary Events (GRACE) study

PubMed Central

Huffman, Jeff C.; Beale, Eleanor E.; Beach, Scott R.; Celano, Christopher M.; Belcher, Arianna M.; Moore, Shannon V.; Suarez, Laura; Gandhi, Parul U.; Motiwala, Shweta R.; Gaggin, Hanna; Januzzi, James L.

2015-01-01

Background Positive psychological constructs, especially optimism, have been linked with superior cardiovascular health. However, there has been minimal study of positive constructs in patients with acute coronary syndrome (ACS), despite the prevalence and importance of this condition. Furthermore, few studies have examined multiple positive psychological constructs and multiple cardiac-related outcomes within the same cohort to determine specifically which positive construct may affect a particular cardiac outcome. Materials and methods The Gratitude Research in Acute Coronary Events (GRACE) study examines the association between optimism/gratitude 2 weeks post-ACS and subsequent clinical outcomes. The primary outcome measure is physical activity at 6 months, measured via accelerometer, and key secondary outcome measures include levels of prognostic biomarkers and rates of nonelective cardiac rehospitalization at 6 months. These relationships will be analyzed using multivariate linear regression, controlling for sociodemographic, medical, and negative psychological factors; associations between baseline positive constructs and subsequent rehospitalizations will be assessed via Cox regression. Results Overall, 164 participants enrolled and completed the baseline 2-week assessment; the cohort had a mean age of 61.5 +/− 10.5 years and was 84% men; this was the first ACS for 58% of participants. Conclusion The GRACE study will determine whether optimism and gratitude are prospectively and independently associated with physical activity and other critical outcomes in the 6 months following an ACS. If these constructs are associated with superior outcomes, this may highlight the importance of these constructs as independent prognostic factors post-ACS. PMID:26166171

Design and baseline data from the Gratitude Research in Acute Coronary Events (GRACE) study.

PubMed

Huffman, Jeff C; Beale, Eleanor E; Beach, Scott R; Celano, Christopher M; Belcher, Arianna M; Moore, Shannon V; Suarez, Laura; Gandhi, Parul U; Motiwala, Shweta R; Gaggin, Hanna; Januzzi, James L

2015-09-01

Positive psychological constructs, especially optimism, have been linked with superior cardiovascular health. However, there has been minimal study of positive constructs in patients with acute coronary syndrome (ACS), despite the prevalence and importance of this condition. Furthermore, few studies have examined multiple positive psychological constructs and multiple cardiac-related outcomes within the same cohort to determine specifically which positive construct may affect a particular cardiac outcome. The Gratitude Research in Acute Coronary Events (GRACE) study examines the association between optimism/gratitude 2weeks post-ACS and subsequent clinical outcomes. The primary outcome measure is physical activity at 6months, measured via accelerometer, and key secondary outcome measures include levels of prognostic biomarkers and rates of nonelective cardiac rehospitalization at 6months. These relationships will be analyzed using multivariable linear regression, controlling for sociodemographic, medical, and negative psychological factors; associations between baseline positive constructs and subsequent rehospitalizations will be assessed via Cox regression. Overall, 164 participants enrolled and completed the baseline 2-week assessment; the cohort had a mean age of 61.5+/?10.5years and was 84% men; this was the first ACS for 58% of participants. The GRACE study will determine whether optimism and gratitude are prospectively and independently associated with physical activity and other critical outcomes in the 6months following an ACS. If these constructs are associated with superior outcomes, this may highlight the importance of these constructs as independent prognostic factors post-ACS. Copyright © 2015 Elsevier Inc. All rights reserved.

Experimental Investigations of Non-Stationary Properties In Radiometer Receivers Using Measurements of Multiple Calibration References

NASA Technical Reports Server (NTRS)

Racette, Paul; Lang, Roger; Zhang, Zhao-Nan; Zacharias, David; Krebs, Carolyn A. (Technical Monitor)

2002-01-01

Radiometers must be periodically calibrated because the receiver response fluctuates. Many techniques exist to correct for the time varying response of a radiometer receiver. An analytical technique has been developed that uses generalized least squares regression (LSR) to predict the performance of a wide variety of calibration algorithms. The total measurement uncertainty including the uncertainty of the calibration can be computed using LSR. The uncertainties of the calibration samples used in the regression are based upon treating the receiver fluctuations as non-stationary processes. Signals originating from the different sources of emission are treated as simultaneously existing random processes. Thus, the radiometer output is a series of samples obtained from these random processes. The samples are treated as random variables but because the underlying processes are non-stationary the statistics of the samples are treated as non-stationary. The statistics of the calibration samples depend upon the time for which the samples are to be applied. The statistics of the random variables are equated to the mean statistics of the non-stationary processes over the interval defined by the time of calibration sample and when it is applied. This analysis opens the opportunity for experimental investigation into the underlying properties of receiver non stationarity through the use of multiple calibration references. In this presentation we will discuss the application of LSR to the analysis of various calibration algorithms, requirements for experimental verification of the theory, and preliminary results from analyzing experiment measurements.

Clinical determinants of the 6-Minute Walk Test in bronchiectasis.

PubMed

Lee, A L; Button, B M; Ellis, S; Stirling, R; Wilson, J W; Holland, A E; Denehy, L

2009-05-01

The 6-Minute Walk Test (6MWT) is a widely used measurement of functional exercise capacity in chronic lung disease. While exercise intolerance has been identified in patients with bronchiectasis, the clinical determinants of the 6MWT in this population have not been examined. The aim of this study was to 1) establish the relationship between the 6-Minute Walk Distance (6MWD), disease severity and Health-Related Quality of Life (HRQOL) and 2) identify predictors of exercise tolerance in adults with bronchiectasis. The 6MWT was performed in 27 patients with bronchiectasis (mean [SD] FEV(1) 73.9% predicted [23.4]). Disease severity was assessed using spirometry and HRCT scoring while HRQOL was evaluated using the St George's Respiratory Questionnaire (SGRQ) and the Short-Form 36 (SF-36). The relationships were evaluated using correlation and multiple regression. The 6MWD correlated positively with FVC (r=0.52, p<0.01), generations of bronchopulmonary divisions (r(s)=0.38, p<0.05) and SF-36 physical summary (r=0.71, p<0.001) while a negative correlation was observed between all domains of the SGRQ (all correlations r>0.5, p<0.001). Multiple regression analysis indicated that the SGRQ activity, symptom scores and generations of bronchial divisions involved were identified as independent predictors of the 6MWD, explaining 76% of the variance. Measures of HRQOL demonstrated a stronger association with the 6MWD compared to physiological measures of disease severity in patients with predominantly mild to moderate bronchiectasis.

[Correlation of retinol binding protein 4 with 
metabolic indexes of glucose and 
lipid, bile cholesterol saturation index].

PubMed

Wang, Wen; Li, Nianfeng

2015-06-01

To measure retinol binding protein 4 (RBP4) levels in serum and bile and to analyze their relationship with insulin resistance, dyslipidemia or cholesterol saturation index (CSI).
 A total of 60 patients with gallstone were divided into a diabetes group (n=30) and a control group (n=30). The concentrations of RBP4 in serum and bile were detected by enzyme-linked immunosorbent assay (ELISA). Enzyme colorimetric method was used to measure the concentration of biliary cholesterol, bile acid and phospholipid. Biliary CSI was calculated by Carey table. Partial correlation and multiple linear regression analysis were used to evaluate the correlation between the RBP4 levels in serum or bile and the above indexes.
 The RBP4 concentrations in serum and bile in the diabetes group were significantly elevated compared with those in the control group (both P<0.01). There was no significant difference in the serum total bile acid (TBA), serum triglyceride (TG), serum high-density lipoprotein (HDL), bile TBA, bile total cholesterol (TC) , bile phospholipids and bile CSI between the 2 groups (all P>0.05); but the serum TC, low density lipoprotein (LDL), fasting blood glucose (FBG), fasting insulin (FINS), and homeostasis model assessment for insulin resistance (HOMA-IR) in the diabetes group were significantly increased compared to those in the control group (all P<0.05). The partial correlation analysis, which was adjusted by age, showed that the bile RBP4 was positively correlated with body mass index (BMI), waist circumference (WC), FINS, FBG, TC, LDL and HOMA-IR (r=0.283, 0.405, 0.685, 0.667, 0.553, 0.424 and 0.735, respectively), and the serum RBP4 was also positively correlated with the WC, FINS, FBG, TC, LDL and HOMA-IR (r=0.317, 0.734, 0.609, 0.528, 0.386 and 0.751, respectively). Stepwise multivariate linear regression analysis suggested that the HOMA-IR, BMI and WC were independently correlated with the level of bile RBP4 (multiple regression equation: Ybile RBP4=2.372XHOMA-IR+0.420XBMI+0.178XWC-26.813), and the serum RBP4 level was correlated with the HOMA-IR and WC independently (multiple regression equation: Yserum RBP4=2.832XHOMA-IR +0.235XWC-20.128). Multiple regression equations showed that HOMA-IR was the strongest correlation factor with RBP4.
 RBP4 concentrations in serum and bile in the diabetes group are significantly higher than those in the control group. HOMA-IR, BMI and WC are independently correlated with the level of bile RBP4. HOMA-IR and WC are independently correlated with the serum RBP4 level. HOMA-IR is the strongest correlation factor with RBP4. RBP4 might play an important role in the course of gallstone formation in Type 2 diabetes mellitus.

A nonparametric multiple imputation approach for missing categorical data.

PubMed

Zhou, Muhan; He, Yulei; Yu, Mandi; Hsu, Chiu-Hsieh

2017-06-06

Incomplete categorical variables with more than two categories are common in public health data. However, most of the existing missing-data methods do not use the information from nonresponse (missingness) probabilities. We propose a nearest-neighbour multiple imputation approach to impute a missing at random categorical outcome and to estimate the proportion of each category. The donor set for imputation is formed by measuring distances between each missing value with other non-missing values. The distance function is calculated based on a predictive score, which is derived from two working models: one fits a multinomial logistic regression for predicting the missing categorical outcome (the outcome model) and the other fits a logistic regression for predicting missingness probabilities (the missingness model). A weighting scheme is used to accommodate contributions from two working models when generating the predictive score. A missing value is imputed by randomly selecting one of the non-missing values with the smallest distances. We conduct a simulation to evaluate the performance of the proposed method and compare it with several alternative methods. A real-data application is also presented. The simulation study suggests that the proposed method performs well when missingness probabilities are not extreme under some misspecifications of the working models. However, the calibration estimator, which is also based on two working models, can be highly unstable when missingness probabilities for some observations are extremely high. In this scenario, the proposed method produces more stable and better estimates. In addition, proper weights need to be chosen to balance the contributions from the two working models and achieve optimal results for the proposed method. We conclude that the proposed multiple imputation method is a reasonable approach to dealing with missing categorical outcome data with more than two levels for assessing the distribution of the outcome. In terms of the choices for the working models, we suggest a multinomial logistic regression for predicting the missing outcome and a binary logistic regression for predicting the missingness probability.

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

USGS Publications Warehouse

Sophocleous, M.

2000-01-01

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

Real-Time Analysis of Isoprene in Breath by Using Ultraviolet-Absorption Spectroscopy with a Hollow Optical Fiber Gas Cell

PubMed Central

Iwata, Takuro; Katagiri, Takashi; Matsuura, Yuji

2016-01-01

A breath analysis system based on ultraviolet-absorption spectroscopy was developed by using a hollow optical fiber as a gas cell for real-time monitoring of isoprene in breath. The hollow optical fiber functions as an ultra-small-volume gas cell with a long path. The measurement sensitivity of the system was evaluated by using nitric-oxide gas as a gas sample. The evaluation result showed that the developed system, using a laser-driven, high-intensity light source and a 3-m-long, aluminum-coated hollow optical fiber, could successfully measure nitric-oxide gas with a 50 ppb concentration. An absorption spectrum of a breath sample in the wavelength region of around 200–300 nm was measured, and the measured spectrum revealed the main absorbing components in breath as water vapor, isoprene, and ozone converted from oxygen by radiation of ultraviolet light. The concentration of isoprene in breath was estimated by multiple linear regression. The regression analysis results showed that the proposed analysis system enables real-time monitoring of isoprene during the exhaling of breath. Accordingly, it is suitable for measuring the circadian variation of isoprene. PMID:27929387

Covariate Measurement Error Correction Methods in Mediation Analysis with Failure Time Data

PubMed Central

Zhao, Shanshan

2014-01-01

Summary Mediation analysis is important for understanding the mechanisms whereby one variable causes changes in another. Measurement error could obscure the ability of the potential mediator to explain such changes. This paper focuses on developing correction methods for measurement error in the mediator with failure time outcomes. We consider a broad definition of measurement error, including technical error and error associated with temporal variation. The underlying model with the ‘true’ mediator is assumed to be of the Cox proportional hazards model form. The induced hazard ratio for the observed mediator no longer has a simple form independent of the baseline hazard function, due to the conditioning event. We propose a mean-variance regression calibration approach and a follow-up time regression calibration approach, to approximate the partial likelihood for the induced hazard function. Both methods demonstrate value in assessing mediation effects in simulation studies. These methods are generalized to multiple biomarkers and to both case-cohort and nested case-control sampling design. We apply these correction methods to the Women's Health Initiative hormone therapy trials to understand the mediation effect of several serum sex hormone measures on the relationship between postmenopausal hormone therapy and breast cancer risk. PMID:25139469

Covariate measurement error correction methods in mediation analysis with failure time data.

PubMed

Zhao, Shanshan; Prentice, Ross L

2014-12-01

Mediation analysis is important for understanding the mechanisms whereby one variable causes changes in another. Measurement error could obscure the ability of the potential mediator to explain such changes. This article focuses on developing correction methods for measurement error in the mediator with failure time outcomes. We consider a broad definition of measurement error, including technical error, and error associated with temporal variation. The underlying model with the "true" mediator is assumed to be of the Cox proportional hazards model form. The induced hazard ratio for the observed mediator no longer has a simple form independent of the baseline hazard function, due to the conditioning event. We propose a mean-variance regression calibration approach and a follow-up time regression calibration approach, to approximate the partial likelihood for the induced hazard function. Both methods demonstrate value in assessing mediation effects in simulation studies. These methods are generalized to multiple biomarkers and to both case-cohort and nested case-control sampling designs. We apply these correction methods to the Women's Health Initiative hormone therapy trials to understand the mediation effect of several serum sex hormone measures on the relationship between postmenopausal hormone therapy and breast cancer risk. © 2014, The International Biometric Society.

Quality of Disease Management and Risk of Mortality in English Primary Care Practices.

PubMed

Dusheiko, Mark; Gravelle, Hugh; Martin, Stephen; Smith, Peter C

2015-10-01

To investigate whether better management of chronic conditions by family practices reduces mortality risk. Two random samples of 5 million patients registered with over 8,000 English family practices followed up for 4 years (2004/5-2007/8). Measures of the quality of disease management for 10 conditions were constructed for each family practice for each year. The outcome measure was an indicator taking the value 1 if the patient died during a specified year, 0 otherwise. Cross-section and multilevel panel data multiple logistic regressions were estimated. Covariates included age, gender, morbidity, hospitalizations, attributed socio-economic characteristics, and local health care supply measures. Although a composite measure of the quality of disease management for all 10 conditions was significantly associated with lower mortality, only the quality of stroke care was significant when all 10 quality measures were entered in the regression. The panel data results suggest that a 1 percent improvement in the quality of stroke care could reduce the annual number of deaths in England by 782 [95 percent CI: 423, 1140]. A longer study period may be necessary to detect any mortality impact of better management of other conditions. © Health Research and Educational Trust.

Real-Time Analysis of Isoprene in Breath by Using Ultraviolet-Absorption Spectroscopy with a Hollow Optical Fiber Gas Cell.

PubMed

Iwata, Takuro; Katagiri, Takashi; Matsuura, Yuji

2016-12-05

A breath analysis system based on ultraviolet-absorption spectroscopy was developed by using a hollow optical fiber as a gas cell for real-time monitoring of isoprene in breath. The hollow optical fiber functions as an ultra-small-volume gas cell with a long path. The measurement sensitivity of the system was evaluated by using nitric-oxide gas as a gas sample. The evaluation result showed that the developed system, using a laser-driven, high-intensity light source and a 3-m-long, aluminum-coated hollow optical fiber, could successfully measure nitric-oxide gas with a 50 ppb concentration. An absorption spectrum of a breath sample in the wavelength region of around 200-300 nm was measured, and the measured spectrum revealed the main absorbing components in breath as water vapor, isoprene, and ozone converted from oxygen by radiation of ultraviolet light. The concentration of isoprene in breath was estimated by multiple linear regression. The regression analysis results showed that the proposed analysis system enables real-time monitoring of isoprene during the exhaling of breath. Accordingly, it is suitable for measuring the circadian variation of isoprene.

Improving Lidar-based Aboveground Biomass Estimation with Site Productivity for Central Hardwood Forests, USA

NASA Astrophysics Data System (ADS)

Shao, G.; Gallion, J.; Fei, S.

