Multiple imputation for handling missing outcome data when estimating the relative risk.

PubMed

Sullivan, Thomas R; Lee, Katherine J; Ryan, Philip; Salter, Amy B

2017-09-06

Multiple imputation is a popular approach to handling missing data in medical research, yet little is known about its applicability for estimating the relative risk. Standard methods for imputing incomplete binary outcomes involve logistic regression or an assumption of multivariate normality, whereas relative risks are typically estimated using log binomial models. It is unclear whether misspecification of the imputation model in this setting could lead to biased parameter estimates. Using simulated data, we evaluated the performance of multiple imputation for handling missing data prior to estimating adjusted relative risks from a correctly specified multivariable log binomial model. We considered an arbitrary pattern of missing data in both outcome and exposure variables, with missing data induced under missing at random mechanisms. Focusing on standard model-based methods of multiple imputation, missing data were imputed using multivariate normal imputation or fully conditional specification with a logistic imputation model for the outcome. Multivariate normal imputation performed poorly in the simulation study, consistently producing estimates of the relative risk that were biased towards the null. Despite outperforming multivariate normal imputation, fully conditional specification also produced somewhat biased estimates, with greater bias observed for higher outcome prevalences and larger relative risks. Deleting imputed outcomes from analysis datasets did not improve the performance of fully conditional specification. Both multivariate normal imputation and fully conditional specification produced biased estimates of the relative risk, presumably since both use a misspecified imputation model. Based on simulation results, we recommend researchers use fully conditional specification rather than multivariate normal imputation and retain imputed outcomes in the analysis when estimating relative risks. However fully conditional specification is not without its shortcomings, and so further research is needed to identify optimal approaches for relative risk estimation within the multiple imputation framework.

Introduction to uses and interpretation of principal component analyses in forest biology.

Treesearch

J. G. Isebrands; Thomas R. Crow

1975-01-01

The application of principal component analysis for interpretation of multivariate data sets is reviewed with emphasis on (1) reduction of the number of variables, (2) ordination of variables, and (3) applications in conjunction with multiple regression.

Biostatistics Series Module 10: Brief Overview of Multivariate Methods.

PubMed

Hazra, Avijit; Gogtay, Nithya

2017-01-01

Multivariate analysis refers to statistical techniques that simultaneously look at three or more variables in relation to the subjects under investigation with the aim of identifying or clarifying the relationships between them. These techniques have been broadly classified as dependence techniques, which explore the relationship between one or more dependent variables and their independent predictors, and interdependence techniques, that make no such distinction but treat all variables equally in a search for underlying relationships. Multiple linear regression models a situation where a single numerical dependent variable is to be predicted from multiple numerical independent variables. Logistic regression is used when the outcome variable is dichotomous in nature. The log-linear technique models count type of data and can be used to analyze cross-tabulations where more than two variables are included. Analysis of covariance is an extension of analysis of variance (ANOVA), in which an additional independent variable of interest, the covariate, is brought into the analysis. It tries to examine whether a difference persists after "controlling" for the effect of the covariate that can impact the numerical dependent variable of interest. Multivariate analysis of variance (MANOVA) is a multivariate extension of ANOVA used when multiple numerical dependent variables have to be incorporated in the analysis. Interdependence techniques are more commonly applied to psychometrics, social sciences and market research. Exploratory factor analysis and principal component analysis are related techniques that seek to extract from a larger number of metric variables, a smaller number of composite factors or components, which are linearly related to the original variables. Cluster analysis aims to identify, in a large number of cases, relatively homogeneous groups called clusters, without prior information about the groups. The calculation intensive nature of multivariate analysis has so far precluded most researchers from using these techniques routinely. The situation is now changing with wider availability, and increasing sophistication of statistical software and researchers should no longer shy away from exploring the applications of multivariate methods to real-life data sets.

Quantitative monitoring of sucrose, reducing sugar and total sugar dynamics for phenotyping of water-deficit stress tolerance in rice through spectroscopy and chemometrics

NASA Astrophysics Data System (ADS)

Das, Bappa; Sahoo, Rabi N.; Pargal, Sourabh; Krishna, Gopal; Verma, Rakesh; Chinnusamy, Viswanathan; Sehgal, Vinay K.; Gupta, Vinod K.; Dash, Sushanta K.; Swain, Padmini

2018-03-01

In the present investigation, the changes in sucrose, reducing and total sugar content due to water-deficit stress in rice leaves were modeled using visible, near infrared (VNIR) and shortwave infrared (SWIR) spectroscopy. The objectives of the study were to identify the best vegetation indices and suitable multivariate technique based on precise analysis of hyperspectral data (350 to 2500 nm) and sucrose, reducing sugar and total sugar content measured at different stress levels from 16 different rice genotypes. Spectral data analysis was done to identify suitable spectral indices and models for sucrose estimation. Novel spectral indices in near infrared (NIR) range viz. ratio spectral index (RSI) and normalised difference spectral indices (NDSI) sensitive to sucrose, reducing sugar and total sugar content were identified which were subsequently calibrated and validated. The RSI and NDSI models had R2 values of 0.65, 0.71 and 0.67; RPD values of 1.68, 1.95 and 1.66 for sucrose, reducing sugar and total sugar, respectively for validation dataset. Different multivariate spectral models such as artificial neural network (ANN), multivariate adaptive regression splines (MARS), multiple linear regression (MLR), partial least square regression (PLSR), random forest regression (RFR) and support vector machine regression (SVMR) were also evaluated. The best performing multivariate models for sucrose, reducing sugars and total sugars were found to be, MARS, ANN and MARS, respectively with respect to RPD values of 2.08, 2.44, and 1.93. Results indicated that VNIR and SWIR spectroscopy combined with multivariate calibration can be used as a reliable alternative to conventional methods for measurement of sucrose, reducing sugars and total sugars of rice under water-deficit stress as this technique is fast, economic, and noninvasive.

Using Logistic Regression To Predict the Probability of Debris Flows Occurring in Areas Recently Burned By Wildland Fires

USGS Publications Warehouse

Rupert, Michael G.; Cannon, Susan H.; Gartner, Joseph E.

2003-01-01

Logistic regression was used to predict the probability of debris flows occurring in areas recently burned by wildland fires. Multiple logistic regression is conceptually similar to multiple linear regression because statistical relations between one dependent variable and several independent variables are evaluated. In logistic regression, however, the dependent variable is transformed to a binary variable (debris flow did or did not occur), and the actual probability of the debris flow occurring is statistically modeled. Data from 399 basins located within 15 wildland fires that burned during 2000-2002 in Colorado, Idaho, Montana, and New Mexico were evaluated. More than 35 independent variables describing the burn severity, geology, land surface gradient, rainfall, and soil properties were evaluated. The models were developed as follows: (1) Basins that did and did not produce debris flows were delineated from National Elevation Data using a Geographic Information System (GIS). (2) Data describing the burn severity, geology, land surface gradient, rainfall, and soil properties were determined for each basin. These data were then downloaded to a statistics software package for analysis using logistic regression. (3) Relations between the occurrence/non-occurrence of debris flows and burn severity, geology, land surface gradient, rainfall, and soil properties were evaluated and several preliminary multivariate logistic regression models were constructed. All possible combinations of independent variables were evaluated to determine which combination produced the most effective model. The multivariate model that best predicted the occurrence of debris flows was selected. (4) The multivariate logistic regression model was entered into a GIS, and a map showing the probability of debris flows was constructed. The most effective model incorporates the percentage of each basin with slope greater than 30 percent, percentage of land burned at medium and high burn severity in each basin, particle size sorting, average storm intensity (millimeters per hour), soil organic matter content, soil permeability, and soil drainage. The results of this study demonstrate that logistic regression is a valuable tool for predicting the probability of debris flows occurring in recently-burned landscapes.

Modelling nitrate pollution pressure using a multivariate statistical approach: the case of Kinshasa groundwater body, Democratic Republic of Congo

NASA Astrophysics Data System (ADS)

Mfumu Kihumba, Antoine; Ndembo Longo, Jean; Vanclooster, Marnik

2016-03-01

A multivariate statistical modelling approach was applied to explain the anthropogenic pressure of nitrate pollution on the Kinshasa groundwater body (Democratic Republic of Congo). Multiple regression and regression tree models were compared and used to identify major environmental factors that control the groundwater nitrate concentration in this region. The analyses were made in terms of physical attributes related to the topography, land use, geology and hydrogeology in the capture zone of different groundwater sampling stations. For the nitrate data, groundwater datasets from two different surveys were used. The statistical models identified the topography, the residential area, the service land (cemetery), and the surface-water land-use classes as major factors explaining nitrate occurrence in the groundwater. Also, groundwater nitrate pollution depends not on one single factor but on the combined influence of factors representing nitrogen loading sources and aquifer susceptibility characteristics. The groundwater nitrate pressure was better predicted with the regression tree model than with the multiple regression model. Furthermore, the results elucidated the sensitivity of the model performance towards the method of delineation of the capture zones. For pollution modelling at the monitoring points, therefore, it is better to identify capture-zone shapes based on a conceptual hydrogeological model rather than to adopt arbitrary circular capture zones.

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

PubMed

Henrard, S; Speybroeck, N; Hermans, C

2015-11-01

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

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.

Partial Least Square Analyses of Landscape and Surface Water Biota Associations in the Savannah River Basin

EPA Science Inventory

Ecologists are often faced with problem of small sample size, correlated and large number of predictors, and high noise-to-signal relationships. This necessitates excluding important variables from the model when applying standard multiple or multivariate regression analyses. In ...

A mixed-effects regression model for longitudinal multivariate ordinal data.

PubMed

Liu, Li C; Hedeker, Donald

2006-03-01

A mixed-effects item response theory model that allows for three-level multivariate ordinal outcomes and accommodates multiple random subject effects is proposed for analysis of multivariate ordinal outcomes in longitudinal studies. This model allows for the estimation of different item factor loadings (item discrimination parameters) for the multiple outcomes. The covariates in the model do not have to follow the proportional odds assumption and can be at any level. Assuming either a probit or logistic response function, maximum marginal likelihood estimation is proposed utilizing multidimensional Gauss-Hermite quadrature for integration of the random effects. An iterative Fisher scoring solution, which provides standard errors for all model parameters, is used. An analysis of a longitudinal substance use data set, where four items of substance use behavior (cigarette use, alcohol use, marijuana use, and getting drunk or high) are repeatedly measured over time, is used to illustrate application of the proposed model.

Confounder summary scores when comparing the effects of multiple drug exposures.

PubMed

Cadarette, Suzanne M; Gagne, Joshua J; Solomon, Daniel H; Katz, Jeffrey N; Stürmer, Til

2010-01-01

Little information is available comparing methods to adjust for confounding when considering multiple drug exposures. We compared three analytic strategies to control for confounding based on measured variables: conventional multivariable, exposure propensity score (EPS), and disease risk score (DRS). Each method was applied to a dataset (2000-2006) recently used to examine the comparative effectiveness of four drugs. The relative effectiveness of risedronate, nasal calcitonin, and raloxifene in preventing non-vertebral fracture, were each compared to alendronate. EPSs were derived both by using multinomial logistic regression (single model EPS) and by three separate logistic regression models (separate model EPS). DRSs were derived and event rates compared using Cox proportional hazard models. DRSs derived among the entire cohort (full cohort DRS) was compared to DRSs derived only among the referent alendronate (unexposed cohort DRS). Less than 8% deviation from the base estimate (conventional multivariable) was observed applying single model EPS, separate model EPS or full cohort DRS. Applying the unexposed cohort DRS when background risk for fracture differed between comparison drug exposure cohorts resulted in -7 to + 13% deviation from our base estimate. With sufficient numbers of exposed and outcomes, either conventional multivariable, EPS or full cohort DRS may be used to adjust for confounding to compare the effects of multiple drug exposures. However, our data also suggest that unexposed cohort DRS may be problematic when background risks differ between referent and exposed groups. Further empirical and simulation studies will help to clarify the generalizability of our findings.

Linking Contextual Affordances: Examining Racial-Ethnic Socialization and Parental Career Support among African American College Students

ERIC Educational Resources Information Center

Blackmon, Sha'Kema M.; Thomas, Anita Jones

2014-01-01

This exploratory investigation examined the link between self-reported racial-ethnic socialization experiences and perceived parental career support among African American undergraduate and graduate students. The results of two separate multivariate multiple regression analyses found that messages about coping with racism positively predicted…

Interaction of African American Learners Online: An Adult Education Perspective

ERIC Educational Resources Information Center

Kang, Haijun; Yang, Yang

2016-01-01

This study examines how various life factors and personal attributes affect African American adult learners' use of the three types of learning interaction-learner-content, learner-instructor, and learner-learner. Multivariate multiple regression analyses were used. The aggregate effect of life factors on African American adult learners' use of…

MULTIVARIATE STATISTICAL MODELS FOR EFFECTS OF PM AND COPOLLUTANTS IN A DAILY TIME SERIES EPIDEMIOLOGY STUDY

EPA Science Inventory

Most analyses of daily time series epidemiology data relate mortality or morbidity counts to PM and other air pollutants by means of single-outcome regression models using multiple predictors, without taking into account the complex statistical structure of the predictor variable...

Artificial Neural Networks in Policy Research: A Current Assessment.

ERIC Educational Resources Information Center

Woelfel, Joseph

1993-01-01

Suggests that artificial neural networks (ANNs) exhibit properties that promise usefulness for policy researchers. Notes that ANNs have found extensive use in areas once reserved for multivariate statistical programs such as regression and multiple classification analysis and are developing an extensive community of advocates for processing text…

An Extension of Dominance Analysis to Canonical Correlation Analysis

ERIC Educational Resources Information Center

Huo, Yan; Budescu, David V.

2009-01-01

Dominance analysis (Budescu, 1993) offers a general framework for determination of relative importance of predictors in univariate and multivariate multiple regression models. This approach relies on pairwise comparisons of the contribution of predictors in all relevant subset models. In this article we extend dominance analysis to canonical…

Relationship between Job Burnout and Personal Wellness in Mental Health Professionals

ERIC Educational Resources Information Center

Puig, Ana; Baggs, Adrienne; Mixon, Kacy; Park, Yang Min; Kim, Bo Young; Lee, Sang Min

2012-01-01

This study aimed to determine the nature of the relationship between job burnout and personal wellness among mental health professionals. The authors performed intercorrelations and multivariate multiple regression analyses to identify the relationship between subscales of job burnout and personal wellness. Results showed that all subscales of job…

Hierarchical Bayesian spatial models for predicting multiple forest variables using waveform LiDAR, hyperspectral imagery, and large inventory datasets

USGS Publications Warehouse

Finley, Andrew O.; Banerjee, Sudipto; Cook, Bruce D.; Bradford, John B.

2013-01-01

In this paper we detail a multivariate spatial regression model that couples LiDAR, hyperspectral and forest inventory data to predict forest outcome variables at a high spatial resolution. The proposed model is used to analyze forest inventory data collected on the US Forest Service Penobscot Experimental Forest (PEF), ME, USA. In addition to helping meet the regression model's assumptions, results from the PEF analysis suggest that the addition of multivariate spatial random effects improves model fit and predictive ability, compared with two commonly applied modeling approaches. This improvement results from explicitly modeling the covariation among forest outcome variables and spatial dependence among observations through the random effects. Direct application of such multivariate models to even moderately large datasets is often computationally infeasible because of cubic order matrix algorithms involved in estimation. We apply a spatial dimension reduction technique to help overcome this computational hurdle without sacrificing richness in modeling.

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.

Exact Interval Estimation, Power Calculation, and Sample Size Determination in Normal Correlation Analysis

ERIC Educational Resources Information Center

Shieh, Gwowen

2006-01-01

This paper considers the problem of analysis of correlation coefficients from a multivariate normal population. A unified theorem is derived for the regression model with normally distributed explanatory variables and the general results are employed to provide useful expressions for the distributions of simple, multiple, and partial-multiple…

Identifying Pedophiles "Eligible" for Community Notification under Megan's Law: A Multivariate Model for Actuarially Anchored Decisions.

ERIC Educational Resources Information Center

Pallone, Nathaniel J.; Hennessy, James J.; Voelbel, Gerald T.

1998-01-01

A scientifically sound methodology for identifying offenders about whose presence the community should be notified is demonstrated. A stepwise multiple regression was calculated among incarcerated pedophiles (N=52) including both psychological and legal data; a precision-weighted equation produced 90.4% "true positives." This methodology can be…

[Influences of environmental factors and interaction of several chemokines gene-environmental on systemic lupus erythematosus].

PubMed

Ye, Dong-qing; Hu, Yi-song; Li, Xiang-pei; Huang, Fen; Yang, Shi-gui; Hao, Jia-hu; Yin, Jing; Zhang, Guo-qing; Liu, Hui-hui

2004-11-01

To explore the impact of environmental factors, daily lifestyle, psycho-social factors and the interactions between environmental factors and chemokines genes on systemic lupus erythematosus (SLE). Case-control study was carried out and environmental factors for SLE were analyzed by univariate and multivariate unconditional logistic regression. Interactions between environmental factors and chemokines polymorphism contributing to systemic lupus erythematosus were also analyzed by logistic regression model. There were nineteen factors associated with SLE when univariate unconditional logistic regression was used. However, when multivariate unconditional logistic regression was used, only five factors showed having impacts on the disease, in which drinking well water (OR=0.099) was protective factor for SLE, and multiple drug allergy (OR=8.174), over-exposure to sunshine (OR=18.339), taking antibiotics (OR=9.630) and oral contraceptives were risk factors for SLE. When unconditional logistic regression model was used, results showed that there was interaction between eating irritable food and -2518MCP-1G/G genotype (OR=4.387). No interaction between environmental factors was found that contributing to SLE in this study. Many environmental factors were related to SLE, and there was an interaction between -2518MCP-1G/G genotype and eating irritable food.

Enhanced ID Pit Sizing Using Multivariate Regression Algorithm

NASA Astrophysics Data System (ADS)

Krzywosz, Kenji

2007-03-01

EPRI is funding a program to enhance and improve the reliability of inside diameter (ID) pit sizing for balance-of plant heat exchangers, such as condensers and component cooling water heat exchangers. More traditional approaches to ID pit sizing involve the use of frequency-specific amplitude or phase angles. The enhanced multivariate regression algorithm for ID pit depth sizing incorporates three simultaneous input parameters of frequency, amplitude, and phase angle. A set of calibration data sets consisting of machined pits of various rounded and elongated shapes and depths was acquired in the frequency range of 100 kHz to 1 MHz for stainless steel tubing having nominal wall thickness of 0.028 inch. To add noise to the acquired data set, each test sample was rotated and test data acquired at 3, 6, 9, and 12 o'clock positions. The ID pit depths were estimated using a second order and fourth order regression functions by relying on normalized amplitude and phase angle information from multiple frequencies. Due to unique damage morphology associated with the microbiologically-influenced ID pits, it was necessary to modify the elongated calibration standard-based algorithms by relying on the algorithm developed solely from the destructive sectioning results. This paper presents the use of transformed multivariate regression algorithm to estimate ID pit depths and compare the results with the traditional univariate phase angle analysis. Both estimates were then compared with the destructive sectioning results.

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.

Identifying maternal and infant factors associated with newborn size in rural Bangladesh by partial least squares (PLS) regression analysis

PubMed Central

Rahman, Md. Jahanur; Shamim, Abu Ahmed; Klemm, Rolf D. W.; Labrique, Alain B.; Rashid, Mahbubur; Christian, Parul; West, Keith P.

2017-01-01

Birth weight, length and circumferences of the head, chest and arm are key measures of newborn size and health in developing countries. We assessed maternal socio-demographic factors associated with multiple measures of newborn size in a large rural population in Bangladesh using partial least squares (PLS) regression method. PLS regression, combining features from principal component analysis and multiple linear regression, is a multivariate technique with an ability to handle multicollinearity while simultaneously handling multiple dependent variables. We analyzed maternal and infant data from singletons (n = 14,506) born during a double-masked, cluster-randomized, placebo-controlled maternal vitamin A or β-carotene supplementation trial in rural northwest Bangladesh. PLS regression results identified numerous maternal factors (parity, age, early pregnancy MUAC, living standard index, years of education, number of antenatal care visits, preterm delivery and infant sex) significantly (p<0.001) associated with newborn size. Among them, preterm delivery had the largest negative influence on newborn size (Standardized β = -0.29 − -0.19; p<0.001). Scatter plots of the scores of first two PLS components also revealed an interaction between newborn sex and preterm delivery on birth size. PLS regression was found to be more parsimonious than both ordinary least squares regression and principal component regression. It also provided more stable estimates than the ordinary least squares regression and provided the effect measure of the covariates with greater accuracy as it accounts for the correlation among the covariates and outcomes. Therefore, PLS regression is recommended when either there are multiple outcome measurements in the same study, or the covariates are correlated, or both situations exist in a dataset. PMID:29261760

Identifying maternal and infant factors associated with newborn size in rural Bangladesh by partial least squares (PLS) regression analysis.

PubMed

Kabir, Alamgir; Rahman, Md Jahanur; Shamim, Abu Ahmed; Klemm, Rolf D W; Labrique, Alain B; Rashid, Mahbubur; Christian, Parul; West, Keith P

2017-01-01

Birth weight, length and circumferences of the head, chest and arm are key measures of newborn size and health in developing countries. We assessed maternal socio-demographic factors associated with multiple measures of newborn size in a large rural population in Bangladesh using partial least squares (PLS) regression method. PLS regression, combining features from principal component analysis and multiple linear regression, is a multivariate technique with an ability to handle multicollinearity while simultaneously handling multiple dependent variables. We analyzed maternal and infant data from singletons (n = 14,506) born during a double-masked, cluster-randomized, placebo-controlled maternal vitamin A or β-carotene supplementation trial in rural northwest Bangladesh. PLS regression results identified numerous maternal factors (parity, age, early pregnancy MUAC, living standard index, years of education, number of antenatal care visits, preterm delivery and infant sex) significantly (p<0.001) associated with newborn size. Among them, preterm delivery had the largest negative influence on newborn size (Standardized β = -0.29 - -0.19; p<0.001). Scatter plots of the scores of first two PLS components also revealed an interaction between newborn sex and preterm delivery on birth size. PLS regression was found to be more parsimonious than both ordinary least squares regression and principal component regression. It also provided more stable estimates than the ordinary least squares regression and provided the effect measure of the covariates with greater accuracy as it accounts for the correlation among the covariates and outcomes. Therefore, PLS regression is recommended when either there are multiple outcome measurements in the same study, or the covariates are correlated, or both situations exist in a dataset.

MGAS: a powerful tool for multivariate gene-based genome-wide association analysis.

PubMed

Van der Sluis, Sophie; Dolan, Conor V; Li, Jiang; Song, Youqiang; Sham, Pak; Posthuma, Danielle; Li, Miao-Xin

2015-04-01

Standard genome-wide association studies, testing the association between one phenotype and a large number of single nucleotide polymorphisms (SNPs), are limited in two ways: (i) traits are often multivariate, and analysis of composite scores entails loss in statistical power and (ii) gene-based analyses may be preferred, e.g. to decrease the multiple testing problem. Here we present a new method, multivariate gene-based association test by extended Simes procedure (MGAS), that allows gene-based testing of multivariate phenotypes in unrelated individuals. Through extensive simulation, we show that under most trait-generating genotype-phenotype models MGAS has superior statistical power to detect associated genes compared with gene-based analyses of univariate phenotypic composite scores (i.e. GATES, multiple regression), and multivariate analysis of variance (MANOVA). Re-analysis of metabolic data revealed 32 False Discovery Rate controlled genome-wide significant genes, and 12 regions harboring multiple genes; of these 44 regions, 30 were not reported in the original analysis. MGAS allows researchers to conduct their multivariate gene-based analyses efficiently, and without the loss of power that is often associated with an incorrectly specified genotype-phenotype models. MGAS is freely available in KGG v3.0 (http://statgenpro.psychiatry.hku.hk/limx/kgg/download.php). Access to the metabolic dataset can be requested at dbGaP (https://dbgap.ncbi.nlm.nih.gov/). The R-simulation code is available from http://ctglab.nl/people/sophie_van_der_sluis. Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press.

The role of chemometrics in single and sequential extraction assays: a review. Part II. Cluster analysis, multiple linear regression, mixture resolution, experimental design and other techniques.

PubMed

Giacomino, Agnese; Abollino, Ornella; Malandrino, Mery; Mentasti, Edoardo

2011-03-04

Single and sequential extraction procedures are used for studying element mobility and availability in solid matrices, like soils, sediments, sludge, and airborne particulate matter. In the first part of this review we reported an overview on these procedures and described the applications of chemometric uni- and bivariate techniques and of multivariate pattern recognition techniques based on variable reduction to the experimental results obtained. The second part of the review deals with the use of chemometrics not only for the visualization and interpretation of data, but also for the investigation of the effects of experimental conditions on the response, the optimization of their values and the calculation of element fractionation. We will describe the principles of the multivariate chemometric techniques considered, the aims for which they were applied and the key findings obtained. The following topics will be critically addressed: pattern recognition by cluster analysis (CA), linear discriminant analysis (LDA) and other less common techniques; modelling by multiple linear regression (MLR); investigation of spatial distribution of variables by geostatistics; calculation of fractionation patterns by a mixture resolution method (Chemometric Identification of Substrates and Element Distributions, CISED); optimization and characterization of extraction procedures by experimental design; other multivariate techniques less commonly applied. Copyright © 2010 Elsevier B.V. All rights reserved.

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).

Error Covariance Penalized Regression: A novel multivariate model combining penalized regression with multivariate error structure.

PubMed

Allegrini, Franco; Braga, Jez W B; Moreira, Alessandro C O; Olivieri, Alejandro C

2018-06-29

A new multivariate regression model, named Error Covariance Penalized Regression (ECPR) is presented. Following a penalized regression strategy, the proposed model incorporates information about the measurement error structure of the system, using the error covariance matrix (ECM) as a penalization term. Results are reported from both simulations and experimental data based on replicate mid and near infrared (MIR and NIR) spectral measurements. The results for ECPR are better under non-iid conditions when compared with traditional first-order multivariate methods such as ridge regression (RR), principal component regression (PCR) and partial least-squares regression (PLS). Copyright © 2018 Elsevier B.V. All rights reserved.

Epidemiologic programs for computers and calculators. A microcomputer program for multiple logistic regression by unconditional and conditional maximum likelihood methods.

PubMed

Campos-Filho, N; Franco, E L

1989-02-01

A frequent procedure in matched case-control studies is to report results from the multivariate unmatched analyses if they do not differ substantially from the ones obtained after conditioning on the matching variables. Although conceptually simple, this rule requires that an extensive series of logistic regression models be evaluated by both the conditional and unconditional maximum likelihood methods. Most computer programs for logistic regression employ only one maximum likelihood method, which requires that the analyses be performed in separate steps. This paper describes a Pascal microcomputer (IBM PC) program that performs multiple logistic regression by both maximum likelihood estimation methods, which obviates the need for switching between programs to obtain relative risk estimates from both matched and unmatched analyses. The program calculates most standard statistics and allows factoring of categorical or continuous variables by two distinct methods of contrast. A built-in, descriptive statistics option allows the user to inspect the distribution of cases and controls across categories of any given variable.

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.

Multivariate logistic regression for predicting total culturable virus presence at the intake of a potable-water treatment plant: novel application of the atypical coliform/total coliform ratio.

PubMed

Black, L E; Brion, G M; Freitas, S J

2007-06-01

Predicting the presence of enteric viruses in surface waters is a complex modeling problem. Multiple water quality parameters that indicate the presence of human fecal material, the load of fecal material, and the amount of time fecal material has been in the environment are needed. This paper presents the results of a multiyear study of raw-water quality at the inlet of a potable-water plant that related 17 physical, chemical, and biological indices to the presence of enteric viruses as indicated by cytopathic changes in cell cultures. It was found that several simple, multivariate logistic regression models that could reliably identify observations of the presence or absence of total culturable virus could be fitted. The best models developed combined a fecal age indicator (the atypical coliform [AC]/total coliform [TC] ratio), the detectable presence of a human-associated sterol (epicoprostanol) to indicate the fecal source, and one of several fecal load indicators (the levels of Giardia species cysts, coliform bacteria, and coprostanol). The best fit to the data was found when the AC/TC ratio, the presence of epicoprostanol, and the density of fecal coliform bacteria were input into a simple, multivariate logistic regression equation, resulting in 84.5% and 78.6% accuracies for the identification of the presence and absence of total culturable virus, respectively. The AC/TC ratio was the most influential input variable in all of the models generated, but producing the best prediction required additional input related to the fecal source and the fecal load. The potential for replacing microbial indicators of fecal load with levels of coprostanol was proposed and evaluated by multivariate logistic regression modeling for the presence and absence of virus.

[Use of multiple regression models in observational studies (1970-2013) and requirements of the STROBE guidelines in Spanish scientific journals].

PubMed

Real, J; Cleries, R; Forné, C; Roso-Llorach, A; Martínez-Sánchez, J M

In medicine and biomedical research, statistical techniques like logistic, linear, Cox and Poisson regression are widely known. The main objective is to describe the evolution of multivariate techniques used in observational studies indexed in PubMed (1970-2013), and to check the requirements of the STROBE guidelines in the author guidelines in Spanish journals indexed in PubMed. A targeted PubMed search was performed to identify papers that used logistic linear Cox and Poisson models. Furthermore, a review was also made of the author guidelines of journals published in Spain and indexed in PubMed and Web of Science. Only 6.1% of the indexed manuscripts included a term related to multivariate analysis, increasing from 0.14% in 1980 to 12.3% in 2013. In 2013, 6.7, 2.5, 3.5, and 0.31% of the manuscripts contained terms related to logistic, linear, Cox and Poisson regression, respectively. On the other hand, 12.8% of journals author guidelines explicitly recommend to follow the STROBE guidelines, and 35.9% recommend the CONSORT guideline. A low percentage of Spanish scientific journals indexed in PubMed include the STROBE statement requirement in the author guidelines. Multivariate regression models in published observational studies such as logistic regression, linear, Cox and Poisson are increasingly used both at international level, as well as in journals published in Spanish. Copyright © 2015 Sociedad Española de Médicos de Atención Primaria (SEMERGEN). Publicado por Elsevier España, S.L.U. All rights reserved.

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

NASA Astrophysics Data System (ADS)

Cepowski, Tomasz

2017-06-01

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

Multivariate Analysis As a Support for Diagnostic Flowcharts in Allergic Bronchopulmonary Aspergillosis: A Proof-of-Concept Study.

PubMed

Vitte, Joana; Ranque, Stéphane; Carsin, Ania; Gomez, Carine; Romain, Thomas; Cassagne, Carole; Gouitaa, Marion; Baravalle-Einaudi, Mélisande; Bel, Nathalie Stremler-Le; Reynaud-Gaubert, Martine; Dubus, Jean-Christophe; Mège, Jean-Louis; Gaudart, Jean

2017-01-01

Molecular-based allergy diagnosis yields multiple biomarker datasets. The classical diagnostic score for allergic bronchopulmonary aspergillosis (ABPA), a severe disease usually occurring in asthmatic patients and people with cystic fibrosis, comprises succinct immunological criteria formulated in 1977: total IgE, anti- Aspergillus fumigatus ( Af ) IgE, anti- Af "precipitins," and anti- Af IgG. Progress achieved over the last four decades led to multiple IgE and IgG(4) Af biomarkers available with quantitative, standardized, molecular-level reports. These newly available biomarkers have not been included in the current diagnostic criteria, either individually or in algorithms, despite persistent underdiagnosis of ABPA. Large numbers of individual biomarkers may hinder their use in clinical practice. Conversely, multivariate analysis using new tools may bring about a better chance of less diagnostic mistakes. We report here a proof-of-concept work consisting of a three-step multivariate analysis of Af IgE, IgG, and IgG4 biomarkers through a combination of principal component analysis, hierarchical ascendant classification, and classification and regression tree multivariate analysis. The resulting diagnostic algorithms might show the way for novel criteria and improved diagnostic efficiency in Af -sensitized patients at risk for ABPA.

An Examination on the Effect of Prior Knowledge, Personal Goals, and Incentive in an Online Employee Training Program

ERIC Educational Resources Information Center

Zha, Shenghua; Adams, Andrea Harpine; Calcagno-Roach, Jamie Marie; Stringham, David A.

2017-01-01

This study explored factors that predicted learners' transformative learning in an online employee training program in a higher education institution in the U.S. A multivariate multiple regression analysis was conducted with a sample of 74 adult learners on their learning of a new learning management system. Four types of participants' behaviors…

Multivariate analysis of cytokine profiles in pregnancy complications.

PubMed

Azizieh, Fawaz; Dingle, Kamaludin; Raghupathy, Raj; Johnson, Kjell; VanderPlas, Jacob; Ansari, Ali

2018-03-01

The immunoregulation to tolerate the semiallogeneic fetus during pregnancy includes a harmonious dynamic balance between anti- and pro-inflammatory cytokines. Several earlier studies reported significantly different levels and/or ratios of several cytokines in complicated pregnancy as compared to normal pregnancy. However, as cytokines operate in networks with potentially complex interactions, it is also interesting to compare groups with multi-cytokine data sets, with multivariate analysis. Such analysis will further examine how great the differences are, and which cytokines are more different than others. Various multivariate statistical tools, such as Cramer test, classification and regression trees, partial least squares regression figures, 2-dimensional Kolmogorov-Smirmov test, principal component analysis and gap statistic, were used to compare cytokine data of normal vs anomalous groups of different pregnancy complications. Multivariate analysis assisted in examining if the groups were different, how strongly they differed, in what ways they differed and further reported evidence for subgroups in 1 group (pregnancy-induced hypertension), possibly indicating multiple causes for the complication. This work contributes to a better understanding of cytokines interaction and may have important implications on targeting cytokine balance modulation or design of future medications or interventions that best direct management or prevention from an immunological approach. © 2018 The Authors. American Journal of Reproductive Immunology Published by John Wiley & Sons Ltd.

Multivariate Linear Regression and CART Regression Analysis of TBM Performance at Abu Hamour Phase-I Tunnel

NASA Astrophysics Data System (ADS)

Jakubowski, J.; Stypulkowski, J. B.; Bernardeau, F. G.

2017-12-01

The first phase of the Abu Hamour drainage and storm tunnel was completed in early 2017. The 9.5 km long, 3.7 m diameter tunnel was excavated with two Earth Pressure Balance (EPB) Tunnel Boring Machines from Herrenknecht. TBM operation processes were monitored and recorded by Data Acquisition and Evaluation System. The authors coupled collected TBM drive data with available information on rock mass properties, cleansed, completed with secondary variables and aggregated by weeks and shifts. Correlations and descriptive statistics charts were examined. Multivariate Linear Regression and CART regression tree models linking TBM penetration rate (PR), penetration per revolution (PPR) and field penetration index (FPI) with TBM operational and geotechnical characteristics were performed for the conditions of the weak/soft rock of Doha. Both regression methods are interpretable and the data were screened with different computational approaches allowing enriched insight. The primary goal of the analysis was to investigate empirical relations between multiple explanatory and responding variables, to search for best subsets of explanatory variables and to evaluate the strength of linear and non-linear relations. For each of the penetration indices, a predictive model coupling both regression methods was built and validated. The resultant models appeared to be stronger than constituent ones and indicated an opportunity for more accurate and robust TBM performance predictions.

Simple linear and multivariate regression models.

PubMed

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

2011-01-01

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

Functional Regression Models for Epistasis Analysis of Multiple Quantitative Traits.

PubMed

Zhang, Futao; Xie, Dan; Liang, Meimei; Xiong, Momiao

2016-04-01

To date, most genetic analyses of phenotypes have focused on analyzing single traits or analyzing each phenotype independently. However, joint epistasis analysis of multiple complementary traits will increase statistical power and improve our understanding of the complicated genetic structure of the complex diseases. Despite their importance in uncovering the genetic structure of complex traits, the statistical methods for identifying epistasis in multiple phenotypes remains fundamentally unexplored. To fill this gap, we formulate a test for interaction between two genes in multiple quantitative trait analysis as a multiple functional regression (MFRG) in which the genotype functions (genetic variant profiles) are defined as a function of the genomic position of the genetic variants. We use large-scale simulations to calculate Type I error rates for testing interaction between two genes with multiple phenotypes and to compare the power with multivariate pairwise interaction analysis and single trait interaction analysis by a single variate functional regression model. To further evaluate performance, the MFRG for epistasis analysis is applied to five phenotypes of exome sequence data from the NHLBI's Exome Sequencing Project (ESP) to detect pleiotropic epistasis. A total of 267 pairs of genes that formed a genetic interaction network showed significant evidence of epistasis influencing five traits. The results demonstrate that the joint interaction analysis of multiple phenotypes has a much higher power to detect interaction than the interaction analysis of a single trait and may open a new direction to fully uncovering the genetic structure of multiple phenotypes.

Calibration of multivariate scatter plots for exploratory analysis of relations within and between sets of variables in genomic research.

PubMed

Graffelman, Jan; van Eeuwijk, Fred

2005-12-01

The scatter plot is a well known and easily applicable graphical tool to explore relationships between two quantitative variables. For the exploration of relations between multiple variables, generalisations of the scatter plot are useful. We present an overview of multivariate scatter plots focussing on the following situations. Firstly, we look at a scatter plot for portraying relations between quantitative variables within one data matrix. Secondly, we discuss a similar plot for the case of qualitative variables. Thirdly, we describe scatter plots for the relationships between two sets of variables where we focus on correlations. Finally, we treat plots of the relationships between multiple response and predictor variables, focussing on the matrix of regression coefficients. We will present both known and new results, where an important original contribution concerns a procedure for the inclusion of scales for the variables in multivariate scatter plots. We provide software for drawing such scales. We illustrate the construction and interpretation of the plots by means of examples on data collected in a genomic research program on taste in tomato.

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

Retrospective Evaluation of Safety, Efficacy and Risk Factors for Pneumothorax in Simultaneous Localizations of Multiple Pulmonary Nodules Using Hook Wire System.

PubMed

Zhong, Yan; Xu, Xiao-Quan; Pan, Xiang-Long; Zhang, Wei; Xu, Hai; Yuan, Mei; Kong, Ling-Yan; Pu, Xue-Hui; Chen, Liang; Yu, Tong-Fu

2017-09-01

To evaluate the safety and efficacy of the hook wire system in the simultaneous localizations for multiple pulmonary nodules (PNs) before video-assisted thoracoscopic surgery (VATS), and to clarify the risk factors for pneumothorax associated with the localization procedure. Between January 2010 and February 2016, 67 patients (147 nodules, Group A) underwent simultaneous localizations for multiple PNs using a hook wire system. The demographic, localization procedure-related information and the occurrence rate of pneumothorax were assessed and compared with a control group (349 patients, 349 nodules, Group B). Multivariate logistic regression analyses were used to determine the risk factors for pneumothorax during the localization procedure. All the 147 nodules were successfully localized. Four (2.7%) hook wires dislodged before VATS procedure, but all these four lesions were successfully resected according to the insertion route of hook wire. Pathological diagnoses were acquired for all 147 nodules. Compared with Group B, Group A demonstrated significantly longer procedure time (p < 0.001) and higher occurrence rate of pneumothorax (p = 0.019). Multivariate logistic regression analysis indicated that position change during localization procedure (OR 2.675, p = 0.021) and the nodules located in the ipsilateral lung (OR 9.404, p < 0.001) were independent risk factors for pneumothorax. Simultaneous localizations for multiple PNs using a hook wire system before VATS procedure were safe and effective. Compared with localization for single PN, simultaneous localizations for multiple PNs were prone to the occurrence of pneumothorax. Position change during localization procedure and the nodules located in the ipsilateral lung were independent risk factors for pneumothorax.

Multivariate calibration on NIR data: development of a model for the rapid evaluation of ethanol content in bakery products.

PubMed

Bello, Alessandra; Bianchi, Federica; Careri, Maria; Giannetto, Marco; Mori, Giovanni; Musci, Marilena

2007-11-05

A new NIR method based on multivariate calibration for determination of ethanol in industrially packed wholemeal bread was developed and validated. GC-FID was used as reference method for the determination of actual ethanol concentration of different samples of wholemeal bread with proper content of added ethanol, ranging from 0 to 3.5% (w/w). Stepwise discriminant analysis was carried out on the NIR dataset, in order to reduce the number of original variables by selecting those that were able to discriminate between the samples of different ethanol concentrations. With the so selected variables a multivariate calibration model was then obtained by multiple linear regression. The prediction power of the linear model was optimized by a new "leave one out" method, so that the number of original variables resulted further reduced.

Aging, not menopause, is associated with higher prevalence of hyperuricemia among older women.

PubMed

Krishnan, Eswar; Bennett, Mihoko; Chen, Linjun

2014-11-01

This work aims to study the associations, if any, of hyperuricemia, gout, and menopause status in the US population. Using multiyear data from the National Health and Nutrition Examination Survey, we performed unmatched comparisons and one to three age-matched comparisons of women aged 20 to 70 years with and without hyperuricemia (serum urate ≥6 mg/dL). Analyses were performed using survey-weighted multiple logistic regression and conditional logistic regression, respectively. Overall, there were 1,477 women with hyperuricemia. Age and serum urate were significantly correlated. In unmatched analyses (n = 9,573 controls), postmenopausal women were older, were heavier, and had higher prevalence of renal impairment, hypertension, diabetes, and hyperlipidemia. In multivariable regression, after accounting for age, body mass index, glomerular filtration rate, and diuretic use, menopause was associated with hyperuricemia (odds ratio, 1.36; 95% CI, 1.05-1.76; P = 0.002). In corresponding multivariable regression using age-matched data (n = 4,431 controls), the odds ratio for menopause was 0.94 (95% CI, 0.83-1.06). Current use of hormone therapy was not associated with prevalent hyperuricemia in both unmatched and matched analyses. Age is a better statistical explanation for the higher prevalence of hyperuricemia among older women than menopause status.

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.

Real estate value prediction using multivariate regression models

NASA Astrophysics Data System (ADS)

Manjula, R.; Jain, Shubham; Srivastava, Sharad; Rajiv Kher, Pranav

2017-11-01

The real estate market is one of the most competitive in terms of pricing and the same tends to vary significantly based on a lot of factors, hence it becomes one of the prime fields to apply the concepts of machine learning to optimize and predict the prices with high accuracy. Therefore in this paper, we present various important features to use while predicting housing prices with good accuracy. We have described regression models, using various features to have lower Residual Sum of Squares error. While using features in a regression model some feature engineering is required for better prediction. Often a set of features (multiple regressions) or polynomial regression (applying a various set of powers in the features) is used for making better model fit. For these models are expected to be susceptible towards over fitting ridge regression is used to reduce it. This paper thus directs to the best application of regression models in addition to other techniques to optimize the result.

Multivariate modelling of density, strength, and stiffness from near infared for mature, juvenile, and pith wood of longleaf pine (Pinus Palustris)

Treesearch

Brian K. Via; Todd F. Shupe; Leslie H. Groom; Michael Stine; Chi-Leung So

2003-01-01

In manufacturing, monitoring the mechanical properties of wood with near infrared spectroscopy (NIR) is an attractive alternative to more conventional methods. However, no attention has been given to see if models differ between juvenile and mature wood. Additionally, it would be convenient if multiple linear regression (MLR) could perform well in the place of more...

Catalog of Air Force Weather Technical Documents, 1941-2006

DTIC Science & Technology

2006-05-19

radiosondes in current use in USA. Elementary discussion of statistical terms and concepts used for expressing accuracy or error is discussed. AWS TR 105...Techniques, Appendix B: Vorticity—An Elementary Discussion of the Concept, August 1956, 27pp. Formerly AWSM 105– 50/1A. Provides the necessary back...steps involved in ordinary multiple linear regression. Conditional probability is calculated using transnormalized variables in the multivariate normal

High-Dimensional Sparse Factor Modeling: Applications in Gene Expression Genomics

PubMed Central

Carvalho, Carlos M.; Chang, Jeffrey; Lucas, Joseph E.; Nevins, Joseph R.; Wang, Quanli; West, Mike

2010-01-01

We describe studies in molecular profiling and biological pathway analysis that use sparse latent factor and regression models for microarray gene expression data. We discuss breast cancer applications and key aspects of the modeling and computational methodology. Our case studies aim to investigate and characterize heterogeneity of structure related to specific oncogenic pathways, as well as links between aggregate patterns in gene expression profiles and clinical biomarkers. Based on the metaphor of statistically derived “factors” as representing biological “subpathway” structure, we explore the decomposition of fitted sparse factor models into pathway subcomponents and investigate how these components overlay multiple aspects of known biological activity. Our methodology is based on sparsity modeling of multivariate regression, ANOVA, and latent factor models, as well as a class of models that combines all components. Hierarchical sparsity priors address questions of dimension reduction and multiple comparisons, as well as scalability of the methodology. The models include practically relevant non-Gaussian/nonparametric components for latent structure, underlying often quite complex non-Gaussianity in multivariate expression patterns. Model search and fitting are addressed through stochastic simulation and evolutionary stochastic search methods that are exemplified in the oncogenic pathway studies. Supplementary supporting material provides more details of the applications, as well as examples of the use of freely available software tools for implementing the methodology. PMID:21218139

Penalized regression procedures for variable selection in the potential outcomes framework

PubMed Central

Ghosh, Debashis; Zhu, Yeying; Coffman, Donna L.

2015-01-01

A recent topic of much interest in causal inference is model selection. In this article, we describe a framework in which to consider penalized regression approaches to variable selection for causal effects. The framework leads to a simple ‘impute, then select’ class of procedures that is agnostic to the type of imputation algorithm as well as penalized regression used. It also clarifies how model selection involves a multivariate regression model for causal inference problems, and that these methods can be applied for identifying subgroups in which treatment effects are homogeneous. Analogies and links with the literature on machine learning methods, missing data and imputation are drawn. A difference LASSO algorithm is defined, along with its multiple imputation analogues. The procedures are illustrated using a well-known right heart catheterization dataset. PMID:25628185

Multivariate adaptive regression splines analysis to predict biomarkers of spontaneous preterm birth.

PubMed

Menon, Ramkumar; Bhat, Geeta; Saade, George R; Spratt, Heidi

2014-04-01

To develop classification models of demographic/clinical factors and biomarker data from spontaneous preterm birth in African Americans and Caucasians. Secondary analysis of biomarker data using multivariate adaptive regression splines (MARS), a supervised machine learning algorithm method. Analysis of data on 36 biomarkers from 191 women was reduced by MARS to develop predictive models for preterm birth in African Americans and Caucasians. Maternal plasma, cord plasma collected at admission for preterm or term labor and amniotic fluid at delivery. Data were partitioned into training and testing sets. Variable importance, a relative indicator (0-100%) and area under the receiver operating characteristic curve (AUC) characterized results. Multivariate adaptive regression splines generated models for combined and racially stratified biomarker data. Clinical and demographic data did not contribute to the model. Racial stratification of data produced distinct models in all three compartments. In African Americans maternal plasma samples IL-1RA, TNF-α, angiopoietin 2, TNFRI, IL-5, MIP1α, IL-1β and TGF-α modeled preterm birth (AUC train: 0.98, AUC test: 0.86). In Caucasians TNFR1, ICAM-1 and IL-1RA contributed to the model (AUC train: 0.84, AUC test: 0.68). African Americans cord plasma samples produced IL-12P70, IL-8 (AUC train: 0.82, AUC test: 0.66). Cord plasma in Caucasians modeled IGFII, PDGFBB, TGF-β1 , IL-12P70, and TIMP1 (AUC train: 0.99, AUC test: 0.82). Amniotic fluid in African Americans modeled FasL, TNFRII, RANTES, KGF, IGFI (AUC train: 0.95, AUC test: 0.89) and in Caucasians, TNF-α, MCP3, TGF-β3 , TNFR1 and angiopoietin 2 (AUC train: 0.94 AUC test: 0.79). Multivariate adaptive regression splines models multiple biomarkers associated with preterm birth and demonstrated racial disparity. © 2014 Nordic Federation of Societies of Obstetrics and Gynecology.

An empirical study of rape in the context of multiple murder.

PubMed

DeLisi, Matt

2014-03-01

In recent years, multiple homicide offending has received increased research attention from criminologists; however, there is mixed evidence about the role of rape toward the perpetration of multiple murder. Drawing on criminal career data from a nonprobability sample of 618 confined male homicide offenders selected from eight U.S. states, the current study examines the role of rape as a predictor of multiple homicide offending. Bivariate analyses indicated a significant association between rape and murder charges. Multivariate path regression models indicated that rape had a significant and robust association with multiple murder. This relationship withstood the confounding effects of kidnapping, prior prison confinement, and prior murder, rape, and kidnapping. These results provide evidence that rape potentially serves as a gateway to multiple murder for some serious offenders. Suggestions for future research are proffered.

Quantifying the impact of between-study heterogeneity in multivariate meta-analyses

PubMed Central

Jackson, Dan; White, Ian R; Riley, Richard D

2012-01-01

Measures that quantify the impact of heterogeneity in univariate meta-analysis, including the very popular I2 statistic, are now well established. Multivariate meta-analysis, where studies provide multiple outcomes that are pooled in a single analysis, is also becoming more commonly used. The question of how to quantify heterogeneity in the multivariate setting is therefore raised. It is the univariate R2 statistic, the ratio of the variance of the estimated treatment effect under the random and fixed effects models, that generalises most naturally, so this statistic provides our basis. This statistic is then used to derive a multivariate analogue of I2, which we call . We also provide a multivariate H2 statistic, the ratio of a generalisation of Cochran's heterogeneity statistic and its associated degrees of freedom, with an accompanying generalisation of the usual I2 statistic, . Our proposed heterogeneity statistics can be used alongside all the usual estimates and inferential procedures used in multivariate meta-analysis. We apply our methods to some real datasets and show how our statistics are equally appropriate in the context of multivariate meta-regression, where study level covariate effects are included in the model. Our heterogeneity statistics may be used when applying any procedure for fitting the multivariate random effects model. Copyright © 2012 John Wiley & Sons, Ltd. PMID:22763950

Transition from a multiport technique to a single-port technique for lung cancer surgery: is lymph node dissection inferior using the single-port technique?†.

PubMed

Liu, Chia-Chuan; Shih, Chih-Shiun; Pennarun, Nicolas; Cheng, Chih-Tao

2016-01-01

The feasibility and radicalism of lymph node dissection for lung cancer surgery by a single-port technique has frequently been challenged. We performed a retrospective cohort study to investigate this issue. Two chest surgeons initiated multiple-port thoracoscopic surgery in a 180-bed cancer centre in 2005 and shifted to a single-port technique gradually after 2010. Data, including demographic and clinical information, from 389 patients receiving multiport thoracoscopic lobectomy or segmentectomy and 149 consecutive patients undergoing either single-port lobectomy or segmentectomy for primary non-small-cell lung cancer were retrieved and entered for statistical analysis by multivariable linear regression models and Box-Cox transformed multivariable analysis. The mean number of total dissected lymph nodes in the lobectomy group was 28.5 ± 11.7 for the single-port group versus 25.2 ± 11.3 for the multiport group; the mean number of total dissected lymph nodes in the segmentectomy group was 19.5 ± 10.8 for the single-port group versus 17.9 ± 10.3 for the multiport group. In linear multivariable and after Box-Cox transformed multivariable analyses, the single-port approach was still associated with a higher total number of dissected lymph nodes. The total number of dissected lymph nodes for primary lung cancer surgery by single-port video-assisted thoracoscopic surgery (VATS) was higher than by multiport VATS in univariable, multivariable linear regression and Box-Cox transformed multivariable analyses. This study confirmed that highly effective lymph node dissection could be achieved through single-port VATS in our setting. © The Author 2015. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved.

Application of multivariate chemometric techniques for simultaneous determination of five parameters of cottonseed oil by single bounce attenuated total reflectance Fourier transform infrared spectroscopy.

PubMed

Talpur, M Younis; Kara, Huseyin; Sherazi, S T H; Ayyildiz, H Filiz; Topkafa, Mustafa; Arslan, Fatma Nur; Naz, Saba; Durmaz, Fatih; Sirajuddin

2014-11-01

Single bounce attenuated total reflectance (SB-ATR) Fourier transform infrared (FTIR) spectroscopy in conjunction with chemometrics was used for accurate determination of free fatty acid (FFA), peroxide value (PV), iodine value (IV), conjugated diene (CD) and conjugated triene (CT) of cottonseed oil (CSO) during potato chips frying. Partial least square (PLS), stepwise multiple linear regression (SMLR), principal component regression (PCR) and simple Beer׳s law (SBL) were applied to develop the calibrations for simultaneous evaluation of five stated parameters of cottonseed oil (CSO) during frying of French frozen potato chips at 170°C. Good regression coefficients (R(2)) were achieved for FFA, PV, IV, CD and CT with value of >0.992 by PLS, SMLR, PCR, and SBL. Root mean square error of prediction (RMSEP) was found to be less than 1.95% for all determinations. Result of the study indicated that SB-ATR FTIR in combination with multivariate chemometrics could be used for accurate and simultaneous determination of different parameters during the frying process without using any toxic organic solvent. Copyright © 2014 Elsevier B.V. All rights reserved.

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

PubMed

Schneider, Astrid; Hommel, Gerhard; Blettner, Maria

2010-11-01

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

Bayesian Estimation of Multivariate Latent Regression Models: Gauss versus Laplace

ERIC Educational Resources Information Center

Culpepper, Steven Andrew; Park, Trevor

2017-01-01

A latent multivariate regression model is developed that employs a generalized asymmetric Laplace (GAL) prior distribution for regression coefficients. The model is designed for high-dimensional applications where an approximate sparsity condition is satisfied, such that many regression coefficients are near zero after accounting for all the model…

A Unified Framework for Association Analysis with Multiple Related Phenotypes

PubMed Central

Stephens, Matthew

2013-01-01

We consider the problem of assessing associations between multiple related outcome variables, and a single explanatory variable of interest. This problem arises in many settings, including genetic association studies, where the explanatory variable is genotype at a genetic variant. We outline a framework for conducting this type of analysis, based on Bayesian model comparison and model averaging for multivariate regressions. This framework unifies several common approaches to this problem, and includes both standard univariate and standard multivariate association tests as special cases. The framework also unifies the problems of testing for associations and explaining associations – that is, identifying which outcome variables are associated with genotype. This provides an alternative to the usual, but conceptually unsatisfying, approach of resorting to univariate tests when explaining and interpreting significant multivariate findings. The method is computationally tractable genome-wide for modest numbers of phenotypes (e.g. 5–10), and can be applied to summary data, without access to raw genotype and phenotype data. We illustrate the methods on both simulated examples, and to a genome-wide association study of blood lipid traits where we identify 18 potential novel genetic associations that were not identified by univariate analyses of the same data. PMID:23861737

Multivariate Regression Analysis and Slaughter Livestock,

DTIC Science & Technology

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

Gene set analysis using variance component tests.

PubMed

Huang, Yen-Tsung; Lin, Xihong

2013-06-28

Gene set analyses have become increasingly important in genomic research, as many complex diseases are contributed jointly by alterations of numerous genes. Genes often coordinate together as a functional repertoire, e.g., a biological pathway/network and are highly correlated. However, most of the existing gene set analysis methods do not fully account for the correlation among the genes. Here we propose to tackle this important feature of a gene set to improve statistical power in gene set analyses. We propose to model the effects of an independent variable, e.g., exposure/biological status (yes/no), on multiple gene expression values in a gene set using a multivariate linear regression model, where the correlation among the genes is explicitly modeled using a working covariance matrix. We develop TEGS (Test for the Effect of a Gene Set), a variance component test for the gene set effects by assuming a common distribution for regression coefficients in multivariate linear regression models, and calculate the p-values using permutation and a scaled chi-square approximation. We show using simulations that type I error is protected under different choices of working covariance matrices and power is improved as the working covariance approaches the true covariance. The global test is a special case of TEGS when correlation among genes in a gene set is ignored. Using both simulation data and a published diabetes dataset, we show that our test outperforms the commonly used approaches, the global test and gene set enrichment analysis (GSEA). We develop a gene set analyses method (TEGS) under the multivariate regression framework, which directly models the interdependence of the expression values in a gene set using a working covariance. TEGS outperforms two widely used methods, GSEA and global test in both simulation and a diabetes microarray data.

Fathers and Daughters: Using a Multivariate Analysis through Multiple Regression to Help Predict a Daughter's Self-Esteem as Experienced by College-Age Women in Rural Nebraska

ERIC Educational Resources Information Center

Beshaler, Mary E.

2010-01-01

Throughout her life, a woman makes decisions about behaviors, relationships, academic accomplishments, and achievements. What propels women to make these choices may be driven by an image of self. This feeling of self-worth or self-esteem is developed early in life with the help of her primary caregivers as found in her biological mother and…

Multi-variant study of obesity risk genes in African Americans: The Jackson Heart Study.

PubMed

Liu, Shijian; Wilson, James G; Jiang, Fan; Griswold, Michael; Correa, Adolfo; Mei, Hao

2016-11-30

Genome-wide association study (GWAS) has been successful in identifying obesity risk genes by single-variant association analysis. For this study, we designed steps of analysis strategy and aimed to identify multi-variant effects on obesity risk among candidate genes. Our analyses were focused on 2137 African American participants with body mass index measured in the Jackson Heart Study and 657 common single nucleotide polymorphisms (SNPs) genotyped at 8 GWAS-identified obesity risk genes. Single-variant association test showed that no SNPs reached significance after multiple testing adjustment. The following gene-gene interaction analysis, which was focused on SNPs with unadjusted p-value<0.10, identified 6 significant multi-variant associations. Logistic regression showed that SNPs in these associations did not have significant linear interactions; examination of genetic risk score evidenced that 4 multi-variant associations had significant additive effects of risk SNPs; and haplotype association test presented that all multi-variant associations contained one or several combinations of particular alleles or haplotypes, associated with increased obesity risk. Our study evidenced that obesity risk genes generated multi-variant effects, which can be additive or non-linear interactions, and multi-variant study is an important supplement to existing GWAS for understanding genetic effects of obesity risk genes. Copyright © 2016 Elsevier B.V. All rights reserved.

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.

Correlative and multivariate analysis of increased radon concentration in underground laboratory.

PubMed

Maletić, Dimitrije M; Udovičić, Vladimir I; Banjanac, Radomir M; Joković, Dejan R; Dragić, Aleksandar L; Veselinović, Nikola B; Filipović, Jelena

2014-11-01

The results of analysis using correlative and multivariate methods, as developed for data analysis in high-energy physics and implemented in the Toolkit for Multivariate Analysis software package, of the relations of the variation of increased radon concentration with climate variables in shallow underground laboratory is presented. Multivariate regression analysis identified a number of multivariate methods which can give a good evaluation of increased radon concentrations based on climate variables. The use of the multivariate regression methods will enable the investigation of the relations of specific climate variable with increased radon concentrations by analysis of regression methods resulting in 'mapped' underlying functional behaviour of radon concentrations depending on a wide spectrum of climate variables. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

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.

[Effect of TNF-alpha gene polymorphism on outcome of thalidomide-based regimens for multiple myeloma].

PubMed

DU, Juan; Yuan, Zhen-Gang; Zhang, Chun-Yang; Fu, Wei-Jun; Jiang, Hua; Chen, Bao-An; Hou, Jian

2009-10-01

To evaluate the effect of polymorphism at the -238 and -308 position of the TNF-alpha promotor region on the clinical outcome of thalidomide (Thal)-based regimens for the treatment of multiple myeloma (MM). The polymorphism at the -238 and -308 position of the TNF-alpha promotor region of 168 MM patients treated with Thal-based regimens were determined by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). Genotypes were tested for association with overall response by logistic regression, and survival was evaluated by univariate and multivariate analysis. In TNF-alpha -238 position, 11 (6.5%) patients had GA genotype and 1 (0.6%) AA genotype. In TNF-alpha -308 position, 19 (11.3%) had GA genotype and 1 (0.6%) AA genotype. In univariate analysis, the TNF-alpha -238 GA + AA genotypes were associated with a significantly prolonged progression free survival (PFS) (P = 0.017), and a better overall survival (OS) (P = 0.150). Multivariate COX regression analysis showed that TNF-alpha -238 polymorphic status was an independent prognostic factor for prolonged PFS (P = 0.049). The TNF-alpha -238 polymorphic status is associated with a favorable clinical outcome in MM patients treated with thalidomide-based regimen. The polymorphism status of TNF-alpha gene might be of promise for developing a more informative stratification system for MM.

Physical victimization, gender identity and suicide risk among transgender men and women.

PubMed

Barboza, Gia Elise; Dominguez, Silvia; Chance, Elena

2016-12-01

We investigated whether being attacked physically due to one's gender identity or expression was associated with suicide risk among trans men and women living in Virginia. The sample consisted of 350 transgender men and women who participated in the Virginia Transgender Health Initiative Survey (THIS). Multivariate multinomial logistic regression was used to explore the competing outcomes associated with suicidal risk. Thirty-seven percent of trans men and women experienced at least one physical attack since the age of 13. On average, individuals experienced 3.97 (SD = 2.86) physical attacks; among these about half were attributed to one's gender identity or expression (mean = 2.08, SD = 1.96). In the multivariate multinomial regression, compared to those with no risk, being physically attacked increased the odds of both attempting and contemplating suicide regardless of gender attribution. Nevertheless, the relative impact of physical victimization on suicidal behavior was higher among those who were targeted on the basis of their gender identity or expression. Finally, no significant association was found between multiple measures of institutional discrimination and suicide risk once discriminatory and non-discriminatory physical victimization was taken into account. Trans men and women experience high levels of physical abuse and face multiple forms of discrimination. They are also at an increased risk for suicidal tendencies. Interventions that help transindividuals cope with discrimination and physical victimization simultaneously may be more effective in saving lives.

A graphical method to evaluate spectral preprocessing in multivariate regression calibrations: example with Savitzky-Golay filters and partial least squares regression

USDA-ARS?s Scientific Manuscript database

In multivariate regression analysis of spectroscopy data, spectral preprocessing is often performed to reduce unwanted background information (offsets, sloped baselines) or accentuate absorption features in intrinsically overlapping bands. These procedures, also known as pretreatments, are commonly ...

Analysis of threats to research validity introduced by audio recording clinic visits: Selection bias, Hawthorne effect, both, or neither?

PubMed Central

Henry, Stephen G.; Jerant, Anthony; Iosif, Ana-Maria; Feldman, Mitchell D.; Cipri, Camille; Kravitz, Richard L.

2015-01-01

Objective To identify factors associated with participant consent to record visits; to estimate effects of recording on patient-clinician interactions Methods Secondary analysis of data from a randomized trial studying communication about depression; participants were asked for optional consent to audio record study visits. Multiple logistic regression was used to model likelihood of patient and clinician consent. Multivariable regression and propensity score analyses were used to estimate effects of audio recording on 6 dependent variables: discussion of depressive symptoms, preventive health, and depression diagnosis; depression treatment recommendations; visit length; visit difficulty. Results Of 867 visits involving 135 primary care clinicians, 39% were recorded. For clinicians, only working in academic settings (P=0.003) and having worked longer at their current practice (P=0.02) were associated with increased likelihood of consent. For patients, white race (P=0.002) and diabetes (P=0.03) were associated with increased likelihood of consent. Neither multivariable regression nor propensity score analyses revealed any significant effects of recording on the variables examined. Conclusion Few clinician or patient characteristics were significantly associated with consent. Audio recording had no significant effect on any dependent variables. Practice Implications Benefits of recording clinic visits likely outweigh the risks of bias in this setting. PMID:25837372

Combinations of Multiple Neuroimaging Markers using Logistic Regression for Auxiliary Diagnosis of Alzheimer Disease and Mild Cognitive Impairment.

PubMed

Mao, Nini; Liu, Yunting; Chen, Kewei; Yao, Li; Wu, Xia

2018-06-05

Multiple neuroimaging modalities have been developed providing various aspects of information on the human brain. Used together and properly, these complementary multimodal neuroimaging data integrate multisource information which can facilitate a diagnosis and improve the diagnostic accuracy. In this study, 3 types of brain imaging data (sMRI, FDG-PET, and florbetapir-PET) were fused in the hope to improve diagnostic accuracy, and multivariate methods (logistic regression) were applied to these trimodal neuroimaging indices. Then, the receiver-operating characteristic (ROC) method was used to analyze the outcomes of the logistic classifier, with either each index, multiples from each modality, or all indices from all 3 modalities, to investigate their differential abilities to identify the disease. With increasing numbers of indices within each modality and across modalities, the accuracy of identifying Alzheimer disease (AD) increases to varying degrees. For example, the area under the ROC curve is above 0.98 when all the indices from the 3 imaging data types are combined. Using a combination of different indices, the results confirmed the initial hypothesis that different biomarkers were potentially complementary, and thus the conjoint analysis of multiple information from multiple sources would improve the capability to identify diseases such as AD and mild cognitive impairment. © 2018 S. Karger AG, Basel.

Exploring the facilitators and barriers to engagement in physical activity for people with multiple sclerosis.

PubMed

Kayes, Nicola M; McPherson, Kathryn M; Schluter, Philip; Taylor, Denise; Leete, Marta; Kolt, Gregory S

2011-01-01

To explore the relationship that cognitive behavioural and other previously identified variables have with physical activity engagement in people with multiple sclerosis (MS). This study adopted a cross-sectional questionnaire design. Participants were 282 individuals with MS. Outcome measures included the Physical Activity Disability Survey--Revised, Cognitive and Behavioural Responses to Symptoms Questionnaire, Barriers to Health Promoting Activities for Disabled Persons Scale, Multiple Sclerosis Self-efficacy Scale, Self-Efficacy for Chronic Diseases Scales and Chalder Fatigue Questionnaire. Multivariable stepwise regression analyses found that greater self-efficacy, greater reported mental fatigue and lower number of perceived barriers to physical activity accounted for a significant proportion of variance in physical activity behaviour, over that accounted for by illness-related variables. Although fear-avoidance beliefs accounted for a significant proportion of variance in the initial analyses, its effect was explained by other factors in the final multivariable analyses. Self-efficacy, mental fatigue and perceived barriers to physical activity are potentially modifiable variables which could be incorporated into interventions designed to improve physical activity engagement. Future research should explore whether a measurement tool tailored to capture beliefs about physical activity identified by people with MS would better predict participation in physical activity.

Ten years of helicopter emergency medical services in Germany: do we still need the helicopter rescue in multiple traumatised patients?

PubMed

Andruszkow, Hagen; Hildebrand, Frank; Lefering, Rolf; Pape, Hans-Christoph; Hoffmann, Reinhard; Schweigkofler, Uwe

2014-10-01

Helicopter emergency medical service (HEMS) has been established in the preclinical treatment of multiple traumatised patients despite an ongoing controversy towards the potential benefit. Celebrating the 20th anniversary of TraumaRegister DGU(®) of the German Trauma Society (DGU) the presented study intended to provide an overview of HEMS rescue in Germany over the last 10 years analysing the potential beneficial impact of a nationwide helicopter rescue in multiple traumatised patients. We analysed TraumaRegister DGU(®) including multiple traumatised patients (ISS ≥ 16) between 2002 and 2012. In-hospital mortality was defined as main outcome. An adjusted, multivariate regression with 13 confounders was performed to evaluate the potential survival benefit. 42,788 patients were included in the present study. 14,275 (33.4%) patients were rescued by HEMS and 28,513 (66.6%) by GEMS. Overall, 66.8% (n=28,569) patients were transported to a level I trauma centre and 28.2% (n=12,052) to a level II trauma centre. Patients rescued by HEMS sustained a higher injury severity compared to GEMS (ISS HEMS: 29.5 ± 12.6 vs. 27.5 ± 11.8). Helicopter rescue teams performed more on-scene interventions, and mission times were increased in HEMS rescue (HEMS: 77.2 ± 28.7 min. vs. GEMS: 60.9 ± 26.9 min.). Linear regression analysis revealed that the frequency of HEMS rescue has decreased significantly between 2002 and 2012. In case of transportation to level I trauma centres a decrease of 1.7% per year was noted (p<0.001) while a decline of 1.6% per year (p<0.001) was measured for level II trauma centre admissions. According to multivariate logistic regression HEMS was proven a positive independent survival predictor between 2002 and 2012 (OR 0.863; 95%-CI 0.800-0.930; Nagelkerkes-R(2) 0.539) with only little differences between each year. This study was able to prove an independent survival benefit of HEMS in multiple traumatised patients during the last 10 years. Despite this fact, a constant decline of HEMS rescue missions was found in multiple trauma patients due to unknown reasons. We concluded that HEMS should be used more often in case of trauma in order to guarantee the proven benefit for multiple traumatised patients. Copyright © 2014 Elsevier Ltd. All rights reserved.

