Estimation of failure criteria in multivariate sensory shelf life testing using survival analysis.

PubMed

Giménez, Ana; Gagliardi, Andrés; Ares, Gastón

2017-09-01

For most food products, shelf life is determined by changes in their sensory characteristics. A predetermined increase or decrease in the intensity of a sensory characteristic has frequently been used to signal that a product has reached the end of its shelf life. Considering all attributes change simultaneously, the concept of multivariate shelf life allows a single measurement of deterioration that takes into account all these sensory changes at a certain storage time. The aim of the present work was to apply survival analysis to estimate failure criteria in multivariate sensory shelf life testing using two case studies, hamburger buns and orange juice, by modelling the relationship between consumers' rejection of the product and the deterioration index estimated using PCA. In both studies, a panel of 13 trained assessors evaluated the samples using descriptive analysis whereas a panel of 100 consumers answered a "yes" or "no" question regarding intention to buy or consume the product. PC1 explained the great majority of the variance, indicating all sensory characteristics evolved similarly with storage time. Thus, PC1 could be regarded as index of sensory deterioration and a single failure criterion could be estimated through survival analysis for 25 and 50% consumers' rejection. The proposed approach based on multivariate shelf life testing may increase the accuracy of shelf life estimations. Copyright © 2017 Elsevier Ltd. All rights reserved.

PYCHEM: a multivariate analysis package for python.

PubMed

Jarvis, Roger M; Broadhurst, David; Johnson, Helen; O'Boyle, Noel M; Goodacre, Royston

2006-10-15

We have implemented a multivariate statistical analysis toolbox, with an optional standalone graphical user interface (GUI), using the Python scripting language. This is a free and open source project that addresses the need for a multivariate analysis toolbox in Python. Although the functionality provided does not cover the full range of multivariate tools that are available, it has a broad complement of methods that are widely used in the biological sciences. In contrast to tools like MATLAB, PyChem 2.0.0 is easily accessible and free, allows for rapid extension using a range of Python modules and is part of the growing amount of complementary and interoperable scientific software in Python based upon SciPy. One of the attractions of PyChem is that it is an open source project and so there is an opportunity, through collaboration, to increase the scope of the software and to continually evolve a user-friendly platform that has applicability across a wide range of analytical and post-genomic disciplines. http://sourceforge.net/projects/pychem

The association between body mass index and severe biliary infections: a multivariate analysis.

PubMed

Stewart, Lygia; Griffiss, J McLeod; Jarvis, Gary A; Way, Lawrence W

2012-11-01

Obesity has been associated with worse infectious disease outcomes. It is a risk factor for cholesterol gallstones, but little is known about associations between body mass index (BMI) and biliary infections. We studied this using factors associated with biliary infections. A total of 427 patients with gallstones were studied. Gallstones, bile, and blood (as applicable) were cultured. Illness severity was classified as follows: none (no infection or inflammation), systemic inflammatory response syndrome (fever, leukocytosis), severe (abscess, cholangitis, empyema), or multi-organ dysfunction syndrome (bacteremia, hypotension, organ failure). Associations between BMI and biliary bacteria, bacteremia, gallstone type, and illness severity were examined using bivariate and multivariate analysis. BMI inversely correlated with pigment stones, biliary bacteria, bacteremia, and increased illness severity on bivariate and multivariate analysis. Obesity correlated with less severe biliary infections. BMI inversely correlated with pigment stones and biliary bacteria; multivariate analysis showed an independent correlation between lower BMI and illness severity. Most patients with severe biliary infections had a normal BMI, suggesting that obesity may be protective in biliary infections. This study examined the correlation between BMI and biliary infection severity. Published by Elsevier Inc.

Sampling effort affects multivariate comparisons of stream assemblages

USGS Publications Warehouse

Cao, Y.; Larsen, D.P.; Hughes, R.M.; Angermeier, P.L.; Patton, T.M.

2002-01-01

Multivariate analyses are used widely for determining patterns of assemblage structure, inferring species-environment relationships and assessing human impacts on ecosystems. The estimation of ecological patterns often depends on sampling effort, so the degree to which sampling effort affects the outcome of multivariate analyses is a concern. We examined the effect of sampling effort on site and group separation, which was measured using a mean similarity method. Two similarity measures, the Jaccard Coefficient and Bray-Curtis Index were investigated with 1 benthic macroinvertebrate and 2 fish data sets. Site separation was significantly improved with increased sampling effort because the similarity between replicate samples of a site increased more rapidly than between sites. Similarly, the faster increase in similarity between sites of the same group than between sites of different groups caused clearer separation between groups. The strength of site and group separation completely stabilized only when the mean similarity between replicates reached 1. These results are applicable to commonly used multivariate techniques such as cluster analysis and ordination because these multivariate techniques start with a similarity matrix. Completely stable outcomes of multivariate analyses are not feasible. Instead, we suggest 2 criteria for estimating the stability of multivariate analyses of assemblage data: 1) mean within-site similarity across all sites compared, indicating sample representativeness, and 2) the SD of within-site similarity across sites, measuring sample comparability.

Identification of Reliable Components in Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS): a Data-Driven Approach across Metabolic Processes.

PubMed

Motegi, Hiromi; Tsuboi, Yuuri; Saga, Ayako; Kagami, Tomoko; Inoue, Maki; Toki, Hideaki; Minowa, Osamu; Noda, Tetsuo; Kikuchi, Jun

2015-11-04

There is an increasing need to use multivariate statistical methods for understanding biological functions, identifying the mechanisms of diseases, and exploring biomarkers. In addition to classical analyses such as hierarchical cluster analysis, principal component analysis, and partial least squares discriminant analysis, various multivariate strategies, including independent component analysis, non-negative matrix factorization, and multivariate curve resolution, have recently been proposed. However, determining the number of components is problematic. Despite the proposal of several different methods, no satisfactory approach has yet been reported. To resolve this problem, we implemented a new idea: classifying a component as "reliable" or "unreliable" based on the reproducibility of its appearance, regardless of the number of components in the calculation. Using the clustering method for classification, we applied this idea to multivariate curve resolution-alternating least squares (MCR-ALS). Comparisons between conventional and modified methods applied to proton nuclear magnetic resonance ((1)H-NMR) spectral datasets derived from known standard mixtures and biological mixtures (urine and feces of mice) revealed that more plausible results are obtained by the modified method. In particular, clusters containing little information were detected with reliability. This strategy, named "cluster-aided MCR-ALS," will facilitate the attainment of more reliable results in the metabolomics datasets.

All-Possible-Subsets for MANOVA and Factorial MANOVAs: Less than a Weekend Project

ERIC Educational Resources Information Center

Nimon, Kim; Zientek, Linda Reichwein; Kraha, Amanda

2016-01-01

Multivariate techniques are increasingly popular as researchers attempt to accurately model a complex world. MANOVA is a multivariate technique used to investigate the dimensions along which groups differ, and how these dimensions may be used to predict group membership. A concern in a MANOVA analysis is to determine if a smaller subset of…

An Individualized Student Term Project for Multivariate Calculus

ERIC Educational Resources Information Center

Gordon, Sheldon P.

2004-01-01

In this article, the author describes an individualized term project that is designed to increase student understanding of some of the major concepts and methods in multivariate calculus. The project involves having each student conduct a complete max-min analysis of a third degree polynomial in x and y that is based on his or her social security…

Enhancing e-waste estimates: Improving data quality by multivariate Input–Output Analysis

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

Wang, Feng, E-mail: fwang@unu.edu; Design for Sustainability Lab, Faculty of Industrial Design Engineering, Delft University of Technology, Landbergstraat 15, 2628CE Delft; Huisman, Jaco

2013-11-15

Highlights: • A multivariate Input–Output Analysis method for e-waste estimates is proposed. • Applying multivariate analysis to consolidate data can enhance e-waste estimates. • We examine the influence of model selection and data quality on e-waste estimates. • Datasets of all e-waste related variables in a Dutch case study have been provided. • Accurate modeling of time-variant lifespan distributions is critical for estimate. - Abstract: Waste electrical and electronic equipment (or e-waste) is one of the fastest growing waste streams, which encompasses a wide and increasing spectrum of products. Accurate estimation of e-waste generation is difficult, mainly due to lackmore » of high quality data referred to market and socio-economic dynamics. This paper addresses how to enhance e-waste estimates by providing techniques to increase data quality. An advanced, flexible and multivariate Input–Output Analysis (IOA) method is proposed. It links all three pillars in IOA (product sales, stock and lifespan profiles) to construct mathematical relationships between various data points. By applying this method, the data consolidation steps can generate more accurate time-series datasets from available data pool. This can consequently increase the reliability of e-waste estimates compared to the approach without data processing. A case study in the Netherlands is used to apply the advanced IOA model. As a result, for the first time ever, complete datasets of all three variables for estimating all types of e-waste have been obtained. The result of this study also demonstrates significant disparity between various estimation models, arising from the use of data under different conditions. It shows the importance of applying multivariate approach and multiple sources to improve data quality for modelling, specifically using appropriate time-varying lifespan parameters. Following the case study, a roadmap with a procedural guideline is provided to enhance e-waste estimation studies.« less

Reproducibility of NMR Analysis of Urine Samples: Impact of Sample Preparation, Storage Conditions, and Animal Health Status

PubMed Central

Schreier, Christina; Kremer, Werner; Huber, Fritz; Neumann, Sindy; Pagel, Philipp; Lienemann, Kai; Pestel, Sabine

2013-01-01

Introduction. Spectroscopic analysis of urine samples from laboratory animals can be used to predict the efficacy and side effects of drugs. This employs methods combining 1H NMR spectroscopy with quantification of biomarkers or with multivariate data analysis. The most critical steps in data evaluation are analytical reproducibility of NMR data (collection, storage, and processing) and the health status of the animals, which may influence urine pH and osmolarity. Methods. We treated rats with a solvent, a diuretic, or a nephrotoxicant and collected urine samples. Samples were titrated to pH 3 to 9, or salt concentrations increased up to 20-fold. The effects of storage conditions and freeze-thaw cycles were monitored. Selected metabolites and multivariate data analysis were evaluated after 1H NMR spectroscopy. Results. We showed that variation of pH from 3 to 9 and increases in osmolarity up to 6-fold had no effect on the quantification of the metabolites or on multivariate data analysis. Storage led to changes after 14 days at 4°C or after 12 months at −20°C, independent of sample composition. Multiple freeze-thaw cycles did not affect data analysis. Conclusion. Reproducibility of NMR measurements is not dependent on sample composition under physiological or pathological conditions. PMID:23865070

Reproducibility of NMR analysis of urine samples: impact of sample preparation, storage conditions, and animal health status.

PubMed

Schreier, Christina; Kremer, Werner; Huber, Fritz; Neumann, Sindy; Pagel, Philipp; Lienemann, Kai; Pestel, Sabine

2013-01-01

Spectroscopic analysis of urine samples from laboratory animals can be used to predict the efficacy and side effects of drugs. This employs methods combining (1)H NMR spectroscopy with quantification of biomarkers or with multivariate data analysis. The most critical steps in data evaluation are analytical reproducibility of NMR data (collection, storage, and processing) and the health status of the animals, which may influence urine pH and osmolarity. We treated rats with a solvent, a diuretic, or a nephrotoxicant and collected urine samples. Samples were titrated to pH 3 to 9, or salt concentrations increased up to 20-fold. The effects of storage conditions and freeze-thaw cycles were monitored. Selected metabolites and multivariate data analysis were evaluated after (1)H NMR spectroscopy. We showed that variation of pH from 3 to 9 and increases in osmolarity up to 6-fold had no effect on the quantification of the metabolites or on multivariate data analysis. Storage led to changes after 14 days at 4°C or after 12 months at -20°C, independent of sample composition. Multiple freeze-thaw cycles did not affect data analysis. Reproducibility of NMR measurements is not dependent on sample composition under physiological or pathological conditions.

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.

Reexamining Sample Size Requirements for Multivariate, Abundance-Based Community Research: When Resources are Limited, the Research Does Not Have to Be.

PubMed

Forcino, Frank L; Leighton, Lindsey R; Twerdy, Pamela; Cahill, James F

2015-01-01

Community ecologists commonly perform multivariate techniques (e.g., ordination, cluster analysis) to assess patterns and gradients of taxonomic variation. A critical requirement for a meaningful statistical analysis is accurate information on the taxa found within an ecological sample. However, oversampling (too many individuals counted per sample) also comes at a cost, particularly for ecological systems in which identification and quantification is substantially more resource consuming than the field expedition itself. In such systems, an increasingly larger sample size will eventually result in diminishing returns in improving any pattern or gradient revealed by the data, but will also lead to continually increasing costs. Here, we examine 396 datasets: 44 previously published and 352 created datasets. Using meta-analytic and simulation-based approaches, the research within the present paper seeks (1) to determine minimal sample sizes required to produce robust multivariate statistical results when conducting abundance-based, community ecology research. Furthermore, we seek (2) to determine the dataset parameters (i.e., evenness, number of taxa, number of samples) that require larger sample sizes, regardless of resource availability. We found that in the 44 previously published and the 220 created datasets with randomly chosen abundances, a conservative estimate of a sample size of 58 produced the same multivariate results as all larger sample sizes. However, this minimal number varies as a function of evenness, where increased evenness resulted in increased minimal sample sizes. Sample sizes as small as 58 individuals are sufficient for a broad range of multivariate abundance-based research. In cases when resource availability is the limiting factor for conducting a project (e.g., small university, time to conduct the research project), statistically viable results can still be obtained with less of an investment.

Integrated GIS and multivariate statistical analysis for regional scale assessment of heavy metal soil contamination: A critical review.

PubMed

Hou, Deyi; O'Connor, David; Nathanail, Paul; Tian, Li; Ma, Yan

2017-12-01

Heavy metal soil contamination is associated with potential toxicity to humans or ecotoxicity. Scholars have increasingly used a combination of geographical information science (GIS) with geostatistical and multivariate statistical analysis techniques to examine the spatial distribution of heavy metals in soils at a regional scale. A review of such studies showed that most soil sampling programs were based on grid patterns and composite sampling methodologies. Many programs intended to characterize various soil types and land use types. The most often used sampling depth intervals were 0-0.10 m, or 0-0.20 m, below surface; and the sampling densities used ranged from 0.0004 to 6.1 samples per km 2 , with a median of 0.4 samples per km 2 . The most widely used spatial interpolators were inverse distance weighted interpolation and ordinary kriging; and the most often used multivariate statistical analysis techniques were principal component analysis and cluster analysis. The review also identified several determining and correlating factors in heavy metal distribution in soils, including soil type, soil pH, soil organic matter, land use type, Fe, Al, and heavy metal concentrations. The major natural and anthropogenic sources of heavy metals were found to derive from lithogenic origin, roadway and transportation, atmospheric deposition, wastewater and runoff from industrial and mining facilities, fertilizer application, livestock manure, and sewage sludge. This review argues that the full potential of integrated GIS and multivariate statistical analysis for assessing heavy metal distribution in soils on a regional scale has not yet been fully realized. It is proposed that future research be conducted to map multivariate results in GIS to pinpoint specific anthropogenic sources, to analyze temporal trends in addition to spatial patterns, to optimize modeling parameters, and to expand the use of different multivariate analysis tools beyond principal component analysis (PCA) and cluster analysis (CA). Copyright © 2017 Elsevier Ltd. All rights reserved.

The contribution of antiphospholipid antibodies to organ damage in systemic lupus erythematosus.

PubMed

Taraborelli, M; Leuenberger, L; Lazzaroni, M G; Martinazzi, N; Zhang, W; Franceschini, F; Salmon, J; Tincani, A; Erkan, D

2016-10-01

The objective of this study was to assess the contribution of clinically significant antiphospholipid antibodies (aPL) to organ damage in systemic lupus erythematosus (SLE). Patients with disease duration of less than 10 years and at least 5 years of follow-up were identified from two SLE registries. A clinically significant antiphospholipid antibody (aPL) profile was defined as: positive lupus anticoagulant, anticardiolipin IgG/M ≥ 40 G phospholipid units (GPL)/M phospholipid units (MPL), and/or anti-β2-glycoprotein-I IgG/M ≥ 99th percentile on two or more occasions, at least 12 weeks apart. Organ damage was assessed by the Systemic Lupus International Collaborating Clinics Damage Index (SDI). Univariate and multivariate analysis compared SLE patients with and without SDI increase during a 15-year follow-up. Among 262 SLE patients, 33% had a clinically significant aPL profile, which was associated with an increased risk of organ damage accrual during a 5-year follow-up in univariate analysis, and during a 15-year follow-up in the multivariate analysis adjusting for age, gender, race, disease duration at registry entry, and time. In the multivariate analysis, older age at diagnosis and male gender were also associated with SDI increase at each time point. A clinically significant aPL profile is associated with an increased risk of organ damage accrual during a 15-year follow-up in SLE patients. © The Author(s) 2016.

Quality-by-design case study: investigation of the role of poloxamer in immediate-release tablets by experimental design and multivariate data analysis.

PubMed

Kaul, Goldi; Huang, Jun; Chatlapalli, Ramarao; Ghosh, Krishnendu; Nagi, Arwinder

2011-12-01

The role of poloxamer 188, water and binder addition rate, on retarding dissolution in immediate-release tablets of a model drug from BCS class II was investigated by means of multivariate data analysis (MVDA) combined with design of experiments (DOE). While the DOE analysis yielded important clues into the cause-and-effect relationship between the responses and design factors, multivariate data analysis of the 40+ variables provided additional information on slowdown in tablet dissolution. A steep dependence of both tablet dissolution and disintegration on the poloxamer and less so on other design variables was observed. Poloxamer was found to increase dissolution rates in granules as expected of surfactants in general but retard dissolution in tablets. The unexpected effect of poloxamer in tablets was accompanied by an increase in tablet-disintegration-time-mediated slowdown of tablet dissolution and by a surrogate binding effect of poloxamer at higher concentrations. It was additionally realized through MVDA that poloxamer in tablets either acts as a binder by itself or promotes binder action of the binder povidone resulting in increased intragranular cohesion. Additionally, poloxamer was found to mediate tablet dissolution on stability as well. In contrast to tablet dissolution at release (time zero), poloxamer appeared to increase tablet dissolution in a concentration-dependent manner on accelerated open-dish stability. Substituting polysorbate 80 as an alternate surfactant in place of poloxamer in the formulation was found to stabilize tablet dissolution.

The source identification of ambient aerosols in Beijing, China by multivariate analysis coupled with {sup 14}C tracer

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

Xiaoyan Tang; Min Shao; Yuanhang Zhang

1996-12-31

Ambient aerosol is one of most important pollutants in China. This paper showed the results of aerosol sources of Beijing area revealed by combination of multivariate analysis models and 14C tracer measured on Accelerator Mass Spectrometry (AMS). The results indicated that the mass concentration of particulate (<100 (M)) didn`t increase rapidly, compared with economic development in Beijing city. The multivariate analysis showed that the predominant source was soil dust which contributed more than 50% to atmospheric particles. However, it would be a risk to conclude that the aerosol pollution from anthropogenic sources was less important in Beijing city based onmore » above phenomenon. Due to lack of reliable tracers, it was very hard to distinguish coal burning from soil source. Thus, it was suspected that the soil source above might be the mixture of soil dust and coal burning. The 14C measurement showed that carbonaceous species of aerosol had quite different emission sources. For carbonaceous aerosols in Beijing, the contribution from fossil fuel to ambient particles was nearly 2/3, as the man-made activities ( coal-burning, etc.) increased, the fossil part would contribute more to atmospheric carbonaceous particles. For example, in downtown Beijing at space-heating seasons, the fossil fuel even contributed more than 95% to carbonaceous particles, which would be potential harmful to population. By using multivariate analysis together with 14C data, two important sources of aerosols in Beijing (soil and coal) combustion were more reliably distinguished, which was critical important for the assessment of aerosol problem in China.« less

Comparative multivariate analyses of transient otoacoustic emissions and distorsion products in normal and impaired hearing.

PubMed

Stamate, Mirela Cristina; Todor, Nicolae; Cosgarea, Marcel

2015-01-01

The clinical utility of otoacoustic emissions as a noninvasive objective test of cochlear function has been long studied. Both transient otoacoustic emissions and distorsion products can be used to identify hearing loss, but to what extent they can be used as predictors for hearing loss is still debated. Most studies agree that multivariate analyses have better test performances than univariate analyses. The aim of the study was to determine transient otoacoustic emissions and distorsion products performance in identifying normal and impaired hearing loss, using the pure tone audiogram as a gold standard procedure and different multivariate statistical approaches. The study included 105 adult subjects with normal hearing and hearing loss who underwent the same test battery: pure-tone audiometry, tympanometry, otoacoustic emission tests. We chose to use the logistic regression as a multivariate statistical technique. Three logistic regression models were developed to characterize the relations between different risk factors (age, sex, tinnitus, demographic features, cochlear status defined by otoacoustic emissions) and hearing status defined by pure-tone audiometry. The multivariate analyses allow the calculation of the logistic score, which is a combination of the inputs, weighted by coefficients, calculated within the analyses. The accuracy of each model was assessed using receiver operating characteristics curve analysis. We used the logistic score to generate receivers operating curves and to estimate the areas under the curves in order to compare different multivariate analyses. We compared the performance of each otoacoustic emission (transient, distorsion product) using three different multivariate analyses for each ear, when multi-frequency gold standards were used. We demonstrated that all multivariate analyses provided high values of the area under the curve proving the performance of the otoacoustic emissions. Each otoacoustic emission test presented high values of area under the curve, suggesting that implementing a multivariate approach to evaluate the performances of each otoacoustic emission test would serve to increase the accuracy in identifying the normal and impaired ears. We encountered the highest area under the curve value for the combined multivariate analysis suggesting that both otoacoustic emission tests should be used in assessing hearing status. Our multivariate analyses revealed that age is a constant predictor factor of the auditory status for both ears, but the presence of tinnitus was the most important predictor for the hearing level, only for the left ear. Age presented similar coefficients, but tinnitus coefficients, by their high value, produced the highest variations of the logistic scores, only for the left ear group, thus increasing the risk of hearing loss. We did not find gender differences between ears for any otoacoustic emission tests, but studies still debate this question as the results are contradictory. Neither gender, nor environment origin had any predictive value for the hearing status, according to the results of our study. Like any other audiological test, using otoacoustic emissions to identify hearing loss is not without error. Even when applying multivariate analysis, perfect test performance is never achieved. Although most studies demonstrated the benefit of using the multivariate analysis, it has not been incorporated into clinical decisions maybe because of the idiosyncratic nature of multivariate solutions or because of the lack of the validation studies.