2016-12-01

Sound forest aboveground biomass estimation is required to monitor diverse forest ecosystems and their impacts on the changing climate. Lidar-based regression models provided promised biomass estimations in most forest ecosystems. However, considerable uncertainties of biomass estimations have been reported in the temperate hardwood and hardwood-dominated mixed forests. Varied site productivities in temperate hardwood forests largely diversified height and diameter growth rates, which significantly reduced the correlation between tree height and diameter at breast height (DBH) in mature and complex forests. It is, therefore, difficult to utilize height-based lidar metrics to predict DBH-based field-measured biomass through a simple regression model regardless the variation of site productivity. In this study, we established a multi-dimension nonlinear regression model incorporating lidar metrics and site productivity classes derived from soil features. In the regression model, lidar metrics provided horizontal and vertical structural information and productivity classes differentiated good and poor forest sites. The selection and combination of lidar metrics were discussed. Multiple regression models were employed and compared. Uncertainty analysis was applied to the best fit model. The effects of site productivity on the lidar-based biomass model were addressed.

Population heterogeneity in the salience of multiple risk factors for adolescent delinquency.

PubMed

Lanza, Stephanie T; Cooper, Brittany R; Bray, Bethany C

2014-03-01

To present mixture regression analysis as an alternative to more standard regression analysis for predicting adolescent delinquency. We demonstrate how mixture regression analysis allows for the identification of population subgroups defined by the salience of multiple risk factors. We identified population subgroups (i.e., latent classes) of individuals based on their coefficients in a regression model predicting adolescent delinquency from eight previously established risk indices drawn from the community, school, family, peer, and individual levels. The study included N = 37,763 10th-grade adolescents who participated in the Communities That Care Youth Survey. Standard, zero-inflated, and mixture Poisson and negative binomial regression models were considered. Standard and mixture negative binomial regression models were selected as optimal. The five-class regression model was interpreted based on the class-specific regression coefficients, indicating that risk factors had varying salience across classes of adolescents. Standard regression showed that all risk factors were significantly associated with delinquency. Mixture regression provided more nuanced information, suggesting a unique set of risk factors that were salient for different subgroups of adolescents. Implications for the design of subgroup-specific interventions are discussed. Copyright © 2014 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

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

PubMed

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

2009-03-01

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

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.

Associations between self-rated health and personality.

PubMed

Aiken-Morgan, Adrienne T; Bichsel, Jacqueline; Savla, Jyoti; Edwards, Christopher L; Whitfield, Keith E

2014-01-01

The goal of our study was to examine how Big Five personality factors predict variability in self-rated health in a sample of older African Americans from the Baltimore Study of Black Aging. Personality was measured by the NEO Personality Inventory-Revised, and self-rated health was assessed by the Health Problems Checklist. The study sample had 202 women and 87 men. Ages ranged from 49 to 90 years (M = 67.2 years, SD = 8.55), and average years of formal education was 10.8 (SD = 3.3). Multiple linear regressions showed that neuroticism and extraversion were significant regression predictors of self-rated health, after controlling for demographic factors. These findings suggest individual personality traits may influence health ratings, behaviors, and decision-making among older African Americans.

Magnitude of income-related disparities in adverse perinatal outcomes.

PubMed

Shankardass, Ketan; O'Campo, Patricia; Dodds, Linda; Fahey, John; Joseph, Ks; Morinis, Julia; Allen, Victoria M

2014-03-04

To assess and compare multiple measurements of socioeconomic position (SEP) in order to determine the relationship with adverse perinatal outcomes across various contexts. A birth registry, the Nova Scotia Atlee Perinatal Database, was confidentially linked to income tax and related information for the year in which delivery occurred. Multiple logistic regression was used to examine odds ratios between multiple indicators of SEP and multiple adverse perinatal outcomes in 117734 singleton births between 1988 and 2003. Models for after tax family income were also adjusted for neighborhood deprivation to gauge the relative magnitude of effects related to SEP at both levels. Effects of SEP were stratified by single- versus multiple-parent family composition, and by urban versus rural location of residence. The risk of small for gestational age and spontaneous preterm birth was higher across all the indicators of lower SEP, while risk for large for gestational age was lower across indicators of lower SEP. Higher risk of postneonatal death was demonstrated for several measures of lower SEP. Higher material deprivation in the neighborhood of residence was associated with increased risk for perinatal death, small for gestational age birth, and iatrogenic and spontaneous preterm birth. Family composition and urbanicity were shown to modify the association between income and some perinatal outcomes. This study highlights the importance of understanding the definitions of SEP and the mechanisms that lead to the association between income and poor perinatal outcomes, and broadening the types of SEP measures used in some cases.

Magnitude of income-related disparities in adverse perinatal outcomes

PubMed Central

2014-01-01

Background To assess and compare multiple measurements of socioeconomic position (SEP) in order to determine the relationship with adverse perinatal outcomes across various contexts. Methods A birth registry, the Nova Scotia Atlee Perinatal Database, was confidentially linked to income tax and related information for the year in which delivery occurred. Multiple logistic regression was used to examine odds ratios between multiple indicators of SEP and multiple adverse perinatal outcomes in 117734 singleton births between 1988 and 2003. Models for after tax family income were also adjusted for neighborhood deprivation to gauge the relative magnitude of effects related to SEP at both levels. Effects of SEP were stratified by single- versus multiple-parent family composition, and by urban versus rural location of residence. Results The risk of small for gestational age and spontaneous preterm birth was higher across all the indicators of lower SEP, while risk for large for gestational age was lower across indicators of lower SEP. Higher risk of postneonatal death was demonstrated for several measures of lower SEP. Higher material deprivation in the neighborhood of residence was associated with increased risk for perinatal death, small for gestational age birth, and iatrogenic and spontaneous preterm birth. Family composition and urbanicity were shown to modify the association between income and some perinatal outcomes. Conclusions This study highlights the importance of understanding the definitions of SEP and the mechanisms that lead to the association between income and poor perinatal outcomes, and broadening the types of SEP measures used in some cases. PMID:24589212

Estimating Causal Effects of Education Interventions Using a Two-Rating Regression Discontinuity Design: Lessons from a Simulation Study and an Application

ERIC Educational Resources Information Center

Porter, Kristin E.; Reardon, Sean F.; Unlu, Fatih; Bloom, Howard S.; Cimpian, Joseph R.

2017-01-01

A valuable extension of the single-rating regression discontinuity design (RDD) is a multiple-rating RDD (MRRDD). To date, four main methods have been used to estimate average treatment effects at the multiple treatment frontiers of an MRRDD: the "surface" method, the "frontier" method, the "binding-score" method, and…

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…

Death Anxiety as a Predictor of Posttraumatic Stress Levels among Individuals with Spinal Cord Injuries

ERIC Educational Resources Information Center

Martz, Erin

2004-01-01

Because the onset of a spinal cord injury may involve a brush with death and because serious injury and disability can act as a reminder of death, death anxiety was examined as a predictor of posttraumatic stress levels among individuals with disabilities. This cross-sectional study used multiple regression and multivariate multiple regression to…

The Impact of Fire on Active Layer Thicknes

NASA Astrophysics Data System (ADS)

Schaefer, K. M.; Parsekian, A.; Natali, S.; Ludwig, S.; Michaelides, R. J.; Zebker, H. A.; Chen, J.

2016-12-01

Fire influences permafrost thermodynamics by darkening the surface to increase solar absorption and removing insulating moss and organic soil, resulting in an increase in Active Layer Thickness (ALT). The summer of 2015 was one of the worst fire years on record in Alaska with multiple fires in the Yukon-Kuskokwim (YK) Delta. To understand the impacts of fire on permafrost, we need large-scale, extensive measurements of ALT both within and outside the fire zones. In August 2016, we surveyed ALT across multiple fire zones in the YK Delta using Ground Penetrating Radar (GPR) and mechanical probing. GPR uses pulsed, radio-frequency electromagnetic waves to noninvasively image the subsurface and is an effective tool to quickly map ALT over large areas. We supplemented this ALT data with measurements of Volumetric Water Content (VWC), Organic Layer Thickness (OLT), and burn severity. We quantified the impacts of fire by statistically comparing the measurements inside and outside the fire zones and statistically regressing ALT against VWC, change in OLT, and burn severity.

Multicollinearity is a red herring in the search for moderator variables: A guide to interpreting moderated multiple regression models and a critique of Iacobucci, Schneider, Popovich, and Bakamitsos (2016).

PubMed

McClelland, Gary H; Irwin, Julie R; Disatnik, David; Sivan, Liron

2017-02-01

Multicollinearity is irrelevant to the search for moderator variables, contrary to the implications of Iacobucci, Schneider, Popovich, and Bakamitsos (Behavior Research Methods, 2016, this issue). Multicollinearity is like the red herring in a mystery novel that distracts the statistical detective from the pursuit of a true moderator relationship. We show multicollinearity is completely irrelevant for tests of moderator variables. Furthermore, readers of Iacobucci et al. might be confused by a number of their errors. We note those errors, but more positively, we describe a variety of methods researchers might use to test and interpret their moderated multiple regression models, including two-stage testing, mean-centering, spotlighting, orthogonalizing, and floodlighting without regard to putative issues of multicollinearity. We cite a number of recent studies in the psychological literature in which the researchers used these methods appropriately to test, to interpret, and to report their moderated multiple regression models. We conclude with a set of recommendations for the analysis and reporting of moderated multiple regression that should help researchers better understand their models and facilitate generalizations across studies.

Body Fat Percentage Prediction Using Intelligent Hybrid Approaches

PubMed Central

Shao, Yuehjen E.

2014-01-01

Excess of body fat often leads to obesity. Obesity is typically associated with serious medical diseases, such as cancer, heart disease, and diabetes. Accordingly, knowing the body fat is an extremely important issue since it affects everyone's health. Although there are several ways to measure the body fat percentage (BFP), the accurate methods are often associated with hassle and/or high costs. Traditional single-stage approaches may use certain body measurements or explanatory variables to predict the BFP. Diverging from existing approaches, this study proposes new intelligent hybrid approaches to obtain fewer explanatory variables, and the proposed forecasting models are able to effectively predict the BFP. The proposed hybrid models consist of multiple regression (MR), artificial neural network (ANN), multivariate adaptive regression splines (MARS), and support vector regression (SVR) techniques. The first stage of the modeling includes the use of MR and MARS to obtain fewer but more important sets of explanatory variables. In the second stage, the remaining important variables are served as inputs for the other forecasting methods. A real dataset was used to demonstrate the development of the proposed hybrid models. The prediction results revealed that the proposed hybrid schemes outperformed the typical, single-stage forecasting models. PMID:24723804

Partial Least Squares Regression Calibration of an Ultraviolet-Visible Spectrophotometer for Measurements of Chemical Oxygen Demand in Dye Wastewater

NASA Astrophysics Data System (ADS)

Mai, W.; Zhang, J.-F.; Zhao, X.-M.; Li, Z.; Xu, Z.-W.

2017-11-01

Wastewater from the dye industry is typically analyzed using a standard method for measurement of chemical oxygen demand (COD) or by a single-wavelength spectroscopic method. To overcome the disadvantages of these methods, ultraviolet-visible (UV-Vis) spectroscopy was combined with principal component regression (PCR) and partial least squares regression (PLSR) in this study. Unlike the standard method, this method does not require digestion of the samples for preparation. Experiments showed that the PLSR model offered high prediction performance for COD, with a mean relative error of about 5% for two dyes. This error is similar to that obtained with the standard method. In this study, the precision of the PLSR model decreased with the number of dye compounds present. It is likely that multiple models will be required in reality, and the complexity of a COD monitoring system would be greatly reduced if the PLSR model is used because it can include several dyes. UV-Vis spectroscopy with PLSR successfully enhanced the performance of COD prediction for dye wastewater and showed good potential for application in on-line water quality monitoring.

Data-driven discovery of partial differential equations.

PubMed

Rudy, Samuel H; Brunton, Steven L; Proctor, Joshua L; Kutz, J Nathan

2017-04-01

We propose a sparse regression method capable of discovering the governing partial differential equation(s) of a given system by time series measurements in the spatial domain. The regression framework relies on sparsity-promoting techniques to select the nonlinear and partial derivative terms of the governing equations that most accurately represent the data, bypassing a combinatorially large search through all possible candidate models. The method balances model complexity and regression accuracy by selecting a parsimonious model via Pareto analysis. Time series measurements can be made in an Eulerian framework, where the sensors are fixed spatially, or in a Lagrangian framework, where the sensors move with the dynamics. The method is computationally efficient, robust, and demonstrated to work on a variety of canonical problems spanning a number of scientific domains including Navier-Stokes, the quantum harmonic oscillator, and the diffusion equation. Moreover, the method is capable of disambiguating between potentially nonunique dynamical terms by using multiple time series taken with different initial data. Thus, for a traveling wave, the method can distinguish between a linear wave equation and the Korteweg-de Vries equation, for instance. The method provides a promising new technique for discovering governing equations and physical laws in parameterized spatiotemporal systems, where first-principles derivations are intractable.

Mapping of the DLQI scores to EQ-5D utility values using ordinal logistic regression.

PubMed

Ali, Faraz Mahmood; Kay, Richard; Finlay, Andrew Y; Piguet, Vincent; Kupfer, Joerg; Dalgard, Florence; Salek, M Sam

2017-11-01

The Dermatology Life Quality Index (DLQI) and the European Quality of Life-5 Dimension (EQ-5D) are separate measures that may be used to gather health-related quality of life (HRQoL) information from patients. The EQ-5D is a generic measure from which health utility estimates can be derived, whereas the DLQI is a specialty-specific measure to assess HRQoL. To reduce the burden of multiple measures being administered and to enable a more disease-specific calculation of health utility estimates, we explored an established mathematical technique known as ordinal logistic regression (OLR) to develop an appropriate model to map DLQI data to EQ-5D-based health utility estimates. Retrospective data from 4010 patients were randomly divided five times into two groups for the derivation and testing of the mapping model. Split-half cross-validation was utilized resulting in a total of ten ordinal logistic regression models for each of the five EQ-5D dimensions against age, sex, and all ten items of the DLQI. Using Monte Carlo simulation, predicted health utility estimates were derived and compared against those observed. This method was repeated for both OLR and a previously tested mapping methodology based on linear regression. The model was shown to be highly predictive and its repeated fitting demonstrated a stable model using OLR as well as linear regression. The mean differences between OLR-predicted health utility estimates and observed health utility estimates ranged from 0.0024 to 0.0239 across the ten modeling exercises, with an average overall difference of 0.0120 (a 1.6% underestimate, not of clinical importance). This modeling framework developed in this study will enable researchers to calculate EQ-5D health utility estimates from a specialty-specific study population, reducing patient and economic burden.

The Correlates of Body Composition with Heart Rate Recovery after Step Test: An Exploratory Study of Malaysian Adolescents

PubMed Central

Abu Hanifah, Redzal; Mohamed, Mohd. Nahar Azmi; Jaafar, Zulkarnain; Abdul Mohsein, Nabilla Al-Sadat; Jalaludin, Muhammad Yazid; Abdul Majid, Hazreen; Murray, Liam; Cantwell, Marie; Su, Tin Tin

2013-01-01

Background In adults, heart rate recovery is a predictor of mortality, while in adolescents it is associated with cardio-metabolic risk factors. The aim of this study was to examine the relationship between body composition measures and heart rate recovery (HRR) after step test in Malaysian secondary school students. Methods In the Malaysian Health and Adolescents Longitudinal Research Team (MyHEART) study, 1071 healthy secondary school students, aged 13 years old, participated in the step test. Parameters for body composition measures were body mass index z-score, body fat percentage, waist circumference, and waist height ratio. The step test was conducted by using a modified Harvard step test. Heart rate recovery of 1 minute (HRR1min) and heart rate recovery of 2 minutes (HRR2min) were calculated by the difference between the peak pulse rate during exercise and the resting pulse rate at 1 and 2 minutes, respectively. Analysis was done separately based on gender. Pearson correlation analysis was used to determine the association between the HRR parameters with body composition measures, while multiple regression analysis was used to determine which body composition measures was the strongest predictor for HRR. Results For both gender groups, all body composition measures were inversely correlated with HRR1min. In girls, all body composition measures were inversely correlated with HRR2min, while in boys all body composition measures, except BMI z-score, were associated with HRR2min. In multiple regression, only waist circumference was inversely associated with HRR2min (p=0.024) in boys, while in girls it was body fat percentage for HRR2min (p=0.008). Conclusion There was an inverse association between body composition measurements and HRR among apparently healthy adolescents. Therefore, it is important to identify cardio-metabolic risk factors in adolescent as an early prevention of consequent adulthood morbidity. This reiterates the importance of healthy living which should start from young. PMID:24349388

Left ventricular diastolic dysfunction and increased left ventricular mass index related to pulmonary hypertension in patients with systemic autoimmune disease without pericardial effusion.