The relationship between hemoglobin level and the type 1 diabetic nephropathy in Anhui Han's patients.

PubMed

Jiang, Jun; Lei, Lan; Zhou, Xiaowan; Li, Peng; Wei, Ren

2018-02-20

Recent studies have shown that low hemoglobin (Hb) level promote the progression of chronic kidney disease. This study assessed the relationship between Hb level and type 1 diabetic nephropathy (DN) in Anhui Han's patients. There were a total of 236 patients diagnosed with type 1 diabetes mellitus and (T1DM) seen between January 2014 and December 2016 in our centre. Hemoglobin levels in patients with DN were compared with those without DN. The relationship between Hb level and the urinary albumin-creatinine ratio (ACR) was examined by Spearman's correlational analysis and multiple stepwise regression analysis. The binary logistic multivariate regression analysis was performed to analyze the correlated factors for type 1 DN, calculate the Odds Ratio (OR) and 95%confidence interval (CI). The predicting value of Hb level for DN was evaluated by area under receiver operation characteristic curve (AUROC) for discrimination and Hosmer-Lemeshow goodness-of-fit test for calibration. The average Hb levels in the DN group (116.1 ± 20.8 g/L) were significantly lower than the non-DN group (131.9 ± 14.4 g/L) , P < 0.001. Hb levels were independently correlated with the urinary ACR in multiple stepwise regression analysis. The logistic multivariate regression analysis showed that the Hb level (OR: 0.936, 95% CI: 0.910 to 0.963, P < 0.001) was inversely correlated with DN in patients with T1DM. In sub-analysis, low Hb level (Hb < 120g/L in female, Hb < 130g/L in male) was still negatively associated with DN in patients with T1DM. The AUROC was 0.721 (95% CI: 0.655 to 0.787) in assessing the discrimination of the Hb level for DN. The value of P was 0.593 in Hosmer-Lemeshow goodness-of-fit test. In Anhui Han's patients with T1DM, the Hb level is inversely correlated with urinary ACR and DN. This article is protected by copyright. All rights reserved.

Application of Machine-Learning Models to Predict Tacrolimus Stable Dose in Renal Transplant Recipients

NASA Astrophysics Data System (ADS)

Tang, Jie; Liu, Rong; Zhang, Yue-Li; Liu, Mou-Ze; Hu, Yong-Fang; Shao, Ming-Jie; Zhu, Li-Jun; Xin, Hua-Wen; Feng, Gui-Wen; Shang, Wen-Jun; Meng, Xiang-Guang; Zhang, Li-Rong; Ming, Ying-Zi; Zhang, Wei

2017-02-01

Tacrolimus has a narrow therapeutic window and considerable variability in clinical use. Our goal was to compare the performance of multiple linear regression (MLR) and eight machine learning techniques in pharmacogenetic algorithm-based prediction of tacrolimus stable dose (TSD) in a large Chinese cohort. A total of 1,045 renal transplant patients were recruited, 80% of which were randomly selected as the “derivation cohort” to develop dose-prediction algorithm, while the remaining 20% constituted the “validation cohort” to test the final selected algorithm. MLR, artificial neural network (ANN), regression tree (RT), multivariate adaptive regression splines (MARS), boosted regression tree (BRT), support vector regression (SVR), random forest regression (RFR), lasso regression (LAR) and Bayesian additive regression trees (BART) were applied and their performances were compared in this work. Among all the machine learning models, RT performed best in both derivation [0.71 (0.67-0.76)] and validation cohorts [0.73 (0.63-0.82)]. In addition, the ideal rate of RT was 4% higher than that of MLR. To our knowledge, this is the first study to use machine learning models to predict TSD, which will further facilitate personalized medicine in tacrolimus administration in the future.

On using summary statistics from an external calibration sample to correct for covariate measurement error.

PubMed

Guo, Ying; Little, Roderick J; McConnell, Daniel S

2012-01-01

Covariate measurement error is common in epidemiologic studies. Current methods for correcting measurement error with information from external calibration samples are insufficient to provide valid adjusted inferences. We consider the problem of estimating the regression of an outcome Y on covariates X and Z, where Y and Z are observed, X is unobserved, but a variable W that measures X with error is observed. Information about measurement error is provided in an external calibration sample where data on X and W (but not Y and Z) are recorded. We describe a method that uses summary statistics from the calibration sample to create multiple imputations of the missing values of X in the regression sample, so that the regression coefficients of Y on X and Z and associated standard errors can be estimated using simple multiple imputation combining rules, yielding valid statistical inferences under the assumption of a multivariate normal distribution. The proposed method is shown by simulation to provide better inferences than existing methods, namely the naive method, classical calibration, and regression calibration, particularly for correction for bias and achieving nominal confidence levels. We also illustrate our method with an example using linear regression to examine the relation between serum reproductive hormone concentrations and bone mineral density loss in midlife women in the Michigan Bone Health and Metabolism Study. Existing methods fail to adjust appropriately for bias due to measurement error in the regression setting, particularly when measurement error is substantial. The proposed method corrects this deficiency.

Calibrated Multivariate Regression with Application to Neural Semantic Basis Discovery.

PubMed

Liu, Han; Wang, Lie; Zhao, Tuo

2015-08-01

We propose a calibrated multivariate regression method named CMR for fitting high dimensional multivariate regression models. Compared with existing methods, CMR calibrates regularization for each regression task with respect to its noise level so that it simultaneously attains improved finite-sample performance and tuning insensitiveness. Theoretically, we provide sufficient conditions under which CMR achieves the optimal rate of convergence in parameter estimation. Computationally, we propose an efficient smoothed proximal gradient algorithm with a worst-case numerical rate of convergence O (1/ ϵ ), where ϵ is a pre-specified accuracy of the objective function value. We conduct thorough numerical simulations to illustrate that CMR consistently outperforms other high dimensional multivariate regression methods. We also apply CMR to solve a brain activity prediction problem and find that it is as competitive as a handcrafted model created by human experts. The R package camel implementing the proposed method is available on the Comprehensive R Archive Network http://cran.r-project.org/web/packages/camel/.

A climate-based multivariate extreme emulator of met-ocean-hydrological events for coastal flooding

NASA Astrophysics Data System (ADS)

Camus, Paula; Rueda, Ana; Mendez, Fernando J.; Tomas, Antonio; Del Jesus, Manuel; Losada, Iñigo J.

2015-04-01

Atmosphere-ocean general circulation models (AOGCMs) are useful to analyze large-scale climate variability (long-term historical periods, future climate projections). However, applications such as coastal flood modeling require climate information at finer scale. Besides, flooding events depend on multiple climate conditions: waves, surge levels from the open-ocean and river discharge caused by precipitation. Therefore, a multivariate statistical downscaling approach is adopted to reproduce relationships between variables and due to its low computational cost. The proposed method can be considered as a hybrid approach which combines a probabilistic weather type downscaling model with a stochastic weather generator component. Predictand distributions are reproduced modeling the relationship with AOGCM predictors based on a physical division in weather types (Camus et al., 2012). The multivariate dependence structure of the predictand (extreme events) is introduced linking the independent marginal distributions of the variables by a probabilistic copula regression (Ben Ayala et al., 2014). This hybrid approach is applied for the downscaling of AOGCM data to daily precipitation and maximum significant wave height and storm-surge in different locations along the Spanish coast. Reanalysis data is used to assess the proposed method. A commonly predictor for the three variables involved is classified using a regression-guided clustering algorithm. The most appropriate statistical model (general extreme value distribution, pareto distribution) for daily conditions is fitted. Stochastic simulation of the present climate is performed obtaining the set of hydraulic boundary conditions needed for high resolution coastal flood modeling. References: Camus, P., Menéndez, M., Méndez, F.J., Izaguirre, C., Espejo, A., Cánovas, V., Pérez, J., Rueda, A., Losada, I.J., Medina, R. (2014b). A weather-type statistical downscaling framework for ocean wave climate. Journal of Geophysical Research, doi: 10.1002/2014JC010141. Ben Ayala, M.A., Chebana, F., Ouarda, T.B.M.J. (2014). Probabilistic Gaussian Copula Regression Model for Multisite and Multivariable Downscaling, Journal of Climate, 27, 3331-3347.

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.

Bayesian multivariate hierarchical transformation models for ROC analysis.

PubMed

O'Malley, A James; Zou, Kelly H

2006-02-15

A Bayesian multivariate hierarchical transformation model (BMHTM) is developed for receiver operating characteristic (ROC) curve analysis based on clustered continuous diagnostic outcome data with covariates. Two special features of this model are that it incorporates non-linear monotone transformations of the outcomes and that multiple correlated outcomes may be analysed. The mean, variance, and transformation components are all modelled parametrically, enabling a wide range of inferences. The general framework is illustrated by focusing on two problems: (1) analysis of the diagnostic accuracy of a covariate-dependent univariate test outcome requiring a Box-Cox transformation within each cluster to map the test outcomes to a common family of distributions; (2) development of an optimal composite diagnostic test using multivariate clustered outcome data. In the second problem, the composite test is estimated using discriminant function analysis and compared to the test derived from logistic regression analysis where the gold standard is a binary outcome. The proposed methodology is illustrated on prostate cancer biopsy data from a multi-centre clinical trial.

Bayesian multivariate hierarchical transformation models for ROC analysis

PubMed Central

O'Malley, A. James; Zou, Kelly H.

2006-01-01

SUMMARY A Bayesian multivariate hierarchical transformation model (BMHTM) is developed for receiver operating characteristic (ROC) curve analysis based on clustered continuous diagnostic outcome data with covariates. Two special features of this model are that it incorporates non-linear monotone transformations of the outcomes and that multiple correlated outcomes may be analysed. The mean, variance, and transformation components are all modelled parametrically, enabling a wide range of inferences. The general framework is illustrated by focusing on two problems: (1) analysis of the diagnostic accuracy of a covariate-dependent univariate test outcome requiring a Box–Cox transformation within each cluster to map the test outcomes to a common family of distributions; (2) development of an optimal composite diagnostic test using multivariate clustered outcome data. In the second problem, the composite test is estimated using discriminant function analysis and compared to the test derived from logistic regression analysis where the gold standard is a binary outcome. The proposed methodology is illustrated on prostate cancer biopsy data from a multi-centre clinical trial. PMID:16217836

Urinary excretion of uric acid is negatively associated with albuminuria in patients with chronic kidney disease: a cross-sectional study.

PubMed

Li, Fengqin; Guo, Hui; Zou, Jianan; Chen, Weijun; Lu, Yijun; Zhang, Xiaoli; Fu, Chensheng; Xiao, Jing; Ye, Zhibin

2018-04-24

Increasing evidence has shown that albuminuria is related to serum uric acid. Little is known about whether this association may be interrelated via renal handling of uric acid. Therefore, we aim to study urinary uric acid excretion and its association with albuminuria in patients with chronic kidney disease (CKD). A cross-sectional study of 200 Chinese CKD patients recruited from department of nephrology of Huadong hospital was conducted. Levels of 24 h urinary excretion of uric acid (24-h Uur), fractional excretion of uric acid (FEur) and uric acid clearance rate (Cur) according to gender, CKD stages, hypertension and albuminuria status were compared by a multivariate analysis. Pearson and Spearman correlation and multiple regression analyses were used to study the correlation of 24-h Uur, FEur and Cur with urinary albumin to creatinine ratio (UACR). The multivariate analysis showed that 24-h Uur and Cur were lower and FEur was higher in the hypertension group, stage 3-5 CKD and macro-albuminuria group (UACR> 30 mg/mmol) than those in the normotensive group, stage 1 CKD group and the normo-albuminuria group (UACR< 3 mg/mmol) (all P < 0.05). Moreover, males had higher 24-h Uur and lower FEur than females (both P < 0.05). Multiple linear regression analysis showed that UACR was negatively associated with 24-h Uur and Cur (P = 0.021, P = 0.007, respectively), but not with FEur (P = 0.759), after adjusting for multiple confounding factors. Our findings suggested that urinary excretion of uric acid is negatively associated with albuminuria in patients with CKD. This phenomenon may help to explain the association between albuminuria and serum uric acid.

Exact and Approximate Statistical Inference for Nonlinear Regression and the Estimating Equation Approach.

PubMed

Demidenko, Eugene

2017-09-01

The exact density distribution of the nonlinear least squares estimator in the one-parameter regression model is derived in closed form and expressed through the cumulative distribution function of the standard normal variable. Several proposals to generalize this result are discussed. The exact density is extended to the estimating equation (EE) approach and the nonlinear regression with an arbitrary number of linear parameters and one intrinsically nonlinear parameter. For a very special nonlinear regression model, the derived density coincides with the distribution of the ratio of two normally distributed random variables previously obtained by Fieller (1932), unlike other approximations previously suggested by other authors. Approximations to the density of the EE estimators are discussed in the multivariate case. Numerical complications associated with the nonlinear least squares are illustrated, such as nonexistence and/or multiple solutions, as major factors contributing to poor density approximation. The nonlinear Markov-Gauss theorem is formulated based on the near exact EE density approximation.

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

PubMed

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

2017-09-01

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

Linear regression analysis and its application to multivariate chromatographic calibration for the quantitative analysis of two-component mixtures.

PubMed

Dinç, Erdal; Ozdemir, Abdil

2005-01-01

Multivariate chromatographic calibration technique was developed for the quantitative analysis of binary mixtures enalapril maleate (EA) and hydrochlorothiazide (HCT) in tablets in the presence of losartan potassium (LST). The mathematical algorithm of multivariate chromatographic calibration technique is based on the use of the linear regression equations constructed using relationship between concentration and peak area at the five-wavelength set. The algorithm of this mathematical calibration model having a simple mathematical content was briefly described. This approach is a powerful mathematical tool for an optimum chromatographic multivariate calibration and elimination of fluctuations coming from instrumental and experimental conditions. This multivariate chromatographic calibration contains reduction of multivariate linear regression functions to univariate data set. The validation of model was carried out by analyzing various synthetic binary mixtures and using the standard addition technique. Developed calibration technique was applied to the analysis of the real pharmaceutical tablets containing EA and HCT. The obtained results were compared with those obtained by classical HPLC method. It was observed that the proposed multivariate chromatographic calibration gives better results than classical HPLC.

Multivariate classification of small order watersheds in the Quabbin Reservoir Basin, Massachusetts

USGS Publications Warehouse

Lent, R.M.; Waldron, M.C.; Rader, J.C.

1998-01-01

A multivariate approach was used to analyze hydrologic, geologic, geographic, and water-chemistry data from small order watersheds in the Quabbin Reservoir Basin in central Massachusetts. Eighty three small order watersheds were delineated and landscape attributes defining hydrologic, geologic, and geographic features of the watersheds were compiled from geographic information system data layers. Principal components analysis was used to evaluate 11 chemical constituents collected bi-weekly for 1 year at 15 surface-water stations in order to subdivide the basin into subbasins comprised of watersheds with similar water quality characteristics. Three principal components accounted for about 90 percent of the variance in water chemistry data. The principal components were defined as a biogeochemical variable related to wetland density, an acid-neutralization variable, and a road-salt variable related to density of primary roads. Three subbasins were identified. Analysis of variance and multiple comparisons of means were used to identify significant differences in stream water chemistry and landscape attributes among subbasins. All stream water constituents were significantly different among subbasins. Multiple regression techniques were used to relate stream water chemistry to landscape attributes. Important differences in landscape attributes were related to wetlands, slope, and soil type.A multivariate approach was used to analyze hydrologic, geologic, geographic, and water-chemistry data from small order watersheds in the Quabbin Reservoir Basin in central Massachusetts. Eighty three small order watersheds were delineated and landscape attributes defining hydrologic, geologic, and geographic features of the watersheds were compiled from geographic information system data layers. Principal components analysis was used to evaluate 11 chemical constituents collected bi-weekly for 1 year at 15 surface-water stations in order to subdivide the basin into subbasins comprised of watersheds with similar water quality characteristics. Three principal components accounted for about 90 percent of the variance in water chemistry data. The principal components were defined as a biogeochemical variable related to wetland density, an acid-neutralization variable, and a road-salt variable related to density of primary roads. Three subbasins were identified. Analysis of variance and multiple comparisons of means were used to identify significant differences in stream water chemistry and landscape attributes among subbasins. All stream water constituents were significantly different among subbasins. Multiple regression techniques were used to relate stream water chemistry to landscape attributes. Important differences in landscape attributes were related to wetlands, slope, and soil type.

Uni- and multi-variable modelling of flood losses: experiences gained from the Secchia river inundation event.

NASA Astrophysics Data System (ADS)

Carisi, Francesca; Domeneghetti, Alessio; Kreibich, Heidi; Schröter, Kai; Castellarin, Attilio

2017-04-01

Flood risk is function of flood hazard and vulnerability, therefore its accurate assessment depends on a reliable quantification of both factors. The scientific literature proposes a number of objective and reliable methods for assessing flood hazard, yet it highlights a limited understanding of the fundamental damage processes. Loss modelling is associated with large uncertainty which is, among other factors, due to a lack of standard procedures; for instance, flood losses are often estimated based on damage models derived in completely different contexts (i.e. different countries or geographical regions) without checking its applicability, or by considering only one explanatory variable (i.e. typically water depth). We consider the Secchia river flood event of January 2014, when a sudden levee-breach caused the inundation of nearly 200 km2 in Northern Italy. In the aftermath of this event, local authorities collected flood loss data, together with additional information on affected private households and industrial activities (e.g. buildings surface and economic value, number of company's employees and others). Based on these data we implemented and compared a quadratic-regression damage function, with water depth as the only explanatory variable, and a multi-variable model that combines multiple regression trees and considers several explanatory variables (i.e. bagging decision trees). Our results show the importance of data collection revealing that (1) a simple quadratic regression damage function based on empirical data from the study area can be significantly more accurate than literature damage-models derived for a different context and (2) multi-variable modelling may outperform the uni-variable approach, yet it is more difficult to develop and apply due to a much higher demand of detailed data.

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.

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

PubMed

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

2016-10-28

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

Kinetic therapy in multiple trauma patients with severe blunt chest trauma: an analysis at a level-1 trauma center.

PubMed

Zeckey, C; Wendt, K; Mommsen, P; Winkelmann, M; Frömke, C; Weidemann, J; Stübig, T; Krettek, C; Hildebrand, F

2015-01-01

Chest trauma is a relevant risk factor for mortality after multiple trauma. Kinetic therapy (KT) represents a potential treatment option in order to restore pulmonary function. Decision criteria for performing kinetic therapy are not fully elucidated. The purpose of this study was to investigate the decision making process to initiate kinetic therapy in a well defined multiple trauma cohort. A retrospective analysis (2000-2009) of polytrauma patients (age > 16 years, ISS ⩾ 16) with severe chest trauma (AIS(Chest) ⩾ 3) was performed. Patients with AIS(Head) ⩾ 3 were excluded. Patients receiving either kinetic (KT+) or lung protective ventilation strategy (KT-) were compared. Chest trauma was classified according to the AIS(Chest), Pulmonary Contusion Score (PCS), Wagner Jamieson Score and Thoracic Trauma Severity Score (TTS). There were multiple outcome parameters investigated included mortality, posttraumatic complications and clinical data. A multivariate regression analysis was performed. Two hundred and eighty-three patients were included (KT+: n=160; KT-: n=123). AIS(Chest), age and gender were comparable in both groups. There were significant higher values of the ISS, PCS, Wagner Jamieson Score and TTS in group KT+. The incidence of posttraumatic complications and mortality was increased compared to group KT- (p< 0.05). Despite that, kinetic therapy failed to be an independent risk factor for mortality in multivariate logistic regression analysis. Kinetic therapy is an option in severely injured patients with severe chest trauma. Decision making is not only based on anatomical aspects such as the AIS(Chest), but on overall injury severity, pulmonary contusions and physiological deterioration. It could be assumed that the increased mortality in patients receiving KT is primarily caused by these factors and does not reflect an independent adverse effect of KT. Furthermore, KT was not shown to be an independent risk factor for mortality.

Optimizing the number of cleavage stage embryos to transfer on day 3 in women 38 years of age and older: a Society for Assisted Reproductive Technology database study.

PubMed

Stern, Judy E; Goldman, Marlene B; Hatasaka, Harry; MacKenzie, Todd A; Surrey, Eric S; Racowsky, Catherine

2009-03-01

To determine the optimal number of day 3 embryos to transfer in women >or=38 years by conducting an evidence-based evaluation. Retrospective analysis of 2000-2004 national SART data. National writing group. A total of 36,103 day 3 embryo transfers in women >or=38 years undergoing their first assisted reproductive technology cycle. None. Logistic regression was used to model the probability of pregnancy, delivery, and multiple births (twin or high order) based on age- and cycle-specific parameters. Pregnancy rates, delivery rates, and multiple rates increased up to transfer of three embryos in 38-year-olds and four in 39-year-olds; beyond this number, only multiple rates increased. In women >or=40 years, delivery rates and multiple rates climbed steadily with increasing numbers transferred. Multivariate analysis confirmed the statistically significant effect of age, number of oocytes retrieved, and embryo cryopreservation on delivery and multiple rates. Maximum FSH level was not an independent predictor by multivariate analysis. Use of intracytoplasmic sperm injection was associated with lowered delivery rate. No more than three or four embryos should be transferred in 38- and 39-year-olds, respectively, whereas up to five embryos could be transferred in >or=40-year-olds. Numbers of embryos to transfer should be adjusted according to number of oocytes retrieved and availability of excess embryos for cryopreservation.

Multiple Chronic Conditions and Labor Force Outcomes: A Population Study of U.S. Adults

PubMed Central

Ward, Brian W.

2015-01-01

Background Although 1-in-5 adults have multiple (≥2) chronic conditions, limited attention has been given to the association between multiple chronic conditions and employment. Methods Cross-sectional data (2011 National Health Interview Survey) and multivariate regression analyses were used to examine the association among multiple chronic conditions, employment, and labor force outcomes for U.S. adults aged 18–64 years, controlling for covariates. Results Among U.S. adults aged 18–64 years (unweighted n=25,458), having multiple chronic conditions reduced employment probability by 11%–29%. Some individual chronic conditions decreased employment probability. Among employed adults (unweighted n=16,096), having multiple chronic conditions increased the average number of work days missed due to injury/illness in the past year by 3–9 days. Conclusions Multiple chronic conditions are be a barrier to employment and increase the number of work days missed, placing affected individuals at a financial disadvantage. Researchers interested in examining consequences of multiple chronic conditions should give consideration to labor force outcomes. PMID:26103096

The importance of extent of choroid plexus cauterization in addition to endoscopic third ventriculostomy for infantile hydrocephalus: a retrospective North American observational study using propensity score-adjusted analysis.

PubMed

Fallah, Aria; Weil, Alexander G; Juraschka, Kyle; Ibrahim, George M; Wang, Anthony C; Crevier, Louis; Tseng, Chi-Hong; Kulkarni, Abhaya V; Ragheb, John; Bhatia, Sanjiv

2017-12-01

OBJECTIVE Combined endoscopic third ventriculostomy (ETC) and choroid plexus cauterization (CPC)-ETV/CPC- is being investigated to increase the rate of shunt independence in infants with hydrocephalus. The degree of CPC necessary to achieve improved rates of shunt independence is currently unknown. METHODS Using data from a single-center, retrospective, observational cohort study involving patients who underwent ETV/CPC for treatment of infantile hydrocephalus, comparative statistical analyses were performed to detect a difference in need for subsequent CSF diversion procedure in patients undergoing partial CPC (describes unilateral CPC or bilateral CPC that only extended from the foramen of Monro [FM] to the atrium on one side) or subtotal CPC (describes CPC extending from the FM to the posterior temporal horn bilaterally) using a rigid neuroendoscope. Propensity scores for extent of CPC were calculated using age and etiology. Propensity scores were used to perform 1) case-matching comparisons and 2) Cox multivariable regression, adjusting for propensity score in the unmatched cohort. Cox multivariable regression adjusting for age and etiology, but not propensity score was also performed as a third statistical technique. RESULTS Eighty-four patients who underwent ETV/CPC had sufficient data to be included in the analysis. Subtotal CPC was performed in 58 patients (69%) and partial CPC in 26 (31%). The ETV/CPC success rates at 6 and 12 months, respectively, were 49% and 41% for patients undergoing subtotal CPC and 35% and 31% for those undergoing partial CPC. Cox multivariate regression in a 48-patient cohort case-matched by propensity score demonstrated no added effect of increased extent of CPC on ETV/CPC survival (HR 0.868, 95% CI 0.422-1.789, p = 0.702). Cox multivariate regression including all patients, with adjustment for propensity score, demonstrated no effect of extent of CPC on ETV/CPC survival (HR 0.845, 95% CI 0.462-1.548, p = 0.586). Cox multivariate regression including all patients, with adjustment for age and etiology, but not propensity score, demonstrated no effect of extent of CPC on ETV/CPC survival (HR 0.908, 95% CI 0.495-1.664, p = 0.755). CONCLUSIONS Using multiple comparative statistical analyses, no difference in need for subsequent CSF diversion procedure was detected between patients in this cohort who underwent partial versus subtotal CPC. Further investigation regarding whether there is truly no difference between partial versus subtotal extent of CPC in larger patient populations and whether further gain in CPC success can be achieved with complete CPC is warranted.

Parent-reported suicidal behavior and correlates among adolescents in China.

PubMed

Liu, Xianchen; Sun, Zhenxiao; Yang, Yanyun

2008-01-01

Suicidal risk begins to increase during adolescence and is associated with multiple biological, psychological, social, and cultural factors. This study examined the prevalence and psychosocial factors of parent-reported suicidal behavior in Chinese adolescents. A community sample of 1920 adolescents in China participated in an epidemiological study. Parents completed a structured questionnaire including child suicidal behavior, illness history, mental health problems, family history, parenting, and family environment. Multiple logistic regression was used for data analysis. Overall, 2.4% of the sample talked about suicide in the previous 6 months, 3.2% had deliberately hurt themselves or attempted suicide, and 5.1% had either suicidal talk or self-harm. The rate of suicidal behavior increased as adolescents aged. Multivariate logistic regression indicated that the following factors were significantly associated with elevated risk for suicidal behavior: depressive/anxious symptoms, poor maternal health, family conflict, and physical punishment of parental discipline style. Suicidal behavior was reported by parents. No causal relationships could be made based on cross-sectional data. The prevalence rate of parent-reported suicidal behavior is markedly lower than self-reported rate in previous research. Depressive/anxious symptoms and multiple family environmental factors are associated with suicidal behavior in Chinese adolescents.

Application of near-infrared spectroscopy for the rapid quality assessment of Radix Paeoniae Rubra

NASA Astrophysics Data System (ADS)

Zhan, Hao; Fang, Jing; Tang, Liying; Yang, Hongjun; Li, Hua; Wang, Zhuju; Yang, Bin; Wu, Hongwei; Fu, Meihong

2017-08-01

Near-infrared (NIR) spectroscopy with multivariate analysis was used to quantify gallic acid, catechin, albiflorin, and paeoniflorin in Radix Paeoniae Rubra, and the feasibility to classify the samples originating from different areas was investigated. A new high-performance liquid chromatography method was developed and validated to analyze gallic acid, catechin, albiflorin, and paeoniflorin in Radix Paeoniae Rubra as the reference. Partial least squares (PLS), principal component regression (PCR), and stepwise multivariate linear regression (SMLR) were performed to calibrate the regression model. Different data pretreatments such as derivatives (1st and 2nd), multiplicative scatter correction, standard normal variate, Savitzky-Golay filter, and Norris derivative filter were applied to remove the systematic errors. The performance of the model was evaluated according to the root mean square of calibration (RMSEC), root mean square error of prediction (RMSEP), root mean square error of cross-validation (RMSECV), and correlation coefficient (r). The results show that compared to PCR and SMLR, PLS had a lower RMSEC, RMSECV, and RMSEP and higher r for all the four analytes. PLS coupled with proper pretreatments showed good performance in both the fitting and predicting results. Furthermore, the original areas of Radix Paeoniae Rubra samples were partly distinguished by principal component analysis. This study shows that NIR with PLS is a reliable, inexpensive, and rapid tool for the quality assessment of Radix Paeoniae Rubra.

A survey of variable selection methods in two Chinese epidemiology journals

PubMed Central

2010-01-01

Background Although much has been written on developing better procedures for variable selection, there is little research on how it is practiced in actual studies. This review surveys the variable selection methods reported in two high-ranking Chinese epidemiology journals. Methods Articles published in 2004, 2006, and 2008 in the Chinese Journal of Epidemiology and the Chinese Journal of Preventive Medicine were reviewed. Five categories of methods were identified whereby variables were selected using: A - bivariate analyses; B - multivariable analysis; e.g. stepwise or individual significance testing of model coefficients; C - first bivariate analyses, followed by multivariable analysis; D - bivariate analyses or multivariable analysis; and E - other criteria like prior knowledge or personal judgment. Results Among the 287 articles that reported using variable selection methods, 6%, 26%, 30%, 21%, and 17% were in categories A through E, respectively. One hundred sixty-three studies selected variables using bivariate analyses, 80% (130/163) via multiple significance testing at the 5% alpha-level. Of the 219 multivariable analyses, 97 (44%) used stepwise procedures, 89 (41%) tested individual regression coefficients, but 33 (15%) did not mention how variables were selected. Sixty percent (58/97) of the stepwise routines also did not specify the algorithm and/or significance levels. Conclusions The variable selection methods reported in the two journals were limited in variety, and details were often missing. Many studies still relied on problematic techniques like stepwise procedures and/or multiple testing of bivariate associations at the 0.05 alpha-level. These deficiencies should be rectified to safeguard the scientific validity of articles published in Chinese epidemiology journals. PMID:20920252

Alternatives for using multivariate regression to adjust prospective payment rates

PubMed Central

Sheingold, Steven H.

1990-01-01

Multivariate regression analysis has been used in structuring three of the adjustments to Medicare's prospective payment rates. Because the indirect-teaching adjustment, the disproportionate-share adjustment, and the adjustment for large cities are responsible for distributing approximately $3 billion in payments each year, the specification of regression models for these adjustments is of critical importance. In this article, the application of regression for adjusting Medicare's prospective rates is discussed, and the implications that differing specifications could have for these adjustments are demonstrated. PMID:10113271

Regression and multivariate models for predicting particulate matter concentration level.

PubMed

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

2018-01-01

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

The Multivariate Regression Statistics Strategy to Investigate Content-Effect Correlation of Multiple Components in Traditional Chinese Medicine Based on a Partial Least Squares Method.

PubMed

Peng, Ying; Li, Su-Ning; Pei, Xuexue; Hao, Kun

2018-03-01

Amultivariate regression statisticstrategy was developed to clarify multi-components content-effect correlation ofpanaxginseng saponins extract and predict the pharmacological effect by components content. In example 1, firstly, we compared pharmacological effects between panax ginseng saponins extract and individual saponin combinations. Secondly, we examined the anti-platelet aggregation effect in seven different saponin combinations of ginsenoside Rb1, Rg1, Rh, Rd, Ra3 and notoginsenoside R1. Finally, the correlation between anti-platelet aggregation and the content of multiple components was analyzed by a partial least squares algorithm. In example 2, firstly, 18 common peaks were identified in ten different batches of panax ginseng saponins extracts from different origins. Then, we investigated the anti-myocardial ischemia reperfusion injury effects of the ten different panax ginseng saponins extracts. Finally, the correlation between the fingerprints and the cardioprotective effects was analyzed by a partial least squares algorithm. Both in example 1 and 2, the relationship between the components content and pharmacological effect was modeled well by the partial least squares regression equations. Importantly, the predicted effect curve was close to the observed data of dot marked on the partial least squares regression model. This study has given evidences that themulti-component content is a promising information for predicting the pharmacological effects of traditional Chinese medicine.

Electricity Consumption in the Industrial Sector of Jordan: Application of Multivariate Linear Regression and Adaptive Neuro-Fuzzy Techniques

NASA Astrophysics Data System (ADS)

Samhouri, M.; Al-Ghandoor, A.; Fouad, R. H.

2009-08-01

In this study two techniques, for modeling electricity consumption of the Jordanian industrial sector, are presented: (i) multivariate linear regression and (ii) neuro-fuzzy models. Electricity consumption is modeled as function of different variables such as number of establishments, number of employees, electricity tariff, prevailing fuel prices, production outputs, capacity utilizations, and structural effects. It was found that industrial production and capacity utilization are the most important variables that have significant effect on future electrical power demand. The results showed that both the multivariate linear regression and neuro-fuzzy models are generally comparable and can be used adequately to simulate industrial electricity consumption. However, comparison that is based on the square root average squared error of data suggests that the neuro-fuzzy model performs slightly better for future prediction of electricity consumption than the multivariate linear regression model. Such results are in full agreement with similar work, using different methods, for other countries.

Psychological distress among re-education through labour camp detainees in Guangxi Autonomous Region, China.

PubMed

Yap, Lorraine; Shu, Su; Zhang, Lei; Liu, Wei; Chen, Yi; Wu, Zunyou; Li, Jianghong; Wand, Handan; Donovan, Basil; Butler, Tony

2017-02-01

There is currently no information about the prevalence of, and factors contributing to psychological distress experienced by re-education through labour camp detainees in China. A cross-sectional face-to-face survey was conducted in three labour camps in Guangxi, China. The questionnaire covered socio-demographic characteristics; sexually transmissible infections (STIs); drug use; psychological distress (K-10); and health service usage and access inside the labour camps. K-10 scores were categorised as ≤30 (low to moderate distress) and  >30 or more (highly distressed). Univariate and multivariate logistic regression models identified factors independently associated with high K-10 scores for men and women separately. In total, 755 detainees, 576 (76%) men and 179 (24%) women, participated in the health survey. The study found 11.6% men versus 11.2% women detainees experienced high psychological distress, but no significant gender differences were observed (p> 0.05). Multivariate logistic regression showed that multiple physical health problems were significantly associated with high psychological distress among men. Drug treatment and forensic mental health services need to be established in detention centres in China to treat more than 10% of detainees with drug use and mental health disorders.

Independent risk factors of morbidity in penetrating colon injuries.

PubMed

Girgin, Sadullah; Gedik, Ercan; Uysal, Ersin; Taçyildiz, Ibrahim Halil

2009-05-01

The present study explored the factors effective on colon-related morbidity in patients with penetrating injury of the colon. The medical records of 196 patients were reviewed for variables including age, gender, factor of trauma, time between injury and operation, shock, duration of operation, Penetrating Abdominal Trauma Index (PATI), Injury Severity Score (ISS), site of colon injury, Colon Injury Score, fecal contamination, number of associated intra- and extraabdominal organ injuries, units of transfused blood within the first 24 hours, and type of surgery. In order to determine the independent risk factors, multivariate logistic regression analysis was performed. Gunshot wounds, interval between injury and operation > or =6 hours, shock, duration of the operation > or =6 hours, PATI > or =25, ISS > or =20, Colon Injury Score > or = grade 3, major fecal contamination, number of associated intraabdominal organ injuries >2, number of associated extraabdominal organ injuries >2, multiple blood transfusions, and diversion were significantly associated with morbidity. Multivariate logistic regression analysis showed diversion and transfusion of > or =4 units in the first 24 hours as independent risk factors affecting colon-related morbidity. Diversion and transfusion of > or =4 units in the first 24 hours were determined to be independent risk factors for colon-related morbidity.

Combined Prediction Model of Death Toll for Road Traffic Accidents Based on Independent and Dependent Variables

PubMed Central

Zhong-xiang, Feng; Shi-sheng, Lu; Wei-hua, Zhang; Nan-nan, Zhang

2014-01-01

In order to build a combined model which can meet the variation rule of death toll data for road traffic accidents and can reflect the influence of multiple factors on traffic accidents and improve prediction accuracy for accidents, the Verhulst model was built based on the number of death tolls for road traffic accidents in China from 2002 to 2011; and car ownership, population, GDP, highway freight volume, highway passenger transportation volume, and highway mileage were chosen as the factors to build the death toll multivariate linear regression model. Then the two models were combined to be a combined prediction model which has weight coefficient. Shapley value method was applied to calculate the weight coefficient by assessing contributions. Finally, the combined model was used to recalculate the number of death tolls from 2002 to 2011, and the combined model was compared with the Verhulst and multivariate linear regression models. The results showed that the new model could not only characterize the death toll data characteristics but also quantify the degree of influence to the death toll by each influencing factor and had high accuracy as well as strong practicability. PMID:25610454

Combined prediction model of death toll for road traffic accidents based on independent and dependent variables.

PubMed

Feng, Zhong-xiang; Lu, Shi-sheng; Zhang, Wei-hua; Zhang, Nan-nan

2014-01-01

In order to build a combined model which can meet the variation rule of death toll data for road traffic accidents and can reflect the influence of multiple factors on traffic accidents and improve prediction accuracy for accidents, the Verhulst model was built based on the number of death tolls for road traffic accidents in China from 2002 to 2011; and car ownership, population, GDP, highway freight volume, highway passenger transportation volume, and highway mileage were chosen as the factors to build the death toll multivariate linear regression model. Then the two models were combined to be a combined prediction model which has weight coefficient. Shapley value method was applied to calculate the weight coefficient by assessing contributions. Finally, the combined model was used to recalculate the number of death tolls from 2002 to 2011, and the combined model was compared with the Verhulst and multivariate linear regression models. The results showed that the new model could not only characterize the death toll data characteristics but also quantify the degree of influence to the death toll by each influencing factor and had high accuracy as well as strong practicability.

[Comparison of arterial stiffness in non-hypertensive and hypertensive population of various age groups].

PubMed

Zhang, Y J; Wu, S L; Li, H Y; Zhao, Q H; Ning, C H; Zhang, R Y; Yu, J X; Li, W; Chen, S H; Gao, J S

2018-01-24

Objective: To investigate the impact of blood pressure and age on arterial stiffness in general population. Methods: Participants who took part in 2010, 2012 and 2014 Kailuan health examination were included. Data of brachial ankle pulse wave velocity (baPWV) examination were analyzed. According to the WHO criteria of age, participants were divided into 3 age groups: 18-44 years group ( n= 11 608), 45-59 years group ( n= 12 757), above 60 years group ( n= 5 002). Participants were further divided into hypertension group and non-hypertension group according to the diagnostic criteria for hypertension (2010 Chinese guidelines for the managemengt of hypertension). Multiple linear regression analysis was used to analyze the association between systolic blood pressure (SBP) with baPWV in the total participants and then stratified by age groups. Multivariate logistic regression model was used to analyze the influence of blood pressure on arterial stiffness (baPWV≥1 400 cm/s) of various groups. Results: (1)The baseline characteristics of all participants: 35 350 participants completed 2010, 2012 and 2014 Kailuan examinations and took part in baPWV examination. 2 237 participants without blood pressure measurement values were excluded, 1 569 participants with history of peripheral artery disease were excluded, we also excluded 1 016 participants with history of cardiac-cerebral vascular disease. Data from 29 367 participants were analyzed. The age was (48.0±12.4) years old, 21 305 were males (72.5%). (2) Distribution of baPWV in various age groups: baPWV increased with aging. In non-hypertension population, baPWV in 18-44 years group, 45-59 years group, above 60 years group were as follows: 1 299.3, 1 428.7 and 1 704.6 cm/s, respectively. For hypertension participants, the respective values of baPWV were: 1 498.4, 1 640.7 and 1 921.4 cm/s. BaPWV was significantly higher in hypertension group than non-hypertension group of respective age groups ( P< 0.05). (3) Multiple linear regression analysis defined risk factors of baPWV: Multivariate linear regression analysis showed that baPWV was positively correlated with SBP( t= 39.30, P< 0.001), and same results were found in the sub-age groups ( t -value was 37.72, 27.30, 9.15, all P< 0.001, respectively) after adjustment for other confounding factors, including age, sex, pulse pressure(PP), body mass index (BMI), fasting blood glucose (FBG), total cholesterol (TC), smoking, drinking, physical exercise, antihypertensive medications, lipid-lowering medication. (4) Multivariate logistic regression analysis of baPWV-related factors: After adjustment for other confounding factors, including age, sex, PP, BMI, FBG, TC, smoking, drinking, physical exercise, antihypertensive medication, lipid-lowering medication, multivariate logistic regression analysis showed that risks for increased arterial stiffness in hypertension group were higher than those in non-hypertension group, the OR in participants with hypertension was 2.54 (2.35-2.74) in the total participants, and same results were also found in sub-age groups, the OR s were 3.22(2.86-3.63), 2.48(2.23-2.76), and 1.91(1.42-2.56), respectively, in each sub-age group. Conclusion: SBP is positively related to arterial stiffness in different age groups, and hypertension is a risk factor for increased arterial stiffness in different age groups. Clinical Trial Registry Chinese Clinical Trial Registry, ChiCTR-TNC-11001489.

Multivariate functions for predicting the sorption of 2,4,6-trinitrotoluene (TNT) and 1,3,5-trinitro-1,3,5-tricyclohexane (RDX) among taxonomically distinct soils.

PubMed

Katseanes, Chelsea K; Chappell, Mark A; Hopkins, Bryan G; Durham, Brian D; Price, Cynthia L; Porter, Beth E; Miller, Lesley F

2016-11-01

After nearly a century of use in numerous munition platforms, TNT and RDX contamination has turned up largely in the environment due to ammunition manufacturing or as part of releases from low-order detonations during training activities. Although the basic knowledge governing the environmental fate of TNT and RDX are known, accurate predictions of TNT and RDX persistence in soil remain elusive, particularly given the universal heterogeneity of pedomorphic soil types. In this work, we proposed a new solution for modeling the sorption and persistence of these munition constituents as multivariate mathematical functions correlating soil attribute data over a variety of taxonomically distinct soil types to contaminant behavior, instead of a single constant or parameter of a specific absolute value. To test this idea, we conducted experiments measuring the sorption of TNT and RDX on taxonomically different soil types that were extensively physical and chemically characterized. Statistical decomposition of the log-transformed, and auto-scaled soil characterization data using the dimension-reduction technique PCA (principal component analysis) revealed a strong latent structure based in the multiple pairwise correlations among the soil properties. TNT and RDX sorption partitioning coefficients (KD-TNT and KD-RDX) were regressed against this latent structure using partial least squares regression (PLSR), generating a 3-factor, multivariate linear functions. Here, PLSR models predicted KD-TNT and KD-RDX values based on attributes contributing to endogenous alkaline/calcareous and soil fertility criteria, respectively, exhibited among the different soil types: We hypothesized that the latent structure arising from the strong covariance of full multivariate geochemical matrix describing taxonomically distinguished soil types may provide the means for potentially predicting complex phenomena in soils. The development of predictive multivariate models tuned to a local soil's taxonomic designation would have direct benefit to military range managers seeking to anticipate the environmental risks of training activities on impact sites. Published by Elsevier Ltd.

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.

Effect of Contact Damage on the Strength of Ceramic Materials.

DTIC Science & Technology

1982-10-01

variables that are important to erosion, and a multivariate , linear regression analysis is used to fit the data to the dimensional analysis. The...of Equations 7 and 8 by a multivariable regression analysis (room tem- perature data) Exponent Regression Standard error Computed coefficient of...1980) 593. WEAVER, Proc. Brit. Ceram. Soc. 22 (1973) 125. 39. P. W. BRIDGMAN, "Dimensional Analaysis ", (Yale 18. R. W. RICE, S. W. FREIMAN and P. F

Spirituality and spiritual care perspectives among baccalaureate nursing students in Saudi Arabia: A cross-sectional study.

PubMed

Cruz, Jonas Preposi; Alshammari, Farhan; Alotaibi, Khalaf Aied; Colet, Paolo C

2017-02-01

No study has been undertaken to understand how spirituality and spiritual care is perceived and implemented by Saudi nursing students undergoing training for their future professional roles as nurses. This study was conducted to investigate the perception of Baccalaureate nursing students toward spirituality and spiritual care. A descriptive, cross-sectional design was employed. A convenience sample of 338 baccalaureate nursing students in two government-run universities in Saudi Arabia was included in this study. A self-administered questionnaire, consisting of a demographic and spiritual care background information sheet and the Spiritual Care-Giving Scale Arabic version (SCGS-A), was used for data collection. A multivariate multiple regression analysis and multiple linear regression analyses were performed accordingly. The mean value on the SCGS-A was 3.84±1.26. Spiritual perspective received the highest mean (4.14±1.45), followed by attribute for spiritual care (3.96±1.48), spiritual care attitude (3.81±1.47), defining spiritual care (3.71±1.51) and spiritual care values (3.57±1.47). Gender, academic level and learning spiritual care from classroom or clinical discussions showed a statistically significant multivariate effect on the five factors of SCGS-A. Efforts should be done to formally integrate holistic concept including all the facets of spirituality and spiritual care in the nursing curriculum. The current findings can be used to inform the development and testing of holistic nursing conceptual framework in nursing education in Saudi Arabia and other Arab Muslim countries. Copyright © 2016 Elsevier Ltd. All rights reserved.

Factors associated with postoperative C5 palsy after expansive open-door laminoplasty: retrospective cohort study using multivariable analysis.

PubMed

Tsuji, Takashi; Matsumoto, Morio; Nakamura, Masaya; Ishii, Ken; Fujita, Nobuyuki; Chiba, Kazuhiro; Watanabe, Kota

2017-09-01

The aim of the present study was to investigate the factors associated with C5 palsy by focusing on radiological parameters using multivariable analysis. The authors retrospectively assessed 190 patients with cervical spondylotic myelopathy treated by open-door laminoplasty. Four radiographic parameters-the number of expanded lamina, C3-C7 angle, lamina open angle and space anterior to the spinal cord-were evaluated to clarify the factors associated with C5 palsy. Of the 190 patients, 11 developed C5 palsy, giving an overall incidence of 5.8%. Although the number of expanded lamina, lamina open angle and space anterior to the spinal cord were significantly larger in C5 palsy group than those in non-palsy group, a multiple logistic regression analysis revealed that only the space anterior to the spinal cord (odds ratio 2.60) was a significant independent factor associated with C5 palsy. A multiple linear regression analysis indicated that the lamina open angle was associated with the space anterior to the spinal cord and the analysis identified the following equation: space anterior to the spinal cord (mm) = 1.54 + 0.09 × lamina open angle (degree). A cut-off value of 53.5° for the lamina open angle predicted the development of C5 palsy with a sensitivity of 72.7% and a specificity of 83.2%. The larger postoperative space anterior to the spinal cord, which was associated with the lamina open angle, was positively correlated with the higher incidence of C5 palsy.

Local polynomial estimation of heteroscedasticity in a multivariate linear regression model and its applications in economics.

PubMed

Su, Liyun; Zhao, Yanyong; Yan, Tianshun; Li, Fenglan

2012-01-01

Multivariate local polynomial fitting is applied to the multivariate linear heteroscedastic regression model. Firstly, the local polynomial fitting is applied to estimate heteroscedastic function, then the coefficients of regression model are obtained by using generalized least squares method. One noteworthy feature of our approach is that we avoid the testing for heteroscedasticity by improving the traditional two-stage method. Due to non-parametric technique of local polynomial estimation, it is unnecessary to know the form of heteroscedastic function. Therefore, we can improve the estimation precision, when the heteroscedastic function is unknown. Furthermore, we verify that the regression coefficients is asymptotic normal based on numerical simulations and normal Q-Q plots of residuals. Finally, the simulation results and the local polynomial estimation of real data indicate that our approach is surely effective in finite-sample situations.

Vocational identity, positive affect, and career thoughts in a group of young adult central nervous system cancer survivors.

PubMed

Lange, Dustin D; Wong, Alex W K; Strauser, David R; Wagner, Stacia

2014-12-01

The aims of this study were as follows: (a) to compare levels of career thoughts and vocational identity between young adult childhood central nervous system (CNS) cancer survivors and noncancer peers and (b) to investigate the contribution of vocational identity and affect on career thoughts among cancer survivors. Participants included 45 young adult CNS cancer survivors and a comparison sample of 60 college students. Participants completed Career Thoughts Inventory, My Vocational Situation, and the Positive and Negative Affect Schedule. Multivariate analysis of variance and multiple regression analysis were used to analyze the data in this study. CNS cancer survivors had a higher level of decision-making confusion than the college students. Multiple regression analysis indicated that vocational identity and positive affect significantly predicted the career thoughts of CNS survivors. The differences in decision-making confusion suggest that young adult CNS survivors would benefit from interventions that focus on providing knowledge of how to make decisions, while increasing vocational identity and positive affect for this specific population could also be beneficial.

imDEV: a graphical user interface to R multivariate analysis tools in Microsoft Excel.

PubMed

Grapov, Dmitry; Newman, John W

2012-09-01

Interactive modules for Data Exploration and Visualization (imDEV) is a Microsoft Excel spreadsheet embedded application providing an integrated environment for the analysis of omics data through a user-friendly interface. Individual modules enables interactive and dynamic analyses of large data by interfacing R's multivariate statistics and highly customizable visualizations with the spreadsheet environment, aiding robust inferences and generating information-rich data visualizations. This tool provides access to multiple comparisons with false discovery correction, hierarchical clustering, principal and independent component analyses, partial least squares regression and discriminant analysis, through an intuitive interface for creating high-quality two- and a three-dimensional visualizations including scatter plot matrices, distribution plots, dendrograms, heat maps, biplots, trellis biplots and correlation networks. Freely available for download at http://sourceforge.net/projects/imdev/. Implemented in R and VBA and supported by Microsoft Excel (2003, 2007 and 2010).

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.

Sex-specific impact of early-life adversity on chronic pain: a large population-based study in Japan.

PubMed

Yamada, Keiko; Matsudaira, Ko; Tanaka, Eizaburo; Oka, Hiroyuki; Katsuhira, Junji; Iso, Hiroyasu

2017-01-01

Responses to early-life adversity may differ by sex. We investigated the sex-specific impact of early-life adversity on chronic pain, chronic multisite pain, and somatizing tendency with chronic pain. We examined 4229 respondents aged 20-79 years who participated in the Pain Associated Cross-Sectional Epidemiological Survey in Japan. Outcomes were: 1) chronic pain prevalence, 2) multisite pain (≥3 sites) prevalence, and 3) multiple somatic symptoms (≥3 symptoms) among respondents with chronic pain related to the presence or absence of early-life adversity. Multivariable-adjusted odds ratios (ORs) were calculated with 95% confidence intervals using a logistic regression model including age, smoking status, exercise routine, sleep time, body mass index, household expenditure, and the full distribution of scores on the Mental Health Inventory-5. We further adjusted for pain intensity when we analyzed the data for respondents with chronic pain. The prevalence of chronic pain was higher among respondents reporting the presence of early-life adversity compared with those reporting its absence, with multivariable ORs of 1.62 (1.22-2.15, p <0.01) in men and 1.47 (1.13-1.90, p <0.01) in women. Among women with chronic pain, early-life adversity was associated with multisite pain and multiple somatic symptoms; multivariable ORs were 1.78 (1.22-2.60, p <0.01) for multisite pain and 1.89 (1.27-2.83, p <0.01) for ≥3 somatic symptoms. No associations were observed between early-life adversity and chronic multisite pain or multiple somatic symptoms among men with chronic pain. Early-life adversity may be linked to a higher prevalence of chronic pain among both sexes and to multisite pain and somatizing tendency among women with chronic pain.

Preference-based Health status in a German outpatient cohort with multiple sclerosis

PubMed Central

2013-01-01

Background To prospectively determine health status and health utility and its predictors in patients with multiple sclerosis (MS). Methods A total of 144 MS patients (mean age: 41.0 ±11.3y) with different subtypes (patterns of progression) and severities of MS were recruited in an outpatient university clinic in Germany. Patients completed a questionnaire at baseline (n = 144), 6 months (n = 65) and 12 months (n = 55). Health utilities were assessed using the EuroQol instrument (EQ-5D, EQ VAS). Health status was assessed by several scales (Expanded Disability Severity Scale (EDSS), Modified Fatigue Impact Scale (M-FIS), Functional Assessment of MS (FAMS), Beck Depression Inventory (BDI-II) and Multiple Sclerosis Functional Composite (MSFC)). Additionally, demographic and socioeconomic parameters were assessed. Multivariate linear and logistic regressions were applied to reveal independent predictors of health status. Results Health status is substantially diminished in MS patients and the EQ VAS was considerably lower than that of the general German population. No significant change in health-status parameters was observed over a 12-months period. Multivariate analyses revealed M-FIS, BDI-II, MSFC, and EDSS to be significant predictors of reduced health status. Socioeconomic and socio-demographic parameters such as working status, family status, number of household inhabitants, age, and gender did not prove significant in multivariate analyses. Conclusion MS considerably impairs patients’ health status. Guidelines aiming to improve self-reported health status should include treatment options for depression and fatigue. Physicians should be aware of depression and fatigue as co-morbidities. Future studies should consider the minimal clinical difference when health status is a primary outcome. PMID:24089999

Regional trends in short-duration precipitation extremes: a flexible multivariate monotone quantile regression approach

NASA Astrophysics Data System (ADS)

Cannon, Alex

2017-04-01

Estimating historical trends in short-duration rainfall extremes at regional and local scales is challenging due to low signal-to-noise ratios and the limited availability of homogenized observational data. In addition to being of scientific interest, trends in rainfall extremes are of practical importance, as their presence calls into question the stationarity assumptions that underpin traditional engineering and infrastructure design practice. Even with these fundamental challenges, increasingly complex questions are being asked about time series of extremes. For instance, users may not only want to know whether or not rainfall extremes have changed over time, they may also want information on the modulation of trends by large-scale climate modes or on the nonstationarity of trends (e.g., identifying hiatus periods or periods of accelerating positive trends). Efforts have thus been devoted to the development and application of more robust and powerful statistical estimators for regional and local scale trends. While a standard nonparametric method like the regional Mann-Kendall test, which tests for the presence of monotonic trends (i.e., strictly non-decreasing or non-increasing changes), makes fewer assumptions than parametric methods and pools information from stations within a region, it is not designed to visualize detected trends, include information from covariates, or answer questions about the rate of change in trends. As a remedy, monotone quantile regression (MQR) has been developed as a nonparametric alternative that can be used to estimate a common monotonic trend in extremes at multiple stations. Quantile regression makes efficient use of data by directly estimating conditional quantiles based on information from all rainfall data in a region, i.e., without having to precompute the sample quantiles. The MQR method is also flexible and can be used to visualize and analyze the nonlinearity of the detected trend. However, it is fundamentally a univariate technique, and cannot incorporate information from additional covariates, for example ENSO state or physiographic controls on extreme rainfall within a region. Here, the univariate MQR model is extended to allow the use of multiple covariates. Multivariate monotone quantile regression (MMQR) is based on a single hidden-layer feedforward network with the quantile regression error function and partial monotonicity constraints. The MMQR model is demonstrated via Monte Carlo simulations and the estimation and visualization of regional trends in moderate rainfall extremes based on homogenized sub-daily precipitation data at stations in Canada.

Variable Selection in Logistic Regression.

DTIC Science & Technology

1987-06-01

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

Importance of Preserving Cross-correlation in developing Statistically Downscaled Climate Forcings and in estimating Land-surface Fluxes and States

NASA Astrophysics Data System (ADS)

Das Bhowmik, R.; Arumugam, S.

2015-12-01

Multivariate downscaling techniques exhibited superiority over univariate regression schemes in terms of preserving cross-correlations between multiple variables- precipitation and temperature - from GCMs. This study focuses on two aspects: (a) develop an analytical solutions on estimating biases in cross-correlations from univariate downscaling approaches and (b) quantify the uncertainty in land-surface states and fluxes due to biases in cross-correlations in downscaled climate forcings. Both these aspects are evaluated using climate forcings available from both historical climate simulations and CMIP5 hindcasts over the entire US. The analytical solution basically relates the univariate regression parameters, co-efficient of determination of regression and the co-variance ratio between GCM and downscaled values. The analytical solutions are compared with the downscaled univariate forcings by choosing the desired p-value (Type-1 error) in preserving the observed cross-correlation. . For quantifying the impacts of biases on cross-correlation on estimating streamflow and groundwater, we corrupt the downscaled climate forcings with different cross-correlation structure.

Adulteration of Argentinean milk fats with animal fats: Detection by fatty acids analysis and multivariate regression techniques.

PubMed

Rebechi, S R; Vélez, M A; Vaira, S; Perotti, M C

2016-02-01

The aims of the present study were to test the accuracy of the fatty acid ratios established by the Argentinean Legislation to detect adulterations of milk fat with animal fats and to propose a regression model suitable to evaluate these adulterations. For this purpose, 70 milk fat, 10 tallow and 7 lard fat samples were collected and analyzed by gas chromatography. Data was utilized to simulate arithmetically adulterated milk fat samples at 0%, 2%, 5%, 10% and 15%, for both animal fats. The fatty acids ratios failed to distinguish adulterated milk fats containing less than 15% of tallow or lard. For each adulterant, Multiple Linear Regression (MLR) was applied, and a model was chosen and validated. For that, calibration and validation matrices were constructed employing genuine and adulterated milk fat samples. The models were able to detect adulterations of milk fat at levels greater than 10% for tallow and 5% for lard. Copyright © 2015 Elsevier Ltd. All rights reserved.

Inherited genetic variants associated with occurrence of multiple primary melanoma.

PubMed

Gibbs, David C; Orlow, Irene; Kanetsky, Peter A; Luo, Li; Kricker, Anne; Armstrong, Bruce K; Anton-Culver, Hoda; Gruber, Stephen B; Marrett, Loraine D; Gallagher, Richard P; Zanetti, Roberto; Rosso, Stefano; Dwyer, Terence; Sharma, Ajay; La Pilla, Emily; From, Lynn; Busam, Klaus J; Cust, Anne E; Ollila, David W; Begg, Colin B; Berwick, Marianne; Thomas, Nancy E

2015-06-01

Recent studies, including genome-wide association studies, have identified several putative low-penetrance susceptibility loci for melanoma. We sought to determine their generalizability to genetic predisposition for multiple primary melanoma in the international population-based Genes, Environment, and Melanoma (GEM) Study. GEM is a case-control study of 1,206 incident cases of multiple primary melanoma and 2,469 incident first primary melanoma participants as the control group. We investigated the odds of developing multiple primary melanoma for 47 SNPs from 21 distinct genetic regions previously reported to be associated with melanoma. ORs and 95% confidence intervals were determined using logistic regression models adjusted for baseline features (age, sex, age by sex interaction, and study center). We investigated univariable models and built multivariable models to assess independent effects of SNPs. Eleven SNPs in 6 gene neighborhoods (TERT/CLPTM1L, TYRP1, MTAP, TYR, NCOA6, and MX2) and a PARP1 haplotype were associated with multiple primary melanoma. In a multivariable model that included only the most statistically significant findings from univariable modeling and adjusted for pigmentary phenotype, back nevi, and baseline features, we found TERT/CLPTM1L rs401681 (P = 0.004), TYRP1 rs2733832 (P = 0.006), MTAP rs1335510 (P = 0.0005), TYR rs10830253 (P = 0.003), and MX2 rs45430 (P = 0.008) to be significantly associated with multiple primary melanoma, while NCOA6 rs4911442 approached significance (P = 0.06). The GEM Study provides additional evidence for the relevance of these genetic regions to melanoma risk and estimates the magnitude of the observed genetic effect on development of subsequent primary melanoma. ©2015 American Association for Cancer Research.

Inherited genetic variants associated with occurrence of multiple primary melanoma

PubMed Central

Gibbs, David C.; Orlow, Irene; Kanetsky, Peter A.; Luo, Li; Kricker, Anne; Armstrong, Bruce K.; Anton-Culver, Hoda; Gruber, Stephen B.; Marrett, Loraine D.; Gallagher, Richard P.; Zanetti, Roberto; Rosso, Stefano; Dwyer, Terence; Sharma, Ajay; La Pilla, Emily; From, Lynn; Busam, Klaus J.; Cust, Anne E.; Ollila, David W.; Begg, Colin B.; Berwick, Marianne; Thomas, Nancy E.

2015-01-01

Recent studies including genome-wide association studies have identified several putative low-penetrance susceptibility loci for melanoma. We sought to determine their generalizability to genetic predisposition for multiple primary melanoma in the international population-based Genes, Environment, and Melanoma (GEM) Study. GEM is a case-control study of 1,206 incident cases of multiple primary melanoma and 2,469 incident first primary melanoma participants as the control group. We investigated the odds of developing multiple primary melanoma for 47 single nucleotide polymorphisms (SNP) from 21 distinct genetic regions previously reported to be associated with melanoma. ORs and 95% CIs were determined using logistic regression models adjusted for baseline features (age, sex, age by sex interaction, and study center). We investigated univariable models and built multivariable models to assess independent effects of SNPs. Eleven SNPs in 6 gene neighborhoods (TERT/CLPTM1L, TYRP1, MTAP, TYR, NCOA6, and MX2) and a PARP1 haplotype were associated with multiple primary melanoma. In a multivariable model that included only the most statistically significant findings from univariable modeling and adjusted for pigmentary phenotype, back nevi, and baseline features, we found TERT/CLPTM1L rs401681 (P = 0.004), TYRP1 rs2733832 (P = 0.006), MTAP rs1335510 (P = 0.0005), TYR rs10830253 (P = 0.003), and MX2 rs45430 (P = 0.008) to be significantly associated with multiple primary melanoma while NCOA6 rs4911442 approached significance (P = 0.06). The GEM study provides additional evidence for the relevance of these genetic regions to melanoma risk and estimates the magnitude of the observed genetic effect on development of subsequent primary melanoma. PMID:25837821

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

PubMed

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

2016-01-01

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

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.

A cross-sectional study to estimate associations between education level and osteoporosis in a Chinese postmenopausal women sample.

PubMed

Piao, Hui-Hong; He, Jiajia; Zhang, Keqin; Tang, Zihui

2015-01-01

Our research aims to investigate the associations between education level and osteoporosis (OP) in Chinese postmenopausal women. A large-scale, community-based, cross-sectional study was conducted to examine the associations between education level and OP. A self-reported questionnaire was used to access the demographical information and medical history of the participants. A total of 1905 postmenopausal women were available for data analysis in this study. Multiple regression models controlling for confounding factors to include education level were performed to investigate the relationship with OP. The prevalence of OP was 28.29% in our study sample. Multivariate linear regression analyses adjusted for relevant potential confounding factors detected significant associations between education level and T-score (β = 0.025, P-value = 0.095, 95% CI: -0.004-0.055 for model 1; and β = 0.092, P-value = 0.032, 95% CI: 0.008-0.175 for model 2). Multivariate logistic regression analyses detected significant associations between education level and OP in model 1 (P-value = 0.070 for model 1, Table 5), while no significant associations was reported in model 2 (P value = 0.131). In participants with high education levels, the OR for OP was 0.914 (95% CI: 0.830-1.007). The findings indicated that education level was independently and significantly associated with OP. The prevalence of OP was more frequent in Chinese postmenopausal women with low educational status.

Identifying Pleiotropic Genes in Genome-Wide Association Studies for Multivariate Phenotypes with Mixed Measurement Scales

PubMed Central

Williams, L. Keoki; Buu, Anne

2017-01-01

We propose a multivariate genome-wide association test for mixed continuous, binary, and ordinal phenotypes. A latent response model is used to estimate the correlation between phenotypes with different measurement scales so that the empirical distribution of the Fisher’s combination statistic under the null hypothesis is estimated efficiently. The simulation study shows that our proposed correlation estimation methods have high levels of accuracy. More importantly, our approach conservatively estimates the variance of the test statistic so that the type I error rate is controlled. The simulation also shows that the proposed test maintains the power at the level very close to that of the ideal analysis based on known latent phenotypes while controlling the type I error. In contrast, conventional approaches–dichotomizing all observed phenotypes or treating them as continuous variables–could either reduce the power or employ a linear regression model unfit for the data. Furthermore, the statistical analysis on the database of the Study of Addiction: Genetics and Environment (SAGE) demonstrates that conducting a multivariate test on multiple phenotypes can increase the power of identifying markers that may not be, otherwise, chosen using marginal tests. The proposed method also offers a new approach to analyzing the Fagerström Test for Nicotine Dependence as multivariate phenotypes in genome-wide association studies. PMID:28081206

Study relationship between inorganic and organic coal analysis with gross calorific value by multiple regression and ANFIS

USGS Publications Warehouse

Chelgani, S.C.; Hart, B.; Grady, W.C.; Hower, J.C.

2011-01-01

The relationship between maceral content plus mineral matter and gross calorific value (GCV) for a wide range of West Virginia coal samples (from 6518 to 15330 BTU/lb; 15.16 to 35.66MJ/kg) has been investigated by multivariable regression and adaptive neuro-fuzzy inference system (ANFIS). The stepwise least square mathematical method comparison between liptinite, vitrinite, plus mineral matter as input data sets with measured GCV reported a nonlinear correlation coefficient (R2) of 0.83. Using the same data set the correlation between the predicted GCV from the ANFIS model and the actual GCV reported a R2 value of 0.96. It was determined that the GCV-based prediction methods, as used in this article, can provide a reasonable estimation of GCV. Copyright ?? Taylor & Francis Group, LLC.

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

Obtaining appropriate interval estimates for age when multiple indicators are used: evaluation of an ad-hoc procedure.

PubMed

Fieuws, Steffen; Willems, Guy; Larsen-Tangmose, Sara; Lynnerup, Niels; Boldsen, Jesper; Thevissen, Patrick

2016-03-01

When an estimate of age is needed, typically multiple indicators are present as found in skeletal or dental information. There exists a vast literature on approaches to estimate age from such multivariate data. Application of Bayes' rule has been proposed to overcome drawbacks of classical regression models but becomes less trivial as soon as the number of indicators increases. Each of the age indicators can lead to a different point estimate ("the most plausible value for age") and a prediction interval ("the range of possible values"). The major challenge in the combination of multiple indicators is not the calculation of a combined point estimate for age but the construction of an appropriate prediction interval. Ignoring the correlation between the age indicators results in intervals being too small. Boldsen et al. (2002) presented an ad-hoc procedure to construct an approximate confidence interval without the need to model the multivariate correlation structure between the indicators. The aim of the present paper is to bring under attention this pragmatic approach and to evaluate its performance in a practical setting. This is all the more needed since recent publications ignore the need for interval estimation. To illustrate and evaluate the method, Köhler et al. (1995) third molar scores are used to estimate the age in a dataset of 3200 male subjects in the juvenile age range.

Cerebellar contribution to motor and cognitive performance in multiple sclerosis: An MRI sub-regional volumetric analysis.

PubMed

D'Ambrosio, Alessandro; Pagani, Elisabetta; Riccitelli, Gianna C; Colombo, Bruno; Rodegher, Mariaemma; Falini, Andrea; Comi, Giancarlo; Filippi, Massimo; Rocca, Maria A

2017-08-01

To investigate the role of cerebellar sub-regions on motor and cognitive performance in multiple sclerosis (MS) patients. Whole and sub-regional cerebellar volumes, brain volumes, T2 hyperintense lesion volumes (LV), and motor performance scores were obtained from 95 relapse-onset MS patients and 32 healthy controls (HC). MS patients also underwent an evaluation of working memory and processing speed functions. Cerebellar anterior and posterior lobes were segmented using the Spatially Unbiased Infratentorial Toolbox (SUIT) from Statistical Parametric Mapping (SPM12). Multivariate linear regression models assessed the relationship between magnetic resonance imaging (MRI) measures and motor/cognitive scores. Compared to HC, only secondary progressive multiple sclerosis (SPMS) patients had lower cerebellar volumes (total and posterior cerebellum). In MS patients, lower anterior cerebellar volume and brain T2 LV predicted worse motor performance, whereas lower posterior cerebellar volume and brain T2 LV predicted poor cognitive performance. Global measures of brain volume and infratentorial T2 LV were not selected by the final multivariate models. Cerebellar volumetric abnormalities are likely to play an important contribution to explain motor and cognitive performance in MS patients. Consistently with functional mapping studies, cerebellar posterior-inferior volume accounted for variance in cognitive measures, whereas anterior cerebellar volume accounted for variance in motor performance, supporting the assessment of cerebellar damage at sub-regional level.