Comparative multivariate analyses of transient otoacoustic emissions and distorsion products in normal and impaired hearing

PubMed Central

STAMATE, MIRELA CRISTINA; TODOR, NICOLAE; COSGAREA, MARCEL

2015-01-01

Background and aim The clinical utility of otoacoustic emissions as a noninvasive objective test of cochlear function has been long studied. Both transient otoacoustic emissions and distorsion products can be used to identify hearing loss, but to what extent they can be used as predictors for hearing loss is still debated. Most studies agree that multivariate analyses have better test performances than univariate analyses. The aim of the study was to determine transient otoacoustic emissions and distorsion products performance in identifying normal and impaired hearing loss, using the pure tone audiogram as a gold standard procedure and different multivariate statistical approaches. Methods The study included 105 adult subjects with normal hearing and hearing loss who underwent the same test battery: pure-tone audiometry, tympanometry, otoacoustic emission tests. We chose to use the logistic regression as a multivariate statistical technique. Three logistic regression models were developed to characterize the relations between different risk factors (age, sex, tinnitus, demographic features, cochlear status defined by otoacoustic emissions) and hearing status defined by pure-tone audiometry. The multivariate analyses allow the calculation of the logistic score, which is a combination of the inputs, weighted by coefficients, calculated within the analyses. The accuracy of each model was assessed using receiver operating characteristics curve analysis. We used the logistic score to generate receivers operating curves and to estimate the areas under the curves in order to compare different multivariate analyses. Results We compared the performance of each otoacoustic emission (transient, distorsion product) using three different multivariate analyses for each ear, when multi-frequency gold standards were used. We demonstrated that all multivariate analyses provided high values of the area under the curve proving the performance of the otoacoustic emissions. Each otoacoustic emission test presented high values of area under the curve, suggesting that implementing a multivariate approach to evaluate the performances of each otoacoustic emission test would serve to increase the accuracy in identifying the normal and impaired ears. We encountered the highest area under the curve value for the combined multivariate analysis suggesting that both otoacoustic emission tests should be used in assessing hearing status. Our multivariate analyses revealed that age is a constant predictor factor of the auditory status for both ears, but the presence of tinnitus was the most important predictor for the hearing level, only for the left ear. Age presented similar coefficients, but tinnitus coefficients, by their high value, produced the highest variations of the logistic scores, only for the left ear group, thus increasing the risk of hearing loss. We did not find gender differences between ears for any otoacoustic emission tests, but studies still debate this question as the results are contradictory. Neither gender, nor environment origin had any predictive value for the hearing status, according to the results of our study. Conclusion Like any other audiological test, using otoacoustic emissions to identify hearing loss is not without error. Even when applying multivariate analysis, perfect test performance is never achieved. Although most studies demonstrated the benefit of using the multivariate analysis, it has not been incorporated into clinical decisions maybe because of the idiosyncratic nature of multivariate solutions or because of the lack of the validation studies. PMID:26733749

Bayesian inference on risk differences: an application to multivariate meta-analysis of adverse events in clinical trials.

PubMed

Chen, Yong; Luo, Sheng; Chu, Haitao; Wei, Peng

2013-05-01

Multivariate meta-analysis is useful in combining evidence from independent studies which involve several comparisons among groups based on a single outcome. For binary outcomes, the commonly used statistical models for multivariate meta-analysis are multivariate generalized linear mixed effects models which assume risks, after some transformation, follow a multivariate normal distribution with possible correlations. In this article, we consider an alternative model for multivariate meta-analysis where the risks are modeled by the multivariate beta distribution proposed by Sarmanov (1966). This model have several attractive features compared to the conventional multivariate generalized linear mixed effects models, including simplicity of likelihood function, no need to specify a link function, and has a closed-form expression of distribution functions for study-specific risk differences. We investigate the finite sample performance of this model by simulation studies and illustrate its use with an application to multivariate meta-analysis of adverse events of tricyclic antidepressants treatment in clinical trials.

Effect of passive smoking on growth and infection rates of breast-fed and non-breast-fed infants.

PubMed

Yilmaz, Gonca; Hizli, Samil; Karacan, Candemir; Yurdakök, Kadriye; Coşkun, Turgay; Dilmen, Uğur

2009-06-01

The aim of the present study was to determine the effect of passive tobacco smoking on growth and infection rate of infants, and to evaluate whether breast-feeding might be protective against harmful effects of cigarette smoke. A cross-sectional study on 254 6-7-month-old infants was carried out. A questionnaire was given to mothers; and infants' head circumference, bodyweight, height, and urinary cotinine levels were measured. Multivariate analysis of factors influencing lower respiratory tract infections showed that smoking mothers increased the rate by 9.1-fold; breast-feeding decreased it by 3.3-fold; formula feeding at birth increased it by a factor of 15.2; another smoker at home increased it by a factor of 40.1. Multivariate analysis of factors influencing upper respiratory tract infections showed that smoking mothers increased the rate by a factor of 23; early formula feeding increased it by a factor of 62; breast-feeding decreased it by a factor of 5; smoking fathers increased it by a factor of 15. Multivariate analysis of factors influencing otitis media found that smoking mothers and fathers increased it by a factor of 9.4 and 6.15, respectively, and breast-feeding decreased it by a factor of 5.4. Tobacco smoke exposure of infants has negative consequences on growth, otitis media, and upper and lower respiratory tract infections. Breast-feeding promoted the growth of infants who were passively exposed to tobacco smoke and protected them against infections. Smoking should not be permitted in households with infants. When this is impossible, breast-feeding should be promoted to protect the infants against the health hazards of passive smoking.

The Covariance Adjustment Approaches for Combining Incomparable Cox Regressions Caused by Unbalanced Covariates Adjustment: A Multivariate Meta-Analysis Study.

PubMed

Dehesh, Tania; Zare, Najaf; Ayatollahi, Seyyed Mohammad Taghi

2015-01-01

Univariate meta-analysis (UM) procedure, as a technique that provides a single overall result, has become increasingly popular. Neglecting the existence of other concomitant covariates in the models leads to loss of treatment efficiency. Our aim was proposing four new approximation approaches for the covariance matrix of the coefficients, which is not readily available for the multivariate generalized least square (MGLS) method as a multivariate meta-analysis approach. We evaluated the efficiency of four new approaches including zero correlation (ZC), common correlation (CC), estimated correlation (EC), and multivariate multilevel correlation (MMC) on the estimation bias, mean square error (MSE), and 95% probability coverage of the confidence interval (CI) in the synthesis of Cox proportional hazard models coefficients in a simulation study. Comparing the results of the simulation study on the MSE, bias, and CI of the estimated coefficients indicated that MMC approach was the most accurate procedure compared to EC, CC, and ZC procedures. The precision ranking of the four approaches according to all above settings was MMC ≥ EC ≥ CC ≥ ZC. This study highlights advantages of MGLS meta-analysis on UM approach. The results suggested the use of MMC procedure to overcome the lack of information for having a complete covariance matrix of the coefficients.

Multivariate Phylogenetic Comparative Methods: Evaluations, Comparisons, and Recommendations.

PubMed

Adams, Dean C; Collyer, Michael L

2018-01-01

Recent years have seen increased interest in phylogenetic comparative analyses of multivariate data sets, but to date the varied proposed approaches have not been extensively examined. Here we review the mathematical properties required of any multivariate method, and specifically evaluate existing multivariate phylogenetic comparative methods in this context. Phylogenetic comparative methods based on the full multivariate likelihood are robust to levels of covariation among trait dimensions and are insensitive to the orientation of the data set, but display increasing model misspecification as the number of trait dimensions increases. This is because the expected evolutionary covariance matrix (V) used in the likelihood calculations becomes more ill-conditioned as trait dimensionality increases, and as evolutionary models become more complex. Thus, these approaches are only appropriate for data sets with few traits and many species. Methods that summarize patterns across trait dimensions treated separately (e.g., SURFACE) incorrectly assume independence among trait dimensions, resulting in nearly a 100% model misspecification rate. Methods using pairwise composite likelihood are highly sensitive to levels of trait covariation, the orientation of the data set, and the number of trait dimensions. The consequences of these debilitating deficiencies are that a user can arrive at differing statistical conclusions, and therefore biological inferences, simply from a dataspace rotation, like principal component analysis. By contrast, algebraic generalizations of the standard phylogenetic comparative toolkit that use the trace of covariance matrices are insensitive to levels of trait covariation, the number of trait dimensions, and the orientation of the data set. Further, when appropriate permutation tests are used, these approaches display acceptable Type I error and statistical power. We conclude that methods summarizing information across trait dimensions, as well as pairwise composite likelihood methods should be avoided, whereas algebraic generalizations of the phylogenetic comparative toolkit provide a useful means of assessing macroevolutionary patterns in multivariate data. Finally, we discuss areas in which multivariate phylogenetic comparative methods are still in need of future development; namely highly multivariate Ornstein-Uhlenbeck models and approaches for multivariate evolutionary model comparisons. © The Author(s) 2017. Published by Oxford University Press on behalf of the Systematic Biology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

The impact of moderate wine consumption on the risk of developing prostate cancer.

PubMed

Vartolomei, Mihai Dorin; Kimura, Shoji; Ferro, Matteo; Foerster, Beat; Abufaraj, Mohammad; Briganti, Alberto; Karakiewicz, Pierre I; Shariat, Shahrokh F

2018-01-01

To investigate the impact of moderate wine consumption on the risk of prostate cancer (PCa). We focused on the differential effect of moderate consumption of red versus white wine. This study was a meta-analysis that includes data from case-control and cohort studies. A systematic search of Web of Science, Medline/PubMed, and Cochrane library was performed on December 1, 2017. Studies were deemed eligible if they assessed the risk of PCa due to red, white, or any wine using multivariable logistic regression analysis. We performed a formal meta-analysis for the risk of PCa according to moderate wine and wine type consumption (white or red). Heterogeneity between studies was assessed using Cochrane's Q test and I 2 statistics. Publication bias was assessed using Egger's regression test. A total of 930 abstracts and titles were initially identified. After removal of duplicates, reviews, and conference abstracts, 83 full-text original articles were screened. Seventeen studies (611,169 subjects) were included for final evaluation and fulfilled the inclusion criteria. In the case of moderate wine consumption: the pooled risk ratio (RR) for the risk of PCa was 0.98 (95% CI 0.92-1.05, p =0.57) in the multivariable analysis. Moderate white wine consumption increased the risk of PCa with a pooled RR of 1.26 (95% CI 1.10-1.43, p =0.001) in the multi-variable analysis. Meanwhile, moderate red wine consumption had a protective role reducing the risk by 12% (RR 0.88, 95% CI 0.78-0.999, p =0.047) in the multivariable analysis that comprised 222,447 subjects. In this meta-analysis, moderate wine consumption did not impact the risk of PCa. Interestingly, regarding the type of wine, moderate consumption of white wine increased the risk of PCa, whereas moderate consumption of red wine had a protective effect. Further analyses are needed to assess the differential molecular effect of white and red wine conferring their impact on PCa risk.

Effects of Flavor and Texture on the Sensory Perception of Gouda-Type Cheese Varieties during Ripening Using Multivariate Analysis.

PubMed

Shiota, Makoto; Iwasawa, Ai; Suzuki-Iwashima, Ai; Iida, Fumiko

2015-12-01

The impact of flavor composition, texture, and other factors on desirability of different commercial sources of Gouda-type cheese using multivariate analyses on the basis of sensory and instrumental analyses were investigated. Volatile aroma compounds were measured using headspace solid-phase microextraction gas chromatography/mass spectrometry (GC/MS) and steam distillation extraction (SDE)-GC/MS, and fatty acid composition, low-molecular-weight compounds, including amino acids, and organic acids, as well pH, texture, and color were measured to determine their relationship with sensory perception. Orthogonal partial least squares-discriminant analysis (OPLS-DA) was performed to discriminate between 2 different ripening periods in 7 sample sets, revealing that ethanol, ethyl acetate, hexanoic acid, and octanoic acid increased with increasing sensory attribute scores for sweetness, fruity, and sulfurous. A partial least squares (PLS) regression model was constructed to predict the desirability of cheese using these parameters. We showed that texture and buttery flavors are important factors affecting the desirability of Gouda-type cheeses for Japanese consumers using these multivariate analyses. © 2015 Institute of Food Technologists®

Hyponatremia in Guillain-Barré Syndrome.

PubMed

Rumalla, Kavelin; Reddy, Adithi Y; Letchuman, Vijay; Mittal, Manoj K

2017-06-01

To evaluate incidence, risk factors, and in-hospital outcomes associated with hyponatremia in patients hospitalized for Guillain-Barré Syndrome (GBS). We identified adult patients with GBS in the Nationwide Inpatient Sample (2002-2011). Univariate and multivariable analyses were used. Among 54,778 patients hospitalized for GBS, the incidence of hyponatremia was 11.8% (compared with 4.0% in non-GBS patients) and increased from 6.9% in 2002 to 13.5% in 2011 (P < 0.0001). Risk factors associated with hyponatremia in multivariable analysis included advanced age, deficiency anemia, alcohol abuse, hypertension, and intravenous immunoglobulin (all P < 0.0001). Hyponatremia was associated with prolonged length of stay (16.07 vs. 10.41, days), increased costs (54,001 vs. 34,125, $USD), and mortality (20.5% vs. 11.6%) (all P < 0.0001). In multivariable analysis, hyponatremia was independently associated with adverse discharge disposition (odds ratio: 2.07, 95% confidence interval, 1.91-2.25, P < 0.0001). Hyponatremia is prevalent in GBS and is detrimental to patient-centered outcomes and health care costs. Sodium levels should be carefully monitored in high-risk patients.

Brain regions with abnormal network properties in severe epilepsy of Lennox-Gastaut phenotype: Multivariate analysis of task-free fMRI.

PubMed

Pedersen, Mangor; Curwood, Evan K; Archer, John S; Abbott, David F; Jackson, Graeme D

2015-11-01

Lennox-Gastaut syndrome, and the similar but less tightly defined Lennox-Gastaut phenotype, describe patients with severe epilepsy, generalized epileptic discharges, and variable intellectual disability. Our previous functional neuroimaging studies suggest that abnormal diffuse association network activity underlies the epileptic discharges of this clinical phenotype. Herein we use a data-driven multivariate approach to determine the spatial changes in local and global networks of patients with severe epilepsy of the Lennox-Gastaut phenotype. We studied 9 adult patients and 14 controls. In 20 min of task-free blood oxygen level-dependent functional magnetic resonance imaging data, two metrics of functional connectivity were studied: Regional homogeneity or local connectivity, a measure of concordance between each voxel to a focal cluster of adjacent voxels; and eigenvector centrality, a global connectivity estimate designed to detect important neural hubs. Multivariate pattern analysis of these data in a machine-learning framework was used to identify spatial features that classified disease subjects. Multivariate pattern analysis was 95.7% accurate in classifying subjects for both local and global connectivity measures (22/23 subjects correctly classified). Maximal discriminating features were the following: increased local connectivity in frontoinsular and intraparietal areas; increased global connectivity in posterior association areas; decreased local connectivity in sensory (visual and auditory) and medial frontal cortices; and decreased global connectivity in the cingulate cortex, striatum, hippocampus, and pons. Using a data-driven analysis method in task-free functional magnetic resonance imaging, we show increased connectivity in critical areas of association cortex and decreased connectivity in primary cortex. This supports previous findings of a critical role for these association cortical regions as a final common pathway in generating the Lennox-Gastaut phenotype. Abnormal function of these areas is likely to be important in explaining the intellectual problems characteristic of this disorder. Wiley Periodicals, Inc. © 2015 International League Against Epilepsy.

A Multitaper, Causal Decomposition for Stochastic, Multivariate Time Series: Application to High-Frequency Calcium Imaging Data.

PubMed

Sornborger, Andrew T; Lauderdale, James D

2016-11-01

Neural data analysis has increasingly incorporated causal information to study circuit connectivity. Dimensional reduction forms the basis of most analyses of large multivariate time series. Here, we present a new, multitaper-based decomposition for stochastic, multivariate time series that acts on the covariance of the time series at all lags, C ( τ ), as opposed to standard methods that decompose the time series, X ( t ), using only information at zero-lag. In both simulated and neural imaging examples, we demonstrate that methods that neglect the full causal structure may be discarding important dynamical information in a time series.

Use of an operating microscope during spine surgery is associated with minor increases in operating room times and no increased risk of infection.

PubMed

Basques, Bryce A; Golinvaux, Nicholas S; Bohl, Daniel D; Yacob, Alem; Toy, Jason O; Varthi, Arya G; Grauer, Jonathan N

2014-10-15

Retrospective database review. To evaluate whether microscope use during spine procedures is associated with increased operating room times or increased risk of infection. Operating microscopes are commonly used in spine procedures. It is debated whether the use of an operating microscope increases operating room time or confers increased risk of infection. The American College of Surgeons National Surgical Quality Improvement Program database, which includes data from more than 370 participating hospitals, was used to identify patients undergoing elective spinal procedures with and without the use of an operating microscope for the years 2011 and 2012. Bivariate and multivariate linear regressions were used to test the association between microscope use and operating room times. Bivariate and multivariate logistic regressions were similarly conducted to test the association between microscope use and infection occurrence within 30 days of surgery. A total of 23,670 elective spine procedures were identified, of which 2226 (9.4%) used an operating microscope. The average patient age was 55.1±14.4 years. The average operative time (incision to closure) was 125.7±82.0 minutes.Microscope use was associated with minor increases in preoperative room time (+2.9 min, P=0.013), operative time (+13.2 min, P<0.001), and total room time (+18.6 min, P<0.001) on multivariate analysis.A total of 328 (1.4%) patients had an infection within 30 days of surgery. Multivariate analysis revealed no significant difference between the microscope and nonmicroscope groups for occurrence of any infection, superficial surgical site infection, deep surgical site infection, organ space infection, or sepsis/septic shock, regardless of surgery type. We did not find operating room times or infection risk to be significant deterrents for use of an operating microscope during spine surgery. 3.

Use of an operating microscope during spine surgery is associated with minor increases in operating room times and no increased risk of infection

PubMed Central

Basques, Bryce A.; Golinvaux, Nicholas S.; Bohl, Daniel D.; Yacob, Alem; Toy, Jason O.; Varthi, Arya G.; Grauer, Jonathan N.

2014-01-01

Study Design Retrospective database review. Objective To evaluate whether microscope use during spine procedures is associated with increased operating room times or increased risk of infection. Summary of Background Data Operating microscopes are commonly used in spine procedures. It is debated whether the use of an operating microscope increases operating room time or confers increased risk of infection. Methods The American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database, which includes data from over 370 participating hospitals, was used to identify patients undergoing elective spinal procedures with and without an operating microscope for the years 2011 and 2012. Bivariate and multivariate linear regressions were used to test the association between microscope use and operating room times. Bivariate and multivariate logistic regressions were similarly conducted to test the association between microscope use and infection occurrence within 30 days of surgery. Results A total of 23,670 elective spine procedures were identified, of which 2,226 (9.4%) used an operating microscope. The average patient age was 55.1 ± 14.4 years. The average operative time (incision to closure) was 125.7 ± 82.0 minutes. Microscope use was associated with minor increases in preoperative room time (+2.9 minutes, p=0.013), operative time (+13.2 minutes, p<0.001), and total room time (+18.6 minutes, p<0.001) on multivariate analysis. A total of 328 (1.4%) patients had an infection within 30 days of surgery. Multivariate analysis revealed no significant difference between the microscope and non-microscope groups for occurrence of any infection, superficial surgical site infection (SSI), deep SSI, organ space infection, or sepsis/septic shock, regardless of surgery type. Conclusions We did not find operating room times or infection risk to be significant deterrents for use of an operating microscope during spine surgery. PMID:25188600

An efficient genome-wide association test for multivariate phenotypes based on the Fisher combination function.

PubMed

Yang, James J; Li, Jia; Williams, L Keoki; Buu, Anne

2016-01-05

In genome-wide association studies (GWAS) for complex diseases, the association between a SNP and each phenotype is usually weak. Combining multiple related phenotypic traits can increase the power of gene search and thus is a practically important area that requires methodology work. This study provides a comprehensive review of existing methods for conducting GWAS on complex diseases with multiple phenotypes including the multivariate analysis of variance (MANOVA), the principal component analysis (PCA), the generalizing estimating equations (GEE), the trait-based association test involving the extended Simes procedure (TATES), and the classical Fisher combination test. We propose a new method that relaxes the unrealistic independence assumption of the classical Fisher combination test and is computationally efficient. To demonstrate applications of the proposed method, we also present the results of statistical analysis on the Study of Addiction: Genetics and Environment (SAGE) data. Our simulation study shows that the proposed method has higher power than existing methods while controlling for the type I error rate. The GEE and the classical Fisher combination test, on the other hand, do not control the type I error rate and thus are not recommended. In general, the power of the competing methods decreases as the correlation between phenotypes increases. All the methods tend to have lower power when the multivariate phenotypes come from long tailed distributions. The real data analysis also demonstrates that the proposed method allows us to compare the marginal results with the multivariate results and specify which SNPs are specific to a particular phenotype or contribute to the common construct. The proposed method outperforms existing methods in most settings and also has great applications in GWAS on complex diseases with multiple phenotypes such as the substance abuse disorders.