PubMed

Sugiura, Atsushi; Funabashi, Nobusada; Ozawa, Koya; Kobayashi, Yoshio

2016-10-01

We investigated the relationship of left ventricular (LV) diastolic dysfunction and LV mass index (LVMI) against pulmonary hypertension (PH) in systemic autoimmune disease (SAD). A total of 84 SAD patients (68 females; 53±17years; systemic lupus erythematosus, 27%; scleroderma, 17%; vasculitis, 16%; mixed connective tissue disease, 13% and polymyositis/dermatomyositis complex, 10%) without significant pericardial effusion (PE) on TTE (Vivid E9, GE) were analyzed. On TTE, PH was defined as peak tricuspid regurgitation velocity (TRV) of ≥2.9m/s based upon 2015 ESC guideline. Left atrial volume index (LAVI) and E/E' were measured as indicators of LV diastolic dysfunction. LVMI was also measured. Seven patients (8%) had PH. PH patients had greater LAVI (p<0.001), E/E' (p=0.004), LVMI (p=0.009) than non-PH patients. LAVI (R=0.458), E/E' (R=0.337), and LVMI (R=0.313) significantly and positively correlated with TRV (all p<0.05). Multiple regression analysis was performed to explore determinants of TRV. Age, female sex, and brain natriuretic peptide (BNP) were included in all the models. Three multiple regression models were generated using 1) LAVI, 2) E/E', and 3) LVMI and included LAVI, E/E', LVMI, and BNP as significant variables influencing TRV. Multi logistic regression analysis for predicting TRV of ≥2.9m/s showed that LAVI, and E/E' were significant predictors (Odds ratio, 1.296, and 1.370, respectively). In SAD patients without PE, LV diastolic dysfunction and increment of LVMI was closely associated with PH based upon TRV. LAVI and E/E' were independent predictors for PH. Measuring LAVI and E/E' may be a key to determine the mechanism of PH in these patients. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Viscoelastic Parameters for Quantifying Liver Fibrosis: Three-Dimensional Multifrequency MR Elastography Study on Thin Liver Rat Slices

PubMed Central

Ronot, Maxime; Lambert, Simon A.; Wagner, Mathilde; Garteiser, Philippe; Doblas, Sabrina; Albuquerque, Miguel; Paradis, Valérie; Vilgrain, Valérie; Sinkus, Ralph; Van Beers, Bernard E.

2014-01-01

Objective To assess in a high-resolution model of thin liver rat slices which viscoelastic parameter at three-dimensional multifrequency MR elastography has the best diagnostic performance for quantifying liver fibrosis. Materials and Methods The study was approved by the ethics committee for animal care of our institution. Eight normal rats and 42 rats with carbon tetrachloride induced liver fibrosis were used in the study. The rats were sacrificed, their livers were resected and three-dimensional MR elastography of 5±2 mm liver slices was performed at 7T with mechanical frequencies of 500, 600 and 700 Hz. The complex shear, storage and loss moduli, and the coefficient of the frequency power law were calculated. At histopathology, fibrosis and inflammation were assessed with METAVIR score, fibrosis was further quantified with morphometry. The diagnostic value of the viscoelastic parameters for assessing fibrosis severity was evaluated with simple and multiple linear regressions, receiver operating characteristic analysis and Obuchowski measures. Results At simple regression, the shear, storage and loss moduli were associated with the severity of fibrosis. At multiple regression, the storage modulus at 600 Hz was the only parameter associated with fibrosis severity (r = 0.86, p<0.0001). This parameter had an Obuchowski measure of 0.89+/−0.03. This measure was significantly larger than that of the loss modulus (0.78+/−0.04, p = 0.028), but not than that of the complex shear modulus (0.88+/−0.03, p = 0.84). Conclusion Our high resolution, three-dimensional multifrequency MR elastography study of thin liver slices shows that the storage modulus is the viscoelastic parameter that has the best association with the severity of liver fibrosis. However, its diagnostic performance does not differ significantly from that of the complex shear modulus. PMID:24722733

The effect of social deprivation on local authority sickness absence rates.

PubMed

Wynn, P; Low, A

2008-06-01

There is an extensive body of research relating to the association between ergonomic and psychosocial factors on sickness absence rates. The impact of deprivation on health indices has also been extensively investigated. However, published research has not investigated the extent of any association between standard measures of deprivation and sickness absence and ill-health retirement rates. To establish if a relationship exists between standard measures of deprivation, used by the UK central government to determine regional health and social welfare funding, and sickness absence and ill-health early retirement rates in English local government employers. Local authority sickness absence rates for 2001-02 were regressed against the 2004 Indices of Multiple Deprivation in a multiple regression model that also included size and type of organization as independent variables. A second model using ill-health retirement as the dependent variable was also estimated. In the full regression models, organization size was not significant and reduced models with deprivation and organization type (depending on whether teachers were employed by the organization or not) were estimated. For the sickness absence model, the adjusted R(2) was 0.20, with 17% of the variation in sickness absence rates being explained by deprivation rank. Ill-health retirement showed a similar relationship with deprivation. In both models, the deprivation coefficients were highly significant: for sickness absence [t = -7.85 (P = 0.00)] and for ill-health retirement [t = -4.79 (P = 0.00)]. A significant proportion of variation in sickness absence and ill-health retirement rates in local government in England are associated with local measures of deprivation. Recognition of the impact of deprivation on sickness absence has implications for a number of different areas of work. These include target setting for Local Government Best Value Performance Indicators, history taking in sickness absence consultations and the role of deprivation as a confounding factor in sickness absence intervention studies.

Verifying the performance of artificial neural network and multiple linear regression in predicting the mean seasonal municipal solid waste generation rate: A case study of Fars province, Iran.

PubMed

Azadi, Sama; Karimi-Jashni, Ayoub

2016-02-01

Predicting the mass of solid waste generation plays an important role in integrated solid waste management plans. In this study, the performance of two predictive models, Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) was verified to predict mean Seasonal Municipal Solid Waste Generation (SMSWG) rate. The accuracy of the proposed models is illustrated through a case study of 20 cities located in Fars Province, Iran. Four performance measures, MAE, MAPE, RMSE and R were used to evaluate the performance of these models. The MLR, as a conventional model, showed poor prediction performance. On the other hand, the results indicated that the ANN model, as a non-linear model, has a higher predictive accuracy when it comes to prediction of the mean SMSWG rate. As a result, in order to develop a more cost-effective strategy for waste management in the future, the ANN model could be used to predict the mean SMSWG rate. Copyright © 2015 Elsevier Ltd. All rights reserved.

[Aggression and related factors in elementary school students].

PubMed

Ji, Eun Sun; Jang, Mi Heui

2010-10-01

This study was done to explore the relationship between aggression and internet over-use, depression-anxiety, self-esteem, all of which are known to be behavior and psychological characteristics linked to "at-risk" children for aggression. Korean-Child Behavior Check List (K-CBCL), Korean-Internet Addiction Self-Test Scale, and Self-Esteem Scale by Rosenberg (1965) were used as measurement tools with a sample of 743, 5th-6th grade students from 3 elementary schools in Jecheon city. Chi-square, t-test, ANOVA, Pearson's correlation and stepwise multiple regression with SPSS/Win 13.0 version were used to analyze the collected data. Aggression for the elementary school students was positively correlated with internet over-use and depression-anxiety, whereas self-esteem was negatively correlated with aggression. Stepwise multiple regression analysis showed that 68.4% of the variance for aggression was significantly accounted for by internet over-use, depression-anxiety, and self-esteem. The most significant factor influencing aggression was depression-anxiety. These results suggest that earlier screening and intervention programs for depression-anxiety and internet over-use for elementary student will be helpful in preventing aggression.

Negative Peer Relationships on Piracy Behavior: A Cross-Sectional Study of the Associations between Cyberbullying Involvement and Digital Piracy.

PubMed

Yubero, Santiago; Larrañaga, Elisa; Villora, Beatriz; Navarro, Raúl

2017-10-05

The present study examines the relationship between different roles in cyberbullying behaviors (cyberbullies, cybervictims, cyberbullies-victims, and uninvolved) and self-reported digital piracy. In a region of central Spain, 643 (49.3% females, 50.7% males) students (grades 7-10) completed a number of self-reported measures, including cyberbullying victimization and perpetration, self-reported digital piracy, ethical considerations of digital piracy, time spent on the Internet, and leisure activities related with digital content. The results of a series of hierarchical multiple regression models for the whole sample indicate that cyberbullies and cyberbullies-victims are associated with more reports of digital piracy. Subsequent hierarchical multiple regression analyses, done separately for males and females, indicate that the relationship between cyberbullying and self-reported digital piracy is sustained only for males. The ANCOVA analysis show that, after controlling for gender, self-reported digital piracy and time spent on the Internet, cyberbullies and cyberbullies-victims believe that digital piracy is a more ethically and morally acceptable behavior than victims and uninvolved adolescents believe. The results provide insight into the association between two deviant behaviors.

Regression Models and Fuzzy Logic Prediction of TBM Penetration Rate

NASA Astrophysics Data System (ADS)

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

2017-03-01

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

Physical disability, life stress, and psychosocial adjustment in multiple sclerosis.

PubMed

Zeldow, P B; Pavlou, M

1984-02-01

Eighty-one outpatients with diagnosed multiple sclerosis were studied in an effort to examine the relative contributions of physical health status, life stress, duration of illness, age, sex, marital status, and social class on various aspects of personal and interpersonal functioning. Stepwise multiple regression analyses were performed to identify the most significant discriminators of the seven psychosocial measures. Physical health status exerted the broadest influence, affecting personal efficiency and well-being, capacity for independent thought and action, self-confidence, self-reliance, and number of meaningful social contacts. Life stress was associated with lowered personal efficiency and sense of well-being. Duration of illness and the demographic variables had few or no effects on psychosocial adjustment. Discussion contrasts the present findings with others in the rehabilitation literature and specifies certain limitations of the study's design.

A statistical methodology for estimating transport parameters: Theory and applications to one-dimensional advectivec-dispersive systems

USGS Publications Warehouse

Wagner, Brian J.; Gorelick, Steven M.

1986-01-01

A simulation nonlinear multiple-regression methodology for estimating parameters that characterize the transport of contaminants is developed and demonstrated. Finite difference contaminant transport simulation is combined with a nonlinear weighted least squares multiple-regression procedure. The technique provides optimal parameter estimates and gives statistics for assessing the reliability of these estimates under certain general assumptions about the distributions of the random measurement errors. Monte Carlo analysis is used to estimate parameter reliability for a hypothetical homogeneous soil column for which concentration data contain large random measurement errors. The value of data collected spatially versus data collected temporally was investigated for estimation of velocity, dispersion coefficient, effective porosity, first-order decay rate, and zero-order production. The use of spatial data gave estimates that were 2–3 times more reliable than estimates based on temporal data for all parameters except velocity. Comparison of estimated linear and nonlinear confidence intervals based upon Monte Carlo analysis showed that the linear approximation is poor for dispersion coefficient and zero-order production coefficient when data are collected over time. In addition, examples demonstrate transport parameter estimation for two real one-dimensional systems. First, the longitudinal dispersivity and effective porosity of an unsaturated soil are estimated using laboratory column data. We compare the reliability of estimates based upon data from individual laboratory experiments versus estimates based upon pooled data from several experiments. Second, the simulation nonlinear regression procedure is extended to include an additional governing equation that describes delayed storage during contaminant transport. The model is applied to analyze the trends, variability, and interrelationship of parameters in a mourtain stream in northern California.

Depression, anxiety and general psychopathology in breast cancer patients: a cross-sectional control study.

PubMed

Fafouti, M; Paparrigopoulos, T; Zervas, Y; Rabavilas, A; Malamos, N; Liappas, I; Tzavara, C

2010-01-01

A significant proportion of breast cancer patients experience psychiatric morbidity. The present study compared the psychopathological profile (depression, anxiety and general psychopathology) of Greek women with breast cancer with a group of healthy controls. Patients (n=109) were recruited from a specialized oncology breast cancer department and healthy controls (n=71) from a breast outpatient clinic. General psychopathology was assessed by the SCL-90-R. The Montgomery-Asberg Depression Rating Scale (MADRS) and the Spielberger State-Trait Anxiety Inventory (STAI) were used for assessing depression and anxiety. Demographics and clinical characteristics were also recorded. Data were modeled using multiple regression analysis. The mean age was 54.7±18.1 years for the control group and 51.2±9.5 years for the patient group (p=0.288). Mean scores on SCL-90-R, MADRS and STAI were significantly higher in the cancer group compared to controls (p<0.05). Multiple regression analysis revealed that breast cancer was independently and positively associated with all psychological measures (p<0.05). Regression coefficients ranged from 0.19 (SCL-90-R, psychotism) to 0.33 (MADRS). Lower anger/aggressiveness and anxiety were found in highly educated women; divorced/widowed women scored higher on obsessionality and MADRS compared to married women. Psychiatric treatment was associated with higher scores on somatization, depression, phobic anxiety and general psychopathology. Anxiety, depression, and overall psychopathology are more frequent in breast cancer patients compared to controls. Disease makes a larger independent contribution to all psychopathological measures than any other investigated variable. Therefore, breast cancer patients should be closely followed up in order to identify and timely treat any mental health problems that may arise.

Use and misuse of motor-vehicle crash death rates in assessing highway-safety performance.

PubMed

O'Neill, Brian; Kyrychenko, Sergey Y

2006-12-01

The objectives of the article are to assess the extent to which comparisons of motor-vehicle crash death rates can be used to determine the effectiveness of highway-safety policies over time in a country or to compare policy effectiveness across countries. Motor-vehicle crash death rates per mile traveled in the 50 U.S. states from 1980 to 2003 are used to show the influence on these rates of factors independent of highway-safety interventions. Multiple regression models relating state death rates to various measures related to urbanization and demographics are used. The analyses demonstrate strong relationships between state death rates and urbanization and demographics. Almost 60% of the variability among the state death rates can be explained by the independent variables in the multiple regression models. When the death rates for passenger vehicle occupants (i.e., excluding motorcycle, pedestrian, and other deaths) are used in the regression models, almost 70% of the variability in the rates can be explained by urbanization and demographics. The analyses presented in the article demonstrate that motor-vehicle crash death rates are strongly influenced by factors unrelated to highway-safety countermeasures. Overall death rates should not be used as a basis for judging the effectiveness (or ineffectiveness) of specific highway-safety countermeasures or to assess overall highway-safety policies, especially across jurisdictions. There can be no substitute for the use of carefully designed scientific evaluations of highway-safety interventions that use outcome measures directly related to the intervention; e.g., motorcyclist deaths should be used to assess the effectiveness of motorcycle helmet laws. While this may seem obvious, there are numerous examples in the literature of death rates from all crashes being used to assess the effectiveness of interventions aimed at specific subsets of crashes.

Evaluation of the Brain Activity Using the Functional Near-Infared Spectroscopy while Having Stimulated by Pleasant and Unpleasant Music

NASA Astrophysics Data System (ADS)

Asano, Hirotoshi; Hiroshige, Satoru; Ide, Hideto

We propose the psychological research and physiological measurements. We used oxyHb as physiological measurements in order to evaluate the emotion of “pleasant-unpleasant”. Concretely, we evaluated the difference in the emotion of “pleasant-unpleasant” from oxyHb of the frontal lobe. The experiment showed that a relation between psychological amount and ⊿oxyHb. Based on the result, we presumed the psychological amount using the multiple regression analysis. As a result, it turned out that we can evaluate the emotion of “pleasant-unpleasant” by fNIRS.

Estimation of instantaneous heat transfer coefficients for a direct-injection stratified-charge rotary engine

NASA Technical Reports Server (NTRS)

Lee, C. M.; Addy, H. E.; Bond, T. H.; Chun, K. S.; Lu, C. Y.

1987-01-01

The main objective of this report was to derive equations to estimate heat transfer coefficients in both the combustion chamber and coolant pasage of a rotary engine. This was accomplished by making detailed temperature and pressure measurements in a direct-injection stratified-charge rotary engine under a range of conditions. For each sppecific measurement point, the local physical properties of the fluids were calculated. Then an empirical correlation of the coefficients was derived by using a multiple regression program. This correlation expresses the Nusselt number as a function of the Prandtl number and Reynolds number.

Prediction of health levels by remote sensing

NASA Technical Reports Server (NTRS)

Rush, M.; Vernon, S.

1975-01-01

Measures of the environment derived from remote sensing were compared to census population/housing measures in their ability to discriminate among health status areas in two urban communities. Three hypotheses were developed to explore the relationships between environmental and health data. Univariate and multiple step-wise linear regression analyses were performed on data from two sample areas in Houston and Galveston, Texas. Environmental data gathered by remote sensing were found to equal or surpass census data in predicting rates of health outcomes. Remote sensing offers the advantages of data collection for any chosen area or time interval, flexibilities not allowed by the decennial census.

Relationships between locus of control and paranormal beliefs.

PubMed

Newby, Robert W; Davis, Jessica Boyette

2004-06-01

The present study investigated the associations between scores on paranormal beliefs, locus of control, and certain psychological processes such as affect and cognitions as measured by the Linguistic Inquiry and Word Count. Analysis yielded significant correlations between scores on Locus of Control and two subscales of Tobacyk's (1988) Revised Paranormal Beliefs Scale, New Age Philosophy and Traditional Paranormal Beliefs. A step-wise multiple regression analysis indicated that Locus of Control was significantly related to New Age Philosophy. Other correlations were found between Tobacyk's subscales, Locus of Control, and three processes measured by the Linguistic Inquiry and Word Count.

Which Measurement of Blood Pressure Is More Associated With Albuminuria in Patients With Type 2 Diabetes: Central Blood Pressure or Peripheral Blood Pressure?