A matrix-based method of moments for fitting the multivariate random effects model for meta-analysis and meta-regression

PubMed Central

Jackson, Dan; White, Ian R; Riley, Richard D

2013-01-01

Multivariate meta-analysis is becoming more commonly used. Methods for fitting the multivariate random effects model include maximum likelihood, restricted maximum likelihood, Bayesian estimation and multivariate generalisations of the standard univariate method of moments. Here, we provide a new multivariate method of moments for estimating the between-study covariance matrix with the properties that (1) it allows for either complete or incomplete outcomes and (2) it allows for covariates through meta-regression. Further, for complete data, it is invariant to linear transformations. Our method reduces to the usual univariate method of moments, proposed by DerSimonian and Laird, in a single dimension. We illustrate our method and compare it with some of the alternatives using a simulation study and a real example. PMID:23401213

SMURC: High-Dimension Small-Sample Multivariate Regression With Covariance Estimation.

PubMed

Bayar, Belhassen; Bouaynaya, Nidhal; Shterenberg, Roman

2017-03-01

We consider a high-dimension low sample-size multivariate regression problem that accounts for correlation of the response variables. The system is underdetermined as there are more parameters than samples. We show that the maximum likelihood approach with covariance estimation is senseless because the likelihood diverges. We subsequently propose a normalization of the likelihood function that guarantees convergence. We call this method small-sample multivariate regression with covariance (SMURC) estimation. We derive an optimization problem and its convex approximation to compute SMURC. Simulation results show that the proposed algorithm outperforms the regularized likelihood estimator with known covariance matrix and the sparse conditional Gaussian graphical model. We also apply SMURC to the inference of the wing-muscle gene network of the Drosophila melanogaster (fruit fly).

Estimation of soil cation exchange capacity using Genetic Expression Programming (GEP) and Multivariate Adaptive Regression Splines (MARS)

NASA Astrophysics Data System (ADS)

Emamgolizadeh, S.; Bateni, S. M.; Shahsavani, D.; Ashrafi, T.; Ghorbani, H.

2015-10-01

The soil cation exchange capacity (CEC) is one of the main soil chemical properties, which is required in various fields such as environmental and agricultural engineering as well as soil science. In situ measurement of CEC is time consuming and costly. Hence, numerous studies have used traditional regression-based techniques to estimate CEC from more easily measurable soil parameters (e.g., soil texture, organic matter (OM), and pH). However, these models may not be able to adequately capture the complex and highly nonlinear relationship between CEC and its influential soil variables. In this study, Genetic Expression Programming (GEP) and Multivariate Adaptive Regression Splines (MARS) were employed to estimate CEC from more readily measurable soil physical and chemical variables (e.g., OM, clay, and pH) by developing functional relations. The GEP- and MARS-based functional relations were tested at two field sites in Iran. Results showed that GEP and MARS can provide reliable estimates of CEC. Also, it was found that the MARS model (with root-mean-square-error (RMSE) of 0.318 Cmol+ kg-1 and correlation coefficient (R2) of 0.864) generated slightly better results than the GEP model (with RMSE of 0.270 Cmol+ kg-1 and R2 of 0.807). The performance of GEP and MARS models was compared with two existing approaches, namely artificial neural network (ANN) and multiple linear regression (MLR). The comparison indicated that MARS and GEP outperformed the MLP model, but they did not perform as good as ANN. Finally, a sensitivity analysis was conducted to determine the most and the least influential variables affecting CEC. It was found that OM and pH have the most and least significant effect on CEC, respectively.

[Overload in the informal caregivers of patients with multiple comorbidities in an urban area].

PubMed

Álvarez-Tello, Margarita; Casado-Mejía, Rosa; Ortega-Calvo, Manuel; Ruiz-Arias, Esperanza

2012-01-01

The aim of the study was, to determine the profile of the family caregiver of patients with multiple pathologies, identify factors associated with overload, and construct predictive models using items from the Caregiver Strain Index (CSI). A cross-sectional study of caregivers of patients with multiple comorbidities who attended an urban health centre. Data were collected from health records and questionnaires (Barthel index, Pfeiffer index, and CSI). Statistical analysis was performed using measures of central tendency and dispersion, and by building multivariate models with binary logistic regression with the CSI items as predictors (program R version 2.14.0). The sample included 67 caregivers, with a mean age of 64.69 years (standard deviation=12.71, median 62 years), of whom 74.6% were women, 35.8% were wives, and 32.8% were daughters. The level of dependence of the patients cared for was total/severe in 77.6%, and moderate in 12% (Barthel), and 47.8% had some level of cognitive impairment (Pfeiffer). A CSI equal or greater than 7 was seen in 47.8% of caregivers, identifying life problems in more than 40% of them such as, restriction of social life, physical exertion, discomfort with change, bad behaviour, personal and family emotional changes, and sleep disturbances. Item 4 of the CSI, analysing the social restriction, was the one that showed a greater significance in the predictive multivariate model. Item 12 (economic burden) was the most significant with age in patients with cognitive impairment. Women tend to take the role of caregiver at an earlier age than men in the urban environment studied, and items from CSI showed that items 4 (social restrictions) and 12 (economic burden) have more significance in the predictive models constructed with Binary Logistic Regression. Copyright © 2012 Elsevier España, S.L. All rights reserved.

White matter hyperintensity patterns in cerebral amyloid angiopathy and hypertensive arteriopathy.

PubMed

Charidimou, Andreas; Boulouis, Gregoire; Haley, Kellen; Auriel, Eitan; van Etten, Ellis S; Fotiadis, Panagiotis; Reijmer, Yael; Ayres, Alison; Vashkevich, Anastasia; Dipucchio, Zora Y; Schwab, Kristin M; Martinez-Ramirez, Sergi; Rosand, Jonathan; Viswanathan, Anand; Greenberg, Steven M; Gurol, M Edip

2016-02-09

To identify different white matter hyperintensity (WMH) patterns between 2 hemorrhage-prone cerebral small vessel diseases (SVD): cerebral amyloid angiopathy (CAA) and hypertensive arteriopathy (HA). Consecutive patients with SVD-related intracerebral hemorrhage (ICH) from a single-center prospective cohort were analyzed. Four predefined subcortical WMH patterns were compared between the CAA and HA groups. These WMH patterns were (1) multiple subcortical spots; (2) peri-basal ganglia (BG); (3) large posterior subcortical patches; and (4) anterior subcortical patches. Their associations with other imaging (cerebral microbleeds [CMBs], enlarged perivascular spaces [EPVS]) and clinical markers of SVD were investigated using multivariable logistic regression. The cohort included 319 patients with CAA and 137 patients with HA. Multiple subcortical spots prevalence was higher in the CAA compared to the HA group (29.8% vs 16.8%; p = 0.004). Peri-BG WMH pattern was more common in the HA- vs the CAA-ICH group (19% vs 7.8%; p = 0.001). In multivariable logistic regression, presence of multiple subcortical spots was associated with lobar CMBs (odds ratio [OR] 1.23; 95% confidence interval [CI] 1.01-1.50, p = 0.039) and high degree of centrum semiovale EPVS (OR 2.43; 95% CI 1.56-3.80, p < 0.0001). By contrast, age (OR 1.05; 95% CI 1.02-1.09, p = 0.002), deep CMBs (OR 2.46; 95% CI 1.44-4.20, p = 0.001), total WMH volume (OR 1.02; 95% CI 1.01-1.04, p = 0.002), and high BG EPVS degree (OR 8.81; 95% CI 3.37-23.02, p < 0.0001) were predictors of peri-BG WMH pattern. Different patterns of subcortical leukoaraiosis visually identified on MRI might provide insights into the dominant underlying microangiopathy type as well as mechanisms of tissue injury in patients with ICH. © 2016 American Academy of Neurology.

Patient and hospital correlates of clinical outcomes and resource utilization in severe pediatric sepsis.

PubMed

Odetola, Folafoluwa O; Gebremariam, Achamyeleh; Freed, Gary L

2007-03-01

Our goal was to describe patient and hospital characteristics associated with in-hospital mortality, length of stay, and charges for critically ill children with severe sepsis. Our study consisted of a retrospective study of children 0 to 19 years of age hospitalized with severe sepsis using the 2003 Kids' Inpatient Database. We generated national estimates of rates of hospitalization and then compared in-hospital mortality, length of stay, and total charges according to patient and hospital characteristics using multivariable regression methods. Severity of illness was measured by using all-patient refined diagnosis-related group severity of illness classification into minor, moderate, major, and extreme severity. There were an estimated 21,448 hospitalizations for severe pediatric sepsis nationally in 2003. The in-hospital mortality rate was 4.2%. Comorbid illness was present in 34% of hospitalized children. Most (70%) of the extremely ill children were admitted to children's hospitals. Length of stay was longer among patients with higher illness severity and nonsurvivors compared with survivors (13.5 vs 8.5 days). Hospitalizations at urban or children's hospitals were also associated with longer length of stay than nonchildren's or rural hospitals, respectively. Higher charges were associated with higher illness severity, and nonsurvivors had 2.5-fold higher total charges than survivors. Also, higher charges were observed among hospitalizations in urban or children's hospitals. In multivariable regression analysis, multiple comorbid illnesses, multiple organ dysfunction, and greater severity of illness were associated with higher odds of mortality and longer length of stay. Higher hospital charges and longer length of stay were observed among transfer hospitalizations and among hospitalizations to children's hospitals and nonchildren's teaching hospitals compared with hospitals, which had neither children's nor teaching status. Mortality from severe pediatric sepsis is associated with patient illness severity, comorbid illness, and multiple organ dysfunction. Many characteristics are associated with resource consumption, including type of hospital, source of admission, and illness severity.

Adjustment of geochemical background by robust multivariate statistics

USGS Publications Warehouse

Zhou, D.

1985-01-01

Conventional analyses of exploration geochemical data assume that the background is a constant or slowly changing value, equivalent to a plane or a smoothly curved surface. However, it is better to regard the geochemical background as a rugged surface, varying with changes in geology and environment. This rugged surface can be estimated from observed geological, geochemical and environmental properties by using multivariate statistics. A method of background adjustment was developed and applied to groundwater and stream sediment reconnaissance data collected from the Hot Springs Quadrangle, South Dakota, as part of the National Uranium Resource Evaluation (NURE) program. Source-rock lithology appears to be a dominant factor controlling the chemical composition of groundwater or stream sediments. The most efficacious adjustment procedure is to regress uranium concentration on selected geochemical and environmental variables for each lithologic unit, and then to delineate anomalies by a common threshold set as a multiple of the standard deviation of the combined residuals. Robust versions of regression and RQ-mode principal components analysis techniques were used rather than ordinary techniques to guard against distortion caused by outliers Anomalies delineated by this background adjustment procedure correspond with uranium prospects much better than do anomalies delineated by conventional procedures. The procedure should be applicable to geochemical exploration at different scales for other metals. ?? 1985.

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

PubMed

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

2017-02-01

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

Time-series panel analysis (TSPA): multivariate modeling of temporal associations in psychotherapy process.

PubMed

Ramseyer, Fabian; Kupper, Zeno; Caspar, Franz; Znoj, Hansjörg; Tschacher, Wolfgang

2014-10-01

Processes occurring in the course of psychotherapy are characterized by the simple fact that they unfold in time and that the multiple factors engaged in change processes vary highly between individuals (idiographic phenomena). Previous research, however, has neglected the temporal perspective by its traditional focus on static phenomena, which were mainly assessed at the group level (nomothetic phenomena). To support a temporal approach, the authors introduce time-series panel analysis (TSPA), a statistical methodology explicitly focusing on the quantification of temporal, session-to-session aspects of change in psychotherapy. TSPA-models are initially built at the level of individuals and are subsequently aggregated at the group level, thus allowing the exploration of prototypical models. TSPA is based on vector auto-regression (VAR), an extension of univariate auto-regression models to multivariate time-series data. The application of TSPA is demonstrated in a sample of 87 outpatient psychotherapy patients who were monitored by postsession questionnaires. Prototypical mechanisms of change were derived from the aggregation of individual multivariate models of psychotherapy process. In a 2nd step, the associations between mechanisms of change (TSPA) and pre- to postsymptom change were explored. TSPA allowed a prototypical process pattern to be identified, where patient's alliance and self-efficacy were linked by a temporal feedback-loop. Furthermore, therapist's stability over time in both mastery and clarification interventions was positively associated with better outcomes. TSPA is a statistical tool that sheds new light on temporal mechanisms of change. Through this approach, clinicians may gain insight into prototypical patterns of change in psychotherapy. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Rate of revisions or conversion after bariatric surgery over 10 years in the state of New York.

PubMed

Altieri, Maria S; Yang, Jie; Nie, Lizhou; Blackstone, Robin; Spaniolas, Konstantinos; Pryor, Aurora

2018-04-01

A primary measure of the success of a procedure is the whether or not additional surgery may be necessary. Multi-institutional studies regarding the need for reoperation after bariatric surgery are scarce. The purpose of this study is to evaluate the rate of revisions/conversions (RC) after 3 common bariatric procedures over 10 years in the state of New York. University Hospital, involving a large database in New York State. The Statewide Planning and Research Cooperative System database was used to identify all patients undergoing laparoscopic adjustable gastric banding (LAGB), sleeve gastrectomy (SG), and Roux-en-Y gastric bypass (RYGB) between 2004 and 2010. Patients were followed for RC to other bariatric procedures for at least 4 years (up to 2014). Multivariable cox proportional hazard regression analysis was performed to identify risk factors for additional surgery after each common bariatric procedure. Multivariable logistic regression was used to check the factors associated with having ≥2 follow-up procedures. There were 40,994 bariatric procedures with 16,444 LAGB, 22,769 RYGB, and 1781 SG. Rate of RC was 26.0% for LAGB, 9.8% for SG, and 4.9% for RYGB. Multiple RC ( = />2) were more common for LAGB (5.7% for LAGB, .5% for RYGB, and .2% for LSG). Band revision/replacements required further procedures compared with patients who underwent conversion to RYGB/SG (939 compared with 48 procedures). Majority of RC were not performed at initial institution (68.2% of LAGB patients, 75.9% for RYGB, 63.7% of SG). Risk factors for multiple procedures included surgery type, as LAGB was more likely to have multiple RC. Reoperation was common for LAGB, but less common for RYGB (4.9%) and SG (9.8%). RC rate are almost twice after SG than after RYGB. LAGB had the highest rate (5.7%) of multiple reoperations. Conversion was the procedure of choice after a failed LAGB. Copyright © 2018 American Society for Bariatric Surgery. Published by Elsevier Inc. All rights reserved.

Multivariate regression model for predicting lumber grade volumes of northern red oak sawlogs

Treesearch

Daniel A. Yaussy; Robert L. Brisbin

1983-01-01

A multivariate regression model was developed to predict green board-foot yields for the seven common factory lumber grades processed from northern red oak (Quercus rubra L.) factory grade logs. The model uses the standard log measurements of grade, scaling diameter, length, and percent defect. It was validated with an independent data set. The model...

Predictive and mechanistic multivariate linear regression models for reaction development

PubMed Central

Santiago, Celine B.; Guo, Jing-Yao

2018-01-01

Multivariate Linear Regression (MLR) models utilizing computationally-derived and empirically-derived physical organic molecular descriptors are described in this review. Several reports demonstrating the effectiveness of this methodological approach towards reaction optimization and mechanistic interrogation are discussed. A detailed protocol to access quantitative and predictive MLR models is provided as a guide for model development and parameter analysis. PMID:29719711

Multivariate regression model for predicting yields of grade lumber from yellow birch sawlogs

Treesearch

Andrew F. Howard; Daniel A. Yaussy

1986-01-01

A multivariate regression model was developed to predict green board-foot yields for the common grades of factory lumber processed from yellow birch factory-grade logs. The model incorporates the standard log measurements of scaling diameter, length, proportion of scalable defects, and the assigned USDA Forest Service log grade. Differences in yields between band and...

G/SPLINES: A hybrid of Friedman's Multivariate Adaptive Regression Splines (MARS) algorithm with Holland's genetic algorithm

NASA Technical Reports Server (NTRS)

Rogers, David

1991-01-01

G/SPLINES are a hybrid of Friedman's Multivariable Adaptive Regression Splines (MARS) algorithm with Holland's Genetic Algorithm. In this hybrid, the incremental search is replaced by a genetic search. The G/SPLINE algorithm exhibits performance comparable to that of the MARS algorithm, requires fewer least squares computations, and allows significantly larger problems to be considered.

Validation of cross-sectional time series and multivariate adaptive regression splines models for the prediction of energy expenditure in children and adolescents using doubly labeled water

USDA-ARS?s Scientific Manuscript database

Accurate, nonintrusive, and inexpensive techniques are needed to measure energy expenditure (EE) in free-living populations. Our primary aim in this study was to validate cross-sectional time series (CSTS) and multivariate adaptive regression splines (MARS) models based on observable participant cha...

Higher-order Multivariable Polynomial Regression to Estimate Human Affective States

NASA Astrophysics Data System (ADS)

Wei, Jie; Chen, Tong; Liu, Guangyuan; Yang, Jiemin

2016-03-01

From direct observations, facial, vocal, gestural, physiological, and central nervous signals, estimating human affective states through computational models such as multivariate linear-regression analysis, support vector regression, and artificial neural network, have been proposed in the past decade. In these models, linear models are generally lack of precision because of ignoring intrinsic nonlinearities of complex psychophysiological processes; and nonlinear models commonly adopt complicated algorithms. To improve accuracy and simplify model, we introduce a new computational modeling method named as higher-order multivariable polynomial regression to estimate human affective states. The study employs standardized pictures in the International Affective Picture System to induce thirty subjects’ affective states, and obtains pure affective patterns of skin conductance as input variables to the higher-order multivariable polynomial model for predicting affective valence and arousal. Experimental results show that our method is able to obtain efficient correlation coefficients of 0.98 and 0.96 for estimation of affective valence and arousal, respectively. Moreover, the method may provide certain indirect evidences that valence and arousal have their brain’s motivational circuit origins. Thus, the proposed method can serve as a novel one for efficiently estimating human affective states.

Higher-order Multivariable Polynomial Regression to Estimate Human Affective States

PubMed Central

Wei, Jie; Chen, Tong; Liu, Guangyuan; Yang, Jiemin

2016-01-01

From direct observations, facial, vocal, gestural, physiological, and central nervous signals, estimating human affective states through computational models such as multivariate linear-regression analysis, support vector regression, and artificial neural network, have been proposed in the past decade. In these models, linear models are generally lack of precision because of ignoring intrinsic nonlinearities of complex psychophysiological processes; and nonlinear models commonly adopt complicated algorithms. To improve accuracy and simplify model, we introduce a new computational modeling method named as higher-order multivariable polynomial regression to estimate human affective states. The study employs standardized pictures in the International Affective Picture System to induce thirty subjects’ affective states, and obtains pure affective patterns of skin conductance as input variables to the higher-order multivariable polynomial model for predicting affective valence and arousal. Experimental results show that our method is able to obtain efficient correlation coefficients of 0.98 and 0.96 for estimation of affective valence and arousal, respectively. Moreover, the method may provide certain indirect evidences that valence and arousal have their brain’s motivational circuit origins. Thus, the proposed method can serve as a novel one for efficiently estimating human affective states. PMID:26996254

Regression Models For Multivariate Count Data

PubMed Central

Zhang, Yiwen; Zhou, Hua; Zhou, Jin; Sun, Wei

2016-01-01

Data with multivariate count responses frequently occur in modern applications. The commonly used multinomial-logit model is limiting due to its restrictive mean-variance structure. For instance, analyzing count data from the recent RNA-seq technology by the multinomial-logit model leads to serious errors in hypothesis testing. The ubiquity of over-dispersion and complicated correlation structures among multivariate counts calls for more flexible regression models. In this article, we study some generalized linear models that incorporate various correlation structures among the counts. Current literature lacks a treatment of these models, partly due to the fact that they do not belong to the natural exponential family. We study the estimation, testing, and variable selection for these models in a unifying framework. The regression models are compared on both synthetic and real RNA-seq data. PMID:28348500

Regression Models For Multivariate Count Data.

PubMed

Zhang, Yiwen; Zhou, Hua; Zhou, Jin; Sun, Wei

2017-01-01

Data with multivariate count responses frequently occur in modern applications. The commonly used multinomial-logit model is limiting due to its restrictive mean-variance structure. For instance, analyzing count data from the recent RNA-seq technology by the multinomial-logit model leads to serious errors in hypothesis testing. The ubiquity of over-dispersion and complicated correlation structures among multivariate counts calls for more flexible regression models. In this article, we study some generalized linear models that incorporate various correlation structures among the counts. Current literature lacks a treatment of these models, partly due to the fact that they do not belong to the natural exponential family. We study the estimation, testing, and variable selection for these models in a unifying framework. The regression models are compared on both synthetic and real RNA-seq data.

Spatio-temporal variations of nitric acid total columns from 9 years of IASI measurements - a driver study

NASA Astrophysics Data System (ADS)

Ronsmans, Gaétane; Wespes, Catherine; Hurtmans, Daniel; Clerbaux, Cathy; Coheur, Pierre-François

2018-04-01

This study aims to understand the spatial and temporal variability of HNO3 total columns in terms of explanatory variables. To achieve this, multiple linear regressions are used to fit satellite-derived time series of HNO3 daily averaged total columns. First, an analysis of the IASI 9-year time series (2008-2016) is conducted based on various equivalent latitude bands. The strong and systematic denitrification of the southern polar stratosphere is observed very clearly. It is also possible to distinguish, within the polar vortex, three regions which are differently affected by the denitrification. Three exceptional denitrification episodes in 2011, 2014 and 2016 are also observed in the Northern Hemisphere, due to unusually low arctic temperatures. The time series are then fitted by multivariate regressions to identify what variables are responsible for HNO3 variability in global distributions and time series, and to quantify their respective influence. Out of an ensemble of proxies (annual cycle, solar flux, quasi-biennial oscillation, multivariate ENSO index, Arctic and Antarctic oscillations and volume of polar stratospheric clouds), only the those defined as significant (p value < 0.05) by a selection algorithm are retained for each equivalent latitude band. Overall, the regression gives a good representation of HNO3 variability, with especially good results at high latitudes (60-80 % of the observed variability explained by the model). The regressions show the dominance of annual variability in all latitudinal bands, which is related to specific chemistry and dynamics depending on the latitudes. We find that the polar stratospheric clouds (PSCs) also have a major influence in the polar regions, and that their inclusion in the model improves the correlation coefficients and the residuals. However, there is still a relatively large portion of HNO3 variability that remains unexplained by the model, especially in the intertropical regions, where factors not included in the regression model (such as vegetation fires or lightning) may be at play.

Chromatography methods and chemometrics for determination of milk fat adulterants

NASA Astrophysics Data System (ADS)

Trbović, D.; Petronijević, R.; Đorđević, V.

2017-09-01

Milk and milk-based products are among the leading food categories according to reported cases of food adulteration. Although many authentication problems exist in all areas of the food industry, adequate control methods are required to evaluate the authenticity of milk and milk products in the dairy industry. Moreover, gas chromatography (GC) analysis of triacylglycerols (TAGs) or fatty acid (FA) profiles of milk fat (MF) in combination with multivariate statistical data processing have been used to detect adulterations of milk and dairy products with foreign fats. The adulteration of milk and butter is a major issue for the dairy industry. The major adulterants of MF are vegetable oils (soybean, sunflower, groundnut, coconut, palm and peanut oil) and animal fat (cow tallow and pork lard). Multivariate analysis enables adulterated MF to be distinguished from authentic MF, while taking into account many analytical factors. Various multivariate analysis methods have been proposed to quantitatively detect levels of adulterant non-MFs, with multiple linear regression (MLR) seemingly the most suitable. There is a need for increased use of chemometric data analyses to detect adulterated MF in foods and for their expanded use in routine quality assurance testing.

Explaining cross-national differences in marriage, cohabitation, and divorce in Europe, 1990-2000.

PubMed

Kalmijn, Matthijs

2007-11-01

European countries differ considerably in their marriage patterns. The study presented in this paper describes these differences for the 1990s and attempts to explain them from a macro-level perspective. We find that different indicators of marriage (i.e., marriage rate, age at marriage, divorce rate, and prevalence of unmarried cohabitation) cannot be seen as indicators of an underlying concept such as the 'strength of marriage'. Multivariate ordinary least squares (OLS) regression analyses are estimated with countries as units and panel regression models are estimated in which annual time series for multiple countries are pooled. Using these models, we find that popular explanations of trends in the indicators - explanations that focus on gender roles, secularization, unemployment, and educational expansion - are also important for understanding differences among countries. We also find evidence for the role of historical continuity and societal disintegration in understanding cross-national differences.

Assessing the response of area burned to changing climate in western boreal North America using a Multivariate Adaptive Regression Splines (MARS) approach

Treesearch

Michael S. Balshi; A. David McGuire; Paul Duffy; Mike Flannigan; John Walsh; Jerry Melillo

2009-01-01

We developed temporally and spatially explicit relationships between air temperature and fuel moisture codes derived from the Canadian Fire Weather Index System to estimate annual area burned at 2.5o (latitude x longitude) resolution using a Multivariate Adaptive Regression Spline (MARS) approach across Alaska and Canada. Burned area was...

"L"-Bivariate and "L"-Multivariate Association Coefficients. Research Report. ETS RR-08-40

ERIC Educational Resources Information Center

Kong, Nan; Lewis, Charles

2008-01-01

Given a system of multiple random variables, a new measure called the "L"-multivariate association coefficient is defined using (conditional) entropy. Unlike traditional correlation measures, the L-multivariate association coefficient measures the multiassociations or multirelations among the multiple variables in the given system; that…

Characterizing multivariate decoding models based on correlated EEG spectral features

PubMed Central

McFarland, Dennis J.

2013-01-01

Objective Multivariate decoding methods are popular techniques for analysis of neurophysiological data. The present study explored potential interpretative problems with these techniques when predictors are correlated. Methods Data from sensorimotor rhythm-based cursor control experiments was analyzed offline with linear univariate and multivariate models. Features were derived from autoregressive (AR) spectral analysis of varying model order which produced predictors that varied in their degree of correlation (i.e., multicollinearity). Results The use of multivariate regression models resulted in much better prediction of target position as compared to univariate regression models. However, with lower order AR features interpretation of the spectral patterns of the weights was difficult. This is likely to be due to the high degree of multicollinearity present with lower order AR features. Conclusions Care should be exercised when interpreting the pattern of weights of multivariate models with correlated predictors. Comparison with univariate statistics is advisable. Significance While multivariate decoding algorithms are very useful for prediction their utility for interpretation may be limited when predictors are correlated. PMID:23466267

Multi-Target Regression via Robust Low-Rank Learning.

PubMed

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

2018-02-01

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

Bone mineral density across a range of physical activity volumes: NHANES 2007-2010.

PubMed

Whitfield, Geoffrey P; Kohrt, Wendy M; Pettee Gabriel, Kelley K; Rahbar, Mohammad H; Kohl, Harold W

2015-02-01

The association between aerobic physical activity volume and bone mineral density (BMD) is not completely understood. The purpose of this study was to clarify the association between BMD and aerobic activity across a broad range of activity volumes, particularly volumes between those recommended in the 2008 Physical Activity Guidelines for Americans and those of trained endurance athletes. Data from the 2007-2010 National Health and Nutrition Examination Survey were used to quantify the association between reported physical activity and BMD at the lumbar spine and proximal femur across the entire range of activity volumes reported by US adults. Participants were categorized into multiples of the minimum guideline-recommended volume based on reported moderate- and vigorous-intensity leisure activity. Lumbar and proximal femur BMD were assessed with dual-energy x-ray absorptiometry. Among women, multivariable-adjusted linear regression analyses revealed no significant differences in lumbar BMD across activity categories, whereas proximal femur BMD was significantly higher among those who exceeded the guidelines by 2-4 times than those who reported no activity. Among men, multivariable-adjusted BMD at both sites neared its highest values among those who exceeded the guidelines by at least 4 times and was not progressively higher with additional activity. Logistic regression estimating the odds of low BMD generally echoed the linear regression results. The association between physical activity volume and BMD is complex. Among women, exceeding guidelines by 2-4 times may be important for maximizing BMD at the proximal femur, whereas among men, exceeding guidelines by ≥4 times may be beneficial for lumbar and proximal femur BMD.

Novel risk score of contrast-induced nephropathy after percutaneous coronary intervention.

PubMed

Ji, Ling; Su, XiaoFeng; Qin, Wei; Mi, XuHua; Liu, Fei; Tang, XiaoHong; Li, Zi; Yang, LiChuan

2015-08-01

Contrast-induced nephropathy (CIN) post-percutaneous coronary intervention (PCI) is a major cause of acute kidney injury. In this study, we established a comprehensive risk score model to assess risk of CIN after PCI procedure, which could be easily used in a clinical environment. A total of 805 PCI patients, divided into analysis cohort (70%) and validation cohort (30%), were enrolled retrospectively in this study. Risk factors for CIN were identified using univariate analysis and multivariate logistic regression in the analysis cohort. Risk score model was developed based on multiple regression coefficients. Sensitivity and specificity of the new risk score system was validated in the validation cohort. Comparisons between the new risk score model and previous reported models were applied. The incidence of post-PCI CIN in the analysis cohort (n = 565) was 12%. Considerably high CIN incidence (50%) was observed in patients with chronic kidney disease (CKD). Age >75, body mass index (BMI) >25, myoglobin level, cardiac function level, hypoalbuminaemia, history of chronic kidney disease (CKD), Intra-aortic balloon pump (IABP) and peripheral vascular disease (PVD) were identified as independent risk factors of post-PCI CIN. A novel risk score model was established using multivariate regression coefficients, which showed highest sensitivity and specificity (0.917, 95%CI 0.877-0.957) compared with previous models. A new post-PCI CIN risk score model was developed based on a retrospective study of 805 patients. Application of this model might be helpful to predict CIN in patients undergoing PCI procedure. © 2015 Asian Pacific Society of Nephrology.

Time-localized wavelet multiple regression and correlation

NASA Astrophysics Data System (ADS)

Fernández-Macho, Javier

2018-02-01

This paper extends wavelet methodology to handle comovement dynamics of multivariate time series via moving weighted regression on wavelet coefficients. The concept of wavelet local multiple correlation is used to produce one single set of multiscale correlations along time, in contrast with the large number of wavelet correlation maps that need to be compared when using standard pairwise wavelet correlations with rolling windows. Also, the spectral properties of weight functions are investigated and it is argued that some common time windows, such as the usual rectangular rolling window, are not satisfactory on these grounds. The method is illustrated with a multiscale analysis of the comovements of Eurozone stock markets during this century. It is shown how the evolution of the correlation structure in these markets has been far from homogeneous both along time and across timescales featuring an acute divide across timescales at about the quarterly scale. At longer scales, evidence from the long-term correlation structure can be interpreted as stable perfect integration among Euro stock markets. On the other hand, at intramonth and intraweek scales, the short-term correlation structure has been clearly evolving along time, experiencing a sharp increase during financial crises which may be interpreted as evidence of financial 'contagion'.

Analysis of Sequence Data Under Multivariate Trait-Dependent Sampling.

PubMed

Tao, Ran; Zeng, Donglin; Franceschini, Nora; North, Kari E; Boerwinkle, Eric; Lin, Dan-Yu

2015-06-01

High-throughput DNA sequencing allows for the genotyping of common and rare variants for genetic association studies. At the present time and for the foreseeable future, it is not economically feasible to sequence all individuals in a large cohort. A cost-effective strategy is to sequence those individuals with extreme values of a quantitative trait. We consider the design under which the sampling depends on multiple quantitative traits. Under such trait-dependent sampling, standard linear regression analysis can result in bias of parameter estimation, inflation of type I error, and loss of power. We construct a likelihood function that properly reflects the sampling mechanism and utilizes all available data. We implement a computationally efficient EM algorithm and establish the theoretical properties of the resulting maximum likelihood estimators. Our methods can be used to perform separate inference on each trait or simultaneous inference on multiple traits. We pay special attention to gene-level association tests for rare variants. We demonstrate the superiority of the proposed methods over standard linear regression through extensive simulation studies. We provide applications to the Cohorts for Heart and Aging Research in Genomic Epidemiology Targeted Sequencing Study and the National Heart, Lung, and Blood Institute Exome Sequencing Project.

The PIT-trap-A "model-free" bootstrap procedure for inference about regression models with discrete, multivariate responses.

PubMed

Warton, David I; Thibaut, Loïc; Wang, Yi Alice

2017-01-01

Bootstrap methods are widely used in statistics, and bootstrapping of residuals can be especially useful in the regression context. However, difficulties are encountered extending residual resampling to regression settings where residuals are not identically distributed (thus not amenable to bootstrapping)-common examples including logistic or Poisson regression and generalizations to handle clustered or multivariate data, such as generalised estimating equations. We propose a bootstrap method based on probability integral transform (PIT-) residuals, which we call the PIT-trap, which assumes data come from some marginal distribution F of known parametric form. This method can be understood as a type of "model-free bootstrap", adapted to the problem of discrete and highly multivariate data. PIT-residuals have the key property that they are (asymptotically) pivotal. The PIT-trap thus inherits the key property, not afforded by any other residual resampling approach, that the marginal distribution of data can be preserved under PIT-trapping. This in turn enables the derivation of some standard bootstrap properties, including second-order correctness of pivotal PIT-trap test statistics. In multivariate data, bootstrapping rows of PIT-residuals affords the property that it preserves correlation in data without the need for it to be modelled, a key point of difference as compared to a parametric bootstrap. The proposed method is illustrated on an example involving multivariate abundance data in ecology, and demonstrated via simulation to have improved properties as compared to competing resampling methods.

The PIT-trap—A “model-free” bootstrap procedure for inference about regression models with discrete, multivariate responses

PubMed Central

Thibaut, Loïc; Wang, Yi Alice

2017-01-01

Bootstrap methods are widely used in statistics, and bootstrapping of residuals can be especially useful in the regression context. However, difficulties are encountered extending residual resampling to regression settings where residuals are not identically distributed (thus not amenable to bootstrapping)—common examples including logistic or Poisson regression and generalizations to handle clustered or multivariate data, such as generalised estimating equations. We propose a bootstrap method based on probability integral transform (PIT-) residuals, which we call the PIT-trap, which assumes data come from some marginal distribution F of known parametric form. This method can be understood as a type of “model-free bootstrap”, adapted to the problem of discrete and highly multivariate data. PIT-residuals have the key property that they are (asymptotically) pivotal. The PIT-trap thus inherits the key property, not afforded by any other residual resampling approach, that the marginal distribution of data can be preserved under PIT-trapping. This in turn enables the derivation of some standard bootstrap properties, including second-order correctness of pivotal PIT-trap test statistics. In multivariate data, bootstrapping rows of PIT-residuals affords the property that it preserves correlation in data without the need for it to be modelled, a key point of difference as compared to a parametric bootstrap. The proposed method is illustrated on an example involving multivariate abundance data in ecology, and demonstrated via simulation to have improved properties as compared to competing resampling methods. PMID:28738071

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

Chronological Age, Cognitions, and Practices in European American Mothers: A Multivariate Study of Parenting

PubMed Central

Bornstein, Marc H.; Putnick, Diane L.

2018-01-01

We studied multiple parenting cognitions and practices in European American mothers (N = 262) who ranged in age from 15 to 47 years. All were first-time parents of 20-month-old children. Some age effects were zero; others were linear or nonlinear. Nonlinear age effects determined by spline regression showed significant associations to a “knot” age (~30 years) with little or no association afterward. For parenting cognitions and practices that are age-sensitive, a two-phase model of parental development is proposed. These findings stress the importance of considering maternal chronological age as a factor in developmental study. PMID:17605519

Nutrition Deficiencies in Children With Intestinal Failure Receiving Chronic Parenteral Nutrition.

PubMed

Namjoshi, Shweta S; Muradian, Sarah; Bechtold, Hannah; Reyen, Laurie; Venick, Robert S; Marcus, Elizabeth A; Vargas, Jorge H; Wozniak, Laura J

2017-02-01

Home parenteral nutrition (PN) is a lifesaving therapy for children with intestinal failure (IF). Our aims were to describe the prevalence of micronutrient deficiencies (vitamin D, zinc, copper, iron, selenium) in a diverse population of children with IF receiving PN and to identify and characterize risk factors associated with micronutrient deficiencies, including hematologic abnormalities. Data were collected on 60 eligible patients through retrospective chart review between May 2012 and February 2015. Descriptive statistics included frequencies, medians, interquartile ranges (IQRs), and odds ratios (ORs). Statistical analyses included χ 2 , Fisher's exact, t tests, and logistic, univariate, and multivariate regressions. Patients were primarily young (median age, 3.3 years; IQR, 0.7-8.4), Latino (62%), and male (56%), with short bowel syndrome (70%). Of 60 study patients, 88% had ≥1 deficiency and 90% were anemic for age. Of 51 patients who had all 5 markers checked, 59% had multiple deficiencies (defined as ≥3). Multivariate analysis shows multiple deficiencies were associated with nonwhite race (OR, 9.4; P = .012) and higher body mass index z score (OR, 2.2; P = .016). Children with severe anemia (hemoglobin <8.5 g/dL) made up 50% of the cohort. Nonwhite race (OR, 6.6; P = .037) and zinc deficiency (OR, 11; P = .003) were multivariate predictors of severe anemia. Micronutrient deficiency and anemia are overwhelmingly prevalent in children with IF using chronic PN. This emphasizes the importance of universal surveillance and supplementation to potentially improve quality of life and developmental outcomes. Future research should investigate how racial disparities might contribute to nutrition outcomes for children using chronic PN.

The Role of Parent Communication and Connectedness in Dating Violence Victimization among Latino Adolescents.

PubMed

Kast, Nicole Rebecca; Eisenberg, Marla E; Sieving, Renee E

2016-06-01

Dating violence among U.S. adolescents is a substantial concern. Previous research indicates that Latino youth are at increased risk of dating violence victimization. This secondary data analysis examined the prevalence of physical and sexual dating violence victimization among subgroups of Latino adolescents and associations of parent communication, parent caring, and dating violence victimization using data from the 2010 Minnesota Student Survey (N = 4,814). Parallel analyses were conducted for Latino-only and multiple-race Latino adolescents, stratified by gender. Multivariate logistic regression models tested associations between race/ethnicity, parent communication, perceived parent caring, and adolescent dating violence experiences. Overall, 7.2% to 16.2% of Latinos reported physical or sexual dating violence. Both types of dating violence were more prevalent among multiple-race Latinos than among Latino-only adolescents, with prevalence rates highest among multiple-race Latino females (19.8% and 19.7% for physical and sexual dating violence victimization, respectively). In multivariate models, perceived parent caring was the most important protective factor against physical and sexual dating violence among males and females. High levels of mother and father communication were associated with less physical violence victimization among males and females and with less sexual violence victimization among females. Results highlight the importance of parent communication and parent caring as buffers against dating violence victimization for Latino youth. These findings indicate potential for preventive interventions with Latino adolescents targeting family connectedness to address dating violence victimization. © The Author(s) 2015.

Is Heart Rate Variability Better Than Routine Vital Signs for Prehospital Identification of Major Hemorrhage

DTIC Science & Technology

2015-01-01

different PRBC transfusion volumes. We performed multivariate regression analysis using HRV metrics and routine vital signs to test the hypothesis that...study sponsors did not have any role in the study design, data collection, analysis and interpretation of data, report writing, or the decision to...primary outcome was hemorrhagic injury plus different PRBC transfusion volumes. We performed multivariate regression analysis using HRV metrics and

imDEV: a graphical user interface to R multivariate analysis tools in Microsoft Excel

PubMed Central

Grapov, Dmitry; Newman, John W.

2012-01-01

Summary: Interactive modules for Data Exploration and Visualization (imDEV) is a Microsoft Excel spreadsheet embedded application providing an integrated environment for the analysis of omics data through a user-friendly interface. Individual modules enables interactive and dynamic analyses of large data by interfacing R's multivariate statistics and highly customizable visualizations with the spreadsheet environment, aiding robust inferences and generating information-rich data visualizations. This tool provides access to multiple comparisons with false discovery correction, hierarchical clustering, principal and independent component analyses, partial least squares regression and discriminant analysis, through an intuitive interface for creating high-quality two- and a three-dimensional visualizations including scatter plot matrices, distribution plots, dendrograms, heat maps, biplots, trellis biplots and correlation networks. Availability and implementation: Freely available for download at http://sourceforge.net/projects/imdev/. Implemented in R and VBA and supported by Microsoft Excel (2003, 2007 and 2010). Contact: John.Newman@ars.usda.gov Supplementary Information: Installation instructions, tutorials and users manual are available at http://sourceforge.net/projects/imdev/. PMID:22815358

MULTIVARIATE LINEAR MIXED MODELS FOR MULTIPLE OUTCOMES. (R824757)

EPA Science Inventory

We propose a multivariate linear mixed (MLMM) for the analysis of multiple outcomes, which generalizes the latent variable model of Sammel and Ryan. The proposed model assumes a flexible correlation structure among the multiple outcomes, and allows a global test of the impact of ...

Multiple pregnancy, short cervix, part-time worker, steroid use, low educational level and male fetus are risk factors for preterm birth in Japan: a multicenter, prospective study.

PubMed

Shiozaki, Arihiro; Yoneda, Satoshi; Nakabayashi, Masao; Takeda, Yoshiharu; Takeda, Satoru; Sugimura, Motoi; Yoshida, Koyo; Tajima, Atsushi; Manabe, Mami; Akagi, Kozo; Nakagawa, Shoko; Tada, Katsuhiko; Imafuku, Noriaki; Ogawa, Masanobu; Mizunoe, Tomoya; Kanayama, Naohiro; Itoh, Hiroaki; Minoura, Shigeki; Ogino, Mitsuharu; Saito, Shigeru

2014-01-01

To examine the relationship between preterm birth and socioeconomic factors, past history, cervical length, cervical interleukin-8, bacterial vaginosis, underlying diseases, use of medication, employment status, sex of the fetus and multiple pregnancy. In a multicenter, prospective, observational study, 1810 Japanese women registering their future delivery were enrolled at 8⁺⁰ to 12⁺⁶ weeks of gestation. Data on cervical length and delivery were obtained from 1365 pregnant women. Multivariate logistic regression analysis was performed. Short cervical length, steroid use, multiple pregnancy and male fetus were risk factors for preterm birth before 34 weeks of gestation. Multiple pregnancy, low educational level, short cervical length and part-timer were risk factors for preterm birth before 37 weeks of gestation. Multiple pregnancy and cervical shortening at 20-24 weeks of gestation was a stronger risk factor for preterm birth. Any pregnant woman being part-time employee or low educational level, having a male fetus and requiring steroid treatment should be watched for the development of preterm birth. © 2013 The Authors. Journal of Obstetrics and Gynaecology Research © 2013 Japan Society of Obstetrics and Gynecology.

A cross-sectional evaluation of the prevalence and associations of HIV among female sex workers in the Gambia.

PubMed

Peitzmeier, Sarah; Mason, Krystal; Ceesay, Nuha; Diouf, Daouda; Drame, Fatou; Loum, Jaegan; Baral, Stefan

2014-03-01

To determine HIV prevalence among female sex workers in the Gambia and HIV risk factors, we accrued participants (n = 251) through peer-referral and venue-based recruitment. Blood samples were screened for HIV and participants were administered a questionnaire. Bivariate and multivariate logistic regression identified factors associated with HIV status. Forty respondents (15.9%) were HIV-positive: 20 (8.0%) were infected with HIV-1 only, 10 (4.0%) with HIV-2 only, and 10 (4.0%) with both HIV-1 and HIV-2; 12.5% (n = 5/40) knew their status. Condom usage at last sex was 97.1% (n = 170/175) with new clients and 44.2% (n = 53/120) with non-paying partners. Having a non-paying partner, living with relatives or friends, having felt scared to walk in public, selling sex in multiple locations, and recent depressive symptoms were positively associated with HIV under multivariate regression. Female sex workers have a higher prevalence of HIV compared to the general Gambian population. Interventions should be rights-based, promote safer sex practices and regular testing for female sex workers and linkage to HIV treatment and care with adherence support for those living with HIV. In addition, service providers should consider non-paying partners of female sex workers, improve knowledge and availability of condoms and lubricant, and address safety and mental health needs.

Cardiovascular comorbidities of pediatric psoriasis among hospitalized children in the United States.

PubMed

Kwa, Lauren; Kwa, Michael C; Silverberg, Jonathan I

2017-12-01

Psoriasis has been shown to be associated with cardiovascular disease in adults. Little is known about cardiovascular risk in pediatric psoriasis. To determine if there is an association between pediatric psoriasis and cardiovascular comorbidities. Data were analyzed from the 2002-2012 Nationwide Inpatient Sample, which included 4,884,448 hospitalized children aged 0-17 years. Bivariate and multivariate survey logistic regression models were created to calculate the odds of psoriasis on cardiovascular comorbidities. In multivariate survey logistic regression models adjusting for age, sex, and race/ethnicity, pediatric psoriasis was significantly associated with 5 of 10 cardiovascular comorbidities (adjusted odds ratio [95% confidence interval]), including obesity (3.15 [2.46-4.05]), hypertension (2.63 [1.93-3.59]), diabetes (2.90 [1.90-4.42]), arrhythmia (1.39 [1.02-1.88]), and valvular heart disease (1.90 [1.07-3.37]). The highest odds of cardiovascular risk factors occurred in blacks and Hispanics and children ages 0-9 years, but there were no sex differences. The study was limited to hospitalized children. We were unable to assess the impact of psoriasis treatment or family history on cardiovascular risk. Pediatric psoriasis is associated with higher odds of multiple cardiovascular comorbidities among hospitalized patients. Strategies for mitigating excess cardiovascular risk in pediatric psoriasis need to be determined. Copyright © 2017 American Academy of Dermatology, Inc. Published by Elsevier Inc. All rights reserved.

Risk factors for indications of intraoperative blood transfusion among patients undergoing surgical treatment for colorectal adenocarcinoma.

PubMed

Gonçalves, Iara; Linhares, Marcelo; Bordin, Jose; Matos, Delcio

2009-01-01

Identification of risk factors for requiring transfusions during surgery for colorectal cancer may lead to preventive actions or alternative measures, towards decreasing the use of blood components in these procedures, and also rationalization of resources use in hemotherapy services. This was a retrospective case-control study using data from 383 patients who were treated surgically for colorectal adenocarcinoma at 'Fundação Pio XII', in Barretos-SP, Brazil, between 1999 and 2003. To recognize significant risk factors for requiring intraoperative blood transfusion in colorectal cancer surgical procedures. Univariate analyses were performed using Fisher's exact test or the chi-squared test for dichotomous variables and Student's t test for continuous variables, followed by multivariate analysis using multiple logistic regression. In the univariate analyses, height (P = 0.06), glycemia (P = 0.05), previous abdominal or pelvic surgery (P = 0.031), abdominoperineal surgery (P<0.001), extended surgery (P<0.001) and intervention with radical intent (P<0.001) were considered significant. In the multivariate analysis using logistic regression, intervention with radical intent (OR = 10.249, P<0.001, 95% CI = 3.071-34.212) and abdominoperineal amputation (OR = 3.096, P = 0.04, 95% CI = 1.445-6.623) were considered to be independently significant. This investigation allows the conclusion that radical intervention and the abdominoperineal procedure in the surgical treatment of colorectal adenocarcinoma are risk factors for requiring intraoperative blood transfusion.

Arteriovenous fistula maturation in patients with permanent access created prior to or after hemodialysis initiation.

PubMed

Duque, Juan C; Martinez, Laisel; Tabbara, Marwan; Dvorquez, Denise; Mehandru, Sushil K; Asif, Arif; Vazquez-Padron, Roberto I; Salman, Loay H

2017-05-15

Multiple factors and comorbidities have been implicated in the ability of arteriovenous fistulas (AVF) to mature, including vessel anatomy, advanced age, and the presence of coronary artery disease or peripheral vascular disease. However, little is known about the role of uremia on AVF primary failure. In this study, we attempt to evaluate the effect of uremia on AVF maturation by comparing AVF outcomes between pre-dialysis chronic kidney disease (CKD) stage five patients and those who had their AVF created after hemodialysis (HD) initiation. We included 612 patients who underwent AVF creation between 2003 and 2015 at the University of Miami Hospital and Jackson Memorial Hospital. Effects of uremia on primary failure were evaluated using univariate statistical comparisons and multivariate logistic regression analyses. Primary failure occurred in 28.1% and 26.3% of patients with an AVF created prior to or after HD initiation, respectively (p = 0.73). The time of HD initiation was not associated with AVF maturation in multivariate logistic regression analysis (p = 0.57). In addition, pre-operative blood urea nitrogen (p = 0.78), estimated glomerular filtration rate (p = 0.66), and serum creatinine levels (p = 0.14) were not associated with AVF primary failure in pre-dialysis patients. Our results show that clearance of uremia with regular HD treatments prior to AVF creation does not improve the frequency of vascular access maturation.

Factors associated with abnormal eating attitudes among Greek adolescents.

PubMed

Bilali, Aggeliki; Galanis, Petros; Velonakis, Emmanuel; Katostaras, Theofanis

2010-01-01

To estimate the prevalence of abnormal eating attitudes among Greek adolescents and identify possible risk factors associated with these attitudes. Cross-sectional, school-based study. Six randomly selected schools in Patras, southern Greece. The study population consisted of 540 Greek students aged 13-18 years, and the response rate was 97%. The dependent variable was scores on the Eating Attitudes Test-26, with scores > or = 20 indicating abnormal eating attitudes. Bivariate analysis included independent Student t test, chi-square test, and Fisher's exact test. Multivariate logistic regression analysis was applied for the identification of the predictive factors, which were associated independently with abnormal eating attitudes. A 2-sided P value of less than .05 was considered statistically significant. The prevalence of abnormal eating attitudes was 16.7%. Multivariate logistic regression analysis demonstrated that females, urban residents, and those with a body mass index outside normal range, a perception of being overweight, body dissatisfaction, and a family member on a diet were independently related to abnormal eating attitudes. The results indicate that a proportion of Greek adolescents report abnormal eating attitudes and suggest that multiple factors contribute to the development of these attitudes. These findings are useful for further research into this topic and would be valuable in designing preventive interventions. Copyright 2010 Society for Nutrition Education. Published by Elsevier Inc. All rights reserved.

Outcome and prognostic factors in critically ill patients with systemic lupus erythematosus: a retrospective study.

PubMed

Hsu, Chia-Lin; Chen, Kuan-Yu; Yeh, Pu-Sheng; Hsu, Yeong-Long; Chang, Hou-Tai; Shau, Wen-Yi; Yu, Chia-Li; Yang, Pan-Chyr

2005-06-01

Systemic lupus erythematosus (SLE) is an archetypal autoimmune disease, involving multiple organ systems with varying course and prognosis. However, there is a paucity of clinical data regarding prognostic factors in SLE patients admitted to the intensive care unit (ICU). From January 1992 to December 2000, all patients admitted to the ICU with a diagnosis of SLE were included. Patients were excluded if the diagnosis of SLE was established at or after ICU admission. A multivariate logistic regression model was applied using Acute Physiology and Chronic Health Evaluation II scores and variables that were at least moderately associated (P < 0.2) with survival in the univariate analysis. A total of 51 patients meeting the criteria were included. The mortality rate was 47%. The most common cause of admission was pneumonia with acute respiratory distress syndrome. Multivariate logistic regression analysis showed that intracranial haemorrhage occurring while the patient was in the ICU (relative risk = 18.68), complicating gastrointestinal bleeding (relative risk = 6.97) and concurrent septic shock (relative risk = 77.06) were associated with greater risk of dying, whereas causes of ICU admission and Acute Physiology and Chronic Health Evaluation II score were not significantly associated with death. The mortality rate in critically ill SLE patients was high. Gastrointestinal bleeding, intracranial haemorrhage and septic shock were significant prognostic factors in SLE patients admitted to the ICU.

Multivariate methods for indoor PM10 and PM2.5 modelling in naturally ventilated schools buildings

NASA Astrophysics Data System (ADS)

Elbayoumi, Maher; Ramli, Nor Azam; Md Yusof, Noor Faizah Fitri; Yahaya, Ahmad Shukri Bin; Al Madhoun, Wesam; Ul-Saufie, Ahmed Zia

2014-09-01

In this study the concentrations of PM10, PM2.5, CO and CO2 concentrations and meteorological variables (wind speed, air temperature, and relative humidity) were employed to predict the annual and seasonal indoor concentration of PM10 and PM2.5 using multivariate statistical methods. The data have been collected in twelve naturally ventilated schools in Gaza Strip (Palestine) from October 2011 to May 2012 (academic year). The bivariate correlation analysis showed that the indoor PM10 and PM2.5 were highly positive correlated with outdoor concentration of PM10 and PM2.5. Further, Multiple linear regression (MLR) was used for modelling and R2 values for indoor PM10 were determined as 0.62 and 0.84 for PM10 and PM2.5 respectively. The Performance indicators of MLR models indicated that the prediction for PM10 and PM2.5 annual models were better than seasonal models. In order to reduce the number of input variables, principal component analysis (PCA) and principal component regression (PCR) were applied by using annual data. The predicted R2 were 0.40 and 0.73 for PM10 and PM2.5, respectively. PM10 models (MLR and PCR) show the tendency to underestimate indoor PM10 concentrations as it does not take into account the occupant's activities which highly affect the indoor concentrations during the class hours.

Aspects of porosity prediction using multivariate linear regression

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

Byrnes, A.P.; Wilson, M.D.

1991-03-01

Highly accurate multiple linear regression models have been developed for sandstones of diverse compositions. Porosity reduction or enhancement processes are controlled by the fundamental variables, Pressure (P), Temperature (T), Time (t), and Composition (X), where composition includes mineralogy, size, sorting, fluid composition, etc. The multiple linear regression equation, of which all linear porosity prediction models are subsets, takes the generalized form: Porosity = C{sub 0} + C{sub 1}(P) + C{sub 2}(T) + C{sub 3}(X) + C{sub 4}(t) + C{sub 5}(PT) + C{sub 6}(PX) + C{sub 7}(Pt) + C{sub 8}(TX) + C{sub 9}(Tt) + C{sub 10}(Xt) + C{sub 11}(PTX) + C{submore » 12}(PXt) + C{sub 13}(PTt) + C{sub 14}(TXt) + C{sub 15}(PTXt). The first four primary variables are often interactive, thus requiring terms involving two or more primary variables (the form shown implies interaction and not necessarily multiplication). The final terms used may also involve simple mathematic transforms such as log X, e{sup T}, X{sup 2}, or more complex transformations such as the Time-Temperature Index (TTI). The X term in the equation above represents a suite of compositional variable and, therefore, a fully expanded equation may include a series of terms incorporating these variables. Numerous published bivariate porosity prediction models involving P (or depth) or Tt (TTI) are effective to a degree, largely because of the high degree of colinearity between p and TTI. However, all such bivariate models ignore the unique contributions of P and Tt, as well as various X terms. These simpler models become poor predictors in regions where colinear relations change, were important variables have been ignored, or where the database does not include a sufficient range or weight distribution for the critical variables.« less

Univariate Analysis of Multivariate Outcomes in Educational Psychology.

ERIC Educational Resources Information Center

Hubble, L. M.

1984-01-01

The author examined the prevalence of multiple operational definitions of outcome constructs and an estimate of the incidence of Type I error rates when univariate procedures were applied to multiple variables in educational psychology. Multiple operational definitions of constructs were advocated and wider use of multivariate analysis was…

Factors predictive of immediate breast reconstruction following mastectomy for invasive breast cancer in Australia.

PubMed

Roder, D; Zorbas, H; Kollias, J; Pyke, C; Walters, D; Campbell, I; Taylor, C; Webster, F

2013-12-01

To investigate person, cancer and treatment determinants of immediate breast reconstruction (IBR) in Australia. Bi-variable and multi-variable analyses of the Quality Audit database. Of 12,707 invasive cancers treated by mastectomy circa 1998-2010, 8% had IBR. This proportion increased over time and reduced from 29% in women below 30 years to approximately 1% in those aged 70 years or more. Multiple regression indicated that other IBR predictors included: high socio-economic status; private health insurance; being asymptomatic; a metropolitan rather than inner regional treatment centre; higher surgeon case load; small tumour size; negative nodal status, positive progesterone receptor status; more cancer foci; multiple affected breast quadrants; synchronous bilateral cancer; not having neo-adjuvant chemotherapy, adjuvant radiotherapy or adjuvant hormone therapy; and receiving ovarian ablation. Variations in access to specialty services and other possible causes of variations in IBR rates need further investigation. Copyright © 2013 Elsevier Ltd. All rights reserved.

Relationships Among Substance Use, Multiple Sexual Partners, and Condomless Sex.

PubMed

Zhao, Yunchuan Lucy; Kim, Heejung; Peltzer, Jill

2017-04-01

Male and female students manifest different behaviors in condomless sex. This cross-sectional, exploratory, correlational study examined the differences in risk factors for condomless sex between male and female high school students, using secondary data from 4,968 sexually active males and females participating in the 2011 National Youth Risk Behavior Survey. Results in descriptive statistics and multivariate binary logistic regressions revealed that condomless sex was reported as 39.70% in general. A greater proportion of females engaged in condomless sex (23.26%) than did males (16.44%). Physical abuse by sex partners was a common reason for failure to use condoms regardless of gender. Lower condom use was found in (1) those experiencing forced sex by a partner in males, (2) female smokers, and (3) female with multiple sex partners. Thus, sexual health education should address the different risk factors and consider gender characteristics to reduce condomless sex.

Characterizing multivariate decoding models based on correlated EEG spectral features.

PubMed

McFarland, Dennis J

2013-07-01

Multivariate decoding methods are popular techniques for analysis of neurophysiological data. The present study explored potential interpretative problems with these techniques when predictors are correlated. Data from sensorimotor rhythm-based cursor control experiments was analyzed offline with linear univariate and multivariate models. Features were derived from autoregressive (AR) spectral analysis of varying model order which produced predictors that varied in their degree of correlation (i.e., multicollinearity). The use of multivariate regression models resulted in much better prediction of target position as compared to univariate regression models. However, with lower order AR features interpretation of the spectral patterns of the weights was difficult. This is likely to be due to the high degree of multicollinearity present with lower order AR features. Care should be exercised when interpreting the pattern of weights of multivariate models with correlated predictors. Comparison with univariate statistics is advisable. While multivariate decoding algorithms are very useful for prediction their utility for interpretation may be limited when predictors are correlated. Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

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

PubMed Central

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

2014-01-01

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

Analyzing Multiple Outcomes in Clinical Research Using Multivariate Multilevel Models

PubMed Central

Baldwin, Scott A.; Imel, Zac E.; Braithwaite, Scott R.; Atkins, David C.

2014-01-01

Objective Multilevel models have become a standard data analysis approach in intervention research. Although the vast majority of intervention studies involve multiple outcome measures, few studies use multivariate analysis methods. The authors discuss multivariate extensions to the multilevel model that can be used by psychotherapy researchers. Method and Results Using simulated longitudinal treatment data, the authors show how multivariate models extend common univariate growth models and how the multivariate model can be used to examine multivariate hypotheses involving fixed effects (e.g., does the size of the treatment effect differ across outcomes?) and random effects (e.g., is change in one outcome related to change in the other?). An online supplemental appendix provides annotated computer code and simulated example data for implementing a multivariate model. Conclusions Multivariate multilevel models are flexible, powerful models that can enhance clinical research. PMID:24491071

The Utility of a Syndemic Framework in Understanding Chronic Disease Management Among HIV-Infected and Type 2 Diabetic Men Who Have Sex with Men.

PubMed

Byg, Blaire; Bazzi, Angela Robertson; Funk, Danielle; James, Bonface; Potter, Jennifer

2016-12-01

Syndemic theory posits that epidemics of multiple physical and psychosocial problems co-occur among disadvantaged groups due to adverse social conditions. Although sexual minority populations are often stigmatized and vulnerable to multiple health problems, the syndemic perspective has been underutilized in understanding chronic disease. To assess the potential utility of this perspective in understanding the management of co-occurring HIV and Type 2 diabetes, we used linear regression to examine glycemic control (A1c) among men who have sex with men (MSM) with both HIV and Type 2 diabetes (n = 88). Bivariable linear regression explored potential syndemic correlates of inadequate glycemic control. Compared to those with adequate glycemic control (A1c ≤ 7.5 %), more men with inadequate glycemic control (A1c > 7.5 %) had hypertension (70 vs. 46 %, p = 0.034), high triglycerides (93 vs. 61 %, p = 0.002), depression (67 vs. 39 %, p = 0.018), current substance abuse (15 vs. 2 %, p = 0.014), and detectable levels of HIV (i.e., viral load ≥75 copies per ml blood; 30 vs. 10 %, p = 0.019). In multivariable regression controlling for age, the factors that were independently associated with higher A1c were high triglycerides, substance use, and detectable HIV viral load, suggesting that chronic disease management among MSM is complex and challenging for patients and providers. Findings also suggest that syndemic theory can be a clarifying lens for understanding chronic disease management among sexual minority stigmatized populations. Interventions targeting single conditions may be inadequate when multiple conditions co-occur; thus, research using a syndemic framework may be helpful in identifying intervention strategies that target multiple co-occurring conditions.

Periodontal disease in Chinese patients with systemic lupus erythematosus.

PubMed

Zhang, Qiuxiang; Zhang, Xiaoli; Feng, Guijaun; Fu, Ting; Yin, Rulan; Zhang, Lijuan; Feng, Xingmei; Li, Liren; Gu, Zhifeng

2017-08-01

Disease of systemic lupus erythematosus (SLE) and periodontal disease (PD) shares the common multiple characteristics. The aims of the present study were to evaluate the prevalence and severity of periodontal disease in Chinese SLE patients and to determine the association between SLE features and periodontal parameters. A cross-sectional study of 108 SLE patients together with 108 age- and sex-matched healthy controls was made. Periodontal status was conducted by two dentists independently. Sociodemographic characteristics, lifestyle factors, medication use, and clinical parameters were also assessed. The periodontal status was significantly worse in SLE patients compared to controls. In univariate logistic regression, SLE had a significant 2.78-fold [95% confidence interval (CI) 1.60-4.82] increase in odds of periodontitis compared to healthy controls. Adjusted for potential risk factors, patients with SLE had 13.98-fold (95% CI 5.10-38.33) increased odds against controls. In multiple linear regression model, the independent variable negatively and significantly associated with gingival index was education (P = 0.005); conversely, disease activity (P < 0.001) and plaque index (P = 0.002) were positively associated; Age was the only variable independently associated with periodontitis of SLE in multivariate logistic regression (OR 1.348; 95% CI: 1.183-1.536, P < 0.001). Chinese SLE patients were likely to suffer from higher odds of PD. These findings confirmed the importance of early interventions in combination with medical therapy. It is necessary for a close collaboration between dentists and clinicians when treating those patients.

EXTENDING MULTIVARIATE DISTANCE MATRIX REGRESSION WITH AN EFFECT SIZE MEASURE AND THE ASYMPTOTIC NULL DISTRIBUTION OF THE TEST STATISTIC

PubMed Central

McArtor, Daniel B.; Lubke, Gitta H.; Bergeman, C. S.

2017-01-01

Person-centered methods are useful for studying individual differences in terms of (dis)similarities between response profiles on multivariate outcomes. Multivariate distance matrix regression (MDMR) tests the significance of associations of response profile (dis)similarities and a set of predictors using permutation tests. This paper extends MDMR by deriving and empirically validating the asymptotic null distribution of its test statistic, and by proposing an effect size for individual outcome variables, which is shown to recover true associations. These extensions alleviate the computational burden of permutation tests currently used in MDMR and render more informative results, thus making MDMR accessible to new research domains. PMID:27738957

Extending multivariate distance matrix regression with an effect size measure and the asymptotic null distribution of the test statistic.

PubMed

McArtor, Daniel B; Lubke, Gitta H; Bergeman, C S

2017-12-01

Person-centered methods are useful for studying individual differences in terms of (dis)similarities between response profiles on multivariate outcomes. Multivariate distance matrix regression (MDMR) tests the significance of associations of response profile (dis)similarities and a set of predictors using permutation tests. This paper extends MDMR by deriving and empirically validating the asymptotic null distribution of its test statistic, and by proposing an effect size for individual outcome variables, which is shown to recover true associations. These extensions alleviate the computational burden of permutation tests currently used in MDMR and render more informative results, thus making MDMR accessible to new research domains.

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

PubMed

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

2017-12-01

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

Lithium and neuroleptics in combination: is there enhancement of neurotoxicity leading to permanent sequelae?

PubMed

Goldman, S A

1996-10-01

Neurotoxicity in relation to concomitant administration of lithium and neuroleptic drugs, particularly haloperidol, has been an ongoing issue. This study examined whether use of lithium with neuroleptic drugs enhances neurotoxicity leading to permanent sequelae. The Spontaneous Reporting System database of the United States Food and Drug Administration and extant literature were reviewed for spectrum cases of lithium/neuroleptic neurotoxicity. Groups taking lithium alone (Li), lithium/haloperidol (LiHal) and lithium/ nonhaloperidol neuroleptics (LiNeuro), each paired for recovery and sequelae, were established for 237 cases. Statistical analyses included pairwise comparisons of lithium levels using the Wilcoxon Rank Sum procedure and logistic regression to analyze the relationship between independent variables and development of sequelae. The Li and Li-Neuro groups showed significant statistical differences in median lithium levels between recovery and sequelae pairs, whereas the LiHal pair did not differ significantly. Lithium level was associated with sequelae development overall and within the Li and LiNeuro groups; no such association was evident in the LiHal group. On multivariable logistic regression analysis, lithium level and taking lithium/haloperidol were significant factors in the development of sequelae, with multiple possibly confounding factors (e.g., age, sex) not statistically significant. Multivariable logistic regression analyses with neuroleptic dose as five discrete dose ranges or actual dose did not show an association between development of sequelae and dose. Database limitations notwithstanding, the lack of apparent impact of serum lithium level on the development of sequelae in patients treated with haloperidol contrasts notably with results in the Li and LiNeuro groups. These findings may suggest a possible effect of pharmacodynamic factors in lithium/neuroleptic combination therapy.

Transport modeling and multivariate adaptive regression splines for evaluating performance of ASR systems in freshwater aquifers

NASA Astrophysics Data System (ADS)

Forghani, Ali; Peralta, Richard C.

2017-10-01

The study presents a procedure using solute transport and statistical models to evaluate the performance of aquifer storage and recovery (ASR) systems designed to earn additional water rights in freshwater aquifers. The recovery effectiveness (REN) index quantifies the performance of these ASR systems. REN is the proportion of the injected water that the same ASR well can recapture during subsequent extraction periods. To estimate REN for individual ASR wells, the presented procedure uses finely discretized groundwater flow and contaminant transport modeling. Then, the procedure uses multivariate adaptive regression splines (MARS) analysis to identify the significant variables affecting REN, and to identify the most recovery-effective wells. Achieving REN values close to 100% is the desire of the studied 14-well ASR system operator. This recovery is feasible for most of the ASR wells by extracting three times the injectate volume during the same year as injection. Most of the wells would achieve RENs below 75% if extracting merely the same volume as they injected. In other words, recovering almost all the same water molecules that are injected requires having a pre-existing water right to extract groundwater annually. MARS shows that REN most significantly correlates with groundwater flow velocity, or hydraulic conductivity and hydraulic gradient. MARS results also demonstrate that maximizing REN requires utilizing the wells located in areas with background Darcian groundwater velocities less than 0.03 m/d. The study also highlights the superiority of MARS over regular multiple linear regressions to identify the wells that can provide the maximum REN. This is the first reported application of MARS for evaluating performance of an ASR system in fresh water aquifers.

Bone Mineral Density across a Range of Physical Activity Volumes: NHANES 2007–2010

PubMed Central

Whitfield, Geoffrey P.; Kohrt, Wendy M.; Pettee Gabriel, Kelley K.; Rahbar, Mohammad H.; Kohl, Harold W.