Multivariate temporal pattern analysis applied to the study of rat behavior in the elevated plus maze: methodological and conceptual highlights.

PubMed

Casarrubea, M; Magnusson, M S; Roy, V; Arabo, A; Sorbera, F; Santangelo, A; Faulisi, F; Crescimanno, G

2014-08-30

Aim of this article is to illustrate the application of a multivariate approach known as t-pattern analysis in the study of rat behavior in elevated plus maze. By means of this multivariate approach, significant relationships among behavioral events in the course of time can be described. Both quantitative and t-pattern analyses were utilized to analyze data obtained from fifteen male Wistar rats following a trial 1-trial 2 protocol. In trial 2, in comparison with the initial exposure, mean occurrences of behavioral elements performed in protected zones of the maze showed a significant increase counterbalanced by a significant decrease of mean occurrences of behavioral elements in unprotected zones. Multivariate t-pattern analysis, in trial 1, revealed the presence of 134 t-patterns of different composition. In trial 2, the temporal structure of behavior become more simple, being present only 32 different t-patterns. Behavioral strings and stripes (i.e. graphical representation of each t-pattern onset) of all t-patterns were presented both for trial 1 and trial 2 as well. Finally, percent distributions in the three zones of the maze show a clear-cut increase of t-patterns in closed arm and a significant reduction in the remaining zones. Results show that previous experience deeply modifies the temporal structure of rat behavior in the elevated plus maze. In addition, this article, by highlighting several conceptual, methodological and illustrative aspects on the utilization of t-pattern analysis, could represent a useful background to employ such a refined approach in the study of rat behavior in elevated plus maze. Copyright © 2014 Elsevier B.V. All rights reserved.

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.

Atmospheric conditions, lunar phases, and childbirth: a multivariate analysis

NASA Astrophysics Data System (ADS)

Ochiai, Angela Megumi; Gonçalves, Fabio Luiz Teixeira; Ambrizzi, Tercio; Florentino, Lucia Cristina; Wei, Chang Yi; Soares, Alda Valeria Neves; De Araujo, Natalucia Matos; Gualda, Dulce Maria Rosa

2012-07-01

Our objective was to assess extrinsic influences upon childbirth. In a cohort of 1,826 days containing 17,417 childbirths among them 13,252 spontaneous labor admissions, we studied the influence of environment upon the high incidence of labor (defined by 75th percentile or higher), analyzed by logistic regression. The predictors of high labor admission included increases in outdoor temperature (odds ratio: 1.742, P = 0.045, 95%CI: 1.011 to 3.001), and decreases in atmospheric pressure (odds ratio: 1.269, P = 0.029, 95%CI: 1.055 to 1.483). In contrast, increases in tidal range were associated with a lower probability of high admission (odds ratio: 0.762, P = 0.030, 95%CI: 0.515 to 0.999). Lunar phase was not a predictor of high labor admission ( P = 0.339). Using multivariate analysis, increases in temperature and decreases in atmospheric pressure predicted high labor admission, and increases of tidal range, as a measurement of the lunar gravitational force, predicted a lower probability of high admission.

The application of ATR-FTIR spectroscopy and multivariate data analysis to study drug crystallisation in the stratum corneum.

PubMed

Goh, Choon Fu; Craig, Duncan Q M; Hadgraft, Jonathan; Lane, Majella E

2017-02-01

Drug permeation through the intercellular lipids, which pack around and between corneocytes, may be enhanced by increasing the thermodynamic activity of the active in a formulation. However, this may also result in unwanted drug crystallisation on and in the skin. In this work, we explore the combination of ATR-FTIR spectroscopy and multivariate data analysis to study drug crystallisation in the skin. Ex vivo permeation studies of saturated solutions of diclofenac sodium (DF Na) in two vehicles, propylene glycol (PG) and dimethyl sulphoxide (DMSO), were carried out in porcine ear skin. Tape stripping and ATR-FTIR spectroscopy were conducted simultaneously to collect spectral data as a function of skin depth. Multivariate data analysis was applied to visualise and categorise the spectral data in the region of interest (1700-1500cm -1 ) containing the carboxylate (COO - ) asymmetric stretching vibrations of DF Na. Spectral data showed the redshifts of the COO - asymmetric stretching vibrations for DF Na in the solution compared with solid drug. Similar shifts were evident following application of saturated solutions of DF Na to porcine skin samples. Multivariate data analysis categorised the spectral data based on the spectral differences and drug crystallisation was found to be confined to the upper layers of the skin. This proof-of-concept study highlights the utility of ATR-FTIR spectroscopy in combination with multivariate data analysis as a simple and rapid approach in the investigation of drug deposition in the skin. The approach described here will be extended to the study of other actives for topical application to the skin. Copyright © 2016 Elsevier B.V. All rights reserved.

The role of area-level deprivation and gender in participation in population-based faecal immunochemical test (FIT) colorectal cancer screening.

PubMed

Clarke, Nicholas; McNamara, Deirdre; Kearney, Patricia M; O'Morain, Colm A; Shearer, Nikki; Sharp, Linda

2016-12-01

This study aimed to investigate the effects of sex and deprivation on participation in a population-based faecal immunochemical test (FIT) colorectal cancer screening programme. The study population included 9785 individuals invited to participate in two rounds of a population-based biennial FIT-based screening programme, in a relatively deprived area of Dublin, Ireland. Explanatory variables included in the analysis were sex, deprivation category of area of residence and age (at end of screening). The primary outcome variable modelled was participation status in both rounds combined (with "participation" defined as having taken part in either or both rounds of screening). Poisson regression with a log link and robust error variance was used to estimate relative risks (RR) for participation. As a sensitivity analysis, data were stratified by screening round. In both the univariable and multivariable models deprivation was strongly associated with participation. Increasing affluence was associated with higher participation; participation was 26% higher in people resident in the most affluent compared to the most deprived areas (multivariable RR=1.26: 95% CI 1.21-1.30). Participation was significantly lower in males (multivariable RR=0.96: 95%CI 0.95-0.97) and generally increased with increasing age (trend per age group, multivariable RR=1.02: 95%CI, 1.01-1.02). No significant interactions between the explanatory variables were found. The effects of deprivation and sex were similar by screening round. Deprivation and male gender are independently associated with lower uptake of population-based FIT colorectal cancer screening, even in a relatively deprived setting. Development of evidence-based interventions to increase uptake in these disadvantaged groups is urgently required. Copyright © 2016. Published by Elsevier Inc.

Estimation and Psychometric Analysis of Component Profile Scores via Multivariate Generalizability Theory

ERIC Educational Resources Information Center

Grochowalski, Joseph H.

2015-01-01

Component Universe Score Profile analysis (CUSP) is introduced in this paper as a psychometric alternative to multivariate profile analysis. The theoretical foundations of CUSP analysis are reviewed, which include multivariate generalizability theory and constrained principal components analysis. Because CUSP is a combination of generalizability…

The impact of lungs from diabetic donors on lung transplant recipients†.

PubMed

Ambur, Vishnu; Taghavi, Sharven; Jayarajan, Senthil; Kadakia, Sagar; Zhao, Huaqing; Gomez-Abraham, Jesus; Toyoda, Yoshiya

2017-02-01

We attempted to determine if transplants of lungs from diabetic donors (DDs) is associated with increased mortality of recipients in the modern era of the lung allocation score (LAS). The United Network for Organ Sharing (UNOS) database was queried for all adult lung transplant recipients from 2006 to 2014. Patients receiving a lung from a DD were compared to those receiving a transplant from a non-DD. Multivariate Cox regression analysis using variables associated with mortality was used to examine survival. A total of 13 159 adult lung transplants were performed between January 2006 and June 2014: 4278 (32.5%) were single-lung transplants (SLT) and 8881 (67.5%) were double-lung transplants (DLT). The log-rank test demonstrated a lower median survival in the DD group (5.6 vs 5.0 years, P = 0.003). We performed additional analysis by dividing this initial cohort into two cohorts by transplant type. On multivariate analysis, receiving an SLT from a DD was associated with increased mortality (HR 1.28, 95% CI 1.07–1.54, P = 0.011). Interestingly, multivariate analysis demonstrated no difference in mortality rates for patients receiving a DLT from a DD (HR 1.12, 95% CI 0.97–1.30, P = 0.14). DLT with DDs can be performed safely without increased mortality, but SLT using DDs results in worse survival and post-transplant outcomes. Preference should be given to DLT when using lungs from donors with diabetes. © The Author 2016. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved.

Multivariate co-integration analysis of the Kaya factors in Ghana.

PubMed

Asumadu-Sarkodie, Samuel; Owusu, Phebe Asantewaa

2016-05-01

The fundamental goal of the Government of Ghana's development agenda as enshrined in the Growth and Poverty Reduction Strategy to grow the economy to a middle income status of US$1000 per capita by the end of 2015 could be met by increasing the labour force, increasing energy supplies and expanding the energy infrastructure in order to achieve the sustainable development targets. In this study, a multivariate co-integration analysis of the Kaya factors namely carbon dioxide, total primary energy consumption, population and GDP was investigated in Ghana using vector error correction model with data spanning from 1980 to 2012. Our research results show an existence of long-run causality running from population, GDP and total primary energy consumption to carbon dioxide emissions. However, there is evidence of short-run causality running from population to carbon dioxide emissions. There was a bi-directional causality running from carbon dioxide emissions to energy consumption and vice versa. In other words, decreasing the primary energy consumption in Ghana will directly reduce carbon dioxide emissions. In addition, a bi-directional causality running from GDP to energy consumption and vice versa exists in the multivariate model. It is plausible that access to energy has a relationship with increasing economic growth and productivity in Ghana.

Risk of Postoperative Complications Among Inflammatory Bowel Disease Patients Treated Preoperatively With Vedolizumab.

PubMed

Yamada, Akihiro; Komaki, Yuga; Patel, Nayan; Komaki, Fukiko; Aelvoet, Arthur S; Tran, Anthony L; Pekow, Joel; Dalal, Sushila; Cohen, Russell D; Cannon, Lisa; Umanskiy, Konstantin; Smith, Radhika; Hurst, Roger; Hyman, Neil; Rubin, David T; Sakuraba, Atsushi

2017-09-01

Vedolizumab is increasingly used to treat patients with ulcerative colitis (UC) and Crohn's disease (CD), however, its safety during the perioperative period remains unclear. We compared the 30-day postoperative complications among patients treated preoperatively with vedolizumab, anti-tumor necrosis factor (TNF)-α agents or non-biological therapy. The retrospective study cohort was comprised of patients receiving vedolizumab, anti-TNF-α agents or non-biological therapy within 4 weeks of surgery. The rates of 30-day postoperative complications were compared between groups using univariate and multivariate analysis. Propensity score-matched analysis was performed to compare the outcome between groups. Among 443 patients (64 vedolizumab, 129 anti-TNF-α agents, and 250 non-biological therapy), a total of 144 patients experienced postoperative complications (32%). In multivariate analysis, age >65 (odds ratio (OR) 3.56, 95% confidence interval (CI) 1.30-9.76) and low-albumin (OR 2.26, 95% CI 1.28-4.00) were associated with increased risk of 30-day postoperative complications. For infectious complications, steroid use (OR 3.67, 95% CI 1.57-8.57, P=0.003) and low hemoglobin (OR 3.03, 95% CI 1.32-6.96, P=0.009) were associated with increased risk in multivariate analysis. Propensity score matched analysis demonstrated that the risks of postoperative complications were not different among patients preoperatively receiving vedolizumab, anti-TNF-α agents or non-biological therapy (UC, P=0.40; CD, P=0.35). In the present study, preoperative vedolizumab exposure did not affect the risk of 30-day postoperative complications in UC and CD. Further, larger studies are required to confirm our findings.

Network structure of multivariate time series.

PubMed

Lacasa, Lucas; Nicosia, Vincenzo; Latora, Vito

2015-10-21

Our understanding of a variety of phenomena in physics, biology and economics crucially depends on the analysis of multivariate time series. While a wide range tools and techniques for time series analysis already exist, the increasing availability of massive data structures calls for new approaches for multidimensional signal processing. We present here a non-parametric method to analyse multivariate time series, based on the mapping of a multidimensional time series into a multilayer network, which allows to extract information on a high dimensional dynamical system through the analysis of the structure of the associated multiplex network. The method is simple to implement, general, scalable, does not require ad hoc phase space partitioning, and is thus suitable for the analysis of large, heterogeneous and non-stationary time series. We show that simple structural descriptors of the associated multiplex networks allow to extract and quantify nontrivial properties of coupled chaotic maps, including the transition between different dynamical phases and the onset of various types of synchronization. As a concrete example we then study financial time series, showing that a multiplex network analysis can efficiently discriminate crises from periods of financial stability, where standard methods based on time-series symbolization often fail.

Analysis of Big Data in Gait Biomechanics: Current Trends and Future Directions.

PubMed

Phinyomark, Angkoon; Petri, Giovanni; Ibáñez-Marcelo, Esther; Osis, Sean T; Ferber, Reed

2018-01-01

The increasing amount of data in biomechanics research has greatly increased the importance of developing advanced multivariate analysis and machine learning techniques, which are better able to handle "big data". Consequently, advances in data science methods will expand the knowledge for testing new hypotheses about biomechanical risk factors associated with walking and running gait-related musculoskeletal injury. This paper begins with a brief introduction to an automated three-dimensional (3D) biomechanical gait data collection system: 3D GAIT, followed by how the studies in the field of gait biomechanics fit the quantities in the 5 V's definition of big data: volume, velocity, variety, veracity, and value. Next, we provide a review of recent research and development in multivariate and machine learning methods-based gait analysis that can be applied to big data analytics. These modern biomechanical gait analysis methods include several main modules such as initial input features, dimensionality reduction (feature selection and extraction), and learning algorithms (classification and clustering). Finally, a promising big data exploration tool called "topological data analysis" and directions for future research are outlined and discussed.

Single Marital Status and Infectious Mortality in Women With Cervical Cancer in the United States.

PubMed

Machida, Hiroko; Eckhardt, Sarah E; Castaneda, Antonio V; Blake, Erin A; Pham, Huyen Q; Roman, Lynda D; Matsuo, Koji

2017-10-01

Unmarried status including single marital status is associated with increased mortality in women bearing malignancy. Infectious disease weights a significant proportion of mortality in patients with malignancy. Here, we examined an association of single marital status and infectious mortality in cervical cancer. This is a retrospective observational study examining 86,555 women with invasive cervical cancer identified in the Surveillance, Epidemiology, and End Results Program between 1973 and 2013. Characteristics of 18,324 single women were compared with 38,713 married women in multivariable binary logistic regression models. Propensity score matching was performed to examine cumulative risk of all-cause and infectious mortality between the 2 groups. Single marital status was significantly associated with young age, black/Hispanic ethnicity, Western US residents, uninsured status, high-grade tumor, squamous histology, and advanced-stage disease on multivariable analysis (all, P < 0.05). In a prematched model, single marital status was significantly associated with increased cumulative risk of all-cause mortality (5-year rate: 32.9% vs 29.7%, P < 0.001) and infectious mortality (0.5% vs 0.3%, P < 0.001) compared with the married status. After propensity score matching, single marital status remained an independent prognostic factor for increased cumulative risk of all-cause mortality (adjusted hazards ratio [HR], 1.15; 95% confidence interval [CI], 1.11-1.20; P < 0.001) and those of infectious mortality on multivariable analysis (adjusted HR, 1.71; 95% CI, 1.27-2.32; P < 0.001). In a sensitivity analysis for stage I disease, single marital status remained significantly increased risk of infectious mortality after propensity score matching (adjusted HR, 2.24; 95% CI, 1.34-3.73; P = 0.002). Single marital status was associated with increased infectious mortality in women with invasive cervical cancer.

Mapping Informative Clusters in a Hierarchial Framework of fMRI Multivariate Analysis

PubMed Central

Xu, Rui; Zhen, Zonglei; Liu, Jia

2010-01-01

Pattern recognition methods have become increasingly popular in fMRI data analysis, which are powerful in discriminating between multi-voxel patterns of brain activities associated with different mental states. However, when they are used in functional brain mapping, the location of discriminative voxels varies significantly, raising difficulties in interpreting the locus of the effect. Here we proposed a hierarchical framework of multivariate approach that maps informative clusters rather than voxels to achieve reliable functional brain mapping without compromising the discriminative power. In particular, we first searched for local homogeneous clusters that consisted of voxels with similar response profiles. Then, a multi-voxel classifier was built for each cluster to extract discriminative information from the multi-voxel patterns. Finally, through multivariate ranking, outputs from the classifiers were served as a multi-cluster pattern to identify informative clusters by examining interactions among clusters. Results from both simulated and real fMRI data demonstrated that this hierarchical approach showed better performance in the robustness of functional brain mapping than traditional voxel-based multivariate methods. In addition, the mapped clusters were highly overlapped for two perceptually equivalent object categories, further confirming the validity of our approach. In short, the hierarchical framework of multivariate approach is suitable for both pattern classification and brain mapping in fMRI studies. PMID:21152081

Estimating multivariate similarity between neuroimaging datasets with sparse canonical correlation analysis: an application to perfusion imaging.

PubMed

Rosa, Maria J; Mehta, Mitul A; Pich, Emilio M; Risterucci, Celine; Zelaya, Fernando; Reinders, Antje A T S; Williams, Steve C R; Dazzan, Paola; Doyle, Orla M; Marquand, Andre F

2015-01-01

An increasing number of neuroimaging studies are based on either combining more than one data modality (inter-modal) or combining more than one measurement from the same modality (intra-modal). To date, most intra-modal studies using multivariate statistics have focused on differences between datasets, for instance relying on classifiers to differentiate between effects in the data. However, to fully characterize these effects, multivariate methods able to measure similarities between datasets are needed. One classical technique for estimating the relationship between two datasets is canonical correlation analysis (CCA). However, in the context of high-dimensional data the application of CCA is extremely challenging. A recent extension of CCA, sparse CCA (SCCA), overcomes this limitation, by regularizing the model parameters while yielding a sparse solution. In this work, we modify SCCA with the aim of facilitating its application to high-dimensional neuroimaging data and finding meaningful multivariate image-to-image correspondences in intra-modal studies. In particular, we show how the optimal subset of variables can be estimated independently and we look at the information encoded in more than one set of SCCA transformations. We illustrate our framework using Arterial Spin Labeling data to investigate multivariate similarities between the effects of two antipsychotic drugs on cerebral blood flow.

Skype Synchronous Interaction Effectiveness in a Quantitative Management Science Course

ERIC Educational Resources Information Center

Strang, Kenneth David

2012-01-01

An experiment compared asynchronous versus synchronous instruction in an online quantitative course. Mann-Whitney U-tests, correlation, analysis of variance, t tests, and multivariate analysis of covariance (MANCOVA) were utilized to test the hypothesis that more high-quality online experiential learning interactions would increase grade.…

Multivariate meta-analysis: potential and promise.

PubMed

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

2011-09-10

The multivariate random effects model is a generalization of the standard univariate model. Multivariate meta-analysis is becoming more commonly used and the techniques and related computer software, although continually under development, are now in place. In order to raise awareness of the multivariate methods, and discuss their advantages and disadvantages, we organized a one day 'Multivariate meta-analysis' event at the Royal Statistical Society. In addition to disseminating the most recent developments, we also received an abundance of comments, concerns, insights, critiques and encouragement. This article provides a balanced account of the day's discourse. By giving others the opportunity to respond to our assessment, we hope to ensure that the various view points and opinions are aired before multivariate meta-analysis simply becomes another widely used de facto method without any proper consideration of it by the medical statistics community. We describe the areas of application that multivariate meta-analysis has found, the methods available, the difficulties typically encountered and the arguments for and against the multivariate methods, using four representative but contrasting examples. We conclude that the multivariate methods can be useful, and in particular can provide estimates with better statistical properties, but also that these benefits come at the price of making more assumptions which do not result in better inference in every case. Although there is evidence that multivariate meta-analysis has considerable potential, it must be even more carefully applied than its univariate counterpart in practice. Copyright © 2011 John Wiley & Sons, Ltd.

Viewpoints: Interactive Exploration of Large Multivariate Earth and Space Science Data Sets

NASA Astrophysics Data System (ADS)

Levit, C.; Gazis, P. R.

2006-05-01

Analysis and visualization of extremely large and complex data sets may be one of the most significant challenges facing earth and space science investigators in the forthcoming decades. While advances in hardware speed and storage technology have roughly kept up with (indeed, have driven) increases in database size, the same is not of our abilities to manage the complexity of these data. Current missions, instruments, and simulations produce so much data of such high dimensionality that they outstrip the capabilities of traditional visualization and analysis software. This problem can only be expected to get worse as data volumes increase by orders of magnitude in future missions and in ever-larger supercomputer simulations. For large multivariate data (more than 105 samples or records with more than 5 variables per sample) the interactive graphics response of most existing statistical analysis, machine learning, exploratory data analysis, and/or visualization tools such as Torch, MLC++, Matlab, S++/R, and IDL stutters, stalls, or stops working altogether. Fortunately, the graphics processing units (GPUs) built in to all professional desktop and laptop computers currently on the market are capable of transforming, filtering, and rendering hundreds of millions of points per second. We present a prototype open-source cross-platform application which leverages much of the power latent in the GPU to enable smooth interactive exploration and analysis of large high- dimensional data using a variety of classical and recent techniques. The targeted application is the interactive analysis of large, complex, multivariate data sets, with dimensionalities that may surpass 100 and sample sizes that may exceed 106-108.