PubMed

Kitagawa, Noriyuki; Okada, Hiroshi; Tanaka, Muhei; Hashimoto, Yoshitaka; Kimura, Toshihiro; Nakano, Koji; Yamazaki, Masahiro; Hasegawa, Goji; Nakamura, Naoto; Fukui, Michiaki

2016-08-01

The aim of this study was to investigate whether central systolic blood pressure (SBP) was associated with albuminuria, defined as urinary albumin excretion (UAE) ≥30 mg/g creatinine, and, if so, whether the relationship of central SBP with albuminuria was stronger than that of peripheral SBP in patients with type 2 diabetes. The authors performed a cross-sectional study in 294 outpatients with type 2 diabetes. The relationship between peripheral SBP or central SBP and UAE using regression analysis was evaluated, and the odds ratios of peripheral SBP or central SBP were calculated to identify albuminuria using logistic regression model. Moreover, the area under the receiver operating characteristic curve (AUC) of central SBP was compared with that of peripheral SBP to identify albuminuria. Multiple regression analysis demonstrated that peripheral SBP (β=0.255, P<.0001) or central SBP (r=0.227, P<.0001) was associated with UAE. Multiple logistic regression analysis demonstrated that peripheral SBP (odds ratio, 1.029; 95% confidence interval, 1.016-1.043) or central SBP (odds ratio, 1.022; 95% confidence interval, 1.011-1.034) was associated with an increased odds of albuminuria. In addition, AUC of peripheral SBP was significantly greater than that of central SBP to identify albuminuria (P=0.035). Peripheral SBP is superior to central SBP in identifying albuminuria, although both peripheral and central SBP are associated with UAE in patients with type 2 diabetes. © 2016 Wiley Periodicals, Inc.

Prevalence and risk factors of non-carious cervical lesions related to occupational exposure to acid mists.

PubMed

Bomfim, Rafael Aiello; Crosato, Edgard; Mazzilli, Luiz Eugênio Nigro; Frias, Antonio Carlos

2015-01-01

This study evaluates the prevalence and risk factors of non-carious cervical lesions (NCCLs) in a Brazilian population of workers exposed and non-exposed to acid mists and chemical products. One hundred workers (46 exposed and 54 non-exposed) were evaluated in a Centro de Referência em Saúde do Trabalhador - CEREST (Worker's Health Reference Center). The workers responded to questionnaires regarding their personal information and about alcohol consumption and tobacco use. A clinical examination was conducted to evaluate the presence of NCCLs, according to WHO parameters. Statistical analyses were performed by unconditional logistic regression and multiple linear regression, with the critical level of p < 0.05. NCCLs were significantly associated with age groups (18-34, 35-44, 45-68 years). The unconditional logistic regression showed that the presence of NCCLs was better explained by age group (OR = 4.04; CI 95% 1.77-9.22) and occupational exposure to acid mists and chemical products (OR = 3.84; CI 95% 1.10-13.49), whereas the linear multiple regression revealed that NCCLs were better explained by years of smoking (p = 0.01) and age group (p = 0.04). The prevalence of NCCLs in the study population was particularly high (76.84%), and the risk factors for NCCLs were age, exposure to acid mists and smoking habit. Controlling risk factors through preventive and educative measures, allied to the use of personal protective equipment to prevent the occupational exposure to acid mists, may contribute to minimizing the prevalence of NCCLs.

Simultaneous multiple non-crossing quantile regression estimation using kernel constraints

PubMed Central

Liu, Yufeng; Wu, Yichao

2011-01-01

Quantile regression (QR) is a very useful statistical tool for learning the relationship between the response variable and covariates. For many applications, one often needs to estimate multiple conditional quantile functions of the response variable given covariates. Although one can estimate multiple quantiles separately, it is of great interest to estimate them simultaneously. One advantage of simultaneous estimation is that multiple quantiles can share strength among them to gain better estimation accuracy than individually estimated quantile functions. Another important advantage of joint estimation is the feasibility of incorporating simultaneous non-crossing constraints of QR functions. In this paper, we propose a new kernel-based multiple QR estimation technique, namely simultaneous non-crossing quantile regression (SNQR). We use kernel representations for QR functions and apply constraints on the kernel coefficients to avoid crossing. Both unregularised and regularised SNQR techniques are considered. Asymptotic properties such as asymptotic normality of linear SNQR and oracle properties of the sparse linear SNQR are developed. Our numerical results demonstrate the competitive performance of our SNQR over the original individual QR estimation. PMID:22190842

Bias-motivated bullying and psychosocial problems: implications for HIV risk behaviors among young men who have sex with men.

PubMed

Li, Michael Jonathan; Distefano, Anthony; Mouttapa, Michele; Gill, Jasmeet K

2014-02-01

The present study aimed to determine whether the experience of bias-motivated bullying was associated with behaviors known to increase the risk of HIV infection among young men who have sex with men (YMSM) aged 18-29, and to assess whether the psychosocial problems moderated this relationship. Using an Internet-based direct marketing approach in sampling, we recruited 545 YMSM residing in the USA to complete an online questionnaire. Multiple linear regression analyses tested three regression models where we controlled for sociodemographics. The first model indicated that bullying during high school was associated with unprotected receptive anal intercourse within the past 12 months, while the second model indicated that bullying after high school was associated with engaging in anal intercourse while under the influence of drugs or alcohol in the past 12 months. In the final regression model, our composite measure of HIV risk behavior was found to be associated with lifetime verbal harassment. None of the psychosocial problems measured in this study - depression, low self-esteem, and internalized homonegativity - moderated any of the associations between bias-motivated bullying victimization and HIV risk behaviors in our regression models. Still, these findings provide novel evidence that bullying prevention programs in schools and communities should be included in comprehensive approaches to HIV prevention among YMSM.

The allometry of coarse root biomass: log-transformed linear regression or nonlinear regression?

PubMed

Lai, Jiangshan; Yang, Bo; Lin, Dunmei; Kerkhoff, Andrew J; Ma, Keping

2013-01-01

Precise estimation of root biomass is important for understanding carbon stocks and dynamics in forests. Traditionally, biomass estimates are based on allometric scaling relationships between stem diameter and coarse root biomass calculated using linear regression (LR) on log-transformed data. Recently, it has been suggested that nonlinear regression (NLR) is a preferable fitting method for scaling relationships. But while this claim has been contested on both theoretical and empirical grounds, and statistical methods have been developed to aid in choosing between the two methods in particular cases, few studies have examined the ramifications of erroneously applying NLR. Here, we use direct measurements of 159 trees belonging to three locally dominant species in east China to compare the LR and NLR models of diameter-root biomass allometry. We then contrast model predictions by estimating stand coarse root biomass based on census data from the nearby 24-ha Gutianshan forest plot and by testing the ability of the models to predict known root biomass values measured on multiple tropical species at the Pasoh Forest Reserve in Malaysia. Based on likelihood estimates for model error distributions, as well as the accuracy of extrapolative predictions, we find that LR on log-transformed data is superior to NLR for fitting diameter-root biomass scaling models. More importantly, inappropriately using NLR leads to grossly inaccurate stand biomass estimates, especially for stands dominated by smaller trees.

Multi-analyte quantification in bioprocesses by Fourier-transform-infrared spectroscopy by partial least squares regression and multivariate curve resolution.

PubMed

Koch, Cosima; Posch, Andreas E; Goicoechea, Héctor C; Herwig, Christoph; Lendl, Bernhard

2014-01-07

This paper presents the quantification of Penicillin V and phenoxyacetic acid, a precursor, inline during Pencillium chrysogenum fermentations by FTIR spectroscopy and partial least squares (PLS) regression and multivariate curve resolution - alternating least squares (MCR-ALS). First, the applicability of an attenuated total reflection FTIR fiber optic probe was assessed offline by measuring standards of the analytes of interest and investigating matrix effects of the fermentation broth. Then measurements were performed inline during four fed-batch fermentations with online HPLC for the determination of Penicillin V and phenoxyacetic acid as reference analysis. PLS and MCR-ALS models were built using these data and validated by comparison of single analyte spectra with the selectivity ratio of the PLS models and the extracted spectral traces of the MCR-ALS models, respectively. The achieved root mean square errors of cross-validation for the PLS regressions were 0.22 g L(-1) for Penicillin V and 0.32 g L(-1) for phenoxyacetic acid and the root mean square errors of prediction for MCR-ALS were 0.23 g L(-1) for Penicillin V and 0.15 g L(-1) for phenoxyacetic acid. A general work-flow for building and assessing chemometric regression models for the quantification of multiple analytes in bioprocesses by FTIR spectroscopy is given. Copyright © 2013 The Authors. Published by Elsevier B.V. All rights reserved.

[Associations of the Employment Status during the First 2 Years Following Medical Rehabilitation and Long Term Occupational Trajectories: Implications for Outcome Measurement].

PubMed

Holstiege, J; Kaluscha, R; Jankowiak, S; Krischak, G

2017-02-01

Study Objectives: The aim was to investigate the predictive value of the employment status measured in the 6 th , 12 th , 18 th and 24 th month after medical rehabilitation for long-term employment trajectories during 4 years. Methods: A retrospective study was conducted based on a 20%-sample of all patients receiving inpatient rehabilitation funded by the German pension fund. Patients aged <62 years who were treated due to musculoskeletal, cardiovascular or psychosomatic disorders during the years 2002-2005 were included and followed for 4 consecutive years. The predictive value of the employment status in 4 predefined months after discharge (6 th , 12 th , 18 th and 24 th month), for the total number of months in employment in 4 years following rehabilitative treatment was analyzed using multiple linear regression. Per time point, separate regression analyses were conducted, including the employment status (employed vs. unemployed) at the respective point in time as explanatory variable, besides a standard set of additional prognostic variables. Results: A total of 252 591 patients were eligible for study inclusion. The level of explained variance of the regression models increased with the point in time used to measure the employment status, included as explanatory variable. Overall the R²-measure increased by 30% from the regression model that included the employment status in the 6 th month (R²=0.60) to the model that included the work status in the 24 th month (R²=0.78). Conclusion: The degree of accuracy in the prognosis of long-term employment biographies increases with the point in time used to measure employment in the first 2 years following rehabilitation. These findings should be taken into consideration for the predefinition of time points used to measure the employment status in future studies. © Georg Thieme Verlag KG Stuttgart · New York.

Comparison of the abbreviated minimal assessment of cognitive function in multiple sclerosis (aMACFIMS) and the brief international cognitive assessment for multiple sclerosis (BICAMS).

PubMed

Gromisch, Elizabeth S; Portnoy, Jeffrey G; Foley, Frederick W

2018-05-15

Cognitive impairment is a prevalent and often intrusive problem among persons with multiple sclerosis (PwMS). Valid and reliable assessments, including quick screening measures, are crucial. The Brief International Cognitive Assessment for MS (BICAMS) was developed for this reason. While it lends itself to use in locations where formal neuropsychological resources might be limited, it does not include measures of verbal fluency or executive functioning, domains assessed as part of the Minimal Assessment of Cognitive Function in MS (MACFIMS). Given previous evidence that shortened MACFIMS measures have strong criterion validity, this study aimed to determine which of these should be included in the abbreviated MACFIMS (aMACFIMS), and how the aMACFIMS compares to the BICAMS. One hundred forty-seven PwMS were included in the analyses. A stepwise logistic regression was used to determine the measures in the aMACFIMS. Receiver-operating-characteristic (ROC) curves assessed the classification accuracy, sensitivity, and specificity. The batteries' sensitivity, specificity, and predictive values were then compared. Compared to the BICAMS, the final aMACFIMS had higher specificity (87% versus 72%) and positive predictive value (86% versus 77%), but lower sensitivity (71% versus 81%). The aMACFIMS has several benefits, including reduced administration time and the addition of a verbal fluency/executive functioning measure. Copyright © 2018 Elsevier B.V. All rights reserved.

Application of Linear Mixed-Effects Models in Human Neuroscience Research: A Comparison with Pearson Correlation in Two Auditory Electrophysiology Studies

PubMed Central

Koerner, Tess K.; Zhang, Yang

2017-01-01

Neurophysiological studies are often designed to examine relationships between measures from different testing conditions, time points, or analysis techniques within the same group of participants. Appropriate statistical techniques that can take into account repeated measures and multivariate predictor variables are integral and essential to successful data analysis and interpretation. This work implements and compares conventional Pearson correlations and linear mixed-effects (LME) regression models using data from two recently published auditory electrophysiology studies. For the specific research questions in both studies, the Pearson correlation test is inappropriate for determining strengths between the behavioral responses for speech-in-noise recognition and the multiple neurophysiological measures as the neural responses across listening conditions were simply treated as independent measures. In contrast, the LME models allow a systematic approach to incorporate both fixed-effect and random-effect terms to deal with the categorical grouping factor of listening conditions, between-subject baseline differences in the multiple measures, and the correlational structure among the predictor variables. Together, the comparative data demonstrate the advantages as well as the necessity to apply mixed-effects models to properly account for the built-in relationships among the multiple predictor variables, which has important implications for proper statistical modeling and interpretation of human behavior in terms of neural correlates and biomarkers. PMID:28264422

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

PubMed Central

Libiger, Ondrej; Schork, Nicholas J.

2015-01-01

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

Features of natural and gonadotropin-releasing hormone antagonist-induced corpus luteum regression and effects of in vivo human chorionic gonadotropin.

PubMed

Del Canto, Felipe; Sierralta, Walter; Kohen, Paulina; Muñoz, Alex; Strauss, Jerome F; Devoto, Luigi

2007-11-01

The natural process of luteolysis and luteal regression is induced by withdrawal of gonadotropin support. The objectives of this study were: 1) to compare the functional changes and apoptotic features of natural human luteal regression and induced luteal regression; 2) to define the ultrastructural characteristics of the corpus luteum at the time of natural luteal regression and induced luteal regression; and 3) to examine the effect of human chorionic gonadotropin (hCG) on the steroidogenic response and apoptotic markers within the regressing corpus luteum. Twenty-three women with normal menstrual cycles undergoing tubal ligation donated corpus luteum at specific stages in the luteal phase. Some women received a GnRH antagonist prior to collection of corpus luteum, others received an injection of hCG with or without prior treatment with a GnRH antagonist. Main outcome measures were plasma hormone levels and analysis of excised luteal tissue for markers of apoptosis, histology, and ultrastructure. The progesterone and estradiol levels, corpus luteum DNA, and protein contents in induced luteal regression resembled those of natural luteal regression. hCG treatment raised progesterone and estradiol in both natural luteal regression and induced luteal regression. The increase in apoptosis detected in induced luteal regression by cytochrome c in the cytosol, activated caspase-3, and nuclear DNA fragmentation, was similar to that observed in natural luteal regression. The antiapoptotic protein Bcl-2 was significantly lower during natural luteal regression. The proapoptotic proteins Bax and Bak were at a constant level. Apoptotic and nonapoptotic death of luteal cells was observed in natural luteal regression and induced luteal regression at the ultrastructural level. hCG prevented apoptotic cell death, but not autophagy. The low number of apoptotic cells disclosed and the frequent autophagocytic suggest that multiple mechanisms are involved in cell death at luteal regression. hCG restores steroidogenic function and restrains the apoptotic process, but not autophagy.

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

PubMed

Yang, Xiaowei; Nie, Kun

2008-03-15

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

Correlates of motivation to change in pathological gamblers completing cognitive-behavioral group therapy.

PubMed

Gómez-Peña, Mónica; Penelo, Eva; Granero, Roser; Fernández-Aranda, Fernando; Alvarez-Moya, Eva; Santamaría, Juan José; Moragas, Laura; Neus Aymamí, Maria; Gunnard, Katarina; Menchón, José M; Jimenez-Murcia, Susana

2012-07-01

The present study analyzes the association between the motivation to change and the cognitive-behavioral group intervention, in terms of dropouts and relapses, in a sample of male pathological gamblers. The specific objectives were as follows: (a) to estimate the predictive value of baseline University of Rhode Island Change Assessment scale (URICA) scores (i.e., at the start of the study) as regards the risk of relapse and dropout during treatment and (b) to assess the incremental predictive ability of URICA scores, as regards the mean change produced in the clinical status of patients between the start and finish of treatment. The relationship between the URICA and the response to treatment was analyzed by means of a pre-post design applied to a sample of 191 patients who were consecutively receiving cognitive-behavioral group therapy. The statistical analysis included logistic regression models and hierarchical multiple linear regression models. The discriminative ability of the models including the four URICA scores regarding the likelihood of relapse and dropout was acceptable (area under the receiver operating haracteristic curve: .73 and .71, respectively). No significant predictive ability was found as regards the differences between baseline and posttreatment scores (changes in R(2) below 5% in the multiple regression models). The availability of useful measures of motivation to change would enable treatment outcomes to be optimized through the application of specific therapeutic interventions. © 2012 Wiley Periodicals, Inc.

Spirometry results (FEV1 and FVC) in young Bantu men from Tanzania vs environmental and family characteristics.

PubMed

Rębacz-Maron, Ewa; Parafiniuk, Mirosław

2014-01-01

The aim of this paper was to examine the extent to which socioeconomic factors, anthropological data and somatic indices influenced the results of spirometric measurements (FEV1 and FVC) in Tanzanian youth. The population studied were young black Bantu men aged 12.8-24.0 years. Analysis was performed for the whole data set (n = 255), as well as separately for two age groups: under 17.5 years (n = 168) and 17.5 + (n = 87). A backward stepwise multiple regression analysis was performed for FEV1 and FVC as dependent variables on socioeconomic and anthropometric data. Multiple regression analysis for the whole group revealed that the socioeconomic and anthropometric data under analysis accounted for 38% of the variation in FEV1. In addition the analysis demonstrated that 34% of the variation in FVC could be accounted for by the variables used in the regression. A significant impact in explaining the variability of FVC was exhibited by the thorax mobility, financial situation of the participants and Pignet-Verwaecka Index. Analysis of the data indicates the significant role of selected socio-economic factors on the development of the biological specimens investigated. There were no perceptible pathologies, and the results can be treated as a credible interpretation of the influence exerted by the environment in which the teenagers under study grew up.