2014-01-01

Introduction The association between aerobic physical activity volume and bone mineral density (BMD) is not completely understood. The purpose of this study was to clarify the association between BMD and aerobic activity across a broad range of activity volumes, in particular volumes between those recommended in the 2008 Physical Activity Guidelines for Americans and those of trained endurance athletes. Methods Data from the 2007–2010 National Health and Nutrition Examination Survey were used to quantify the association between reported physical activity and BMD at the lumbar spine and proximal femur across the entire range of activity volumes reported by US adults. Participants were categorized into multiples of the minimum guideline-recommended volume based on reported moderate and vigorous intensity leisure activity. Lumbar and proximal femur BMD was assessed with dual-energy x-ray absorptiometry. Results Among women, multivariable-adjusted linear regression analyses revealed no significant differences in lumbar BMD across activity categories, while proximal femur BMD was significantly higher among those who exceeded guidelines by 2–4 times than those who reported no activity. Among men, multivariable-adjusted BMD at both sites neared its highest values among those who exceeded guidelines by at least 4 times and was not progressively higher with additional activity. Logistic regression estimating the odds of low BMD generally echoed the linear regression results. Conclusion The association between physical activity volume and BMD is complex. Among women, exceeding guidelines by 2–4 times may be important for maximizing BMD at the proximal femur, while among men, exceeding guidelines by 4+ times may be beneficial for lumbar and proximal femur BMD. PMID:24870584

Logistic models--an odd(s) kind of regression.

PubMed

Jupiter, Daniel C

2013-01-01

The logistic regression model bears some similarity to the multivariable linear regression with which we are familiar. However, the differences are great enough to warrant a discussion of the need for and interpretation of logistic regression. Copyright © 2013 American College of Foot and Ankle Surgeons. Published by Elsevier Inc. All rights reserved.

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.

Gas-induced susceptibility artefacts on diffusion-weighted MRI of the rectum at 1.5 T - Effect of applying a micro-enema to improve image quality.

PubMed

van Griethuysen, Joost J M; Bus, Elyse M; Hauptmann, Michael; Lahaye, Max J; Maas, Monique; Ter Beek, Leon C; Beets, Geerard L; Bakers, Frans C H; Beets-Tan, Regina G H; Lambregts, Doenja M J

2018-02-01

Assess whether application of a micro-enema can reduce gas-induced susceptibility artefacts in Single-shot Echo Planar Imaging (EPI) Diffusion-weighted imaging of the rectum at 1.5 T. Retrospective analysis of n = 50 rectal cancer patients who each underwent multiple DWI-MRIs (1.5 T) from 2012 to 2016 as part of routine follow-up during a watch-and-wait approach after chemoradiotherapy. From March 2014 DWI-MRIs were routinely acquired after application of a preparatory micro-enema (Microlax ® ; 5 ml; self-administered shortly before acquisition); before March 2014 no bowel preparation was given. In total, 335 scans were scored by an experienced reader for the presence/severity of gas-artefacts (on b1000 DWI), ranging from 0 (no artefact) to 5 (severe artefact). A score ≥3 (moderate-severe) was considered a clinically relevant artefact. A random sample of 100 scans was re-assessed by a second independent reader to study inter-observer effects. Scores were compared between the scans performed without and with a preparatory micro-enema using univariable and multivariable logistic regression taking into account potential confounding factors (age/gender, acquisition parameters, MRI-hardware, rectoscopy prior to MRI). Clinically relevant gas-artefacts were seen in 24.3% (no micro-enema) vs. 3.7% (micro-enema), odds ratios were 0.118 in univariable and 0.230 in multivariable regression (P = 0.0005 and 0.0291). Mean severity score (±SD) was 1.19 ± 1.71 (no-enema) vs 0.32 ± 0.77 (micro-enema), odds ratios were 0.321 (P < 0.0001) and 0.489 (P = 0.0461) in uni- and multivariable regression, respectively. Inter-observer agreement was excellent (κ0.85). Use of a preparatory micro-enema shortly before rectal EPI-DWI examinations performed at 1.5 T MRI significantly reduces both the incidence and severity of gas-induced artefacts, compared to examinations performed without bowel preparation. Copyright © 2017 Elsevier B.V. All rights reserved.

Linear Multivariable Regression Models for Prediction of Eddy Dissipation Rate from Available Meteorological Data

NASA Technical Reports Server (NTRS)

MCKissick, Burnell T. (Technical Monitor); Plassman, Gerald E.; Mall, Gerald H.; Quagliano, John R.

2005-01-01

Linear multivariable regression models for predicting day and night Eddy Dissipation Rate (EDR) from available meteorological data sources are defined and validated. Model definition is based on a combination of 1997-2000 Dallas/Fort Worth (DFW) data sources, EDR from Aircraft Vortex Spacing System (AVOSS) deployment data, and regression variables primarily from corresponding Automated Surface Observation System (ASOS) data. Model validation is accomplished through EDR predictions on a similar combination of 1994-1995 Memphis (MEM) AVOSS and ASOS data. Model forms include an intercept plus a single term of fixed optimal power for each of these regression variables; 30-minute forward averaged mean and variance of near-surface wind speed and temperature, variance of wind direction, and a discrete cloud cover metric. Distinct day and night models, regressing on EDR and the natural log of EDR respectively, yield best performance and avoid model discontinuity over day/night data boundaries.

Optical characteristics of fine and coarse particulates at Grand Canyon, Arizona

NASA Astrophysics Data System (ADS)

Malm, William C.; Johnson, Christopher E.

The relationship between airborne particulate matter and atmospheric light extinction was examined using the multivariate techniques of principal component analysis and multiple linear regression on data gathered at the Grand Canyon, Arizona, from December 1979 to November 1981. Results showed that, on the average, fine sulfates were most strongly associated with light attenuation in the atmosphere. Other fine mass (nitrates, organics, soot and carbonaceous material) and coarse mass (primarily windblown dust) were much less associated with atmospheric extinction. Fine sulfate mass at the Grand Canyon was responsible for 63% of atmospheric light extinction while other fine mass and coarse mass were responsible for 17 and 20% of atmospheric extinction, respectively.

Suicidal ideation and Attempts in North American School-Based Surveys

PubMed Central

Saewyc, Elizabeth M.; Skay, Carol L.; Hynds, Patricia; Pettingell, Sandra; Bearinger, Linda H.; Resnick, Michael D.; Reis, Elizabeth

2008-01-01

This study explored the prevalence, disparity, and cohort trends in suicidality among bisexual teens vs. heterosexual and gay/lesbian peers in 9 population-based high school surveys in Canada and the U.S. Multivariate logistic regressions were used to calculate age-adjusted odds ratios separately by gender; 95% confidence intervals tested cohort trends where surveys were repeated over multiple years. Results showed remarkable consistency: bisexual youth reported higher odds of recent suicidal ideation and attempts vs. heterosexual peers, with increasing odds in most surveys over the past decade. Results compared to gay and lesbian peers were mixed, with varying gender differences in prevalence and disparity trends in the different regions. PMID:19835039

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

PubMed Central

2011-01-01

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

Field applications of stand-off sensing using visible/NIR multivariate optical computing

NASA Astrophysics Data System (ADS)

Eastwood, DeLyle; Soyemi, Olusola O.; Karunamuni, Jeevanandra; Zhang, Lixia; Li, Hongli; Myrick, Michael L.

2001-02-01

12 A novel multivariate visible/NIR optical computing approach applicable to standoff sensing will be demonstrated with porphyrin mixtures as examples. The ultimate goal is to develop environmental or counter-terrorism sensors for chemicals such as organophosphorus (OP) pesticides or chemical warfare simulants in the near infrared spectral region. The mathematical operation that characterizes prediction of properties via regression from optical spectra is a calculation of inner products between the spectrum and the pre-determined regression vector. The result is scaled appropriately and offset to correspond to the basis from which the regression vector is derived. The process involves collecting spectroscopic data and synthesizing a multivariate vector using a pattern recognition method. Then, an interference coating is designed that reproduces the pattern of the multivariate vector in its transmission or reflection spectrum, and appropriate interference filters are fabricated. High and low refractive index materials such as Nb2O5 and SiO2 are excellent choices for the visible and near infrared regions. The proof of concept has now been established for this system in the visible and will later be extended to chemicals such as OP compounds in the near and mid-infrared.

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

PubMed

Keithley, Richard B; Wightman, R Mark

2011-06-07

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

Reported prevalence of gestational diabetes in Scotland: The relationship with obesity, age, socioeconomic status, smoking and macrosomia, and how many are we missing?

PubMed

Collier, Andrew; Abraham, E Christie; Armstrong, Julie; Godwin, Jon; Monteath, Kirsten; Lindsay, Robert

2017-03-01

Gestational diabetes mellitus (GDM) is defined as 'carbohydrate intolerance of varying degrees of severity with onset or first recognition during pregnancy,' and is associated with increased fetal and maternal risks. The aims of the present study were to investigate the prevalence of GDM in Scotland over 32 years (1981-2012), and using the data from 2012, to assess how GDM related to maternal body mass index, maternal age, parity, smoking, Scottish Index of Multiple Deprivation, infant gender and macrosomia status. GDM prevalence along with anthropometric, obstetric and demographic data were collected on a total of 1,891,097 women with a delivery episode between 1 January 1981 and 31 December 2012 using data extracted from the Scottish Morbidity Record 02. Univariate and multivariate logistic regression analysis was undertaken to investigate their association with GDM. A ninefold increase in GDM prevalence was observed from 1981 to 2012 (P < 0.001). GDM prevalence in 2012 was 1.9%. Maternal body mass index, age, parity status, Scottish index of multiple deprivation and fetal macrosomia were positively associated with GDM. Reported smoking status at booking was inversely associated with GDM. Multivariable analysis showed that fetal macrosomia was not associated with GDM status. The present study confirmed that the reporting of GDM is low in Scotland, and that GDM is associated with maternal body mass index, maternal age, multiparity and social deprivation. GDM was negatively associated with smoking and requires further investigation. The lack of association between GDM and macrosomia (following multivariate analysis) might reflect the screening processes undertaken in Scotland. © 2016 The Authors. Journal of Diabetes Investigation published by Asian Association for the Study of Diabetes (AASD) and John Wiley & Sons Australia, Ltd.

Missing Data and Multiple Imputation in the Context of Multivariate Analysis of Variance

ERIC Educational Resources Information Center

Finch, W. Holmes

2016-01-01

Multivariate analysis of variance (MANOVA) is widely used in educational research to compare means on multiple dependent variables across groups. Researchers faced with the problem of missing data often use multiple imputation of values in place of the missing observations. This study compares the performance of 2 methods for combining p values in…

A refined method for multivariate meta-analysis and meta-regression.

PubMed

Jackson, Daniel; Riley, Richard D

2014-02-20

Making inferences about the average treatment effect using the random effects model for meta-analysis is problematic in the common situation where there is a small number of studies. This is because estimates of the between-study variance are not precise enough to accurately apply the conventional methods for testing and deriving a confidence interval for the average effect. We have found that a refined method for univariate meta-analysis, which applies a scaling factor to the estimated effects' standard error, provides more accurate inference. We explain how to extend this method to the multivariate scenario and show that our proposal for refined multivariate meta-analysis and meta-regression can provide more accurate inferences than the more conventional approach. We explain how our proposed approach can be implemented using standard output from multivariate meta-analysis software packages and apply our methodology to two real examples. Copyright © 2013 John Wiley & Sons, Ltd.

Newer classification and regression tree techniques: Bagging and Random Forests for ecological prediction

Treesearch

Anantha M. Prasad; Louis R. Iverson; Andy Liaw; Andy Liaw

2006-01-01

We evaluated four statistical models - Regression Tree Analysis (RTA), Bagging Trees (BT), Random Forests (RF), and Multivariate Adaptive Regression Splines (MARS) - for predictive vegetation mapping under current and future climate scenarios according to the Canadian Climate Centre global circulation model.

Response of dissolved trace metals to land use/land cover and their source apportionment using a receptor model in a subtropic river, China.

PubMed

Li, Siyue; Zhang, Quanfa

2011-06-15

Water samples were collected for determination of dissolved trace metals in 56 sampling sites throughout the upper Han River, China. Multivariate statistical analyses including correlation analysis, stepwise multiple linear regression models, and principal component and factor analysis (PCA/FA) were employed to examine the land use influences on trace metals, and a receptor model of factor analysis-multiple linear regression (FA-MLR) was used for source identification/apportionment of anthropogenic heavy metals in the surface water of the River. Our results revealed that land use was an important factor in water metals in the snow melt flow period and land use in the riparian zone was not a better predictor of metals than land use away from the river. Urbanization in a watershed and vegetation along river networks could better explain metals, and agriculture, regardless of its relative location, however slightly explained metal variables in the upper Han River. FA-MLR analysis identified five source types of metals, and mining, fossil fuel combustion, and vehicle exhaust were the dominant pollutions in the surface waters. The results demonstrated great impacts of human activities on metal concentrations in the subtropical river of China. Copyright © 2011 Elsevier B.V. All rights reserved.

Features predictive of brain arteriovenous malformation hemorrhage: extrapolation to a physiologic model.

PubMed

Sahlein, Daniel H; Mora, Paloma; Becske, Tibor; Huang, Paul; Jafar, Jafar J; Connolly, E Sander; Nelson, Peter K

2014-07-01

Although there is generally thought to be a 2% to 4% per annum rupture risk for brain arteriovenous malformations (bAVMs), there is no way to estimate risk for an individual patient. In this retrospective study, patients were eligible who had nidiform bAVMs and underwent detailed pretreatment diagnostic cerebral angiography at our medical center from 1996 to 2006. All patients had superselective microcatheter angiography, and films were reviewed for the purpose of this project. Patient demographics, clinical presentation, and angioarchitectural characteristics were analyzed. A univariate analysis was performed, and angioarchitectural features with potential physiological significance that showed at least a trend toward significance were added to a multivariate logistic regression model. One hundred twenty-two bAVMs met criteria for study entry. bAVMs with single venous drainage anatomy were more likely to present with hemorrhage. In addition, patients with multiple draining veins and a venous stenosis reverted to a risk similar to those with 1 draining vein, whereas those with multiple draining veins and without stenosis had diminished association with hemorrhage presentation. Those bAVMs with associated aneurysms were more likely to present with hemorrhage. These findings were robust in both univariate and multivariate models. The results of this article lead to the first physiological, internally consistent model of individual bAVM hemorrhage risk, where 1 draining vein, venous stenosis, and associated aneurysms increase risk. © 2014 American Heart Association, Inc.

"Photographing money" task pricing

NASA Astrophysics Data System (ADS)

Jia, Zhongxiang

2018-05-01

"Photographing money" [1]is a self-service model under the mobile Internet. The task pricing is reasonable, related to the success of the commodity inspection. First of all, we analyzed the position of the mission and the membership, and introduced the factor of membership density, considering the influence of the number of members around the mission on the pricing. Multivariate regression of task location and membership density using MATLAB to establish the mathematical model of task pricing. At the same time, we can see from the life experience that membership reputation and the intensity of the task will also affect the pricing, and the data of the task success point is more reliable. Therefore, the successful point of the task is selected, and its reputation, task density, membership density and Multiple regression of task positions, according to which a nhew task pricing program. Finally, an objective evaluation is given of the advantages and disadvantages of the established model and solution method, and the improved method is pointed out.

Gambling disorder-related illegal acts: Regression model of associated factors

PubMed Central

Gorsane, Mohamed Ali; Reynaud, Michel; Vénisse, Jean-Luc; Legauffre, Cindy; Valleur, Marc; Magalon, David; Fatséas, Mélina; Chéreau-Boudet, Isabelle; Guilleux, Alice; JEU Group; Challet-Bouju, Gaëlle; Grall-Bronnec, Marie

2017-01-01

Background and aims Gambling disorder-related illegal acts (GDRIA) are often crucial events for gamblers and/or their entourage. This study was designed to determine the predictive factors of GDRIA. Methods Participants were 372 gamblers reporting at least three DSM-IV-TR (American Psychiatric Association, 2000) criteria. They were assessed on the basis of sociodemographic characteristics, gambling-related characteristics, their personality profile, and psychiatric comorbidities. A multiple logistic regression was performed to identify the relevant predictors of GDRIA and their relative contribution to the prediction of the presence of GDRIA. Results Multivariate analysis revealed a higher South Oaks Gambling Scale score, comorbid addictive disorders, and a lower level of income as GDRIA predictors. Discussion and conclusion An original finding of this study was that the comorbid addictive disorder effect might be mediated by a disinhibiting effect of stimulant substances on GDRIA. Further studies are necessary to replicate these results, especially in a longitudinal design, and to explore specific therapeutic interventions. PMID:28198636

Gambling disorder-related illegal acts: Regression model of associated factors.

PubMed

Gorsane, Mohamed Ali; Reynaud, Michel; Vénisse, Jean-Luc; Legauffre, Cindy; Valleur, Marc; Magalon, David; Fatséas, Mélina; Chéreau-Boudet, Isabelle; Guilleux, Alice; Challet-Bouju, Gaëlle; Grall-Bronnec, Marie

2017-03-01

Background and aims Gambling disorder-related illegal acts (GDRIA) are often crucial events for gamblers and/or their entourage. This study was designed to determine the predictive factors of GDRIA. Methods Participants were 372 gamblers reporting at least three DSM-IV-TR (American Psychiatric Association, 2000) criteria. They were assessed on the basis of sociodemographic characteristics, gambling-related characteristics, their personality profile, and psychiatric comorbidities. A multiple logistic regression was performed to identify the relevant predictors of GDRIA and their relative contribution to the prediction of the presence of GDRIA. Results Multivariate analysis revealed a higher South Oaks Gambling Scale score, comorbid addictive disorders, and a lower level of income as GDRIA predictors. Discussion and conclusion An original finding of this study was that the comorbid addictive disorder effect might be mediated by a disinhibiting effect of stimulant substances on GDRIA. Further studies are necessary to replicate these results, especially in a longitudinal design, and to explore specific therapeutic interventions.

Shared Decision-Making among Caregivers and Health Care Providers of Youth with Type 1 Diabetes

PubMed Central

Valenzuela, Jessica M.; Smith, Laura B.; Stafford, Jeanette M.; Andrews, S.; D’Agostino, Ralph B.; Lawrence, Jean M.; Yi-Frazier, Joyce P.; Seid, Michael; Dolan, Lawrence M.

2014-01-01

The present study aimed to examine perceptions of shared decision-making (SDM) in caregivers of youth with type 1 diabetes (T1D). Interview, survey data, and HbA1c assays were gathered from caregivers of 439 youth with T1D aged 3–18 years. Caregiver-report indicated high perceived SDM during medical visits. Multivariable linear regression indicated that greater SDM is associated with lower HbA1c, older child age, and having a pediatric endocrinologist provider. Multiple logistic regression found that caregivers who did not perceive having made any healthcare decisions in the past year were more likely to identify a non-pediatric endocrinologist provider and to report less optimal diabetes self-care. Findings suggest that youth whose caregivers report greater SDM may show benefits in terms of self-care and glycemic control. Future research should examine the role of youth in SDM and how best to identify youth and families with low SDM in order to improve care. PMID:24952739

Efficient Determination of Free Energy Landscapes in Multiple Dimensions from Biased Umbrella Sampling Simulations Using Linear Regression.

PubMed

Meng, Yilin; Roux, Benoît

2015-08-11

The weighted histogram analysis method (WHAM) is a standard protocol for postprocessing the information from biased umbrella sampling simulations to construct the potential of mean force with respect to a set of order parameters. By virtue of the WHAM equations, the unbiased density of state is determined by satisfying a self-consistent condition through an iterative procedure. While the method works very effectively when the number of order parameters is small, its computational cost grows rapidly in higher dimension. Here, we present a simple and efficient alternative strategy, which avoids solving the self-consistent WHAM equations iteratively. An efficient multivariate linear regression framework is utilized to link the biased probability densities of individual umbrella windows and yield an unbiased global free energy landscape in the space of order parameters. It is demonstrated with practical examples that free energy landscapes that are comparable in accuracy to WHAM can be generated at a small fraction of the cost.

Efficient Determination of Free Energy Landscapes in Multiple Dimensions from Biased Umbrella Sampling Simulations Using Linear Regression

PubMed Central

2015-01-01

The weighted histogram analysis method (WHAM) is a standard protocol for postprocessing the information from biased umbrella sampling simulations to construct the potential of mean force with respect to a set of order parameters. By virtue of the WHAM equations, the unbiased density of state is determined by satisfying a self-consistent condition through an iterative procedure. While the method works very effectively when the number of order parameters is small, its computational cost grows rapidly in higher dimension. Here, we present a simple and efficient alternative strategy, which avoids solving the self-consistent WHAM equations iteratively. An efficient multivariate linear regression framework is utilized to link the biased probability densities of individual umbrella windows and yield an unbiased global free energy landscape in the space of order parameters. It is demonstrated with practical examples that free energy landscapes that are comparable in accuracy to WHAM can be generated at a small fraction of the cost. PMID:26574437

A Pilot Study of Reasons and Risk Factors for "No-Shows" in a Pediatric Neurology Clinic.

PubMed

Guzek, Lindsay M; Fadel, William F; Golomb, Meredith R

2015-09-01

Missed clinic appointments lead to decreased patient access, worse patient outcomes, and increased healthcare costs. The goal of this pilot study was to identify reasons for and risk factors associated with missed pediatric neurology outpatient appointments ("no-shows"). This was a prospective cohort study of patients scheduled for 1 week of clinic. Data on patient clinical and demographic information were collected by record review; data on reasons for missed appointments were collected by phone interviews. Univariate and multivariate analyses were conducted using chi-square tests and multiple logistic regression to assess risk factors for missed appointments. Fifty-nine (25%) of 236 scheduled patients were no-shows. Scheduling conflicts (25.9%) and forgetting (20.4%) were the most common reasons for missed appointments. When controlling for confounding factors in the logistic regression, Medicaid (odds ratio 2.36), distance from clinic, and time since appointment was scheduled were associated with missed appointments. Further work in this area is needed. © The Author(s) 2014.

Functional Relationships and Regression Analysis.

ERIC Educational Resources Information Center

Preece, Peter F. W.

1978-01-01

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

Prognostic factors in multiple myeloma: selection using Cox's proportional hazard model.

PubMed

Pasqualetti, P; Collacciani, A; Maccarone, C; Casale, R

1996-01-01

The pretreatment characteristics of 210 patients with multiple myeloma, observed between 1980 and 1994, were evaluated as potential prognostic factors for survival. Multivariate analysis according to Cox's proportional hazard model identified in the 160 dead patients with myeloma, among 26 different single prognostic variables, the following factors in order of importance: beta 2-microglobulin; bone marrow plasma cell percentage, hemoglobinemia, degree of lytic bone lesions, serum creatinine, and serum albumin. By analysis of these variables a prognostic index (PI), that considers the regression coefficients derived by Cox's model of all significant factors, was obtained. Using this it was possible to separate the whole patient group into three stages: stage I (PI < 1.485, 67 patients), stage II (PI: 1.485-2.090, 76 patients), and stage III (PI > 2.090, 67 patients), with a median survivals of 68, 36 and 13 months (P < 0.0001), respectively. Also the responses to therapy (P < 0.0001) and the survival curves (P < 0.00001) presented significant differences among the three subgroups. Knowledge of these factors could be of value in predicting prognosis and in planning therapy in patients with multiple myeloma.

Prognostic value of Sequential Organ Failure Assessment and Simplified Acute Physiology II Score compared with trauma scores in the outcome of multiple-trauma patients.

PubMed

Fueglistaler, Philipp; Amsler, Felix; Schüepp, Marcel; Fueglistaler-Montali, Ida; Attenberger, Corinna; Pargger, Hans; Jacob, Augustinus Ludwig; Gross, Thomas

2010-08-01

Prospective data regarding the prognostic value of the Sequential Organ Failure Assessment (SOFA) score in comparison with the Simplified Acute Physiology Score (SAPS II) and trauma scores on the outcome of multiple-trauma patients are lacking. Single-center evaluation (n = 237, Injury Severity Score [ISS] >16; mean ISS = 29). Uni- and multivariate analysis of SAPS II, SOFA, revised trauma, polytrauma, and trauma and ISS scores (TRISS) was performed. The 30-day mortality was 22.8% (n = 54). SOFA day 1 was significantly higher in nonsurvivors compared with survivors (P < .001) and correlated well with the length of intensive care unit stay (r = .50, P < .001). Logistic regression revealed SAPS II to have the best predictive value of 30-day mortality (area under the receiver operating characteristic = .86 +/- .03). The SOFA score significantly added prognostic information with regard to mortality to both SAPS II and TRISS. The combination of critically ill and trauma scores may increase the accuracy of mortality prediction in multiple-trauma patients. 2010 Elsevier Inc. All rights reserved.

Membrane Introduction Mass Spectrometry Combined with an Orthogonal Partial-Least Squares Calibration Model for Mixture Analysis.

PubMed

Li, Min; Zhang, Lu; Yao, Xiaolong; Jiang, Xingyu

2017-01-01

The emerging membrane introduction mass spectrometry technique has been successfully used to detect benzene, toluene, ethyl benzene and xylene (BTEX), while overlapped spectra have unfortunately hindered its further application to the analysis of mixtures. Multivariate calibration, an efficient method to analyze mixtures, has been widely applied. In this paper, we compared univariate and multivariate analyses for quantification of the individual components of mixture samples. The results showed that the univariate analysis creates poor models with regression coefficients of 0.912, 0.867, 0.440 and 0.351 for BTEX, respectively. For multivariate analysis, a comparison to the partial-least squares (PLS) model shows that the orthogonal partial-least squares (OPLS) regression exhibits an optimal performance with regression coefficients of 0.995, 0.999, 0.980 and 0.976, favorable calibration parameters (RMSEC and RMSECV) and a favorable validation parameter (RMSEP). Furthermore, the OPLS exhibits a good recovery of 73.86 - 122.20% and relative standard deviation (RSD) of the repeatability of 1.14 - 4.87%. Thus, MIMS coupled with the OPLS regression provides an optimal approach for a quantitative BTEX mixture analysis in monitoring and predicting water pollution.

Factors associated with active commuting to work among women.

PubMed

Bopp, Melissa; Child, Stephanie; Campbell, Matthew

2014-01-01

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

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

Factors associated with parasite dominance in fishes from Brazil.

PubMed

Amarante, Cristina Fernandes do; Tassinari, Wagner de Souza; Luque, Jose Luis; Pereira, Maria Julia Salim

2016-06-14

The present study used regression models to evaluate the existence of factors that may influence the numerical parasite dominance with an epidemiological approximation. A database including 3,746 fish specimens and their respective parasites were used to evaluate the relationship between parasite dominance and biotic characteristics inherent to the studied hosts and the parasite taxa. Multivariate, classical, and mixed effects linear regression models were fitted. The calculations were performed using R software (95% CI). In the fitting of the classical multiple linear regression model, freshwater and planktivorous fish species and body length, as well as the species of the taxa Trematoda, Monogenea, and Hirudinea, were associated with parasite dominance. However, the fitting of the mixed effects model showed that the body length of the host and the species of the taxa Nematoda, Trematoda, Monogenea, Hirudinea, and Crustacea were significantly associated with parasite dominance. Studies that consider specific biological aspects of the hosts and parasites should expand the knowledge regarding factors that influence the numerical dominance of fish in Brazil. The use of a mixed model shows, once again, the importance of the appropriate use of a model correlated with the characteristics of the data to obtain consistent results.

Role of BMI and age in predicting pathologic vertebral fractures in newly diagnosed multiple myeloma patients: A retrospective cohort study.

PubMed

Chen, Yi-Lun; Liu, Yao-Chung; Wu, Chia-Hung; Yeh, Chiu-Mei; Chiu, Hsun-I; Lee, Gin-Yi; Lee, Yu-Ting; Hsu, Pei; Lin, Ting-Wei; Gau, Jyh-Pyng; Hsiao, Liang-Tsai; Chiou, Tzeon-Jye; Liu, Jin-Hwang; Liu, Chia-Jen

2018-04-01

Vertebral fractures affect approximately 30% of myeloma patients and lead to a poor impact on survival and life quality. In general, age and body mass index (BMI) are reported to have an important role in vertebral fractures. However, the triangle relationship among age, BMI, and vertebral fractures is still unclear in newly diagnosed multiple myeloma (NDMM) patients. This study recruited consecutive 394 patients with NDMM at Taipei Veterans General Hospital between January 1, 2005 and December 31, 2015. Risk factors for vertebral fractures in NDMM patients were collected and analyzed. The survival curves were demonstrated using Kaplan-Meier estimate. In total, 301 (76.4%) NDMM patients were enrolled in the cohort. In the median follow-up period of 18.0 months, the median survival duration in those with vertebral fractures ≥ 2 was shorter than those with vertebral fracture < 2 (59.3 vs 28.6 months; P = 0.017). In multivariate Poisson regression, BMI < 18.5 kg/m 2 declared increased vertebral fractures compared with BMI ≥ 24.0 kg/m 2 (adjusted RR, 2.79; 95% CI, 1.44-5.43). In multivariable logistic regression, BMI < 18.5 kg/m 2 was an independent risk factor for vertebral fractures ≥ 2 compared with BMI ≥ 24.0 kg/m 2 (adjusted OR, 6.05; 95% CI, 2.43-15.08). Among age stratifications, patients with both old age and low BMI were at a greater risk suffering from increased vertebral fractures, especially in patients > 75 years and BMI < 18.5 kg/m 2 (adjusted RR, 12.22; 95% CI, 3.02-49.40). This is the first study that demonstrated that age had a significant impact on vertebral fractures in NDMM patients with low BMI. Elder patients with low BMI should consider to routinely receive spinal radiographic examinations and regular follow-up. Copyright © 2017 John Wiley & Sons, Ltd.

Analysis of Factors Related to Hypopituitarism in Patients with Nonsellar Intracranial Tumor.

PubMed

Lu, Song-Song; Gu, Jian-Jun; Luo, Xiao-Hong; Zhang, Jian-He; Wang, Shou-Sen

2017-09-01

Previous studies have suggested that postoperative hypopituitarism in patients with nonsellar intracranial tumors is caused by traumatic surgery. However, with development of minimally invasive and precise neurosurgical techniques, the degree of injury to brain tissue has been reduced significantly, especially for parenchymal tumors. Therefore, understanding preexisting hypopituitarism and related risk factors can improve perioperative management for patients with nonsellar intracranial tumors. Chart data were collected retrospectively from 83 patients with nonsellar intracranial tumors admitted to our hospital from May 2014 to April 2015. Pituitary function of each subject was determined based on results of preoperative serum pituitary hormone analysis. Univariate and multivariate logistic regression methods were used to analyze relationships between preoperative hypopituitarism and factors including age, sex, history of hypertension and secondary epilepsy, course of disease, tumor mass effect, site of tumor, intracranial pressure (ICP), cerebrospinal fluid content, and pituitary morphology. A total of 30 patients (36.14%) presented with preoperative hypopituitarism in either 1 axis or multiple axes; 23 (27.71%) were affected in 1 axis, and 7 (8.43%) were affected in multiple axes. Univariate analysis showed that risk factors for preoperative hypopituitarism in patients with a nonsellar intracranial tumor include an acute or subacute course (≤3 months), intracranial hypertension (ICP >200 mm H 2 O), and mass effect (P < 0.05). Multivariate logistic regression analysis showed that mass effect is an independent risk factor for preoperative hypopituitarism in patients with nonsellar intracranial tumors (P < 0.05; odds ratio, 3.197). Prevalence of hypopituitarism is high in patients with nonsellar intracranial tumors. The occurrence of hypopituitarism is correlated with factors including an acute or subacute course (≤3 months), intracranial hypertension (ICP >200 mm H 2 O), and mass effect (P < 0.05). Mass effect is an independent risk factor for hypopituitarism. Copyright © 2017 Elsevier Inc. All rights reserved.

Relationship between non-standard work arrangements and work-related accident absence in Belgium

PubMed Central

Alali, Hanan; Braeckman, Lutgart; Van Hecke, Tanja; De Clercq, Bart; Janssens, Heidi; Wahab, Magd Abdel

2017-01-01

Objectives: The main objective of this study is to examine the relationship between indicators of non-standard work arrangements, including precarious contract, long working hours, multiple jobs, shift work, and work-related accident absence, using a representative Belgian sample and considering several socio-demographic and work characteristics. Methods: This study was based on the data of the fifth European Working Conditions Survey (EWCS). For the analysis, the sample was restricted to 3343 respondents from Belgium who were all employed workers. The associations between non-standard work arrangements and work-related accident absence were studied with multivariate logistic regression modeling techniques while adjusting for several confounders. Results: During the last 12 months, about 11.7% of workers were absent from work because of work-related accident. A multivariate regression model showed an increased injury risk for those performing shift work (OR 1.546, 95% CI 1.074-2.224). The relationship between contract type and occupational injuries was not significant (OR 1.163, 95% CI 0.739-1.831). Furthermore, no statistically significant differences were observed for those performing long working hours (OR 1.217, 95% CI 0.638-2.321) and those performing multiple jobs (OR 1.361, 95% CI 0.827-2.240) in relation to work-related accident absence. Those who rated their health as bad, low educated workers, workers from the construction sector, and those exposed to biomechanical exposure (BM) were more frequent victims of work-related accident absence. No significant gender difference was observed. Conclusion: Indicators of non-standard work arrangements under this study, except shift work, were not significantly associated with work-related accident absence. To reduce the burden of occupational injuries, not only risk reduction strategies and interventions are needed but also policy efforts are to be undertaken to limit shift work. In general, preventive measures and more training on the job are needed to ensure the safety and well-being of all workers. PMID:28111414

Relationship between non-standard work arrangements and work-related accident absence in Belgium.

PubMed

Alali, Hanan; Braeckman, Lutgart; Van Hecke, Tanja; De Clercq, Bart; Janssens, Heidi; Wahab, Magd Abdel

2017-03-28

The main objective of this study is to examine the relationship between indicators of non-standard work arrangements, including precarious contract, long working hours, multiple jobs, shift work, and work-related accident absence, using a representative Belgian sample and considering several socio-demographic and work characteristics. This study was based on the data of the fifth European Working Conditions Survey (EWCS). For the analysis, the sample was restricted to 3343 respondents from Belgium who were all employed workers. The associations between non-standard work arrangements and work-related accident absence were studied with multivariate logistic regression modeling techniques while adjusting for several confounders. During the last 12 months, about 11.7% of workers were absent from work because of work-related accident. A multivariate regression model showed an increased injury risk for those performing shift work (OR 1.546, 95% CI 1.074-2.224). The relationship between contract type and occupational injuries was not significant (OR 1.163, 95% CI 0.739-1.831). Furthermore, no statistically significant differences were observed for those performing long working hours (OR 1.217, 95% CI 0.638-2.321) and those performing multiple jobs (OR 1.361, 95% CI 0.827-2.240) in relation to work-related accident absence. Those who rated their health as bad, low educated workers, workers from the construction sector, and those exposed to biomechanical exposure (BM) were more frequent victims of work-related accident absence. No significant gender difference was observed. Indicators of non-standard work arrangements under this study, except shift work, were not significantly associated with work-related accident absence. To reduce the burden of occupational injuries, not only risk reduction strategies and interventions are needed but also policy efforts are to be undertaken to limit shift work. In general, preventive measures and more training on the job are needed to ensure the safety and well-being of all workers.

Blood lead level association with lower body weight in NHANES 1999–2006

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

Scinicariello, Franco, E-mail: fes6@cdc.gov; Buser, Melanie C.; Mevissen, Meike

Background: Lead exposure is associated with low birth-weight. The objective of this study is to determine whether lead exposure is associated with lower body weight in children, adolescents and adults. Methods: We analyzed data from NHANES 1999–2006 for participants aged ≥ 3 using multiple logistic and multivariate linear regression. Using age- and sex-standardized BMI Z-scores, overweight and obese children (ages 3–19) were classified by BMI ≥ 85th and ≥ 95th percentiles, respectively. The adult population (age ≥ 20) was classified as overweight and obese with BMI measures of 25–29.9 and ≥ 30, respectively. Blood lead level (BLL) was categorized bymore » weighted quartiles. Results: Multivariate linear regressions revealed a lower BMI Z-score in children and adolescents when the highest lead quartile was compared to the lowest lead quartile (β (SE) = − 0.33 (0.07), p < 0.001), and a decreased BMI in adults (β (SE) = − 2.58 (0.25), p < 0.001). Multiple logistic analyses in children and adolescents found a negative association between BLL and the percentage of obese and overweight with BLL in the highest quartile compared to the lowest quartile (OR = 0.42, 95% CI: 0.30–0.59; and OR = 0.67, 95% CI: 0.52–0.88, respectively). Adults in the highest lead quartile were less likely to be obese (OR = 0.42, 95% CI: 0.35–0.50) compared to those in the lowest lead quartile. Further analyses with blood lead as restricted cubic splines, confirmed the dose-relationship between blood lead and body weight outcomes. Conclusions: BLLs are associated with lower body mass index and obesity in children, adolescents and adults. - Highlights: • NHANES analysis of BLL and body weight outcomes • Increased BLL associated with decreased body weight in children and adolescent • Increased BLL associated with decreased body weight in adults.« less

Numerically accurate computational techniques for optimal estimator analyses of multi-parameter models

NASA Astrophysics Data System (ADS)

Berger, Lukas; Kleinheinz, Konstantin; Attili, Antonio; Bisetti, Fabrizio; Pitsch, Heinz; Mueller, Michael E.

2018-05-01

Modelling unclosed terms in partial differential equations typically involves two steps: First, a set of known quantities needs to be specified as input parameters for a model, and second, a specific functional form needs to be defined to model the unclosed terms by the input parameters. Both steps involve a certain modelling error, with the former known as the irreducible error and the latter referred to as the functional error. Typically, only the total modelling error, which is the sum of functional and irreducible error, is assessed, but the concept of the optimal estimator enables the separate analysis of the total and the irreducible errors, yielding a systematic modelling error decomposition. In this work, attention is paid to the techniques themselves required for the practical computation of irreducible errors. Typically, histograms are used for optimal estimator analyses, but this technique is found to add a non-negligible spurious contribution to the irreducible error if models with multiple input parameters are assessed. Thus, the error decomposition of an optimal estimator analysis becomes inaccurate, and misleading conclusions concerning modelling errors may be drawn. In this work, numerically accurate techniques for optimal estimator analyses are identified and a suitable evaluation of irreducible errors is presented. Four different computational techniques are considered: a histogram technique, artificial neural networks, multivariate adaptive regression splines, and an additive model based on a kernel method. For multiple input parameter models, only artificial neural networks and multivariate adaptive regression splines are found to yield satisfactorily accurate results. Beyond a certain number of input parameters, the assessment of models in an optimal estimator analysis even becomes practically infeasible if histograms are used. The optimal estimator analysis in this paper is applied to modelling the filtered soot intermittency in large eddy simulations using a dataset of a direct numerical simulation of a non-premixed sooting turbulent flame.

Suicide attempters examined in a Parisian Emergency Department: Contrasting characteristics associated with multiple suicide attempts or with the motive to die.

PubMed

Perquier, Florence; Duroy, David; Oudinet, Camille; Maamar, Alya; Choquet, Christophe; Casalino, Enrique; Lejoyeux, Michel

2017-07-01

Among patients examined after a suicide attempt in a Parisian emergency department, we aimed to compare individual characteristics of i) first time and multiple suicide attempters, ii) attempters whose principal motive was "to die" and attempters who had any other motive. Information regarding sociodemographics, clinical characteristics, prior mental health care and outgoing referral was collected in 168 suicide attempters using a standardized form. Associations of these variables with suicide attempt repetition (yes or no) and with the motive underlying the attempt (to die or not) were examined using descriptive statistics and multivariable logistic regression models. Multiple attempters were more likely to have no occupation and to report previous mental health care: mental health follow-up, psychiatric medication or psychiatric hospitalization. The motive to die was not associated with the risk of multiple suicide attempts but related to past suicidal ideation and to some specific precipitating factors, including psychiatric disorder. Patients who intended to die were also more likely to be referred to inpatient than to outpatient psychiatric care. Multiple attempters and attempters who desire to die might represent two distinct high-risk groups regarding clinical characteristics and care pathways. They would probably not benefit from the same intervention strategies. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

Solving large mixed linear models using preconditioned conjugate gradient iteration.

PubMed

Strandén, I; Lidauer, M

1999-12-01

Continuous evaluation of dairy cattle with a random regression test-day model requires a fast solving method and algorithm. A new computing technique feasible in Jacobi and conjugate gradient based iterative methods using iteration on data is presented. In the new computing technique, the calculations in multiplication of a vector by a matrix were recorded to three steps instead of the commonly used two steps. The three-step method was implemented in a general mixed linear model program that used preconditioned conjugate gradient iteration. Performance of this program in comparison to other general solving programs was assessed via estimation of breeding values using univariate, multivariate, and random regression test-day models. Central processing unit time per iteration with the new three-step technique was, at best, one-third that needed with the old technique. Performance was best with the test-day model, which was the largest and most complex model used. The new program did well in comparison to other general software. Programs keeping the mixed model equations in random access memory required at least 20 and 435% more time to solve the univariate and multivariate animal models, respectively. Computations of the second best iteration on data took approximately three and five times longer for the animal and test-day models, respectively, than did the new program. Good performance was due to fast computing time per iteration and quick convergence to the final solutions. Use of preconditioned conjugate gradient based methods in solving large breeding value problems is supported by our findings.

Periodontal inflamed surface area as a novel numerical variable describing periodontal conditions

PubMed Central

2017-01-01

Purpose A novel index, the periodontal inflamed surface area (PISA), represents the sum of the periodontal pocket depth of bleeding on probing (BOP)-positive sites. In the present study, we evaluated correlations between PISA and periodontal classifications, and examined PISA as an index integrating the discrete conventional periodontal indexes. Methods This study was a cross-sectional subgroup analysis of data from a prospective cohort study investigating the association between chronic periodontitis and the clinical features of ankylosing spondylitis. Data from 84 patients without systemic diseases (the control group in the previous study) were analyzed in the present study. Results PISA values were positively correlated with conventional periodontal classifications (Spearman correlation coefficient=0.52; P<0.01) and with periodontal indexes, such as BOP and the plaque index (PI) (r=0.94; P<0.01 and r=0.60; P<0.01, respectively; Pearson correlation test). Porphyromonas gingivalis (P. gingivalis) expression and the presence of serum P. gingivalis antibodies were significant factors affecting PISA values in a simple linear regression analysis, together with periodontal classification, PI, bleeding index, and smoking, but not in the multivariate analysis. In the multivariate linear regression analysis, PISA values were positively correlated with the quantity of current smoking, PI, and severity of periodontal disease. Conclusions PISA integrates multiple periodontal indexes, such as probing pocket depth, BOP, and PI into a numerical variable. PISA is advantageous for quantifying periodontal inflammation and plaque accumulation. PMID:29093989

Nocturnal hypoglycaemia in Type 1 diabetic patients, assessed with continuous glucose monitoring: frequency, duration and associations.

PubMed

Wentholt, I M E; Maran, A; Masurel, N; Heine, R J; Hoekstra, J B L; DeVries, J H

2007-05-01

We quantified the occurrence and duration of nocturnal hypoglycaemia in individuals with Type 1 diabetes treated with continuous subcutaneous insulin infusion (CSII) or multiple-injection therapy (MIT) using a continuous subcutaneous glucose sensor. A microdialysis sensor was worn at home by 24 patients on CSII (mean HbA(1c) 7.8 +/- 0.9%) and 33 patients on MIT (HbA(1c) 8.7 +/- 1.3%) for 48 h. Occurrence and duration of nocturnal hypoglycaemia were assessed and using multivariate regression analysis, the association between HbA(1c), diabetes duration, treatment type (CSII vs. MIT), fasting and bedtime blood glucose values, total daily insulin dose and mean nocturnal glucose concentrations, and hypoglycaemia occurrence and duration was investigated. Nocturnal hypoglycaemia < or = 3.9 mmol/l occurred in 33.3% of both the CSII- (8/24) and MIT-treated patients (11/33). Mean (+/- sd; median, interquartile range) duration of hypoglycaemia < or = 3.9 mmol/l was 78 (+/- 76; 57, 23-120) min per night for the CSII- and 98 (+/- 80; 81, 32-158) min per night for the MIT-treated group. Multivariate regression analysis showed that bedtime glucose value had the strongest association with the occurrence (P = 0.026) and duration (P = 0.032) of nocturnal hypoglycaemia. Microdialysis continuous glucose monitoring has enabled more precise quantification of nocturnal hypoglycaemia occurrence and duration in Type 1 diabetic patients. Occurrence and duration of nocturnal hypoglycaemia were mainly associated with bedtime glucose value.

Predictors of Depression in Youth With Crohn Disease

PubMed Central

Clark, Jeffrey G.; Srinath, Arvind I.; Youk, Ada O.; Kirshner, Margaret A.; McCarthy, F. Nicole; Keljo, David J.; Bousvaros, Athos; DeMaso, David R.; Szigethy, Eva M.

2014-01-01

Objective The aim of the study was to determine whether infliximab use and other potential predictors are associated with decreased prevalence and severity of depression in pediatric patients with Crohn disease (CD). Methods A total of 550 (n = 550) youth ages 9 to 17 years with biopsy-confirmed CD were consecutively recruited as part of a multicenter randomized controlled trial. Out of the 550, 499 patients met study criteria and were included in the analysis. At recruitment, each subject and a parent completed the Children’s Depression Inventory (CDI). A child or parent CDI score ≥ 12 was used to denote clinically significant depressive symptoms (CSDS). Child and parent CDI scores were summed to form total CDI (CDIT). Infliximab use, demographic information, steroid use, laboratory values, and Pediatric Crohn’s Disease Activity Index (PCDAI) were collected as the potential predictors of depression. Univariate regression models were constructed to determine the relations among predictors, CSDS, and CDIT. Stepwise multivariate regression models were constructed to predict the relation between infliximab use and depression while controlling for other predictors of depression. Results Infliximab use was not associated with a decreased proportion of CSDS and CDIT after adjusting for multiple comparisons. CSDS and CDIT were positively associated with PCDAI, erythrocyte sedimentation rate, and steroid dose (P<0.01) and negatively associated with socioeconomic status (SES) (P<0.001). In multivariate models, PCDAI and SES were the strongest predictors of depression. Conclusions Disease activity and SES are significant predictors of depression in youth with Crohn disease. PMID:24343281

From clinical to tissue-based dual TIA: Validation and refinement of ABCD3-I score.

PubMed

Dai, Qiliang; Sun, Wen; Xiong, Yunyun; Hankey, Graeme J; Xiao, Lulu; Zhu, Wusheng; Ma, Minmin; Liu, Wenhua; Liu, Dezhi; Cai, Qiankun; Han, Yunfei; Duan, Lihui; Chen, Xiangliang; Xu, Gelin; Liu, Xinfeng

2015-04-07

To investigate whether dual tissue-defined ischemic attacks, defined as multiple diffusion-weighted imaging lesions of different age and/or arterial territory (dual DWI), are an independent and stronger predictor of 90-day stroke than dual clinical TIAs (dual TIA). Consecutive patients with clinically defined TIA were enrolled and assessed clinically and by MRI within 3 days. The predictive ability of the ABCD clinical factors, dual TIA, and dual DWI was evaluated by means of multivariate logistic regression. Among 658 patients who were included in the study and completed 90 days of follow-up, a total of 70 patients (10.6%) experienced subsequent stroke by 90 days. Multivariate logistic regression indicated that dual DWI was an independent predictor for subsequent stroke (odds ratio 4.64, 95% confidence interval 2.15-10.01), while dual TIA was not (odds ratio 1.18, 95% confidence interval 0.69-2.01). C statistics was higher when the item of dual TIA in ABCD3-I score was replaced by dual DWI (0.759 vs 0.729, p = 0.035). The net reclassification value for 90-day stroke risk was also improved (continuous net reclassification improvement 0.301, p = 0.017). Dual DWI independently predicted future stroke in patients with TIA. A new ABCD3-I score with dual DWI instead of dual clinical TIA may improve risk stratification for early stroke risk after TIA. © 2015 American Academy of Neurology.

Relationships between seminal plasma metals/metalloids and semen quality, sperm apoptosis and DNA integrity.

PubMed

Wang, Yi-Xin; Wang, Peng; Feng, Wei; Liu, Chong; Yang, Pan; Chen, Ying-Jun; Sun, Li; Sun, Yang; Yue, Jing; Gu, Long-Jie; Zeng, Qiang; Lu, Wen-Qing

2017-05-01

This study aimed to investigate the relationships between environmental exposure to metals/metalloids and semen quality, sperm apoptosis and DNA integrity using the metal/metalloids levels in seminal plasma as biomarkers. We determined 18 metals/metalloids in seminal plasma using an inductively coupled plasma-mass spectrometry among 746 men recruited from a reproductive medicine center. Associations of these metals/metalloids with semen quality (n = 746), sperm apoptosis (n = 331) and DNA integrity (n = 404) were evaluated using multivariate linear and logistic regression models. After accounting for multiple comparisons and confounders, seminal plasma arsenic (As) quartiles were negatively associated with progressive and total sperm motility using multivariable linear regression analysis, which were in accordance with the trends for increased odds ratios (ORs) for below-reference semen quality parameters in the logistic models. We also found inverse correlations between cadmium (Cd) quartiles and progressive and total sperm motility, whereas positive correlations between zinc (Zn) quartiles and sperm concentration, between copper (Cu) and As quartiles and the percentage of tail DNA, between As and selenium (Se) quartiles and tail extent and tail distributed moment, and between tin (Sn) categories and the percentage of necrotic spermatozoa (all P trend <0.05). These relationships remained after the simultaneous consideration of various elements. Our results indicate that environmental exposure to As, Cd, Cu, Se and Sn may impair male reproductive health, whereas Zn may be beneficial to sperm concentration. Copyright © 2017 Elsevier Ltd. All rights reserved.

Associations Between Physician Empathy, Physician Characteristics, and Standardized Measures of Patient Experience.

PubMed

Chaitoff, Alexander; Sun, Bob; Windover, Amy; Bokar, Daniel; Featherall, Joseph; Rothberg, Michael B; Misra-Hebert, Anita D

2017-10-01

To identify correlates of physician empathy and determine whether physician empathy is related to standardized measures of patient experience. Demographic, professional, and empathy data were collected during 2013-2015 from Cleveland Clinic Health System physicians prior to participation in mandatory communication skills training. Empathy was assessed using the Jefferson Scale of Empathy. Data were also collected for seven measures (six provider communication items and overall provider rating) from the visit-specific and 12-month Consumer Assessment of Healthcare Providers and Systems Clinician and Group (CG-CAHPS) surveys. Associations between empathy and provider characteristics were assessed by linear regression, ANOVA, or a nonparametric equivalent. Significant predictors were included in a multivariable linear regression model. Correlations between empathy and CG-CAHPS scores were assessed using Spearman rank correlation coefficients. In bivariable analysis (n = 847 physicians), female sex (P < .001), specialty (P < .01), outpatient practice setting (P < .05), and DO degree (P < .05) were associated with higher empathy scores. In multivariable analysis, female sex (P < .001) and four specialties (obstetrics-gynecology, pediatrics, psychiatry, and thoracic surgery; all P < .05) were significantly associated with higher empathy scores. Of the seven CG-CAHPS measures, scores on five for the 583 physicians with visit-specific data and on three for the 277 physicians with 12-month data were positively correlated with empathy. Specialty and sex were independently associated with physician empathy. Empathy was correlated with higher scores on multiple CG-CAHPS items, suggesting improving physician empathy might play a role in improving patient experience.

Risk Factors for Venous Thromboembolism in Chronic Obstructive Pulmonary Disease

PubMed Central

Kim, Victor; Goel, Nishant; Gangar, Jinal; Zhao, Huaqing; Ciccolella, David E.; Silverman, Edwin K.; Crapo, James D.; Criner, Gerard J.

2014-01-01

Background: COPD patients are at increased risk for venous thromboembolism (VTE). VTE however remains under-diagnosed in this population and the clinical profile of VTE in COPD is unclear. Methods: Global initiative for chronic Obstructive Lung Disease (GOLD) stages II-IV participants in the COPD Genetic Epidemiology (COPDGene) study were divided into 2 groups: VTE+, those who reported a history of VTE by questionnaire, and VTE-, those who did not. We compared variables in these 2 groups with either t-test or chi-squared test for continuous and categorical variables, respectively. We performed a univariate logistic regression for VTE, and then a multivariate logistic regression using the significant predictors of interest in the univariate analysis to ascertain the determinants of VTE. Results: The VTE+ group was older, more likely to be Caucasian, had a higher body mass index (BMI), smoking history, used oxygen, had a lower 6-minute walk distance, worse quality of life scores, and more dyspnea and respiratory exacerbations than the VTE- group. Lung function was not different between groups. A greater percentage of the VTE+ group described multiple medical comorbidities. On multivariate analysis, BMI, 6-minute walk distance, pneumothorax, peripheral vascular disease, and congestive heart failure significantly increased the odds for VTE by history. Conclusions: BMI, exercise capacity, and medical comorbidities were significantly associated with VTE in moderate to severe COPD. Clinicians should suspect VTE in patients who present with dyspnea and should consider possibilities other than infection as causes of COPD exacerbation. PMID:25844397

Loneliness in senior housing communities.

PubMed

Taylor, Harry Owen; Wang, Yi; Morrow-Howell, Nancy

2018-05-23

There are many studies on loneliness among community-dwelling older adults; however, there is limited research examining the extent and correlates of loneliness among older adults who reside in senior housing communities. This study examines the extent and correlates of loneliness in three public senior housing communities in the St. Louis area. Data for this project was collected with survey questionnaires with a total sample size of 148 respondents. Loneliness was measured using the Hughes 3-item loneliness scale. Additionally, the questionnaire contained measures on socio-demographics, health/mental health, social engagement, and social support. Missing data for the hierarchical multivariate regression models were imputed using multiple imputation methods. Results showed approximately 30.8% of the sample was not lonely, 42.7% was moderately lonely, and 26.6% was severely lonely. In the multivariate analyses, loneliness was primarily associated with depressive symptoms. Contrary to popular opinion, our study found the prevalence of loneliness was high in senior housing communities. Nevertheless, senior housing communities could be ideal locations for reducing loneliness among older adults. Interventions should focus on concomitantly addressing both an individual's loneliness and mental health.

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

PubMed

Barrantes, Gilbert; Sandoval, Luis

2009-09-01

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

Multiple Imputation for Multivariate Missing-Data Problems: A Data Analyst's Perspective.

ERIC Educational Resources Information Center

Schafer, Joseph L.; Olsen, Maren K.

1998-01-01

The key ideas of multiple imputation for multivariate missing data problems are reviewed. Software programs available for this analysis are described, and their use is illustrated with data from the Adolescent Alcohol Prevention Trial (W. Hansen and J. Graham, 1991). (SLD)

Flood-frequency prediction methods for unregulated streams of Tennessee, 2000

USGS Publications Warehouse

Law, George S.; Tasker, Gary D.

2003-01-01

Up-to-date flood-frequency prediction methods for unregulated, ungaged rivers and streams of Tennessee have been developed. Prediction methods include the regional-regression method and the newer region-of-influence method. The prediction methods were developed using stream-gage records from unregulated streams draining basins having from 1 percent to about 30 percent total impervious area. These methods, however, should not be used in heavily developed or storm-sewered basins with impervious areas greater than 10 percent. The methods can be used to estimate 2-, 5-, 10-, 25-, 50-, 100-, and 500-year recurrence-interval floods of most unregulated rural streams in Tennessee. A computer application was developed that automates the calculation of flood frequency for unregulated, ungaged rivers and streams of Tennessee. Regional-regression equations were derived by using both single-variable and multivariable regional-regression analysis. Contributing drainage area is the explanatory variable used in the single-variable equations. Contributing drainage area, main-channel slope, and a climate factor are the explanatory variables used in the multivariable equations. Deleted-residual standard error for the single-variable equations ranged from 32 to 65 percent. Deleted-residual standard error for the multivariable equations ranged from 31 to 63 percent. These equations are included in the computer application to allow easy comparison of results produced by the different methods. The region-of-influence method calculates multivariable regression equations for each ungaged site and recurrence interval using basin characteristics from 60 similar sites selected from the study area. Explanatory variables that may be used in regression equations computed by the region-of-influence method include contributing drainage area, main-channel slope, a climate factor, and a physiographic-region factor. Deleted-residual standard error for the region-of-influence method tended to be only slightly smaller than those for the regional-regression method and ranged from 27 to 62 percent.

Multivariate logistic regression analysis of postoperative complications and risk model establishment of gastrectomy for gastric cancer: A single-center cohort report.

PubMed

Zhou, Jinzhe; Zhou, Yanbing; Cao, Shougen; Li, Shikuan; Wang, Hao; Niu, Zhaojian; Chen, Dong; Wang, Dongsheng; Lv, Liang; Zhang, Jian; Li, Yu; Jiao, Xuelong; Tan, Xiaojie; Zhang, Jianli; Wang, Haibo; Zhang, Bingyuan; Lu, Yun; Sun, Zhenqing

2016-01-01

Reporting of surgical complications is common, but few provide information about the severity and estimate risk factors of complications. If have, but lack of specificity. We retrospectively analyzed data on 2795 gastric cancer patients underwent surgical procedure at the Affiliated Hospital of Qingdao University between June 2007 and June 2012, established multivariate logistic regression model to predictive risk factors related to the postoperative complications according to the Clavien-Dindo classification system. Twenty-four out of 86 variables were identified statistically significant in univariate logistic regression analysis, 11 significant variables entered multivariate analysis were employed to produce the risk model. Liver cirrhosis, diabetes mellitus, Child classification, invasion of neighboring organs, combined resection, introperative transfusion, Billroth II anastomosis of reconstruction, malnutrition, surgical volume of surgeons, operating time and age were independent risk factors for postoperative complications after gastrectomy. Based on logistic regression equation, p=Exp∑BiXi / (1+Exp∑BiXi), multivariate logistic regression predictive model that calculated the risk of postoperative morbidity was developed, p = 1/(1 + e((4.810-1.287X1-0.504X2-0.500X3-0.474X4-0.405X5-0.318X6-0.316X7-0.305X8-0.278X9-0.255X10-0.138X11))). The accuracy, sensitivity and specificity of the model to predict the postoperative complications were 86.7%, 76.2% and 88.6%, respectively. This risk model based on Clavien-Dindo grading severity of complications system and logistic regression analysis can predict severe morbidity specific to an individual patient's risk factors, estimate patients' risks and benefits of gastric surgery as an accurate decision-making tool and may serve as a template for the development of risk models for other surgical groups.

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

EPA Science Inventory

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

Causal diagrams and multivariate analysis II: precision work.

PubMed

Jupiter, Daniel C

2014-01-01

In this Investigators' Corner, I continue my discussion of when and why we researchers should include variables in multivariate regression. My examination focuses on studies comparing treatment groups and situations for which we can either exclude variables from multivariate analyses or include them for reasons of precision. Copyright © 2014 American College of Foot and Ankle Surgeons. Published by Elsevier Inc. All rights reserved.

Why are we regressing?

PubMed

Jupiter, Daniel C

2012-01-01

In this first of a series of statistical methodology commentaries for the clinician, we discuss the use of multivariate linear regression. Copyright © 2012 American College of Foot and Ankle Surgeons. Published by Elsevier Inc. All rights reserved.

The base rates and factors associated with reported access to firearms in psychiatric inpatients.

PubMed

Kolla, Bhanu Prakash; O'Connor, Stephen S; Lineberry, Timothy W

2011-01-01

The aim of this study was to define whether specific patient demographic groups, diagnoses or other factors are associated with psychiatric inpatients reporting firearms access. A retrospective medical records review study was conducted using information on access to firearms from electronic medical records for all patients 16 years and older admitted between July 2007 and May 2008 at the Mayo Clinic Psychiatric Hospital in Rochester, MN. Data were obtained only on patients providing authorization for record review. Data were analyzed using univariate and multivariate logistic regression analyses accounting for gender, diagnostic groups, comorbid substance use, history of suicide attempts and family history of suicide/suicide attempts. Seventy-four percent (1169/1580) of patients provided research authorization. The ratio of men to women was identical in both research and nonresearch authorization groups. There were 14.6% of inpatients who reported firearms access. In univariate analysis, men were more likely (P<.0001) to report access than women, and a history of previous suicide attempt(s) was associated with decreased access (P=.02). Multiple logistic regression analyses controlling for other factors found females and patients with history of previous suicide attempt(s) less likely to report access, while patients with a family history of suicide or suicide attempts reported increased firearms access. Diagnostic groups were not associated with access on univariate or multiple logistic regression analyses. Men and inpatients with a family history of suicide/suicide attempts were more likely to report firearms access. Clinicians should develop standardized systems of identification of firearms access and provide guidance on removal. Copyright © 2011 Elsevier Inc. All rights reserved.

PM10 modeling in the Oviedo urban area (Northern Spain) by using multivariate adaptive regression splines

NASA Astrophysics Data System (ADS)

Nieto, Paulino José García; Antón, Juan Carlos Álvarez; Vilán, José Antonio Vilán; García-Gonzalo, Esperanza

2014-10-01

The aim of this research work is to build a regression model of the particulate matter up to 10 micrometers in size (PM10) by using the multivariate adaptive regression splines (MARS) technique in the Oviedo urban area (Northern Spain) at local scale. This research work explores the use of a nonparametric regression algorithm known as multivariate adaptive regression splines (MARS) which has the ability to approximate the relationship between the inputs and outputs, and express the relationship mathematically. In this sense, hazardous air pollutants or toxic air contaminants refer to any substance that may cause or contribute to an increase in mortality or serious illness, or that may pose a present or potential hazard to human health. To accomplish the objective of this study, the experimental dataset of nitrogen oxides (NOx), carbon monoxide (CO), sulfur dioxide (SO2), ozone (O3) and dust (PM10) were collected over 3 years (2006-2008) and they are used to create a highly nonlinear model of the PM10 in the Oviedo urban nucleus (Northern Spain) based on the MARS technique. One main objective of this model is to obtain a preliminary estimate of the dependence between PM10 pollutant in the Oviedo urban area at local scale. A second aim is to determine the factors with the greatest bearing on air quality with a view to proposing health and lifestyle improvements. The United States National Ambient Air Quality Standards (NAAQS) establishes the limit values of the main pollutants in the atmosphere in order to ensure the health of healthy people. Firstly, this MARS regression model captures the main perception of statistical learning theory in order to obtain a good prediction of the dependence among the main pollutants in the Oviedo urban area. Secondly, the main advantages of MARS are its capacity to produce simple, easy-to-interpret models, its ability to estimate the contributions of the input variables, and its computational efficiency. Finally, on the basis of these numerical calculations, using the multivariate adaptive regression splines (MARS) technique, conclusions of this research work are exposed.

Multivariate meta-analysis: a robust approach based on the theory of U-statistic.

PubMed

Ma, Yan; Mazumdar, Madhu

2011-10-30

Meta-analysis is the methodology for combining findings from similar research studies asking the same question. When the question of interest involves multiple outcomes, multivariate meta-analysis is used to synthesize the outcomes simultaneously taking into account the correlation between the outcomes. Likelihood-based approaches, in particular restricted maximum likelihood (REML) method, are commonly utilized in this context. REML assumes a multivariate normal distribution for the random-effects model. This assumption is difficult to verify, especially for meta-analysis with small number of component studies. The use of REML also requires iterative estimation between parameters, needing moderately high computation time, especially when the dimension of outcomes is large. A multivariate method of moments (MMM) is available and is shown to perform equally well to REML. However, there is a lack of information on the performance of these two methods when the true data distribution is far from normality. In this paper, we propose a new nonparametric and non-iterative method for multivariate meta-analysis on the basis of the theory of U-statistic and compare the properties of these three procedures under both normal and skewed data through simulation studies. It is shown that the effect on estimates from REML because of non-normal data distribution is marginal and that the estimates from MMM and U-statistic-based approaches are very similar. Therefore, we conclude that for performing multivariate meta-analysis, the U-statistic estimation procedure is a viable alternative to REML and MMM. Easy implementation of all three methods are illustrated by their application to data from two published meta-analysis from the fields of hip fracture and periodontal disease. We discuss ideas for future research based on U-statistic for testing significance of between-study heterogeneity and for extending the work to meta-regression setting. Copyright © 2011 John Wiley & Sons, Ltd.

ECOPASS - a multivariate model used as an index of growth performance of poplar clones

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

Ceulemans, R.; Impens, I.

The model (ECOlogical PASSport) reported was constructed by principal component analysis from a combination of biochemical, anatomical/morphological and ecophysiological gas exchange parameters measured on 5 fast growing poplar clones. Productivity data were 10 selected trees in 3 plantations in Belgium and given as m.a.i.(b.a.). The model is shown to be able to reflect not only genetic origin and the relative effects of the different parameters of the clones, but also their production potential. Multiple regression analysis of the 4 principal components showed a high cumulative correlation (96%) between the 3 components related to ecophysiological, biochemical and morphological parameters, and productivity;more » the ecophysiological component alone correlated 85% with productivity.« less

A refined method for multivariate meta-analysis and meta-regression

PubMed Central

Jackson, Daniel; Riley, Richard D

2014-01-01

Making inferences about the average treatment effect using the random effects model for meta-analysis is problematic in the common situation where there is a small number of studies. This is because estimates of the between-study variance are not precise enough to accurately apply the conventional methods for testing and deriving a confidence interval for the average effect. We have found that a refined method for univariate meta-analysis, which applies a scaling factor to the estimated effects’ standard error, provides more accurate inference. We explain how to extend this method to the multivariate scenario and show that our proposal for refined multivariate meta-analysis and meta-regression can provide more accurate inferences than the more conventional approach. We explain how our proposed approach can be implemented using standard output from multivariate meta-analysis software packages and apply our methodology to two real examples. © 2013 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd. PMID:23996351

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

NASA Astrophysics Data System (ADS)

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

2009-10-01

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

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

PubMed

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

2016-04-01

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

Access disparities to Magnet hospitals for patients undergoing neurosurgical operations

PubMed Central

Missios, Symeon; Bekelis, Kimon

2017-01-01

Background Centers of excellence focusing on quality improvement have demonstrated superior outcomes for a variety of surgical interventions. We investigated the presence of access disparities to hospitals recognized by the Magnet Recognition Program of the American Nurses Credentialing Center (ANCC) for patients undergoing neurosurgical operations. Methods We performed a cohort study of all neurosurgery patients who were registered in the New York Statewide Planning and Research Cooperative System (SPARCS) database from 2009–2013. We examined the association of African-American race and lack of insurance with Magnet status hospitalization for neurosurgical procedures. A mixed effects propensity adjusted multivariable regression analysis was used to control for confounding. Results During the study period, 190,535 neurosurgical patients met the inclusion criteria. Using a multivariable logistic regression, we demonstrate that African-Americans had lower admission rates to Magnet institutions (OR 0.62; 95% CI, 0.58–0.67). This persisted in a mixed effects logistic regression model (OR 0.77; 95% CI, 0.70–0.83) to adjust for clustering at the patient county level, and a propensity score adjusted logistic regression model (OR 0.75; 95% CI, 0.69–0.82). Additionally, lack of insurance was associated with lower admission rates to Magnet institutions (OR 0.71; 95% CI, 0.68–0.73), in a multivariable logistic regression model. This persisted in a mixed effects logistic regression model (OR 0.72; 95% CI, 0.69–0.74), and a propensity score adjusted logistic regression model (OR 0.72; 95% CI, 0.69–0.75). Conclusions Using a comprehensive all-payer cohort of neurosurgery patients in New York State we identified an association of African-American race and lack of insurance with lower rates of admission to Magnet hospitals. PMID:28684152

Employment characteristics and socioeconomic factors associated with disparities in smoking abstinence and former smoking among U.S. workers.

PubMed

Fagan, Pebbles; Shavers, Vickie L; Lawrence, Deirdre; Gibson, James Todd; O'Connell, Mary E

2007-11-01

This study examines the associations among employment and socioeconomic factors and the outcomes, current smoking, cigarette abstinence and former smoking among adult U.S. workers ages 18-64 (n=288,813). Multivariate logistic regression was used to examine the associations among the variables using cross-sectional data from the 1998-1999 and 2001-2002 Tobacco Use Supplements to the Current Population Survey. Lower odds of current smoking was observed among part-time workers compared to those working variable hours and multiple job holders compared to persons holding one job. The self-employed, part-time workers and multiple job holders had higher odds of former smoking than comparison groups. Employment factors were not associated with short-term abstinence or 12-month abstinence from smoking, but income, education, marital status, and duration of smoking were associated with 12-month abstinence. These data suggest that while employment factors are associated with current and former smoking, socioeconomic factors are associated with long-term quitting.

Occupational and non-occupational factors associated with work-related injuries among construction workers in the USA

PubMed Central

Dong, Xiuwen Sue; Wang, Xuanwen; Largay, Julie A.