Multivariate Models for Normal and Binary Responses in Intervention Studies

ERIC Educational Resources Information Center

Pituch, Keenan A.; Whittaker, Tiffany A.; Chang, Wanchen

2016-01-01

Use of multivariate analysis (e.g., multivariate analysis of variance) is common when normally distributed outcomes are collected in intervention research. However, when mixed responses--a set of normal and binary outcomes--are collected, standard multivariate analyses are no longer suitable. While mixed responses are often obtained in…

The impact of moderate wine consumption on the risk of developing prostate cancer

PubMed Central

Ferro, Matteo; Foerster, Beat; Abufaraj, Mohammad; Briganti, Alberto; Karakiewicz, Pierre I; Shariat, Shahrokh F

2018-01-01

Objective To investigate the impact of moderate wine consumption on the risk of prostate cancer (PCa). We focused on the differential effect of moderate consumption of red versus white wine. Design This study was a meta-analysis that includes data from case–control and cohort studies. Materials and methods A systematic search of Web of Science, Medline/PubMed, and Cochrane library was performed on December 1, 2017. Studies were deemed eligible if they assessed the risk of PCa due to red, white, or any wine using multivariable logistic regression analysis. We performed a formal meta-analysis for the risk of PCa according to moderate wine and wine type consumption (white or red). Heterogeneity between studies was assessed using Cochrane’s Q test and I2 statistics. Publication bias was assessed using Egger’s regression test. Results A total of 930 abstracts and titles were initially identified. After removal of duplicates, reviews, and conference abstracts, 83 full-text original articles were screened. Seventeen studies (611,169 subjects) were included for final evaluation and fulfilled the inclusion criteria. In the case of moderate wine consumption: the pooled risk ratio (RR) for the risk of PCa was 0.98 (95% CI 0.92–1.05, p=0.57) in the multivariable analysis. Moderate white wine consumption increased the risk of PCa with a pooled RR of 1.26 (95% CI 1.10–1.43, p=0.001) in the multi-variable analysis. Meanwhile, moderate red wine consumption had a protective role reducing the risk by 12% (RR 0.88, 95% CI 0.78–0.999, p=0.047) in the multivariable analysis that comprised 222,447 subjects. Conclusions In this meta-analysis, moderate wine consumption did not impact the risk of PCa. Interestingly, regarding the type of wine, moderate consumption of white wine increased the risk of PCa, whereas moderate consumption of red wine had a protective effect. Further analyses are needed to assess the differential molecular effect of white and red wine conferring their impact on PCa risk. PMID:29713200

Multivariate diallel analysis allows multiple gains in segregating populations for agronomic traits in Jatropha.

PubMed

Teodoro, P E; Rodrigues, E V; Peixoto, L A; Silva, L A; Laviola, B G; Bhering, L L

2017-03-22

Jatropha is research target worldwide aimed at large-scale oil production for biodiesel and bio-kerosene. Its production potential is among 1200 and 1500 kg/ha of oil after the 4th year. This study aimed to estimate combining ability of Jatropha genotypes by multivariate diallel analysis to select parents and crosses that allow gains in important agronomic traits. We performed crosses in diallel complete genetic design (3 x 3) arranged in blocks with five replications and three plants per plot. The following traits were evaluated: plant height, stem diameter, canopy projection between rows, canopy projection on the line, number of branches, mass of hundred grains, and grain yield. Data were submitted to univariate and multivariate diallel analysis. Genotypes 107 and 190 can be used in crosses for establishing a base population of Jatropha, since it has favorable alleles for increasing the mass of hundred grains and grain yield and reducing the plant height. The cross 190 x 107 is the most promising to perform the selection of superior genotypes for the simultaneous breeding of these traits.

Deconstructing multivariate decoding for the study of brain function.

PubMed

Hebart, Martin N; Baker, Chris I

2017-08-04

Multivariate decoding methods were developed originally as tools to enable accurate predictions in real-world applications. The realization that these methods can also be employed to study brain function has led to their widespread adoption in the neurosciences. However, prior to the rise of multivariate decoding, the study of brain function was firmly embedded in a statistical philosophy grounded on univariate methods of data analysis. In this way, multivariate decoding for brain interpretation grew out of two established frameworks: multivariate decoding for predictions in real-world applications, and classical univariate analysis based on the study and interpretation of brain activation. We argue that this led to two confusions, one reflecting a mixture of multivariate decoding for prediction or interpretation, and the other a mixture of the conceptual and statistical philosophies underlying multivariate decoding and classical univariate analysis. Here we attempt to systematically disambiguate multivariate decoding for the study of brain function from the frameworks it grew out of. After elaborating these confusions and their consequences, we describe six, often unappreciated, differences between classical univariate analysis and multivariate decoding. We then focus on how the common interpretation of what is signal and noise changes in multivariate decoding. Finally, we use four examples to illustrate where these confusions may impact the interpretation of neuroimaging data. We conclude with a discussion of potential strategies to help resolve these confusions in interpreting multivariate decoding results, including the potential departure from multivariate decoding methods for the study of brain function. Copyright © 2017. Published by Elsevier Inc.

Association between Daily Hospital Outpatient Visits for Accidents and Daily Ambient Air Temperatures in an Industrial City.

PubMed

Chau, Tang-Tat; Wang, Kuo-Ying

2016-01-01

An accident is an unwanted hazard to a person. However, accidents occur. In this work, we search for correlations between daily accident rates and environmental factors. To study daily hospital outpatients who were admitted for accidents during a 5-year period, 2007-2011, we analyzed data regarding 168,366 outpatients using univariate regression models; we also used multivariable regression models to account for confounding factors. Our analysis indicates that the number of male outpatients admitted for accidents was approximately 1.31 to 1.47 times the number of female outpatients (P < 0.0001). Of the 12 parameters (regarding air pollution and meteorology) considered, only daily temperature exhibited consistent and significant correlations with the daily number of hospital outpatient visits for accidents throughout the 5-year analysis period. The univariate regression models indicate that older people (greater than 66 years old) had the fewest accidents per 1-degree increase in temperature, followed by young people (0-15 years old). Middle-aged people (16-65 years old) were the group of outpatients that were more prone to accidents, with an increase in accident rates of 0.8-1.2 accidents per degree increase in temperature. The multivariable regression models also reveal that the temperature variation was the dominant factor in determining the daily number of outpatient visits for accidents. Our further multivariable model analysis of temperature with respect to air pollution variables show that, through the increases in emissions and concentrations of CO, photochemical O3 production and NO2 loss in the ambient air, increases in vehicular emissions are associated with increases in temperatures. As such, increases in hospital visits for accidents are related to vehicular emissions and usage. This finding is consistent with clinical experience which shows about 60% to 80% of accidents are related to traffic, followed by accidents occurred in work place.

Association between Daily Hospital Outpatient Visits for Accidents and Daily Ambient Air Temperatures in an Industrial City

PubMed Central

Chau, Tang-Tat; Wang, Kuo-Ying

2016-01-01

An accident is an unwanted hazard to a person. However, accidents occur. In this work, we search for correlations between daily accident rates and environmental factors. To study daily hospital outpatients who were admitted for accidents during a 5-year period, 2007–2011, we analyzed data regarding 168,366 outpatients using univariate regression models; we also used multivariable regression models to account for confounding factors. Our analysis indicates that the number of male outpatients admitted for accidents was approximately 1.31 to 1.47 times the number of female outpatients (P < 0.0001). Of the 12 parameters (regarding air pollution and meteorology) considered, only daily temperature exhibited consistent and significant correlations with the daily number of hospital outpatient visits for accidents throughout the 5-year analysis period. The univariate regression models indicate that older people (greater than 66 years old) had the fewest accidents per 1-degree increase in temperature, followed by young people (0–15 years old). Middle-aged people (16–65 years old) were the group of outpatients that were more prone to accidents, with an increase in accident rates of 0.8–1.2 accidents per degree increase in temperature. The multivariable regression models also reveal that the temperature variation was the dominant factor in determining the daily number of outpatient visits for accidents. Our further multivariable model analysis of temperature with respect to air pollution variables show that, through the increases in emissions and concentrations of CO, photochemical O3 production and NO2 loss in the ambient air, increases in vehicular emissions are associated with increases in temperatures. As such, increases in hospital visits for accidents are related to vehicular emissions and usage. This finding is consistent with clinical experience which shows about 60% to 80% of accidents are related to traffic, followed by accidents occurred in work place. PMID:26815039

Applications of modern statistical methods to analysis of data in physical science

NASA Astrophysics Data System (ADS)

Wicker, James Eric

Modern methods of statistical and computational analysis offer solutions to dilemmas confronting researchers in physical science. Although the ideas behind modern statistical and computational analysis methods were originally introduced in the 1970's, most scientists still rely on methods written during the early era of computing. These researchers, who analyze increasingly voluminous and multivariate data sets, need modern analysis methods to extract the best results from their studies. The first section of this work showcases applications of modern linear regression. Since the 1960's, many researchers in spectroscopy have used classical stepwise regression techniques to derive molecular constants. However, problems with thresholds of entry and exit for model variables plagues this analysis method. Other criticisms of this kind of stepwise procedure include its inefficient searching method, the order in which variables enter or leave the model and problems with overfitting data. We implement an information scoring technique that overcomes the assumptions inherent in the stepwise regression process to calculate molecular model parameters. We believe that this kind of information based model evaluation can be applied to more general analysis situations in physical science. The second section proposes new methods of multivariate cluster analysis. The K-means algorithm and the EM algorithm, introduced in the 1960's and 1970's respectively, formed the basis of multivariate cluster analysis methodology for many years. However, several shortcomings of these methods include strong dependence on initial seed values and inaccurate results when the data seriously depart from hypersphericity. We propose new cluster analysis methods based on genetic algorithms that overcomes the strong dependence on initial seed values. In addition, we propose a generalization of the Genetic K-means algorithm which can accurately identify clusters with complex hyperellipsoidal covariance structures. We then use this new algorithm in a genetic algorithm based Expectation-Maximization process that can accurately calculate parameters describing complex clusters in a mixture model routine. Using the accuracy of this GEM algorithm, we assign information scores to cluster calculations in order to best identify the number of mixture components in a multivariate data set. We will showcase how these algorithms can be used to process multivariate data from astronomical observations.

Multivariate meta-analysis: Potential and promise

PubMed Central

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

2011-01-01

The multivariate random effects model is a generalization of the standard univariate model. Multivariate meta-analysis is becoming more commonly used and the techniques and related computer software, although continually under development, are now in place. In order to raise awareness of the multivariate methods, and discuss their advantages and disadvantages, we organized a one day ‘Multivariate meta-analysis’ event at the Royal Statistical Society. In addition to disseminating the most recent developments, we also received an abundance of comments, concerns, insights, critiques and encouragement. This article provides a balanced account of the day's discourse. By giving others the opportunity to respond to our assessment, we hope to ensure that the various view points and opinions are aired before multivariate meta-analysis simply becomes another widely used de facto method without any proper consideration of it by the medical statistics community. We describe the areas of application that multivariate meta-analysis has found, the methods available, the difficulties typically encountered and the arguments for and against the multivariate methods, using four representative but contrasting examples. We conclude that the multivariate methods can be useful, and in particular can provide estimates with better statistical properties, but also that these benefits come at the price of making more assumptions which do not result in better inference in every case. Although there is evidence that multivariate meta-analysis has considerable potential, it must be even more carefully applied than its univariate counterpart in practice. Copyright © 2011 John Wiley & Sons, Ltd. PMID:21268052

Multivariate analysis and geochemical approach for assessment of metal pollution state in sediment cores.

PubMed

Jamshidi-Zanjani, Ahmad; Saeedi, Mohsen

2017-07-01

Vertical distribution of metals (Cu, Zn, Cr, Fe, Mn, Pb, Ni, Cd, and Li) in four sediment core samples (C 1 , C 2 , C 3 , and C 4 ) from Anzali international wetland located southwest of the Caspian Sea was examined. Background concentration of each metal was calculated according to different statistical approaches. The results of multivariate statistical analysis showed that Fe and Mn might have significant role in the fate of Ni and Zn in sediment core samples. Different sediment quality indexes were utilized to assess metal pollution in sediment cores. Moreover, a new sediment quality index named aggregative toxicity index (ATI) based on sediment quality guidelines (SQGs) was developed to assess the degree of metal toxicity in an aggregative manner. The increasing pattern of metal pollution and their toxicity degree in upper layers of core samples indicated increasing effects of anthropogenic sources in the study area.

Multivariate Longitudinal Analysis with Bivariate Correlation Test

PubMed Central

Adjakossa, Eric Houngla; Sadissou, Ibrahim; Hounkonnou, Mahouton Norbert; Nuel, Gregory

2016-01-01

In the context of multivariate multilevel data analysis, this paper focuses on the multivariate linear mixed-effects model, including all the correlations between the random effects when the dimensional residual terms are assumed uncorrelated. Using the EM algorithm, we suggest more general expressions of the model’s parameters estimators. These estimators can be used in the framework of the multivariate longitudinal data analysis as well as in the more general context of the analysis of multivariate multilevel data. By using a likelihood ratio test, we test the significance of the correlations between the random effects of two dependent variables of the model, in order to investigate whether or not it is useful to model these dependent variables jointly. Simulation studies are done to assess both the parameter recovery performance of the EM estimators and the power of the test. Using two empirical data sets which are of longitudinal multivariate type and multivariate multilevel type, respectively, the usefulness of the test is illustrated. PMID:27537692

Multivariate Longitudinal Analysis with Bivariate Correlation Test.

PubMed

Adjakossa, Eric Houngla; Sadissou, Ibrahim; Hounkonnou, Mahouton Norbert; Nuel, Gregory

2016-01-01

In the context of multivariate multilevel data analysis, this paper focuses on the multivariate linear mixed-effects model, including all the correlations between the random effects when the dimensional residual terms are assumed uncorrelated. Using the EM algorithm, we suggest more general expressions of the model's parameters estimators. These estimators can be used in the framework of the multivariate longitudinal data analysis as well as in the more general context of the analysis of multivariate multilevel data. By using a likelihood ratio test, we test the significance of the correlations between the random effects of two dependent variables of the model, in order to investigate whether or not it is useful to model these dependent variables jointly. Simulation studies are done to assess both the parameter recovery performance of the EM estimators and the power of the test. Using two empirical data sets which are of longitudinal multivariate type and multivariate multilevel type, respectively, the usefulness of the test is illustrated.

Association between serum CA 19-9 and metabolic syndrome: A cross-sectional study.

PubMed

Du, Rui; Cheng, Di; Lin, Lin; Sun, Jichao; Peng, Kui; Xu, Yu; Xu, Min; Chen, Yuhong; Bi, Yufang; Wang, Weiqing; Lu, Jieli; Ning, Guang

2017-11-01

Increasing evidence suggests that serum CA 19-9 is associated with abnormal glucose metabolism. However, data on the association between CA 19-9 and metabolic syndrome is limited. The aim of the present study was to investigate the association between serum CA 19-9 and metabolic syndrome. A cross-sectional study was conducted on 3641 participants aged ≥40 years from the Songnan Community, Baoshan District in Shanghai, China. Logistic regression analysis was used to evaluate the association between serum CA 19-9 and metabolic syndrome. Multivariate logistic regression analysis showed that compared with participants in the first tertile of serum CA 19-9, those in the second and third tertiles had increased odds ratios (OR) for prevalent metabolic syndrome (multivariate adjusted OR 1.46 [95% confidence interval {CI} 1.11-1.92] and 1.51 [95% CI 1.14-1.98]; P trend  = 0.005). In addition, participants with elevated serum CA 19-9 (≥37 U/mL) had an increased risk of prevalent metabolic syndrome compared with those with serum CA 19-9 < 37 U/mL (multivariate adjusted OR 2.10; 95% CI 1.21-3.65). Serum CA 19-9 is associated with an increased risk of prevalent metabolic syndrome. In order to confirm this association and identify potential mechanisms, prospective cohort and mechanic studies should be performed. © 2017 Ruijin Hospital, Shanghai Jiaotong University School of Medicine and John Wiley & Sons Australia, Ltd.

Multivariate analysis of infant death in England and Wales in 2005-06, with focus on socio-economic status and deprivation.

PubMed

Oakley, Laura; Maconochie, Noreen; Doyle, Pat; Dattani, Nirupa; Moser, Kath

2009-01-01

Current health inequality targets include the goal of reducing the differential in infant mortality between social groups. This article reports on a multivariate analysis of risk factors for infant mortality, with specific focus on deprivation and socio-economic status. Data on all singleton live births in England and Wales in 2005-06 were used, and deprivation quintile (Carstairs index) was assigned to each birth using postcode at birth registration. Deprivation had a strong independent effect on infant mortality, risk of death tending to increase with increasing levels of deprivation. The strength of this relationship depended, however, on whether the babies were low birthweight, preterm or small-for-gestational-age. Trends of increasing mortality risk with increasing deprivation were strongest in the postneonatal period. Uniquely, this article reports the number and proportion of all infant deaths which would potentially be avoided if all levels of deprivation were reduced to that of the least deprived group. It estimates that one quarter of all infant deaths would potentially be avoided if deprivation levels were reduced in this way.

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.

PHI and PCA3 improve the prognostic performance of PRIAS and Epstein criteria in predicting insignificant prostate cancer in men eligible for active surveillance.

PubMed

Cantiello, Francesco; Russo, Giorgio Ivan; Cicione, Antonio; Ferro, Matteo; Cimino, Sebastiano; Favilla, Vincenzo; Perdonà, Sisto; De Cobelli, Ottavio; Magno, Carlo; Morgia, Giuseppe; Damiano, Rocco

2016-04-01

To assess the performance of prostate health index (PHI) and prostate cancer antigen 3 (PCA3) when added to the PRIAS or Epstein criteria in predicting the presence of pathologically insignificant prostate cancer (IPCa) in patients who underwent radical prostatectomy (RP) but eligible for active surveillance (AS). An observational retrospective study was performed in 188 PCa patients treated with laparoscopic or robot-assisted RP but eligible for AS according to Epstein or PRIAS criteria. Blood and urinary specimens were collected before initial prostate biopsy for PHI and PCA3 measurements. Multivariate logistic regression analyses and decision curve analysis were carried out to identify predictors of IPCa using the updated ERSPC definition. At the multivariate analyses, the inclusion of both PCA3 and PHI significantly increased the accuracy of the Epstein multivariate model in predicting IPCa with an increase of 17 % (AUC = 0.77) and of 32 % (AUC = 0.92), respectively. The inclusion of both PCA3 and PHI also increased the predictive accuracy of the PRIAS multivariate model with an increase of 29 % (AUC = 0.87) and of 39 % (AUC = 0.97), respectively. DCA revealed that the multivariable models with the addition of PHI or PCA3 showed a greater net benefit and performed better than the reference models. In a direct comparison, PHI outperformed PCA3 performance resulting in higher net benefit. In a same cohort of patients eligible for AS, the addition of PHI and PCA3 to Epstein or PRIAS models improved their prognostic performance. PHI resulted in greater net benefit in predicting IPCa compared to PCA3.

PyMVPA: A python toolbox for multivariate pattern analysis of fMRI data.

PubMed

Hanke, Michael; Halchenko, Yaroslav O; Sederberg, Per B; Hanson, Stephen José; Haxby, James V; Pollmann, Stefan

2009-01-01

Decoding patterns of neural activity onto cognitive states is one of the central goals of functional brain imaging. Standard univariate fMRI analysis methods, which correlate cognitive and perceptual function with the blood oxygenation-level dependent (BOLD) signal, have proven successful in identifying anatomical regions based on signal increases during cognitive and perceptual tasks. Recently, researchers have begun to explore new multivariate techniques that have proven to be more flexible, more reliable, and more sensitive than standard univariate analysis. Drawing on the field of statistical learning theory, these new classifier-based analysis techniques possess explanatory power that could provide new insights into the functional properties of the brain. However, unlike the wealth of software packages for univariate analyses, there are few packages that facilitate multivariate pattern classification analyses of fMRI data. Here we introduce a Python-based, cross-platform, and open-source software toolbox, called PyMVPA, for the application of classifier-based analysis techniques to fMRI datasets. PyMVPA makes use of Python's ability to access libraries written in a large variety of programming languages and computing environments to interface with the wealth of existing machine learning packages. We present the framework in this paper and provide illustrative examples on its usage, features, and programmability.

PyMVPA: A Python toolbox for multivariate pattern analysis of fMRI data

PubMed Central

Hanke, Michael; Halchenko, Yaroslav O.; Sederberg, Per B.; Hanson, Stephen José; Haxby, James V.; Pollmann, Stefan

2009-01-01

Decoding patterns of neural activity onto cognitive states is one of the central goals of functional brain imaging. Standard univariate fMRI analysis methods, which correlate cognitive and perceptual function with the blood oxygenation-level dependent (BOLD) signal, have proven successful in identifying anatomical regions based on signal increases during cognitive and perceptual tasks. Recently, researchers have begun to explore new multivariate techniques that have proven to be more flexible, more reliable, and more sensitive than standard univariate analysis. Drawing on the field of statistical learning theory, these new classifier-based analysis techniques possess explanatory power that could provide new insights into the functional properties of the brain. However, unlike the wealth of software packages for univariate analyses, there are few packages that facilitate multivariate pattern classification analyses of fMRI data. Here we introduce a Python-based, cross-platform, and open-source software toolbox, called PyMVPA, for the application of classifier-based analysis techniques to fMRI datasets. PyMVPA makes use of Python's ability to access libraries written in a large variety of programming languages and computing environments to interface with the wealth of existing machine-learning packages. We present the framework in this paper and provide illustrative examples on its usage, features, and programmability. PMID:19184561

Potential of non-invasive esophagus cancer detection based on urine surface-enhanced Raman spectroscopy

NASA Astrophysics Data System (ADS)

Huang, Shaohua; Wang, Lan; Chen, Weisheng; Feng, Shangyuan; Lin, Juqiang; Huang, Zufang; Chen, Guannan; Li, Buhong; Chen, Rong

2014-11-01

Non-invasive esophagus cancer detection based on urine surface-enhanced Raman spectroscopy (SERS) analysis was presented. Urine SERS spectra were measured on esophagus cancer patients (n = 56) and healthy volunteers (n = 36) for control analysis. Tentative assignments of the urine SERS spectra indicated some interesting esophagus cancer-specific biomolecular changes, including a decrease in the relative content of urea and an increase in the percentage of uric acid in the urine of esophagus cancer patients compared to that of healthy subjects. Principal component analysis (PCA) combined with linear discriminant analysis (LDA) was employed to analyze and differentiate the SERS spectra between normal and esophagus cancer urine. The diagnostic algorithms utilizing a multivariate analysis method achieved a diagnostic sensitivity of 89.3% and specificity of 83.3% for separating esophagus cancer samples from normal urine samples. These results from the explorative work suggested that silver nano particle-based urine SERS analysis coupled with PCA-LDA multivariate analysis has potential for non-invasive detection of esophagus cancer.