Estimating Time to Event From Longitudinal Categorical Data: An Analysis of Multiple Sclerosis Progression.

PubMed

Mandel, Micha; Gauthier, Susan A; Guttmann, Charles R G; Weiner, Howard L; Betensky, Rebecca A

2007-12-01

The expanded disability status scale (EDSS) is an ordinal score that measures progression in multiple sclerosis (MS). Progression is defined as reaching EDSS of a certain level (absolute progression) or increasing of one point of EDSS (relative progression). Survival methods for time to progression are not adequate for such data since they do not exploit the EDSS level at the end of follow-up. Instead, we suggest a Markov transitional model applicable for repeated categorical or ordinal data. This approach enables derivation of covariate-specific survival curves, obtained after estimation of the regression coefficients and manipulations of the resulting transition matrix. Large sample theory and resampling methods are employed to derive pointwise confidence intervals, which perform well in simulation. Methods for generating survival curves for time to EDSS of a certain level, time to increase of EDSS of at least one point, and time to two consecutive visits with EDSS greater than three are described explicitly. The regression models described are easily implemented using standard software packages. Survival curves are obtained from the regression results using packages that support simple matrix calculation. We present and demonstrate our method on data collected at the Partners MS center in Boston, MA. We apply our approach to progression defined by time to two consecutive visits with EDSS greater than three, and calculate crude (without covariates) and covariate-specific curves.

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.

The association between financial literacy and Problematic Internet Shopping in a multinational sample.

PubMed

Lam, Lawrence T; Lam, Mary K

2017-12-01

To examine the association between financial literacy and Problematic Internet Shopping in adults. This cross-sectional online survey recruited participants, aged between 18 and 60 years, through an online research facility. The sample consisted of multinational participants from mainly three continents including Europe, North America, and Asia. Problematic Internet Shopping was assessed using the Bergen Shopping Addiction Scale (BSAS). Financial Literacy was measured by the Financial Literacy subscale of the Financial Wellbeing Questionnaire. Multiple linear regression analyses were conducted to elucidate the relationship between the study and outcome variables with adjustment for other potential risk factors. Of the total of 997 respondents with an average age of 30.9 (s.d. = 8.8), 135 (13.8%) could be classified as having a high risk of being Problematic Internet Shoppers. Results from the multiple regression analyses suggested a significant and negative relationship between financial literacy and Problematic Internet Shopping with a regression coefficient of - 0.13, after controlling for the effects of potential risk factors such as age, region of birth, employment, income, shopping frequency, self-regulation and anxiety (t = - 6.42, p < 0.001). The clinical management of PIS should include a financial counselling as a component of the treatment regime. Enhancement of financial literacy in the general population, particularly among young people, will likely have a positive effect on the occurrence of PIS.

Simultaneous determination of hydroquinone, resorcinol, phenol, m-cresol and p-cresol in untreated air samples using spectrofluorimetry and a custom multiple linear regression-successive projection algorithm.

PubMed

Pistonesi, Marcelo F; Di Nezio, María S; Centurión, María E; Lista, Adriana G; Fragoso, Wallace D; Pontes, Márcio J C; Araújo, Mário C U; Band, Beatriz S Fernández

2010-12-15

In this study, a novel, simple, and efficient spectrofluorimetric method to determine directly and simultaneously five phenolic compounds (hydroquinone, resorcinol, phenol, m-cresol and p-cresol) in air samples is presented. For this purpose, variable selection by the successive projections algorithm (SPA) is used in order to obtain simple multiple linear regression (MLR) models based on a small subset of wavelengths. For comparison, partial least square (PLS) regression is also employed in full-spectrum. The concentrations of the calibration matrix ranged from 0.02 to 0.2 mg L(-1) for hydroquinone, from 0.05 to 0.6 mg L(-1) for resorcinol, and from 0.05 to 0.4 mg L(-1) for phenol, m-cresol and p-cresol; incidentally, such ranges are in accordance with the Argentinean environmental legislation. To verify the accuracy of the proposed method a recovery study on real air samples of smoking environment was carried out with satisfactory results (94-104%). The advantage of the proposed method is that it requires only spectrofluorimetric measurements of samples and chemometric modeling for simultaneous determination of five phenols. With it, air is simply sampled and no pre-treatment sample is needed (i.e., separation steps and derivatization reagents are avoided) that means a great saving of time. Copyright © 2010 Elsevier B.V. All rights reserved.

Using Network Science Measures to Predict the Lexical Decision Performance of Adults Who Stutter.

PubMed

Castro, Nichol; Pelczarski, Kristin M; Vitevitch, Michael S

2017-07-12

Methods from network science have examined various aspects of language processing. Clinical populations may also benefit from these novel analyses. Phonological and lexical factors have been examined in adults who stutter (AWS) as potential contributing factors to stuttering, although differences reported are often subtle. We reexamined the performance of AWS and adults who do not stutter (AWNS) from a previously conducted lexical decision task in an attempt to determine if network science measures would provide additional insight into the phonological network of AWS beyond traditional psycholinguistic measures. Multiple regression was used to examine the influence of several traditional psycholinguistic measures as well as several new measures from network science on response times. AWS responded to low-frequency words more slowly than AWNS; responses for both groups were equivalent for high-frequency words. AWS responded to shorter words more slowly than AWNS, producing a reverse word-length effect. For the network measures, degree/neighborhood density and closeness centrality, but not whether a word was inside or outside the giant component, influenced response times similarly between groups. Network analyses suggest that multiple levels of the phonological network might influence phonological processing, not just the micro-level traditionally considered by mainstream psycholinguistics.

With a little help from my goals: integrating intergoal facilitation with the theory of planned behaviour to predict physical activity.

PubMed

Presseau, Justin; Sniehotta, Falko F; Francis, Jill J; Gebhardt, Winifred A

2010-11-01

Integration of a multiple goal theory approach into the theory of planned behaviour (TPB) to investigate how the perceived facilitating and conflicting relationships in multiple goal pursuit predict performance of a health-related behaviour. Prospective design with 8-week follow-up. At baseline, perceived intergoal facilitation and intergoal conflict were measured using personal projects analysis supplemented with standard TPB measures for physical activity (PA). Self-reported PA was measured at follow-up 8 weeks later. N=137 participants completed measures at both time points (55.4% response rate at follow-up). Hierarchical regression showed that perceived intergoal facilitation, but not intergoal conflict, directly predicted PA beyond intention and perceived behavioural control (PBC), accounting for more than 4% of additional variance in PA. Intergoal facilitation had an indirect effect on intention through attitude and PBC, and intention partially mediated the effect of intergoal facilitation on behaviour. The perceived facilitating effect of pursuing other personal goals predicts the performance of a health-related behaviour over and above single behaviour-focused social cognitions.

A spatial analysis of health-related resources in three diverse metropolitan areas

PubMed Central

Smiley, Melissa J.; Diez Roux, Ana V.; Brines, Shannon J.; Brown, Daniel G.; Evenson, Kelly R.; Rodriguez, Daniel A.

2010-01-01

Few studies have investigated the spatial clustering of multiple health-related resources. We constructed 0.5-mile kernel densities of resources for census areas in New York City, NY (n=819 block groups), Baltimore, MD (n=737), and Winston-Salem, NC (n=169). Three of the four resource densities (supermarkets/produce stores, retail areas, and recreational facilities) tended to be correlated with each other, whereas park density was less consistently and sometimes negatively correlated with the others. Blacks were more likely to live in block groups with multiple low resource densities. Spatial regression models showed that block groups with higher proportions of black residents tended to have lower supermarket/produce, retail, and recreational facility densities, although these associations did not always achieve statistical significance. A measure that combined local and neighboring block group racial composition was often a stronger predictor of resources than the local measure alone. Overall, our results from three diverse U.S. cities show that health-related resources are not randomly distributed across space and that disadvantage in multiple domains often clusters with residential racial patterning. PMID:20478737

Neuropsychological and structural brain lesions in multiple sclerosis: a regional analysis.

PubMed

Swirsky-Sacchetti, T; Mitchell, D R; Seward, J; Gonzales, C; Lublin, F; Knobler, R; Field, H L

1992-07-01

Quantified lesion scores derived from MRI correlate significantly with neuropsychological testing in patients with multiple sclerosis (MS). Variables used to reflect disease severity include total lesion area (TLA), ventricular-brain ratio, and size of the corpus callosum. We used these general measures of cerebral lesion involvement as well as specific ratings of lesion involvement by frontal, temporal, and parieto-occipital regions to quantify the topographic distribution of lesions and consequent effects upon cognitive function. Lesions were heavily distributed in the parieto-occipital regions bilaterally. Neuropsychological tests were highly related to all generalized measures of cerebral involvement, with TLA being the best predictor of neuropsychological deficit. Mean TLA for the cognitively impaired group was 28.30 cm2 versus 7.41 cm2 for the cognitively intact group (p less than 0.0001). Multiple regression analyses revealed that left frontal lobe involvement best predicted impaired abstract problem solving, memory, and word fluency. Left parieto-occipital lesion involvement best predicted deficits in verbal learning and complex visual-integrative skills. Analysis of regional cerebral lesion load may assist in understanding the particular pattern and course of cognitive deficits in MS.

Influence of ozone and meteorological parameters on levels of polycyclic aromatic hydrocarbons in the air

NASA Astrophysics Data System (ADS)

Pehnec, Gordana; Jakovljević, Ivana; Šišović, Anica; Bešlić, Ivan; Vađić, Vladimira

2016-04-01

Concentrations of ten polycyclic aromatic hydrocarbons (PAHs) in the PM10 particle fraction were measured together with ozone and meteorological parameters at an urban site (Zagreb, Croatia) over a one-year period. Data were subjected to regression analysis in order to determine the relationship between the measured pollutants and selected meteorological variables. All of the PAHs showed seasonal variations with high concentrations in winter and autumn and very low concentrations during summer and spring. All of the ten PAHs concentrations also correlated well with each other. A statistically significant negative correlation was found between the concentrations of PAHs and ozone concentrations and concentrations of PAHs and temperature, as well as a positive correlation between concentrations of PAHs and PM10 mass concentration and relative humidity. Multiple regression analysis showed that concentrations of PM10 and ozone, temperature, relative humidity and pressure accounted for 43-70% of PAHs variability. Concentrations of PM10 and temperature were significant variables for all of the measured PAH's concentrations in all seasons. Ozone concentrations were significant for only some of the PAHs, particularly 6-ring PAHs.

Relationships among personality traits, metabolic syndrome, and metabolic syndrome scores: The Kakegawa cohort study.

PubMed

Ohseto, Hisashi; Ishikuro, Mami; Kikuya, Masahiro; Obara, Taku; Igarashi, Yuko; Takahashi, Satomi; Kikuchi, Daisuke; Shigihara, Michiko; Yamanaka, Chizuru; Miyashita, Masako; Mizuno, Satoshi; Nagai, Masato; Matsubara, Hiroko; Sato, Yuki; Metoki, Hirohito; Tachibana, Hirofumi; Maeda-Yamamoto, Mari; Kuriyama, Shinichi

2018-04-01

Metabolic syndrome and the presence of metabolic syndrome components are risk factors for cardiovascular disease (CVD). However, the association between personality traits and metabolic syndrome remains controversial, and few studies have been conducted in East Asian populations. We measured personality traits using the Japanese version of the Eysenck Personality Questionnaire (Revised Short Form) and five metabolic syndrome components-elevated waist circumference, elevated triglycerides, reduced high-density lipoprotein cholesterol, elevated blood pressure, and elevated fasting glucose-in 1322 participants aged 51.1±12.7years old from Kakegawa city, Japan. Metabolic syndrome score (MS score) was defined as the number of metabolic syndrome components present, and metabolic syndrome as having the MS score of 3 or higher. We performed multiple logistic regression analyses to examine the relationship between personality traits and metabolic syndrome components and multiple regression analyses to examine the relationship between personality traits and MS scores adjusted for age, sex, education, income, smoking status, alcohol use, and family history of CVD and diabetes mellitus. We also examine the relationship between personality traits and metabolic syndrome presence by multiple logistic regression analyses. "Extraversion" scores were higher in those with metabolic syndrome components (elevated waist circumference: P=0.001; elevated triglycerides: P=0.01; elevated blood pressure: P=0.004; elevated fasting glucose: P=0.002). "Extraversion" was associated with the MS score (coefficient=0.12, P=0.0003). No personality trait was significantly associated with the presence of metabolic syndrome. Higher "extraversion" scores were related to higher MS scores, but no personality trait was significantly associated with the presence of metabolic syndrome. Copyright © 2018 Elsevier Inc. All rights reserved.

Forecasting USAF JP-8 Fuel Needs

DTIC Science & Technology

2009-03-01

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

The Overall Odds Ratio as an Intuitive Effect Size Index for Multiple Logistic Regression: Examination of Further Refinements

ERIC Educational Resources Information Center

Le, Huy; Marcus, Justin

2012-01-01

This study used Monte Carlo simulation to examine the properties of the overall odds ratio (OOR), which was recently introduced as an index for overall effect size in multiple logistic regression. It was found that the OOR was relatively independent of study base rate and performed better than most commonly used R-square analogs in indexing model…

FIRE: an SPSS program for variable selection in multiple linear regression analysis via the relative importance of predictors.

PubMed

Lorenzo-Seva, Urbano; Ferrando, Pere J

2011-03-01

We provide an SPSS program that implements currently recommended techniques and recent developments for selecting variables in multiple linear regression analysis via the relative importance of predictors. The approach consists of: (1) optimally splitting the data for cross-validation, (2) selecting the final set of predictors to be retained in the equation regression, and (3) assessing the behavior of the chosen model using standard indices and procedures. The SPSS syntax, a short manual, and data files related to this article are available as supplemental materials from brm.psychonomic-journals.org/content/supplemental.

Standardized Regression Coefficients as Indices of Effect Sizes in Meta-Analysis

ERIC Educational Resources Information Center

Kim, Rae Seon

2011-01-01

When conducting a meta-analysis, it is common to find many collected studies that report regression analyses, because multiple regression analysis is widely used in many fields. Meta-analysis uses effect sizes drawn from individual studies as a means of synthesizing a collection of results. However, indices of effect size from regression analyses…

Bayesian adjustment for measurement error in continuous exposures in an individually matched case-control study.

PubMed

Espino-Hernandez, Gabriela; Gustafson, Paul; Burstyn, Igor

2011-05-14

In epidemiological studies explanatory variables are frequently subject to measurement error. The aim of this paper is to develop a Bayesian method to correct for measurement error in multiple continuous exposures in individually matched case-control studies. This is a topic that has not been widely investigated. The new method is illustrated using data from an individually matched case-control study of the association between thyroid hormone levels during pregnancy and exposure to perfluorinated acids. The objective of the motivating study was to examine the risk of maternal hypothyroxinemia due to exposure to three perfluorinated acids measured on a continuous scale. Results from the proposed method are compared with those obtained from a naive analysis. Using a Bayesian approach, the developed method considers a classical measurement error model for the exposures, as well as the conditional logistic regression likelihood as the disease model, together with a random-effect exposure model. Proper and diffuse prior distributions are assigned, and results from a quality control experiment are used to estimate the perfluorinated acids' measurement error variability. As a result, posterior distributions and 95% credible intervals of the odds ratios are computed. A sensitivity analysis of method's performance in this particular application with different measurement error variability was performed. The proposed Bayesian method to correct for measurement error is feasible and can be implemented using statistical software. For the study on perfluorinated acids, a comparison of the inferences which are corrected for measurement error to those which ignore it indicates that little adjustment is manifested for the level of measurement error actually exhibited in the exposures. Nevertheless, a sensitivity analysis shows that more substantial adjustments arise if larger measurement errors are assumed. In individually matched case-control studies, the use of conditional logistic regression likelihood as a disease model in the presence of measurement error in multiple continuous exposures can be justified by having a random-effect exposure model. The proposed method can be successfully implemented in WinBUGS to correct individually matched case-control studies for several mismeasured continuous exposures under a classical measurement error model.

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

PubMed

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

2017-08-01

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

Financial burden is associated with worse health-related quality of life in adults with multiple endocrine neoplasia type 1.

PubMed

Peipert, Benjamin J; Goswami, Sneha; Helenowski, Irene; Yount, Susan E; Sturgeon, Cord

2017-12-01

Health-related quality of life and financial burden among patients with multiple endocrine neoplasia type 1 is poorly described. It is not known how financial burden influences health-related quality of life in this population. We hypothesized that the financial burden attributable to multiple endocrine neoplasia type 1 is associated with worse health-related quality of life. United States adults (≥18 years) with multiple endocrine neoplasia type 1 were recruited from the AMENSupport MEN online support group. Patient demographics, clinical characteristics, and financial burden were assessed via an online survey. The instrument Patient-Reported Outcomes Measurement Information System 29-item profile measure was used to assess health-related quality of life. Multivariable linear regression was used to identify significant variables in each Patient-Reported Outcomes Measurement Information System domain. Out of 1,378 members in AMENSupport, our survey link was accessed 449 times (33%). Of 153 US respondents who completed our survey, 84% reported financial burden attributable to multiple endocrine neoplasia type 1. The degree of financial burden had a linear relationship with worse health-related quality of life across all Patient-Reported Outcomes Measurement Information System domains (r = 0.36-0.55, P < .001); 63% reported experiencing ≥1 negative financial event(s). Borrowing money from friends/family (30%), unemployment (13%), and spending >$100/month out-of-pocket on prescription medications (46%) were associated consistently with impaired health-related quality of life (ß = 3.75-6.77, P < .05). Respondents were 3- and 34-times more likely to be unemployed and declare bankruptcy than the US population, respectively. This study characterizes the financial burden in patients with multiple endocrine neoplasia type 1. Individuals with multiple endocrine neoplasia type 1 report a high degree of financial burden, negative financial events, and unemployment. Each of these factors was associated with worse health-related quality of life. Copyright © 2017 Elsevier Inc. All rights reserved.