2015-01-01

Background: Many factors contribute to occupational injuries. However, these factors have been compartmentalized and isolated in most studies. Objective: To examine the relationship between work-related injuries and multiple occupational and non-occupational factors among construction workers in the USA. Methods: Data from the 1988–2000 National Longitudinal Survey of Youth, 1979 cohort (N = 12,686) were analyzed. Job exposures and health behaviors were examined and used as independent variables in four multivariate logistic regression models to identify associations with occupational injuries. Results: After controlling for demographic variables, occupational injuries were 18% (95% CI: 1.04–1.34) more likely in construction than in non-construction. Blue-collar occupations, job physical efforts, multiple jobs, and long working hours accounted for the escalated risk in construction. Smoking, obesity/overweight, and cocaine use significantly increased the risk of work-related injury when demographics and occupational factors were held constant. Conclusions: Workplace injuries are better explained by simultaneously examining occupational and non-occupational characteristics. PMID:25816923

Liquid detection with InGaAsP semiconductor lasers having multiple short external cavities.

PubMed

Zhu, X; Cassidy, D T

1996-08-20

A liquid detection system consisting of a diode laser with multiple short external cavities (MSXC's) is reported. The MSXC diode laser operates single mode on one of 18 distinct modes that span a range of 72 nm. We selected the modes by setting the length of one of the external cavities using a piezoelectric positioner. One can measure the transmission through cells by modulating the injection current at audio frequencies and using phase-sensitive detection to reject the ambient light and reduce 1/f noise. A method to determine regions of single-mode operation by the rms of the output of the laser is described. The transmission data were processed by multivariate calibration techniques, i.e., partial least squares and principal component regression. Water concentration in acetone was used to demonstrate the performance of the system. A correlation coefficient of R(2) = 0.997 and 0.29% root-mean-square error of prediction are found for water concentration over the range of 2-19%.

Self-esteem is associated with perceived stress in multiple sclerosis patients.

PubMed

N Ifantopoulou, Parthena; K Artemiadis, Artemios; Triantafyllou, Nikolaos; Chrousos, George; Papanastasiou, Ioannis; Darviri, Christina

2015-07-01

Previous studies have showed that perceived stress (PS) in patients with multiple sclerosis (MS) constitutes an important factor for disease onset, relapse, symptomatology and psychological adjustment. The aim of this pilot cross-sectional study was to examine the role of self-esteem in PS, after controlling for sociodemographical characteristics, depression and personality in MS patients. Sixty-six relapsing-remitting MS patients (66.67% females, mean age of 40 ± 11.1 years old, mean duration of disease 133.6 ± 128.8 months) were studied. Perceived stress, self-esteem, depression and personality type were assessed using self-administered questionnaires. Hierarchical multivariate regression modelling was used. Higher education and depression and lower self-esteem were independently and significantly associated with increased PS, accounting for 40.5% of its variance. Univariate analyses revealed that low extroversion and openness and higher neurotism were associated with higher PS, although no significant after adjusting for other factors. Although our findings need further confirmation, psychological interventions targetting self-esteem are strongly encouraged.

Remote sensing and GIS-based landslide hazard analysis and cross-validation using multivariate logistic regression model on three test areas in Malaysia

NASA Astrophysics Data System (ADS)

Pradhan, Biswajeet

2010-05-01

This paper presents the results of the cross-validation of a multivariate logistic regression model using remote sensing data and GIS for landslide hazard analysis on the Penang, Cameron, and Selangor areas in Malaysia. Landslide locations in the study areas were identified by interpreting aerial photographs and satellite images, supported by field surveys. SPOT 5 and Landsat TM satellite imagery were used to map landcover and vegetation index, respectively. Maps of topography, soil type, lineaments and land cover were constructed from the spatial datasets. Ten factors which influence landslide occurrence, i.e., slope, aspect, curvature, distance from drainage, lithology, distance from lineaments, soil type, landcover, rainfall precipitation, and normalized difference vegetation index (ndvi), were extracted from the spatial database and the logistic regression coefficient of each factor was computed. Then the landslide hazard was analysed using the multivariate logistic regression coefficients derived not only from the data for the respective area but also using the logistic regression coefficients calculated from each of the other two areas (nine hazard maps in all) as a cross-validation of the model. For verification of the model, the results of the analyses were then compared with the field-verified landslide locations. Among the three cases of the application of logistic regression coefficient in the same study area, the case of Selangor based on the Selangor logistic regression coefficients showed the highest accuracy (94%), where as Penang based on the Penang coefficients showed the lowest accuracy (86%). Similarly, among the six cases from the cross application of logistic regression coefficient in other two areas, the case of Selangor based on logistic coefficient of Cameron showed highest (90%) prediction accuracy where as the case of Penang based on the Selangor logistic regression coefficients showed the lowest accuracy (79%). Qualitatively, the cross application model yields reasonable results which can be used for preliminary landslide hazard mapping.

Predictors of unsafe sexual behavior among people living with human immunodeficiency virus/AIDS attending antiretroviral therapy center in Western India.

PubMed

Mehta, Kedar G; Baxi, Rajendra; Chavda, Parag; Patel, Sangita; Mazumdar, Vihang

2016-01-01

As more and more people with human immunodeficiency virus (HIV) live longer and healthier lives because of antiretroviral therapy (ART), an increasing number of sexual transmissions of HIV may arise from these people living with HIV/AIDS (PLWHA). Hence, this study is conducted to assess the predictors of unsafe sexual behavior among PLWHA on ART in Western India. The current cross-sectional study was carried out among 175 PLWHAs attending ART center of a Tertiary Care Hospital in Western India. Unsafe sex was defined as inconsistent and/or incorrect condom use. A total of 39 variables from four domains viz., sociodemographic, relationship-related, medical and psycho-social factors were studied for their relationship to unsafe sexual behavior. The variables found to be significantly associated with unsafe sex practices in bivariate analysis were explored by multivariate analysis using multiple logistic regression in SPSS 17.0 version. Fifty-eight percentage of PLWHAs were practicing unsafe sex. 15 out of total 39 variables showed significant association in bivariate analysis. Finally, 11 of them showed significant association in multivariate analysis. Young age group, illiteracy, lack of counseling, misbeliefs about condom use, nondisclosure to spouse and lack of partner communication were the major factors found to be independently associated with unsafe sex in multivariate analysis. Appropriate interventions like need-based counseling are required to address risk factors associated with unsafe sex.

Multi-disease analysis of maternal antibody decay using non-linear mixed models accounting for censoring.

PubMed

Goeyvaerts, Nele; Leuridan, Elke; Faes, Christel; Van Damme, Pierre; Hens, Niel

2015-09-10

Biomedical studies often generate repeated measures of multiple outcomes on a set of subjects. It may be of interest to develop a biologically intuitive model for the joint evolution of these outcomes while assessing inter-subject heterogeneity. Even though it is common for biological processes to entail non-linear relationships, examples of multivariate non-linear mixed models (MNMMs) are still fairly rare. We contribute to this area by jointly analyzing the maternal antibody decay for measles, mumps, rubella, and varicella, allowing for a different non-linear decay model for each infectious disease. We present a general modeling framework to analyze multivariate non-linear longitudinal profiles subject to censoring, by combining multivariate random effects, non-linear growth and Tobit regression. We explore the hypothesis of a common infant-specific mechanism underlying maternal immunity using a pairwise correlated random-effects approach and evaluating different correlation matrix structures. The implied marginal correlation between maternal antibody levels is estimated using simulations. The mean duration of passive immunity was less than 4 months for all diseases with substantial heterogeneity between infants. The maternal antibody levels against rubella and varicella were found to be positively correlated, while little to no correlation could be inferred for the other disease pairs. For some pairs, computational issues occurred with increasing correlation matrix complexity, which underlines the importance of further developing estimation methods for MNMMs. Copyright © 2015 John Wiley & Sons, Ltd.

Understanding and predicting the impact of critical dissolution variables for nifedipine immediate release capsules by multivariate data analysis.

PubMed

Mercuri, A; Pagliari, M; Baxevanis, F; Fares, R; Fotaki, N

2017-02-25

In this study the selection of in vivo predictive in vitro dissolution experimental set-ups using a multivariate analysis approach, in line with the Quality by Design (QbD) principles, is explored. The dissolution variables selected using a design of experiments (DoE) were the dissolution apparatus [USP1 apparatus (basket) and USP2 apparatus (paddle)], the rotational speed of the basket/or paddle, the operator conditions (dissolution apparatus brand and operator), the volume, the pH, and the ethanol content of the dissolution medium. The dissolution profiles of two nifedipine capsules (poorly soluble compound), under conditions mimicking the intake of the capsules with i. water, ii. orange juice and iii. an alcoholic drink (orange juice and ethanol) were analysed using multiple linear regression (MLR). Optimised dissolution set-ups, generated based on the mathematical model obtained via MLR, were used to build predicted in vitro-in vivo correlations (IVIVC). IVIVC could be achieved using physiologically relevant in vitro conditions mimicking the intake of the capsules with an alcoholic drink (orange juice and ethanol). The multivariate analysis revealed that the concentration of ethanol used in the in vitro dissolution experiments (47% v/v) can be lowered to less than 20% v/v, reflecting recently found physiological conditions. Copyright © 2016 Elsevier B.V. All rights reserved.

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

PubMed

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

2017-12-30

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

Independent Prognostic Factors for Acute Organophosphorus Pesticide Poisoning.

PubMed

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

2016-07-01

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

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

PubMed

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

2016-05-01

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

Factors associated with burnout among US neurosurgery residents: a nationwide survey.

PubMed

Attenello, Frank J; Buchanan, Ian A; Wen, Timothy; Donoho, Daniel A; McCartney, Shirley; Cen, Steven Y; Khalessi, Alexander A; Cohen-Gadol, Aaron A; Cheng, Joseph S; Mack, William J; Schirmer, Clemens M; Swartz, Karin R; Prall, J Adair; Stroink, Ann R; Giannotta, Steven L; Klimo, Paul

2018-02-09

OBJECTIVE Excessive dissatisfaction and stress among physicians can precipitate burnout, which results in diminished productivity, quality of care, and patient satisfaction and treatment adherence. Given the multiplicity of its harms and detriments to workforce retention and in light of the growing physician shortage, burnout has garnered much attention in recent years. Using a national survey, the authors formally evaluated burnout among neurosurgery trainees. METHODS An 86-item questionnaire was disseminated to residents in the American Association of Neurological Surgeons database between June and November 2015. Questions evaluated personal and workplace stressors, mentorship, career satisfaction, and burnout. Burnout was assessed using the previously validated Maslach Burnout Inventory. Factors associated with burnout were determined using univariate and multivariate logistic regression. RESULTS The response rate with completed surveys was 21% (346/1643). The majority of residents were male (78%), 26-35 years old (92%), in a stable relationship (70%), and without children (73%). Respondents were equally distributed across all residency years. Eighty-one percent of residents were satisfied with their career choice, although 41% had at some point given serious thought to quitting. The overall burnout rate was 67%. In the multivariate analysis, notable factors associated with burnout included inadequate operating room exposure (OR 7.57, p = 0.011), hostile faculty (OR 4.07, p = 0.008), and social stressors outside of work (OR 4.52, p = 0.008). Meaningful mentorship was protective against burnout in the multivariate regression models (OR 0.338, p = 0.031). CONCLUSIONS Rates of burnout and career satisfaction are paradoxically high among neurosurgery trainees. While several factors were predictive of burnout, including inadequate operative exposure and social stressors, meaningful mentorship proved to be protective against burnout. The documented negative effects of burnout on patient care and health care economics necessitate further studies for potential solutions to curb its rise.

4-protein signature predicting tamoxifen treatment outcome in recurrent breast cancer.

PubMed

De Marchi, Tommaso; Liu, Ning Qing; Stingl, Cristoph; Timmermans, Mieke A; Smid, Marcel; Look, Maxime P; Tjoa, Mila; Braakman, Rene B H; Opdam, Mark; Linn, Sabine C; Sweep, Fred C G J; Span, Paul N; Kliffen, Mike; Luider, Theo M; Foekens, John A; Martens, John W M; Umar, Arzu

2016-01-01

Estrogen receptor (ER) positive tumors represent the majority of breast malignancies, and are effectively treated with hormonal therapies, such as tamoxifen. However, in the recurrent disease resistance to tamoxifen therapy is common and a major cause of death. In recent years, in-depth proteome analyses have enabled identification of clinically useful biomarkers, particularly, when heterogeneity in complex tumor tissue was reduced using laser capture microdissection (LCM). In the current study, we performed high resolution proteomic analysis on two cohorts of ER positive breast tumors derived from patients who either manifested good or poor outcome to tamoxifen treatment upon recurrence. A total of 112 fresh frozen tumors were collected from multiple medical centers and divided into two sets: an in-house training and a multi-center test set. Epithelial tumor cells were enriched with LCM and analyzed by nano-LC Orbitrap mass spectrometry (MS), which yielded >3000 and >4000 quantified proteins in the training and test sets, respectively. Raw data are available via ProteomeXchange with identifiers PXD000484 and PXD000485. Statistical analysis showed differential abundance of 99 proteins, of which a subset of 4 proteins was selected through a multivariate step-down to develop a predictor for tamoxifen treatment outcome. The 4-protein signature significantly predicted poor outcome patients in the test set, independent of predictive histopathological characteristics (hazard ratio [HR] = 2.17; 95% confidence interval [CI] = 1.15 to 4.17; multivariate Cox regression p value = 0.017). Immunohistochemical (IHC) staining of PDCD4, one of the signature proteins, on an independent set of formalin-fixed paraffin-embedded tumor tissues provided and independent technical validation (HR = 0.72; 95% CI = 0.57 to 0.92; multivariate Cox regression p value = 0.009). We hereby report the first validated protein predictor for tamoxifen treatment outcome in recurrent ER-positive breast cancer. IHC further showed that PDCD4 is an independent marker. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

Prognostic Impact of Stone-Scoring Systems After Percutaneous Nephrolithotomy for Staghorn Calculi: A Single Center's Experience Over 10 Years.

PubMed

Choi, Sae Woong; Bae, Woong Jin; Ha, U-Syn; Hong, Sung-Hoo; Lee, Ji Youl; Kim, Sae Woong; Cho, Hyuk Jin

2016-09-01

To investigate the prognostic factors associated with stone-free rate (SFR) and complications after percutaneous nephrolithotomy (PCNL) for the treatment of staghorn stone and to compare the predictive value and accuracy of three stone-scoring systems for the treatment success of staghorn stone. We retrospectively reviewed all patients undergoing PCNL at our center from June 2003 to June 2014. On the basis of noncontrast computed tomography (NCCT) scan images, we calculated Guy's score, S.T.O.N.E. nephrolithometry, and Clinical Research Office of the Endourological Society (CROES) nomogram to assess the association with stone-free status and complications. For statistical evaluation, univariate and multivariate logistic regression analyses were used. During the study period, 886 cases had medical records available. Cases who underwent PCNL for the treatment of staghorn calculi accounted for 34.4% (305/886 cases). Preoperative NCCT was performed in 217 cases. The 217 procedures (205 patients, 12 simultaneous bilateral PCNLs) had a mean stone size of 1358.3 ± 760.7 mm(2), with 111 (51.2%) partial staghorn and 106 (48.8%) complete staghorn stones. The initial and overall SFRs of PCNL were 53.9% and 70.1%, respectively. The overall complication rate was 32.7% (71/217 cases). On a multivariate logistic regression analysis, independent predictors for SFR were number of involved calices, S.T.O.N.E. nephrolithometry, and pre-existent urinary tract infection (UTI) (odds ratios [ORs] = 1.311, 1.933, and 2.340, respectively). Stone burden was an independent risk factor for the development of complications on multivariate analysis (OR = 2.846 and p = 0.001). The results of this study show that multiple involved calices, high grades of S.T.O.N.E. nephrolithometry, and pre-existent UTIs were associated with lower SFR after PCNL for staghorn calculi. Stone burden was an independent risk factor for the development of complications.

Combined high-field intraoperative magnetic resonance imaging and endoscopy increase extent of resection and progression-free survival for pituitary adenomas

PubMed Central

Sylvester, Peter T.; Evans, John A.; Zipfel, Gregory J.; Chole, Richard A.; Uppaluri, Ravindra; Haughey, Bruce H.; Getz, Anne E.; Silverstein, Julie; Rich, Keith M.; Kim, Albert H.; Dacey, Ralph G.

2014-01-01

Purpose The clinical benefit of combined intraoperative magnetic resonance imaging (iMRI) and endoscopy for transsphenoidal pituitary adenoma resection has not been completely characterized. This study assessed the impact of microscopy, endoscopy, and/or iMRI on progression-free survival, extent of resection status (gross-, near-, and subtotal resection), and operative complications. Methods Retrospective analyses were performed on 446 transsphenoidal pituitary adenoma surgeries at a single institution between 1998 and 2012. Multivariate analyses were used to control for baseline characteristics, differences during extent of resection status, and progression-free survival analysis. Results Additional surgery was performed after iMRI in 56/156 cases (35.9 %), which led to increased extent of resection status in 15/156 cases (9.6 %). Multivariate ordinal logistic regression revealed no increase in extent of resection status following iMRI or endoscopy alone; however, combining these modalities increased extent of resection status (odds ratio 2.05, 95 % CI 1.21–3.46) compared to conventional transsphenoidal microsurgery. Multivariate Cox regression revealed that reduced extent of resection status shortened progression-free survival for near- versus gross-total resection [hazard ratio (HR) 2.87, 95 % CI 1.24–6.65] and sub- versus near-total resection (HR 2.10; 95 % CI 1.00–4.40). Complication comparisons between microscopy, endoscopy, and iMRI revealed increased perioperative deaths for endoscopy versus microscopy (4/209 and 0/237, respectively), but this difference was non-significant considering multiple post hoc comparisons (Fisher exact, p = 0.24). Conclusions Combined use of endoscopy and iMRI increased pituitary adenoma extent of resection status compared to conventional transsphenoidal microsurgery, and increased extent of resection status was associated with longer progression-free survival. Treatment modality combination did not significantly impact complication rate. PMID:24599833

Incremental Benefit of a Home Visit Following Discharge for Patients with Multiple Chronic Conditions Receiving Transitional Care

PubMed Central

Jackson, Carlos; Kasper, Elizabeth W.; Williams, Christianna

2016-01-01

Abstract Transitional care management is effective at reducing hospital readmissions among patients with multiple chronic conditions, but evidence is lacking on the relative benefit of the home visit as a component of transitional care. The sample included non-dual Medicaid recipients with multiple chronic conditions enrolled in Community Care of North Carolina (CCNC), with a hospital discharge between July 2010 and December 2012. Using claims data and care management records, this study retrospectively examined whether home visits reduced the odds of 30-day readmission compared to less intensive transitional care support, using multivariate logistic regression to control for demographic and clinical characteristics. Additionally, the researchers examined group differences within clinical risk strata on inpatient admissions and total cost of care in the 6 months following hospital discharge. Of 35,174 discharges receiving transitional care from a CCNC care manager, 21% (N = 7468) included a home visit. In multivariate analysis, home visits significantly reduced the odds of readmission within 30 days (odds ratio = 0.52, 95% confidence interval 0.48–0.57). At the 6-month follow-up, home visits were associated with fewer inpatient admissions within 4 of 6 clinical risk strata, and lower total costs of care for highest risk patients (average per member per month cost difference $970; P < 0.01). For complex chronic patients, home visits reduced the likelihood of a 30-day readmission by almost half compared to less intensive forms of nurse-led transitional care support. Higher risk patients experienced the greatest benefit in terms of number of inpatient admissions and total cost of care in the 6 months following discharge. (Population Health Management 2016;19:163–170) PMID:26431255

MODELING SNAKE MICROHABITAT FROM RADIOTELEMETRY STUDIES USING POLYTOMOUS LOGISTIC REGRESSION

EPA Science Inventory

Multivariate analysis of snake microhabitat has historically used techniques that were derived under assumptions of normality and common covariance structure (e.g., discriminant function analysis, MANOVA). In this study, polytomous logistic regression (PLR which does not require ...

[Predicting the outcome in severe injuries: an analysis of 2069 patients from the trauma register of the German Society of Traumatology (DGU)].

PubMed

Rixen, D; Raum, M; Bouillon, B; Schlosser, L E; Neugebauer, E

2001-03-01

On hospital admission numerous variables are documented from multiple trauma patients. The value of these variables to predict outcome are discussed controversially. The aim was the ability to initially determine the probability of death of multiple trauma patients. Thus, a multivariate probability model was developed based on data obtained from the trauma registry of the Deutsche Gesellschaft für Unfallchirurgie (DGU). On hospital admission the DGU trauma registry collects more than 30 variables prospectively. In the first step of analysis those variables were selected, that were assumed to be clinical predictors for outcome from literature. In a second step a univariate analysis of these variables was performed. For all primary variables with univariate significance in outcome prediction a multivariate logistic regression was performed in the third step and a multivariate prognostic model was developed. 2069 patients from 20 hospitals were prospectively included in the trauma registry from 01.01.1993-31.12.1997 (age 39 +/- 19 years; 70.0% males; ISS 22 +/- 13; 18.6% lethality). From more than 30 initially documented variables, the age, the GCS, the ISS, the base excess (BE) and the prothrombin time were the most important prognostic factors to predict the probability of death (P(death)). The following prognostic model was developed: P(death) = 1/1 + e(-[k + beta 1(age) + beta 2(GCS) + beta 3(ISS) + beta 4(BE) + beta 5(prothrombin time)]) where: k = -0.1551, beta 1 = 0.0438 with p < 0.0001, beta 2 = -0.2067 with p < 0.0001, beta 3 = 0.0252 with p = 0.0071, beta 4 = -0.0840 with p < 0.0001 and beta 5 = -0.0359 with p < 0.0001. Each of the five variables contributed significantly to the multifactorial model. These data show that the age, GCS, ISS, base excess and prothrombin time are potentially important predictors to initially identify multiple trauma patients with a high risk of lethality. With the base excess and prothrombin time value, as only variables of this multifactorial model that can be therapeutically influenced, it might be possible to better guide early and aggressive therapy.

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

Maternal Risk Factors for Fetal Alcohol Spectrum Disorders in a Province in Italy*

PubMed Central

Ceccanti, Mauro; Fiorentino, Daniela; Coriale, Giovanna; Kalberg, Wendy O.; Buckley, David; Hoyme, H. Eugene; Gossage, J. Phillip; Robinson, Luther K.; Manning, Melanie; Romeo, Marina; Hasken, Julie M.; Tabachnick, Barbara; Blankenship, Jason

2016-01-01

Background Maternal risk factors for fetal alcohol spectrum disorders (FASD) in Italy and Mediterranean cultures need clarification, as there are few studies and most are plagued by inaccurate reporting of antenatal alcohol use. Methods Maternal interviews (n=905) were carried out in a population-based study of the prevalence and characteristics of FASD in the Lazio region of Italy which provided data for multivariate case control comparisons and multiple correlation models. Results Case control findings from interviews seven years post-partum indicate that mothers of children with FASD are significantly more likely than randomly-selected controls or community mothers to: be shorter; have higher body mass indexes (BMI); be married to a man with legal problems; report more drinking three months pre-pregnancy; engage in more current drinking and drinking alone; and have alcohol problems in her family. Logistic regression analysis of multiple candidate predictors of a FASD diagnosis indicates that alcohol problems in the child’s family is the most significant risk factor, making a diagnosis within the continuum of FASD 9 times more likely (95% C.I. = 1.6 to 50.7). Sequential multiple regression analysis of the child’s neuropsychological performance also identifies alcohol problems in the child’s family as the only significant maternal risk variable (p<.001) when controlling for other potential risk factors. Conclusions Underreporting of prenatal alcohol use has been demonstrated among Italian and other Mediterranean antenatal samples, and it was suspected in this sample. Nevertheless, several significant maternal risk factors for FASD have been identified. PMID:25456331

Maternal risk factors for fetal alcohol spectrum disorders in a province in Italy.

PubMed

Ceccanti, Mauro; Fiorentino, Daniela; Coriale, Giovanna; Kalberg, Wendy O; Buckley, David; Hoyme, H Eugene; Gossage, J Phillip; Robinson, Luther K; Manning, Melanie; Romeo, Marina; Hasken, Julie M; Tabachnick, Barbara; Blankenship, Jason; May, Philip A

2014-12-01

Maternal risk factors for fetal alcohol spectrum disorders (FASD) in Italy and Mediterranean cultures need clarification, as there are few studies and most are plagued by inaccurate reporting of antenatal alcohol use. Maternal interviews (n = 905) were carried out in a population-based study of the prevalence and characteristics of FASD in the Lazio region of Italy which provided data for multivariate case control comparisons and multiple correlation models. Case control findings from interviews seven years post-partum indicate that mothers of children with FASD are significantly more likely than randomly-selected controls or community mothers to: be shorter; have higher body mass indexes (BMI); be married to a man with legal problems; report more drinking three months pre-pregnancy; engage in more current drinking and drinking alone; and have alcohol problems in her family. Logistic regression analysis of multiple candidate predictors of a FASD diagnosis indicates that alcohol problems in the child's family is the most significant risk factor, making a diagnosis within the continuum of FASD 9 times more likely (95%C.I. = 1.6 to 50.7). Sequential multiple regression analysis of the child's neuropsychological performance also identifies alcohol problems in the child's family as the only significant maternal risk variable (p < .001) when controlling for other potential risk factors. Underreporting of prenatal alcohol use has been demonstrated among Italian and other Mediterranean antenatal samples, and it was suspected in this sample. Nevertheless, several significant maternal risk factors for FASD have been identified. Copyright © 2014. Published by Elsevier Ireland Ltd.

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

PubMed

Chowdhury, Nilotpal; Sapru, Shantanu

2015-01-01

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

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

PubMed Central

Chowdhury, Nilotpal; Sapru, Shantanu

2015-01-01

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

Salting-out assisted liquid-liquid extraction and partial least squares regression to assay low molecular weight polycyclic aromatic hydrocarbons leached from soils and sediments

NASA Astrophysics Data System (ADS)

Bressan, Lucas P.; do Nascimento, Paulo Cícero; Schmidt, Marcella E. P.; Faccin, Henrique; de Machado, Leandro Carvalho; Bohrer, Denise

2017-02-01

A novel method was developed to determine low molecular weight polycyclic aromatic hydrocarbons in aqueous leachates from soils and sediments using a salting-out assisted liquid-liquid extraction, synchronous fluorescence spectrometry and a multivariate calibration technique. Several experimental parameters were controlled and the optimum conditions were: sodium carbonate as the salting-out agent at concentration of 2 mol L- 1, 3 mL of acetonitrile as extraction solvent, 6 mL of aqueous leachate, vortexing for 5 min and centrifuging at 4000 rpm for 5 min. The partial least squares calibration was optimized to the lowest values of root mean squared error and five latent variables were chosen for each of the targeted compounds. The regression coefficients for the true versus predicted concentrations were higher than 0.99. Figures of merit for the multivariate method were calculated, namely sensitivity, multivariate detection limit and multivariate quantification limit. The selectivity was also evaluated and other polycyclic aromatic hydrocarbons did not interfere in the analysis. Likewise, high performance liquid chromatography was used as a comparative methodology, and the regression analysis between the methods showed no statistical difference (t-test). The proposed methodology was applied to soils and sediments of a Brazilian river and the recoveries ranged from 74.3% to 105.8%. Overall, the proposed methodology was suitable for the targeted compounds, showing that the extraction method can be applied to spectrofluorometric analysis and that the multivariate calibration is also suitable for these compounds in leachates from real samples.

Relationships (II) of International Classification of High-resolution Computed Tomography for Occupational and Environmental Respiratory Diseases with ventilatory functions indices for parenchymal abnormalities.

PubMed

Tamura, Taro; Suganuma, Narufumi; Hering, Kurt G; Vehmas, Tapio; Itoh, Harumi; Akira, Masanori; Takashima, Yoshihiro; Hirano, Harukazu; Kusaka, Yukinori

2015-01-01

The International Classification of High-Resolution Computed Tomography (HRCT) for Occupational and Environmental Respiratory Diseases (ICOERD) is used to screen and diagnose respiratory illnesses. Using univariate and multivariate analysis, we investigated the relationship between subject characteristics and parenchymal abnormalities according to ICOERD, and the results of ventilatory function tests (VFT). Thirty-five patients with and 27 controls without mineral-dust exposure underwent VFT and HRCT. We recorded all subjects' occupational history for mineral dust exposure and smoking history. Experts independently assessed HRCT using the ICOERD parenchymal abnormalities (Items) grades for well-defined rounded opacities (RO), linear and/or irregular opacities (IR), and emphysema (EM). High-resolution computed tomography showed that 11 patients had RO; 15 patients, IR; and 19 patients, EM. According to the multiple regression model, age and height had significant associations with many indices ventilatory functions such as vital capacity, forced vital capacity, and forced expiratory volume in 1 s (FEV1). The EM summed grades on the upper, middle, and lower zones of the right and left lungs also had significant associations with FEV1 and the maximum mid-expiratory flow rate. The results suggest the ICOERD notation is adequate based on the good and significant multiple regression modeling of ventilatory function with the EM summed grades.

Factors associated to clinical learning in nursing students in primary health care: an analytical cross-sectional study

PubMed Central

Serrano-Gallardo, Pilar; Martínez-Marcos, Mercedes; Espejo-Matorrales, Flora; Arakawa, Tiemi; Magnabosco, Gabriela Tavares; Pinto, Ione Carvalho

2016-01-01

ABSTRACT Objective: to identify the students' perception about the quality of clinical placements and asses the influence of the different tutoring processes in clinical learning. Methods: analytical cross-sectional study on second and third year nursing students (n=122) about clinical learning in primary health care. The Clinical Placement Evaluation Tool and a synthetic index of attitudes and skills were computed to give scores to the clinical learning (scale 0-10). Univariate, bivariate and multivariate (multiple linear regression) analyses were performed. Results: the response rate was 91.8%. The most commonly identified tutoring process was "preceptor-professor" (45.2%). The clinical placement was assessed as "optimal" by 55.1%, relationship with team-preceptor was considered good by 80.4% of the cases and the average grade for clinical learning was 7.89. The multiple linear regression model with more explanatory capacity included the variables "Academic year" (beta coefficient = 1.042 for third-year students), "Primary Health Care Area (PHC)" (beta coefficient = 0.308 for Area B) and "Clinical placement perception" (beta coefficient = - 0.204 for a suboptimal perception). Conclusions: timeframe within the academic program, location and clinical placement perception were associated with students' clinical learning. Students' perceptions of setting quality were positive and a good team-preceptor relationship is a matter of relevance. PMID:27627124

Social consequences of multiple sclerosis: clinical and demographic predictors - a historical prospective cohort study.

PubMed

Pfleger, C C H; Flachs, E M; Koch-Henriksen, N

2010-11-01

Time to disability pension is one of the endpoints to be used to determine the prognosis of multiple sclerosis (MS) in prospective studies.   To assess the time to cessation of work and receiving disability pension in MS, and how it may depend on gender, type of work and age and symptom at onset. A total of 2240 Danes with onset of definite/probable MS 1980-1989, identified from the Danish MS-Registry, were included. Information on social endpoints was retrieved from Statistics Denmark. Cox regression analyses were used with onset as starting point. Afferent onset symptoms [hazard ratio (HR 0.57)] and non-physical type of work (HR 0.70) were favourable prognostic factors compared with high age at onset, physical work and efferent symptoms at onset. The mean time to disability pension was 13 years for patients with afferent/brainstem onset symptom but 8.7 years for those with efferent onset symptoms (P < 0.0001). The effect of onset symptom was reduced and the effect of sex became significant when all covariates and age at onset were included in multivariate Cox regression. Onset age, type of onset symptom and work are robust predictors of disability pension in MS. Disability pension proves to be a reliable milestone in estimation of the prognosis of MS. © 2010 The Author(s). Journal compilation © 2010 EFNS.

Relationships among symptom severity, coping styles, and quality of life in community-dwelling women with urinary incontinence: a multiple mediator model.

PubMed

Xu, Dongjuan; Liu, Nana; Qu, Haili; Chen, Liqin; Wang, Kefang

2016-01-01

To investigate the relationships among symptom severity, coping styles, and quality of life (QOL) in community-dwelling women with urinary incontinence (UI). A total of 592 women with UI participated in this cross-sectional study. Bivariate Pearson's correlation was used to examine the correlations between symptom severity, coping styles, and QOL. Multivariate regression models and Sobel tests were used to test the mediating effect of coping styles. Additionally, a multiple mediator model was used to examine the mediating role of coping styles collectively. All regression models were adjusted for age, education, marital status, income, duration of UI, and type of UI. Participants tended to use avoidant and palliative coping styles and not use instrumental coping style. Avoidant and palliative coping styles were associated with poor QOL, and partially mediated the association between symptom severity and QOL. Nearly 73% of the adverse effect of symptom severity on QOL was mediated by avoidant and palliative coping styles. The use of avoidant and palliative coping styles was higher with more severe urine leakage, and QOL tended to be poorer. Coping styles should be addressed in UI management. It may be of particular value to look closely at negative coping styles and implement education and training of patients in improving their coping skills related to managing UI, which will in turn improve their QOL.

Potential predictors of risk sexual behavior among private college students in Mekelle City, North Ethiopia.

PubMed

Gebresllasie, Fanna; Tsadik, Mache; Berhane, Eyoel

2017-01-01

Risk sexual practice among students from public universities/colleges is common in Ethiopia. However, little has been known about risk sexual behavior of students in private colleges where more students are potentially enrolled. Therefore, this study aimed to assess the magnitude of risky sexual behaviors and predictors among students of Private Colleges in Mekelle City. A mixed design of both quantitative and qualitative methods was used among 627 randomly selected students of private colleges from February to march 2013. Self administered questionnaire and focus group discussion was used to collect data. A thematic content analysis was used for the qualitative part. For the quantitative study, Univariate, Bivariate and multivariable analysis was made using SPSS version 16 statistical package and p value less than 0.05 was used as cut off point for a statistical significance. Among the total 590 respondents, 151 (29.1%) have ever had sex. Among the sexually active students, 30.5% reported having had multiple sexual partners and consistent condom use was nearly 39%. In multivariable logistic regression analysis, variables such as sex, age group, sex last twelve months and condom use last twelve months was found significantly associated with risky sexual behavior. The findings of qualitative and quantitative study showed consistency in presence of risk factors. Finding of this study showed sexual risk behaviors is high among private colleges such as multiple sexual partners and substance use. So that colleges should emphasis on promoting healthy sexual and reproductive health programs.

Advanced multivariate data analysis to determine the root cause of trisulfide bond formation in a novel antibody–peptide fusion

PubMed Central

Goldrick, Stephen; Holmes, William; Bond, Nicholas J.; Lewis, Gareth; Kuiper, Marcel; Turner, Richard

2017-01-01

ABSTRACT Product quality heterogeneities, such as a trisulfide bond (TSB) formation, can be influenced by multiple interacting process parameters. Identifying their root cause is a major challenge in biopharmaceutical production. To address this issue, this paper describes the novel application of advanced multivariate data analysis (MVDA) techniques to identify the process parameters influencing TSB formation in a novel recombinant antibody–peptide fusion expressed in mammalian cell culture. The screening dataset was generated with a high‐throughput (HT) micro‐bioreactor system (AmbrTM 15) using a design of experiments (DoE) approach. The complex dataset was firstly analyzed through the development of a multiple linear regression model focusing solely on the DoE inputs and identified the temperature, pH and initial nutrient feed day as important process parameters influencing this quality attribute. To further scrutinize the dataset, a partial least squares model was subsequently built incorporating both on‐line and off‐line process parameters and enabled accurate predictions of the TSB concentration at harvest. Process parameters identified by the models to promote and suppress TSB formation were implemented on five 7 L bioreactors and the resultant TSB concentrations were comparable to the model predictions. This study demonstrates the ability of MVDA to enable predictions of the key performance drivers influencing TSB formation that are valid also upon scale‐up. Biotechnol. Bioeng. 2017;114: 2222–2234. © 2017 The Authors. Biotechnology and Bioengineering Published by Wiley Periodicals, Inc. PMID:28500668

Predicting volumes in four Hawaii hardwoods...first multivariate equations developed

Treesearch

David A. Sharpnack

1966-01-01

Multivariate regression equations were developed for predicting board-foot (Int. 1/ 4-inch log rule ) and cubic-foot volumes in each 8.15-foot section of trees of four Hawaii hardwood species. The species are koa (Acacia koa), ohia (Metrosideros polymorpha), robusta eucalyptus (Eucalyptus robusta), and...

A Multivariate Test of the Bott Hypothesis in an Urban Irish Setting

ERIC Educational Resources Information Center

Gordon, Michael; Downing, Helen

1978-01-01

Using a sample of 686 married Irish women in Cork City the Bott hypothesis was tested, and the results of a multivariate regression analysis revealed that neither network connectedness nor the strength of the respondent's emotional ties to the network had any explanatory power. (Author)

Prevalence and correlates of substance use among trans*female youth ages 16–24 years in the San Francisco Bay Area

PubMed Central

Rowe, Chris; Santos, Glenn-Milo; McFarland, Willi; Wilson, Erin C.

2014-01-01

Background Substance use is highly prevalent among transgender (trans*) females and has been associated with negative health outcomes, including HIV infection. Little is known about psychosocial risk factors that may influence the onset of substance use among trans*female youth, which can contribute to health disparities during adulthood. Methods We conducted a secondary data analysis of a study on HIV risk and resilience among trans*female youth (N=292). Prevalence of substance use was assessed and multivariable logistic regression models were used to examine the relationship between posttraumatic stress disorder (PTSD), psychological distress, gender-related discrimination, parental drug or alcohol problems (PDAP) and multiple substance use outcomes. Results Most (69%) of the trans*female youth reported recent drug use. In multivariable analyses, those with PTSD had increased odds of drug use [AOR=1.94 (95%CI=1.09–3.44)]. Those who experienced gender-related discrimination had increased odds of drug use [AOR=2.28 (95%CI=1.17–4.44)], drug use concurrent with sex [AOR=2.35 (95%CI=1.11–4.98)] and use of multiple drugs [AOR=3.24 (95%CI=1.52–6.88)]. Those with psychological distress had increased odds of using multiple heavy drugs [AOR=2.27 (95%CI=1.01–5.12)]. Those with PDAP had increased odds of drugs use [AOR=2.62 (95%CI=1.43–4.82)], drug use concurrent with sex [AOR=2.01 (95%CI, 1.15–3.51)] and use of multiple drugs [AOR=2.10 (95%CI=1.22–3.62)]. Conclusions Substance use is highly prevalent among trans*female youth and was significantly associated with psychosocial risk factors. In order to effectively address substance use among trans*female youth, efforts must address coping related to gender-based discrimination and trauma. Furthermore, structural level interventions aiming to reduce stigma and gender-identity discrimination might also be effective. PMID:25548025

Prevalence and correlates of substance use among trans female youth ages 16-24 years in the San Francisco Bay Area.

PubMed

Rowe, Chris; Santos, Glenn-Milo; McFarland, Willi; Wilson, Erin C

2015-02-01

Substance use is highly prevalent among transgender (trans*) females and has been associated with negative health outcomes, including HIV infection. Little is known about psychosocial risk factors that may influence the onset of substance use among trans*female youth, which can contribute to health disparities during adulthood. We conducted a secondary data analysis of a study on HIV risk and resilience among trans*female youth (N=292). Prevalence of substance use was assessed and multivariable logistic regression models were used to examine the relationship between posttraumatic stress disorder (PTSD), psychological distress, gender-related discrimination, parental drug or alcohol problems (PDAP) and multiple substance use outcomes. Most (69%) of the trans*female youth reported recent drug use. In multivariable analyses, those with PTSD had increased odds of drug use [AOR=1.94 (95% CI=1.09-3.44)]. Those who experienced gender-related discrimination had increased odds of drug use [AOR=2.28 (95% CI=1.17-4.44)], drug use concurrent with sex [AOR=2.35 (95% CI=1.11-4.98)] and use of multiple drugs [AOR=3.24 (95% CI=1.52-6.88)]. Those with psychological distress had increased odds of using multiple heavy drugs [AOR=2.27 (95% CI=1.01-5.12)]. Those with PDAP had increased odds of drugs use [AOR=2.62 (95% CI=1.43-4.82)], drug use concurrent with sex [AOR=2.01 (95% CI, 1.15-3.51)] and use of multiple drugs [AOR=2.10 (95% CI=1.22-3.62)]. Substance use is highly prevalent among trans*female youth and was significantly associated with psychosocial risk factors. In order to effectively address substance use among trans*female youth, efforts must address coping related to gender-based discrimination and trauma. Furthermore, structural level interventions aiming to reduce stigma and gender-identity discrimination might also be effective. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

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.

[Risk factors for iron deficiency anemia in infants aged 6 to 12 months and its effects on neuropsychological development].

PubMed

Xu, Kang; Zhang, Cui-Mei; Huang, Lian-Hong; Fu, Si-Mao; Liu, Yu-Ling; Chen, Ang; Ou, Jun-Bin

2015-08-01

To study the risk factors for moderate and severe iron deficiency anemia (IDA) in infants aged 6-12 months, and to preliminarily investigate the effects of IDA on the neuromotor development and temperament characteristics of infants. A total of 326 infants aged 6-12 months with IDA were classified into three groups: mild IDA (n=176), moderate IDA (n=111), and severe IDA (n=39) according to the severity of anemia. The risk factors for moderate or severe IDA were investigated by multivariate logistic regression analysis. Three hundred and forty-six infants without IDA who showed matched age, sex, and other backgrounds were selected as the control group. The Gesell Development Diagnosis Scale was used to evaluate children's mental development. The Temperament Scale for infants was used for evaluating children's temperament. The univariate analysis showed that the severity of IDA was associated with sex, birth weight, gestational age, multiple birth, maternal anemia during pregnancy, and mother's lack of knowledge about IDA (P<0.05). Setting the mild IDA group as control, the multivariate logistic regression analysis showed that multiple birth, premature birth, low birth weight (<2500 g), maternal anemia during pregnancy, breast feeding, and mother's lack of knowledge about IDA were the risk factors for severe IDA (OR>1; P<0.05); premature birth, breast feeding, and mixed feeding were the risk factors for moderate IDA (OR>1; P<0.05). The IDA group had significantly lower scores in Gesell general development quotient, gross motor, adaptive behavior, and fine motor than the control group (P<0.05). The IDA group had higher percentages of children with difficulty and intermediate difficulty temperaments than the control group (P<0.05). The IDA group had significantly higher scores in activity level, rhythmicity, adaptability, and perseverance than the control group (P<0.05). The severity of IDA is associated with premature birth, multiple birth, low birth weight, feeding pattern, maternal anemia during pregnancy and mother's lack of knowledge about IDA in infants aged 6-12 months. Infants with IDA have delayed neuromotor development and most of them have negative temperaments. More attention should be paid to mental and behavior problems for the infants. It is necessary to provide guidance for their parents in feeding and education.

Is the impact of fatigue related to walking capacity and perceived ability in persons with multiple sclerosis? A multicenter study.

PubMed

Dalgas, U; Langeskov-Christensen, M; Skjerbæk, A; Jensen, E; Baert, I; Romberg, A; Santoyo Medina, C; Gebara, B; Maertens de Noordhout, B; Knuts, K; Béthoux, F; Rasova, K; Severijns, D; Bibby, B M; Kalron, A; Norman, B; Van Geel, F; Wens, I; Feys, P

2018-04-15

The relationship between fatigue impact and walking capacity and perceived ability in patients with multiple sclerosis (MS) is inconclusive in the existing literature. A better understanding might guide new treatment avenues for fatigue and/or walking capacity in patients with MS. To investigate the relationship between the subjective impact of fatigue and objective walking capacity as well as subjective walking ability in MS patients. A cross-sectional multicenter study design was applied. Ambulatory MS patients (n = 189, age: 47.6 ± 10.5 years; gender: 115/74 women/men; Expanded Disability Status Scale (EDSS): 4.1 ± 1.8 [range: 0-6.5]) were tested at 11 sites. Objective tests of walking capacity included short walking tests (Timed 25-Foot Walk (T25FW), 10-Metre Walk Test (10mWT) at usual and fastest speed and the timed up and go (TUG)), and long walking tests (2- and 6-Minute Walk Tests (MWT). Subjective walking ability was tested applying the Multiple Sclerosis Walking Scale-12 (MSWS-12). Fatigue impact was measured by the self-reported modified fatigue impact scale (MFIS) consisting of a total score (MFIS total ) and three subscales (MFIS physical , MFIS cognitive and MFIS psychosocial ). Uni- and multivariate regression analysis were performed to evaluate the relation between walking and fatigue impact. MFIS total was negatively related with long (6MWT, r = -0.14, p = 0.05) and short composite (TUG, r = -0.22, p = 0.003) walking measures. MFIS physical showed a significant albeit weak relationship to walking speed in all walking capacity tests (r = -0.22 to -0.33, p < .0001), which persisted in the multivariate linear regression analysis. Subjective walking ability (MSWS-12) was related to MFIS total (r = 0.49, p < 0.0001), as well as to all other subscales of MFIS (r = 0.24-0.63, p < 0.001), showing stronger relationships than objective measures of walking. The physical impact of fatigue is weakly related to objective walking capacity, while general, physical, cognitive and psychosocial fatigue impact are weakly to moderately related to subjective walking ability, when analysed in a large heterogeneous sample of MS patients. Copyright © 2018 Elsevier B.V. All rights reserved.

Multivariate two-part statistics for analysis of correlated mass spectrometry data from multiple biological specimens.

PubMed

Taylor, Sandra L; Ruhaak, L Renee; Weiss, Robert H; Kelly, Karen; Kim, Kyoungmi

2017-01-01

High through-put mass spectrometry (MS) is now being used to profile small molecular compounds across multiple biological sample types from the same subjects with the goal of leveraging information across biospecimens. Multivariate statistical methods that combine information from all biospecimens could be more powerful than the usual univariate analyses. However, missing values are common in MS data and imputation can impact between-biospecimen correlation and multivariate analysis results. We propose two multivariate two-part statistics that accommodate missing values and combine data from all biospecimens to identify differentially regulated compounds. Statistical significance is determined using a multivariate permutation null distribution. Relative to univariate tests, the multivariate procedures detected more significant compounds in three biological datasets. In a simulation study, we showed that multi-biospecimen testing procedures were more powerful than single-biospecimen methods when compounds are differentially regulated in multiple biospecimens but univariate methods can be more powerful if compounds are differentially regulated in only one biospecimen. We provide R functions to implement and illustrate our method as supplementary information CONTACT: sltaylor@ucdavis.eduSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Temperature variability during targeted temperature management is not associated with neurological outcomes following cardiac arrest.

PubMed

Nayeri, Arash; Bhatia, Nirmanmoh; Holmes, Benjamin; Borges, Nyal; Armstrong, William; Xu, Meng; Farber-Eger, Eric; Wells, Quinn S; McPherson, John A

2017-06-01

Recent studies on comatose survivors of cardiac arrest undergoing targeted temperature management (TTM) have shown similar outcomes at multiple target temperatures. However, details regarding core temperature variability during TTM and its prognostic implications remain largely unknown. We sought to assess the association between core temperature variability and neurological outcomes in patients undergoing TTM following cardiac arrest. We analyzed a prospectively collected cohort of 242 patients treated with TTM following cardiac arrest at a tertiary care hospital between 2007 and 2014. Core temperature variability was defined as the statistical variance (i.e. standard deviation squared) amongst all core temperature recordings during the maintenance phase of TTM. Poor neurological outcome at hospital discharge, defined as a Cerebral Performance Category (CPC) score>2, was the primary outcome. Death prior to hospital discharge was assessed as the secondary outcome. Multivariable logistic regression was used to examine the association between temperature variability and neurological outcome or death at hospital discharge. A poor neurological outcome was observed in 147 (61%) patients and 136 (56%) patients died prior to hospital discharge. In multivariable logistic regression, increased core temperature variability was not associated with increased odds of poor neurological outcomes (OR 0.38, 95% CI 0.11-1.38, p=0.142) or death (OR 0.43, 95% CI 0.12-1.53, p=0.193) at hospital discharge. In this study, individual core temperature variability during TTM was not associated with poor neurological outcomes or death at hospital discharge. Copyright © 2017 Elsevier Inc. All rights reserved.

The use of surgery in the treatment of ER+ early stage breast cancer in England: Variation by time, age and patient characteristics.

PubMed

Richards, P; Ward, S; Morgan, J; Lagord, C; Reed, M; Collins, K; Wyld, L

2016-04-01

To assess whether the proportion of patients aged 70 and over with ER+ operable breast cancer in England who are treated with surgery has changed since 2002, and to determine whether age and individual level factors including tumour characteristics and co-morbidity influence treatment choice. A retrospective cohort analysis of routinely collected cancer registration data from two English regions (West Midlands, Northern & Yorkshire) was carried out (n = 17,129). Trends in surgical use over time for different age groups were assessed graphically and with linear regression. Uni- and multivariable logistic regressions were used to assess the effects of age, comorbidity, deprivation and disease characteristics on treatment choice. Missing data was handled using multiple imputation. There is no evidence of a change in the proportion of patients treated surgically over time. The multivariable model shows that age remains an important predictor of whether or not a woman with ER+ operable breast cancer receives surgery after covariate adjustment (Odds ratio of surgery vs no surgery, 0.82 (per year over 70)). Co-morbidity, deprivation, symptomatic presentation, later stage at diagnosis and low grade are also associated with increased probability of non-surgical treatment. Contrary to current NICE guidance in England, age appears to be an important factor in the decision to treat operable ER+ breast cancer non-surgically. Further research is needed to assess the role of other age-related factors on treatment choice, and the effect that current practice has on survival and mortality from breast cancer for older women. Copyright © 2016 Elsevier Ltd. All rights reserved.

The association between patient participation and functional gain following inpatient rehabilitation.

PubMed

Morghen, Sara; Morandi, Alessandro; Guccione, Andrew A; Bozzini, Michela; Guerini, Fabio; Gatti, Roberto; Del Santo, Francesco; Gentile, Simona; Trabucchi, Marco; Bellelli, Giuseppe

2017-08-01

To evaluate patients' participation during physical therapy sessions as assessed with the Pittsburgh rehabilitation participation scale (PRPS) as a possible predictor of functional gain after rehabilitation training. All patients aged 65 years or older consecutively admitted to a Department of Rehabilitation and Aged Care (DRAC) were evaluated on admission regarding their health, nutritional, functional and cognitive status. Functional status was assessed with the functional independence measure (FIM) on admission and at discharge. Participation during rehabilitation sessions was measured with the PRPS. Functional gain was evaluated using the Montebello rehabilitation factor score (MRFS efficacy), and patients stratified in two groups according to their level of functional gain and their sociodemographic, clinical and functional characteristics were compared. Predictors of poor functional gain were evaluated using a multivariable logistic regression model adjusted for confounding factors. A total of 556 subjects were included in this study. Patients with poor functional gain at discharge demonstrated lower participation during physical therapy sessions were significantly older, more cognitively and functionally impaired on admission, more depressed, more comorbid, and more frequently admitted for cardiac disease or immobility syndrome than their counterparts. There was a significant linear association between PRPS scores and MRFS efficacy. In a multivariable logistic regression model, participation was independently associated with functional gain at discharge (odds ratio 1.51, 95 % confidence interval 1.19-1.91). This study showed that participation during physical therapy affects the extent of functional gain at discharge in a large population of older patients with multiple diseases receiving in-hospital rehabilitation.

Using Multivariate Adaptive Regression Spline and Artificial Neural Network to Simulate Urbanization in Mumbai, India

NASA Astrophysics Data System (ADS)

Ahmadlou, M.; Delavar, M. R.; Tayyebi, A.; Shafizadeh-Moghadam, H.

2015-12-01

Land use change (LUC) models used for modelling urban growth are different in structure and performance. Local models divide the data into separate subsets and fit distinct models on each of the subsets. Non-parametric models are data driven and usually do not have a fixed model structure or model structure is unknown before the modelling process. On the other hand, global models perform modelling using all the available data. In addition, parametric models have a fixed structure before the modelling process and they are model driven. Since few studies have compared local non-parametric models with global parametric models, this study compares a local non-parametric model called multivariate adaptive regression spline (MARS), and a global parametric model called artificial neural network (ANN) to simulate urbanization in Mumbai, India. Both models determine the relationship between a dependent variable and multiple independent variables. We used receiver operating characteristic (ROC) to compare the power of the both models for simulating urbanization. Landsat images of 1991 (TM) and 2010 (ETM+) were used for modelling the urbanization process. The drivers considered for urbanization in this area were distance to urban areas, urban density, distance to roads, distance to water, distance to forest, distance to railway, distance to central business district, number of agricultural cells in a 7 by 7 neighbourhoods, and slope in 1991. The results showed that the area under the ROC curve for MARS and ANN was 94.77% and 95.36%, respectively. Thus, ANN performed slightly better than MARS to simulate urban areas in Mumbai, India.

External root resorption of the second molar associated with mesially and horizontally impacted mandibular third molar: evidence from cone beam computed tomography.

PubMed

Wang, Dongmiao; He, Xiaotong; Wang, Yanling; Li, Zhongwu; Zhu, Yumin; Sun, Chao; Ye, Jinhai; Jiang, Hongbing; Cheng, Jie

2017-05-01

The aim of the present study was to assess the incidence and risk factors of ERR in second molars with mesially and horizontally impacted mandibular third molars using cone beam computed tomography (CBCT) images from patients in a Chinese tertiary referral hospital. A total number of 216 patients with 362 mesially and horizontally impacted mandibular third molars who were treated at our institution from 2014 to 2015 was retrospectively included. The ERR in second molars was identified on CBCT multiplanar images. The associations between incidence of ERR and multiple clinical parameters were statistically analyzed by Chi-square test. Moreover, the risk factors for ERR in second molars were further assessed by multivariate regression analysis. The overall incidence of ERR in second molars was 20.17 % (73/362) as detected on CBCT images. The presence of ERR significantly associated with patients age and impaction depth of mandibular third molars. However, no significant relationship was found between ERR severity and impaction depth or ERR location. Multivariate regression analyses further revealed age over 35 years and impaction depth as important risk factors affecting the ERR incidence caused by mesial and horizontal impaction of mandibular third molar. ERR in second molar resulted from mesially and horizontally impacted mandibular third molar is not very rare and can be reliably identified via CBCT scan. Given the possibility of ERR associated with third molar impaction, the prophylactic removal of these impacted teeth could be considered especially for those patients with over 35 years and mesially and horizontally impacted teeth.

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

PubMed

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

2017-03-01

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

National Trends of Simple Prostatectomy for Benign Prostatic Hyperplasia With an Analysis of Risk Factors for Adverse Perioperative Outcomes.

PubMed

Pariser, Joseph J; Pearce, Shane M; Patel, Sanjay G; Bales, Gregory T

2015-10-01

To examine the national trends of simple prostatectomy (SP) for benign prostatic hyperplasia (BPH) focusing on perioperative outcomes and risk factors for complications. The National Inpatient Sample (2002-2012) was utilized to identify patients with BPH undergoing SP. Analysis included demographics, hospital details, associated procedures, and operative approach (open, robotic, or laparoscopic). Outcomes included complications, length of stay, charges, and mortality. Multivariate logistic regression was used to determine the risk factors for perioperative complications. Linear regression was used to assess the trends in the national annual utilization of SP. The study population included 35,171 patients. Median length of stay was 4 days (interquartile range 3-6). Cystolithotomy was performed concurrently in 6041 patients (17%). The overall complication rate was 28%, with bleeding occurring most commonly. In total, 148 (0.4%) patients experienced in-hospital mortality. On multivariate analysis, older age, black race, and overall comorbidity were associated with greater risk of complications while the use of a minimally invasive approach and concurrent cystolithotomy had a decreased risk. Over the study period, the national use of simple prostatectomy decreased, on average, by 145 cases per year (P = .002). By 2012, 135/2580 procedures (5%) were performed using a minimally invasive approach. The nationwide utilization of SP for BPH has decreased. Bleeding complications are common, but perioperative mortality is low. Patients who are older, black race, or have multiple comorbidities are at higher risk of complications. Minimally invasive approaches, which are becoming increasingly utilized, may reduce perioperative morbidity. Copyright © 2015 Elsevier Inc. All rights reserved.

A regressive methodology for estimating missing data in rainfall daily time series

NASA Astrophysics Data System (ADS)

Barca, E.; Passarella, G.

2009-04-01

The "presence" of gaps in environmental data time series represents a very common, but extremely critical problem, since it can produce biased results (Rubin, 1976). Missing data plagues almost all surveys. The problem is how to deal with missing data once it has been deemed impossible to recover the actual missing values. Apart from the amount of missing data, another issue which plays an important role in the choice of any recovery approach is the evaluation of "missingness" mechanisms. When data missing is conditioned by some other variable observed in the data set (Schafer, 1997) the mechanism is called MAR (Missing at Random). Otherwise, when the missingness mechanism depends on the actual value of the missing data, it is called NCAR (Not Missing at Random). This last is the most difficult condition to model. In the last decade interest arose in the estimation of missing data by using regression (single imputation). More recently multiple imputation has become also available, which returns a distribution of estimated values (Scheffer, 2002). In this paper an automatic methodology for estimating missing data is presented. In practice, given a gauging station affected by missing data (target station), the methodology checks the randomness of the missing data and classifies the "similarity" between the target station and the other gauging stations spread over the study area. Among different methods useful for defining the similarity degree, whose effectiveness strongly depends on the data distribution, the Spearman correlation coefficient was chosen. Once defined the similarity matrix, a suitable, nonparametric, univariate, and regressive method was applied in order to estimate missing data in the target station: the Theil method (Theil, 1950). Even though the methodology revealed to be rather reliable an improvement of the missing data estimation can be achieved by a generalization. A first possible improvement consists in extending the univariate technique to the multivariate approach. Another approach follows the paradigm of the "multiple imputation" (Rubin, 1987; Rubin, 1988), which consists in using a set of "similar stations" instead than the most similar. This way, a sort of estimation range can be determined allowing the introduction of uncertainty. Finally, time series can be grouped on the basis of monthly rainfall rates defining classes of wetness (i.e.: dry, moderately rainy and rainy), in order to achieve the estimation using homogeneous data subsets. We expect that integrating the methodology with these enhancements will certainly improve its reliability. The methodology was applied to the daily rainfall time series data registered in the Candelaro River Basin (Apulia - South Italy) from 1970 to 2001. REFERENCES D.B., Rubin, 1976. Inference and Missing Data. Biometrika 63 581-592 D.B. Rubin, 1987. Multiple Imputation for Nonresponce in Surveys, New York: John Wiley & Sons, Inc. D.B. Rubin, 1988. An overview of multiple imputation. In Survey Research Section, pp. 79-84, American Statistical Association, 1988. J.L., Schafer, 1997. Analysis of Incomplete Multivariate Data, Chapman & Hall. J., Scheffer, 2002. Dealing with Missing Data. Res. Lett. Inf. Math. Sci. 3, 153-160. Available online at http://www.massey.ac.nz/~wwiims/research/letters/ H. Theil, 1950. A rank-invariant method of linear and polynomial regression analysis. Indicationes Mathematicae, 12, pp.85-91.

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

NASA Astrophysics Data System (ADS)

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

2018-05-01

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

Regional flow duration curves: Geostatistical techniques versus multivariate regression

USGS Publications Warehouse

Pugliese, Alessio; Farmer, William H.; Castellarin, Attilio; Archfield, Stacey A.; Vogel, Richard M.

2016-01-01

A period-of-record flow duration curve (FDC) represents the relationship between the magnitude and frequency of daily streamflows. Prediction of FDCs is of great importance for locations characterized by sparse or missing streamflow observations. We present a detailed comparison of two methods which are capable of predicting an FDC at ungauged basins: (1) an adaptation of the geostatistical method, Top-kriging, employing a linear weighted average of dimensionless empirical FDCs, standardised with a reference streamflow value; and (2) regional multiple linear regression of streamflow quantiles, perhaps the most common method for the prediction of FDCs at ungauged sites. In particular, Top-kriging relies on a metric for expressing the similarity between catchments computed as the negative deviation of the FDC from a reference streamflow value, which we termed total negative deviation (TND). Comparisons of these two methods are made in 182 largely unregulated river catchments in the southeastern U.S. using a three-fold cross-validation algorithm. Our results reveal that the two methods perform similarly throughout flow-regimes, with average Nash-Sutcliffe Efficiencies 0.566 and 0.662, (0.883 and 0.829 on log-transformed quantiles) for the geostatistical and the linear regression models, respectively. The differences between the reproduction of FDC's occurred mostly for low flows with exceedance probability (i.e. duration) above 0.98.

Herpes simplex virus type 2 (HSV-2) as a coronary atherosclerosis risk factor in HIV-infected men: Multicenter AIDS Cohort Study

PubMed Central

Hechter, Rulin C.; Budoff, Matthew; Hodis, Howard N.; Rinaldo, Charles R.; Jenkins, Frank J.; Jacobson, Lisa P.; Kingsley, Lawrence A.; Taiwo, Babafemi; Post, Wendy S.; Margolick, Joseph B.; Detels, Roger

2012-01-01

We assessed associations of herpes simplex virus types 1 and 2 (HSV-1 and -2), cytomegalovirus (CMV), and human herpesvirus 8 (HHV-8) infection with subclinical coronary atherosclerosis in 291 HIV-infected men in the Multicenter AIDS Cohort Study. Coronary artery calcium (CAC) was measured by non-contrast coronary CT imaging. Markers for herpesviruses infection were measured in frozen specimens collected 10-12 years prior to case identification. Multivariable logistic regression models and ordinal logistic regression models were performed. HSV-2 seropositivity was associated with coronary atherosclerosis (adjusted odds ratio [AOR] =4.12, 95% confidence interval [CI] =1.58-10.85) after adjustment for age, race/ethnicity, cardiovascular risk factors, and HIV infection related factors. Infection with a greater number of herpesviruses was associated with elevated CAC levels (AOR=1.58, 95% CI=1.06-2.36). Our findings suggest HSV-2 may be a risk factor for subclinical coronary atherosclerosis in HIV-infected men. Infection with multiple herpesviruses may contribute to the increased burden of atherosclerosis. PMID:22472456

Recurrent shoulder dystocia: is it predictable?

PubMed

Kleitman, Vered; Feldman, Roi; Walfisch, Asnat; Toledano, Ronen; Sheiner, Eyal

2016-11-01

To examine the course and outcome of deliveries occurring in women who previously experienced shoulder dystocia. In addition, recurrent shoulder dystocia risk factors were assessed. A retrospective cohort analysis comparing all singleton deliveries with and without shoulder dystocia in their preceding delivery was conducted. Independent predictors of recurrent shoulder dystocia were investigated using a multiple logistic regression model. Of the 201,422 deliveries included in the analysis, 307 occurred in women with a previous shoulder dystocia (0.015 %). Women with a history of shoulder dystocia were more likely to be older, experienced higher rates of gestational diabetes mellitus, polyhydramnios, prolonged second stage, operative delivery and macrosomia (>4000 g) in the following delivery. Previous shoulder dystocia was found to be an independent risk factor for recurrent shoulder dystocia (OR = 6.1, 95 % CI 3.2-11.8, p value <0.001) in the multivariable regression analysis. Shoulder dystocia is an independent risk factor for recurrent shoulder dystocia. Deliveries in women with a history of shoulder dystocia are characterized by higher rates of operative delivery, prolonged second stage of labor and macrosomia.

Herpes simplex virus type 2 (HSV-2) as a coronary atherosclerosis risk factor in HIV-infected men: multicenter AIDS cohort study.

PubMed

Hechter, Rulin C; Budoff, Matthew; Hodis, Howard N; Rinaldo, Charles R; Jenkins, Frank J; Jacobson, Lisa P; Kingsley, Lawrence A; Taiwo, Babafemi; Post, Wendy S; Margolick, Joseph B; Detels, Roger

2012-08-01

We assessed associations of herpes simplex virus types 1 and 2 (HSV-1 and -2), cytomegalovirus (CMV), and human herpesvirus 8 (HHV-8) infection with subclinical coronary atherosclerosis in 291 HIV-infected men in the Multicenter AIDS Cohort Study. Coronary artery calcium (CAC) was measured by non-contrast coronary CT imaging. Markers for herpesviruses infection were measured in frozen specimens collected 10-12 years prior to case identification. Multivariable logistic regression models and ordinal logistic regression models were performed. HSV-2 seropositivity was associated with coronary atherosclerosis (adjusted odds ratio [AOR]=4.12, 95% confidence interval [CI]=1.58-10.85) after adjustment for age, race/ethnicity, cardiovascular risk factors, and HIV infection related factors. Infection with a greater number of herpesviruses was associated with elevated CAC levels (AOR=1.58, 95% CI=1.06-2.36). Our findings suggest HSV-2 may be a risk factor for subclinical coronary atherosclerosis in HIV-infected men. Infection with multiple herpesviruses may contribute to the increased burden of atherosclerosis. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

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

PubMed

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

1989-08-01

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

Computation of nonlinear least squares estimator and maximum likelihood using principles in matrix calculus

NASA Astrophysics Data System (ADS)

Mahaboob, B.; Venkateswarlu, B.; Sankar, J. Ravi; Balasiddamuni, P.

2017-11-01

This paper uses matrix calculus techniques to obtain Nonlinear Least Squares Estimator (NLSE), Maximum Likelihood Estimator (MLE) and Linear Pseudo model for nonlinear regression model. David Pollard and Peter Radchenko [1] explained analytic techniques to compute the NLSE. However the present research paper introduces an innovative method to compute the NLSE using principles in multivariate calculus. This study is concerned with very new optimization techniques used to compute MLE and NLSE. Anh [2] derived NLSE and MLE of a heteroscedatistic regression model. Lemcoff [3] discussed a procedure to get linear pseudo model for nonlinear regression model. In this research article a new technique is developed to get the linear pseudo model for nonlinear regression model using multivariate calculus. The linear pseudo model of Edmond Malinvaud [4] has been explained in a very different way in this paper. David Pollard et.al used empirical process techniques to study the asymptotic of the LSE (Least-squares estimation) for the fitting of nonlinear regression function in 2006. In Jae Myung [13] provided a go conceptual for Maximum likelihood estimation in his work “Tutorial on maximum likelihood estimation

Rex fortran 4 system for combinatorial screening or conventional analysis of multivariate regressions

Treesearch

L.R. Grosenbaugh

1967-01-01

Describes an expansible computerized system that provides data needed in regression or covariance analysis of as many as 50 variables, 8 of which may be dependent. Alternatively, it can screen variously generated combinations of independent variables to find the regression with the smallest mean-squared-residual, which will be fitted if desired. The user can easily...

[Study on the correlation among adolescents' family function, negative life events stress amount and suicide ideation].

PubMed

Zhang, Dongdong; Chen, Ling; Yin, Dan; Miao, Jinping; Sun, Yehuan

2014-07-01

To explore the correlation between suicide ideation and family function & negative life events, as well as other influential factors in adolescents, thus present a theoretical base for clinicians and school staff to develop intervention for those problems. By adopting current situation random sampling method, Self-Rating Idea of Suicide Scale, Adolescent Self-Rating Life Events Check List and Family APGAR Index were used to assess adolescents at random in a hygiene vocational school in Changzhou City, Jiangsu Province and a collage in Wuhu City, Anhui Province. 3700 questionnaires were granted, 3675 questionnaires were collected, among which 3620 were valid. Chi-square test, t-test, and univariate logistic regression were employed in univariate analysis, multivariate logistic regression was used in multivariate analysis. The detection rate of suicide ideation is 7.0%, and the top five suicide ideation characteristics were: poor academic performance (33.6%), serious family functional impairment (25.8%), lower-middle academic performance (11.7%), bad economic conditions (10.8%) and study in Grade Three (9.9%). Multiple logistic regression showed that the following three high-level stress amount in negative life events are most crucial for suicide ideation. They are "relationships" (OR = 1.135, 95% CI 1.071 - 1. 202), "academic pressure" (OR = 1.169, 95% CI 1.101 - 1.241), and "external events" (OR = 1.278, 95% CI 1.187 - 1.376). What' s more, the stress of attending higher grades (OR = 1.980, 95% CI 1.302 - 3.008), poor academic performance (OR = 7.206, 95% CI 1.745 - 9.789), moderate family functional impairment (OR = 2.562, 95% CI 1.527 - 2.892) and its serious level (OR = 8.287, 95% CI 3.154 - 6.917) are also influential factors for suicide ideation. Severe family functional impairment and high-level stress amount of negative life events produced the main factors of suicide ideation. Therefore, necessary and sufficient support should be given to adolescents by families and schools.