Brain natriuretic peptide predicts functional outcome in ischemic stroke

PubMed Central

Rost, Natalia S; Biffi, Alessandro; Cloonan, Lisa; Chorba, John; Kelly, Peter; Greer, David; Ellinor, Patrick; Furie, Karen L

2011-01-01

Background Elevated serum levels of brain natriuretic peptide (BNP) have been associated with cardioembolic (CE) stroke and increased post-stroke mortality. We sought to determine whether BNP levels were associated with functional outcome after ischemic stroke. Methods We measured BNP in consecutive patients aged ≥18 years admitted to our Stroke Unit between 2002–2005. BNP quintiles were used for analysis. Stroke subtypes were assigned using TOAST criteria. Outcomes were measured as 6-month modified Rankin Scale score (“good outcome” = 0–2 vs. “poor”) as well as mortality. Multivariate logistic regression was used to assess association between the quintiles of BNP and outcomes. Predictive performance of BNP as compared to clinical model alone was assessed by comparing ROC curves. Results Of 569 ischemic stroke patients, 46% were female; mean age was 67.9 ± 15 years. In age- and gender-adjusted analysis, elevated BNP was associated with lower ejection fraction (p<0.0001) and left atrial dilatation (p<0.001). In multivariate analysis, elevated BNP decreased the odds of good functional outcome (OR 0.64, 95%CI 0.41–0.98) and increased the odds of death (OR 1.75, 95%CI 1.36–2.24) in these patients. Addition of BNP to multivariate models increased their predictive performance for functional outcome (p=0.013) and mortality (p<0.03) after CE stroke. Conclusions Serum BNP levels are strongly associated with CE stroke and functional outcome at 6 months after ischemic stroke. Inclusion of BNP improved prediction of mortality in patients with CE stroke. PMID:22116811

The Removal of EOG Artifacts From EEG Signals Using Independent Component Analysis and Multivariate Empirical Mode Decomposition.

PubMed

Wang, Gang; Teng, Chaolin; Li, Kuo; Zhang, Zhonglin; Yan, Xiangguo

2016-09-01

The recorded electroencephalography (EEG) signals are usually contaminated by electrooculography (EOG) artifacts. In this paper, by using independent component analysis (ICA) and multivariate empirical mode decomposition (MEMD), the ICA-based MEMD method was proposed to remove EOG artifacts (EOAs) from multichannel EEG signals. First, the EEG signals were decomposed by the MEMD into multiple multivariate intrinsic mode functions (MIMFs). The EOG-related components were then extracted by reconstructing the MIMFs corresponding to EOAs. After performing the ICA of EOG-related signals, the EOG-linked independent components were distinguished and rejected. Finally, the clean EEG signals were reconstructed by implementing the inverse transform of ICA and MEMD. The results of simulated and real data suggested that the proposed method could successfully eliminate EOAs from EEG signals and preserve useful EEG information with little loss. By comparing with other existing techniques, the proposed method achieved much improvement in terms of the increase of signal-to-noise and the decrease of mean square error after removing EOAs.

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

Assessment of trace elements levels in patients with Type 2 diabetes using multivariate statistical analysis.

PubMed

Badran, M; Morsy, R; Soliman, H; Elnimr, T

2016-01-01

The trace elements metabolism has been reported to possess specific roles in the pathogenesis and progress of diabetes mellitus. Due to the continuous increase in the population of patients with Type 2 diabetes (T2D), this study aims to assess the levels and inter-relationships of fast blood glucose (FBG) and serum trace elements in Type 2 diabetic patients. This study was conducted on 40 Egyptian Type 2 diabetic patients and 36 healthy volunteers (Hospital of Tanta University, Tanta, Egypt). The blood serum was digested and then used to determine the levels of 24 trace elements using an inductive coupled plasma mass spectroscopy (ICP-MS). Multivariate statistical analysis depended on correlation coefficient, cluster analysis (CA) and principal component analysis (PCA), were used to analysis the data. The results exhibited significant changes in FBG and eight of trace elements, Zn, Cu, Se, Fe, Mn, Cr, Mg, and As, levels in the blood serum of Type 2 diabetic patients relative to those of healthy controls. The statistical analyses using multivariate statistical techniques were obvious in the reduction of the experimental variables, and grouping the trace elements in patients into three clusters. The application of PCA revealed a distinct difference in associations of trace elements and their clustering patterns in control and patients group in particular for Mg, Fe, Cu, and Zn that appeared to be the most crucial factors which related with Type 2 diabetes. Therefore, on the basis of this study, the contributors of trace elements content in Type 2 diabetic patients can be determine and specify with correlation relationship and multivariate statistical analysis, which confirm that the alteration of some essential trace metals may play a role in the development of diabetes mellitus. Copyright © 2015 Elsevier GmbH. All rights reserved.

Ripening of salami: assessment of colour and aspect evolution using image analysis and multivariate image analysis.

PubMed

Fongaro, Lorenzo; Alamprese, Cristina; Casiraghi, Ernestina

2015-03-01

During ripening of salami, colour changes occur due to oxidation phenomena involving myoglobin. Moreover, shrinkage due to dehydration results in aspect modifications, mainly ascribable to fat aggregation. The aim of this work was the application of image analysis (IA) and multivariate image analysis (MIA) techniques to the study of colour and aspect changes occurring in salami during ripening. IA results showed that red, green, blue, and intensity parameters decreased due to the development of a global darker colour, while Heterogeneity increased due to fat aggregation. By applying MIA, different salami slice areas corresponding to fat and three different degrees of oxidised meat were identified and quantified. It was thus possible to study the trend of these different areas as a function of ripening, making objective an evaluation usually performed by subjective visual inspection. Copyright © 2014 Elsevier Ltd. All rights reserved.

Immediate versus delayed intramedullary nailing for open fractures of the tibial shaft: a multivariate analysis of factors affecting deep infection and fracture healing.

PubMed

Yokoyama, Kazuhiko; Itoman, Moritoshi; Uchino, Masataka; Fukushima, Kensuke; Nitta, Hiroshi; Kojima, Yoshiaki

2008-10-01

The purpose of this study was to evaluate contributing factors affecting deep infection and fracture healing of open tibia fractures treated with locked intramedullary nailing (IMN) by multivariate analysis. We examined 99 open tibial fractures (98 patients) treated with immediate or delayed locked IMN in static fashion from 1991 to 2002. Multivariate analyses following univariate analyses were derived to determine predictors of deep infection, nonunion, and healing time to union. The following predictive variables of deep infection were selected for analysis: age, sex, Gustilo type, fracture grade by AO type, fracture location, timing or method of IMN, reamed or unreamed nailing, debridement time (< or =6 h or >6 h), method of soft-tissue management, skin closure time (< or =1 week or >1 week), existence of polytrauma (ISS< 18 or ISS> or =18), existence of floating knee injury, and existence of superficial/pin site infection. The predictive variables of nonunion selected for analysis was the same as those for deep infection, with the addition of deep infection for exchange of pin site infection. The predictive variables of union time selected for analysis was the same as those for nonunion, excluding of location, debridement time, and existence of floating knee and superficial infection. Six (6.1%; type II Gustilo n=1, type IIIB Gustilo n=5) of the 99 open tibial fractures developed deep infections. Multivariate analysis revealed that timing or method of IMN, debridement time, method of soft-tissue management, and existence of superficial or pin site infection significantly correlated with the occurrence of deep infection (P< 0.0001). In the immediate nailing group alone, the deep infection rate in type IIIB + IIIC was significantly higher than those in type I + II and IIIA (P = 0.016). Nonunion occurred in 17 fractures (20.3%, 17/84). Multivariate analysis revealed that Gustilo type, skin closure time, and existence of deep infection significantly correlated with occurrence of nonunion (P < 0.05). Gustilo type and existence of deep infection were significantly correlated with healing time to union on multivariate analysis (r(2) = 0.263, P = 0.0001). Multivariate analyses for open tibial fractures treated with IMN showed that IMN after EF (especially in existence of pin site infection) was at high risk of deep infection, and that debridement within 6 h and appropriate soft-tissue managements were also important factor in preventing deep infections. These analyses postulated that both the Gustilo type and the existence of deep infection is related with fracture healing in open fractures treated with IMN. In addition, immediate IMN for type IIIB and IIIC is potentially risky, and canal reaming did not increase the risk of complication for open tibial fractures treated with IMN.

Multivariate Classification of Original and Fake Perfumes by Ion Analysis and Ethanol Content.

PubMed

Gomes, Clêrton L; de Lima, Ari Clecius A; Loiola, Adonay R; da Silva, Abel B R; Cândido, Manuela C L; Nascimento, Ronaldo F

2016-07-01

The increased marketing of fake perfumes has encouraged us to investigate how to identify such products by their chemical characteristics and multivariate analysis. The aim of this study was to present an alternative approach to distinguish original from fake perfumes by means of the investigation of sodium, potassium, chloride ions, and ethanol contents by chemometric tools. For this, 50 perfumes were used (25 original and 25 counterfeit) for the analysis of ions (ion chromatography) and ethanol (gas chromatography). The results demonstrated that the fake perfume had low levels of ethanol and high levels of chloride compared to the original product. The data were treated by chemometric tools such as principal component analysis and linear discriminant analysis. This study proved that the analysis of ethanol is an effective method of distinguishing original from the fake products, and it may potentially be used to assist legal authorities in such cases. © 2016 American Academy of Forensic Sciences.

Combined data preprocessing and multivariate statistical analysis characterizes fed-batch culture of mouse hybridoma cells for rational medium design.

PubMed

Selvarasu, Suresh; Kim, Do Yun; Karimi, Iftekhar A; Lee, Dong-Yup

2010-10-01

We present an integrated framework for characterizing fed-batch cultures of mouse hybridoma cells producing monoclonal antibody (mAb). This framework systematically combines data preprocessing, elemental balancing and statistical analysis technique. Initially, specific rates of cell growth, glucose/amino acid consumptions and mAb/metabolite productions were calculated via curve fitting using logistic equations, with subsequent elemental balancing of the preprocessed data indicating the presence of experimental measurement errors. Multivariate statistical analysis was then employed to understand physiological characteristics of the cellular system. The results from principal component analysis (PCA) revealed three major clusters of amino acids with similar trends in their consumption profiles: (i) arginine, threonine and serine, (ii) glycine, tyrosine, phenylalanine, methionine, histidine and asparagine, and (iii) lysine, valine and isoleucine. Further analysis using partial least square (PLS) regression identified key amino acids which were positively or negatively correlated with the cell growth, mAb production and the generation of lactate and ammonia. Based on these results, the optimal concentrations of key amino acids in the feed medium can be inferred, potentially leading to an increase in cell viability and productivity, as well as a decrease in toxic waste production. The study demonstrated how the current methodological framework using multivariate statistical analysis techniques can serve as a potential tool for deriving rational medium design strategies. Copyright © 2010 Elsevier B.V. All rights reserved.

Multivariate calibration in Laser-Induced Breakdown Spectroscopy quantitative analysis: The dangers of a 'black box' approach and how to avoid them

NASA Astrophysics Data System (ADS)

Safi, A.; Campanella, B.; Grifoni, E.; Legnaioli, S.; Lorenzetti, G.; Pagnotta, S.; Poggialini, F.; Ripoll-Seguer, L.; Hidalgo, M.; Palleschi, V.

2018-06-01

The introduction of multivariate calibration curve approach in Laser-Induced Breakdown Spectroscopy (LIBS) quantitative analysis has led to a general improvement of the LIBS analytical performances, since a multivariate approach allows to exploit the redundancy of elemental information that are typically present in a LIBS spectrum. Software packages implementing multivariate methods are available in the most diffused commercial and open source analytical programs; in most of the cases, the multivariate algorithms are robust against noise and operate in unsupervised mode. The reverse of the coin of the availability and ease of use of such packages is the (perceived) difficulty in assessing the reliability of the results obtained which often leads to the consideration of the multivariate algorithms as 'black boxes' whose inner mechanism is supposed to remain hidden to the user. In this paper, we will discuss the dangers of a 'black box' approach in LIBS multivariate analysis, and will discuss how to overcome them using the chemical-physical knowledge that is at the base of any LIBS quantitative analysis.

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.

Resemblance profiles as clustering decision criteria: Estimating statistical power, error, and correspondence for a hypothesis test for multivariate structure.

PubMed

Kilborn, Joshua P; Jones, David L; Peebles, Ernst B; Naar, David F

2017-04-01

Clustering data continues to be a highly active area of data analysis, and resemblance profiles are being incorporated into ecological methodologies as a hypothesis testing-based approach to clustering multivariate data. However, these new clustering techniques have not been rigorously tested to determine the performance variability based on the algorithm's assumptions or any underlying data structures. Here, we use simulation studies to estimate the statistical error rates for the hypothesis test for multivariate structure based on dissimilarity profiles (DISPROF). We concurrently tested a widely used algorithm that employs the unweighted pair group method with arithmetic mean (UPGMA) to estimate the proficiency of clustering with DISPROF as a decision criterion. We simulated unstructured multivariate data from different probability distributions with increasing numbers of objects and descriptors, and grouped data with increasing overlap, overdispersion for ecological data, and correlation among descriptors within groups. Using simulated data, we measured the resolution and correspondence of clustering solutions achieved by DISPROF with UPGMA against the reference grouping partitions used to simulate the structured test datasets. Our results highlight the dynamic interactions between dataset dimensionality, group overlap, and the properties of the descriptors within a group (i.e., overdispersion or correlation structure) that are relevant to resemblance profiles as a clustering criterion for multivariate data. These methods are particularly useful for multivariate ecological datasets that benefit from distance-based statistical analyses. We propose guidelines for using DISPROF as a clustering decision tool that will help future users avoid potential pitfalls during the application of methods and the interpretation of results.

Multivariate analysis: A statistical approach for computations

NASA Astrophysics Data System (ADS)

Michu, Sachin; Kaushik, Vandana

2014-10-01

Multivariate analysis is a type of multivariate statistical approach commonly used in, automotive diagnosis, education evaluating clusters in finance etc and more recently in the health-related professions. The objective of the paper is to provide a detailed exploratory discussion about factor analysis (FA) in image retrieval method and correlation analysis (CA) of network traffic. Image retrieval methods aim to retrieve relevant images from a collected database, based on their content. The problem is made more difficult due to the high dimension of the variable space in which the images are represented. Multivariate correlation analysis proposes an anomaly detection and analysis method based on the correlation coefficient matrix. Anomaly behaviors in the network include the various attacks on the network like DDOs attacks and network scanning.

Multivariate Cluster Analysis.

ERIC Educational Resources Information Center

McRae, Douglas J.

Procedures for grouping students into homogeneous subsets have long interested educational researchers. The research reported in this paper is an investigation of a set of objective grouping procedures based on multivariate analysis considerations. Four multivariate functions that might serve as criteria for adequate grouping are given and…

Moving Beyond Univariate Post-Hoc Testing in Exercise Science: A Primer on Descriptive Discriminate Analysis.

PubMed

Barton, Mitch; Yeatts, Paul E; Henson, Robin K; Martin, Scott B

2016-12-01

There has been a recent call to improve data reporting in kinesiology journals, including the appropriate use of univariate and multivariate analysis techniques. For example, a multivariate analysis of variance (MANOVA) with univariate post hocs and a Bonferroni correction is frequently used to investigate group differences on multiple dependent variables. However, this univariate approach decreases power, increases the risk for Type 1 error, and contradicts the rationale for conducting multivariate tests in the first place. The purpose of this study was to provide a user-friendly primer on conducting descriptive discriminant analysis (DDA), which is a post-hoc strategy to MANOVA that takes into account the complex relationships among multiple dependent variables. A real-world example using the Statistical Package for the Social Sciences syntax and data from 1,095 middle school students on their body composition and body image are provided to explain and interpret the results from DDA. While univariate post hocs increased the risk for Type 1 error to 76%, the DDA identified which dependent variables contributed to group differences and which groups were different from each other. For example, students in the very lean and Healthy Fitness Zone categories for body mass index experienced less pressure to lose weight, more satisfaction with their body, and higher physical self-concept than the Needs Improvement Zone groups. However, perceived pressure to gain weight did not contribute to group differences because it was a suppressor variable. Researchers are encouraged to use DDA when investigating group differences on multiple correlated dependent variables to determine which variables contributed to group differences.

Comparative forensic soil analysis of New Jersey state parks using a combination of simple techniques with multivariate statistics.

PubMed

Bonetti, Jennifer; Quarino, Lawrence

2014-05-01

This study has shown that the combination of simple techniques with the use of multivariate statistics offers the potential for the comparative analysis of soil samples. Five samples were obtained from each of twelve state parks across New Jersey in both the summer and fall seasons. Each sample was examined using particle-size distribution, pH analysis in both water and 1 M CaCl2 , and a loss on ignition technique. Data from each of the techniques were combined, and principal component analysis (PCA) and canonical discriminant analysis (CDA) were used for multivariate data transformation. Samples from different locations could be visually differentiated from one another using these multivariate plots. Hold-one-out cross-validation analysis showed error rates as low as 3.33%. Ten blind study samples were analyzed resulting in no misclassifications using Mahalanobis distance calculations and visual examinations of multivariate plots. Seasonal variation was minimal between corresponding samples, suggesting potential success in forensic applications. © 2014 American Academy of Forensic Sciences.

Effect of altered sensory conditions on multivariate descriptors of human postural sway

NASA Technical Reports Server (NTRS)

Kuo, A. D.; Speers, R. A.; Peterka, R. J.; Horak, F. B.; Peterson, B. W. (Principal Investigator)

1998-01-01

Multivariate descriptors of sway were used to test whether altered sensory conditions result not only in changes in amount of sway but also in postural coordination. Eigenvalues and directions of eigenvectors of the covariance of shnk and hip angles were used as a set of multivariate descriptors. These quantities were measured in 14 healthy adult subjects performing the Sensory Organization test, which disrupts visual and somatosensory information used for spatial orientation. Multivariate analysis of variance and discriminant analysis showed that resulting sway changes were at least bivariate in character, with visual and somatosensory conditions producing distinct changes in postural coordination. The most significant changes were found when somatosensory information was disrupted by sway-referencing of the support surface (P = 3.2 x 10(-10)). The resulting covariance measurements showed that subjects not only swayed more but also used increased hip motion analogous to the hip strategy. Disruption of vision, by either closing the eyes or sway-referencing the visual surround, also resulted in altered sway (P = 1.7 x 10(-10)), with proportionately more motion of the center of mass than with platform sway-referencing. As shown by discriminant analysis, an optimal univariate measure could explain at most 90% of the behavior due to altered sensory conditions. The remaining 10%, while smaller, are highly significant changes in posture control that depend on sensory conditions. The results imply that normal postural coordination of the trunk and legs requires both somatosensory and visual information and that each sensory modality makes a unique contribution to posture control. Descending postural commands are multivariate in nature, and the motion at each joint is affected uniquely by input from multiple sensors.

Combining microwave resonance technology to multivariate data analysis as a novel PAT tool to improve process understanding in fluid bed granulation.

PubMed

Lourenço, Vera; Herdling, Thorsten; Reich, Gabriele; Menezes, José C; Lochmann, Dirk

2011-08-01

A set of 192 fluid bed granulation batches at industrial scale were in-line monitored using microwave resonance technology (MRT) to determine moisture, temperature and density of the granules. Multivariate data analysis techniques such as multiway partial least squares (PLS), multiway principal component analysis (PCA) and multivariate batch control charts were applied onto collected batch data sets. The combination of all these techniques, along with off-line particle size measurements, led to significantly increased process understanding. A seasonality effect could be put into evidence that impacted further processing through its influence on the final granule size. Moreover, it was demonstrated by means of a PLS that a relation between the particle size and the MRT measurements can be quantitatively defined, highlighting a potential ability of the MRT sensor to predict information about the final granule size. This study has contributed to improve a fluid bed granulation process, and the process knowledge obtained shows that the product quality can be built in process design, following Quality by Design (QbD) and Process Analytical Technology (PAT) principles. Copyright © 2011. Published by Elsevier B.V.

Multivariate synaptic and behavioral profiling reveals new developmental endophenotypes in the prefrontal cortex

PubMed Central

Iafrati, Jillian; Malvache, Arnaud; Gonzalez Campo, Cecilia; Orejarena, M. Juliana; Lassalle, Olivier; Bouamrane, Lamine; Chavis, Pascale

2016-01-01

The postnatal maturation of the prefrontal cortex (PFC) represents a period of increased vulnerability to risk factors and emergence of neuropsychiatric disorders. To disambiguate the pathophysiological mechanisms contributing to these disorders, we revisited the endophenotype approach from a developmental viewpoint. The extracellular matrix protein reelin which contributes to cellular and network plasticity, is a risk factor for several psychiatric diseases. We mapped the aggregate effect of the RELN risk allele on postnatal development of PFC functions by cross-sectional synaptic and behavioral analysis of reelin-haploinsufficient mice. Multivariate analysis of bootstrapped datasets revealed subgroups of phenotypic traits specific to each maturational epoch. The preeminence of synaptic AMPA/NMDA receptor content to pre-weaning and juvenile endophenotypes shifts to long-term potentiation and memory renewal during adolescence followed by NMDA-GluN2B synaptic content in adulthood. Strikingly, multivariate analysis shows that pharmacological rehabilitation of reelin haploinsufficient dysfunctions is mediated through induction of new endophenotypes rather than reversion to wild-type traits. By delineating previously unknown developmental endophenotypic sequences, we conceived a promising general strategy to disambiguate the molecular underpinnings of complex psychiatric disorders and for the rational design of pharmacotherapies in these disorders. PMID:27765946

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.