What is the relationship between renal function and visit-to-visit blood pressure variability in primary care? Retrospective cohort study from routinely collected healthcare data.

PubMed

Lasserson, Daniel S; Scherpbier de Haan, Nynke; de Grauw, Wim; van der Wel, Mark; Wetzels, Jack F; O'Callaghan, Christopher A

2016-06-10

To determine the relationship between renal function and visit-to-visit blood pressure (BP) variability in a cohort of primary care patients. Retrospective cohort study from routinely collected healthcare data. Primary care in Nijmegen, the Netherlands, from 2007 to 2012. 19 175 patients who had a measure of renal function, and 7 separate visits with BP readings in the primary care record. Visit-to-visit variability in systolic BP, calculated from the first 7 office measurements, including SD, successive variation, absolute real variation and metrics of variability shown to be independent of mean. Multiple linear regression was used to analyse the influence of estimated glomerular filtration rate (eGFR) on BP variability measures with adjustment for age, sex, diabetes, mean BP, proteinuria, cardiovascular disease, time interval between measures and antihypertensive use. In the patient cohort, 57% were women, mean (SD) age was 65.5 (12.3) years, mean (SD) eGFR was 75.6 (18.0) mL/min/1.73m(2) and SD systolic BP 148.3 (21.4) mm Hg. All BP variability measures were negatively correlated with eGFR and positively correlated with age. However, multiple linear regressions demonstrated consistent, small magnitude negative relationships between eGFR and all measures of BP variability adjusting for confounding variables. Worsening renal function is associated with small increases in measures of visit-to-visit BP variability after adjustment for confounding factors. This is seen across the spectrum of renal function in the population, and provides a mechanism whereby chronic kidney disease may raise the risk of cardiovascular events. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Destination bedside: using research findings to visualize optimal unit layouts and health information technology in support of bedside care.

PubMed

Watkins, Nicholas; Kennedy, Mary; Lee, Nelson; O'Neill, Michael; Peavey, Erin; Ducharme, Maria; Padula, Cynthia

2012-05-01

This study explored the impact of unit design and healthcare information technology (HIT) on nursing workflow and patient-centered care (PCC). Healthcare information technology and unit layout-related predictors of nursing workflow and PCC were measured during a 3-phase study involving questionnaires and work sampling methods. Stepwise multiple linear regressions demonstrated several HIT and unit layout-related factors that impact nursing workflow and PCC.

Factors associated with variation in financial condition among voluntary hospitals.

PubMed Central

Brecher, C; Nesbitt, S

1985-01-01

This article uses multiple regression analysis to identify factors which affect variations in the financial condition of voluntary hospitals in New York State. Six separate ratios are used to measure financial condition and 18 independent variables are considered. The factors affecting financial conditions were found to vary among dimensions of financial health, and different causal relationships were evident among hospitals in New York City than among those in the rest of the state. PMID:4019212

The interaction between stratospheric monthly mean regional winds and sporadic-E

NASA Astrophysics Data System (ADS)

Çetin, Kenan; Özcan, Osman; Korlaelçi, Serhat

2017-03-01

In the present study, a statistical investigation is carried out to explore whether there is a relationship between the critical frequency (foEs) of the sporadic-E layer that is occasionally seen on the E region of the ionosphere and the quasi-biennial oscillation (QBO) that flows in the east-west direction in the equatorial stratosphere. Multiple regression model as a statistical tool was used to determine the relationship between variables. In this model, the stationarity of the variables (foEs and QBO) was firstly analyzed for each station (Cocos Island, Gibilmanna, Niue Island, and Tahiti). Then, a co-integration test was made to determine the existence of a long-term relationship between QBO and foEs. After verifying the presence of a long-term relationship between the variables, the magnitude of the relationship between variables was further determined using the multiple regression model. As a result, it is concluded that the variations in foEs were explainable with QBO measured at 10 hPa altitude at the rate of 69%, 94%, 79%, and 58% for Cocos Island, Gibilmanna, Niue Island, and Tahiti stations, respectively. It is observed that the variations in foEs were explainable with QBO measured at 70 hPa altitude at the rate of 66%, 69%, 53%, and 47% for Cocos Island, Gibilmanna, Niue Island, and Tahiti stations, respectively.

Association between Social Activities and Cognitive Function among the Elderly in China: A Cross-Sectional Study.

PubMed

Fu, Chang; Li, Zhen; Mao, Zongfu

2018-01-30

Participation in social activities is one of important factors for older adults' health. The present study aims to examine the cross-sectional association between social activities and cognitive function among Chinese elderly. A total of 8966 individuals aged 60 and older from the 2015 China Health and Retirement Longitudinal Study were obtained for this study. Telephone interviews of cognitive status, episodic memory, and visuospatial abilities were assessed by questionnaire. We used the sum of all three of the above measures to represent the respondent's cognitive status as a whole. Types and frequencies of participation in social groups were used to measure social activities. Multiple linear regression analysis was used to explore the relationship between social activities and cognitive function. After adjustment for demographics, smoking, drinking, depression, hypertension, diabetes, basic activities of daily living, instrumental activities of daily living, and self-rated health, multiple linear regression analysis revealed that interaction with friends, participating in hobby groups, and sports groups were associated with better cognitive function among both men and women ( p < 0.05); doing volunteer work was associated with better cognitive function among women but not among men ( p < 0.05). These findings suggest that there is a cross-sectional association between participation in social activities and cognitive function among Chinese elderly. Longitudinal studies are needed to examine the effects of social activities on cognitive function.

Association between Social Activities and Cognitive Function among the Elderly in China: A Cross-Sectional Study

PubMed Central

Fu, Chang; Li, Zhen; Mao, Zongfu

2018-01-01

Participation in social activities is one of important factors for older adults’ health. The present study aims to examine the cross-sectional association between social activities and cognitive function among Chinese elderly. A total of 8966 individuals aged 60 and older from the 2015 China Health and Retirement Longitudinal Study were obtained for this study. Telephone interviews of cognitive status, episodic memory, and visuospatial abilities were assessed by questionnaire. We used the sum of all three of the above measures to represent the respondent’s cognitive status as a whole. Types and frequencies of participation in social groups were used to measure social activities. Multiple linear regression analysis was used to explore the relationship between social activities and cognitive function. After adjustment for demographics, smoking, drinking, depression, hypertension, diabetes, basic activities of daily living, instrumental activities of daily living, and self-rated health, multiple linear regression analysis revealed that interaction with friends, participating in hobby groups, and sports groups were associated with better cognitive function among both men and women (p < 0.05); doing volunteer work was associated with better cognitive function among women but not among men (p < 0.05). These findings suggest that there is a cross-sectional association between participation in social activities and cognitive function among Chinese elderly. Longitudinal studies are needed to examine the effects of social activities on cognitive function. PMID:29385773

Comparisons of Office and 24-Hour Ambulatory Blood Pressure Monitoring in Children with Obstructive Sleep Apnea.

PubMed

Kang, Kun-Tai; Chiu, Shuenn-Nan; Weng, Wen-Chin; Lee, Pei-Lin; Hsu, Wei-Chung

2017-03-01

To compare office blood pressure (BP) and 24-hour ambulatory BP (ABP) monitoring to facilitate the diagnosis and management of hypertension in children with obstructive sleep apnea (OSA). Children aged 4-16 years with OSA-related symptoms were recruited from a tertiary referral medical center. All children underwent overnight polysomnography, office BP, and 24-hour ABP studies. Multiple linear regression analyses were applied to elucidate the association between the apnea-hypopnea index and BP. Correlation and consistency between office BP and 24-hour ABP were measured by Pearson correlation, intraclass correlation, and Bland-Altman analyses. In the 163 children enrolled (mean age, 8.2 ± 3.3 years; 67% male). The prevalence of systolic hypertension at night was significantly higher in children with moderate-to-severe OSA than in those with primary snoring (44.9% vs 16.1%, P = .006). Pearson correlation and intraclass correlation analyses revealed associations between office BP and 24-hour BP, and Bland-Altman analysis indicated an agreement between office and 24-hour BP measurements. However, multiple linear regression analyses demonstrated that 24-hour BP (nighttime systolic BP and mean arterial pressure), unlike office BP, was independently associated with the apnea-hypopnea index, after adjustment for adiposity variables. Twenty-four-hour ABP is more strongly correlated with OSA in children, compared with office BP. Copyright © 2016 Elsevier Inc. All rights reserved.

Decision making of heavy cannabis users on the Iowa Gambling Task: stronger association with THC of hair analysis than with personality traits of the Tridimensional Personality Questionnaire.

PubMed

Hermann, Derik; Leménager, Tagrid; Gelbke, Jan; Welzel, Helga; Skopp, Gisela; Mann, Karl

2009-01-01

It is unclear whether impairment in decision making, measured by the Iowa Gambling Task (IGT), in addiction is substance-induced or the consequence of personality structure. Analysis of the IGT, the Tridimensional Personality Questionnaire (TPQ) and cannabinoids in hair and urine were performed in 13 cannabis users and matched controls. Hair Delta(9)-tetrahydrocannabinol (THC) correlated negatively with the last subtrial (cards 80-100) of the IGT (R = -0.67). In all participants (n = 26) the TPQ dimension, harm avoidance, correlated negatively with the total IGT score (R = -0.46). The last IGT-subtrial correlated with adventure seeking (R = 0.43), harm avoidance (R = -0.39) and reward dependence (R = -0.44). The last subtrial gives information on whether a participant has learned the IGT strategy. Multiple regression confirmed the impact of THC on the last subtrial, whereas TPQ personality traits did not additionally explain variance. Former indications of the IGT performance depending on the amount of cannabis consumed were replicated with an objective measurement of chronic cannabis consumption (hair THC). Multiple regression analysis argues for a stronger impact of chronic THC consumption than personality traits, but does not provide a causal relationship. Other factors (e.g. genetic) may also play a role. 2009 S. Karger AG, Basel.

The association between subgingival periodontal pathogens and systemic inflammation.

PubMed

Winning, Lewis; Patterson, Christopher C; Cullen, Kathy M; Stevenson, Kathryn A; Lundy, Fionnuala T; Kee, Frank; Linden, Gerard J

2015-09-01

To investigate associations between periodontal disease pathogens and levels of systemic inflammation measured by C-reactive protein (CRP). A representative sample of dentate 60-70-year-old men in Northern Ireland had a comprehensive periodontal examination. Men taking statins were excluded. Subgingival plaque samples were analysed by quantitative real time PCR to identify the presence of Aggregatibacter actinomycetemcomitans, Porphyromonas gingivalis, Treponema denticola and Tannerella forsythia. High-sensitivity CRP (mg/l) was measured from fasting blood samples. Multiple linear regression analysis was performed using log-transformed CRP concentration as the dependent variable, with the presence of each periodontal pathogen as predictor variables, with adjustment for various potential confounders. A total of 518 men (mean age 63.6 SD 3.0 years) were included in the analysis. Multiple regression analysis showed that body mass index (p < 0.001), current smoking (p < 0.01), the detectable presence of P. gingivalis (p < 0.01) and hypertension (p = 0.01), were independently associated with an increased CRP. The detectable presence of P. gingivalis was associated with a 20% (95% confidence interval 4-35%) increase in CRP (mg/l) after adjustment for all other predictor variables. In these 60-70-year-old dentate men, the presence of P. gingivalis in subgingival plaque was significantly associated with a raised level of C-reactive protein. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Correlation and simple linear regression.

PubMed

Eberly, Lynn E

2007-01-01

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

A simple measure of cognitive reserve is relevant for cognitive performance in MS patients.

PubMed

Della Corte, Marida; Santangelo, Gabriella; Bisecco, Alvino; Sacco, Rosaria; Siciliano, Mattia; d'Ambrosio, Alessandro; Docimo, Renato; Cuomo, Teresa; Lavorgna, Luigi; Bonavita, Simona; Tedeschi, Gioacchino; Gallo, Antonio

2018-05-04

Cognitive reserve (CR) contributes to preserve cognition despite brain damage. This theory has been applied to multiple sclerosis (MS) to explain the partial relationship between cognition and MRI markers of brain pathology. Our aim was to determine the relationship between two measures of CR and cognition in MS. One hundred and forty-seven MS patients were enrolled. Cognition was assessed using the Rao's Brief Repeatable Battery and the Stroop Test. CR was measured as the vocabulary subtest of the WAIS-R score (VOC) and the number of years of formal education (EDU). Regression analysis included raw score data on each neuropsychological (NP) test as dependent variables and demographic/clinical parameters, VOC, and EDU as independent predictors. A binary logistic regression analysis including clinical/CR parameters as covariates and absence/presence of cognitive deficits as dependent variables was performed too. VOC, but not EDU, was strongly correlated with performances at all ten NP tests. EDU was correlated with executive performances. The binary logistic regression showed that only the Expanded Disability Status Scale (EDSS) and VOC were independently correlated with the presence/absence of CD. The lower the VOC and/or the higher the EDSS, the higher the frequency of CD. In conclusion, our study supports the relevance of CR in subtending cognitive performances and the presence of CD in MS patients.

Suicidal Ideation and Schizophrenia: Contribution of Appraisal, Stigmatization, and Cognition.

PubMed

Stip, Emmanuel; Caron, Jean; Tousignant, Michel; Lecomte, Yves

2017-10-01

To predict suicidal ideation in people with schizophrenia, certain studies have measured its relationship with the variables of defeat and entrapment. The relationships are positive, but their interactions remain undefined. To further their understanding, this research sought to measure the relationship between suicidal ideation with the variables of loss, entrapment, and humiliation. The convenience sample included 30 patients with schizophrenia spectrum disorders. The study was prospective (3 measurement times) during a 6-month period. Results were analyzed by stepwise multiple regression. The contribution of the 3 variables to the variance of suicidal ideation was not significant at any of the 3 times (T1: 16.2%, P = 0.056; T2: 19.9%, P = 0.117; T3: 11.2%, P = 0.109). Further analyses measured the relationship between the variables of stigmatization, perceived cognitive dysfunction, symptoms, depression, self-esteem, reason to live, spirituality, social provision, and suicidal ideation. Stepwise multiple regression demonstrated that the contribution of the variables of stigmatization and perceived cognitive dysfunction to the variance of suicidal ideation was significant at all 3 times (T1: 41.7.5%, P = 0.000; T2: 35.2%, P = 0.001; T3: 21.5%, P = 0.012). Yet, over time, the individual contribution of the variables changed: T1, stigmatization (β = 0.518; P = 0.002); T2, stigmatization (β = 0.394; P = 0.025) and perceived cognitive dysfunction (β = 0.349; P = 0.046). Then, at T3, only perceived cognitive dysfunction contributed significantly to suicidal ideation (β = 0.438; P = 0.016). The results highlight the importance of the contribution of the variables of perceived cognitive dysfunction and stigmatization in the onset of suicidal ideation in people with schizophrenia spectrum disorders.

Measures of Heart Rate Variability in Individuals With Somatic Symptom Disorder.

PubMed

Huang, Wei-Lieh; Liao, Shih-Cheng; Yang, Cheryl C H; Kuo, Terry B J; Chen, Tzu-Ting; Chen, I-Ming; Gau, Susan Shur-Fen

2017-01-01

Little is known about autonomic nervous system activity in individuals with somatic symptom disorder (SSD) as defined by DSM-V criteria. The aims of this study were to investigate whether individuals with SSD differ from healthy controls in heart rate variability (HRV) measures of autonomic nervous system activity and whether sex has a moderating effect on this association. We recruited 168 individuals with SSD (35.1% men) and 106 healthy controls (27.4% men). Demographics, HRV, and psychological factors were measured using the Patient Health Questionnaire-15, Health Anxiety Questionnaire, Beck Depression Inventory-II (BDI-II), and Beck Anxiety Inventory. Multiple regression analysis was used to examine the association of SSD with HRV, adjusting for demographic and psychological measures. Individuals with SSD had lower levels of total-power HRV and low-frequency HRV, but no differences in high-frequency (HF)-HRV were found. HRV differences between SSD and controls varied by sex and age (triple interaction TP-HRV β = -0.222, p < .001; low-frequency HRV β = -0.332, p < .001; and HF-HRV β = -0.167, p = .006). Whole-sample multiple regression analyses revealed significant sex differences in the magnitudes of the association between BDI-II with HF-HRV (β of sex*BDI-II: 0.761, p = .005) and analyses stratified by sex indicated that HF-HRV was significantly correlated with depression in men with SSD (r = -0.491, p < .001) but not in women with SSD (r = 0.057, p = .558). These results suggest that patients with SSD demonstrate different patterns of HRV and the patterns of association between HRV indices and psychological factors vary between men and women.

Workplace bullying and mental distress - a prospective study of Norwegian employees.