Association between overuse of mobile phones on quality of sleep and general health among occupational health and safety students.

PubMed

Eyvazlou, Meysam; Zarei, Esmaeil; Rahimi, Azin; Abazari, Malek

2016-01-01

Concerns about health problems due to the increasing use of mobile phones are growing. Excessive use of mobile phones can affect the quality of sleep as one of the important issues in the health literature and general health of people. Therefore, this study investigated the relationship between the excessive use of mobile phones and general health and quality of sleep on 450 Occupational Health and Safety (OH&S) students in five universities of medical sciences in the North East of Iran in 2014. To achieve this objective, special questionnaires that included Cell Phone Overuse Scale, Pittsburgh's Sleep Quality Index (PSQI) and General Health Questionnaire (GHQ) were used, respectively. In addition to descriptive statistical methods, independent t-test, Pearson correlation, analysis of variance (ANOVA) and multiple regression tests were performed. The results revealed that half of the students had a poor level of sleep quality and most of them were considered unhealthy. The Pearson correlation co-efficient indicated a significant association between the excessive use of mobile phones and the total score of general health and the quality of sleep. In addition, the results of the multiple regression showed that the excessive use of mobile phones has a significant relationship between each of the four subscales of general health and the quality of sleep. Furthermore, the results of the multivariate regression indicated that the quality of sleep has a simultaneous effect on each of the four scales of the general health. Overall, a simultaneous study of the effects of the mobile phones on the quality of sleep and the general health could be considered as a trigger to employ some intervention programs to improve their general health status, quality of sleep and consequently educational performance.

Frequency of early vascular aging and associated risk factors among an adult population in Latin America: the OPTIMO study.

PubMed

Botto, Fernando; Obregon, Sebastian; Rubinstein, Fernando; Scuteri, Angelo; Nilsson, Peter M; Kotliar, Carol

2018-03-01

The main objective was to estimate the frequency of early vascular aging (EVA) in a sample of subjects from Latin America, with emphasis in young adults. We included 1416 subjects from 12 countries in Latin America who provided information about lifestyle, cardiovascular risk factors (CVRF), and anthropometrics. We measured pulse wave velocity (PWV) as a marker of arterial stiffness, and blood pressure (BP) using an oscillometric device (Mobil-O-Graph). To determine the frequency of EVA, we used multiple linear regression to estimate each subject's PWV expected for his/her age and systolic BP, and compared with observed values to obtain standardized residuals (z-scores). We defined EVA when z-score was ≥1.96. Finally, a multivariable logistic regression analysis was performed to determine baseline characteristics associated with EVA. Mean age was 49.9 ± 15.5 years, male gender was 50.3%. Mean PWV was 7.52 m/s (SD 1.97), mean systolic BP was 125.3 mmHg (SD 16.7) and mean diastolic BP was 78.9 mmHg (SD 12.2). The frequency of EVA was 5.7% in the total population, 9.8% in adults of 40 years or less and 18.7% in those 30 years or less. In these young adults, multiple logistic regression analyses demonstrated that dyslipidemia and hypertension showed an independent association with EVA, and smoking a borderline association (p  =  0.07). In conclusion, the frequency of EVA in a sample from Latin America was around 6%, with higher rates in young adults. These results would support the search of CVRF and EVA during early adulthood.

An 8-year study of people with multiple sclerosis in Isfahan, Iran: Association between environmental air pollutants and severity of disease.

PubMed

Ashtari, Fereshte; Esmaeil, Nafiseh; Mansourian, Marjan; Poursafa, Parinaz; Mirmosayyeb, Omid; Barzegar, Mahdi; Pourgheisari, Hajar

2018-06-15

The evidence for an impact of ambient air pollution on the incidence and severity of multiple sclerosis (MS) is still limited. In the present study, we assessed the association between daily air pollution levels and MS prevalence and severity in Isfahan city, Iran. Data related to MS patients has been collected from 2008 to 2016 in a referral university clinic. The air quality index (AQI) data, were collected from 6 monitoring stations of Isfahan department of environment. The distribution map presenting the sites of air pollution monitoring stations as well as the residential address of MS patients was plotted on geographical information system (GIS). An increase in AQI level in four areas of the city (north, west, east and south) was associated with higher expanded disability status scale (EDSS) of MS patients[logistic regression odds ratio = 1.01 (95% CI = 1.008,1.012)]. Moreover, significant inverse association between the complete remission after the first attack with AQI level in total areas [logistic regression odds ratio = 0.987 (95% CI = 0.977, 0.997)] was found in crude model. However, after adjustment for confounding variables through multivariate logistic regression, AQI level was associated with degree of complete remission after first attack 1.005 (95% CI = 1.004, 1.006). The results of our study suggest that air pollution could play a role in the severity and remission of MS disease. However, more detailed studies with considering the complex involvement of different environmental factors including sunlight exposure, diet, depression and vitamin D are needed to determine the outcome of MS. Copyright © 2018 Elsevier B.V. All rights reserved.

Wetlands in Changed Landscapes: The Influence of Habitat Transformation on the Physico-Chemistry of Temporary Depression Wetlands

PubMed Central

Bird, Matthew S.; Day, Jenny A.

2014-01-01

Temporary wetlands dominate the wet season landscape of temperate, semi-arid and arid regions, yet, other than their direct loss to development and agriculture, little information exists on how remaining wetlands have been altered by anthropogenic conversion of surrounding landscapes. This study investigates relationships between the extent and type of habitat transformation around temporary wetlands and their water column physico-chemical characteristics. A set of 90 isolated depression wetlands (seasonally inundated) occurring on coastal plains of the south-western Cape mediterranean-climate region of South Africa was sampled during the winter/spring wet season of 2007. Wetlands were sampled across habitat transformation gradients according to the areal cover of agriculture, urban development and alien invasive vegetation within 100 and 500 m radii of each wetland edge. We hypothesized that the principal drivers of physico-chemical conditions in these wetlands (e.g. soil properties, basin morphology) are altered by habitat transformation. Multivariate multiple regression analyses (distance-based Redundancy Analysis) indicated significant associations between wetland physico-chemistry and habitat transformation (overall transformation within 100 and 500 m, alien vegetation cover within 100 and 500 m, urban cover within 100 m); although for significant regressions the amount of variation explained was very low (range: ∼2 to ∼5.5%), relative to that explained by purely spatio-temporal factors (range: ∼35.5 to ∼43%). The nature of the relationships between each type of transformation in the landscape and individual physico-chemical variables in wetlands were further explored with univariate multiple regressions. Results suggest that conservation of relatively narrow (∼100 m) buffer strips around temporary wetlands is likely to be effective in the maintenance of natural conditions in terms of physico-chemical water quality. PMID:24533161

Some Recent Developments on Complex Multivariate Distributions

ERIC Educational Resources Information Center

Krishnaiah, P. R.

1976-01-01

In this paper, the author gives a review of the literature on complex multivariate distributions. Some new results on these distributions are also given. Finally, the author discusses the applications of the complex multivariate distributions in the area of the inference on multiple time series. (Author)

Multivariate Methods for Meta-Analysis of Genetic Association Studies.

PubMed

Dimou, Niki L; Pantavou, Katerina G; Braliou, Georgia G; Bagos, Pantelis G

2018-01-01

Multivariate meta-analysis of genetic association studies and genome-wide association studies has received a remarkable attention as it improves the precision of the analysis. Here, we review, summarize and present in a unified framework methods for multivariate meta-analysis of genetic association studies and genome-wide association studies. Starting with the statistical methods used for robust analysis and genetic model selection, we present in brief univariate methods for meta-analysis and we then scrutinize multivariate methodologies. Multivariate models of meta-analysis for a single gene-disease association studies, including models for haplotype association studies, multiple linked polymorphisms and multiple outcomes are discussed. The popular Mendelian randomization approach and special cases of meta-analysis addressing issues such as the assumption of the mode of inheritance, deviation from Hardy-Weinberg Equilibrium and gene-environment interactions are also presented. All available methods are enriched with practical applications and methodologies that could be developed in the future are discussed. Links for all available software implementing multivariate meta-analysis methods are also provided.

MRI morphometry of mamillary bodies, caudate nuclei, and prefrontal cortices after chemotherapy for childhood leukemia: multivariate models of early and late developing memory subsystems.

PubMed

Ciesielski, K T; Lesnik, P G; Benzel, E C; Hart, B L; Sanders, J A

1999-06-01

Neurotoxic intrathecal chemotherapy for childhood acute lymphoblastic leukemia (ALL) affects developing structures and functions of memory and learning subsystems selectively. Results show significant reductions in magnetic resonance imaging morphometry of mamillary bodies, components of the corticolimbic-diencephalic subsystem subserving functionally later developing, single-trial memory, nonsignificant changes in bilateral heads of the caudate nuclei, components of the corticostriatal subsystem subserving functionally earlier developing, multitrial learning, significant reductions in prefrontal cortical volume, visual and verbal single-trial memory deficits, and visuospatial, but not verbal, multitrial learning deficits. Multiple regression models provide evidence for partial dissociation and connectivity between the subsystems, and suggest that greater involvement of caudate may compensate for inefficient corticolimbic-diencephalic components.

Perceived discrimination, psychological distress, and current smoking status: results from the Behavioral Risk Factor Surveillance System Reactions to Race module, 2004-2008.

PubMed

Purnell, Jason Q; Peppone, Luke J; Alcaraz, Kassandra; McQueen, Amy; Guido, Joseph J; Carroll, Jennifer K; Shacham, Enbal; Morrow, Gary R

2012-05-01

We examined the association between perceived discrimination and smoking status and whether psychological distress mediated this relationship in a large, multiethnic sample. We used 2004 through 2008 data from the Behavioral Risk Factor Surveillance System Reactions to Race module to conduct multivariate logistic regression analyses and tests of mediation examining associations between perceived discrimination in health care and workplace settings, psychological distress, and current smoking status. Regardless of race/ethnicity, perceived discrimination was associated with increased odds of current smoking. Psychological distress was also a significant mediator of the discrimination-smoking association. Our results indicate that individuals who report discriminatory treatment in multiple domains may be more likely to smoke, in part, because of the psychological distress associated with such treatment.

Integrated Employee Occupational Health and Organizational-Level Registered Nurse Outcomes.

PubMed

Mohr, David C; Schult, Tamara; Eaton, Jennifer Lipkowitz; Awosika, Ebi; McPhaul, Kathleen M

2016-05-01

The study examined organizational culture, structural supports, and employee health program integration influence on registered nurse (RN) outcomes. An organizational health survey, employee health clinical operations survey, employee attitudes survey, and administration data were collected. Multivariate regression models examined outcomes of sick leave, leave without pay, voluntary turnover, intention to leave, and organizational culture using 122 medical centers. Lower staffing ratios were associated with greater sick leave, higher turnover, and intention to leave. Safety climate was favorably associated with each of the five outcomes. Both onsite employee occupational health services and a robust health promotion program were associated with more positive organizational culture perceptions. Findings highlight the positive influence of integrating employee health and health promotion services on organizational health outcomes. Attention to promoting employee health may benefit organizations in multiple, synergistic ways.

Determination of boiling point of petrochemicals by gas chromatography-mass spectrometry and multivariate regression analysis of structural activity relationship.

PubMed

Fakayode, Sayo O; Mitchell, Breanna S; Pollard, David A

2014-08-01

Accurate understanding of analyte boiling points (BP) is of critical importance in gas chromatographic (GC) separation and crude oil refinery operation in petrochemical industries. This study reported the first combined use of GC separation and partial-least-square (PLS1) multivariate regression analysis of petrochemical structural activity relationship (SAR) for accurate BP determination of two commercially available (D3710 and MA VHP) calibration gas mix samples. The results of the BP determination using PLS1 multivariate regression were further compared with the results of traditional simulated distillation method of BP determination. The developed PLS1 regression was able to correctly predict analytes BP in D3710 and MA VHP calibration gas mix samples, with a root-mean-square-%-relative-error (RMS%RE) of 6.4%, and 10.8% respectively. In contrast, the overall RMS%RE of 32.9% and 40.4%, respectively obtained for BP determination in D3710 and MA VHP using a traditional simulated distillation method were approximately four times larger than the corresponding RMS%RE of BP prediction using MRA, demonstrating the better predictive ability of MRA. The reported method is rapid, robust, and promising, and can be potentially used routinely for fast analysis, pattern recognition, and analyte BP determination in petrochemical industries. Copyright © 2014 Elsevier B.V. All rights reserved.

Prevalence and characteristics of cancer patients receiving care from single vs. multiple institutions.

PubMed

Clarke, Christina A; Glaser, Sally L; Leung, Rita; Davidson-Allen, Kathleen; Gomez, Scarlett L; Keegan, Theresa H M

2017-02-01

Patients may receive cancer care from multiple institutions. However, at the population level, such patterns of cancer care are poorly described, complicating clinical research. To determine the population-based prevalence and characteristics of patients seen by multiple institutions, we used operations data from a state-mandated cancer registry. 59,672 invasive cancers diagnosed in 1/1/2010-12/31/2011 in the Greater Bay Area of northern California were categorized as having been reported to the cancer registry within 365days of diagnosis by: 1) ≥1 institution within an integrated health system (IHS); 2) IHS institution(s) and ≥1 non-IHS institution (e.g., private hospital); 3) 1 non-IHS institution; or 4) ≥2 non-IHS institutions. Multivariable logistic regression was used to characterize patients reported by multiple vs. single institutions. Overall in this region, 17% of cancers were reported by multiple institutions. Of the 33% reported by an IHS, 8% were also reported by a non-IHS. Of non-IHS patients, 21% were reported by multiple institutions, with 28% for breast and 27% for pancreatic cancer, but 19%% for lung and 18% for prostate cancer. Generally, patients more likely to be seen by multiple institutions were younger or had more severe disease at diagnosis. Population-based data show that one in six newly diagnosed cancer patients received care from multiple institutions, and differed from patients seen only at a single institution. Cancer care data from single institutions may be incomplete and possibly biased. Copyright © 2016. Published by Elsevier Ltd.

Borrowing of strength and study weights in multivariate and network meta-analysis.

PubMed

Jackson, Dan; White, Ian R; Price, Malcolm; Copas, John; Riley, Richard D

2017-12-01

Multivariate and network meta-analysis have the potential for the estimated mean of one effect to borrow strength from the data on other effects of interest. The extent of this borrowing of strength is usually assessed informally. We present new mathematical definitions of 'borrowing of strength'. Our main proposal is based on a decomposition of the score statistic, which we show can be interpreted as comparing the precision of estimates from the multivariate and univariate models. Our definition of borrowing of strength therefore emulates the usual informal assessment. We also derive a method for calculating study weights, which we embed into the same framework as our borrowing of strength statistics, so that percentage study weights can accompany the results from multivariate and network meta-analyses as they do in conventional univariate meta-analyses. Our proposals are illustrated using three meta-analyses involving correlated effects for multiple outcomes, multiple risk factor associations and multiple treatments (network meta-analysis).

Borrowing of strength and study weights in multivariate and network meta-analysis

PubMed Central

Jackson, Dan; White, Ian R; Price, Malcolm; Copas, John; Riley, Richard D

2016-01-01

Multivariate and network meta-analysis have the potential for the estimated mean of one effect to borrow strength from the data on other effects of interest. The extent of this borrowing of strength is usually assessed informally. We present new mathematical definitions of ‘borrowing of strength’. Our main proposal is based on a decomposition of the score statistic, which we show can be interpreted as comparing the precision of estimates from the multivariate and univariate models. Our definition of borrowing of strength therefore emulates the usual informal assessment. We also derive a method for calculating study weights, which we embed into the same framework as our borrowing of strength statistics, so that percentage study weights can accompany the results from multivariate and network meta-analyses as they do in conventional univariate meta-analyses. Our proposals are illustrated using three meta-analyses involving correlated effects for multiple outcomes, multiple risk factor associations and multiple treatments (network meta-analysis). PMID:26546254

Inference for multivariate regression model based on multiply imputed synthetic data generated via posterior predictive sampling

NASA Astrophysics Data System (ADS)

Moura, Ricardo; Sinha, Bimal; Coelho, Carlos A.

2017-06-01

The recent popularity of the use of synthetic data as a Statistical Disclosure Control technique has enabled the development of several methods of generating and analyzing such data, but almost always relying in asymptotic distributions and in consequence being not adequate for small sample datasets. Thus, a likelihood-based exact inference procedure is derived for the matrix of regression coefficients of the multivariate regression model, for multiply imputed synthetic data generated via Posterior Predictive Sampling. Since it is based in exact distributions this procedure may even be used in small sample datasets. Simulation studies compare the results obtained from the proposed exact inferential procedure with the results obtained from an adaptation of Reiters combination rule to multiply imputed synthetic datasets and an application to the 2000 Current Population Survey is discussed.

Extending local canonical correlation analysis to handle general linear contrasts for FMRI data.

PubMed

Jin, Mingwu; Nandy, Rajesh; Curran, Tim; Cordes, Dietmar

2012-01-01

Local canonical correlation analysis (CCA) is a multivariate method that has been proposed to more accurately determine activation patterns in fMRI data. In its conventional formulation, CCA has several drawbacks that limit its usefulness in fMRI. A major drawback is that, unlike the general linear model (GLM), a test of general linear contrasts of the temporal regressors has not been incorporated into the CCA formalism. To overcome this drawback, a novel directional test statistic was derived using the equivalence of multivariate multiple regression (MVMR) and CCA. This extension will allow CCA to be used for inference of general linear contrasts in more complicated fMRI designs without reparameterization of the design matrix and without reestimating the CCA solutions for each particular contrast of interest. With the proper constraints on the spatial coefficients of CCA, this test statistic can yield a more powerful test on the inference of evoked brain regional activations from noisy fMRI data than the conventional t-test in the GLM. The quantitative results from simulated and pseudoreal data and activation maps from fMRI data were used to demonstrate the advantage of this novel test statistic.

Differences in temperament, character and psychopathology among subjects with different patterns of cannabis use.

PubMed

Spalletta, Gianfranco; Bria, Pietro; Caltagirone, Carlo

2007-01-01

Patients who use illicit drugs and suffer from comorbid psychiatric illnesses have worse outcomes than drug users without a dual diagnosis. For this reason we aimed at identifying predictors of cannabis use severity using a multivariate model in which different clinical and socio-demographic variables were included. We administered the Temperament and Character Inventory, SCID-P, SCID-II, the Beck Depression Inventory and the State-Trait Anxiety Inventory. Of the 84 subjects included, 25 were occasional users, 37 were abusers, and 22 were dependent on cannabis. A stepwise multiple regression analysis identified increased self-transcendence scores and state anxiety severity as the only predictors of a increased cannabis use severity (F = 6.635; d.f. = 2, 81; p = 0.0021). In particular, in a further multivariate analysis of variance, the transpersonal identification issue of self-transcendence was associated significantly (F = 4.267; d.f. = 2, 81; p = 0.017) with greater severity of cannabis use. Character dimension of self-transcendence and symptoms of state anxiety should be taken into consideration during the assessment procedure of patients with cannabis use as they may be helpful in the discrimination of cannabis use severity.

Extending Local Canonical Correlation Analysis to Handle General Linear Contrasts for fMRI Data

PubMed Central

Jin, Mingwu; Nandy, Rajesh; Curran, Tim; Cordes, Dietmar

2012-01-01

Local canonical correlation analysis (CCA) is a multivariate method that has been proposed to more accurately determine activation patterns in fMRI data. In its conventional formulation, CCA has several drawbacks that limit its usefulness in fMRI. A major drawback is that, unlike the general linear model (GLM), a test of general linear contrasts of the temporal regressors has not been incorporated into the CCA formalism. To overcome this drawback, a novel directional test statistic was derived using the equivalence of multivariate multiple regression (MVMR) and CCA. This extension will allow CCA to be used for inference of general linear contrasts in more complicated fMRI designs without reparameterization of the design matrix and without reestimating the CCA solutions for each particular contrast of interest. With the proper constraints on the spatial coefficients of CCA, this test statistic can yield a more powerful test on the inference of evoked brain regional activations from noisy fMRI data than the conventional t-test in the GLM. The quantitative results from simulated and pseudoreal data and activation maps from fMRI data were used to demonstrate the advantage of this novel test statistic. PMID:22461786

Chronic osteomyelitis correlates with increased risk of acute pancreatitis in a case-control study in Taiwan.

PubMed

Lai, Shih-Wei; Lai, Hsueh-Chou; Lin, Cheng-Li; Liao, Kuan-Fu; Tseng, Chun-Hung

2015-07-01

The objective of this study was to examine the relationship between chronic osteomyelitis and acute pancreatitis in Taiwan. This was a population-based case-control study utilizing the database of the Taiwan National Health Insurance Program. We identified 7678 cases aged 20-84 with newly diagnosed acute pancreatitis during the period of 1998 to 2011. From the same database, 30,712 subjects without diagnosis of acute pancreatitis were selected as controls. The cases and controls were matched with sex, age and index year of diagnosing acute pancreatitis. The odds ratio with 95% confidence interval of acute pancreatitis associated with chronic osteomyelitis was examined by the multivariable unconditional logistic regression analysis. After adjustment for multiple confounders, the multivariable analysis showed that the adjusted odds ratio of acute pancreatitis was 1.93 for subjects with chronic osteomyelitis (95% confidence interval 1.01, 3.69), when compared with subjects without chronic osteomyelitis. Chronic osteomyelitis correlates with increased risk of acute pancreatitis. Patients with chronic osteomyelitis should be carefully monitored about the risk of acute pancreatitis. Copyright © 2015 European Federation of Internal Medicine. Published by Elsevier B.V. All rights reserved.

Assessment of changes of vector borne diseases with wetland characteristics using multivariate analysis.

PubMed

Sheela, A M; Sarun, S; Justus, J; Vineetha, P; Sheeja, R V

2015-04-01

Vector borne diseases are a threat to human health. Little attention has been paid to the prevention of these diseases. We attempted to identify the significant wetland characteristics associated with the spread of chikungunya, dengue fever and malaria in Kerala, a tropical region of South West India using multivariate analyses (hierarchical cluster analysis, factor analysis and multiple regression). High/medium turbid coastal lagoons and inland water-logged wetlands with aquatic vegetation have significant effect on the incidence of chikungunya while dengue influenced by high turbid coastal beaches and malaria by medium turbid coastal beaches. The high turbidity in water is due to the urban waste discharge namely sewage, sullage and garbage from the densely populated cities and towns. The large extent of wetland is low land area favours the occurrence of vector borne diseases. Hence the provision of pollution control measures at source including soil erosion control measures is vital. The identification of vulnerable zones favouring the vector borne diseases will help the authorities to control pollution especially from urban areas and prevent these vector borne diseases. Future research should cover land use cover changes, climatic factors, seasonal variations in weather and pollution factors favouring the occurrence of vector borne diseases.

Variation of Water Quality Parameters with Siltation Depth for River Ichamati Along International Border with Bangladesh Using Multivariate Statistical Techniques

NASA Astrophysics Data System (ADS)

Roy, P. K.; Pal, S.; Banerjee, G.; Biswas Roy, M.; Ray, D.; Majumder, A.

2014-12-01

River is considered as one of the main sources of freshwater all over the world. Hence analysis and maintenance of this water resource is globally considered a matter of major concern. This paper deals with the assessment of surface water quality of the Ichamati river using multivariate statistical techniques. Eight distinct surface water quality observation stations were located and samples were collected. For the samples collected statistical techniques were applied to the physico-chemical parameters and depth of siltation. In this paper cluster analysis is done to determine the relations between surface water quality and siltation depth of river Ichamati. Multiple regressions and mathematical equation modeling have been done to characterize surface water quality of Ichamati river on the basis of physico-chemical parameters. It was found that surface water quality of the downstream river was different from the water quality of the upstream. The analysis of the water quality parameters of the Ichamati river clearly indicate high pollution load on the river water which can be accounted to agricultural discharge, tidal effect and soil erosion. The results further reveal that with the increase in depth of siltation, water quality degraded.

The Contribution of Missed Clinic Visits to Disparities in HIV Viral Load Outcomes

PubMed Central

Westfall, Andrew O.; Gardner, Lytt I.; Giordano, Thomas P.; Wilson, Tracey E.; Drainoni, Mari-Lynn; Keruly, Jeanne C.; Rodriguez, Allan E.; Malitz, Faye; Batey, D. Scott; Mugavero, Michael J.

2015-01-01

Objectives. We explored the contribution of missed primary HIV care visits (“no-show”) to observed disparities in virological failure (VF) among Black persons and persons with injection drug use (IDU) history. Methods. We used patient-level data from 6 academic clinics, before the Centers for Disease Control and Prevention and Health Resources and Services Administration Retention in Care intervention. We employed staged multivariable logistic regression and multivariable models stratified by no-show visit frequency to evaluate the association of sociodemographic factors with VF. We used multiple imputations to assign missing viral load values. Results. Among 10 053 patients (mean age = 46 years; 35% female; 64% Black; 15% with IDU history), 31% experienced VF. Although Black patients and patients with IDU history were significantly more likely to experience VF in initial analyses, race and IDU parameter estimates were attenuated after sequential addition of no-show frequency. In stratified models, race and IDU were not statistically significantly associated with VF at any no-show level. Conclusions. Because missed clinic visits contributed to observed differences in viral load outcomes among Black and IDU patients, achieving an improved understanding of differential visit attendance is imperative to reducing disparities in HIV. PMID:26270301

Multivariate random-parameters zero-inflated negative binomial regression model: an application to estimate crash frequencies at intersections.

PubMed

Dong, Chunjiao; Clarke, David B; Yan, Xuedong; Khattak, Asad; Huang, Baoshan

2014-09-01

Crash data are collected through police reports and integrated with road inventory data for further analysis. Integrated police reports and inventory data yield correlated multivariate data for roadway entities (e.g., segments or intersections). Analysis of such data reveals important relationships that can help focus on high-risk situations and coming up with safety countermeasures. To understand relationships between crash frequencies and associated variables, while taking full advantage of the available data, multivariate random-parameters models are appropriate since they can simultaneously consider the correlation among the specific crash types and account for unobserved heterogeneity. However, a key issue that arises with correlated multivariate data is the number of crash-free samples increases, as crash counts have many categories. In this paper, we describe a multivariate random-parameters zero-inflated negative binomial (MRZINB) regression model for jointly modeling crash counts. The full Bayesian method is employed to estimate the model parameters. Crash frequencies at urban signalized intersections in Tennessee are analyzed. The paper investigates the performance of MZINB and MRZINB regression models in establishing the relationship between crash frequencies, pavement conditions, traffic factors, and geometric design features of roadway intersections. Compared to the MZINB model, the MRZINB model identifies additional statistically significant factors and provides better goodness of fit in developing the relationships. The empirical results show that MRZINB model possesses most of the desirable statistical properties in terms of its ability to accommodate unobserved heterogeneity and excess zero counts in correlated data. Notably, in the random-parameters MZINB model, the estimated parameters vary significantly across intersections for different crash types. Copyright © 2014 Elsevier Ltd. All rights reserved.

Identification of Differential Item Functioning in Multiple-Group Settings: A Multivariate Outlier Detection Approach

ERIC Educational Resources Information Center

Magis, David; De Boeck, Paul

2011-01-01

We focus on the identification of differential item functioning (DIF) when more than two groups of examinees are considered. We propose to consider items as elements of a multivariate space, where DIF items are outlying elements. Following this approach, the situation of multiple groups is a quite natural case. A robust statistics technique is…

A Multivariate Model for the Meta-Analysis of Study Level Survival Data at Multiple Times

ERIC Educational Resources Information Center

Jackson, Dan; Rollins, Katie; Coughlin, Patrick

2014-01-01

Motivated by our meta-analytic dataset involving survival rates after treatment for critical leg ischemia, we develop and apply a new multivariate model for the meta-analysis of study level survival data at multiple times. Our data set involves 50 studies that provide mortality rates at up to seven time points, which we model simultaneously, and…

Utility of Intermediate-Delay Washout CT Images for Differentiation of Malignant and Benign Adrenal Lesions: A Multivariate Analysis.

PubMed

Ng, Chaan S; Altinmakas, Emre; Wei, Wei; Ghosh, Payel; Li, Xiao; Grubbs, Elizabeth G; Perrier, Nancy D; Lee, Jeffrey E; Prieto, Victor G; Hobbs, Brian P

2018-06-27

The objective of this study was to identify features that impact the diagnostic performance of intermediate-delay washout CT for distinguishing malignant from benign adrenal lesions. This retrospective study evaluated 127 pathologically proven adrenal lesions (82 malignant, 45 benign) in 126 patients who had undergone portal venous phase and intermediate-delay washout CT (1-3 minutes after portal venous phase) with or without unenhanced images. Unenhanced images were available for 103 lesions. Quantitatively, lesion CT attenuation on unenhanced (UA) and delayed (DL) images, absolute and relative percentage of enhancement washout (APEW and RPEW, respectively), descriptive CT features (lesion size, margin characteristics, heterogeneity or homogeneity, fat, calcification), patient demographics, and medical history were evaluated for association with lesion status using multiple logistic regression with stepwise model selection. Area under the ROC curve (A z ) was calculated from both univariate and multivariate analyses. The predictive diagnostic performance of multivariate evaluations was ascertained through cross-validation. A z for DL, APEW, RPEW, and UA was 0.751, 0.795, 0.829, and 0.839, respectively. Multivariate analyses yielded the following significant CT quantitative features and associated A z when combined: RPEW and DL (A z = 0.861) when unenhanced images were not available and APEW and UA (A z = 0.889) when unenhanced images were available. Patient demographics and presence of a prior malignancy were additional significant factors, increasing A z to 0.903 and 0.927, respectively. The combined predictive classifier, without and with UA available, yielded 85.7% and 87.3% accuracies with cross-validation, respectively. When appropriately combined with other CT features, washout derived from intermediate-delay CT with or without additional clinical data has potential utility in differentiating malignant from benign adrenal lesions.

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.

Learning investment indicators through data extension

NASA Astrophysics Data System (ADS)

Dvořák, Marek

2017-07-01

Stock prices in the form of time series were analysed using single and multivariate statistical methods. After simple data preprocessing in the form of logarithmic differences, we augmented this single variate time series to a multivariate representation. This method makes use of sliding windows to calculate several dozen of new variables using simple statistic tools like first and second moments as well as more complicated statistic, like auto-regression coefficients and residual analysis, followed by an optional quadratic transformation that was further used for data extension. These were used as a explanatory variables in a regularized logistic LASSO regression which tried to estimate Buy-Sell Index (BSI) from real stock market data.

Fast Detection of Copper Content in Rice by Laser-Induced Breakdown Spectroscopy with Uni- and Multivariate Analysis.

PubMed

Liu, Fei; Ye, Lanhan; Peng, Jiyu; Song, Kunlin; Shen, Tingting; Zhang, Chu; He, Yong

2018-02-27

Fast detection of heavy metals is very important for ensuring the quality and safety of crops. Laser-induced breakdown spectroscopy (LIBS), coupled with uni- and multivariate analysis, was applied for quantitative analysis of copper in three kinds of rice (Jiangsu rice, regular rice, and Simiao rice). For univariate analysis, three pre-processing methods were applied to reduce fluctuations, including background normalization, the internal standard method, and the standard normal variate (SNV). Linear regression models showed a strong correlation between spectral intensity and Cu content, with an R 2 more than 0.97. The limit of detection (LOD) was around 5 ppm, lower than the tolerance limit of copper in foods. For multivariate analysis, partial least squares regression (PLSR) showed its advantage in extracting effective information for prediction, and its sensitivity reached 1.95 ppm, while support vector machine regression (SVMR) performed better in both calibration and prediction sets, where R c 2 and R p 2 reached 0.9979 and 0.9879, respectively. This study showed that LIBS could be considered as a constructive tool for the quantification of copper contamination in rice.

Fast Detection of Copper Content in Rice by Laser-Induced Breakdown Spectroscopy with Uni- and Multivariate Analysis

PubMed Central

Ye, Lanhan; Song, Kunlin; Shen, Tingting

2018-01-01

Fast detection of heavy metals is very important for ensuring the quality and safety of crops. Laser-induced breakdown spectroscopy (LIBS), coupled with uni- and multivariate analysis, was applied for quantitative analysis of copper in three kinds of rice (Jiangsu rice, regular rice, and Simiao rice). For univariate analysis, three pre-processing methods were applied to reduce fluctuations, including background normalization, the internal standard method, and the standard normal variate (SNV). Linear regression models showed a strong correlation between spectral intensity and Cu content, with an R2 more than 0.97. The limit of detection (LOD) was around 5 ppm, lower than the tolerance limit of copper in foods. For multivariate analysis, partial least squares regression (PLSR) showed its advantage in extracting effective information for prediction, and its sensitivity reached 1.95 ppm, while support vector machine regression (SVMR) performed better in both calibration and prediction sets, where Rc2 and Rp2 reached 0.9979 and 0.9879, respectively. This study showed that LIBS could be considered as a constructive tool for the quantification of copper contamination in rice. PMID:29495445

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

PubMed Central

2011-01-01

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

The relationship between depressive symptoms among female workers and job stress and sleep quality.

PubMed

Cho, Ho-Sung; Kim, Young-Wook; Park, Hyoung-Wook; Lee, Kang-Ho; Jeong, Baek-Geun; Kang, Yune-Sik; Park, Ki-Soo

2013-07-22

Recently, workers' mental health has become important focus in the field of occupational health management. Depression is a psychiatric illness with a high prevalence. The association between job stress and depressive symptoms has been demonstrated in many studies. Recently, studies about the association between sleep quality and depressive symptoms have been reported, but there has been no large-scaled study in Korean female workers. Therefore, this study was designed to investigate the relationship between job stress and sleep quality, and depressive symptoms in female workers. From Mar 2011 to Aug 2011, 4,833 female workers in the manufacturing, finance, and service fields at 16 workplaces in Yeungnam province participated in this study, conducted in combination with a worksite-based health checkup initiated by the National Health Insurance Service (NHIS). In this study, a questionnaire survey was carried out using the Korean Occupational Stress Scale-Short Form(KOSS-SF), Pittsburgh Sleep Quality Index(PSQI) and Center for Epidemiological Studies-Depression Scale(CES-D). The collected data was entered in the system and analyzed using the PASW (version 18.0) program. A correlation analysis, cross analysis, multivariate logistic regression analysis, and hierarchical multiple regression analysis were conducted. Among the 4,883 subjects, 978 subjects (20.0%) were in the depression group. Job stress(OR=3.58, 95% CI=3.06-4.21) and sleep quality(OR=3.81, 95% CI=3.18-4.56) were strongly associated with depressive symptoms. Hierarchical multiple regression analysis revealed that job stress displayed explanatory powers of 15.6% on depression while sleep quality displayed explanatory powers of 16.2%, showing that job stress and sleep quality had a closer relationship with depressive symptoms, compared to the other factors. The multivariate logistic regression analysis yielded odds ratios between the 7 subscales of job stress and depressive symptoms in the range of 1.30-2.72 and the odds ratio for the lack of reward was the highest(OR=2.72, 95% CI=2.32-3.19). In the partial correlation analysis between each of the 7 subscales of sleep quality (PSQI) and depressive symptoms, the correlation coefficient of subjective sleep quality and daytime dysfunction were 0.352 and 0.362, respectively. This study showed that the depressive symptoms of female workers are closely related to their job stress and sleep quality. In particular, the lack of reward and subjective sleep factors are the greatest contributors to depression. In the future, a large-scale study should be performed to augment the current study and to reflect all age groups in a balanced manner. The findings on job stress, sleep, and depression can be utilized as source data to establish standards for mental health management of the ever increasing numbers of female members of the workplace.

Kernel canonical-correlation Granger causality for multiple time series

NASA Astrophysics Data System (ADS)

Wu, Guorong; Duan, Xujun; Liao, Wei; Gao, Qing; Chen, Huafu

2011-04-01

Canonical-correlation analysis as a multivariate statistical technique has been applied to multivariate Granger causality analysis to infer information flow in complex systems. It shows unique appeal and great superiority over the traditional vector autoregressive method, due to the simplified procedure that detects causal interaction between multiple time series, and the avoidance of potential model estimation problems. However, it is limited to the linear case. Here, we extend the framework of canonical correlation to include the estimation of multivariate nonlinear Granger causality for drawing inference about directed interaction. Its feasibility and effectiveness are verified on simulated data.

Predictors of neonatal outcome in women with severe preeclampsia or eclampsia between 24 and 33 weeks' gestation.

PubMed

Witlin, A G; Saade, G R; Mattar, F; Sibai, B M

2000-03-01

We sought to characterize predictors of neonatal outcome in women with severe preeclampsia or eclampsia who were delivered of their infants preterm. We performed a retrospective analysis of 195 pregnancies delivered between 24 and 33 weeks' gestation because of severe preeclampsia or eclampsia. Multiple logistic regression and univariate chi(2) analysis were performed for the dependent outcome variables of survival and respiratory distress syndrome by use of independent fetal and maternal variables. A P value of <.05 was considered significant. In the multivariate analysis, respiratory distress syndrome was inversely related to gestational age at delivery (P =.0018) and directly related to cesarean delivery (P =.02), whereas survival was directly related to birth weight (P =.00025). There was no correlation in the multivariate analysis between respiratory distress syndrome or survival and corticosteroid use, composite neonatal morbidity, mean arterial pressure, eclampsia, or abruptio placentae. In the univariate analysis respiratory distress syndrome was associated with cesarean delivery (odds ratio, 7.19; 95% confidence interval, 2. 91-18.32). The incidence of intrauterine growth restriction increased as gestational age advanced. Furthermore, intrauterine growth restriction decreased survival in both the multivariate (P =. 038; odds ratio, 13.2; 95% confidence interval, 1.16-151.8) and univariate (P =.001; odds ratio, 5.88; 95% confidence interval, 1. 81-19.26) analyses. The presence of intrauterine growth restriction adversely affected survival independently of other variables. Presumed intrauterine stress, as reflected by the severity of maternal disease, did not improve neonatal outcome.

Circulating miRNAs in Pediatric Pulmonary Hypertension Show Promise as Biomarkers of Vascular Function

PubMed Central

Sucharov, Carmen C.; Truong, Uyen; Dunning, Jamie; Ivy, Dunbar; Miyamoto, Shelley; Shandas, Robin

2017-01-01

Background/Objectives The objective of this study was to evaluate the utility of circulating miRNAs as biomarkers of vascular function in pediatric pulmonary hypertension. Method Fourteen pediatric pulmonary arterial hypertension patients underwent simultaneous right heart catheterization (RHC) and blood biochemical analysis. Univariate and stepwise multivariate linear regression was used to identify and correlate measures of reactive and resistive afterload with circulating miRNA levels. Furthermore, circulating miRNA candidates that classified patients according to a 20% decrease in resistive afterload in response to oxygen (O2) or inhaled nitric oxide (iNO) were identified using receiver-operating curves. Results Thirty-two circulating miRNAs correlated with the pulmonary vascular resistance index (PVRi), pulmonary arterial distensibility, and PVRi decrease in response to O2 and/or iNO. Multivariate models, combining the predictive capability of multiple promising miRNA candidates, revealed a good correlation with resistive (r = 0.97, P2−tailed < 0.0001) and reactive (r = 0.86, P2−tailed < 0.005) afterloads. Bland-Altman plots showed that 95% of the differences between multivariate models and RHC would fall within 0.13 (mmHg−min/L)m2 and 0.0085/mmHg for resistive and reactive afterloads, respectively. Circulating miR-663 proved to be a good classifier for vascular responsiveness to acute O2 and iNO challenges. Conclusion This study suggests that circulating miRNAs may be biomarkers to phenotype vascular function in pediatric PAH. PMID:28819545

Usual Dietary Intakes: SAS Macros for Fitting Multivariate Measurement Error Models & Estimating Multivariate Usual Intake Distributions

Cancer.gov

The following SAS macros can be used to create a multivariate usual intake distribution for multiple dietary components that are consumed nearly every day or episodically. A SAS macro for performing balanced repeated replication (BRR) variance estimation is also included.

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

NASA Astrophysics Data System (ADS)

Olivas Saunders, Rolando

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

Multivariate regression model for partitioning tree volume of white oak into round-product classes

Treesearch

Daniel A. Yaussy; David L. Sonderman

1984-01-01

Describes the development of multivariate equations that predict the expected cubic volume of four round-product classes from independent variables composed of individual tree-quality characteristics. Although the model has limited application at this time, it does demonstrate the feasibility of partitioning total tree cubic volume into round-product classes based on...

Multivariate decoding of brain images using ordinal regression.

PubMed

Doyle, O M; Ashburner, J; Zelaya, F O; Williams, S C R; Mehta, M A; Marquand, A F

2013-11-01

Neuroimaging data are increasingly being used to predict potential outcomes or groupings, such as clinical severity, drug dose response, and transitional illness states. In these examples, the variable (target) we want to predict is ordinal in nature. Conventional classification schemes assume that the targets are nominal and hence ignore their ranked nature, whereas parametric and/or non-parametric regression models enforce a metric notion of distance between classes. Here, we propose a novel, alternative multivariate approach that overcomes these limitations - whole brain probabilistic ordinal regression using a Gaussian process framework. We applied this technique to two data sets of pharmacological neuroimaging data from healthy volunteers. The first study was designed to investigate the effect of ketamine on brain activity and its subsequent modulation with two compounds - lamotrigine and risperidone. The second study investigates the effect of scopolamine on cerebral blood flow and its modulation using donepezil. We compared ordinal regression to multi-class classification schemes and metric regression. Considering the modulation of ketamine with lamotrigine, we found that ordinal regression significantly outperformed multi-class classification and metric regression in terms of accuracy and mean absolute error. However, for risperidone ordinal regression significantly outperformed metric regression but performed similarly to multi-class classification both in terms of accuracy and mean absolute error. For the scopolamine data set, ordinal regression was found to outperform both multi-class and metric regression techniques considering the regional cerebral blood flow in the anterior cingulate cortex. Ordinal regression was thus the only method that performed well in all cases. Our results indicate the potential of an ordinal regression approach for neuroimaging data while providing a fully probabilistic framework with elegant approaches for model selection. Copyright © 2013. Published by Elsevier Inc.

Health-related quality of life in multiple sclerosis: role of cognitive appraisals of self, illness and treatment.

PubMed

Wilski, Maciej; Tasiemski, Tomasz

2016-07-01

Health-related quality of life (HRQoL) is considered an important measure of treatment and rehabilitation outcomes in multiple sclerosis (MS) patients. In this study, we used multivariate regression analysis to examine the role of cognitive appraisals, adjusted for clinical, socioeconomic and demographic variables, as correlates of HRQoL in MS. The cross-sectional study included 257 MS patients, who completed Multiple Sclerosis Impact Scale, Generalized Self-Efficacy Scale, Rosenberg Self-Esteem Scale, Brief Illness Perception Questionnaire, Treatment Beliefs Scale, Actually Received Support Scale (a part of Berlin Social Support Scale) and Socioeconomic Resources Scale. Demographic and clinical characteristics of the participants were collected with a self-report survey. Correlation and regression analyses were conducted to determine associations between the variables. Five variables, illness identity (β = 0.29, p ≤ 0.001), self-esteem (β = -0.22, p ≤ 0.001), general self-efficacy (β = -0.21, p ≤ 0.001), disability subgroup "EDSS" (β = 0.14, p = 0.006) and age (β = 0.12, p = 0.012), were significant correlates of HRQoL in MS. These variables explained 46 % of variance in the dependent variable. Moreover, we identified correlates of physical and psychological dimensions of HRQoL. Cognitive appraisals, such as general self-efficacy, self-esteem and illness perception, are more salient correlates of HRQoL than social support, socioeconomic resources and clinical characteristics, such as type and duration of MS. Therefore, interventions aimed at cognitive appraisals may also improve HRQoL of MS patients.

Patterns of Drug Use, Risky Behavior, and Health Status Among Persons Who Inject Drugs Living in San Diego, California: A Latent Class Analysis

PubMed Central

Roth, Alexis M.; Armenta, Richard A.; Wagner, Karla D.; Roesch, Scott C.; Bluthenthal, Ricky N.; Cuevas-Mota, Jazmine; Garfein, Richard S.

2015-01-01

Background Among persons who inject drugs (PWID), polydrug use (the practice of mixing multiple drugs/alcohol sequentially or simultaneously) increases risk for HIV transmission and unintentional overdose deaths. Research has shown local drug markets influence drug use practices. However, little is known about the impact of drug mixing in markets dominated by black tar heroin and methamphetamine, such as the western United States. Methods Data were collected through an ongoing longitudinal study examining drug use, risk behavior, and health status among PWID. Latent class analysis (LCA) was used to identify patterns of substance use (heroin, methamphetamine, prescription drugs, alcohol, and marijuana) via multiple administration routes (injecting, smoking, and swallowing). Logistic regression was used to identify behaviors and health indicators associated with drug use class. Results The sample included 511 mostly white (51.5%) males (73.8%), with mean age of 43.5 years. Two distinct classes of drug users predominated: methamphetamine by multiple routes (51%) and heroin by injection (49%). In multivariable logistic regression, class membership was associated with age, race, and housing status. PWID who were HIV-seropositive and reported prior sexually transmitted infections had increased odds of belonging to the methamphetamine class. Those who were HCV positive and reported previous opioid overdose had an increased odds of being in the primarily heroin injection class (all P-values < .05). Conclusion Risk behaviors and health outcomes differed between PWID who primarily inject heroin vs. those who use methamphetamine. The findings suggest that in a region where PWID mainly use black tar heroin or methamphetamine, interventions tailored to sub-populations of PWID could improve effectiveness. PMID:25313832

Pregnancy interruption after second trimester diagnosis of fetal structural anomalies: the New Jersey Fetal Abnormalities Registry.

PubMed

Rauch, Eden R; Smulian, John C; DePrince, Kristin; Ananth, Cande V; Marcella, Stephen W

2005-10-01

The purpose of this study was to identify factors that predict a decision to interrupt a pregnancy in which there are fetal anomalies in the second trimester. The New Jersey Fetal Abnormalities Registry prospectively recruits and collects information on pregnancies (> or = 15 weeks of gestation) from New Jersey residents in whom a fetal structural anomaly has been suspected by maternal-fetal medicine specialists. Enrolled pregnancies that have major fetal structural abnormalities identified from 15 to 23 weeks of gestation were included. Outcomes were classified as either elective interruption or a natural pregnancy course, which might include a spontaneous fetal death or live birth. Predictors of elective interruption of pregnancy were examined with univariable and multivariable logistic regression analyses. Of the 97 cases, 33% of the women (n = 32) interrupted the pregnancy. Significant variables in the regression model that were associated with a decision to interrupt a pregnancy were earlier identification of fetal anomalies (19.0 +/- 2 weeks of gestation vs 20.5 +/- 2 weeks of gestation; P = .003), the presence of multiple anomalies (78% [25/32] vs 52% [33/63]; P = .01], and a presumption of lethality (56% [18/32] vs 14% [9/65]; P = .0001). These variables corresponded to an odds ratio for pregnancy interruption of 4.2 (95% CI, 1.0, 17.0) for multiple anomalies, 0.8 (95% CI, 0.7, 1.0) for each week of advancing gestational age, and 36.1 (95% CI, 2.9, 450.7) for presumed lethal abnormalities. Early diagnosis, the identification of multiple abnormalities, and an assessment of likely lethality of fetal anomalies are important factors for the optimization of parental autonomy in deciding pregnancy management.

SCOPA and META-SCOPA: software for the analysis and aggregation of genome-wide association studies of multiple correlated phenotypes.

PubMed

Mägi, Reedik; Suleimanov, Yury V; Clarke, Geraldine M; Kaakinen, Marika; Fischer, Krista; Prokopenko, Inga; Morris, Andrew P

2017-01-11

Genome-wide association studies (GWAS) of single nucleotide polymorphisms (SNPs) have been successful in identifying loci contributing genetic effects to a wide range of complex human diseases and quantitative traits. The traditional approach to GWAS analysis is to consider each phenotype separately, despite the fact that many diseases and quantitative traits are correlated with each other, and often measured in the same sample of individuals. Multivariate analyses of correlated phenotypes have been demonstrated, by simulation, to increase power to detect association with SNPs, and thus may enable improved detection of novel loci contributing to diseases and quantitative traits. We have developed the SCOPA software to enable GWAS analysis of multiple correlated phenotypes. The software implements "reverse regression" methodology, which treats the genotype of an individual at a SNP as the outcome and the phenotypes as predictors in a general linear model. SCOPA can be applied to quantitative traits and categorical phenotypes, and can accommodate imputed genotypes under a dosage model. The accompanying META-SCOPA software enables meta-analysis of association summary statistics from SCOPA across GWAS. Application of SCOPA to two GWAS of high-and low-density lipoprotein cholesterol, triglycerides and body mass index, and subsequent meta-analysis with META-SCOPA, highlighted stronger association signals than univariate phenotype analysis at established lipid and obesity loci. The META-SCOPA meta-analysis also revealed a novel signal of association at genome-wide significance for triglycerides mapping to GPC5 (lead SNP rs71427535, p = 1.1x10 -8 ), which has not been reported in previous large-scale GWAS of lipid traits. The SCOPA and META-SCOPA software enable discovery and dissection of multiple phenotype association signals through implementation of a powerful reverse regression approach.

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

Time Series Model Identification by Estimating Information.

DTIC Science & Technology

1982-11-01

principle, Applications of Statistics, P. R. Krishnaiah , ed., North-Holland: Amsterdam, 27-41. Anderson, T. W. (1971). The Statistical Analysis of Time Series...E. (1969). Multiple Time Series Modeling, Multivariate Analysis II, edited by P. Krishnaiah , Academic Press: New York, 389-409. Parzen, E. (1981...Newton, H. J. (1980). Multiple Time Series Modeling, II Multivariate Analysis - V, edited by P. Krishnaiah , North Holland: Amsterdam, 181-197. Shibata, R

The effect of tobacco consumption and body mass index on complications and hospital stay after inguinal hernia surgery.

PubMed

Lindström, D; Sadr Azodi, O; Bellocco, R; Wladis, A; Linder, S; Adami, J

2007-04-01

The extent to which lifestyle factors such as tobacco consumption and obesity affect the outcome after inguinal hernia surgery has been poorly studied. This study was undertaken to assess the effect of smoking, smokeless tobacco consumption and obesity on postoperative complications after inguinal hernia surgery. The second aim was to evaluate the effect of tobacco consumption and obesity on the length of hospital stay. A cohort of 12,697 Swedish construction workers with prospectively collected exposure data on tobacco consumption and body mass index (BMI) from 1968 onward were linked to the Swedish inpatient register. Information on inguinal hernia procedures was collected from the inpatient register. Any postoperative complication occurring within 30 days was registered. In addition to this, the length of hospitalization was calculated. The risk of postoperative complications due to tobacco exposure and BMI was estimated using a multiple logistic regression model and the length of hospital stay was estimated in a multiple linear regression model. After adjusting for the other covariates in the multivariate analysis, current smokers had a 34% (OR 1.34, 95% CI 1.04, 1.72) increased risk of postoperative complications compared to never smokers. Use of "Swedish oral moist snuff" (snus) and pack-years of tobacco smoking were not found to be significantly associated with an increased risk of postoperative complications. BMI was found to be significantly associated with an increased risk of postoperative complications (P = 0.04). This effect was mediated by the underweighted group (OR 2.94; 95% CI 1.15, 7.51). In a multivariable model, increased BMI was also found to be significantly associated with an increased mean length of hospital stay (P < 0.001). There was no statistically significant association between smoking or using snus, and the mean length of hospitalization after adjusting for the other covariates in the model. Smoking increases the risk of postoperative complications even in minor surgery such as inguinal hernia procedures. Obesity increases hospitalization after inguinal hernia surgery. The Swedish version of oral moist tobacco, snus, does not seem to affect the complication rate after hernia surgery at all.

Bayesian inference for multivariate meta-analysis Box-Cox transformation models for individual patient data with applications to evaluation of cholesterol lowering drugs

PubMed Central

Kim, Sungduk; Chen, Ming-Hui; Ibrahim, Joseph G.; Shah, Arvind K.; Lin, Jianxin

2013-01-01

In this paper, we propose a class of Box-Cox transformation regression models with multidimensional random effects for analyzing multivariate responses for individual patient data (IPD) in meta-analysis. Our modeling formulation uses a multivariate normal response meta-analysis model with multivariate random effects, in which each response is allowed to have its own Box-Cox transformation. Prior distributions are specified for the Box-Cox transformation parameters as well as the regression coefficients in this complex model, and the Deviance Information Criterion (DIC) is used to select the best transformation model. Since the model is quite complex, a novel Monte Carlo Markov chain (MCMC) sampling scheme is developed to sample from the joint posterior of the parameters. This model is motivated by a very rich dataset comprising 26 clinical trials involving cholesterol lowering drugs where the goal is to jointly model the three dimensional response consisting of Low Density Lipoprotein Cholesterol (LDL-C), High Density Lipoprotein Cholesterol (HDL-C), and Triglycerides (TG) (LDL-C, HDL-C, TG). Since the joint distribution of (LDL-C, HDL-C, TG) is not multivariate normal and in fact quite skewed, a Box-Cox transformation is needed to achieve normality. In the clinical literature, these three variables are usually analyzed univariately: however, a multivariate approach would be more appropriate since these variables are correlated with each other. A detailed analysis of these data is carried out using the proposed methodology. PMID:23580436

Bayesian inference for multivariate meta-analysis Box-Cox transformation models for individual patient data with applications to evaluation of cholesterol-lowering drugs.

PubMed

Kim, Sungduk; Chen, Ming-Hui; Ibrahim, Joseph G; Shah, Arvind K; Lin, Jianxin

2013-10-15

In this paper, we propose a class of Box-Cox transformation regression models with multidimensional random effects for analyzing multivariate responses for individual patient data in meta-analysis. Our modeling formulation uses a multivariate normal response meta-analysis model with multivariate random effects, in which each response is allowed to have its own Box-Cox transformation. Prior distributions are specified for the Box-Cox transformation parameters as well as the regression coefficients in this complex model, and the deviance information criterion is used to select the best transformation model. Because the model is quite complex, we develop a novel Monte Carlo Markov chain sampling scheme to sample from the joint posterior of the parameters. This model is motivated by a very rich dataset comprising 26 clinical trials involving cholesterol-lowering drugs where the goal is to jointly model the three-dimensional response consisting of low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C), and triglycerides (TG) (LDL-C, HDL-C, TG). Because the joint distribution of (LDL-C, HDL-C, TG) is not multivariate normal and in fact quite skewed, a Box-Cox transformation is needed to achieve normality. In the clinical literature, these three variables are usually analyzed univariately; however, a multivariate approach would be more appropriate because these variables are correlated with each other. We carry out a detailed analysis of these data by using the proposed methodology. Copyright © 2013 John Wiley & Sons, Ltd.

Skeletal height estimation from regression analysis of sternal lengths in a Northwest Indian population of Chandigarh region: a postmortem study.

PubMed

Singh, Jagmahender; Pathak, R K; Chavali, Krishnadutt H

2011-03-20

Skeletal height estimation from regression analysis of eight sternal lengths in the subjects of Chandigarh zone of Northwest India is the topic of discussion in this study. Analysis of eight sternal lengths (length of manubrium, length of mesosternum, combined length of manubrium and mesosternum, total sternal length and first four intercostals lengths of mesosternum) measured from 252 male and 91 female sternums obtained at postmortems revealed that mean cadaver stature and sternal lengths were more in North Indians and males than the South Indians and females. Except intercostal lengths, all the sternal lengths were positively correlated with stature of the deceased in both sexes (P < 0.001). The multiple regression analysis of sternal lengths was found more useful than the linear regression for stature estimation. Using multivariate regression analysis, the combined length of manubrium and mesosternum in both sexes and the length of manubrium along with 2nd and 3rd intercostal lengths of mesosternum in males were selected as best estimators of stature. Nonetheless, the stature of males can be predicted with SEE of 6.66 (R(2) = 0.16, r = 0.318) from combination of MBL+BL_3+LM+BL_2, and in females from MBL only, it can be estimated with SEE of 6.65 (R(2) = 0.10, r = 0.318), whereas from the multiple regression analysis of pooled data, stature can be known with SEE of 6.97 (R(2) = 0.387, r = 575) from the combination of MBL+LM+BL_2+TSL+BL_3. The R(2) and F-ratio were found to be statistically significant for almost all the variables in both the sexes, except 4th intercostal length in males and 2nd to 4th intercostal lengths in females. The 'major' sternal lengths were more useful than the 'minor' ones for stature estimation The universal regression analysis used by Kanchan et al. [39] when applied to sternal lengths, gave satisfactory estimates of stature for males only but female stature was comparatively better estimated from simple linear regressions. But they are not proposed for the subjects of known sex, as they underestimate the male and overestimate female stature. However, intercostal lengths were found to be the poor estimators of stature (P < 0.05). And also sternal lengths exhibit weaker correlation coefficients and higher standard errors of estimate. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Does the quality of parent-child connectedness matter for adolescents' sexual behaviors in Nairobi informal settlements?

PubMed

Sidze, Estelle M; Elungata'a, Patricia; Maina, Beatrice W; Mutua, Michael M

2015-04-01

This study investigated the associations between parent-child connectedness and sexual behaviors among adolescents living in informal settlements in Nairobi, Kenya, a vulnerable group with respect to reproductive health outcomes. The study was based on data from the Transition to Adulthood project, a study designed to follow adolescents aged 12-22 for 3 years in the informal settlements of Korogocho and Viwandani. Direct face-to-face questions were asked to adolescents about parenting variables and sexual behaviors. This study used a subsample of 689 sexually experienced 12-22-years-olds at Wave 2. Bivariate analysis compared gender differences for three outcomes-sexual activity in the 12 months prior to the survey and, among those who had had sex in this period, multiple sexual partners and condom use at last sex. Multivariate logistic regressions were used to identify associations between these outcomes and the quality of parent-child connectedness. About 60% of adolescent females and males were sexually active in the 12 months prior to the survey. The multivariate results showed a strong association between the quality of parent-child connectedness and condom use among adolescent males. Living with related or unrelated guardians (versus living with biological parents) was also associated with higher odds of multiple sexual partners and lower odds of condom use at last sex among adolescent females and with higher odds of sexual activity among adolescent males. Sexual and reproductive health programs targeting adolescents living in Nairobi informal settlements would benefit from attention to assisting parents to improve their ability to play the connectedness role.

Advanced multivariate data analysis to determine the root cause of trisulfide bond formation in a novel antibody-peptide fusion.

PubMed

Goldrick, Stephen; Holmes, William; Bond, Nicholas J; Lewis, Gareth; Kuiper, Marcel; Turner, Richard; Farid, Suzanne S

2017-10-01

Product quality heterogeneities, such as a trisulfide bond (TSB) formation, can be influenced by multiple interacting process parameters. Identifying their root cause is a major challenge in biopharmaceutical production. To address this issue, this paper describes the novel application of advanced multivariate data analysis (MVDA) techniques to identify the process parameters influencing TSB formation in a novel recombinant antibody-peptide fusion expressed in mammalian cell culture. The screening dataset was generated with a high-throughput (HT) micro-bioreactor system (Ambr TM 15) using a design of experiments (DoE) approach. The complex dataset was firstly analyzed through the development of a multiple linear regression model focusing solely on the DoE inputs and identified the temperature, pH and initial nutrient feed day as important process parameters influencing this quality attribute. To further scrutinize the dataset, a partial least squares model was subsequently built incorporating both on-line and off-line process parameters and enabled accurate predictions of the TSB concentration at harvest. Process parameters identified by the models to promote and suppress TSB formation were implemented on five 7 L bioreactors and the resultant TSB concentrations were comparable to the model predictions. This study demonstrates the ability of MVDA to enable predictions of the key performance drivers influencing TSB formation that are valid also upon scale-up. Biotechnol. Bioeng. 2017;114: 2222-2234. © 2017 The Authors. Biotechnology and Bioengineering Published by Wiley Periodicals, Inc. © 2017 The Authors. Biotechnology and Bioengineering Published by Wiley Periodicals, Inc.

Modelling the immunological response to a tetravalent dengue vaccine from multiple phase-2 trials in Latin America and South East Asia.

PubMed

Dorigatti, Ilaria; Aguas, Ricardo; Donnelly, Christl A; Guy, Bruno; Coudeville, Laurent; Jackson, Nicholas; Saville, Melanie; Ferguson, Neil M

2015-07-17

The most advanced dengue vaccine candidate is a live-attenuated recombinant vaccine containing the four dengue viruses on the yellow fever vaccine backbone (CYD-TDV) developed by Sanofi Pasteur. Several analyses have been published on the safety and immunogenicity of the CYD-TDV vaccine from single trials but none modelled the heterogeneity observed in the antibody responses elicited by the vaccine. We analyse the immunogenicity data collected in five phase-2 trials of the CYD-TDV vaccine. We provide a descriptive analysis of the aggregated datasets and fit the observed post-vaccination PRNT50 titres against the four dengue (DENV) serotypes using multivariate regression models. We find that the responses to CYD-TDV are principally predicted by the baseline immunological status against DENV, but the trial is also a significant predictor. We find that the CYD-TDV vaccine generates similar titres against all serotypes following the third dose, though DENV4 is immunodominant after the first dose. This study contributes to a better understanding of the immunological responses elicited by CYD-TDV. The recent availability of phase-3 data is a unique opportunity to further investigate the immunogenicity and efficacy of the CYD-TDV vaccine, especially in subjects with different levels of pre-existing immunity against DENV. Modelling multiple immunological outcomes with a single multivariate model offers advantages over traditional approaches, capturing correlations between response variables, and the statistical method adopted in this study can be applied to a variety of infections with interacting strains. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Analysis of Multivariate Experimental Data Using A Simplified Regression Model Search Algorithm

NASA Technical Reports Server (NTRS)

Ulbrich, Norbert M.

2013-01-01

A new regression model search algorithm was developed that may be applied to both general multivariate experimental data sets and wind tunnel strain-gage balance calibration data. The algorithm is a simplified version of a more complex algorithm that was originally developed for the NASA Ames Balance Calibration Laboratory. The new algorithm performs regression model term reduction to prevent overfitting of data. It has the advantage that it needs only about one tenth of the original algorithm's CPU time for the completion of a regression model search. In addition, extensive testing showed that the prediction accuracy of math models obtained from the simplified algorithm is similar to the prediction accuracy of math models obtained from the original algorithm. The simplified algorithm, however, cannot guarantee that search constraints related to a set of statistical quality requirements are always satisfied in the optimized regression model. Therefore, the simplified algorithm is not intended to replace the original algorithm. Instead, it may be used to generate an alternate optimized regression model of experimental data whenever the application of the original search algorithm fails or requires too much CPU time. Data from a machine calibration of NASA's MK40 force balance is used to illustrate the application of the new search algorithm.

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.

Anticholinergic load negatively correlates with recovery of cognitive activities of daily living for geriatric patients after stroke in the convalescent stage.

PubMed

Kose, E; Hirai, T; Seki, T; Hidaka, S; Hamamoto, T

2018-05-16

Anticholinergic drugs are associated with risks of falls, confusion and cognitive dysfunction. However, the effect of anticholinergic drug use on rehabilitation outcomes after a stroke is poorly documented. We therefore aimed to establish whether the anticholinergic load was associated with functional recovery among geriatric patients convalescing after stroke. Consecutive geriatric stroke patients admitted and discharged from a convalescence rehabilitation ward between 2010 and 2016 were included in this retrospective cohort study. Anticholinergic load was assessed by the Anticholinergic Risk Scale (ARS), and functional recovery was assessed by the Functional Independence Measure (FIM). The primary outcome was cognitive FIM (FIM-C) gain, but we also assessed the interaction of other putative factors identified from univariate analysis. Multivariate analyses were performed, adjusting for confounding factors. We included 418 participants (171 males, 247 females) with a median age of 78 years (interquartile range, 72-84 years). Multiple regression analysis revealed that ARS change, length of stay, and epilepsy were independently and negatively correlated with cognitive FIM gain. Multiple logistic regression analysis indicated that the "Comprehension" and "Memory" items of the cognitive FIM gain were independently and negatively associated with anticholinergic load. A causal relationship cannot be established, but increased ARS scores during hospitalization may predict limited cognitive functional improvement in geriatric patients after stroke. Alternatively, cognitive impairment may lead to increased use of anticholinergic drugs. © 2018 John Wiley & Sons Ltd.

Determinants of child maltreatment in Nepal: Results from the 2014 Nepal multiple indicator cluster survey (the 2014 NMICS).

PubMed

Atteraya, Madhu Sudhan; Ebrahim, Nasser B; Gnawali, Shreejana

2018-02-01

We examined the prevalence of child maltreatment as measured by the level of physical (moderate to severe) and emotional abuse and child labor, and the associated household level determinants of child maltreatment in Nepal. We used a nationally representative data set from the fifth round of the Nepal Multiple Indicator Cluster Survey (the 2014 NMICS). The main independent variables were household level characteristics. Dependent variables included child experience of moderate to severe physical abuse, emotional abuse, and child labor (domestic work and economic activities). Bivariate analyses and logistic regressions were used to examine the associations between independent and dependent variables. The results showed that nearly half of the children (49.8%) had experienced moderate physical abuse, 21.5% experienced severe physical abuse, and 77.3% experienced emotional abuse. About 27% of the children had engaged in domestic work and 46.7% in various economic activities. At bivariate level, educational level of household's head and household wealth status had shown significant statistical association with child maltreatment (p<0.001). Results from multivariate logistic regressions showed that higher education levels and higher household wealth status protected children from moderate to severe physical abuse, emotional abuse and child labor. In general, child maltreatment is a neglected social issue in Nepal and the high rates of child maltreatment calls for mass awareness programs focusing on parents, and involving all stakeholders including governments, local, and international organizations. Copyright © 2017 Elsevier Ltd. All rights reserved.

Entry characteristics and performance in a Masters module in Tropical Medicine: a 5-year analysis.

PubMed

Weigel, R; Robinson, D; Stewart, M; Assinder, S

2016-06-01

Postgraduate courses can contribute to better-qualified personnel in resource-limited settings. We aimed to identify how entry characteristics of applicants predict performance in order to provide support measures early. We describe demographic data and end-of-module examination marks of medical doctors who enrolled in a first semester module of two one-year MSc programmes between 2010 and 2014. We used t-tests and one-way anova to compare, and post hoc tests to locate differences of mean marks between categories of entry characteristics in univariate analysis. After exclusion of collinear variables, multiple regression examined the effect of several characteristics in multivariable analysis. Eighty-nine students (47% male) with a mean age of 32 (SD 6.4) years who received their medical degree in the UK (19%), other European (22%), African (35%) or other countries (24%) attended the 3-months module. Their mean mark was 69.1% (SD 10.9). Medical graduates from UK universities achieved significantly higher mean marks than graduates from other countries. Students' age was significantly negatively correlated with the module mark. In multiple linear regression, place of medical degree (β = -0.44, P < 0.001) and time since graduation (β = -0.28, P = 0.007) were strongest predictors of performance, explaining 32% of the variation of mean marks. Students' performance substantially differs based on their entry criteria in this 1st semester module. Non-UK graduates and mature students might benefit from early support. © 2016 John Wiley & Sons Ltd.

Elevated risk of adverse obstetric outcomes in pregnant women with depression.

PubMed

Kim, Deborah R; Sockol, Laura E; Sammel, Mary D; Kelly, Caroline; Moseley, Marian; Epperson, C Neill

2013-12-01

In this study, we evaluated the association between prenatal depression symptoms adverse birth outcomes in African-American women. We conducted a retrospective cohort study of 261 pregnant African-American women who were screened with the Edinburgh Postnatal Depression Scale (EPDS) at their initial prenatal visit. Medical records were reviewed to assess pregnancy and neonatal outcomes, specifically preeclampsia, preterm birth, intrauterine growth retardation, and low birth weight. Using multivariable logistic regression models, an EPDS score ≥10 was associated with increased risk for preeclampsia, preterm birth, and low birth weight. An EPDS score ≥10 was associated with increased risk for intrauterine growth retardation, but after controlling for behavioral risk factors, this association was no longer significant. Patients who screen positive for depression symptoms during pregnancy are at increased risk for multiple adverse birth outcomes. In a positive, patient-rated depression screening at the initial obstetrics visit, depression is associated with increased risk for multiple adverse birth outcomes. Given the retrospective study design and small sample size, these findings should be confirmed in a prospective cohort study.