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

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

Analysis of risk factors for central venous port failure in cancer patients

PubMed Central

Hsieh, Ching-Chuan; Weng, Hsu-Huei; Huang, Wen-Shih; Wang, Wen-Ke; Kao, Chiung-Lun; Lu, Ming-Shian; Wang, Chia-Siu

2009-01-01

AIM: To analyze the risk factors for central port failure in cancer patients administered chemotherapy, using univariate and multivariate analyses. METHODS: A total of 1348 totally implantable venous access devices (TIVADs) were implanted into 1280 cancer patients in this cohort study. A Cox proportional hazard model was applied to analyze risk factors for failure of TIVADs. Log-rank test was used to compare actuarial survival rates. Infection, thrombosis, and surgical complication rates (χ2 test or Fisher’s exact test) were compared in relation to the risk factors. RESULTS: Increasing age, male gender and open-ended catheter use were significant risk factors reducing survival of TIVADs as determined by univariate and multivariate analyses. Hematogenous malignancy decreased the survival time of TIVADs; this reduction was not statistically significant by univariate analysis [hazard ratio (HR) = 1.336, 95% CI: 0.966-1.849, P = 0.080)]. However, it became a significant risk factor by multivariate analysis (HR = 1.499, 95% CI: 1.079-2.083, P = 0.016) when correlated with variables of age, sex and catheter type. Close-ended (Groshong) catheters had a lower thrombosis rate than open-ended catheters (2.5% vs 5%, P = 0.015). Hematogenous malignancy had higher infection rates than solid malignancy (10.5% vs 2.5%, P < 0.001). CONCLUSION: Increasing age, male gender, open-ended catheters and hematogenous malignancy were risk factors for TIVAD failure. Close-ended catheters had lower thrombosis rates and hematogenous malignancy had higher infection rates. PMID:19787834

Carbapenemase-producing Enterobacteriaceae: Risk factors for infection and impact of resistance on outcomes

PubMed Central

Mariappan, Shanthi; Sekar, Uma; Kamalanathan, Arunagiri

2017-01-01

Background: Carbapenemase-producing Enterobacteriaceae (CPE) have increased in recent years leading to limitations of treatment options. The present study was undertaken to detect CPE, risk factors for acquiring them and their impact on clinical outcomes. Methods: This retrospective observational study included 111 clinically significant Enterobacteriaceae resistant to cephalosporins subclass III and exhibiting a positive modified Hodge test. Screening for carbapenemase production was done by phenotypic methods, and polymerase chain reaction was performed to detect genes encoding them. Retrospectively, the medical records of the patients were perused to assess risk factors for infections with CPE and their impact. The data collected were duration of hospital stay, Intensive Care Unit (ICU) stay, use of invasive devices, mechanical ventilation, the presence of comorbidities, and antimicrobial therapy. The outcome was followed up. Univariate and multivariate analysis of the data were performed using SPSS software. Results: Carbapenemase-encoding genes were detected in 67 isolates. The genes detected were New Delhi metallo-β-lactamase, Verona integron-encoded metallo-β-lactamase, and oxacillinase-181.Although univariate analysis identified risk factors associated with acquiring CPE infections as ICU stay (P = 0.021), mechanical ventilation (P = 0.013), indwelling device (P = 0.011), diabetes mellitus (P = 0.036), usage of multiple antimicrobial agents (P = 0.007), administration of carbapenems (P = 0.042), presence of focal infection or sepsis (P = 0.013), and surgical interventions (P = 0.016), multivariate analysis revealed that all these factors were insignificant. Mortality rate was 56.7% in patients with CPE infections. By both univariate and multivariate analysis of impact of the variables on mortality in these patients, the significant factors were mechanical ventilation (odds ratio [OR]: 0.141, 95% confidence interval [CI]: 0.024–0.812) and presence of indwelling invasive device (OR: 8.034; 95% CI: 2.060–31.335). Conclusion: In this study, no specific factor was identified as an independent risk for acquisition of CPE infection. However, as it is evident by multivariate analysis, there is an increased risk of mortality in patients with CPE infections when they are ventilated and are supported by indwelling devices. PMID:28251105

Attention deficit hyperactivity disorder in cocaine-dependent adults: a psychiatric comorbidity analysis.

PubMed

Daigre, Constanza; Roncero, Carlos; Grau-López, Lara; Martínez-Luna, Nieves; Prat, Gemma; Valero, Sergi; Tejedor, Rosa; Ramos-Quiroga, Josep A; Casas, Miguel

2013-01-01

Attention deficit hyperactivity disorder (ADHD) is highly prevalent among drug abusers. We studied the psychiatric comorbidity and characteristics of cocaine use in relation to the presence of ADHD among patients with cocaine dependence. A total of 200 cocaine-dependent patients attending an Outpatient Drug Clinic participated in the study. A systematic evaluation of ADHD (CAADID-II), the severity of addiction (EuropASI) and other axes I and II psychiatric disorders was made (SCID-I and SCID-II). A descriptive, bivariate, and multivariate analysis of the data was performed. In the multivariate analysis, the identified risk factors for the development of ADHD were a history of behavioral disorder in childhood (OR: 3.04), a lifetime history of cannabis dependence in the course of life (OR: 2.68), and age at the start of treatment (OR: 1.08). The bivariate analysis showed ADHD to be associated with other factors such as male gender, age at start of cocaine use and dependence, the amount of cocaine consumed weekly, increased occupational alteration, alcohol consumption, general psychological discomfort, depressive disorder, and antisocial personality disorder. We conclude that ADHD is associated with increased psychiatric comorbidity and greater severity of addiction. Copyright © American Academy of Addiction Psychiatry.

Order-restricted inference for multivariate longitudinal data with applications to the natural history of hearing loss.

PubMed

Rosen, Sophia; Davidov, Ori

2012-07-20

Multivariate outcomes are often measured longitudinally. For example, in hearing loss studies, hearing thresholds for each subject are measured repeatedly over time at several frequencies. Thus, each patient is associated with a multivariate longitudinal outcome. The multivariate mixed-effects model is a useful tool for the analysis of such data. There are situations in which the parameters of the model are subject to some restrictions or constraints. For example, it is known that hearing thresholds, at every frequency, increase with age. Moreover, this age-related threshold elevation is monotone in frequency, that is, the higher the frequency, the higher, on average, is the rate of threshold elevation. This means that there is a natural ordering among the different frequencies in the rate of hearing loss. In practice, this amounts to imposing a set of constraints on the different frequencies' regression coefficients modeling the mean effect of time and age at entry to the study on hearing thresholds. The aforementioned constraints should be accounted for in the analysis. The result is a multivariate longitudinal model with restricted parameters. We propose estimation and testing procedures for such models. We show that ignoring the constraints may lead to misleading inferences regarding the direction and the magnitude of various effects. Moreover, simulations show that incorporating the constraints substantially improves the mean squared error of the estimates and the power of the tests. We used this methodology to analyze a real hearing loss study. Copyright © 2012 John Wiley & Sons, Ltd.

Structural brain connectivity and cognitive ability differences: A multivariate distance matrix regression analysis.

PubMed

Ponsoda, Vicente; Martínez, Kenia; Pineda-Pardo, José A; Abad, Francisco J; Olea, Julio; Román, Francisco J; Barbey, Aron K; Colom, Roberto

2017-02-01

Neuroimaging research involves analyses of huge amounts of biological data that might or might not be related with cognition. This relationship is usually approached using univariate methods, and, therefore, correction methods are mandatory for reducing false positives. Nevertheless, the probability of false negatives is also increased. Multivariate frameworks have been proposed for helping to alleviate this balance. Here we apply multivariate distance matrix regression for the simultaneous analysis of biological and cognitive data, namely, structural connections among 82 brain regions and several latent factors estimating cognitive performance. We tested whether cognitive differences predict distances among individuals regarding their connectivity pattern. Beginning with 3,321 connections among regions, the 36 edges better predicted by the individuals' cognitive scores were selected. Cognitive scores were related to connectivity distances in both the full (3,321) and reduced (36) connectivity patterns. The selected edges connect regions distributed across the entire brain and the network defined by these edges supports high-order cognitive processes such as (a) (fluid) executive control, (b) (crystallized) recognition, learning, and language processing, and (c) visuospatial processing. This multivariate study suggests that one widespread, but limited number, of regions in the human brain, supports high-level cognitive ability differences. Hum Brain Mapp 38:803-816, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

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

Folate Deficiency, Atopy, and Severe Asthma Exacerbations in Puerto Rican Children.

PubMed

Blatter, Joshua; Brehm, John M; Sordillo, Joanne; Forno, Erick; Boutaoui, Nadia; Acosta-Pérez, Edna; Alvarez, María; Colón-Semidey, Angel; Weiss, Scott T; Litonjua, Augusto A; Canino, Glorisa; Celedón, Juan C

2016-02-01

Little is known about folate and atopy or severe asthma exacerbations. We examined whether folate deficiency is associated with number of positive skin tests to allergens or severe asthma exacerbations in a high-risk population and further assessed whether such association is explained or modified by vitamin D status. Cross-sectional study of 582 children aged 6 to 14 years with (n = 304) and without (n = 278) asthma in San Juan, Puerto Rico. Folate deficiency was defined as plasma folate less than or equal to 20 ng/ml. Our outcomes were the number of positive skin tests to allergens (range, 0-15) in all children and (in children with asthma) one or more severe exacerbations in the previous year. Logistic and negative binomial regression models were used for the multivariate analysis. All multivariate models were adjusted for age, sex, household income, residential proximity to a major road, and (for atopy) case/control status; those for severe exacerbations were also adjusted for use of inhaled corticosteroids and vitamin D insufficiency (a plasma 25[OH]D < 30 ng/ml). In a multivariate analysis, folate deficiency was significantly associated with an increased degree of atopy and 2.2 times increased odds of at least one severe asthma exacerbation (95% confidence interval for odds ratio, 1.1-4.6). Compared with children who had normal levels of both folate and vitamin D, those with both folate deficiency and vitamin D insufficiency had nearly eightfold increased odds of one or more severe asthma exacerbation (95% confidence interval for adjusted odds ratio, 2.7-21.6). Folate deficiency is associated with increased degree of atopy and severe asthma exacerbations in school-aged Puerto Ricans. Vitamin D insufficiency may further increase detrimental effects of folate deficiency on severe asthma exacerbations.

Medicaid Participation among Liver Transplant Candidates after the Affordable Care Act Medicaid Expansion.

PubMed

Tumin, Dmitry; Beal, Eliza W; Mumtaz, Khalid; Hayes, Don; Tobias, Joseph D; Pawlik, Timothy M; Washburn, W Kenneth; Black, Sylvester M

2017-08-01

The 2014 Medicaid expansion in participating states increased insurance coverage among people with chronic health conditions, but its implications for access to surgical care remain unclear. We investigated how Medicaid expansion influenced the insurance status of candidates for liver transplantation (LT) and transplant center payor mix. Data on LT candidates aged 18 to 64 years, in 2012 to 2013 (pre-expansion) and 2014 to 2015 (post-expansion), were obtained from the United Network for Organ Sharing registry. Change between the 2 periods in the percent of LT candidates using Medicaid was compared between expansion and nonexpansion states. Multivariable logistic regression was used to determine how Medicaid expansion influenced individual LT candidates' likelihood of using Medicaid insurance. The study included 33,017 LT candidates, of whom 29,666 had complete data for multivariable analysis. Medicaid enrollment increased by 4% after Medicaid expansion in participating states. One-quarter of the transplant centers in these states experienced ≥10% increase in the proportion of LT candidates using Medicaid insurance. Multivariable analysis confirmed that Medicaid expansion was associated with increased odds of LT candidates using Medicaid insurance (odds ratio 1.49; 95% CI 1.34, 1.66; p < 0.001). However, the absolute number and demographic characteristics of patients listed for LT did not change in Medicaid expansion states during the post-expansion period. Candidates for LT became more likely to use Medicaid after the 2014 Medicaid expansion policy came into effect. Enactment of this policy did not appear to increase access to LT or address socioeconomic and demographic disparities in access to the LT wait list. Copyright © 2017 American College of Surgeons. Published by Elsevier Inc. All rights reserved.

Enhancing e-waste estimates: improving data quality by multivariate Input-Output Analysis.

PubMed

Wang, Feng; Huisman, Jaco; Stevels, Ab; Baldé, Cornelis Peter

2013-11-01

Waste electrical and electronic equipment (or e-waste) is one of the fastest growing waste streams, which encompasses a wide and increasing spectrum of products. Accurate estimation of e-waste generation is difficult, mainly due to lack of high quality data referred to market and socio-economic dynamics. This paper addresses how to enhance e-waste estimates by providing techniques to increase data quality. An advanced, flexible and multivariate Input-Output Analysis (IOA) method is proposed. It links all three pillars in IOA (product sales, stock and lifespan profiles) to construct mathematical relationships between various data points. By applying this method, the data consolidation steps can generate more accurate time-series datasets from available data pool. This can consequently increase the reliability of e-waste estimates compared to the approach without data processing. A case study in the Netherlands is used to apply the advanced IOA model. As a result, for the first time ever, complete datasets of all three variables for estimating all types of e-waste have been obtained. The result of this study also demonstrates significant disparity between various estimation models, arising from the use of data under different conditions. It shows the importance of applying multivariate approach and multiple sources to improve data quality for modelling, specifically using appropriate time-varying lifespan parameters. Following the case study, a roadmap with a procedural guideline is provided to enhance e-waste estimation studies. Copyright © 2013 Elsevier Ltd. All rights reserved.

Anal sphincter lacerations and upright delivery postures--a risk analysis from a randomized controlled trial.

PubMed

Altman, Daniel; Ragnar, Inga; Ekström, Asa; Tydén, Tanja; Olsson, Sven-Eric

2007-02-01

To evaluate obstetric sphincter lacerations after a kneeling or sitting position at second stage of labor in a multivariate risk analysis model. Two hundred and seventy-one primiparous women with normal pregnancies and spontaneous labor were randomized, 138 to a kneeling position and 133 to a sitting position. Medical data were retrieved from delivery charts and partograms. Risk factors were tested in a multivariate logistic regression model in a stepwise manner. The trial was completed by 106 subjects in the kneeling group and 112 subjects in the sitting group. There were no significant differences with regard to duration of second stage of labor or pre-trial maternal characteristics between the two groups. Obstetrical sphincter tears did not differ significantly between the two groups but an intact perineum was more common in the kneeling group (p<0.03) and episiotomy (mediolateral) was more common in the sitting group (p<0.05). Three grade IV sphincter lacerations occurred in the sitting group compared to none in the kneeling group (NS). Multivariate risk analysis indicated that prolonged duration of second stage of labor and episiotomy were associated with an increased risk of third- or fourth-degree sphincter tears (p<0.01 and p<0.05, respectively). Delivery posture, maternal age, fetal weight, use of oxytocin, and use of epidural analgesia did not increase the risk of obstetrical anal sphincter lacerations in the two upright postures. Obstetrical anal sphincter lacerations did not differ significantly between a kneeling or sitting upright delivery posture. Episiotomy was more common after a sitting delivery posture, which may be associated with an increased risk of anal sphincter lacerations. Upright delivery postures may be encouraged in healthy women with normal, full-term pregnancy.

Risk Assessment of Hepatocellular Carcinoma in Patients with Hepatitis C in China and the USA.

PubMed

Parikh, Neehar D; Fu, Sherry; Rao, Huiying; Yang, Ming; Li, Yumeng; Powell, Corey; Wu, Elizabeth; Lin, Andy; Xing, Baocai; Wei, Lai; Lok, Anna S F

2017-11-01

Hepatitis C (HCV) infection is an increasingly common cause of hepatocellular carcinoma (HCC) in China. We aimed to determine differences in demographic and behavioral profiles associated with HCC in HCV+ patients in China and the USA. Consecutive HCV+ patients were recruited from centers in China and the USA. Clinical data and lifestyle profiles were obtained through standardized questionnaires. Multivariable analysis was conducted to determine factors associated with HCC diagnosis within groups. We included 41 HCC patients from China and 71 from the USA, and 931 non-HCC patients in China and 859 in China. Chinese patients with HCC were significantly younger, less likely to be male and to be obese than US patients with HCC (all p < 0.001). Chinese patients with HCC had a significantly lower rate of cirrhosis diagnosis (36.6 vs. 78.9%, p < 0.001); however, they also had a higher rate of hepatitis B core antibody positivity (63.4 vs. 36.8%, p = 0.007). In a multivariable analysis of the entire Chinese cohort, age > 55, male sex, the presence of diabetes, and time from maximum weight were associated with HCC, while tea consumption was associated with a decreased HCC risk (OR 0.37, 95% CI 0.16-0.88). In the US cohort, age > 55, male sex, and cirrhosis were associated with HCC on multivariable analysis. With the aging Chinese population and increasing rates of diabetes, there will likely be continued increase in the incidence of HCV-related HCC in China. The protective effect of tea consumption on HCC development deserves further validation.

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.

Analysis techniques for multivariate root loci. [a tool in linear control systems

NASA Technical Reports Server (NTRS)

Thompson, P. M.; Stein, G.; Laub, A. J.

1980-01-01

Analysis and techniques are developed for the multivariable root locus and the multivariable optimal root locus. The generalized eigenvalue problem is used to compute angles and sensitivities for both types of loci, and an algorithm is presented that determines the asymptotic properties of the optimal root locus.

Methods for presentation and display of multivariate data

NASA Technical Reports Server (NTRS)

Myers, R. H.

1981-01-01

Methods for the presentation and display of multivariate data are discussed with emphasis placed on the multivariate analysis of variance problems and the Hotelling T(2) solution in the two-sample case. The methods utilize the concepts of stepwise discrimination analysis and the computation of partial correlation coefficients.

A Primer on Multivariate Analysis of Variance (MANOVA) for Behavioral Scientists

ERIC Educational Resources Information Center

Warne, Russell T.

2014-01-01

Reviews of statistical procedures (e.g., Bangert & Baumberger, 2005; Kieffer, Reese, & Thompson, 2001; Warne, Lazo, Ramos, & Ritter, 2012) show that one of the most common multivariate statistical methods in psychological research is multivariate analysis of variance (MANOVA). However, MANOVA and its associated procedures are often not…

Job insecurity and risk of diabetes: a meta-analysis of individual participant data.

PubMed

Ferrie, Jane E; Virtanen, Marianna; Jokela, Markus; Madsen, Ida E H; Heikkilä, Katriina; Alfredsson, Lars; Batty, G David; Bjorner, Jakob B; Borritz, Marianne; Burr, Hermann; Dragano, Nico; Elovainio, Marko; Fransson, Eleonor I; Knutsson, Anders; Koskenvuo, Markku; Koskinen, Aki; Kouvonen, Anne; Kumari, Meena; Nielsen, Martin L; Nordin, Maria; Oksanen, Tuula; Pahkin, Krista; Pejtersen, Jan H; Pentti, Jaana; Salo, Paula; Shipley, Martin J; Suominen, Sakari B; Tabák, Adam; Theorell, Töres; Väänänen, Ari; Vahtera, Jussi; Westerholm, Peter J M; Westerlund, Hugo; Rugulies, Reiner; Nyberg, Solja T; Kivimäki, Mika

2016-12-06

Job insecurity has been associated with certain health outcomes. We examined the role of job insecurity as a risk factor for incident diabetes. We used individual participant data from 8 cohort studies identified in 2 open-access data archives and 11 cohort studies participating in the Individual-Participant-Data Meta-analysis in Working Populations Consortium. We calculated study-specific estimates of the association between job insecurity reported at baseline and incident diabetes over the follow-up period. We pooled the estimates in a meta-analysis to produce a summary risk estimate. The 19 studies involved 140 825 participants from Australia, Europe and the United States, with a mean follow-up of 9.4 years and 3954 incident cases of diabetes. In the preliminary analysis adjusted for age and sex, high job insecurity was associated with an increased risk of incident diabetes compared with low job insecurity (adjusted odds ratio [OR] 1.19, 95% confidence interval [CI] 1.09-1.30). In the multivariable-adjusted analysis restricted to 15 studies with baseline data for all covariates (age, sex, socioeconomic status, obesity, physical activity, alcohol and smoking), the association was slightly attenuated (adjusted OR 1.12, 95% CI 1.01-1.24). Heterogeneity between the studies was low to moderate (age- and sex-adjusted model: I 2 = 24%, p = 0.2; multivariable-adjusted model: I 2 = 27%, p = 0.2). In the multivariable-adjusted analysis restricted to high-quality studies, in which the diabetes diagnosis was ascertained from electronic medical records or clinical examination, the association was similar to that in the main analysis (adjusted OR 1.19, 95% CI 1.04-1.35). Our findings suggest that self-reported job insecurity is associated with a modest increased risk of incident diabetes. Health care personnel should be aware of this association among workers reporting job insecurity. © 2016 Canadian Medical Association or its licensors.

Economic Impact of Increased Utilization of Multivariate Assay Testing to Guide the Treatment of Ovarian Cancer: Implications for Payers.