PubMed

Finne, Live Bakke; Knardahl, Stein; Lau, Bjørn

2011-07-01

Using a prospective design, the objective of this study was to determine the relationship between workplace bullying and mental distress. Altogether, 1971 Norwegian employees, recruited from 20 organizations, answered questions regarding workplace bullying and mental distress at both baseline and follow-up. Baseline data were gathered between 2004-2006, and follow-up data were gathered between 2006-2009. The time-lag between baseline and follow-up was approximately two years for all the respondents in all the organizations. The factors measured in the study were individual characteristics, mental distress measured with the Hopkins Symptom Checklist (HSCL-10), self-reported workplace bullying measured with a single item from the General Nordic Questionnaire for Psychological and Social Factors at Work (QPSNordic) and job demands and job control assessed by QPSNordic. A multiple linear regression analysis adjusted for mental distress, sex, age, job demands and job control at baseline [β=0.05, 95% confidence interval (95% CI) 0.03-0.17] and a repeated measures ANOVA adjusted for sex and age [F(3,1965)=38.37; partial η (2)=0.06] showed that workplace bullying predicted mental distress. Furthermore, a multiple binary logistic regression analysis adjusted for bullying, sex, age, job demands and job control at baseline [odds ratio (OR) 2.30, 95% CI 1.43-3.69] showed that mental distress was a predictor of bullying. We found support for the notion that self-reported workplace bullying is a predictor of mental distress two years later. Bullying had an independent effect on mental distress after adjusting for job demands and job control. Mental distress was also found to be a predictor of bullying, indicating that the reverse relationship is also important.

Suicidal Ideation and Schizophrenia: Contribution of Appraisal, Stigmatization, and Cognition

PubMed Central

Stip, Emmanuel; Caron, Jean; Tousignant, Michel

2017-01-01

Objective: To predict suicidal ideation in people with schizophrenia, certain studies have measured its relationship with the variables of defeat and entrapment. The relationships are positive, but their interactions remain undefined. To further their understanding, this research sought to measure the relationship between suicidal ideation with the variables of loss, entrapment, and humiliation. Method: The convenience sample included 30 patients with schizophrenia spectrum disorders. The study was prospective (3 measurement times) during a 6-month period. Results were analyzed by stepwise multiple regression. Results: The contribution of the 3 variables to the variance of suicidal ideation was not significant at any of the 3 times (T1: 16.2%, P = 0.056; T2: 19.9%, P = 0.117; T3: 11.2%, P = 0.109). Further analyses measured the relationship between the variables of stigmatization, perceived cognitive dysfunction, symptoms, depression, self-esteem, reason to live, spirituality, social provision, and suicidal ideation. Stepwise multiple regression demonstrated that the contribution of the variables of stigmatization and perceived cognitive dysfunction to the variance of suicidal ideation was significant at all 3 times (T1: 41.7.5%, P = 0.000; T2: 35.2%, P = 0.001; T3: 21.5%, P = 0.012). Yet, over time, the individual contribution of the variables changed: T1, stigmatization (β = 0.518; P = 0.002); T2, stigmatization (β = 0.394; P = 0.025) and perceived cognitive dysfunction (β = 0.349; P = 0.046). Then, at T3, only perceived cognitive dysfunction contributed significantly to suicidal ideation (β = 0.438; P = 0.016). Conclusion: The results highlight the importance of the contribution of the variables of perceived cognitive dysfunction and stigmatization in the onset of suicidal ideation in people with schizophrenia spectrum disorders. PMID:28673099

Principal component analysis of early alcohol, drug and tobacco use with major depressive disorder in US adults.

PubMed

Wang, Kesheng; Liu, Ying; Ouedraogo, Youssoufou; Wang, Nianyang; Xie, Xin; Xu, Chun; Luo, Xingguang

2018-05-01

Early alcohol, tobacco and drug use prior to 18 years old are comorbid and correlated. This study included 6239 adults with major depressive disorder (MDD) in the past year and 72,010 controls from the combined data of 2013 and 2014 National Survey on Drug Use and Health (NSDUH). To deal with multicollinearity existing among 17 variables related to early alcohol, tobacco and drug use prior to 18 years old, we used principal component analysis (PCA) to infer PC scores and then use weighted multiple logistic regression analyses to estimate the associations of potential factors and PC scores with MDD. The odds ratios (ORs) with 95% confidence intervals (CIs) were estimated. The overall prevalence of MDD was 6.7%. The first four PCs could explain 57% of the total variance. Weighted multiple logistic regression showed that PC 1 (a measure of psychotherapeutic drugs and illicit drugs other than marijuana use), PC 2 (a measure of cocaine and hallucinogens), PC 3 (a measure of early alcohol, cigarettes, and marijuana use), and PC 4 (a measure of cigar, smokeless tobacco use and illicit drugs use) revealed significant associations with MDD (OR = 1.12, 95% CI = 1.08-1.16, OR = 1.08, 95% CI = 1.04-1.12, OR = 1.13, 95% CI = 1.07-1.18, and OR = 1.15, 95% CI = 1.09-1.21, respectively). In conclusion, PCA can be used to reduce the indicators in complex survey data. Early alcohol, tobacco and drug use prior to 18 years old were found to be associated with increased odds of adult MDD. Copyright © 2018 Elsevier Ltd. All rights reserved.

Prediction of Maximal Oxygen Uptake by Six-Minute Walk Test and Body Mass Index in Healthy Boys.

PubMed

Jalili, Majid; Nazem, Farzad; Sazvar, Akbar; Ranjbar, Kamal

2018-05-14

To develop an equation to predict maximal oxygen uptake (VO2max) based on the 6-minute walk test (6MWT) and body composition in healthy boys. Direct VO2max, 6-minute walk distance, and anthropometric characteristics were measured in 349 healthy boys (12.49 ± 2.72 years). Multiple regression analysis was used to generate VO2max prediction equations. Cross-validation of the VO2max prediction equations was assessed with predicted residual sum of squares statistics. Pearson correlation was used to assess the correlation between measured and predicted VO2max. Objectively measured VO2max had a significant correlation with demographic and 6MWT characteristics (R = 0.11-0.723, P < .01). Multiple regression analysis revealed the following VO2max prediction equation: VO2max (mL/kg/min) = 12.701 + (0.06 × 6-minute walk distance m ) - (0.732 × body mass index kg/m2 ) (R 2 = 0.79, standard error of the estimate [SEE] = 2.91 mL/kg/min, %SEE = 6.9%). There was strong correlation between measured and predicted VO2max (r = 0.875, P < .001). Cross-validation revealed minimal shrinkage (R 2 p = 0.78 and predicted residual sum of squares SEE = 2.99 mL/kg/min). This study provides a relatively accurate and convenient VO2max prediction equation based on the 6MWT and body mass index in healthy boys. This model can be used for evaluation of cardiorespiratory fitness of boys in different settings. Copyright © 2018 Elsevier Inc. All rights reserved.

Detection and Measurement of Unhealthy, Environment-Derived Aerosol Materials in an Emergency Department.

PubMed

Choi, Bryan Y; Kobayashi, Leo; Pathania, Shivany; Miller, Courtney B; Locke, Emma R; Stearns, Branden C; Hudepohl, Nathan J; Patefield, Scott S; Suner, Selim; Williams, Kenneth A; Machan, Jason T; Jay, Gregory D

2015-01-01

To measure unhealthy aerosol materials in an Emergency Department (ED) and identify their sources for mitigation efforts. Based on pilot findings of elevated ED particulate matter (PM) levels, investigators hypothesized that unhealthy aerosol materials derive from exogenous (vehicular) sources at ambulance receiving entrances. The Aerosol Environmental Toxicity in Healthcare-related Exposure and Risk program was conducted as an observational study. Calibrated sensors monitored PM and toxic gases at Ambulance Triage Exterior (ATE), Ambulance Triage Desk (ATD), and control Public Triage Desk (PTD) on a 3/3/3-day cycle. Cassette sampling characterized PM; meteorological and ambulance traffic data were logged. Descriptive and multiple linear regression analyses assessed for interactions between aerosol material levels, location, temporal variables, ambulance activity, and meteorological factors. Sensors acquired 93,682 PM0.3, 90,250 PM2.5, and 93,768 PM5 measurements over 366 days to generate a data set representing at least 85.6% of planned measurements. PM0.3, PM2.5, and PM5 mean counts were lowest in PTD; 56%, 224%, and 223% higher in ATD; and 996%, 200%, and 63% higher in ATE, respectively (all p < .001). Qualitative analyses showed similar PM compositions in ATD and ATE. On multiple linear regression analysis, PM0.3 counts correlated primarily with location; PM2.5 and PM5 counts correlated most strongly with location and ambulance presence. PM < 2.5 and toxic gas concentrations at ATD and PTD patient care areas did not exceed hazard levels; PM0.3 counts did not have formal safety thresholds for comparison. Higher levels of PM were linked with ED ambulance areas, although their health impact is unclear. © The Author(s) 2015.

New Method for the Approximation of Corrected Calcium Concentrations in Chronic Kidney Disease Patients.

PubMed

Kaku, Yoshio; Ookawara, Susumu; Miyazawa, Haruhisa; Ito, Kiyonori; Ueda, Yuichirou; Hirai, Keiji; Hoshino, Taro; Mori, Honami; Yoshida, Izumi; Morishita, Yoshiyuki; Tabei, Kaoru

2016-02-01

The following conventional calcium correction formula (Payne) is broadly applied for serum calcium estimation: corrected total calcium (TCa) (mg/dL) = TCa (mg/dL) + (4 - albumin (g/dL)); however, it is inapplicable to chronic kidney disease (CKD) patients. A total of 2503 venous samples were collected from 942 all-stage CKD patients, and levels of TCa (mg/dL), ionized calcium ([iCa(2+) ] mmol/L), phosphate (mg/dL), albumin (g/dL), and pH, and other clinical parameters were measured. We assumed corrected TCa (the gold standard) to be equal to eight times the iCa(2+) value (measured corrected TCa). Then, we performed stepwise multiple linear regression analysis by using the clinical parameters and derived a simple formula for corrected TCa approximation. The following formula was devised from multiple linear regression analysis: Approximated  corrected TCa (mg/dL) = TCa + 0.25 × (4 - albumin) + 4 × (7.4 - p H) + 0.1 × (6 - phosphate) + 0.3. Receiver operating characteristic curves analysis illustrated that area under the curve of approximated corrected TCa for detection of measured corrected TCa ≥ 8.4 mg/dL and ≤ 10.4 mg/dL were 0.994 and 0.919, respectively. The intraclass correlation coefficient demonstrated superior agreement using this new formula compared to other formulas (new formula: 0.826, Payne: 0.537, Jain: 0.312, Portale: 0.582, Ferrari: 0.362). In CKD patients, TCa correction should include not only albumin but also pH and phosphate. The approximated corrected TCa from this formula demonstrates superior agreement with the measured corrected TCa in comparison to other formulas. © 2016 International Society for Apheresis, Japanese Society for Apheresis, and Japanese Society for Dialysis Therapy.

An Examination of Postsecondary Faculty and the Extent of Critical Reading Taught in 100-Level Introductory Biology and American History Courses in Publicly Funded Two-Year and Four-Year Pennsylvania Institutions

NASA Astrophysics Data System (ADS)

Sand, Dianna

This research examined the responses of postsecondary faculty on a critical reading inventory. The research is quantitative, non-experimental, and incorporates a multiple regression model in the analyses. Three research questions guided this study: (1) By institution type: To what degree does institution type predict the extent to which postsecondary faculty teach critical reading as measured by the Reading Goals Inventory (Jones, 1996)? (2) By faculty status: To what degree does faculty status predict the extent to which postsecondary faculty teach critical reading as measured by the Reading Goals Inventory (Jones, 1996)? (3) By disciplinary area: To what degree does disciplinary area predict the extent to which postsecondary faculty teach critical reading as measured by the Reading Goals Inventory (Jones, 1996)? Faculty from 28 Pennsylvania postsecondary institutions participated in this study. Faculty respondents taught 100-level introductory biology or American history courses either part-time or full-time at Pennsylvania community colleges or Pennsylvania State System of Higher Education (PASSHE) universities. Fifty-four faculty respondents completed the Reading Goals Inventory (Jones, 1996). The researcher conducted multiple regression analyses using a hierarchical method. Predictor variables included Institution Type, Faculty Status, and Disciplinary Area; criterion or outcome variables included seven sub-scales of the critical reading inventory. In this study, Institution Type and Faculty Status were not significant predictors. Disciplinary Area was a consistent significant predictor of the amount of critical reading taught as measured in the Interpretation, Analysis, Evaluation, and Reflection sub-scales of the Reading Goals Inventory (Jones, 1996).

HIV-related ocular microangiopathic syndrome and color contrast sensitivity.

PubMed

Geier, S A; Hammel, G; Bogner, J R; Kronawitter, U; Berninger, T; Goebel, F D

1994-06-01

Color vision deficits in patients with acquired immunodeficiency syndrome (AIDS) or human immunodeficiency virus (HIV) disease were reported, and a retinal pathogenic mechanism was proposed. The purpose of this study was to evaluate the association of color vision deficits with HIV-related retinal microangiopathy. A computer graphics system was used to measure protan, deutan, and tritan color contrast sensitivity (CCS) thresholds in 60 HIV-infected patients. Retinal microangiopathy was measured by counting the number of cotton-wool spots, and conjunctival blood-flow sludging was determined. Additional predictors were CD4+ count, age, time on aerosolized pentamidine, time on zidovudine, and Walter Reed staging. The relative influence of each predictor was calculated by stepwise multiple regression analysis (inclusion criterion; incremental P value = < 0.05) using data for the right eyes (RE). The results were validated by using data for the left eyes (LE) and both eyes (BE). The only included predictors in multiple regression analyses for the RE were number of cotton-wool spots (tritan: R = .70; deutan: R = .46; and protan: R = .58; P < .0001 for all axes) and age (tritan: increment of R [Ri] = .05, P = .002; deutan: Ri = .10, P = .004; and protan: Ri = .05, P = .002). The predictors time on zidovudine (Ri = .05, P = .002) and Walter Reed staging (Ri = .03, P = .01) were additionally included in multiple regression analysis for tritan LE. The results for deutan LE were comparable to those for the RE. In the analysis for protan LE, the only included predictor was number of cotton-wool spots. In the analyses for BE, no further predictors were included. The predictors Walter Reed staging and CD4+ count showed a significant association with all three criteria in univariate analysis. Additionally, tritan CCS was significantly associated with conjunctival blood-flow sludging. CCS deficits in patients with HIV disease are primarily associated with the number of cotton-wool spots. Results of this study are in accordance with the hypothesis that CCS deficits are in a relevant part caused by neuroretinal damage secondary to HIV-related microangiopathy.

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.

Non-contact measurement of pulse wave velocity using RGB cameras

NASA Astrophysics Data System (ADS)

Nakano, Kazuya; Aoki, Yuta; Satoh, Ryota; Hoshi, Akira; Suzuki, Hiroyuki; Nishidate, Izumi

2016-03-01

Non-contact measurement of pulse wave velocity (PWV) using red, green, and blue (RGB) digital color images is proposed. Generally, PWV is used as the index of arteriosclerosis. In our method, changes in blood volume are calculated based on changes in the color information, and is estimated by combining multiple regression analysis (MRA) with a Monte Carlo simulation (MCS) model of the transit of light in human skin. After two pulse waves of human skins were measured using RGB cameras, and the PWV was calculated from the difference of the pulse transit time and the distance between two measurement points. The measured forehead-finger PWV (ffPWV) was on the order of m/s and became faster as the values of vital signs raised. These results demonstrated the feasibility of this method.

The left hand second to fourth digit ratio (2D:4D) is not related to any physical fitness component in adolescent girls.

PubMed

Peeters, Maarten W; Van Aken, Katrijn; Claessens, Albrecht L

2013-01-01

The second to fourth-digit-ratio (2D:4D), a putative marker of prenatal androgen action and a sexually dimorphic trait, has been suggested to be related with fitness and sports performance, although results are not univocal. Most studies however focus on a single aspect of physical fitness or one sports discipline. In this study the 2D:4D ratio of 178 adolescent girls (age 13.5-18 y) was measured on X-rays of the left hand. The relation between 2D:4D digit ratio and multiple aspects of physical fitness (balance, speed of limb movement, flexibility, explosive strength, static strength, trunk strength, functional strength, running speed/agility, and endurance) was studied by correlation analyses and stepwise multiple regression. For comparison the relation between these physical fitness components and a selected number of objectively measured anthropometric traits (stature, mass, BMI, somatotype components and the Bayer & Bailey androgyny index) are presented alongside the results of 2D:4D digit ratio. Left hand 2D:4D digit ratio (0.925±0.019) was not significantly correlated with any of the physical fitness components nor any of the anthropometric variables included in the present study. 2D:4D did not enter the multiple stepwise regression for any of the physical fitness components in which other anthropometric traits explained between 9.2% (flexibility) and 33.9% (static strength) of variance. Unlike other anthropometric traits the 2D:4D digit ratio does not seem to be related to any physical fitness component in adolescent girls and therefore most likely should not be considered in talent detection programs for sporting ability in girls.

Characteristics of low-slope streams that affect O2 transfer rates

USGS Publications Warehouse

Parker, Gene W.; Desimone, Leslie A.

1991-01-01

Multiple-regression techniques were used to derive the reaeration coefficients estimating equation for low sloped streams: K2 = 3.83 MBAS-0.41 SL0.20 H-0.76, where K2 is the reaeration coefficient in base e units per day; MBAS is the methylene blue active substances concentration in milligrams per liter; SL is the water-surface slope in foot per foot; and H is the mean-flow depth in feet. Fourteen hydraulic, physical, and water-quality characteristics were regressed against 29 measured-reaeration coefficients for low-sloped (water surface slopes less than 0.002 foot per foot) streams in Massachusetts and New York. Reaeration coefficients measured from May 1985 to October 1988 ranged from 0.2 to 11.0 base e units per day for 29 low-sloped tracer studies. Concentration of methylene blue active substances is significant because it is thought to be an indicator of concentration of surfactants which could change the surface tension at the air-water interface.