Emotional, physical, and social needs among 0-5-year-old children displaced by the 2010 Chilean earthquake: associated characteristics and exposures.

PubMed

Arbour, MaryCatherine; Murray, Kara A; Yoshikawa, Hirokazu; Arriet, Felipe; Moraga, Cecilia; Vega, Miguel Angel Cordero

2017-04-01

An 8.8-magnitude earthquake occurred off the coast of Chile on 27 February 2010, displacing nearly 2,000 children aged less than five years to emergency housing camps. Nine months later, this study assessed the needs of 140 displaced 0-5-year-old children in six domains: caregiver stability and protection; health; housing; nutrition; psychosocial situation; and stimulation. Multivariate regression was applied to examine the degree to which emotional, physical, and social needs were associated with baseline characteristics and exposure to the earthquake, to stressful events, and to ongoing risks in the proximal post-earthquake context. In each domain, 20 per cent or fewer children had unmet needs. Of all children in the sample, 20 per cent had unmet needs in multiple domains. Children's emotional, physical, and social needs were associated with ongoing exposures amenable to intervention, more than with baseline characteristics or epicentre proximity. Relief efforts should address multiple interrelated domains of child well-being and ongoing risks in post-disaster settings. © 2017 The Author(s). Disasters © Overseas Development Institute, 2017.

A spline-based regression parameter set for creating customized DARTEL MRI brain templates from infancy to old age.

PubMed

Wilke, Marko

2018-02-01

This dataset contains the regression parameters derived by analyzing segmented brain MRI images (gray matter and white matter) from a large population of healthy subjects, using a multivariate adaptive regression splines approach. A total of 1919 MRI datasets ranging in age from 1-75 years from four publicly available datasets (NIH, C-MIND, fCONN, and IXI) were segmented using the CAT12 segmentation framework, writing out gray matter and white matter images normalized using an affine-only spatial normalization approach. These images were then subjected to a six-step DARTEL procedure, employing an iterative non-linear registration approach and yielding increasingly crisp intermediate images. The resulting six datasets per tissue class were then analyzed using multivariate adaptive regression splines, using the CerebroMatic toolbox. This approach allows for flexibly modelling smoothly varying trajectories while taking into account demographic (age, gender) as well as technical (field strength, data quality) predictors. The resulting regression parameters described here can be used to generate matched DARTEL or SHOOT templates for a given population under study, from infancy to old age. The dataset and the algorithm used to generate it are publicly available at https://irc.cchmc.org/software/cerebromatic.php.

Bootstrap Enhanced Penalized Regression for Variable Selection with Neuroimaging Data.

PubMed

Abram, Samantha V; Helwig, Nathaniel E; Moodie, Craig A; DeYoung, Colin G; MacDonald, Angus W; Waller, Niels G

2016-01-01

Recent advances in fMRI research highlight the use of multivariate methods for examining whole-brain connectivity. Complementary data-driven methods are needed for determining the subset of predictors related to individual differences. Although commonly used for this purpose, ordinary least squares (OLS) regression may not be ideal due to multi-collinearity and over-fitting issues. Penalized regression is a promising and underutilized alternative to OLS regression. In this paper, we propose a nonparametric bootstrap quantile (QNT) approach for variable selection with neuroimaging data. We use real and simulated data, as well as annotated R code, to demonstrate the benefits of our proposed method. Our results illustrate the practical potential of our proposed bootstrap QNT approach. Our real data example demonstrates how our method can be used to relate individual differences in neural network connectivity with an externalizing personality measure. Also, our simulation results reveal that the QNT method is effective under a variety of data conditions. Penalized regression yields more stable estimates and sparser models than OLS regression in situations with large numbers of highly correlated neural predictors. Our results demonstrate that penalized regression is a promising method for examining associations between neural predictors and clinically relevant traits or behaviors. These findings have important implications for the growing field of functional connectivity research, where multivariate methods produce numerous, highly correlated brain networks.

Bootstrap Enhanced Penalized Regression for Variable Selection with Neuroimaging Data

PubMed Central

Abram, Samantha V.; Helwig, Nathaniel E.; Moodie, Craig A.; DeYoung, Colin G.; MacDonald, Angus W.; Waller, Niels G.

2016-01-01

Recent advances in fMRI research highlight the use of multivariate methods for examining whole-brain connectivity. Complementary data-driven methods are needed for determining the subset of predictors related to individual differences. Although commonly used for this purpose, ordinary least squares (OLS) regression may not be ideal due to multi-collinearity and over-fitting issues. Penalized regression is a promising and underutilized alternative to OLS regression. In this paper, we propose a nonparametric bootstrap quantile (QNT) approach for variable selection with neuroimaging data. We use real and simulated data, as well as annotated R code, to demonstrate the benefits of our proposed method. Our results illustrate the practical potential of our proposed bootstrap QNT approach. Our real data example demonstrates how our method can be used to relate individual differences in neural network connectivity with an externalizing personality measure. Also, our simulation results reveal that the QNT method is effective under a variety of data conditions. Penalized regression yields more stable estimates and sparser models than OLS regression in situations with large numbers of highly correlated neural predictors. Our results demonstrate that penalized regression is a promising method for examining associations between neural predictors and clinically relevant traits or behaviors. These findings have important implications for the growing field of functional connectivity research, where multivariate methods produce numerous, highly correlated brain networks. PMID:27516732

Numerically stable algorithm for combining census and sample estimates with the multivariate composite estimator

Treesearch

R. L. Czaplewski

2009-01-01

The minimum variance multivariate composite estimator is a relatively simple sequential estimator for complex sampling designs (Czaplewski 2009). Such designs combine a probability sample of expensive field data with multiple censuses and/or samples of relatively inexpensive multi-sensor, multi-resolution remotely sensed data. Unfortunately, the multivariate composite...

Predictive equations for the estimation of body size in seals and sea lions (Carnivora: Pinnipedia)

PubMed Central

Churchill, Morgan; Clementz, Mark T; Kohno, Naoki

2014-01-01

Body size plays an important role in pinniped ecology and life history. However, body size data is often absent for historical, archaeological, and fossil specimens. To estimate the body size of pinnipeds (seals, sea lions, and walruses) for today and the past, we used 14 commonly preserved cranial measurements to develop sets of single variable and multivariate predictive equations for pinniped body mass and total length. Principal components analysis (PCA) was used to test whether separate family specific regressions were more appropriate than single predictive equations for Pinnipedia. The influence of phylogeny was tested with phylogenetic independent contrasts (PIC). The accuracy of these regressions was then assessed using a combination of coefficient of determination, percent prediction error, and standard error of estimation. Three different methods of multivariate analysis were examined: bidirectional stepwise model selection using Akaike information criteria; all-subsets model selection using Bayesian information criteria (BIC); and partial least squares regression. The PCA showed clear discrimination between Otariidae (fur seals and sea lions) and Phocidae (earless seals) for the 14 measurements, indicating the need for family-specific regression equations. The PIC analysis found that phylogeny had a minor influence on relationship between morphological variables and body size. The regressions for total length were more accurate than those for body mass, and equations specific to Otariidae were more accurate than those for Phocidae. Of the three multivariate methods, the all-subsets approach required the fewest number of variables to estimate body size accurately. We then used the single variable predictive equations and the all-subsets approach to estimate the body size of two recently extinct pinniped taxa, the Caribbean monk seal (Monachus tropicalis) and the Japanese sea lion (Zalophus japonicus). Body size estimates using single variable regressions generally under or over-estimated body size; however, the all-subset regression produced body size estimates that were close to historically recorded body length for these two species. This indicates that the all-subset regression equations developed in this study can estimate body size accurately. PMID:24916814

[Correlation between gaseous exchange rate, body temperature, and mitochondrial protein content in the liver of mice].

PubMed

Muradian, Kh K; Utko, N O; Mozzhukhina, T H; Pishel', I M; Litoshenko, O Ia; Bezrukov, V V; Fraĭfel'd, V E

2002-01-01

Correlative and regressive relations between the gaseous exchange, thermoregulation and mitochondrial protein content were analyzed by two- and three-dimensional statistics in mice. It has been shown that the pair wise linear methods of analysis did not reveal any significant correlation between the parameters under exploration. However, it became evident at three-dimensional and non-linear plotting for which the coefficients of multivariable correlation reached and even exceeded 0.7-0.8. The calculations based on partial differentiation of the multivariable regression equations allow to conclude that at certain values of VO2, VCO2 and body temperature negative relations between the systems of gaseous exchange and thermoregulation become dominating.

Use of Multiple Imputation to Estimate the Proportion of Respiratory Virus Detections Among Patients Hospitalized With Community-Acquired Pneumonia.

PubMed

Bozio, Catherine H; Flanders, W Dana; Finelli, Lyn; Bramley, Anna M; Reed, Carrie; Gandhi, Neel R; Vidal, Jorge E; Erdman, Dean; Levine, Min Z; Lindstrom, Stephen; Ampofo, Krow; Arnold, Sandra R; Self, Wesley H; Williams, Derek J; Grijalva, Carlos G; Anderson, Evan J; McCullers, Jonathan A; Edwards, Kathryn M; Pavia, Andrew T; Wunderink, Richard G; Jain, Seema

2018-04-01

Real-time polymerase chain reaction (PCR) on respiratory specimens and serology on paired blood specimens are used to determine the etiology of respiratory illnesses for research studies. However, convalescent serology is often not collected. We used multiple imputation to assign values for missing serology results to estimate virus-specific prevalence among pediatric and adult community-acquired pneumonia hospitalizations using data from an active population-based surveillance study. Presence of adenoviruses, human metapneumovirus, influenza viruses, parainfluenza virus types 1-3, and respiratory syncytial virus was defined by positive PCR on nasopharyngeal/oropharyngeal specimens or a 4-fold rise in paired serology. We performed multiple imputation by developing a multivariable regression model for each virus using data from patients with available serology results. We calculated absolute and relative differences in the proportion of each virus detected comparing the imputed to observed (nonimputed) results. Among 2222 children and 2259 adults, 98.8% and 99.5% had nasopharyngeal/oropharyngeal specimens and 43.2% and 37.5% had paired serum specimens, respectively. Imputed results increased viral etiology assignments by an absolute difference of 1.6%-4.4% and 0.8%-2.8% in children and adults, respectively; relative differences were 1.1-3.0 times higher. Multiple imputation can be used when serology results are missing, to refine virus-specific prevalence estimates, and these will likely increase estimates.

Correlated Heterospectral Lipidomics for Biomolecular Profiling of Remyelination in Multiple Sclerosis

PubMed Central

2017-01-01

Analyzing lipid composition and distribution within the brain is important to study white matter pathologies that present focal demyelination lesions, such as multiple sclerosis. Some lesions can endogenously re-form myelin sheaths. Therapies aim to enhance this repair process in order to reduce neurodegeneration and disability progression in patients. In this context, a lipidomic analysis providing both precise molecular classification and well-defined localization is crucial to detect changes in myelin lipid content. Here we develop a correlated heterospectral lipidomic (HSL) approach based on coregistered Raman spectroscopy, desorption electrospray ionization mass spectrometry (DESI-MS), and immunofluorescence imaging. We employ HSL to study the structural and compositional lipid profile of demyelination and remyelination in an induced focal demyelination mouse model and in multiple sclerosis lesions from patients ex vivo. Pixelwise coregistration of Raman spectroscopy and DESI-MS imaging generated a heterospectral map used to interrelate biomolecular structure and composition of myelin. Multivariate regression analysis enabled Raman-based assessment of highly specific lipid subtypes in complex tissue for the first time. This method revealed the temporal dynamics of remyelination and provided the first indication that newly formed myelin has a different lipid composition compared to normal myelin. HSL enables detailed molecular myelin characterization that can substantially improve upon the current understanding of remyelination in multiple sclerosis and provides a strategy to assess remyelination treatments in animal models. PMID:29392175

A High-Dimensional, Multivariate Copula Approach to Modeling Multivariate Agricultural Price Relationships and Tail Dependencies

Treesearch

Xuan Chi; Barry Goodwin

2012-01-01

Spatial and temporal relationships among agricultural prices have been an important topic of applied research for many years. Such research is used to investigate the performance of markets and to examine linkages up and down the marketing chain. This research has empirically evaluated price linkages by using correlation and regression models and, later, linear and...

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

PubMed

Pourahmadi, Mohsen; Noorbaloochi, Siamak

2016-04-01

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

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)

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

PubMed Central

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

2015-01-01

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

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

PubMed

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

2015-12-01

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

The Impact of Metastatic Spinal Tumor Location on 30-Day Perioperative Mortality and Morbidity After Surgical Decompression.

PubMed

Hussain, Awais K; Vig, Khushdeep S; Cheung, Zoe B; Phan, Kevin; Lima, Mauricio C; Kim, Jun S; Kaji, Deepak A; Arvind, Varun; Cho, Samuel Kang-Wook

2018-06-01

A retrospective cohort study from 2011 to 2014 was performed using the American College of Surgeons National Surgical Quality Improvement Program database. The purpose of this study was to assess the impact of tumor location in the cervical, thoracic, or lumbosacral spine on 30-day perioperative mortality and morbidity after surgical decompression of metastatic extradural spinal tumors. Operative treatment of metastatic spinal tumors involves extensive procedures that are associated with significant complication rates and healthcare costs. Past studies have examined various risk factors for poor clinical outcomes after surgical decompression procedures for spinal tumors, but few studies have specifically investigated the impact of tumor location on perioperative mortality and morbidity. We identified 2238 patients in the American College of Surgeons National Surgical Quality Improvement Program database who underwent laminectomy for excision of metastatic extradural tumors in the cervical, thoracic, or lumbosacral spine. Baseline patient characteristics were collected from the database. Univariate and multivariate regression analyses were performed to examine the association between spinal tumor location and 30-day perioperative mortality and morbidity. On univariate analysis, cervical spinal tumors were associated with the highest rate of pulmonary complications. Multivariate regression analysis demonstrated that cervical spinal tumors had the highest odds of multiple perioperative complications. However, thoracic spinal tumors were associated with the highest risk of intra- or postoperative blood transfusion. In contrast, patients with metastatic tumors in the lumbosacral spine had lower odds of perioperative mortality, pulmonary complications, and sepsis. Tumor location is an independent risk factor for perioperative mortality and morbidity after surgical decompression of metastatic spinal tumors. The addition of tumor location to existing prognostic scoring systems may help to improve their predictive accuracy. 3.

Multivariate analysis on unilateral cleft lip and palate treatment outcome by EUROCRAN index: A retrospective study.

PubMed

Yew, Ching Ching; Alam, Mohammad Khursheed; Rahman, Shaifulizan Abdul

2016-10-01

This study is to evaluate the dental arch relationship and palatal morphology of unilateral cleft lip and palate patients by using EUROCRAN index, and to assess the factors that affect them using multivariate statistical analysis. A total of one hundred and seven patients from age five to twelve years old with non-syndromic unilateral cleft lip and palate were included in the study. These patients have received cheiloplasty and one stage palatoplasty surgery but yet to receive alveolar bone grafting procedure. Five assessors trained in the use of the EUROCRAN index underwent calibration exercise and ranked the dental arch relationships and palatal morphology of the patients' study models. For intra-rater agreement, the examiners scored the models twice, with two weeks interval in between sessions. Variable factors of the patients were collected and they included gender, site, type and, family history of unilateral cleft lip and palate; absence of lateral incisor on cleft side, cheiloplasty and palatoplasty technique used. Associations between various factors and dental arch relationships were assessed using logistic regression analysis. Dental arch relationship among unilateral cleft lip and palate in local population had relatively worse scoring than other parts of the world. Crude logistics regression analysis did not demonstrate any significant associations among the various socio-demographic factors, cheiloplasty and palatoplasty techniques used with the dental arch relationship outcome. This study has limitations that might have affected the results, example: having multiple operators performing the surgeries and the inability to access the influence of underlying genetic predisposed cranio-facial variability. These may have substantial influence on the treatment outcome. The factors that can affect unilateral cleft lip and palate treatment outcome is multifactorial in nature and remained controversial in general. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Perceived resource support for chronic illnesses among diabetics in north-western China.

PubMed

Zhong, Huiqin; Shao, Ya; Fan, Ling; Zhong, Tangshen; Ren, Lu; Wang, Yan

2016-06-01

A high level of social support can improve long-term diabetes self-management. Support from a single source has been evaluated. This study aims to analyze support from multiple and multilevel sources for diabetic patients by using the Chronic Illness Resources Survey (CIRS). Factors influencing the utilization of the CIRS were also evaluated. A total of 297 patients with diabetes were investigated using the CIRS and Perceived Diabetes Self-management Scale in Shihezi City, China. Descriptive statistics were used to explain demographic variables and scores of the scales. Factors affecting the utilization of chronic illness resources were determined through univariate analysis and then examined by multivariate logistic regression analysis. Of the 297 diabetic patients surveyed, 67% failed to reach the standard (more than 3 points) of utilizing chronic illness resources. Moreover, utilization of chronic illness resources was positively moderately correlated with self-management of diabetes (r = 0.75, P < 0.05). According to the multivariate logistic regression analysis, age (OR, 3.42; 95%CI, 1.19-9.84) and monthly income (OR, 5.27; 95%CI, 1.86-14.90) were significantly positively associated with the CIRS score. Individuals with high school (OR, 2.61; 95%CI, 1.13-6.05) and college (OR, 3.02; 95%CI, 1.13-8.04) degrees obtained higher scores in the survey than those with elementary school education. Results indicated that utilization of resources and support for chronic illness self-management, particularly personal adjustment and organization, were not ideal among diabetics in the communities of north-western China. Improved utilization of chronic illness resources was conducive for proper diabetes self-management. Furthermore, the level of utilization of chronic illness resources increased with age, literacy level, and monthly income.

Risk factors for reinsertion of urinary catheter after early removal in thoracic surgical patients.

PubMed

Young, John; Geraci, Travis; Milman, Steven; Maslow, Andrew; Jones, Richard N; Ng, Thomas

2018-03-08

To reduce the incidence of urinary tract infection, Surgical Care Improvement Project 9 mandates the removal of urinary catheters within 48 hours postoperatively. In patients with thoracic epidural anesthesia, we sought to determine the rate of catheter reinsertion, the complications of reinsertion, and the factors associated with reinsertion. We conducted a prospective observational study of consecutive patients undergoing major pulmonary or esophageal resection with thoracic epidural analgesia over a 2-year period. As per Surgical Care Improvement Project 9, all urinary catheters were removed within 48 hours postoperatively. Excluded were patients with chronic indwelling catheter, patients with urostomy, and patients requiring continued strict urine output monitoring. Multivariable logistic regression analysis was used to identify independent risk factors for urinary catheter reinsertion. Thirteen patients met exclusion criteria. Of the 275 patients evaluated, 60 (21.8%) required reinsertion of urinary catheter. There was no difference in the urinary tract infection rate between patients requiring reinsertion (1/60 [1.7%]) versus patients not requiring reinsertion (1/215 [0.5%], P = .389). Urethral trauma during reinsertion was seen in 1 of 60 patients (1.7%). After reinsertion, discharge with urinary catheter was required in 4 of 60 patients (6.7%). Multivariable logistic regression analysis found esophagectomy, lower body mass index, and benign prostatic hypertrophy to be independent risk factors associated with catheter reinsertion after early removal in the presence of thoracic epidural analgesia. When applying Surgical Care Improvement Project 9 to patients undergoing thoracic procedures with thoracic epidural analgesia, consideration to delayed removal of urinary catheter may be warranted in patients with multiple risk factors for reinsertion. Copyright © 2018 The American Association for Thoracic Surgery. Published by Elsevier Inc. All rights reserved.

Socioeconomic disparities in the quality of life in children with cancer or brain tumors: the mediating role of family factors.

PubMed

Litzelman, Kristin; Barker, Emily; Catrine, Kristine; Puccetti, Diane; Possin, Peggy; Witt, Whitney P

2013-05-01

This study aimed to determine if and to what extent (i) socioeconomic disparities exist in the health-related quality of life (QOL) of children with cancer or brain tumors and healthy children; and (ii) family functioning and burden mediate the relationship between socioeconomic status and children's QOL. In this cross-sectional study, parents of children ages 2-18 with (n = 71) and without (n = 135) cancer or brain tumors completed in-person interviewer-assisted surveys assessing sociodemographics (including income and parental education), child QOL (measure: PedsQL), family functioning (measure: Family Adaptability and Cohesion Evaluation Scale IV) and burden (measure: Impact on the Family Scale). For children with cancer, clinical characteristics were captured through medical record abstraction. Multiple linear regression was used to determine the relationship between income and child QOL; the interaction between group status and income was assessed. Staged multivariate regression models were used to assess the role of family factors in this relationship among children with cancer. In multivariate analyses, the effect of income differed by cancer status; lower income was associated with worse QOL in children with cancer but not among healthy children. Among children with cancer, this relationship was significantly attenuated by family burden. Significant socioeconomic disparities exist in the QOL of children with cancer. Family factors partially explain the relationship between low income and poor QOL outcomes among these children. Lower-income families may have fewer resources to cope with their child's cancer. Increased support, monitoring, and referrals to reduce burden for these families may lead to improved QOL in children with cancer. Copyright © 2012 John Wiley & Sons, Ltd.

The Effects of Optimism and Gratitude on Physical Activity, Biomarkers, and Readmissions Following an Acute Coronary Syndrome: The Gratitude Research in Acute Coronary Events (GRACE) Study

PubMed Central

Huffman, Jeff C.; Beale, Eleanor E.; Celano, Christopher M.; Beach, Scott R.; Belcher, Arianna M.; Moore, Shannon V.; Suarez, Laura; Motiwala, Shweta R.; Gandhi, Parul U.; Gaggin, Hanna; Januzzi, James L.

2015-01-01

Background Positive psychological constructs, such as optimism, are associated with beneficial health outcomes. However, no study has separately examined the effects of multiple positive psychological constructs on behavioral, biological, and clinical outcomes after an acute coronary syndrome (ACS). Accordingly, we aimed to investigate associations of baseline optimism and gratitude with subsequent physical activity, prognostic biomarkers, and cardiac rehospitalizations in post-ACS patients. Methods and Results Participants were enrolled during admission for ACS and underwent assessments at baseline (2 weeks post-ACS) and follow-up (6 months later). Associations between baseline positive psychological constructs and subsequent physical activity/biomarkers were analyzed using multivariable linear regression. Associations between baseline positive constructs and 6-month rehospitalizations were assessed via multivariable Cox regression. Overall, 164 participants enrolled and completed the baseline 2-week assessments. Baseline optimism was significantly associated with greater physical activity at 6 months (n=153; β=102.5; 95% confidence interval [13.6-191.5]; p=.024), controlling for baseline activity and sociodemographic, medical, and negative psychological covariates. Baseline optimism was also associated with lower rates of cardiac readmissions at 6 months (N=164), controlling for age, gender, and medical comorbidity (hazard ratio=.92; 95% confidence interval [.86-.98]; p=.006). There were no significant relationships between optimism and biomarkers. Gratitude was minimally associated with post-ACS outcomes. Conclusions Post-ACS optimism, but not gratitude, was prospectively and independently associated with superior physical activity and fewer cardiac readmissions. Whether interventions that target optimism can successfully increase optimism or improve cardiovascular outcomes in post-ACS patients is not yet known, but can be tested in future studies. Clinical Trial Registration URL: http://www.clinicaltrials.gov. Unique identifier: NCT01709669. PMID:26646818

Body mass index in ambulatory cerebral palsy patients.

PubMed

Feeley, Brian T; Gollapudi, Kiran; Otsuka, Norman Y

2007-05-01

Malnutrition is a common problem in children with cerebral palsy. Although malnutrition is often recognized in patients with severe cerebral palsy, it can be unrecognized in less severely affected patients. The consequences of malnutrition are serious, and include decreased muscle strength, poor immune status, and depressed cerebral functioning. Low body mass index has been used as a marker for malnutrition. The purpose of this study was to determine which patients in an ambulatory cerebral palsy patient population were at risk for low body mass index. A retrospective chart review was performed on 75 patients. Age, sex, height, weight, type of cerebral palsy, and functional status [gross motor functional classification system (GMFCS) level] was recorded from the chart. Descriptive statistics with bivariate and multivariate regression analyses were performed. Thirty-eight boys and 37 girls with an average age of 8.11 years were included in the study. Unique to our patient population, all cerebral palsy patients were independent ambulators. Patients with quadriplegic cerebral palsy had a significantly lower body mass index than those with diplegic and hemiplegic cerebral palsy. Patients with a GMFCS III had significantly lower body mass index than those with GMFCS I and II. When multivariate regression analysis to control for age and sex was performed, low body mass index remained associated with quadriplegic cerebral palsy and GMFCS III. Malnutrition is a common health problem in patients with cerebral palsy, leading to significant morbidity in multiple organ systems. We found that in an ambulatory cerebral palsy population, patients with lower functional status or quadriplegia had significantly lower body mass index, suggesting that even highly functioning ambulatory cerebral palsy patients are at risk for malnutrition.

Socioeconomic Disparities in the Quality of Life in Children with Cancer or Brain Tumors: The Mediating Role of Family Factors

PubMed Central

Litzelman, Kristin; Barker, Emily; Catrine, Kristine; Puccetti, Diane; Possin, Peggy; Witt, Whitney P

2012-01-01

Objective This study aimed to determine if and to what extent: (1) socioeconomic disparities exist in the health-related quality of life (QOL) of children with cancer or brain tumors and healthy children; and (2) family functioning and burden mediate the relationship between socioeconomic status and children’s QOL. Methods In this cross-sectional study, parents of children ages 2–18 with (n=71) and without (n=135) cancer or brain tumors completed in-person interviewer-assisted surveys assessing sociodemographics (including income and parental education), child QOL (measure: PedsQL), family functioning (measure: FACES IV) and burden (measure: Impact on the Family Scale). For children with cancer, clinical characteristics were captured through medical record abstraction. Multiple linear regression was used to determine the relationship between income and child QOL; the interaction between group status and income was assessed. Staged multivariate regression models were used to assess the role of family factors in this relationship among children with cancer. Results In multivariate analyses, the effect of income differed by cancer status; lower income was associated with worse QOL in children with cancer, but not among healthy children. Among children with cancer, this relationship was significantly attenuated by family burden. Conclusions Significant socioeconomic disparities exist in the QOL of children with cancer. Family factors partially explain the relationship between low income and poor QOL outcomes among these children. Lower income families may have fewer resources to cope with their child’s cancer. Increased support, monitoring, and referrals to reduce burden for these families may lead to improved QOL in children with cancer. PMID:22645071

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

PubMed

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

2013-01-01

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

Risk factors for mortality before age 18 years in cystic fibrosis.

PubMed

McColley, Susanna A; Schechter, Michael S; Morgan, Wayne J; Pasta, David J; Craib, Marcia L; Konstan, Michael W

2017-07-01

Understanding early-life risk factors for childhood death in cystic fibrosis (CF) is important for clinical care, including the identification of effective interventions. Data from the Epidemiologic Study of Cystic Fibrosis (ESCF) collected 1994-2005 were linked with the Cystic Fibrosis Foundation Patient Registry (CFFPR) demographic and mortality data from 2013. Inclusion criteria were ≥1 visit annually at age 3-5 years and ≥1 FEV 1 measurement at age 6-8 years. Demographic data, nutritional parameters, pulmonary signs and symptoms, microbiology, and FEV 1 were evaluated as risk factors for death before age 18 years. Multivariable Cox proportional hazards regression was used to model the simultaneous effects of risk factors associated with death before age 18 years. Among 5365 patients enrolled in ESCF who met inclusion criteria, 3880 (72%) were linked to the CFFPR. Among these, 191 (5.7%) died before age 18 years; median age at death was 13.4 ± 3.1 years. Multivariable regression showed clubbing, crackles, female sex, unknown CFTR genotype, minority race or ethnicity, Medicaid insurance (a proxy of low socioeconomic status), Pseudomonas aeruginosa on 2 or more cultures, and weight-for-age <50th percentile were significant risk factors for death regardless of inclusion of FEV 1 at age 6-8 years in the model. We identified multiple risk factors for childhood death of patients with CF, all of which remained important after incorporating FEV 1 at age 6-8 years. Among the factors identified were the presence of clubbing or crackles at age 3-5 years, signs which are not routinely collected in registries. © 2017 Wiley Periodicals, Inc.

Increased risk of SSEs in bone-only metastatic breast cancer patients treated with zoledronic acid.

PubMed

Yanae, Masashi; Fujimoto, Shinichiro; Tane, Kaori; Tanioka, Maki; Fujiwara, Kimiko; Tsubaki, Masanobu; Yamazoe, Yuzuru; Morishima, Yoshiyuki; Chiba, Yasutaka; Takao, Shintaro; Komoike, Yoshifumi; Tsurutani, Junji; Nakagawa, Kazuhiko; Nishida, Shozo

2017-09-01

Bone represents one of the most common sites to which breast cancer cells metastasize. Patients experience skeletal related adverse events (pathological fractures, spinal cord compressions, and irradiation for deteriorated pain on bone) even during treatment with zoledronic acid (ZA). Therefore, we conducted a retrospective cohort study to investigate the predictive factors for symptomatic skeletal events (SSEs) in bone-metastasized breast cancer (b-MBC) patients. We retrospectively collected data on b-MBC patients treated with ZA. Patient characteristics, including age, subtype, the presence of non-bone lesions, the presence of multiple bone metastases at the commencement of ZA therapy, duration of ZA therapy, the time interval between breast cancer diagnosis and the initiation of ZA therapy, and type of systemic therapy, presence of previous SSE were analyzed using multivariable logistic regression analysis. The medical records of 183 patients were reviewed and 176 eligible patients were analyzed. The median age was 59 (range, 30-87) years. Eighty-seven patients were aged ≥60 years and 89 patients were aged < 60 years. The proportions of patients with estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2-positive disease were 81.8%, 63.1%, and 17.6%, respectively. Fifty-three patients had bone-only MBC at the commencement of ZA therapy. SSEs were observed in 42 patients. In the multivariable logistic regression analysis, bone-only MBC but not a breast cancer subtype was an independent risk factor for an SSE during ZA therapy (odds ratio: 3.878, 95% confidence interval: 1.647-9.481; p = 0.002). Bone-only MBC patients are more likely to experience an SSE even after treatment with ZA.

Only-Child Status in Relation to Perceived Stress and Studying-Related Life Satisfaction among University Students in China: A Comparison with International Students.

PubMed

Chu, Janet Junqing; Khan, Mobarak Hossain; Jahn, Heiko J; Kraemer, Alexander

2015-01-01

University students in general face multiple challenges, which may affect their levels of perceived stress and life satisfaction. Chinese students currently face specific strains due to the One-Child Policy (OCP). The aim of this study was to assess (1) whether the levels of perceived stress and studying-related life satisfaction are associated with only-child (OC) status after controlling for demographic and socio-economic characteristics and (2) whether these associations differ between Chinese and international students. A cross-sectional health survey based on a self-administrated standardised questionnaire was conducted among 1,843 (1,543 Chinese, 300 international) students at two Chinese universities in 2010-2011. Cohen's Perceived Stress Scale (PSS-14) and Stock and Kraemer's Studying-related Life Satisfaction Scale were used to measure perceived stress and studying-related life satisfaction respectively. Multivariable logistic regression analyses were used to examine the associations of OC status with perceived stress and studying-related life satisfaction by sex for Chinese students and international students separately. The Chinese non-only-children (NOCs) were more likely to come from small cities. Multivariable regression models indicate that the Chinese NOCs were more stressed than OCs (OR = 1.39, 1.11-1.74) with a stronger association in men (OR = 1.48, 1.08-2.02) than women (OR = 1.26, 0.89-1.77). NOCs were also more dissatisfied than their OC fellows in the Chinese subsample (OR = 1.37, 1.09-1.73). Among international students, no associations between OC status and perceived stress or studying-related life satisfaction were found. To promote equality between OCs and NOCs at Chinese universities, the causes of more stress and less studying-related life satisfaction among NOCs compared to OCs need further exploration.

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

PubMed Central

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

2016-01-01

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

Gender, position of authority, and the risk of depression and post-traumatic stress disorder among a national sample of U.S. Reserve Component Personnel

PubMed Central

Cohen, Gregory H.; Sampson, Laura A.; Fink, David S.; Wang, Jing; Russell, Dale; Gifford, Robert; Fullerton, Carol; Ursano, Robert; Galea, Sandro

2016-01-01

BACKGROUND Recent United States military operations in Iraq and Afghanistan have seen dramatic increases in the proportion of women serving, and the breadth of their occupational roles. General population studies suggest that women, compared to men, and persons with lower, as compared to higher, social position may be at greater risk of post-traumatic stress disorder (PTSD) and depression. However, these relations remain unclear in military populations. Accordingly, we aimed to estimate the effects of (1) gender, (2) military authority (i.e., rank) and (3) the interaction of gender and military authority upon: (a) risk of most-recent-deployment-related PTSD, and (b) risk of depression since most-recent-deployment. METHODS Using a nationally representative sample of 1024 previously deployed Reserve Component personnel surveyed in 2010, we constructed multivariable logistic regression models to estimate effects of interest. RESULTS Weighted multivariable logistic regression models demonstrated no statistically significant associations between gender or authority, and either PTSD or depression. Interaction models demonstrated multiplicative statistical interaction between gender and authority for PTSD (beta= −2.37;p=0.01), and depression (beta=-1.21; p=0.057). Predicted probabilities of PTSD and depression, respectively, were lowest in male officers (0.06, 0.09), followed by male enlisted (0.07, 0.14), female enlisted (0.07, 0.15), and female officers (0.30, 0.25). CONCLUSIONS Female officers in the Reserve Component may be at greatest risk for PTSD and depression following deployment, relative to their male and enlisted counterparts, and this relation is not explained by deployment trauma exposure. Future studies may fruitfully examine whether social support, family responsibilities peri-deployment, or contradictory class status may explain these findings. PMID:26899583

Ankylosing Spondylitis Is Associated with Increased Prevalence of Left Ventricular Hypertrophy.

PubMed

Midtbø, Helga; Gerdts, Eva; Berg, Inger Jorid; Rollefstad, Silvia; Jonsson, Roland; Semb, Anne Grete

2018-06-01

Ankylosing spondylitis (AS) is associated with increased risk for cardiovascular disease (CVD). Left ventricular (LV) hypertrophy is a strong precursor for clinical CVD. The aim of our study was to assess whether having AS was associated with increased prevalence of LV hypertrophy. Clinical and echocardiographic data from 139 AS patients and 126 age- and sex-matched controls was used. LV mass was calculated according to guidelines and indexed to height 2.7 . LV hypertrophy was considered present if LV mass index was > 49.2 g/m 2.7 in men and > 46.7 g/m 2.7 in women. Patients with AS were on average 49 ± 12 years old, and 60% were men. The prevalence of hypertension (HTN; 35% vs 41%) and diabetes (5% vs 2%) was similar among patients and controls, while patients with AS had higher serum C-reactive protein level (CRP; p < 0.001). The prevalence of LV hypertrophy was higher in patients with AS compared to controls (15% vs 6%, p = 0.01). In multivariable logistic regression analysis, having AS was associated with OR 6.3 (95% CI 2.1-19.3, p = 0.001) of having LV hypertrophy independent of the presence of HTN, diabetes, and obesity. In multivariable linear regression analyses, having AS was also associated with higher LV mass (β 0.15, p = 0.007) after adjusting for CVD risk factors including sex, body mass index, systolic blood pressure, diabetes, and serum CRP (multiple R 2 = 0.41, p < 0.001). Having AS was associated with increased prevalence of LV hypertrophy independent of CVD risk factors. This finding strengthens the indication for thorough CVD risk assessment in patients with AS.

Assessing the Role of Environmental Conditions on Efficacy Rates of Heterorhabditis indica (Nematoda: Heterorhabditidae) for Controlling Aethina tumida (Coleoptera: Nitidulidae) in Honey Bee (Hymenoptera: Apidae) Colonies: a Citizen Science Approach.

PubMed

Hill, Elizabeth S; Smythe, Ashleigh B; Delaney, Deborah A

2016-02-01

Certain species of entomopathogenic nematodes, such as Heterorhabditis indica Poinar, Karunakar & David, have the potential to be effective controls for Aethina tumida (Murray), or small hive beetles, when applied to the soil surrounding honey bee (Apis mellifera L.) hives. Despite the efficacy of H. indica, beekeepers have struggled to use them successfully as a biocontrol. It is believed that the sensitivity of H. indica to certain environmental conditions is the primary reason for this lack of success. Although research has been conducted to explore the impact of specific environmental conditions--such as soil moisture or soil temperature-on entomopathogenic nematode infectivity, no study to date has taken a comprehensive approach that considers the impact of multiple environmental conditions simultaneously. In exploring this, a multivariate logistic regression model was used to determine what environmental conditions resulted in reductions of A. tumida populations in honey bee colonies. To obtain the sample sizes necessary to run a multivariate logistic regression, this study utilized citizen scientist beekeepers and their hives from across the mid-Atlantic region of the United States. Results suggest that soil moisture, soil temperatures, sunlight exposure, and groundcover contribute to the efficacy of H. indica in reducing A. tumida populations in A. mellifera colonies. The results of this study offer direction for future research on the environmental preferences of H. indica and can be used to educate beekeepers about methods for better utilizing H. indica as a biological control. © The Authors 2015. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Factors favoring regain of the lost vertical spinal height through posterior spinal fusion in adolescent idiopathic scoliosis.

PubMed

Shi, Benlong; Mao, Saihu; Xu, Leilei; Sun, Xu; Liu, Zhen; Zhu, Zezhang; Lam, Tsz Ping; Cheng, Jack Cy; Ng, Bobby; Qiu, Yong

2016-07-04

Height gain is a common beneficial consequence following correction surgery in adolescent idiopathic scoliosis (AIS), yet little is known concerning factors favoring regain of the lost vertical spinal height (SH) through posterior spinal fusion. A consecutive series of AIS patients from February 2013 to August 2015 were reviewed. Surgical changes in SH (ΔSH), as well as the multiple coronal and sagittal deformity parameters were measured and correlated. Factors associated with ΔSH were identified through Pearson correlation analysis and multivariate regression analysis. A total of 172 single curve and 104 double curve patients were reviewed. The ΔSH averaged 2.5 ± 0.9 cm in single curve group and 2.9 ± 1.0 cm in double curve group. The multivariate regression analysis revealed the following pre-operative variables contributed significantly to ΔSH: pre-op Cobb angle, pre-op TK (single curve group only), pre-op GK (double curve group only) and pre-op LL (double curve group only) (p < 0.05). Thus change in height (in cm) = 0.044 × (pre-op Cobb angle) + 0.012 × (pre-op TK) (Single curve, adjusted R(2) = 0.549) or 0.923 + 0.021 × (pre-op Cobb angle1) + 0.028 × (pre-op Cobb angle2) + 0.015 × (pre-op GK)-0.012 × (pre-op LL) (Double curve, adjusted R(2) = 0.563). Severer pre-operative coronal Cobb angle and greater sagittal curves were beneficial factors favoring more contribution to the surgical lengthening effect in vertical spinal height in AIS.

Effects of Optimism and Gratitude on Physical Activity, Biomarkers, and Readmissions After an Acute Coronary Syndrome: The Gratitude Research in Acute Coronary Events Study.

PubMed

Huffman, Jeff C; Beale, Eleanor E; Celano, Christopher M; Beach, Scott R; Belcher, Arianna M; Moore, Shannon V; Suarez, Laura; Motiwala, Shweta R; Gandhi, Parul U; Gaggin, Hanna K; Januzzi, James L

2016-01-01

Positive psychological constructs, such as optimism, are associated with beneficial health outcomes. However, no study has separately examined the effects of multiple positive psychological constructs on behavioral, biological, and clinical outcomes after an acute coronary syndrome (ACS). Accordingly, we aimed to investigate associations of baseline optimism and gratitude with subsequent physical activity, prognostic biomarkers, and cardiac rehospitalizations in post-ACS patients. Participants were enrolled during admission for ACS and underwent assessments at baseline (2 weeks post-ACS) and follow-up (6 months later). Associations between baseline positive psychological constructs and subsequent physical activity/biomarkers were analyzed using multivariable linear regression. Associations between baseline positive constructs and 6-month rehospitalizations were assessed via multivariable Cox regression. Overall, 164 participants enrolled and completed the baseline 2-week assessments. Baseline optimism was significantly associated with greater physical activity at 6 months (n=153; β=102.5; 95% confidence interval, 13.6-191.5; P=0.024), controlling for baseline activity and sociodemographic, medical, and negative psychological covariates. Baseline optimism was also associated with lower rates of cardiac readmissions at 6 months (n=164), controlling for age, sex, and medical comorbidity (hazard ratio, 0.92; 95% confidence interval, [0.86-0.98]; P=0.006). There were no significant relationships between optimism and biomarkers. Gratitude was minimally associated with post-ACS outcomes. Post-ACS optimism, but not gratitude, was prospectively and independently associated with superior physical activity and fewer cardiac readmissions. Whether interventions that target optimism can successfully increase optimism or improve cardiovascular outcomes in post-ACS patients is not yet known, but can be tested in future studies. URL: http://www.clinicaltrials.gov. Unique identifier: NCT01709669. © 2015 American Heart Association, Inc.

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

PubMed

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

2016-08-01

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

The 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)

Risks and burden of viral respiratory tract infections in patients with multiple myeloma in the era of immunomodulatory drugs and bortezomib: experience at an Australian Cancer Hospital.

PubMed

Teh, Benjamin W; Worth, Leon J; Harrison, Simon J; Thursky, Karin A; Slavin, Monica A

2015-07-01

Infections are a leading cause of morbidity and mortality in patients with multiple myeloma. The epidemiology, risk factors and outcomes of viral respiratory tract infections (vRTI) are not well described in patients with multiple myeloma managed with novel agents, the current standard of care. Patients with myeloma from 2009 to 2012 who tested positive on respiratory virus multiplex polymerase chain reaction had clinical, radiological and microbiological records reviewed. The Fourth European Conference on Infections in Leukaemia (ECIL-4) definitions of RTI were applied. Univariate and multivariate regression analysis of risk factors was performed using vRTI as the evaluable outcome. Of 330 patients, 75 (22.7%) tested positive for a total of 100 vRTI episodes. All patients received thalidomide, lenalidomide or bortezomib in combination with myeloma therapies (median of three treatment regimens). vRTI occurred most commonly in patients with progressive disease, and receipt of more than three lines of myeloma therapy was associated with an increased risk of vRTI (p < 0.01). Amongst key respiratory pathogens, influenza was associated with the highest hospital admission rate (66.7%), ICU admission rate (41.6%) and mortality (33.3%) whilst RSV was associated with prolonged hospital stay. Patients with multiple myeloma and advanced disease managed with multiple lines of therapy are at risk for vRTI, and targeted interventions for prevention/treatment are required.

Moving from Descriptive to Causal Analytics: Case Study of the Health Indicators Warehouse

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

Schryver, Jack C.; Shankar, Mallikarjun; Xu, Songhua

The KDD community has described a multitude of methods for knowledge discovery on large datasets. We consider some of these methods and integrate them into an analyst s workflow that proceeds from the data-centric descriptive level to the model-centric causal level. Examples of the workflow are shown for the Health Indicators Warehouse, which is a public database for community health information that is a potent resource for conducting data science on a medium scale. We demonstrate the potential of HIW as a source of serious visual analytics efforts by showing correlation matrix visualizations, multivariate outlier analysis, multiple linear regression ofmore » Medicare costs, and scatterplot matrices for a broad set of health indicators. We conclude by sketching the first steps toward a causal dependence hypothesis.« less

Measures of work-family conflict predict sickness absence from work.

PubMed

Clays, Els; Kittel, France; Godin, Isabelle; Bacquer, Dirk De; Backer, Guy De

2009-08-01

To examine the relation between work-family conflict and sickness absence. The BELSTRESS III study comprised 2983 middle-aged workers. Strain-based work-home interference (WHI) and home-work interference (HWI) were assessed by means of self-administered questionnaires. Prospective data of registered sickness absence during 12-months follow-up were collected. Multiple logistic regression analysis was conducted. HWI was positively and significantly related to high sickness absence duration (at least 10 sick leave days) and high sickness absence frequency (at least 3 sick leave episodes) in men and women, also after adjustments were made for sociodemographic variables, health indicators, and environmental psychosocial factors. In multivariate analysis, no association between WHI and sickness absence was found. HWI was positively and significantly related to high sickness absence duration and frequency during 12-months follow-up in male and female workers.

Perceived Discrimination, Psychological Distress, and Current Smoking Status: Results From the Behavioral Risk Factor Surveillance System Reactions to Race Module, 2004–2008

PubMed Central

Peppone, Luke J.; Alcaraz, Kassandra; McQueen, Amy; Guido, Joseph J.; Carroll, Jennifer K.; Shacham, Enbal; Morrow, Gary R.

2012-01-01

Objectives. We examined the association between perceived discrimination and smoking status and whether psychological distress mediated this relationship in a large, multiethnic sample. Methods. We used 2004 through 2008 data from the Behavioral Risk Factor Surveillance System Reactions to Race module to conduct multivariate logistic regression analyses and tests of mediation examining associations between perceived discrimination in health care and workplace settings, psychological distress, and current smoking status. Results. Regardless of race/ethnicity, perceived discrimination was associated with increased odds of current smoking. Psychological distress was also a significant mediator of the discrimination–smoking association. Conclusions. Our results indicate that individuals who report discriminatory treatment in multiple domains may be more likely to smoke, in part, because of the psychological distress associated with such treatment. PMID:22420821

A Comparison of Conventional Linear Regression Methods and Neural Networks for Forecasting Educational Spending.

ERIC Educational Resources Information Center

Baker, Bruce D.; Richards, Craig E.

1999-01-01

Applies neural network methods for forecasting 1991-95 per-pupil expenditures in U.S. public elementary and secondary schools. Forecasting models included the National Center for Education Statistics' multivariate regression model and three neural architectures. Regarding prediction accuracy, neural network results were comparable or superior to…

"Let Me Count the Ways:" Fostering Reasons for Living among Low-Income, Suicidal, African American Women

ERIC Educational Resources Information Center

West, Lindsey M.; Davis, Telsie A.; Thompson, Martie P.; Kaslow, Nadine J.

2011-01-01

Protective factors for fostering reasons for living were examined among low-income, suicidal, African American women. Bivariate logistic regressions revealed that higher levels of optimism, spiritual well-being, and family social support predicted reasons for living. Multivariate logistic regressions indicated that spiritual well-being showed…

Fresh Biomass Estimation in Heterogeneous Grassland Using Hyperspectral Measurements and Multivariate Statistical Analysis

NASA Astrophysics Data System (ADS)

Darvishzadeh, R.; Skidmore, A. K.; Mirzaie, M.; Atzberger, C.; Schlerf, M.

2014-12-01

Accurate estimation of grassland biomass at their peak productivity can provide crucial information regarding the functioning and productivity of the rangelands. Hyperspectral remote sensing has proved to be valuable for estimation of vegetation biophysical parameters such as biomass using different statistical techniques. However, in statistical analysis of hyperspectral data, multicollinearity is a common problem due to large amount of correlated hyper-spectral reflectance measurements. The aim of this study was to examine the prospect of above ground biomass estimation in a heterogeneous Mediterranean rangeland employing multivariate calibration methods. Canopy spectral measurements were made in the field using a GER 3700 spectroradiometer, along with concomitant in situ measurements of above ground biomass for 170 sample plots. Multivariate calibrations including partial least squares regression (PLSR), principal component regression (PCR), and Least-Squared Support Vector Machine (LS-SVM) were used to estimate the above ground biomass. The prediction accuracy of the multivariate calibration methods were assessed using cross validated R2 and RMSE. The best model performance was obtained using LS_SVM and then PLSR both calibrated with first derivative reflectance dataset with R2cv = 0.88 & 0.86 and RMSEcv= 1.15 & 1.07 respectively. The weakest prediction accuracy was appeared when PCR were used (R2cv = 0.31 and RMSEcv= 2.48). The obtained results highlight the importance of multivariate calibration methods for biomass estimation when hyperspectral data are used.

Predicting exposure-response associations of ambient particulate matter with mortality in 73 Chinese cities.

PubMed

Madaniyazi, Lina; Guo, Yuming; Chen, Renjie; Kan, Haidong; Tong, Shilu

2016-01-01

Estimating the burden of mortality associated with particulates requires knowledge of exposure-response associations. However, the evidence on exposure-response associations is limited in many cities, especially in developing countries. In this study, we predicted associations of particulates smaller than 10 μm in aerodynamic diameter (PM10) with mortality in 73 Chinese cities. The meta-regression model was used to test and quantify which city-specific characteristics contributed significantly to the heterogeneity of PM10-mortality associations for 16 Chinese cities. Then, those city-specific characteristics with statistically significant regression coefficients were treated as independent variables to build multivariate meta-regression models. The model with the best fitness was used to predict PM10-mortality associations in 73 Chinese cities in 2010. Mean temperature, PM10 concentration and green space per capita could best explain the heterogeneity in PM10-mortality associations. Based on city-specific characteristics, we were able to develop multivariate meta-regression models to predict associations between air pollutants and health outcomes reasonably well. Copyright © 2015 Elsevier Ltd. All rights reserved.

Multiple tobacco product use among US adolescents and young adults

PubMed Central

Soneji, Samir; Sargent, James; Tanski, Susanne

2016-01-01

Objective To assess the extent to which multiple tobacco product use among adolescents and young adults falls outside current Food and Drug Administration (FDA) regulatory authority. Methods We conducted a web-based survey of 1596 16–26-year-olds to assess use of 11 types of tobacco products. We ascertained current (past 30 days) tobacco product use among 927 respondents who ever used tobacco. Combustible tobacco products included cigarettes, cigars (little filtered, cigarillos, premium) and hookah; non-combustible tobacco products included chew, dip, dissolvables, e-cigarettes, snuff and snus. We then fitted an ordinal logistic regression model to assess demographic and behavioural associations with higher levels of current tobacco product use (single, dual and multiple product use). Results Among 448 current tobacco users, 54% were single product users, 25% dual users and 21% multiple users. The largest single use category was cigarettes (49%), followed by hookah (23%), little filtered cigars (17%) and e-cigarettes (5%). Most dual and multiple product users smoked cigarettes, along with little filtered cigars, hookah and e-cigarettes. Forty-six per cent of current single, 84% of dual and 85% of multiple tobacco product users consumed a tobacco product outside FDA regulatory authority. In multivariable analysis, the adjusted risk of multiple tobacco use was higher for males, first use of a non-combustible tobacco product, high sensation seeking respondents and declined for each additional year of age that tobacco initiation was delayed. Conclusions Nearly half of current adolescent and young adult tobacco users in this study engaged in dual and multiple tobacco product use; the majority of them used products that fall outside current FDA regulatory authority. This study supports FDA deeming of these products and their incorporation into the national media campaign to address youth tobacco use. PMID:25361744

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…

Multiplication factor versus regression analysis in stature estimation from hand and foot dimensions.

PubMed

Krishan, Kewal; Kanchan, Tanuj; Sharma, Abhilasha

2012-05-01

Estimation of stature is an important parameter in identification of human remains in forensic examinations. The present study is aimed to compare the reliability and accuracy of stature estimation and to demonstrate the variability in estimated stature and actual stature using multiplication factor and regression analysis methods. The study is based on a sample of 246 subjects (123 males and 123 females) from North India aged between 17 and 20 years. Four anthropometric measurements; hand length, hand breadth, foot length and foot breadth taken on the left side in each subject were included in the study. Stature was measured using standard anthropometric techniques. Multiplication factors were calculated and linear regression models were derived for estimation of stature from hand and foot dimensions. Derived multiplication factors and regression formula were applied to the hand and foot measurements in the study sample. The estimated stature from the multiplication factors and regression analysis was compared with the actual stature to find the error in estimated stature. The results indicate that the range of error in estimation of stature from regression analysis method is less than that of multiplication factor method thus, confirming that the regression analysis method is better than multiplication factor analysis in stature estimation. Copyright © 2012 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

Does Employee Safety Matter for Patients Too? Employee Safety Climate and Patient Safety Culture in Health Care.

PubMed

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

2015-04-22

We examined relationships between employee safety climate and patient safety culture. Because employee safety may be a precondition for the development of patient safety, we hypothesized that employee safety culture would be strongly and positively related to patient safety culture. An employee safety climate survey was administered in 2010 and assessed employees' views and experiences of safety for employees. The patient safety survey administered in 2011 assessed the safety culture for patients. We performed Pearson correlations and multiple regression analysis to examine the relationships between a composite measure of employee safety with subdimensions of patient safety culture. The regression models controlled for size, geographic characteristics, and teaching affiliation. Analyses were conducted at the group level using data from 132 medical centers. Higher employee safety climate composite scores were positively associated with all 9 patient safety culture measures examined. Standardized multivariate regression coefficients ranged from 0.44 to 0.64. Medical facilities where staff have more positive perceptions of health care workplace safety climate tended to have more positive assessments of patient safety culture. This suggests that patient safety culture and employee safety climate could be mutually reinforcing, such that investments and improvements in one domain positively impacts the other. Further research is needed to better understand the nexus between health care employee and patient safety to generalize and act upon findings.

Barriers to health-care and psychological distress among mothers living with HIV in Quebec (Canada).

PubMed

Blais, Martin; Fernet, Mylène; Proulx-Boucher, Karène; Lebouché, Bertrand; Rodrigue, Carl; Lapointe, Normand; Otis, Joanne; Samson, Johanne

2015-01-01

Health-care providers play a major role in providing good quality care and in preventing psychological distress among mothers living with HIV (MLHIV). The objectives of this study are to explore the impact of health-care services and satisfaction with care providers on psychological distress in MLHIV. One hundred MLHIV were recruited from community and clinical settings in the province of Quebec (Canada). Prevalence estimation of clinical psychological distress and univariate and multivariable logistic regression models were performed to predict clinical psychological distress. Forty-five percent of the participants reported clinical psychological distress. In the multivariable regression, the following variables were significantly associated with psychological distress while controlling for sociodemographic variables: resilience, quality of communication with the care providers, resources, and HIV disclosure concerns. The multivariate results support the key role of personal, structural, and medical resources in understanding psychological distress among MLHIV. Interventions that can support the psychological health of MLHIV are discussed.

Timing of Adjuvant Surgical Oophorectomy in the Menstrual Cycle and Disease-Free and Overall Survival in Premenopausal Women With Operable Breast Cancer

PubMed Central

Laudico, Adriano V.; Van Dinh, Nguyen; Allred, D. Craig; Uy, Gemma B.; Quang, Le Hong; Salvador, Jonathan Disraeli S.; Siguan, Stephen Sixto S.; Mirasol-Lumague, Maria Rica; Tung, Nguyen Dinh; Benjaafar, Noureddine; Navarro, Narciso S.; Quy, Tran Tu; De La Peña, Arturo S.; Dofitas, Rodney B.; Bisquera, Orlino C.; Linh, Nguyen Dieu; To, Ta Van; Young, Gregory S.; Hade, Erinn M.; Jarjoura, David

2015-01-01

Background: For women with hormone receptor–positive, operable breast cancer, surgical oophorectomy plus tamoxifen is an effective adjuvant therapy. We conducted a phase III randomized clinical trial to test the hypothesis that oophorectomy surgery performed during the luteal phase of the menstrual cycle was associated with better outcomes. Methods: Seven hundred forty premenopausal women entered a clinical trial in which those women estimated not to be in the luteal phase of their menstrual cycle for the next one to six days (n = 509) were randomly assigned to receive treatment with surgical oophorectomy either delayed to be during a five-day window in the history-estimated midluteal phase of the menstrual cycles, or in the next one to six days. Women who were estimated to be in the luteal phase of the menstrual cycle for the next one to six days (n = 231) were excluded from random assignment and received immediate surgical treatments. All patients began tamoxifen within 6 days of surgery and continued this for 5 years. Kaplan-Meier methods, the log-rank test, and multivariable Cox regression models were used to assess differences in five-year disease-free survival (DFS) between the groups. All statistical tests were two-sided. Results: The randomized midluteal phase surgery group had a five-year DFS of 64%, compared with 71% for the immediate surgery random assignment group (hazard ratio [HR] = 1.24, 95% confidence interval [CI] = 0.91 to 1.68, P = .18). Multivariable Cox regression models, which included important prognostic variables, gave similar results (aHR = 1.28, 95% CI = 0.94 to 1.76, P = .12). For overall survival, the univariate hazard ratio was 1.33 (95% CI = 0.94 to 1.89, P = .11) and the multivariable aHR was 1.43 (95% CI = 1.00 to 2.06, P = .05). Better DFS for follicular phase surgery, which was unanticipated, proved consistent across multiple exploratory analyses. Conclusions: The hypothesized benefit of adjuvant luteal phase oophorectomy was not shown in this large trial. PMID:25794890

Excess risk of chronic physical conditions associated with depression and anxiety

PubMed Central

2014-01-01

Background Depression and anxiety have been reported to be associated with chronic physical conditions. We examined the excess risk of chronic physical conditions associated with depression and/or anxiety within a multivariate framework controlling for demographic and modifiable lifestyle risk factors. Methods We used a retrospective cross-sectional study design. Study participants were adults aged 22–64 years from 2007 and 2009 Medical Expenditure Panel Survey. We defined presence of depression-anxiety based on self-reported depression and anxiety and classified adults into 4 groups: 1) depression only; 2) anxiety only; 3) comorbid depression and anxiety 4) no depression and no anxiety. We included presence/absence of arthritis, asthma, chronic obstructive pulmonary disorder, diabetes, heart disease, hypertension, and osteoporosis as dependent variables. Complementary log-log regressions were used to examine the excess risk associated with depression and/or anxiety for chronic physical conditions using a multivariate framework that controlled for demographic (gender, age, race/ethnicity) and modifiable lifestyle (obesity, lack of physical activity, smoking) risk factors. Bonferroni correction for multiple comparisons was applied and p ≤0.007 was considered statistically significant. Results Overall, 7% had only depression, 5.2% had only anxiety and 2.5% had comorbid depression and anxiety. Results from multivariable regressions indicated that compared to individuals with no depression and no anxiety, individuals with comorbid depression and anxiety, with depression only and with anxiety only, all had higher risk of all the chronic physical conditions. ARRs for comorbid depression and anxiety ranged from 2.47 (95% CI: 1.47, 4.15; P = 0.0007) for osteoporosis to 1.64 (95% CI: 1.33, 2.04; P < 0.0001) for diabetes. Presence of depression only was also found to be significantly associated with all chronic conditions except for osteoporosis. Individuals with anxiety only were found to have a higher risk for arthritis, COPD, heart disease and hypertension. Conclusion Presence of depression and/or anxiety conferred an independent risk for having chronic physical conditions after adjusting for demographic and modifiable lifestyle risk factors. PMID:24433257

Compensator improvement for multivariable control systems

NASA Technical Reports Server (NTRS)

Mitchell, J. R.; Mcdaniel, W. L., Jr.; Gresham, L. L.

1977-01-01

A theory and the associated numerical technique are developed for an iterative design improvement of the compensation for linear, time-invariant control systems with multiple inputs and multiple outputs. A strict constraint algorithm is used in obtaining a solution of the specified constraints of the control design. The result of the research effort is the multiple input, multiple output Compensator Improvement Program (CIP). The objective of the Compensator Improvement Program is to modify in an iterative manner the free parameters of the dynamic compensation matrix so that the system satisfies frequency domain specifications. In this exposition, the underlying principles of the multivariable CIP algorithm are presented and the practical utility of the program is illustrated with space vehicle related examples.

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

PubMed

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

2015-11-01

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

Physical Function in Older Men With Hyperkyphosis

PubMed Central

Harrison, Stephanie L.; Fink, Howard A.; Marshall, Lynn M.; Orwoll, Eric; Barrett-Connor, Elizabeth; Cawthon, Peggy M.; Kado, Deborah M.

2015-01-01

Background. Age-related hyperkyphosis has been associated with poor physical function and is a well-established predictor of adverse health outcomes in older women, but its impact on health in older men is less well understood. Methods. We conducted a cross-sectional study to evaluate the association of hyperkyphosis and physical function in 2,363 men, aged 71–98 (M = 79) from the Osteoporotic Fractures in Men Study. Kyphosis was measured using the Rancho Bernardo Study block method. Measurements of grip strength and lower extremity function, including gait speed over 6 m, narrow walk (measure of dynamic balance), repeated chair stands ability and time, and lower extremity power (Nottingham Power Rig) were included separately as primary outcomes. We investigated associations of kyphosis and each outcome in age-adjusted and multivariable linear or logistic regression models, controlling for age, clinic, education, race, bone mineral density, height, weight, diabetes, and physical activity. Results. In multivariate linear regression, we observed a dose-related response of worse scores on each lower extremity physical function test as number of blocks increased, p for trend ≤.001. Using a cutoff of ≥4 blocks, 20% (N = 469) of men were characterized with hyperkyphosis. In multivariate logistic regression, men with hyperkyphosis had increased odds (range 1.5–1.8) of being in the worst quartile of performing lower extremity physical function tasks (p < .001 for each outcome). Kyphosis was not associated with grip strength in any multivariate analysis. Conclusions. Hyperkyphosis is associated with impaired lower extremity physical function in older men. Further studies are needed to determine the direction of causality. PMID:25431353

Multivariate functional response regression, with application to fluorescence spectroscopy in a cervical pre-cancer study.

PubMed

Zhu, Hongxiao; Morris, Jeffrey S; Wei, Fengrong; Cox, Dennis D

2017-07-01

Many scientific studies measure different types of high-dimensional signals or images from the same subject, producing multivariate functional data. These functional measurements carry different types of information about the scientific process, and a joint analysis that integrates information across them may provide new insights into the underlying mechanism for the phenomenon under study. Motivated by fluorescence spectroscopy data in a cervical pre-cancer study, a multivariate functional response regression model is proposed, which treats multivariate functional observations as responses and a common set of covariates as predictors. This novel modeling framework simultaneously accounts for correlations between functional variables and potential multi-level structures in data that are induced by experimental design. The model is fitted by performing a two-stage linear transformation-a basis expansion to each functional variable followed by principal component analysis for the concatenated basis coefficients. This transformation effectively reduces the intra-and inter-function correlations and facilitates fast and convenient calculation. A fully Bayesian approach is adopted to sample the model parameters in the transformed space, and posterior inference is performed after inverse-transforming the regression coefficients back to the original data domain. The proposed approach produces functional tests that flag local regions on the functional effects, while controlling the overall experiment-wise error rate or false discovery rate. It also enables functional discriminant analysis through posterior predictive calculation. Analysis of the fluorescence spectroscopy data reveals local regions with differential expressions across the pre-cancer and normal samples. These regions may serve as biomarkers for prognosis and disease assessment.

Pan evaporation modeling using six different heuristic computing methods in different climates of China

NASA Astrophysics Data System (ADS)

Wang, Lunche; Kisi, Ozgur; Zounemat-Kermani, Mohammad; Li, Hui

2017-01-01

Pan evaporation (Ep) plays important roles in agricultural water resources management. One of the basic challenges is modeling Ep using limited climatic parameters because there are a number of factors affecting the evaporation rate. This study investigated the abilities of six different soft computing methods, multi-layer perceptron (MLP), generalized regression neural network (GRNN), fuzzy genetic (FG), least square support vector machine (LSSVM), multivariate adaptive regression spline (MARS), adaptive neuro-fuzzy inference systems with grid partition (ANFIS-GP), and two regression methods, multiple linear regression (MLR) and Stephens and Stewart model (SS) in predicting monthly Ep. Long-term climatic data at various sites crossing a wide range of climates during 1961-2000 are used for model development and validation. The results showed that the models have different accuracies in different climates and the MLP model performed superior to the other models in predicting monthly Ep at most stations using local input combinations (for example, the MAE (mean absolute errors), RMSE (root mean square errors), and determination coefficient (R2) are 0.314 mm/day, 0.405 mm/day and 0.988, respectively for HEB station), while GRNN model performed better in Tibetan Plateau (MAE, RMSE and R2 are 0.459 mm/day, 0.592 mm/day and 0.932, respectively). The accuracies of above models ranked as: MLP, GRNN, LSSVM, FG, ANFIS-GP, MARS and MLR. The overall results indicated that the soft computing techniques generally performed better than the regression methods, but MLR and SS models can be more preferred at some climatic zones instead of complex nonlinear models, for example, the BJ (Beijing), CQ (Chongqing) and HK (Haikou) stations. Therefore, it can be concluded that Ep could be successfully predicted using above models in hydrological modeling studies.

Age and disability drive cognitive impairment in multiple sclerosis across disease subtypes.

PubMed

Ruano, Luis; Portaccio, Emilio; Goretti, Benedetta; Niccolai, Claudia; Severo, Milton; Patti, Francesco; Cilia, Sabina; Gallo, Paolo; Grossi, Paola; Ghezzi, Angelo; Roscio, Marco; Mattioli, Flavia; Stampatori, Chiara; Trojano, Maria; Viterbo, Rosa Gemma; Amato, Maria Pia

2017-08-01

There is limited and inconsistent information on the clinical determinants of cognitive impairment (CI) in multiple sclerosis (MS). The aim of this study was to compare the prevalence and profile of CI across MS disease subtypes and assess its clinical determinants. Cognitive performance was assessed through the Brief Repeatable Battery and the Stroop test in consecutive patients with MS referred to six Italian centers. CI was defined as impairment in ⩾ 2 cognitive domains. A total of 1040 patients were included, 167 with clinically isolated syndrome (CIS), 759 with relapsing remitting (RR), 74 with secondary progressive (SP), and 40 with primary progressive (PP) disease course. The overall prevalence of CI was 46.3%; 34.5% in CIS, 44.5% in RR, 79.4% in SP, and 91.3% in PP. The severity of impairment and the number of involved domains were significantly higher in SP and primary progressive multiple sclerosis (PPMS) than in CIS and RR. In multivariable logistic regression analysis, the presence of CI was significantly associated with higher Expanded Disability Status Scale (EDSS) and older age. CI is present in all MS subtypes since the clinical onset and its frequency is increased in the progressive forms, but these differences seem to be more associated with patient age and physical disability than to disease subtype per se.

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.

Logistic regression analysis of factors associated with avascular necrosis of the femoral head following femoral neck fractures in middle-aged and elderly patients.

PubMed

Ai, Zi-Sheng; Gao, You-Shui; Sun, Yuan; Liu, Yue; Zhang, Chang-Qing; Jiang, Cheng-Hua

2013-03-01

Risk factors for femoral neck fracture-induced avascular necrosis of the femoral head have not been elucidated clearly in middle-aged and elderly patients. Moreover, the high incidence of screw removal in China and its effect on the fate of the involved femoral head require statistical methods to reflect their intrinsic relationship. Ninety-nine patients older than 45 years with femoral neck fracture were treated by internal fixation between May 1999 and April 2004. Descriptive analysis, interaction analysis between associated factors, single factor logistic regression, multivariate logistic regression, and detailed interaction analysis were employed to explore potential relationships among associated factors. Avascular necrosis of the femoral head was found in 15 cases (15.2 %). Age × the status of implants (removal vs. maintenance) and gender × the timing of reduction were interactive according to two-factor interactive analysis. Age, the displacement of fractures, the quality of reduction, and the status of implants were found to be significant factors in single factor logistic regression analysis. Age, age × the status of implants, and the quality of reduction were found to be significant factors in multivariate logistic regression analysis. In fine interaction analysis after multivariate logistic regression analysis, implant removal was the most important risk factor for avascular necrosis in 56-to-85-year-old patients, with a risk ratio of 26.00 (95 % CI = 3.076-219.747). The middle-aged and elderly have less incidence of avascular necrosis of the femoral head following femoral neck fractures treated by cannulated screws. The removal of cannulated screws can induce a significantly high incidence of avascular necrosis of the femoral head in elderly patients, while a high-quality reduction is helpful to reduce avascular necrosis.

Young Adult and Usual Adult Body Mass Index and Multiple Myeloma Risk: A Pooled Analysis in the International Multiple Myeloma Consortium (IMMC).