PubMed

Brodsky, Burton S; Owens, Gary M; Scotti, Dennis J; Needham, Keith A; Cool, Christina L

2017-10-01

Ovarian cancer is the eighth most common cancer among women, but ranks fifth in cancer-related causes of death, the majority of which are detected in late stages, after the cancer has metastasized. The CA125 test is the standard of care for assessing suspicious pelvic masses. However, the primary use of CA125 is to monitor treatment progress rather than to screen for disease, and its sensitivity is exceedingly low, unlike the multivariate assay OVA1. A cost-effective treatment of ovarian cancer requires early and accurate diagnosis of pelvic masses and reduced referrals of patients with benign tumors to a gynecologic oncologist. To analyze the economic impact of increased utilization of a multivariate assay, such as OVA1, to guide the treatment of ovarian cancer. The study population was drawn from Medicare and commercial health plan claims data. A budget impact model was constructed to estimate the economic consequences of substituting the multivariate assay OVA1 to replace the single biomarker assay CA125 to assess the likelihood of pelvic mass malignancy in premenopausal and/or postmenopausal women. All patients selected for the analysis had CA125 testing before surgical intervention. A total of 92,843 health plan members were included for analysis, comprising 48,113 commercially insured members and 44,730 Medicare beneficiaries. Estimates of future health plan expenditures, which were calculated from base-case assumptions, projected overall savings of $0.05 per-member per-month (PMPM) for commercially insured members and $0.01 PMPM for Medicare beneficiaries as a result of increased utilization of OVA1. Sensitivity analysis revealed potential savings of up to $0.17 PMPM for commercially insured patients and up to $0.05 for Medicare beneficiaries. The results of the budget impact model support the use of OVA1 instead of CA125 by indicating that modest cost-savings can be achieved, while reaping the clinical benefits of improved diagnostic accuracy, early disease detection, and reductions in multiple, and possibly unnecessary, referrals to gynecologic oncologists.

Quality by design case study: an integrated multivariate approach to drug product and process development.

PubMed

Huang, Jun; Kaul, Goldi; Cai, Chunsheng; Chatlapalli, Ramarao; Hernandez-Abad, Pedro; Ghosh, Krishnendu; Nagi, Arwinder

2009-12-01

To facilitate an in-depth process understanding, and offer opportunities for developing control strategies to ensure product quality, a combination of experimental design, optimization and multivariate techniques was integrated into the process development of a drug product. A process DOE was used to evaluate effects of the design factors on manufacturability and final product CQAs, and establish design space to ensure desired CQAs. Two types of analyses were performed to extract maximal information, DOE effect & response surface analysis and multivariate analysis (PCA and PLS). The DOE effect analysis was used to evaluate the interactions and effects of three design factors (water amount, wet massing time and lubrication time), on response variables (blend flow, compressibility and tablet dissolution). The design space was established by the combined use of DOE, optimization and multivariate analysis to ensure desired CQAs. Multivariate analysis of all variables from the DOE batches was conducted to study relationships between the variables and to evaluate the impact of material attributes/process parameters on manufacturability and final product CQAs. The integrated multivariate approach exemplifies application of QbD principles and tools to drug product and process development.

Structural changes in cross-border liabilities: A multidimensional approach

NASA Astrophysics Data System (ADS)

Araújo, Tanya; Spelta, Alessandro

2014-01-01

We study the international interbank market through a geometric analysis of empirical data. The geometric analysis of the time series of cross-country liabilities shows that the systematic information of the interbank international market is contained in a space of small dimension. Geometric spaces of financial relations across countries are developed, for which the space volume, multivariate skewness and multivariate kurtosis are computed. The behavior of these coefficients reveals an important modification acting in the financial linkages since 1997 and allows us to relate the shape of the geometric space that emerges in recent years to the globally turbulent period that has characterized financial systems since the late 1990s. Here we show that, besides a persistent decrease in the volume of the geometric space since 1997, the observation of a generalized increase in the values of the multivariate skewness and kurtosis sheds some light on the behavior of cross-border interdependencies during periods of financial crises. This was found to occur in such a systematic fashion, that these coefficients may be used as a proxy for systemic risk.

Multiscale analysis of information dynamics for linear multivariate processes.

PubMed

Faes, Luca; Montalto, Alessandro; Stramaglia, Sebastiano; Nollo, Giandomenico; Marinazzo, Daniele

2016-08-01

In the study of complex physical and physiological systems represented by multivariate time series, an issue of great interest is the description of the system dynamics over a range of different temporal scales. While information-theoretic approaches to the multiscale analysis of complex dynamics are being increasingly used, the theoretical properties of the applied measures are poorly understood. This study introduces for the first time a framework for the analytical computation of information dynamics for linear multivariate stochastic processes explored at different time scales. After showing that the multiscale processing of a vector autoregressive (VAR) process introduces a moving average (MA) component, we describe how to represent the resulting VARMA process using statespace (SS) models and how to exploit the SS model parameters to compute analytical measures of information storage and information transfer for the original and rescaled processes. The framework is then used to quantify multiscale information dynamics for simulated unidirectionally and bidirectionally coupled VAR processes, showing that rescaling may lead to insightful patterns of information storage and transfer but also to potentially misleading behaviors.

Estimating an Effect Size in One-Way Multivariate Analysis of Variance (MANOVA)

ERIC Educational Resources Information Center

Steyn, H. S., Jr.; Ellis, S. M.

2009-01-01

When two or more univariate population means are compared, the proportion of variation in the dependent variable accounted for by population group membership is eta-squared. This effect size can be generalized by using multivariate measures of association, based on the multivariate analysis of variance (MANOVA) statistics, to establish whether…

Dangers in Using Analysis of Covariance Procedures.

ERIC Educational Resources Information Center

Campbell, Kathleen T.

Problems associated with the use of analysis of covariance (ANCOVA) as a statistical control technique are explained. Three problems relate to the use of "OVA" methods (analysis of variance, analysis of covariance, multivariate analysis of variance, and multivariate analysis of covariance) in general. These are: (1) the wasting of information when…

The association between glucose levels and hospital outcomes in patients with acute exacerbations of chronic obstructive pulmonary disease

PubMed Central

Islam, Ebtesam A.; Limsuwat, Chok; Nantsupawat, Teerapat; Berdine, Gilbert G.; Nugent, Kenneth M.

2015-01-01

BACKGROUND: Corticosteroids used for chronic obstructive pulmonary disease (COPD) exacerbations can cause hyperglycemia in hospitalized patients, and hyperglycemia may be associated with increased mortality, length of stay (LOS), and re-admissions in these patients. MATERIALS AND METHODS: We did three retrospective studies using charts from July 2008 through June 2009, January 2006 through December 2010, and October 2010 through March 2011. We collected demographic and clinical information, laboratory results, radiographic results, and information on LOS, mortality, and re-admission. RESULTS: Glucose levels did not predict outcomes in any of the studied cohorts, after adjustment for covariates in multivariable analysis. The first database included 30 patients admitted to non-intensive care unit (ICU) hospital beds. Six of 20 non-diabetic patients had peak glucoses above 200 mg/dl. Nine of the ten diabetic patients had peak glucoses above 200 mg/dl. The maximum daily corticosteroid dose had no apparent effect on the glucose levels. The second database included 217 patients admitted to ICUs. The initial blood glucose was higher in patients who died than those who survived using bivariate analysis (P = 0.015; odds ratio, OR, 1.01) but not in multivariable analysis. Multivariable logistic regression analysis also demonstrated that glucose levels did not affect LOS. The third database analyzing COPD re-admission rates included 81 patients; the peak glucose levels were not associated with re-admission. CONCLUSIONS: Our data demonstrate that COPD patients treated with corticosteroids developed significant hyperglycemia, but the increase in blood glucose levels did not correlate with the maximum dose of corticosteroids. Blood glucose levels were not associated with mortality, LOS, or re-admission rates. PMID:25829959

Influence of microclimatic ammonia levels on productive performance of different broilers' breeds estimated with univariate and multivariate approaches.

PubMed

Soliman, Essam S; Moawed, Sherif A; Hassan, Rania A

2017-08-01

Birds litter contains unutilized nitrogen in the form of uric acid that is converted into ammonia; a fact that does not only affect poultry performance but also has a negative effect on people's health around the farm and contributes in the environmental degradation. The influence of microclimatic ammonia emissions on Ross and Hubbard broilers reared in different housing systems at two consecutive seasons (fall and winter) was evaluated using a discriminant function analysis to differentiate between Ross and Hubbard breeds. A total number of 400 air samples were collected and analyzed for ammonia levels during the experimental period. Data were analyzed using univariate and multivariate statistical methods. Ammonia levels were significantly higher (p< 0.01) in the Ross compared to the Hubbard breed farm, although no significant differences (p>0.05) were found between the two farms in body weight, body weight gain, feed intake, feed conversion ratio, and performance index (PI) of broilers. Body weight; weight gain and PI had increased values (p< 0.01) during fall compared to winter irrespective of broiler breed. Ammonia emissions were positively (although weekly) correlated with the ambient relative humidity (r=0.383; p< 0.01), but not with the ambient temperature (r=-0.045; p>0.05). Test of significance of discriminant function analysis did not show a classification based on the studied traits suggesting that they cannot been used as predictor variables. The percentage of correct classification was 52% and it was improved after deletion of highly correlated traits to 57%. The study revealed that broiler's growth was negatively affected by increased microclimatic ammonia concentrations and recommended the analysis of broilers' growth performance parameters data using multivariate discriminant function analysis.

Influence of microclimatic ammonia levels on productive performance of different broilers’ breeds estimated with univariate and multivariate approaches

PubMed Central

Soliman, Essam S.; Moawed, Sherif A.; Hassan, Rania A.

2017-01-01

Background and Aim: Birds litter contains unutilized nitrogen in the form of uric acid that is converted into ammonia; a fact that does not only affect poultry performance but also has a negative effect on people’s health around the farm and contributes in the environmental degradation. The influence of microclimatic ammonia emissions on Ross and Hubbard broilers reared in different housing systems at two consecutive seasons (fall and winter) was evaluated using a discriminant function analysis to differentiate between Ross and Hubbard breeds. Materials and Methods: A total number of 400 air samples were collected and analyzed for ammonia levels during the experimental period. Data were analyzed using univariate and multivariate statistical methods. Results: Ammonia levels were significantly higher (p< 0.01) in the Ross compared to the Hubbard breed farm, although no significant differences (p>0.05) were found between the two farms in body weight, body weight gain, feed intake, feed conversion ratio, and performance index (PI) of broilers. Body weight; weight gain and PI had increased values (p< 0.01) during fall compared to winter irrespective of broiler breed. Ammonia emissions were positively (although weekly) correlated with the ambient relative humidity (r=0.383; p< 0.01), but not with the ambient temperature (r=−0.045; p>0.05). Test of significance of discriminant function analysis did not show a classification based on the studied traits suggesting that they cannot been used as predictor variables. The percentage of correct classification was 52% and it was improved after deletion of highly correlated traits to 57%. Conclusion: The study revealed that broiler’s growth was negatively affected by increased microclimatic ammonia concentrations and recommended the analysis of broilers’ growth performance parameters data using multivariate discriminant function analysis. PMID:28919677

Home-measured heart rate is associated with albuminuria in patients with type 2 diabetes mellitus: a post-hoc analysis of a cross-sectional multicenter study.

PubMed

Ushigome, Emi; Fukui, Michiaki; Hamaguchi, Masahide; Tanaka, Toru; Atsuta, Haruhiko; Ohnishi, Masayoshi; Tsunoda, Sei; Yamazaki, Masahiro; Hasegawa, Goji; Nakamura, Naoto

2014-06-01

Epidemiological studies have shown that elevated heart rate (HR) is associated with an increased risk of diabetic nephropathy, as well as cardiovascular events and mortality, in patients with type 2 diabetes mellitus. Recently, the advantages of the self-measurement of blood pressure (BP) at home have been recognized. The aim of this study was to investigate the relationship between home-measured HR and albuminuria in patients with type 2 diabetes mellitus. We designed a cross-sectional multicenter analysis of 1245 patients with type 2 diabetes mellitus. We investigated the relationship between the logarithm of urinary albumin excretion (log UAE) and home-measured HR or other factors that may be related to nephropathy using univariate and multivariate analyses. Multivariate linear regression analysis indicated that age, duration of diabetes mellitus, morning HR (β=0.131, P<0.001), morning systolic BP (β=0.311, P<0.001), hemoglobin A1C, triglycerides, daily consumption of alcohol, use of angiotensin II receptor blockers and use of beta-blockers were independently associated with the log UAE. Multivariate logistic regression analysis indicated that the odds ratio (95% confidence interval) associated with 1 beat per min and 1 mm Hg increases in the morning HR and morning systolic BP for albuminuria were 1.024 ((1.008-1.040), P=0.004) and 1.039 ((1.029-1.048), P<0.001), respectively. In conclusion, home-measured HR was significantly associated with albuminuria independent of the known risk factors for nephropathy, including home-measured systolic BP, in patients with type 2 diabetes mellitus.

Transforming growth factor-β and toll-like receptor-4 polymorphisms are not associated with fibrosis in haemochromatosis

PubMed Central

Wood, Marnie J; Powell, Lawrie W; Dixon, Jeannette L; Subramaniam, V Nathan; Ramm, Grant A

2013-01-01

AIM: To investigate the role of genetic polymorphisms in the progression of hepatic fibrosis in hereditary haemochromatosis. METHODS: A cohort of 245 well-characterised C282Y homozygous patients with haemochromatosis was studied, with all subjects having liver biopsy data and DNA available for testing. This study assessed the association of eight single nucleotide polymorphisms (SNPs) in a total of six genes including toll-like receptor 4 (TLR4), transforming growth factor-beta (TGF-β), oxoguanine DNA glycosylase, monocyte chemoattractant protein 1, chemokine C-C motif receptor 2 and interleukin-10 with liver disease severity. Genotyping was performed using high resolution melt analysis and sequencing. The results were analysed in relation to the stage of hepatic fibrosis in multivariate analysis incorporating other cofactors including alcohol consumption and hepatic iron concentration. RESULTS: There were significant associations between the cofactors of male gender (P = 0.0001), increasing age (P = 0.006), alcohol consumption (P = 0.0001), steatosis (P = 0.03), hepatic iron concentration (P < 0.0001) and the presence of hepatic fibrosis. Of the candidate gene polymorphisms studied, none showed a significant association with hepatic fibrosis in univariate or multivariate analysis incorporating cofactors. We also specifically studied patients with hepatic iron loading above threshold levels for cirrhosis and compared the genetic polymorphisms between those with no fibrosis vs cirrhosis however there was no significant effect from any of the candidate genes studied. Importantly, in this large, well characterised cohort of patients there was no association between SNPs for TGF-β or TLR4 and the presence of fibrosis, cirrhosis or increasing fibrosis stage in multivariate analysis. CONCLUSION: In our large, well characterised group of haemochromatosis subjects we did not demonstrate any relationship between candidate gene polymorphisms and hepatic fibrosis or cirrhosis. PMID:24409064

Transforming growth factor-β and toll-like receptor-4 polymorphisms are not associated with fibrosis in haemochromatosis.

PubMed

Wood, Marnie J; Powell, Lawrie W; Dixon, Jeannette L; Subramaniam, V Nathan; Ramm, Grant A

2013-12-28

To investigate the role of genetic polymorphisms in the progression of hepatic fibrosis in hereditary haemochromatosis. A cohort of 245 well-characterised C282Y homozygous patients with haemochromatosis was studied, with all subjects having liver biopsy data and DNA available for testing. This study assessed the association of eight single nucleotide polymorphisms (SNPs) in a total of six genes including toll-like receptor 4 (TLR4), transforming growth factor-beta (TGF-β), oxoguanine DNA glycosylase, monocyte chemoattractant protein 1, chemokine C-C motif receptor 2 and interleukin-10 with liver disease severity. Genotyping was performed using high resolution melt analysis and sequencing. The results were analysed in relation to the stage of hepatic fibrosis in multivariate analysis incorporating other cofactors including alcohol consumption and hepatic iron concentration. There were significant associations between the cofactors of male gender (P = 0.0001), increasing age (P = 0.006), alcohol consumption (P = 0.0001), steatosis (P = 0.03), hepatic iron concentration (P < 0.0001) and the presence of hepatic fibrosis. Of the candidate gene polymorphisms studied, none showed a significant association with hepatic fibrosis in univariate or multivariate analysis incorporating cofactors. We also specifically studied patients with hepatic iron loading above threshold levels for cirrhosis and compared the genetic polymorphisms between those with no fibrosis vs cirrhosis however there was no significant effect from any of the candidate genes studied. Importantly, in this large, well characterised cohort of patients there was no association between SNPs for TGF-β or TLR4 and the presence of fibrosis, cirrhosis or increasing fibrosis stage in multivariate analysis. In our large, well characterised group of haemochromatosis subjects we did not demonstrate any relationship between candidate gene polymorphisms and hepatic fibrosis or cirrhosis.

Alkalosis in Critically Ill Patients with Severe Sepsis and Septic Shock

PubMed Central

Jazrawi, Allan; Miller, Jan; Baigi, Amir; Chew, Michelle

2017-01-01

Introduction Although metabolic alkalosis is a common occurrence in intensive care units (ICUs), no study has evaluated its prevalence or outcomes in patients with severe sepsis or septic shock. Methods This is a retrospective cohort study of critically ill patients suffering from severe sepsis and septic shock admitted to the ICUs of Halmstad and Varberg County hospitals. From 910 patient records, 627 patients met the inclusion criteria. We investigated the relationship between metabolic alkalosis and mortality. Further, we studied the relationship between metabolic alkalosis and ICU length of stay (LOS). Results Metabolic alkalosis was associated with decreased 30-day and 12-month mortalities. This effect was however lost when a multivariate analysis was conducted, correcting for age, gender, pH on admission, base excess (BE) on admission, Simplified Acute Physiology Score III (SAPS III) and acute kidney injury (AKI). We then analyzed for any dose-response effect between the severity of metabolic alkalosis and mortality and found no relationship. Bivariate analysis showed that metabolic alkalosis had a significant effect on the length of ICU stay. When adjusting for age, sex, pH at admission, BE at admission, SAPS III and AKI in a multivariate analysis, metabolic alkalosis significantly contributed to prolonged ICU length of stay. In two separate sensitivity analyses pure metabolic alkalosis and late metabolic alkalosis (time of onset >48 hours) were the only significant predictor of increased ICU length of stay. Conclusion Metabolic alkalosis did not have any effect on 30-day and 12-month mortalities after adjusting for age, sex, SAPS III-score, pH and BE on admission and AKI in a multivariate analysis. The presence of metabolic alkalosis was independently associated with an increased ICU length of stay. PMID:28045915

FTIR Imaging Coupled with Multivariate Analysis for Study of Initial Diffusion of Different Solvents in Cellulose Acetate Butyrate Films

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

Lindblad, M.S.; Keyes, B.; Gedvilas, L.

Fourier transform infrared (FTIR) spectroscopic imaging was used to study the initial diffusion of different solvents in cellulose acetate butyrate (CAB) films containing different amounts of acetyl and butyryl substituents. Different solvents and solvent/non-solvent mixtures were also studied. The FTIR imaging system allowed acquisition of sequential images of the CAB films as solvent penetration proceeded without disturbing the system. The interface between the non-swollen polymer and the initial swelling front could be identified using multivariate data analysis tools. For a series of ketone solvents the initial diffusion coefficients and diffusion rates could be quantified and were found to be relatedmore » to the polar and hydrogen interaction parameters in the Hansen solubility parameters of the solvents. For the solvent/non-solvent system the initial diffusion rate decreased less than linearly with the weight-percent of non-solvent present in the solution, which probably was due to the swelling characteristic of the non-solvent. For a given solvent, increasing the butyryl content of the CAB increased the initial diffusion rate. Increasing the butyryl content from 17 wt.% butyryl to 37 wt.% butyryl produced a considerably larger increase in initial diffusion rate compared to an increase in butyryl content from 37 wt.% to 50 wt.% butyryl.« less

Changes in Concurrent Risk of Warm and Dry Years under Impact of Climate Change

NASA Astrophysics Data System (ADS)

Sarhadi, A.; Wiper, M.; Touma, D. E.; Ausín, M. C.; Diffenbaugh, N. S.

2017-12-01

Anthropogenic global warming has changed the nature and the risk of extreme climate phenomena. The changing concurrence of multiple climatic extremes (warm and dry years) may result in intensification of undesirable consequences for water resources, human and ecosystem health, and environmental equity. The present study assesses how global warming influences the probability that warm and dry years co-occur in a global scale. In the first step of the study a designed multivariate Mann-Kendall trend analysis is used to detect the areas in which the concurrence of warm and dry years has increased in the historical climate records and also climate models in the global scale. The next step investigates the concurrent risk of the extremes under dynamic nonstationary conditions. A fully generalized multivariate risk framework is designed to evolve through time under dynamic nonstationary conditions. In this methodology, Bayesian, dynamic copulas are developed to model the time-varying dependence structure between the two different climate extremes (warm and dry years). The results reveal an increasing trend in the concurrence risk of warm and dry years, which are in agreement with the multivariate trend analysis from historical and climate models. In addition to providing a novel quantification of the changing probability of compound extreme events, the results of this study can help decision makers develop short- and long-term strategies to prepare for climate stresses now and in the future.

Multivariate pattern analysis reveals subtle brain anomalies relevant to the cognitive phenotype in neurofibromatosis type 1.

PubMed

Duarte, João V; Ribeiro, Maria J; Violante, Inês R; Cunha, Gil; Silva, Eduardo; Castelo-Branco, Miguel

2014-01-01

Neurofibromatosis Type 1 (NF1) is a common genetic condition associated with cognitive dysfunction. However, the pathophysiology of the NF1 cognitive deficits is not well understood. Abnormal brain structure, including increased total brain volume, white matter (WM) and grey matter (GM) abnormalities have been reported in the NF1 brain. These previous studies employed univariate model-driven methods preventing detection of subtle and spatially distributed differences in brain anatomy. Multivariate pattern analysis allows the combination of information from multiple spatial locations yielding a discriminative power beyond that of single voxels. Here we investigated for the first time subtle anomalies in the NF1 brain, using a multivariate data-driven classification approach. We used support vector machines (SVM) to classify whole-brain GM and WM segments of structural T1 -weighted MRI scans from 39 participants with NF1 and 60 non-affected individuals, divided in children/adolescents and adults groups. We also employed voxel-based morphometry (VBM) as a univariate gold standard to study brain structural differences. SVM classifiers correctly classified 94% of cases (sensitivity 92%; specificity 96%) revealing the existence of brain structural anomalies that discriminate NF1 individuals from controls. Accordingly, VBM analysis revealed structural differences in agreement with the SVM weight maps representing the most relevant brain regions for group discrimination. These included the hippocampus, basal ganglia, thalamus, and visual cortex. This multivariate data-driven analysis thus identified subtle anomalies in brain structure in the absence of visible pathology. Our results provide further insight into the neuroanatomical correlates of known features of the cognitive phenotype of NF1. Copyright © 2012 Wiley Periodicals, Inc.