The relationship between apical root resorption and orthodontic tooth movement in growing subjects.

PubMed

Xu, Tianmin; Baumrind, S

2002-07-01

To investigate the relationship between apical root resorption and orthodontic tooth movement in growing subjects. 58 growing subjects were collected randomly into the study sample and another 40 non-treated cases were used as control. The apical resoption of the upper central incisors was measured on periapical film and the incisor displacement was measured on lateral cephalogram. Using multiple linear regression analysis to examine the relationship between root resoption and the displacement of the upper incisor apex in each of four direction (retraction, advancement, intrusion and extrusion). The statistically significant negative association were found between resorption and both intrusion (P < 0.001) and extrusion (P < 0.05), but no significant association was found between resorption and both retraction and advancement. The regression analysis implied an average of 2.29 mm resorption in the absence of apical displacement. The likelihood that the magnitude of displacement of the incisor root is positively associated with root resoption in the population of treated growing subjects is very small.

Obsessional personality features in employed Japanese adults with a lifetime history of depression: assessment by the Munich Personality Test (MPT).

PubMed

Sakado, K; Sakado, M; Seki, T; Kuwabara, H; Kojima, M; Sato, T; Someya, T

2001-06-01

Although a number of studies have reported on the association between obsessional personality features as measured by the Munich Personality Test (MPT) "Rigidity" scale and depression, there has been no examination of these relationships in a non-clinical sample. The dimensional scores on the MPT were compared between subjects with and without lifetime depression, using a sample of employed Japanese adults. The odds ratio for suffering from lifetime depression was estimated by multiple logistic regression analysis. To diagnose a lifetime history of depression, the Inventory to Diagnose Depression, Lifetime version (IDDL) was used. The subjects with lifetime depression scored significantly higher on the "Rigidity" scale than the subjects without lifetime depression. In our logistic regression analysis, three risk factors were identified as each independently increasing a person's risk for suffering from lifetime depression: higher levels of "Rigidity", being of the female gender, and suffering from current depressive symptoms. The MPT "Rigidity" scale is a sensitive measure of personality features that occur with depression.

A methodology to measure cervical vertebral bone maturation in a sample from low-income children.

PubMed

Aguiar, Luciana Barreto Vieira; Caldas, Maria de Paula; Haiter Neto, Francisco; Ambrosano, Glaucia Maria Bovi

2013-01-01

This study evaluated the applicability of the regression method for determining vertebral age developed by Caldas et al. (2007) by testing this method in children from low-income families of the rural zone. The sample comprised cephalometric and hand-wrist radiographs of 76 boys and 64 girls aged 7.0 to 14.9 years living in a medium-sized city in the desert region of the northeastern region of Brazil, with an HDI of 0.678. C3 and C4 vertebrae were traced and measured on cephalometric radiographs to estimate the bone age. The average age, average hand-wrist age and average error estimated for girls and boys were, respectively, 10.62 and 10.44 years, 11.28 and 10.57 years, and 1.42 and 1.18 years. Based on these results, the formula proposed by Caldas et al. (2007) was not applicable to the studied population, and new multiple regression models were developed to obtain the children's vertebral bone age accurately.

[Association of mineral and bone disorder with increasing PWV in CKD 1-5 patients].

PubMed

Shiota, Jun; Watanabe, Mitsuhiro

2007-01-01

The association between pulse wave velocity(PWV) and chronic kidney disease mineral and bone disorder(CKD-MBD) was investigated in CKD 1-5 patients without dialysis. Pulse pressure(PP), PWV, serum Cr, non-HDL-cholesterol, Alb, Ca, Pi, calcitriol, intact-PTH and BAP were measured in sixty patients not receiving a phosphate binder or vitamin D. Using the relationship between age and baPWV in healthy subjects, we determined delta baPWV(measured baPWV-calculated baPWV) as an index for the effect of CKD-related factors. delta baPWV was significantly higher in diabetic patients (p < 0.00001). Simple regression analysis revealed that delta baPWV was positively correlated with PP (p < 0.05) and Log(intact-PTH) (p < 0.01), but negatively correlated with Log(estimated GFR) and Log(calcitriol) (p < 0.01). Multiple regression analysis revealed that delta baPWV was significantly associated with PP and calcitriol, or PP and intact-PTH. These results suggest a relationship between PWV and CKD-MBD.

Weather Impact on Airport Arrival Meter Fix Throughput

NASA Technical Reports Server (NTRS)

Wang, Yao

2017-01-01

Time-based flow management provides arrival aircraft schedules based on arrival airport conditions, airport capacity, required spacing, and weather conditions. In order to meet a scheduled time at which arrival aircraft can cross an airport arrival meter fix prior to entering the airport terminal airspace, air traffic controllers make regulations on air traffic. Severe weather may create an airport arrival bottleneck if one or more of airport arrival meter fixes are partially or completely blocked by the weather and the arrival demand has not been reduced accordingly. Under these conditions, aircraft are frequently being put in holding patterns until they can be rerouted. A model that predicts the weather impacted meter fix throughput may help air traffic controllers direct arrival flows into the airport more efficiently, minimizing arrival meter fix congestion. This paper presents an analysis of air traffic flows across arrival meter fixes at the Newark Liberty International Airport (EWR). Several scenarios of weather impacted EWR arrival fix flows are described. Furthermore, multiple linear regression and regression tree ensemble learning approaches for translating multiple sector Weather Impacted Traffic Indexes (WITI) to EWR arrival meter fix throughputs are examined. These weather translation models are developed and validated using the EWR arrival flight and weather data for the period of April-September in 2014. This study also compares the performance of the regression tree ensemble with traditional multiple linear regression models for estimating the weather impacted throughputs at each of the EWR arrival meter fixes. For all meter fixes investigated, the results from the regression tree ensemble weather translation models show a stronger correlation between model outputs and observed meter fix throughputs than that produced from multiple linear regression method.

Determination of osteoporosis risk factors using a multiple logistic regression model in postmenopausal Turkish women.

PubMed

Akkus, Zeki; Camdeviren, Handan; Celik, Fatma; Gur, Ali; Nas, Kemal

2005-09-01

To determine the risk factors of osteoporosis using a multiple binary logistic regression method and to assess the risk variables for osteoporosis, which is a major and growing health problem in many countries. We presented a case-control study, consisting of 126 postmenopausal healthy women as control group and 225 postmenopausal osteoporotic women as the case group. The study was carried out in the Department of Physical Medicine and Rehabilitation, Dicle University, Diyarbakir, Turkey between 1999-2002. The data from the 351 participants were collected using a standard questionnaire that contains 43 variables. A multiple logistic regression model was then used to evaluate the data and to find the best regression model. We classified 80.1% (281/351) of the participants using the regression model. Furthermore, the specificity value of the model was 67% (84/126) of the control group while the sensitivity value was 88% (197/225) of the case group. We found the distribution of residual values standardized for final model to be exponential using the Kolmogorow-Smirnow test (p=0.193). The receiver operating characteristic curve was found successful to predict patients with risk for osteoporosis. This study suggests that low levels of dietary calcium intake, physical activity, education, and longer duration of menopause are independent predictors of the risk of low bone density in our population. Adequate dietary calcium intake in combination with maintaining a daily physical activity, increasing educational level, decreasing birth rate, and duration of breast-feeding may contribute to healthy bones and play a role in practical prevention of osteoporosis in Southeast Anatolia. In addition, the findings of the present study indicate that the use of multivariate statistical method as a multiple logistic regression in osteoporosis, which maybe influenced by many variables, is better than univariate statistical evaluation.

Practical Guidance for Conducting Mediation Analysis With Multiple Mediators Using Inverse Odds Ratio Weighting

PubMed Central

Nguyen, Quynh C.; Osypuk, Theresa L.; Schmidt, Nicole M.; Glymour, M. Maria; Tchetgen Tchetgen, Eric J.

2015-01-01

Despite the recent flourishing of mediation analysis techniques, many modern approaches are difficult to implement or applicable to only a restricted range of regression models. This report provides practical guidance for implementing a new technique utilizing inverse odds ratio weighting (IORW) to estimate natural direct and indirect effects for mediation analyses. IORW takes advantage of the odds ratio's invariance property and condenses information on the odds ratio for the relationship between the exposure (treatment) and multiple mediators, conditional on covariates, by regressing exposure on mediators and covariates. The inverse of the covariate-adjusted exposure-mediator odds ratio association is used to weight the primary analytical regression of the outcome on treatment. The treatment coefficient in such a weighted regression estimates the natural direct effect of treatment on the outcome, and indirect effects are identified by subtracting direct effects from total effects. Weighting renders treatment and mediators independent, thereby deactivating indirect pathways of the mediators. This new mediation technique accommodates multiple discrete or continuous mediators. IORW is easily implemented and is appropriate for any standard regression model, including quantile regression and survival analysis. An empirical example is given using data from the Moving to Opportunity (1994–2002) experiment, testing whether neighborhood context mediated the effects of a housing voucher program on obesity. Relevant Stata code (StataCorp LP, College Station, Texas) is provided. PMID:25693776

Estimating magnitude and frequency of peak discharges for rural, unregulated, streams in West Virginia

USGS Publications Warehouse

Wiley, J.B.; Atkins, John T.; Tasker, Gary D.

2000-01-01

Multiple and simple least-squares regression models for the log10-transformed 100-year discharge with independent variables describing the basin characteristics (log10-transformed and untransformed) for 267 streamflow-gaging stations were evaluated, and the regression residuals were plotted as areal distributions that defined three regions of the State, designated East, North, and South. Exploratory data analysis procedures identified 31 gaging stations at which discharges are different than would be expected for West Virginia. Regional equations for the 2-, 5-, 10-, 25-, 50-, 100-, 200-, and 500-year peak discharges were determined by generalized least-squares regression using data from 236 gaging stations. Log10-transformed drainage area was the most significant independent variable for all regions.Equations developed in this study are applicable only to rural, unregulated, streams within the boundaries of West Virginia. The accuracy of estimating equations is quantified by measuring the average prediction error (from 27.7 to 44.7 percent) and equivalent years of record (from 1.6 to 20.0 years).

Above-ground biomass of mangrove species. I. Analysis of models

NASA Astrophysics Data System (ADS)

Soares, Mário Luiz Gomes; Schaeffer-Novelli, Yara

2005-10-01

This study analyzes the above-ground biomass of Rhizophora mangle and Laguncularia racemosa located in the mangroves of Bertioga (SP) and Guaratiba (RJ), Southeast Brazil. Its purpose is to determine the best regression model to estimate the total above-ground biomass and compartment (leaves, reproductive parts, twigs, branches, trunk and prop roots) biomass, indirectly. To do this, we used structural measurements such as height, diameter at breast-height (DBH), and crown area. A combination of regression types with several compositions of independent variables generated 2.272 models that were later tested. Subsequent analysis of the models indicated that the biomass of reproductive parts, branches, and prop roots yielded great variability, probably because of environmental factors and seasonality (in the case of reproductive parts). It also indicated the superiority of multiple regression to estimate above-ground biomass as it allows researchers to consider several aspects that affect above-ground biomass, specially the influence of environmental factors. This fact has been attested to the models that estimated the biomass of crown compartments.

A Statistical Multimodel Ensemble Approach to Improving Long-Range Forecasting in Pakistan

DTIC Science & Technology

2012-03-01

Impact of global warming on monsoon variability in Pakistan. J. Anim. Pl. Sci., 21, no. 1, 107–110. Gillies, S., T. Murphree, and D. Meyer, 2012...are generated by multiple regression models that relate globally distributed oceanic and atmospheric predictors to local predictands. The...generated by multiple regression models that relate globally distributed oceanic and atmospheric predictors to local predictands. The predictands are

Suppression Situations in Multiple Linear Regression

ERIC Educational Resources Information Center

Shieh, Gwowen

2006-01-01

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

Quantitative assessment of cervical vertebral maturation using cone beam computed tomography in Korean girls.

PubMed

Byun, Bo-Ram; Kim, Yong-Il; Yamaguchi, Tetsutaro; Maki, Koutaro; Son, Woo-Sung

2015-01-01

This study was aimed to examine the correlation between skeletal maturation status and parameters from the odontoid process/body of the second vertebra and the bodies of third and fourth cervical vertebrae and simultaneously build multiple regression models to be able to estimate skeletal maturation status in Korean girls. Hand-wrist radiographs and cone beam computed tomography (CBCT) images were obtained from 74 Korean girls (6-18 years of age). CBCT-generated cervical vertebral maturation (CVM) was used to demarcate the odontoid process and the body of the second cervical vertebra, based on the dentocentral synchondrosis. Correlation coefficient analysis and multiple linear regression analysis were used for each parameter of the cervical vertebrae (P < 0.05). Forty-seven of 64 parameters from CBCT-generated CVM (independent variables) exhibited statistically significant correlations (P < 0.05). The multiple regression model with the greatest R (2) had six parameters (PH2/W2, UW2/W2, (OH+AH2)/LW2, UW3/LW3, D3, and H4/W4) as independent variables with a variance inflation factor (VIF) of <2. CBCT-generated CVM was able to include parameters from the second cervical vertebral body and odontoid process, respectively, for the multiple regression models. This suggests that quantitative analysis might be used to estimate skeletal maturation status.

Using Regression Equations Built from Summary Data in the Psychological Assessment of the Individual Case: Extension to Multiple Regression

ERIC Educational Resources Information Center

Crawford, John R.; Garthwaite, Paul H.; Denham, Annie K.; Chelune, Gordon J.

2012-01-01

Regression equations have many useful roles in psychological assessment. Moreover, there is a large reservoir of published data that could be used to build regression equations; these equations could then be employed to test a wide variety of hypotheses concerning the functioning of individual cases. This resource is currently underused because…

Higher cortisol levels at diurnal trough predict greater attentional bias towards threat in healthy young adults.

PubMed

Hakamata, Yuko; Izawa, Shuhei; Sato, Eisuke; Komi, Shotaro; Murayama, Norio; Moriguchi, Yoshiya; Hanakawa, Takashi; Inoue, Yusuke; Tagaya, Hirokuni

2013-11-01

Attentional bias (AB), selective information processing towards threat, can exacerbate anxiety and depression. Despite growing interest, physiological determinants of AB are yet to be understood. We examined whether stress hormone cortisol and its diurnal variation pattern contribute to AB. Eighty-seven healthy young adults underwent assessments for AB, anxious personality traits, depressive symptoms, and attentional function. Salivary cortisol was collected at three time points daily (at awakening, 30 min after awakening, and bedtime) for 2 consecutive days. We performed: (1) multiple regression analysis to examine the relationships between AB and the other measures and (2) analysis of variance (ANOVA) between groups with different cortisol variation patterns for the other measures. Multiple regression analysis revealed that higher cortisol levels at bedtime (p<0.001), an anxious personality trait (p=0.011), and years of education (p=0.036) were included in the optimal model to predict AB (adjusted R(2)=0.234, p<0.001). ANOVA further demonstrated significant mean differences in AB and depressive symptoms; individuals with blunted cortisol variation exhibited significantly greater AB and depression than those with moderate variation (p=0.037 and p=0.009, respectively). Neuropsychological assessment focused on attention and cortisol measurement at three time points daily. We showed that higher cortisol levels at bedtime and blunted cortisol variation are associated with greater AB. Individuals who have higher cortisol levels at diurnal trough might be at risk of clinical anxiety or depression but could also derive more benefits from the attentional-bias-modification program. © 2013 Elsevier B.V. All rights reserved.

Does the Aristotle Score predict outcome in congenital heart surgery?

PubMed

Kang, Nicholas; Tsang, Victor T; Elliott, Martin J; de Leval, Marc R; Cole, Timothy J

2006-06-01

The Aristotle Score has been proposed as a measure of 'complexity' in congenital heart surgery, and a tool for comparing performance amongst different centres. To date, however, it remains unvalidated. We examined whether the Basic Aristotle Score was a useful predictor of mortality following open-heart surgery, and compared it to the Risk Adjustment in Congenital Heart Surgery (RACHS-1) system. We also examined the ability of the Aristotle Score to measure performance. The Basic Aristotle Score and RACHS-1 risk categories were assigned retrospectively to 1085 operations involving cardiopulmonary bypass in children less than 18 years of age. Multiple logistic regression analysis was used to determine the significance of the Aristotle Score and RACHS-1 category as independent predictors of in-hospital mortality. Operative performance was calculated using the Aristotle equation: performance = complexity x survival. Multiple logistic regression identified RACHS-1 category to be a powerful predictor of mortality (Wald 17.7, p < 0.0001), whereas Aristotle Score was only weakly associated with mortality (Wald 4.8, p = 0.03). Age at operation and bypass time were also highly significant predictors of postoperative death (Wald 13.7 and 33.8, respectively, p < 0.0001 for both). Operative performance was measured at 7.52 units. The Basic Aristotle Score was only weakly associated with postoperative mortality in this series. Operative performance appeared to be inflated by the fact that the overall complexity of cases was relatively high in this series. An alternative equation (performance = complexity/mortality) is proposed as a fairer and more logical method of risk-adjustment.