PubMed

Birmann, Brenda M; Andreotti, Gabriella; De Roos, Anneclaire J; Camp, Nicola J; Chiu, Brian C H; Spinelli, John J; Becker, Nikolaus; Benhaim-Luzon, Véronique; Bhatti, Parveen; Boffetta, Paolo; Brennan, Paul; Brown, Elizabeth E; Cocco, Pierluigi; Costas, Laura; Cozen, Wendy; de Sanjosé, Silvia; Foretová, Lenka; Giles, Graham G; Maynadié, Marc; Moysich, Kirsten; Nieters, Alexandra; Staines, Anthony; Tricot, Guido; Weisenburger, Dennis; Zhang, Yawei; Baris, Dalsu; Purdue, Mark P

2017-06-01

Background: Multiple myeloma risk increases with higher adult body mass index (BMI). Emerging evidence also supports an association of young adult BMI with multiple myeloma. We undertook a pooled analysis of eight case-control studies to further evaluate anthropometric multiple myeloma risk factors, including young adult BMI. Methods: We conducted multivariable logistic regression analysis of usual adult anthropometric measures of 2,318 multiple myeloma cases and 9,609 controls, and of young adult BMI (age 25 or 30 years) for 1,164 cases and 3,629 controls. Results: In the pooled sample, multiple myeloma risk was positively associated with usual adult BMI; risk increased 9% per 5-kg/m 2 increase in BMI [OR, 1.09; 95% confidence interval (CI), 1.04-1.14; P = 0.007]. We observed significant heterogeneity by study design ( P = 0.04), noting the BMI-multiple myeloma association only for population-based studies ( P trend = 0.0003). Young adult BMI was also positively associated with multiple myeloma (per 5-kg/m 2 ; OR, 1.2; 95% CI, 1.1-1.3; P = 0.0002). Furthermore, we observed strong evidence of interaction between younger and usual adult BMI ( P interaction <0.0001); we noted statistically significant associations with multiple myeloma for persons overweight (25-<30 kg/m 2 ) or obese (30+ kg/m 2 ) in both younger and usual adulthood (vs. individuals consistently <25 kg/m 2 ), but not for those overweight or obese at only one time period. Conclusions: BMI-associated increases in multiple myeloma risk were highest for individuals who were overweight or obese throughout adulthood. Impact: These findings provide the strongest evidence to date that earlier and later adult BMI may increase multiple myeloma risk and suggest that healthy BMI maintenance throughout life may confer an added benefit of multiple myeloma prevention. Cancer Epidemiol Biomarkers Prev; 26(6); 876-85. ©2017 AACR . ©2017 American Association for Cancer Research.

An Investigation of Multivariate Adaptive Regression Splines for Modeling and Analysis of Univariate and Semi-Multivariate Time Series Systems

DTIC Science & Technology

1991-09-01

However, there is no guarantee that this would work; for instance if the data were generated by an ARCH model (Tong, 1990 pp. 116-117) then a simple...Hill, R., Griffiths, W., Lutkepohl, H., and Lee, T., Introduction to the Theory and Practice of Econometrics , 2th ed., Wiley, 1985. Kendall, M., Stuart

Willingness-to-accept and purchase genetically modified rice with high folate content in Shanxi Province, China.

PubMed

De Steur, H; Gellynck, X; Storozhenko, S; Liqun, G; Lambert, W; Van Der Straeten, D; Viaene, J

2010-02-01

Neural-tube defects (NTDs) are considered to be the most common congenital malformations. As Shanxi Province, a poor region in the North of China, has one of the highest reported prevalence rates of NTDs in the world, folate fortification of rice is an excellent alternative to low intake of folate acid pills in this region. This paper investigates the relations between socio-demographic indicators, consumer characteristics (knowledge, consumer perceptions on benefits, risks, safety and price), willingness-to-accept and willingness-to-pay genetically modified (GM) rice. The consumer survey compromises 944 face-to-face interviews with rice consumers in Shanxi Province, China. Multivariate analyses consist of multinomial logistic regression and multiple regression. The results indicate that consumers generally are willing-to-accept GM rice, with an acceptance rate of 62.2%. Acceptance is influenced by objective knowledge and consumers' perceptions on benefits and risks. Willingness-to-pay GM rice is influenced by objective knowledge, risk perception and acceptance. Communication towards the use of GM rice should target mainly improving knowledge and consumers' perceptions on high-risk groups within Shanxi Province, in particular low educated women. 2009 Elsevier Ltd. All rights reserved.

Novel spectrophotometric determination of chloramphenicol and dexamethasone in the presence of non labeled interfering substances using univariate methods and multivariate regression model updating

NASA Astrophysics Data System (ADS)

Hegazy, Maha A.; Lotfy, Hayam M.; Rezk, Mamdouh R.; Omran, Yasmin Rostom

2015-04-01

Smart and novel spectrophotometric and chemometric methods have been developed and validated for the simultaneous determination of a binary mixture of chloramphenicol (CPL) and dexamethasone sodium phosphate (DSP) in presence of interfering substances without prior separation. The first method depends upon derivative subtraction coupled with constant multiplication. The second one is ratio difference method at optimum wavelengths which were selected after applying derivative transformation method via multiplying by a decoding spectrum in order to cancel the contribution of non labeled interfering substances. The third method relies on partial least squares with regression model updating. They are so simple that they do not require any preliminary separation steps. Accuracy, precision and linearity ranges of these methods were determined. Moreover, specificity was assessed by analyzing synthetic mixtures of both drugs. The proposed methods were successfully applied for analysis of both drugs in their pharmaceutical formulation. The obtained results have been statistically compared to that of an official spectrophotometric method to give a conclusion that there is no significant difference between the proposed methods and the official ones with respect to accuracy and precision.

Examining the Link Between Public Transit Use and Active Commuting

PubMed Central

Bopp, Melissa; Gayah, Vikash V.; Campbell, Matthew E.

2015-01-01

Background: An established relationship exists between public transportation (PT) use and physical activity. However, there is limited literature that examines the link between PT use and active commuting (AC) behavior. This study examines this link to determine if PT users commute more by active modes. Methods: A volunteer, convenience sample of adults (n = 748) completed an online survey about AC/PT patterns, demographic, psychosocial, community and environmental factors. t-test compared differences between PT riders and non-PT riders. Binary logistic regression analyses examined the effect of multiple factors on AC and a full logistic regression model was conducted to examine AC. Results: Non-PT riders (n = 596) reported less AC than PT riders. There were several significant relationships with AC for demographic, interpersonal, worksite, community and environmental factors when considering PT use. The logistic multivariate analysis for included age, number of children and perceived distance to work as negative predictors and PT use, feelings of bad weather and lack of on-street bike lanes as a barrier to AC, perceived behavioral control and spouse AC were positive predictors. Conclusions: This study revealed the complex relationship between AC and PT use. Further research should investigate how AC and public transit use are related. PMID:25898405

Standing on the shoulders of apes: Analyzing the form and function of the hominoid scapula using geometric morphometrics and finite element analysis.

PubMed

Püschel, Thomas A; Sellers, William I

2016-02-01

The aim was to analyze the relationship between scapular form and function in hominoids by using geometric morphometrics (GM) and finite element analysis (FEA). FEA was used to analyze the biomechanical performance of different hominoid scapulae by simulating static postural scenarios. GM was used to quantify scapular shape differences and the relationship between form and function was analyzed by applying both multivariate-multiple regressions and phylogenetic generalized least-squares regressions (PGLS). Although it has been suggested that primate scapular morphology is mainly a product of function rather than phylogeny, our results showed that shape has a significant phylogenetic signal. There was a significant relationship between scapular shape and its biomechanical performance; hence at least part of the scapular shape variation is due to non-phylogenetic factors, probably related to functional demands. This study has shown that a combined approach using GM and FEA was able to cast some light regarding the functional and phylogenetic contributions in hominoid scapular morphology, thus contributing to a better insight of the association between scapular form and function. © 2015 Wiley Periodicals, Inc.

Body Mass Index (BMI) Is Associated with Microalbuminuria in Chinese Hypertensive Patients

PubMed Central

Liu, Xinyu; Liu, Yu; Chen, Youming; Li, Yongqiang; Shao, Xiaofei; Liang, Yan; Li, Bin; Holthöfer, Harry; Zhang, Guanjing; Zou, Hequn

2015-01-01

There is no general consensus on possible factors associated with microalbuminuria in hypertensive patients nor any reported study about this issue in Chinese patients. To examine this issues, 944 hypertensive patients were enrolled in a study based on a cross-sectional survey conducted in Southern China. Multivariate regression analyses were performed to identify the factors related with the presence of microalbuminuria and urinary excretion of albumin. The prevalence of microalbuminuria in hypertensive and non-diabetic hypertensive patients were 17.16% and 15.25%, respectively. Body mass index (BMI), but not waist circumference (WC), were independently associated with microalbuminuria and the values of urinary albumin to creatinine ratio (ACR) based on multiple regression analyses, even after excluding diabetic patients and patients taking inhibitors of the renin-angiotensin system from the analyses. Furthermore, patients with obesity (BMI ≥28) had higher levels of ACR, compared with those with normal weight (BMI <24 kg/m2) and overweight (24 kg/m2≤ BMI < 28). In conclusion, BMI, as a modifiable factor, is closely associated with microalbuminuria among Chinese hypertensive patients, which may provide a basis for future development of intervention approaches for these patients. PMID:25674785

Workplace exposure to environmental tobacco smoke and high density lipoprotein cholesterol among nonsmokers.

PubMed

Mizoue, T; Ueda, R; Hino, Y; Yoshimura, T

1999-11-15

There are few epidemiologic studies among adult nonsmokers on the effects of workplace environmental tobacco smoke on high density lipoprotein cholesterol (HDL-C). The authors investigated this relation, using data from health examinations conducted in 1995 on 3,062 Japanese nonsmokers in a total of 27 municipal offices with few smoking restrictions. Multiple regression analysis with adjustments for age, body mass index, alcohol drinking, and sports activities showed that in women, and in men lacking both alcohol consumption and sports activities characteristics, there were inverse linear relations between workplace smoking indices and HDL-C levels. Multivariate logistic regression showed that nonsmoking women in the upper two thirds of offices ranked by smoking intensity had an increased risk of low HDL-C levels (<45 mg), taking those in the lowest third of offices as reference (the medium third: odds ratio = 1.7; 95% confidence interval: 1.2, 2.5; the highest third: odds ratio = 1.6; 95% confidence interval: 1.1, 2.4). The results indicated that workplace environmental tobacco smoke exposure is associated with HDL-C among nonsmokers. However, the lack of data on home exposure limits causal inferences about the effects of workplace exposure.

Aspergillus Colonization of the Lung Allograft is a Risk Factor for Bronchiolitis Obliterans Syndrome

PubMed Central

Weigt, S. Samuel; Elashoff, Robert M.; Huang, Cathy; Ardehali, Abbas; Gregson, Aric L.; Kubak, Bernard; Fishbein, Michael C.; Saggar, Rajeev; Keane, Michael P.; Saggar, Rajan; Lynch, Joseph P.; Zisman, David A.; Ross, David J.; Belperio, John A.

2014-01-01

Multiple infections have been linked with the development of bronchiolitis obliterans syndrome (BOS) post-lung transplantation. Lung allograft airway colonization by Aspergillus species is common among lung transplant recipients. We hypothesized that Aspergillus colonization may promote the development of BOS and may decrease survival post-lung transplantation. We reviewed all lung transplant recipients transplanted in our center between 1/2000 and 6/2006. Bronchoscopy was performed according to a surveillance protocol and when clinically indicated. Aspergillus colonization was defined as a positive culture from bronchoalveolar lavage or two sputum cultures positive for the same Aspergillus species, in the absence of invasive pulmonary Aspergillosis. We found that Aspergillus colonization was strongly associated with BOS and BOS related mortality in Cox regression analyses. Aspergillus colonization typically preceded the development of BOS by a median of 261 days (95% CI 87 to 520). Furthermore, in a multivariate Cox regression model, Aspergillus colonization was a distinct risk factor for BOS, independent of acute rejection. These data suggest a potential causative role for Aspergillus colonization in the development of BOS post-lung transplantation and raise the possibility that strategies aimed to prevent Aspergillus colonization may help delay or reduce the incidence of BOS. PMID:19459819

Examining the link between public transit use and active commuting.

PubMed

Bopp, Melissa; Gayah, Vikash V; Campbell, Matthew E

2015-04-17

An established relationship exists between public transportation (PT) use and physical activity. However, there is limited literature that examines the link between PT use and active commuting (AC) behavior. This study examines this link to determine if PT users commute more by active modes. A volunteer, convenience sample of adults (n = 748) completed an online survey about AC/PT patterns, demographic, psychosocial, community and environmental factors. t-test compared differences between PT riders and non-PT riders. Binary logistic regression analyses examined the effect of multiple factors on AC and a full logistic regression model was conducted to examine AC. Non-PT riders (n = 596) reported less AC than PT riders. There were several significant relationships with AC for demographic, interpersonal, worksite, community and environmental factors when considering PT use. The logistic multivariate analysis for included age, number of children and perceived distance to work as negative predictors and PT use, feelings of bad weather and lack of on-street bike lanes as a barrier to AC, perceived behavioral control and spouse AC were positive predictors. This study revealed the complex relationship between AC and PT use. Further research should investigate how AC and public transit use are related.

Development of a Risk Assessment Tool to Predict Fall-Related Severe Injuries Occurring in a Hospital

PubMed Central

Toyabe, Shin-ichi

2014-01-01

Inpatient falls are the most common adverse events that occur in a hospital, and about 3 to 10% of falls result in serious injuries such as bone fractures and intracranial haemorrhages. We previously reported that bone fractures and intracranial haemorrhages were two major fall-related injuries and that risk assessment score for osteoporotic bone fracture was significantly associated not only with bone fractures after falls but also with intracranial haemorrhage after falls. Based on the results, we tried to establish a risk assessment tool for predicting fall-related severe injuries in a hospital. Possible risk factors related to fall-related serious injuries were extracted from data on inpatients that were admitted to a tertiary-care university hospital by using multivariate Cox’ s regression analysis and multiple logistic regression analysis. We found that fall risk score and fracture risk score were the two significant factors, and we constructed models to predict fall-related severe injuries incorporating these factors. When the prediction model was applied to another independent dataset, the constructed model could detect patients with fall-related severe injuries efficiently. The new assessment system could identify patients prone to severe injuries after falls in a reproducible fashion. PMID:25168984

Prediction of Excess Weight Loss after Laparoscopic Roux-en-Y Gastric Bypass: Data from an Artificial Neural Network

PubMed Central

Wise, Eric S.; Hocking, Kyle M.; Kavic, Stephen M.

2015-01-01

Introduction Laparoscopic Roux-en-Y Gastric Bypass (LRYGB) has become the gold standard for surgical weight loss. The success of LRYGB may be measured by excess body-mass index loss (%EBMIL) over 25 kg/m2, which is partially determined by multiple patient factors. In this study, artificial neural network (ANN) modeling was used to derive a reasonable estimate of expected postoperative weight loss using only known preoperative patient variables. Additionally, ANN modeling allowed for the discriminant prediction of achievement of benchmark 50% EBMIL at one year postoperatively. Methods Six-hundred and forty-seven LRYGB included patients were retrospectively reviewed for preoperative factors independently associated with EBMIL at 180 and 365 days postoperatively (EBMIL180 and EBMIL365, respectively). Previously validated factors were selectively analyzed, including age; race; gender; preoperative BMI (BMI0); hemoglobin; and diagnoses of hypertension (HTN), diabetes mellitus (DM), and depression or anxiety disorder. Variables significant upon multivariate analysis (P<.05) were modeled by “traditional” multiple linear regression and an ANN, to predict %EBMIL180 and %EBMIL365. Results The mean EBMIL180 and EBMIL365 were 56.4%±16.5% and 73.5%±21.5%, corresponding to total body weight losses of 25.7%±5.9% and 33.6%±8.0%, respectively. Upon multivariate analysis, independent factors associated with EBMIL180 included black race (B=−6.3%, P<.001), BMI0 (B=−1.1%/unit BMI, P<.001) and DM (B=−3.2%, P<.004). For EBMIL365, independently associated factors were female gender (B=6.4%, P<.001), black race (B=−6.7%, P<.001), BMI0 (B=−1.2%/unit BMI, P<.001), HTN (B=−3.7%, P=.03) and DM (B=−6.0%, P<.001). Pearson r2 values for the multiple linear regression and ANN models were .38 (EBMIL180) and .35 (EBMIL365), and .42 (EBMIL180) and .38 (EBMIL365), respectively. ANN-prediction of benchmark 50% EBMIL at 365 days generated an area under the curve of 0.78±0.03 in the training set (n=518), and 0.83±0.04 (n=129) in the validation set. Conclusions Available at https://redcap.vanderbilt.edu/surveys/?s=3HCR43AKXR, this, or other ANN models may be used to provide an optimized estimate of postoperative EBMIL following LRYGB. PMID:26017908

Meta-analysis of quantitative pleiotropic traits for next-generation sequencing with multivariate functional linear models

PubMed Central

Chiu, Chi-yang; Jung, Jeesun; Chen, Wei; Weeks, Daniel E; Ren, Haobo; Boehnke, Michael; Amos, Christopher I; Liu, Aiyi; Mills, James L; Ting Lee, Mei-ling; Xiong, Momiao; Fan, Ruzong

2017-01-01

To analyze next-generation sequencing data, multivariate functional linear models are developed for a meta-analysis of multiple studies to connect genetic variant data to multiple quantitative traits adjusting for covariates. The goal is to take the advantage of both meta-analysis and pleiotropic analysis in order to improve power and to carry out a unified association analysis of multiple studies and multiple traits of complex disorders. Three types of approximate F -distributions based on Pillai–Bartlett trace, Hotelling–Lawley trace, and Wilks's Lambda are introduced to test for association between multiple quantitative traits and multiple genetic variants. Simulation analysis is performed to evaluate false-positive rates and power of the proposed tests. The proposed methods are applied to analyze lipid traits in eight European cohorts. It is shown that it is more advantageous to perform multivariate analysis than univariate analysis in general, and it is more advantageous to perform meta-analysis of multiple studies instead of analyzing the individual studies separately. The proposed models require individual observations. The value of the current paper can be seen at least for two reasons: (a) the proposed methods can be applied to studies that have individual genotype data; (b) the proposed methods can be used as a criterion for future work that uses summary statistics to build test statistics to meta-analyze the data. PMID:28000696

Meta-analysis of quantitative pleiotropic traits for next-generation sequencing with multivariate functional linear models.

PubMed

Chiu, Chi-Yang; Jung, Jeesun; Chen, Wei; Weeks, Daniel E; Ren, Haobo; Boehnke, Michael; Amos, Christopher I; Liu, Aiyi; Mills, James L; Ting Lee, Mei-Ling; Xiong, Momiao; Fan, Ruzong

2017-02-01

To analyze next-generation sequencing data, multivariate functional linear models are developed for a meta-analysis of multiple studies to connect genetic variant data to multiple quantitative traits adjusting for covariates. The goal is to take the advantage of both meta-analysis and pleiotropic analysis in order to improve power and to carry out a unified association analysis of multiple studies and multiple traits of complex disorders. Three types of approximate F -distributions based on Pillai-Bartlett trace, Hotelling-Lawley trace, and Wilks's Lambda are introduced to test for association between multiple quantitative traits and multiple genetic variants. Simulation analysis is performed to evaluate false-positive rates and power of the proposed tests. The proposed methods are applied to analyze lipid traits in eight European cohorts. It is shown that it is more advantageous to perform multivariate analysis than univariate analysis in general, and it is more advantageous to perform meta-analysis of multiple studies instead of analyzing the individual studies separately. The proposed models require individual observations. The value of the current paper can be seen at least for two reasons: (a) the proposed methods can be applied to studies that have individual genotype data; (b) the proposed methods can be used as a criterion for future work that uses summary statistics to build test statistics to meta-analyze the data.

Cervical human papillomavirus infection among young women engaged in sex work in Phnom Penh, Cambodia: prevalence, genotypes, risk factors and association with HIV infection.

PubMed

Couture, Marie-Claude; Page, Kimberly; Stein, Ellen S; Sansothy, Neth; Sichan, Keo; Kaldor, John; Evans, Jennifer L; Maher, Lisa; Palefsky, Joel

2012-07-28

Although cervical cancer is the leading cancer in Cambodia, most women receive no routine screening for cervical cancer and few treatment options exist. Moreover, nothing is known regarding the prevalence of cervical HPV or the genotypes present among women in the country. Young sexually active women, especially those with multiple sex partners are at highest risk of HPV infection. We examine the prevalence and genotypes of cervical HPV, as well as the associated risk factors among young women engaged in sex work in Phnom Penh, Cambodia. We conducted a cross-sectional study among 220 young women (15-29 years) engaged in sex work in different venues including brothels or entertainment establishments, and on a freelance basis in streets, parks and private apartments. Cervical specimens were collected using standard cytobrush technique. HPV DNA was tested for by polymerase chain reaction (PCR) and genotyping using type-specific probes for 29 individual HPV types, as well as for a mixture of 10 less common HPV types. All participants were also screened for HIV status using blood samples. Multivariate logistic regression analyses were conducted to assess risk factors for any or multiple HPV infection. The prevalence of cervical HPV 41.1%. HPV 51 and 70 were the most common (5.0%), followed by 16 (4.6%), 71 (4.1%) and 81 (3.7%). Thirty-six women (16.4%) were infected with multiple genotypes and 23.3% were infected with at least one oncogenic HPV type. In multivariate analyses, having HIV infection and a higher number of sexual partners were associated with cervical HPV infection. Risk factors for infection with multiple genotypes included working as freelance female sex workers (FSW) or in brothels, recent binge use of drugs, high number of sexual partners, and HIV infection. This is the first Cambodian study on cervical HPV prevalence and genotypes. We found that HPV infection was common among young FSW, especially among women infected with HIV. These results underscore the urgent need for accessible cervical cancer screening and treatment, as well as for a prophylactic vaccine that covers the HPV subtypes present in Cambodia.

Predictors of clinical outcome in pediatric oligodendroglioma: meta-analysis of individual patient data and multiple imputation.

PubMed

Wang, Kevin Yuqi; Vankov, Emilian R; Lin, Doris Da May

2018-02-01

OBJECTIVE Oligodendroglioma is a rare primary CNS neoplasm in the pediatric population, and only a limited number of studies in the literature have characterized this entity. Existing studies are limited by small sample sizes and discrepant interstudy findings in identified prognostic factors. In the present study, the authors aimed to increase the statistical power in evaluating for potential prognostic factors of pediatric oligodendrogliomas and sought to reconcile the discrepant findings present among existing studies by performing an individual-patient-data (IPD) meta-analysis and using multiple imputation to address data not directly available from existing studies. METHODS A systematic search was performed, and all studies found to be related to pediatric oligodendrogliomas and associated outcomes were screened for inclusion. Each study was searched for specific demographic and clinical characteristics of each patient and the duration of event-free survival (EFS) and overall survival (OS). Given that certain demographic and clinical information of each patient was not available within all studies, a multivariable imputation via chained equations model was used to impute missing data after the mechanism of missing data was determined. The primary end points of interest were hazard ratios for EFS and OS, as calculated by the Cox proportional-hazards model. Both univariate and multivariate analyses were performed. The multivariate model was adjusted for age, sex, tumor grade, mixed pathologies, extent of resection, chemotherapy, radiation therapy, tumor location, and initial presentation. A p value of less than 0.05 was considered statistically significant. RESULTS A systematic search identified 24 studies with both time-to-event and IPD characteristics available, and a total of 237 individual cases were available for analysis. A median of 19.4% of the values among clinical, demographic, and outcome variables in the compiled 237 cases were missing. Multivariate Cox regression analysis revealed subtotal resection (p = 0.007 [EFS] and 0.043 [OS]), initial presentation of headache (p = 0.006 [EFS] and 0.004 [OS]), mixed pathologies (p = 0.005 [EFS] and 0.049 [OS]), and location of the tumor in the parietal lobe (p = 0.044 [EFS] and 0.030 [OS]) to be significant predictors of tumor progression or recurrence and death. CONCLUSIONS The use of IPD meta-analysis provides a valuable means for increasing statistical power in investigations of disease entities with a very low incidence. Missing data are common in research, and multiple imputation is a flexible and valid approach for addressing this issue, when it is used conscientiously. Undergoing subtotal resection, having a parietal tumor, having tumors with mixed pathologies, and suffering headaches at the time of diagnosis portended a poorer prognosis in pediatric patients with oligodendroglioma.

Effects of Social Class and School Conditions on Educational Enrollment and Achievement of Boys and Girls in Rural Viet Nam

ERIC Educational Resources Information Center

Nguyen, Phuong L.

2006-01-01

This study examines the effects of parental SES, school quality, and community factors on children's enrollment and achievement in rural areas in Viet Nam, using logistic regression and ordered logistic regression. Multivariate analysis reveals significant differences in educational enrollment and outcomes by level of household expenditures and…

Multivariate Autoregressive Modeling and Granger Causality Analysis of Multiple Spike Trains

PubMed Central

Krumin, Michael; Shoham, Shy

2010-01-01

Recent years have seen the emergence of microelectrode arrays and optical methods allowing simultaneous recording of spiking activity from populations of neurons in various parts of the nervous system. The analysis of multiple neural spike train data could benefit significantly from existing methods for multivariate time-series analysis which have proven to be very powerful in the modeling and analysis of continuous neural signals like EEG signals. However, those methods have not generally been well adapted to point processes. Here, we use our recent results on correlation distortions in multivariate Linear-Nonlinear-Poisson spiking neuron models to derive generalized Yule-Walker-type equations for fitting ‘‘hidden” Multivariate Autoregressive models. We use this new framework to perform Granger causality analysis in order to extract the directed information flow pattern in networks of simulated spiking neurons. We discuss the relative merits and limitations of the new method. PMID:20454705

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.

A comparison of model-based imputation methods for handling missing predictor values in a linear regression model: A simulation study

NASA Astrophysics Data System (ADS)

Hasan, Haliza; Ahmad, Sanizah; Osman, Balkish Mohd; Sapri, Shamsiah; Othman, Nadirah

2017-08-01

In regression analysis, missing covariate data has been a common problem. Many researchers use ad hoc methods to overcome this problem due to the ease of implementation. However, these methods require assumptions about the data that rarely hold in practice. Model-based methods such as Maximum Likelihood (ML) using the expectation maximization (EM) algorithm and Multiple Imputation (MI) are more promising when dealing with difficulties caused by missing data. Then again, inappropriate methods of missing value imputation can lead to serious bias that severely affects the parameter estimates. The main objective of this study is to provide a better understanding regarding missing data concept that can assist the researcher to select the appropriate missing data imputation methods. A simulation study was performed to assess the effects of different missing data techniques on the performance of a regression model. The covariate data were generated using an underlying multivariate normal distribution and the dependent variable was generated as a combination of explanatory variables. Missing values in covariate were simulated using a mechanism called missing at random (MAR). Four levels of missingness (10%, 20%, 30% and 40%) were imposed. ML and MI techniques available within SAS software were investigated. A linear regression analysis was fitted and the model performance measures; MSE, and R-Squared were obtained. Results of the analysis showed that MI is superior in handling missing data with highest R-Squared and lowest MSE when percent of missingness is less than 30%. Both methods are unable to handle larger than 30% level of missingness.

Procedures for using signals from one sensor as substitutes for signals of another

NASA Technical Reports Server (NTRS)

Suits, G.; Malila, W.; Weller, T.

1988-01-01

Long-term monitoring of surface conditions may require a transfer from using data from one satellite sensor to data from a different sensor having different spectral characteristics. Two general procedures for spectral signal substitution are described in this paper, a principal-components procedure and a complete multivariate regression procedure. They are evaluated through a simulation study of five satellite sensors (MSS, TM, AVHRR, CZCS, and HRV). For illustration, they are compared to another recently described procedure for relating AVHRR and MSS signals. The multivariate regression procedure is shown to be best. TM can accurately emulate the other sensors, but they, on the other hand, have difficulty in accurately emulating its shortwave infrared bands (TM5 and TM7).

Multivariate Analysis of Seismic Field Data

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

Alam, M. Kathleen

1999-06-01

This report includes the details of the model building procedure and prediction of seismic field data. Principal Components Regression, a multivariate analysis technique, was used to model seismic data collected as two pieces of equipment were cycled on and off. Models built that included only the two pieces of equipment of interest had trouble predicting data containing signals not included in the model. Evidence for poor predictions came from the prediction curves as well as spectral F-ratio plots. Once the extraneous signals were included in the model, predictions improved dramatically. While Principal Components Regression performed well for the present datamore » sets, the present data analysis suggests further work will be needed to develop more robust modeling methods as the data become more complex.« less

Non-proportional odds multivariate logistic regression of ordinal family data.

PubMed

Zaloumis, Sophie G; Scurrah, Katrina J; Harrap, Stephen B; Ellis, Justine A; Gurrin, Lyle C

2015-03-01

Methods to examine whether genetic and/or environmental sources can account for the residual variation in ordinal family data usually assume proportional odds. However, standard software to fit the non-proportional odds model to ordinal family data is limited because the correlation structure of family data is more complex than for other types of clustered data. To perform these analyses we propose the non-proportional odds multivariate logistic regression model and take a simulation-based approach to model fitting using Markov chain Monte Carlo methods, such as partially collapsed Gibbs sampling and the Metropolis algorithm. We applied the proposed methodology to male pattern baldness data from the Victorian Family Heart Study. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A Framework and Algorithms for Multivariate Time Series Analytics (MTSA): Learning, Monitoring, and Recommendation

ERIC Educational Resources Information Center

Ngan, Chun-Kit

2013-01-01

Making decisions over multivariate time series is an important topic which has gained significant interest in the past decade. A time series is a sequence of data points which are measured and ordered over uniform time intervals. A multivariate time series is a set of multiple, related time series in a particular domain in which domain experts…

Adolescent suicide and health risk behaviors: Rhode Island's 2007 Youth Risk Behavior Survey.

PubMed

Jiang, Yongwen; Perry, Donald K; Hesser, Jana E

2010-05-01

Suicide is the third-leading cause of death among high school students in the U.S. This study examined the relationships among indicators of depressed mood, suicidal thoughts, suicide attempts, and demographics and risk behaviors in Rhode Island high school students. Data from Rhode Island's 2007 Youth Risk Behavior Survey were utilized for this study. The statewide sample contained 2210 randomly selected public high school students. Data were analyzed in 2008 to model for each of five depressed mood/suicide indicators using multivariable logistic regression. By examining depressed mood and suicide indicators through a multivariable approach, the strongest predictors were identified, for multiple as well as specific suicide indicators. These predictors included being female, having low grades, speaking a language other than English at home, being lesbian/gay/bisexual/unsure of sexual orientation, not going to school as a result of feeling unsafe, having been a victim of forced sexual intercourse, being a current cigarette smoker, and having a self-perception of being overweight. The strength of associations between three factors (immigrant status, feeling unsafe, and having forced sex) and suicide indicators adds new information about potential predictors of suicidal behavior in adolescents. 2010 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.

The influence of hydrology and waterway distance on population structure of Chinook salmon Oncorhynchus tshawytscha in a large river.

PubMed

Olsen, J B; Beacham, T D; Wetklo, M; Seeb, L W; Smith, C T; Flannery, B G; Wenburg, J K

2010-04-01

Adult Chinook salmon Oncorhynchus tshawytscha navigate in river systems using olfactory cues that may be influenced by hydrologic factors such as flow and the number, size and spatial distribution of tributaries. Thus, river hydrology may influence both homing success and the level of straying (gene flow), which in turn influences population structure. In this study, two methods of multivariate analysis were used to examine the extent to which four indicators of hydrology and waterway distance explained population structure of O. tshawytscha in the Yukon River. A partial Mantel test showed that the indicators of hydrology were positively associated with broad-scale (Yukon basin) population structure, when controlling for the influence of waterway distance. Multivariate multiple regression showed that waterway distance, supplemented with the number and flow of major drainage basins, explained more variation in broad-scale population structure than any single indicator. At an intermediate spatial scale, indicators of hydrology did not appear to influence population structure after accounting for waterway distance. These results suggest that habitat changes in the Yukon River, which alter hydrology, may influence the basin-wide pattern of population structure in O. tshawytscha. Further research is warranted on the role of hydrology in concert with waterway distance in influencing population structure in Pacific salmon.

Low vascularity predicts favourable outcomes in leiomyoma patients treated with uterine artery embolization.

PubMed

Tang, Yixin; Chen, Chunlin; Duan, Hui; Ma, Ben; Liu, Ping

2016-10-01

To investigate the clinical factors predicting outcomes of leiomyoma treated with uterine artery embolization (UAE). A total of 183 uterine leiomyoma patients undergoing UAE were retrospectively analyzed. Patient age, characteristics of vascular supply in magnetic resonance imaging (MRI)/digital subtraction angiography (DSA), number, size and location of leiomyoma were recorded. Leiomyoma regrowth, new leiomyoma appearance and recurrence of any previously reported symptoms were carefully monitored over a mean follow-up of 30 months (median 32 months, range 12-80). Potential recurrence risk factors were analyzed by univariate and multivariate cox regression analysis. Twenty-three recurrences were recorded. The difference in the vascularity classification systems between MRI and DSA was not statistically significant (P = 0.059). High vascularity in MRI, high vascularity in DSA and multiple leiomyoma showed a significant risk of recurrence using univariate and multivariate analysis (P = 0.004, P < 0.001 and P = 0.023, respectively). The other factors were not significantly associated with leiomyoma recurrence (P > 0.05). Low vascularity and solitary leiomyoma indicated favourable outcomes in patients treated with UAE. • Low vascularity and solitary mass predicted favourable outcomes in UAE-treated patients. • MRI might provide information on vascularity in leiomyoma before UAE. • Variations in vascular supply, age, size, location were not associated with recurrence.

Spatial assessment of air quality patterns in Malaysia using multivariate analysis

NASA Astrophysics Data System (ADS)

Dominick, Doreena; Juahir, Hafizan; Latif, Mohd Talib; Zain, Sharifuddin M.; Aris, Ahmad Zaharin

2012-12-01

This study aims to investigate possible sources of air pollutants and the spatial patterns within the eight selected Malaysian air monitoring stations based on a two-year database (2008-2009). The multivariate analysis was applied on the dataset. It incorporated Hierarchical Agglomerative Cluster Analysis (HACA) to access the spatial patterns, Principal Component Analysis (PCA) to determine the major sources of the air pollution and Multiple Linear Regression (MLR) to assess the percentage contribution of each air pollutant. The HACA results grouped the eight monitoring stations into three different clusters, based on the characteristics of the air pollutants and meteorological parameters. The PCA analysis showed that the major sources of air pollution were emissions from motor vehicles, aircraft, industries and areas of high population density. The MLR analysis demonstrated that the main pollutant contributing to variability in the Air Pollutant Index (API) at all stations was particulate matter with a diameter of less than 10 μm (PM10). Further MLR analysis showed that the main air pollutant influencing the high concentration of PM10 was carbon monoxide (CO). This was due to combustion processes, particularly originating from motor vehicles. Meteorological factors such as ambient temperature, wind speed and humidity were also noted to influence the concentration of PM10.

Characterizing Durations of Heroin Abstinence in the California Civil Addict Program: Results From a 33-Year Observational Cohort Study

PubMed Central

Nosyk, Bohdan; Anglin, M. Douglas; Brecht, Mary-Lynn; Lima, Viviane Dias; Hser, Yih-Ing

2013-01-01

In accordance with the chronic disease model of opioid dependence, cessation is often observed as a longitudinal process rather than a discrete endpoint. We aimed to characterize and identify predictors of periods of heroin abstinence in the natural history of recovery from opioid dependence. Data were collected on participants from California who were enrolled in the Civil Addict Program from 1962 onward by use of a natural history interview. Multivariate regression using proportional hazards frailty models was applied to identify independent predictors and correlates of repeated abstinence episode durations. Among 471 heroin-dependent males, 387 (82.2%) reported 932 abstinence episodes, 60.3% of which lasted at least 1 year. Multivariate analysis revealed several important findings. First, demographic factors such as age and ethnicity did not explain variation in durations of abstinence episodes. However, employment and lower drug use severity predicted longer episodes. Second, abstinence durations were longer following sustained treatment versus incarceration. Third, individuals with multiple abstinence episodes remained abstinent for longer durations in successive episodes. Finally, abstinence episodes initiated >10 and ≤20 years after first use lasted longer than others. Public policy facilitating engagement of opioid-dependent individuals in maintenance-oriented drug treatment and employment is recommended to achieve and sustain opioid abstinence. PMID:23445901

Development of an accelerometer-based multivariate model to predict free-living energy expenditure in a large military cohort.

PubMed

Horner, Fleur; Bilzon, James L; Rayson, Mark; Blacker, Sam; Richmond, Victoria; Carter, James; Wright, Anthony; Nevill, Alan

2013-01-01

This study developed a multivariate model to predict free-living energy expenditure (EE) in independent military cohorts. Two hundred and eighty-eight individuals (20.6 ± 3.9 years, 67.9 ± 12.0 kg, 1.71 ± 0.10 m) from 10 cohorts wore accelerometers during observation periods of 7 or 10 days. Accelerometer counts (PAC) were recorded at 1-minute epochs. Total energy expenditure (TEE) and physical activity energy expenditure (PAEE) were derived using the doubly labelled water technique. Data were reduced to n = 155 based on wear-time. Associations between PAC and EE were assessed using allometric modelling. Models were derived using multiple log-linear regression analysis and gender differences assessed using analysis of covariance. In all models PAC, height and body mass were related to TEE (P < 0.01). For models predicting TEE (r (2) = 0.65, SE = 462 kcal · d(-1) (13.0%)), PAC explained 4% of the variance. For models predicting PAEE (r (2) = 0.41, SE = 490 kcal · d(-1) (32.0%)), PAC accounted for 6% of the variance. Accelerometry increases the accuracy of EE estimation in military populations. However, the unique nature of military life means accurate prediction of individual free-living EE is highly dependent on anthropometric measurements.

Prediction of the birch pollen season characteristics in Cracow, Poland using an 18-year data series.

PubMed

Dorota, Myszkowska

2013-03-01

The aim of the study was to construct the model forecasting the birch pollen season characteristics in Cracow on the basis of an 18-year data series. The study was performed using the volumetric method (Lanzoni/Burkard trap). The 98/95 % method was used to calculate the pollen season. The Spearman's correlation test was applied to find the relationship between the meteorological parameters and pollen season characteristics. To construct the predictive model, the backward stepwise multiple regression analysis was used including the multi-collinearity of variables. The predictive models best fitted the pollen season start and end, especially models containing two independent variables. The peak concentration value was predicted with the higher prediction error. Also the accuracy of the models predicting the pollen season characteristics in 2009 was higher in comparison with 2010. Both, the multi-variable model and one-variable model for the beginning of the pollen season included air temperature during the last 10 days of February, while the multi-variable model also included humidity at the beginning of April. The models forecasting the end of the pollen season were based on temperature in March-April, while the peak day was predicted using the temperature during the last 10 days of March.

Perception of control, coping and psychological stress of infertile women undergoing IVF.

PubMed

Gourounti, Kleanthi; Anagnostopoulos, Fotios; Potamianos, Grigorios; Lykeridou, Katerina; Schmidt, Lone; Vaslamatzis, Grigorios

2012-06-01

The study aimed to examine: (i) the association between perception of infertility controllability and coping strategies; and (ii) the association between perception of infertility controllability and coping strategies to psychological distress, applying multivariate statistical techniques to control for the effects of demographic variables. This cross-sectional study included 137 women with fertility problems undergoing IVF in a public hospital. All participants completed questionnaires that measured fertility-related stress, state anxiety, depressive symptomatology, perception of control and coping strategies. Pearson's correlation coefficients were calculated between all study variables, followed by hierarchical multiple linear regression. Low perception of personal and treatment controllability was associated with frequent use of avoidance coping and high perception of treatment controllability was positively associated with problem-focused coping. Multivariate analysis showed that, when controlling for demographic factors, low perception of personal control and avoidance coping were positively associated with fertility-related stress and state anxiety, and problem-appraisal coping was negatively and significantly associated with fertility-related stress and depressive symptomatology scores. The findings of this study merit the understanding of the role of control perception and coping in psychological stress of infertile women to identify beforehand those women who might be at risk of experiencing high stress and in need of support. Copyright © 2012 Reproductive Healthcare Ltd. Published by Elsevier Ltd. All rights reserved.

Repair of pediatric bladder rupture improves survival: results from the National Trauma Data Bank.

PubMed

Deibert, Christopher M; Glassberg, Kenneth I; Spencer, Benjamin A

2012-09-01

The urinary bladder is the second most commonly injured genitourinary organ. The objective of this study was to describe the management of pediatric traumatic bladder ruptures in the United States and their association with surgical repair and mortality. We searched the 2002-2008 National Trauma Data Bank for all pediatric (<18 years old) subjects with bladder rupture. Demographics, mechanism of injury, coexisting injury severity, and operative interventions for bladder and other abdominal trauma are described. Multivariate logistic regression analysis was used to examine the relationship between bladder rupture and both bladder surgery and in-hospital mortality. We identified 816 children who sustained bladder trauma. Forty-four percent underwent bladder surgery, including 17% with an intraperitoneal injury. Eighteen percent had 2 intra-abdominal injuries, and 40% underwent surgery to other abdominal organs. In multivariate analysis, operative bladder repair reduced the likelihood of in-hospital mortality by 82%. A greater likelihood of dying was seen among the uninsured and those with more severe injuries and multiple abdominal injuries. After bladder trauma, pediatric patients demonstrate significantly improved survival when the bladder is surgically repaired. With only 67% of intraperitoneal bladder injuries being repaired, there appears to be underuse of a life-saving procedure. Copyright © 2012 Elsevier Inc. All rights reserved.

[Retrospective statistical analysis of clinical factors of recurrence in chronic subdural hematoma: correlation between univariate and multivariate analysis].

PubMed

Takayama, Motoharu; Terui, Keita; Oiwa, Yoshitsugu

2012-10-01

Chronic subdural hematoma is common in elderly individuals and surgical procedures are simple. The recurrence rate of chronic subdural hematoma, however, varies from 9.2 to 26.5% after surgery. The authors studied factors of the recurrence using univariate and multivariate analyses in patients with chronic subdural hematoma We retrospectively reviewed 239 consecutive cases of chronic subdural hematoma who received burr-hole surgery with irrigation and closed-system drainage. We analyzed the relationships between recurrence of chronic subdural hematoma and factors such as sex, age, laterality, bleeding tendency, other complicated diseases, density on CT, volume of the hematoma, residual air in the hematoma cavity, use of artificial cerebrospinal fluid. Twenty-one patients (8.8%) experienced a recurrence of chronic subdural hematoma. Multiple logistic regression found that the recurrence rate was higher in patients with a large volume of the residual air, and was lower in patients using artificial cerebrospinal fluid. No statistical differences were found in bleeding tendency. Techniques to reduce the air in the hematoma cavity are important for good outcome in surgery of chronic subdural hematoma. Also, the use of artificial cerebrospinal fluid reduces recurrence of chronic subdural hematoma. The surgical procedures can be the same for patients with bleeding tendencies.

Cognitive function and dialysis adequacy: no clear relationship.

PubMed

Giang, Lena M; Weiner, Daniel E; Agganis, Brian T; Scott, Tammy; Sorensen, Eric P; Tighiouart, Hocine; Sarnak, Mark J

2011-01-01

Cognitive impairment is common in hemodialysis patients and may be impacted by multiple patient and treatment characteristics. The impact of dialysis dose on cognitive function remains uncertain, particularly in the current era of increased dialysis dose and flux. We explored the cross-sectional relationship between dialysis adequacy and cognitive function in a cohort of maintenance hemodialysis patients. Adequacy was defined as the average of the 3 most proximate single pool Kt/V assessments. A detailed neurocognitive battery was administered during the 1st hour of dialysis. Multivariable linear regression models were adjusted for age, sex, education, race and other clinical and demographic characteristics. Among 273 patients who underwent cognitive testing, the mean (SD) age was 63 (17) years and the median dialysis duration was 13 months, 47% were woman, 22% were African American, and 48% had diabetes. The mean (SD) Kt/V was 1.51 (0.24). In univariate, parsimonious and multivariable models, there were no significant relationships between decreased cognitive function and lower Kt/V. In contrast to several older studies, there is no association between lower Kt/V and worse cognitive performance in the current era of increased dialysis dose. Future studies should address the longitudinal relationship between adequacy of dialysis and cognitive function to confirm these findings. Copyright © 2010 S. Karger AG, Basel.

Racial-ethnic Disparities in Postpartum Hemorrhage in Native Hawaiians, Pacific Islanders, and Asians.

PubMed

Harvey, Scott A; Lim, Eunjung; Gandhi, Krupa R; Miyamura, Jill; Nakagawa, Kazuma

2017-05-01

The objective of this study was to assess racial-ethnic differences in the prevalence of postpartum hemorrhage (PPH) among Native Hawaiians and other Pacific Islanders (NHOPI), Asians, and Whites. We performed a retrospective study on statewide inpatient data for delivery hospitalizations in Hawai'i between January 1995 and December 2013. A total of 243,693 in-hospital delivery discharges (35.0% NHOPI, 44.0% Asian, and 21.0% White) were studied. Among patients with PPH, there were more NHOPI (37.1%) and Asians (47.6%), compared to Whites (15.3%). Multivariable logistic regression was used to assess the impact of maternal race-ethnicity on the prevalence of PPH after adjusting for delivery type, labor induction, prolonged labor, multiple gestation, gestational hypertension, gestational diabetes, preeclampsia, chorioamnionitis, placental abruption, placenta previa, obesity, and period with different diagnostic criteria for preeclampsia. In the multivariable analyses, NHOPI (adjusted odds ratio [aOR], 1.40; 95% confidence interval [CI], 1.32-1.48) and Asians (aOR, 1.45; 95% CI, 1.37-1.53) were more likely to have PPH compared to Whites. In the secondary analyses of 12,142 discharges with PPH, NHOPI and Asians had higher prevalence of uterine atony than Whites (NHOPI: 77.2%, Asians: 73.9% vs Whites: 65.1%, P < .001 for both comparisons).

The Unmet Health Care Needs of Homeless Adults: A National Study

PubMed Central

O'Connell, James J.; Singer, Daniel E.; Rigotti, Nancy A.

2010-01-01

Objectives. We assessed the prevalence and predictors of past-year unmet needs for 5 types of health care services in a national sample of homeless adults. Methods. We analyzed data from 966 adult respondents to the 2003 Health Care for the Homeless User Survey, a sample representing more than 436 000 individuals nationally. Using multivariable logistic regression, we determined the independent predictors of each type of unmet need. Results. Seventy-three percent of the respondents reported at least one unmet health need, including an inability to obtain needed medical or surgical care (32%), prescription medications (36%), mental health care (21%), eyeglasses (41%), and dental care (41%). In multivariable analyses, significant predictors of unmet needs included food insufficiency, out-of-home placement as a minor, vision impairment, and lack of health insurance. Individuals who had been employed in the past year were more likely than those who had not to be uninsured and to have unmet needs for medical care and prescription medications. Conclusions. This national sample of homeless adults reported substantial unmet needs for multiple types of health care. Expansion of health insurance may improve health care access for homeless adults, but addressing the unique challenges inherent to homelessness will also be required. PMID:20466953

Statistical mixture design and multivariate analysis of inkjet printed a-WO3/TiO2/WOX electrochromic films.

PubMed

Wojcik, Pawel Jerzy; Pereira, Luís; Martins, Rodrigo; Fortunato, Elvira

2014-01-13

An efficient mathematical strategy in the field of solution processed electrochromic (EC) films is outlined as a combination of an experimental work, modeling, and information extraction from massive computational data via statistical software. Design of Experiment (DOE) was used for statistical multivariate analysis and prediction of mixtures through a multiple regression model, as well as the optimization of a five-component sol-gel precursor subjected to complex constraints. This approach significantly reduces the number of experiments to be realized, from 162 in the full factorial (L=3) and 72 in the extreme vertices (D=2) approach down to only 30 runs, while still maintaining a high accuracy of the analysis. By carrying out a finite number of experiments, the empirical modeling in this study shows reasonably good prediction ability in terms of the overall EC performance. An optimized ink formulation was employed in a prototype of a passive EC matrix fabricated in order to test and trial this optically active material system together with a solid-state electrolyte for the prospective application in EC displays. Coupling of DOE with chromogenic material formulation shows the potential to maximize the capabilities of these systems and ensures increased productivity in many potential solution-processed electrochemical applications.

Hostility among adolescents in Switzerland? multivariate relations between excessive media use and forms of violence.

PubMed

Kuntsche, Emmanuel N

2004-03-01

To determine what kind of violence-related behavior or opinion is directly related to excessive media use among adolescents in Switzerland. A national representative sample of 4222 schoolchildren (7th- and 8th-graders; mean age 13.9 years) answered questions on the frequency of television-viewing, electronic game-playing, feeling unsafe at school, bullying others, hitting others, and fighting with others, as part of the Health Behaviour in School-Aged Children (HBSC) international collaborative study protocol. The Chi-square tests and multiple logistic regression analyses were applied to high-risk groups of adolescents. For the total sample, all bivariate relationships between television-viewing/electronic game-playing and each violence-related variable are significant. In the multivariate comparison, physical violence among boys ceases to be significant. For girls, only television-viewing is linked to indirect violence. Against the hypothesis, females' electronic game-playing only had a bearing on hitting others. Experimental designs are needed that take into account gender, different forms of media, and violence to answer the question of whether excessive media use leads to violent behavior. With the exception of excessive electronic game-playing among girls, this study found that electronic media are not thought to lead directly to real-life violence but to hostility and indirect violence.

Community characteristics associated with child abuse in Iowa.

PubMed

Weissman, Alicia M; Jogerst, Gerald J; Dawson, Jeffrey D

2003-10-01

Various demographic and community characteristics are associated with child abuse rates in national and urban samples, but similar analyses have not been done within rural areas. This study analyzes the relationships between reported and substantiated rates of child abuse and county demographic, health care resource and social services factors in a predominantly rural state in the US. County-level data from Iowa between 1984-1993 were analyzed for associations between county characteristics and rates of child abuse using univariate correlations and multivariate stagewise regression analysis. Population-adjusted rates of reported and substantiated child abuse were correlated with rates of children in poverty, single-parent families, marriage and divorce, unemployment, high-school dropouts, median family income, elder abuse, birth and death rates, numbers of physicians and other healthcare providers, hospital, social workers, and number of caseworkers in the Department of Human Services. Rates of single-parent families, divorce and elder abuse were significantly associated with reported and substantiated child abuse in multivariate analysis, while economic and most health care factors were not. Reporting and substantiation rates differed across districts after adjustment for multiple factors including caseworker workload. In this rural state, family structure is more significantly associated with child abuse report and substantiation rates than are socioeconomic factors. The level of health care resources in a county does not appear to affect these rates.

A Review of Multivariate Distributions for Count Data Derived from the Poisson Distribution.

PubMed

Inouye, David; Yang, Eunho; Allen, Genevera; Ravikumar, Pradeep

2017-01-01

The Poisson distribution has been widely studied and used for modeling univariate count-valued data. Multivariate generalizations of the Poisson distribution that permit dependencies, however, have been far less popular. Yet, real-world high-dimensional count-valued data found in word counts, genomics, and crime statistics, for example, exhibit rich dependencies, and motivate the need for multivariate distributions that can appropriately model this data. We review multivariate distributions derived from the univariate Poisson, categorizing these models into three main classes: 1) where the marginal distributions are Poisson, 2) where the joint distribution is a mixture of independent multivariate Poisson distributions, and 3) where the node-conditional distributions are derived from the Poisson. We discuss the development of multiple instances of these classes and compare the models in terms of interpretability and theory. Then, we empirically compare multiple models from each class on three real-world datasets that have varying data characteristics from different domains, namely traffic accident data, biological next generation sequencing data, and text data. These empirical experiments develop intuition about the comparative advantages and disadvantages of each class of multivariate distribution that was derived from the Poisson. Finally, we suggest new research directions as explored in the subsequent discussion section.

Lateralization of temporal lobe epilepsy by multimodal multinomial hippocampal response-driven models.

PubMed

Nazem-Zadeh, Mohammad-Reza; Elisevich, Kost V; Schwalb, Jason M; Bagher-Ebadian, Hassan; Mahmoudi, Fariborz; Soltanian-Zadeh, Hamid

2014-12-15

Multiple modalities are used in determining laterality in mesial temporal lobe epilepsy (mTLE). It is unclear how much different imaging modalities should be weighted in decision-making. The purpose of this study is to develop response-driven multimodal multinomial models for lateralization of epileptogenicity in mTLE patients based upon imaging features in order to maximize the accuracy of noninvasive studies. The volumes, means and standard deviations of FLAIR intensity and means of normalized ictal-interictal SPECT intensity of the left and right hippocampi were extracted from preoperative images of a retrospective cohort of 45 mTLE patients with Engel class I surgical outcomes, as well as images of a cohort of 20 control, nonepileptic subjects. Using multinomial logistic function regression, the parameters of various univariate and multivariate models were estimated. Based on the Bayesian model averaging (BMA) theorem, response models were developed as compositions of independent univariate models. A BMA model composed of posterior probabilities of univariate response models of hippocampal volumes, means and standard deviations of FLAIR intensity, and means of SPECT intensity with the estimated weighting coefficients of 0.28, 0.32, 0.09, and 0.31, respectively, as well as a multivariate response model incorporating all mentioned attributes, demonstrated complete reliability by achieving a probability of detection of one with no false alarms to establish proper laterality in all mTLE patients. The proposed multinomial multivariate response-driven model provides a reliable lateralization of mesial temporal epileptogenicity including those patients who require phase II assessment. Copyright © 2014 Elsevier B.V. All rights reserved.

Factors associated with seasonal influenza vaccination in pregnant women.

PubMed

Henninger, Michelle L; Irving, Stephanie A; Thompson, Mark; Avalos, Lyndsay Ammon; Ball, Sarah W; Shifflett, Pat; Naleway, Allison L

2015-05-01

This observational study followed a cohort of pregnant women during the 2010-2011 influenza season to determine factors associated with vaccination. Participants were 1105 pregnant women who completed a survey assessing health beliefs related to vaccination upon enrollment and were then followed to determine vaccination status by the end of the 2010-2011 influenza season. We conducted univariate and multivariate analyses to explore factors associated with vaccination status and a factor analysis of survey items to identify health beliefs associated with vaccination. Sixty-three percent (n=701) of the participants were vaccinated. In the univariate analyses, multiple factors were associated with vaccination status, including maternal age, race, marital status, educational level, and gravidity. Factor analysis identified two health belief factors associated with vaccination: participant's positive views (factor 1) and negative views (factor 2) of influenza vaccination. In a multivariate logistic regression model, factor 1 was associated with increased likelihood of vaccination (adjusted odds ratio [aOR]=2.18; 95% confidence interval [CI]=1.72-2.78), whereas factor 2 was associated with decreased likelihood of vaccination (aOR=0.36; 95% CI=0.28-0.46). After controlling for the two health belief factors in multivariate analyses, demographic factors significant in univariate analyses were no longer significant. Women who received a provider recommendation were about three times more likely to be vaccinated (aOR=3.14; 95% CI=1.99-4.96). Pregnant women's health beliefs about vaccination appear to be more important than demographic and maternal factors previously associated with vaccination status. Provider recommendation remains one of the most critical factors influencing vaccination during pregnancy.

The role of objective cognitive dysfunction in subjective cognitive complaints after stroke.

PubMed

van Rijsbergen, M W A; Mark, R E; Kop, W J; de Kort, P L M; Sitskoorn, M M

2017-03-01

Objective cognitive performance (OCP) is often impaired in patients post-stroke but the consequences of OCP for patient-reported subjective cognitive complaints (SCC) are poorly understood. We performed a detailed analysis on the association between post-stroke OCP and SCC. Assessments of OCP and SCC were obtained in 208 patients 3 months after stroke. OCP was evaluated using conventional and ecologically valid neuropsychological tests. Levels of SCC were measured using the CheckList for Cognitive and Emotional (CLCE) consequences following stroke inventory. Multivariate hierarchical regression analyses were used to evaluate the association of OCP with CLCE scores adjusting for age, sex and intelligence quotient. Analyses were performed to examine the global extent of OCP dysfunction (based on the total number of impaired neuropsychological tests, i.e. objective cognitive impairment index) and for each OCP test separately using the raw neuropsychological (sub)test scores. The objective cognitive impairment index for global OCP was positively correlated with the CLCE score (Spearman's rho = 0.22, P = 0.003), which remained significant in multivariate adjusted models (β = 0.25, P = 0.01). Results for the separate neuropsychological tests indicated that only one task (the ecologically valid Rivermead Behavioural Memory Test) was independently associated with the CLCE in multivariate adjusted models (β = -0.34, P < 0.001). Objective neuropsychological test performance, as measured by the global dysfunction index or an ecologically valid memory task, was associated with SCC. These data suggest that cumulative deficits in multiple cognitive domains contribute to subjectively experienced poor cognitive abilities in daily life in patients post-stroke. © 2016 EAN.

The significance of peripartum fever in women undergoing vaginal deliveries.

PubMed

Bensal, Adi; Weintraub, Adi Y; Levy, Amalia; Holcberg, Gershon; Sheiner, Eyal

2008-10-01

We investigated whether patients undergoing vaginal delivery who developed peripartum fever (PPF) had increased rates of other gestational complications. A retrospective study was undertaken comparing pregnancy complications of patients who developed PPF with those who did not. A multivariable logistic regression model was constructed to control for confounders. To avoid ascertainment bias, the year of birth was included in the model. Women who underwent cesarean delivery and those with multiple pregnancies were excluded from the study. During the study period, there were 169,738 singleton vaginal deliveries, and 0.4% of the women suffered from PPF. Hypertensive disorders, induction of labor, dystocia of labor in the second stage, suspected fetal distress, meconium-stained amniotic fluid, postpartum hemorrhage, manual lysis of a retained placenta, and revision of the uterine cavity and cervix were found to be independently associated with PPF by multivariable analysis. Year of birth was found to be a risk factor for fever. Apgar scores lower than 7 at 1 but not 5 minutes were significantly higher in the PPF group. Perinatal mortality rates were significantly higher among women with PPF (6.7% versus 1.3%, odds ratio [OR] = 5.4; 95% confidence interval [CI] 3.9 to 7.3; P < 0.001). Using another multivariable analysis, with perinatal mortality as the outcome variable, PPF was found as an independent risk factor for perinatal mortality (OR = 2.9; 95% CI 1.9 to 4.6; P < 0.001). PPF in women undergoing vaginal deliveries is associated with adverse perinatal outcomes and specifically is an independent risk factor for perinatal mortality.

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.

A general framework for multivariate multi-index drought prediction based on Multivariate Ensemble Streamflow Prediction (MESP)

NASA Astrophysics Data System (ADS)

Hao, Zengchao; Hao, Fanghua; Singh, Vijay P.

2016-08-01

Drought is among the costliest natural hazards worldwide and extreme drought events in recent years have caused huge losses to various sectors. Drought prediction is therefore critically important for providing early warning information to aid decision making to cope with drought. Due to the complicated nature of drought, it has been recognized that the univariate drought indicator may not be sufficient for drought characterization and hence multivariate drought indices have been developed for drought monitoring. Alongside the substantial effort in drought monitoring with multivariate drought indices, it is of equal importance to develop a drought prediction method with multivariate drought indices to integrate drought information from various sources. This study proposes a general framework for multivariate multi-index drought prediction that is capable of integrating complementary prediction skills from multiple drought indices. The Multivariate Ensemble Streamflow Prediction (MESP) is employed to sample from historical records for obtaining statistical prediction of multiple variables, which is then used as inputs to achieve multivariate prediction. The framework is illustrated with a linearly combined drought index (LDI), which is a commonly used multivariate drought index, based on climate division data in California and New York in the United States with different seasonality of precipitation. The predictive skill of LDI (represented with persistence) is assessed by comparison with the univariate drought index and results show that the LDI prediction skill is less affected by seasonality than the meteorological drought prediction based on SPI. Prediction results from the case study show that the proposed multivariate drought prediction outperforms the persistence prediction, implying a satisfactory performance of multivariate drought prediction. The proposed method would be useful for drought prediction to integrate drought information from various sources for early drought warning.

Genetic parameters for growth characteristics of free-range chickens under univariate random regression models.

PubMed

Rovadoscki, Gregori A; Petrini, Juliana; Ramirez-Diaz, Johanna; Pertile, Simone F N; Pertille, Fábio; Salvian, Mayara; Iung, Laiza H S; Rodriguez, Mary Ana P; Zampar, Aline; Gaya, Leila G; Carvalho, Rachel S B; Coelho, Antonio A D; Savino, Vicente J M; Coutinho, Luiz L; Mourão, Gerson B

2016-09-01

Repeated measures from the same individual have been analyzed by using repeatability and finite dimension models under univariate or multivariate analyses. However, in the last decade, the use of random regression models for genetic studies with longitudinal data have become more common. Thus, the aim of this research was to estimate genetic parameters for body weight of four experimental chicken lines by using univariate random regression models. Body weight data from hatching to 84 days of age (n = 34,730) from four experimental free-range chicken lines (7P, Caipirão da ESALQ, Caipirinha da ESALQ and Carijó Barbado) were used. The analysis model included the fixed effects of contemporary group (gender and rearing system), fixed regression coefficients for age at measurement, and random regression coefficients for permanent environmental effects and additive genetic effects. Heterogeneous variances for residual effects were considered, and one residual variance was assigned for each of six subclasses of age at measurement. Random regression curves were modeled by using Legendre polynomials of the second and third orders, with the best model chosen based on the Akaike Information Criterion, Bayesian Information Criterion, and restricted maximum likelihood. Multivariate analyses under the same animal mixed model were also performed for the validation of the random regression models. The Legendre polynomials of second order were better for describing the growth curves of the lines studied. Moderate to high heritabilities (h(2) = 0.15 to 0.98) were estimated for body weight between one and 84 days of age, suggesting that selection for body weight at all ages can be used as a selection criteria. Genetic correlations among body weight records obtained through multivariate analyses ranged from 0.18 to 0.96, 0.12 to 0.89, 0.06 to 0.96, and 0.28 to 0.96 in 7P, Caipirão da ESALQ, Caipirinha da ESALQ, and Carijó Barbado chicken lines, respectively. Results indicate that genetic gain for body weight can be achieved by selection. Also, selection for body weight at 42 days of age can be maintained as a selection criterion. © 2016 Poultry Science Association Inc.

Somatosensory impairment and its association with balance limitation in people with multiple sclerosis.

PubMed

Jamali, Akram; Sadeghi-Demneh, Ebrahim; Fereshtenajad, Niloufar; Hillier, Susan

2017-09-01

Somatosensory impairments are common in multiple sclerosis. However, little data are available to characterize the nature and frequency of these problems in people with multiple sclerosis. To investigate the frequency of somatosensory impairments and identify any association with balance limitations in people with multiple sclerosis. The design was a prospective cross-sectional study, involving 82 people with multiple sclerosis and 30 healthy controls. Tactile and proprioceptive sensory acuity were measured using the Rivermead Assessment of Somatosensory Performance. Vibration duration was assessed using a tuning fork. Duration for the Timed Up and Go Test and reaching distance of the Functional Reach Test were measured to assess balance limitations. The normative range of sensory modalities was defined using cut-off points in the healthy participants. The multivariate linear regression was used to identify the significant predictors of balance in people with multiple sclerosis. Proprioceptive impairments (66.7%) were more common than tactile (60.8%) and vibration impairments (44.9%). Somatosensory impairments were more frequent in the lower limb (78.2%) than the upper limb (64.1%). All sensory modalities were significantly associated with the Timed Up and Go and Functional Reach tests (p<0.05). The Timed Up and Go test was independently predicted by the severity of the neurological lesion, Body Mass Index, ataxia, and tactile sensation (R2=0.58), whereas the Functional Reach test was predicted by the severity of the neurological lesion, lower limb strength, and vibration sense (R2=0.49). Somatosensory impairments are very common in people with multiple sclerosis. These impairments are independent predictors of balance limitation. Copyright © 2017 Elsevier B.V. All rights reserved.

False Positives in Multiple Regression: Unanticipated Consequences of Measurement Error in the Predictor Variables

ERIC Educational Resources Information Center

Shear, Benjamin R.; Zumbo, Bruno D.

2013-01-01

Type I error rates in multiple regression, and hence the chance for false positive research findings, can be drastically inflated when multiple regression models are used to analyze data that contain random measurement error. This article shows the potential for inflated Type I error rates in commonly encountered scenarios and provides new…

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...

Multivariate generalized hidden Markov regression models with random covariates: Physical exercise in an elderly population.

PubMed

Punzo, Antonio; Ingrassia, Salvatore; Maruotti, Antonello

2018-04-22

A time-varying latent variable model is proposed to jointly analyze multivariate mixed-support longitudinal data. The proposal can be viewed as an extension of hidden Markov regression models with fixed covariates (HMRMFCs), which is the state of the art for modelling longitudinal data, with a special focus on the underlying clustering structure. HMRMFCs are inadequate for applications in which a clustering structure can be identified in the distribution of the covariates, as the clustering is independent from the covariates distribution. Here, hidden Markov regression models with random covariates are introduced by explicitly specifying state-specific distributions for the covariates, with the aim of improving the recovering of the clusters in the data with respect to a fixed covariates paradigm. The hidden Markov regression models with random covariates class is defined focusing on the exponential family, in a generalized linear model framework. Model identifiability conditions are sketched, an expectation-maximization algorithm is outlined for parameter estimation, and various implementation and operational issues are discussed. Properties of the estimators of the regression coefficients, as well as of the hidden path parameters, are evaluated through simulation experiments and compared with those of HMRMFCs. The method is applied to physical activity data. Copyright © 2018 John Wiley & Sons, Ltd.

A novel strategy for forensic age prediction by DNA methylation and support vector regression model

PubMed Central

Xu, Cheng; Qu, Hongzhu; Wang, Guangyu; Xie, Bingbing; Shi, Yi; Yang, Yaran; Zhao, Zhao; Hu, Lan; Fang, Xiangdong; Yan, Jiangwei; Feng, Lei

2015-01-01

High deviations resulting from prediction model, gender and population difference have limited age estimation application of DNA methylation markers. Here we identified 2,957 novel age-associated DNA methylation sites (P < 0.01 and R2 > 0.5) in blood of eight pairs of Chinese Han female monozygotic twins. Among them, nine novel sites (false discovery rate < 0.01), along with three other reported sites, were further validated in 49 unrelated female volunteers with ages of 20–80 years by Sequenom Massarray. A total of 95 CpGs were covered in the PCR products and 11 of them were built the age prediction models. After comparing four different models including, multivariate linear regression, multivariate nonlinear regression, back propagation neural network and support vector regression, SVR was identified as the most robust model with the least mean absolute deviation from real chronological age (2.8 years) and an average accuracy of 4.7 years predicted by only six loci from the 11 loci, as well as an less cross-validated error compared with linear regression model. Our novel strategy provides an accurate measurement that is highly useful in estimating the individual age in forensic practice as well as in tracking the aging process in other related applications. PMID:26635134

Analysis of Multivariate Experimental Data Using A Simplified Regression Model Search Algorithm

NASA Technical Reports Server (NTRS)

Ulbrich, Norbert Manfred

2013-01-01

A new regression model search algorithm was developed in 2011 that may be used to analyze both general multivariate experimental data sets and wind tunnel strain-gage balance calibration data. The new algorithm is a simplified version of a more complex search algorithm that was originally developed at the NASA Ames Balance Calibration Laboratory. The new algorithm has the advantage that it needs only about one tenth of the original algorithm's CPU time for the completion of a search. In addition, extensive testing showed that the prediction accuracy of math models obtained from the simplified algorithm is similar to the prediction accuracy of math models obtained from the original algorithm. The simplified algorithm, however, cannot guarantee that search constraints related to a set of statistical quality requirements are always satisfied in the optimized regression models. Therefore, the simplified search algorithm is not intended to replace the original search algorithm. Instead, it may be used to generate an alternate optimized regression model of experimental data whenever the application of the original search algorithm either fails or requires too much CPU time. Data from a machine calibration of NASA's MK40 force balance is used to illustrate the application of the new regression model search algorithm.