Association of maternal serum PAPP-A levels, nuchal translucency and crown-rump length in first trimester with adverse pregnancy outcomes: retrospective cohort study.

PubMed

Bilagi, Ashwini; Burke, Danielle L; Riley, Richard D; Mills, Ian; Kilby, Mark D; Katie Morris, R

2017-07-01

Are first trimester serum pregnancy-associated plasma protein-A (PAPP-A), nuchal translucency (NT) and crown-rump length (CRL) prognostic factors for adverse pregnancy outcomes? Retrospective cohort, women, singleton pregnancies (UK 2011-2015). Unadjusted and multivariable logistic regression. small for gestational age (SGA), pre-eclampsia (PE), preterm birth (PTB), miscarriage, stillbirth, perinatal mortality and neonatal death (NND). A total of 12 592 pregnancies: 852 (6.8%) PTB, 352 (2.8%) PE, 1824 (14.5%) SGA, 73 (0.6%) miscarriages, 37(0.3%) stillbirths, 73 perinatal deaths (0.6%) and 38 (0.30%) NND. Multivariable analysis: lower odds of SGA [adjusted odds ratio (aOR) 0.88 (95% CI 0.85,0.91)], PTB [0.92 (95%CI 0.88,0.97)], PE [0.91 (95% CI 0.85,0.97)] and stillbirth [0.71 (95% CI 0.52,0.98)] as PAPP-A increases. Lower odds of SGA [aOR 0.79 (95% CI 0.70,0.89)] but higher odds of miscarriage [aOR 1.75 95% CI (1.12,2.72)] as NT increases, and lower odds of stillbirth as CRL increases [aOR 0.94 95% CI (0.89,0.99)]. Multivariable analysis of three factors together demonstrated strong associations: a) PAPP-A, NT, CRL and SGA, b) PAPP-A and PTB, c) PAPP-A, CRL and PE, d) NT and miscarriage. Pregnancy-associated plasma protein-A, NT and CRL are independent prognostic factors for adverse pregnancy outcomes, particularly PAPP-A and SGA with lower PAPP-A associated with increased risk. © 2017 John Wiley & Sons, Ltd. © 2017 John Wiley & Sons, Ltd.

Single-Isocenter Multiple-Target Stereotactic Radiosurgery: Risk of Compromised Coverage

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

Roper, Justin, E-mail: justin.roper@emory.edu; Department of Biostatistics and Bioinformatics, Winship Cancer Institute of Emory University, Atlanta, Georgia; Chanyavanich, Vorakarn

2015-11-01

Purpose: To determine the dosimetric effects of rotational errors on target coverage using volumetric modulated arc therapy (VMAT) for multitarget stereotactic radiosurgery (SRS). Methods and Materials: This retrospective study included 50 SRS cases, each with 2 intracranial planning target volumes (PTVs). Both PTVs were planned for simultaneous treatment to 21 Gy using a single-isocenter, noncoplanar VMAT SRS technique. Rotational errors of 0.5°, 1.0°, and 2.0° were simulated about all axes. The dose to 95% of the PTV (D95) and the volume covered by 95% of the prescribed dose (V95) were evaluated using multivariate analysis to determine how PTV coverage was relatedmore » to PTV volume, PTV separation, and rotational error. Results: At 0.5° rotational error, D95 values and V95 coverage rates were ≥95% in all cases. For rotational errors of 1.0°, 7% of targets had D95 and V95 values <95%. Coverage worsened substantially when the rotational error increased to 2.0°: D95 and V95 values were >95% for only 63% of the targets. Multivariate analysis showed that PTV volume and distance to isocenter were strong predictors of target coverage. Conclusions: The effects of rotational errors on target coverage were studied across a broad range of SRS cases. In general, the risk of compromised coverage increased with decreasing target volume, increasing rotational error and increasing distance between targets. Multivariate regression models from this study may be used to quantify the dosimetric effects of rotational errors on target coverage given patient-specific input parameters of PTV volume and distance to isocenter.« less

Dose-dependent effect of mammographic breast density on the risk of contralateral breast cancer.

PubMed

Chowdhury, Marzana; Euhus, David; O'Donnell, Maureen; Onega, Tracy; Choudhary, Pankaj K; Biswas, Swati

2018-07-01

Increased mammographic breast density is a significant risk factor for breast cancer. It is not clear if it is also a risk factor for the development of contralateral breast cancer. The data were obtained from Breast Cancer Surveillance Consortium and included women diagnosed with invasive breast cancer or ductal carcinoma in situ between ages 18 and 88 and years 1995 and 2009. Each case of contralateral breast cancer was matched with three controls based on year of first breast cancer diagnosis, race, and length of follow-up. A total of 847 cases and 2541 controls were included. The risk factors included in the study were mammographic breast density, age of first breast cancer diagnosis, family history of breast cancer, anti-estrogen treatment, hormone replacement therapy, menopausal status, and estrogen receptor status, all from the time of first breast cancer diagnosis. Both univariate analysis and multivariate conditional logistic regression analysis were performed. In the final multivariate model, breast density, family history of breast cancer, and anti-estrogen treatment remained significant with p values less than 0.01. Increasing breast density had a dose-dependent effect on the risk of contralateral breast cancer. Relative to 'almost entirely fat' category of breast density, the adjusted odds ratios (and p values) in the multivariate analysis for 'scattered density,' 'heterogeneously dense,' and 'extremely dense' categories were 1.65 (0.036), 2.10 (0.002), and 2.32 (0.001), respectively. Breast density is an independent and significant risk factor for development of contralateral breast cancer. This risk factor should contribute to clinical decision making.

Unbiased metabolite profiling by liquid chromatography-quadrupole time-of-flight mass spectrometry and multivariate data analysis for herbal authentication: classification of seven Lonicera species flower buds.

PubMed

Gao, Wen; Yang, Hua; Qi, Lian-Wen; Liu, E-Hu; Ren, Mei-Ting; Yan, Yu-Ting; Chen, Jun; Li, Ping

2012-07-06

Plant-based medicines become increasingly popular over the world. Authentication of herbal raw materials is important to ensure their safety and efficacy. Some herbs belonging to closely related species but differing in medicinal properties are difficult to be identified because of similar morphological and microscopic characteristics. Chromatographic fingerprinting is an alternative method to distinguish them. Existing approaches do not allow a comprehensive analysis for herbal authentication. We have now developed a strategy consisting of (1) full metabolic profiling of herbal medicines by rapid resolution liquid chromatography (RRLC) combined with quadrupole time-of-flight mass spectrometry (QTOF MS), (2) global analysis of non-targeted compounds by molecular feature extraction algorithm, (3) multivariate statistical analysis for classification and prediction, and (4) marker compounds characterization. This approach has provided a fast and unbiased comparative multivariate analysis of the metabolite composition of 33-batch samples covering seven Lonicera species. Individual metabolic profiles are performed at the level of molecular fragments without prior structural assignment. In the entire set, the obtained classifier for seven Lonicera species flower buds showed good prediction performance and a total of 82 statistically different components were rapidly obtained by the strategy. The elemental compositions of discriminative metabolites were characterized by the accurate mass measurement of the pseudomolecular ions and their chemical types were assigned by the MS/MS spectra. The high-resolution, comprehensive and unbiased strategy for metabolite data analysis presented here is powerful and opens the new direction of authentication in herbal analysis. Copyright © 2012 Elsevier B.V. All rights reserved.

CD147 as a novel biomarker for predicting the prognosis and clinicopathological features of bladder cancer: a meta-analysis

PubMed Central

Li, Hongru; Xu, Yadong; Li, Hui

2017-01-01

Objective To assess the prognostic and clinicopathological characteristics of CD147 in human bladder cancer. Methods Studies on CD147 expression in bladder cancer were retrieved from PubMed, EMBASE, the Cochrane Library, Web of Science, China National Knowledge Infrastructure, and the WanFang databases. Outcomes were pooled with meta-analyzing softwares RevMan 5.3 and STATA 14.0. Results Twenty-four studies with 25 datasets demonstrated that CD147 expression was higher in bladder cancer than in non-cancer tissues (OR=43.64, P<0.00001). Moreover, this increase was associated with more advanced clinical stages (OR=73.89, P<0.0001), deeper invasion (OR=3.22, P<0.00001), lower histological differentiation (OR=4.54, P=0.0005), poorer overall survival (univariate analysis, HR=2.63, P<0.00001; multivariate analysis, HR=1.86, P=0.00036), disease specific survival (univariate analysis, HR=1.65, P=0.002), disease recurrence-free survival (univariate analysis, HR=2.78, P=0.001; multivariate analysis, HR=5.51, P=0.017), rate of recurrence (OR=1.91, P=0.0006), invasive depth (pT2∼T4 vs. pTa∼T1; OR=3.22, P<0.00001), and histological differentiation (low versus moderate-to-high; OR=4.54, P=0.0005). No difference was found among disease specific survival in multivariate analysis (P=0.067), lymph node metastasis (P=0.12), and sex (P=0.15). Conclusion CD147 could be a biomarker for early diagnosis, treatment, and prognosis of bladder cancer. PMID:28977970

Chemical Discrimination of Cortex Phellodendri amurensis and Cortex Phellodendri chinensis by Multivariate Analysis Approach.

PubMed

Sun, Hui; Wang, Huiyu; Zhang, Aihua; Yan, Guangli; Han, Ying; Li, Yuan; Wu, Xiuhong; Meng, Xiangcai; Wang, Xijun

2016-01-01

As herbal medicines have an important position in health care systems worldwide, their current assessment, and quality control are a major bottleneck. Cortex Phellodendri chinensis (CPC) and Cortex Phellodendri amurensis (CPA) are widely used in China, however, how to identify species of CPA and CPC has become urgent. In this study, multivariate analysis approach was performed to the investigation of chemical discrimination of CPA and CPC. Principal component analysis showed that two herbs could be separated clearly. The chemical markers such as berberine, palmatine, phellodendrine, magnoflorine, obacunone, and obaculactone were identified through the orthogonal partial least squared discriminant analysis, and were identified tentatively by the accurate mass of quadruple-time-of-flight mass spectrometry. A total of 29 components can be used as the chemical markers for discrimination of CPA and CPC. Of them, phellodenrine is significantly higher in CPC than that of CPA, whereas obacunone and obaculactone are significantly higher in CPA than that of CPC. The present study proves that multivariate analysis approach based chemical analysis greatly contributes to the investigation of CPA and CPC, and showed that the identified chemical markers as a whole should be used to discriminate the two herbal medicines, and simultaneously the results also provided chemical information for their quality assessment. Multivariate analysis approach was performed to the investigate the herbal medicineThe chemical markers were identified through multivariate analysis approachA total of 29 components can be used as the chemical markers. UPLC-Q/TOF-MS-based multivariate analysis method for the herbal medicine samples Abbreviations used: CPC: Cortex Phellodendri chinensis, CPA: Cortex Phellodendri amurensis, PCA: Principal component analysis, OPLS-DA: Orthogonal partial least squares discriminant analysis, BPI: Base peaks ion intensity.

Body composition status and the risk of migraine: A meta-analysis.

PubMed

Gelaye, Bizu; Sacco, Simona; Brown, Wendy J; Nitchie, Haley L; Ornello, Raffaele; Peterlin, B Lee

2017-05-09

To evaluate the association between migraine and body composition status as estimated based on body mass index and WHO physical status categories. Systematic electronic database searches were conducted for relevant studies. Two independent reviewers performed data extraction and quality appraisal. Odds ratios (OR) and confidence intervals (CI) were pooled using a random effects model. Significant values, weighted effect sizes, and tests of homogeneity of variance were calculated. A total of 12 studies, encompassing data from 288,981 unique participants, were included. The age- and sex-adjusted pooled risk of migraine in those with obesity was increased by 27% compared with those of normal weight (odds ratio [OR] 1.27; 95% confidence interval [CI] 1.16-1.37, p < 0.001) and remained increased after multivariate adjustments. Although the age- and sex-adjusted pooled migraine risk was increased in overweight individuals (OR 1.08; 95% CI 1.04, 1.12, p < 0.001), significance was lost after multivariate adjustments. The age- and sex-adjusted pooled risk of migraine in underweight individuals was marginally increased by 13% compared with those of normal weight (OR 1.13; 95% CI 1.02, 1.24, p < 0.001) and remained increased after multivariate adjustments. The current body of evidence shows that the risk of migraine is increased in obese and underweight individuals. Studies are needed to confirm whether interventions that modify obesity status decrease the risk of migraine. © 2017 American Academy of Neurology.

Multivariate information-theoretic measures reveal directed information structure and task relevant changes in fMRI connectivity.

PubMed

Lizier, Joseph T; Heinzle, Jakob; Horstmann, Annette; Haynes, John-Dylan; Prokopenko, Mikhail

2011-02-01

The human brain undertakes highly sophisticated information processing facilitated by the interaction between its sub-regions. We present a novel method for interregional connectivity analysis, using multivariate extensions to the mutual information and transfer entropy. The method allows us to identify the underlying directed information structure between brain regions, and how that structure changes according to behavioral conditions. This method is distinguished in using asymmetric, multivariate, information-theoretical analysis, which captures not only directional and non-linear relationships, but also collective interactions. Importantly, the method is able to estimate multivariate information measures with only relatively little data. We demonstrate the method to analyze functional magnetic resonance imaging time series to establish the directed information structure between brain regions involved in a visuo-motor tracking task. Importantly, this results in a tiered structure, with known movement planning regions driving visual and motor control regions. Also, we examine the changes in this structure as the difficulty of the tracking task is increased. We find that task difficulty modulates the coupling strength between regions of a cortical network involved in movement planning and between motor cortex and the cerebellum which is involved in the fine-tuning of motor control. It is likely these methods will find utility in identifying interregional structure (and experimentally induced changes in this structure) in other cognitive tasks and data modalities.

SOCR Motion Charts: An Efficient, Open-Source, Interactive and Dynamic Applet for Visualizing Longitudinal Multivariate Data

PubMed Central

Al-Aziz, Jameel; Christou, Nicolas; Dinov, Ivo D.

2011-01-01

The amount, complexity and provenance of data have dramatically increased in the past five years. Visualization of observed and simulated data is a critical component of any social, environmental, biomedical or scientific quest. Dynamic, exploratory and interactive visualization of multivariate data, without preprocessing by dimensionality reduction, remains a nearly insurmountable challenge. The Statistics Online Computational Resource (www.SOCR.ucla.edu) provides portable online aids for probability and statistics education, technology-based instruction and statistical computing. We have developed a new Java-based infrastructure, SOCR Motion Charts, for discovery-based exploratory analysis of multivariate data. This interactive data visualization tool enables the visualization of high-dimensional longitudinal data. SOCR Motion Charts allows mapping of ordinal, nominal and quantitative variables onto time, 2D axes, size, colors, glyphs and appearance characteristics, which facilitates the interactive display of multidimensional data. We validated this new visualization paradigm using several publicly available multivariate datasets including Ice-Thickness, Housing Prices, Consumer Price Index, and California Ozone Data. SOCR Motion Charts is designed using object-oriented programming, implemented as a Java Web-applet and is available to the entire community on the web at www.socr.ucla.edu/SOCR_MotionCharts. It can be used as an instructional tool for rendering and interrogating high-dimensional data in the classroom, as well as a research tool for exploratory data analysis. PMID:21479108

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.

Using Interactive Graphics to Teach Multivariate Data Analysis to Psychology Students

ERIC Educational Resources Information Center

Valero-Mora, Pedro M.; Ledesma, Ruben D.

2011-01-01

This paper discusses the use of interactive graphics to teach multivariate data analysis to Psychology students. Three techniques are explored through separate activities: parallel coordinates/boxplots; principal components/exploratory factor analysis; and cluster analysis. With interactive graphics, students may perform important parts of the…

Can texture analysis of tooth microwear detect within guild niche partitioning in extinct species?

NASA Astrophysics Data System (ADS)

Purnell, Mark; Nedza, Christopher; Rychlik, Leszek

2017-04-01

Recent work shows that tooth microwear analysis can be applied further back in time and deeper into the phylogenetic history of vertebrate clades than previously thought (e.g. niche partitioning in early Jurassic insectivorous mammals; Gill et al., 2014, Nature). Furthermore, quantitative approaches to analysis based on parameterization of surface roughness are increasing the robustness and repeatability of this widely used dietary proxy. Discriminating between taxa within dietary guilds has the potential to significantly increase our ability to determine resource use and partitioning in fossil vertebrates, but how sensitive is the technique? To address this question we analysed tooth microwear texture in sympatric populations of shrew species (Neomys fodiens, Neomys anomalus, Sorex araneus, Sorex minutus) from BiaŁ owieza Forest, Poland. These populations are known to exhibit varying degrees of niche partitioning (Churchfield & Rychlik, 2006, J. Zool.) with greatest overlap between the Neomys species. Sorex araneus also exhibits some niche overlap with N. anomalus, while S. minutus is the most specialised. Multivariate analysis based only on tooth microwear textures recovers the same pattern of niche partitioning. Our results also suggest that tooth textures track seasonal differences in diet. Projecting data from fossils into the multivariate dietary space defined using microwear from extant taxa demonstrates that the technique is capable of subtle dietary discrimination in extinct insectivores.

Characteristics of foodborne outbreaks in which use of analytical epidemiological studies contributed to identification of suspected vehicles, European Union, 2007 to 2011.

PubMed

Schlinkmann, K M; Razum, O; Werber, D

2017-04-01

Foodborne disease outbreaks (FBDOs) occur frequently in Europe. Employing analytical epidemiological study designs increases the likelihood of identifying the suspected vehicle(s), but these studies are rarely applied in FBDO investigations. We used multivariable binary logistic regression analysis to identify characteristics of investigated FBDOs reported to the European Food Safety Authority (2007-2011) that were associated with analytical epidemiological evidence (compared to evidence from microbiological investigations/descriptive epidemiology only). The analysis was restricted to FBDO investigations, where the evidence for the suspected vehicle was considered 'strong', i.e. convincing. The presence of analytical epidemiological evidence was reported in 2012 (50%) of these 4038 outbreaks. In multivariable analysis, increasing outbreak size, number of hospitalizations, causative (i.e. aetiological) agent (whether identified and, if so, which one), and the setting in which these outbreaks occurred (e.g. geographically dispersed outbreaks) were independently associated with presence of analytical evidence. The number of investigations with reported analytical epidemiological evidence was unexpectedly high, likely indicating the need for quality assurance within the European Union foodborne outbreak reporting system, and warranting cautious interpretation of our findings. This first analysis of evidence implicating a food vehicle in FBDOs may help to inform public health authorities on when to use analytical epidemiological study designs.

Fourier Transform Infrared Spectroscopy (FTIR) and Multivariate Analysis for Identification of Different Vegetable Oils Used in Biodiesel Production

PubMed Central

Mueller, Daniela; Ferrão, Marco Flôres; Marder, Luciano; da Costa, Adilson Ben; de Cássia de Souza Schneider, Rosana

2013-01-01

The main objective of this study was to use infrared spectroscopy to identify vegetable oils used as raw material for biodiesel production and apply multivariate analysis to the data. Six different vegetable oil sources—canola, cotton, corn, palm, sunflower and soybeans—were used to produce biodiesel batches. The spectra were acquired by Fourier transform infrared spectroscopy using a universal attenuated total reflectance sensor (FTIR-UATR). For the multivariate analysis principal component analysis (PCA), hierarchical cluster analysis (HCA), interval principal component analysis (iPCA) and soft independent modeling of class analogy (SIMCA) were used. The results indicate that is possible to develop a methodology to identify vegetable oils used as raw material in the production of biodiesel by FTIR-UATR applying multivariate analysis. It was also observed that the iPCA found the best spectral range for separation of biodiesel batches using FTIR-UATR data, and with this result, the SIMCA method classified 100% of the soybean biodiesel samples. PMID:23539030

Multivariate meta-analysis for non-linear and other multi-parameter associations

PubMed Central

Gasparrini, A; Armstrong, B; Kenward, M G

2012-01-01

In this paper, we formalize the application of multivariate meta-analysis and meta-regression to synthesize estimates of multi-parameter associations obtained from different studies. This modelling approach extends the standard two-stage analysis used to combine results across different sub-groups or populations. The most straightforward application is for the meta-analysis of non-linear relationships, described for example by regression coefficients of splines or other functions, but the methodology easily generalizes to any setting where complex associations are described by multiple correlated parameters. The modelling framework of multivariate meta-analysis is implemented in the package mvmeta within the statistical environment R. As an illustrative example, we propose a two-stage analysis for investigating the non-linear exposure–response relationship between temperature and non-accidental mortality using time-series data from multiple cities. Multivariate meta-analysis represents a useful analytical tool for studying complex associations through a two-stage procedure. Copyright © 2012 John Wiley & Sons, Ltd. PMID:22807043

Measures of dependence for multivariate Lévy distributions

NASA Astrophysics Data System (ADS)

Boland, J.; Hurd, T. R.; Pivato, M.; Seco, L.

2001-02-01

Recent statistical analysis of a number of financial databases is summarized. Increasing agreement is found that logarithmic equity returns show a certain type of asymptotic behavior of the largest events, namely that the probability density functions have power law tails with an exponent α≈3.0. This behavior does not vary much over different stock exchanges or over time, despite large variations in trading environments. The present paper proposes a class of multivariate distributions which generalizes the observed qualities of univariate time series. A new consequence of the proposed class is the "spectral measure" which completely characterizes the multivariate dependences of the extreme tails of the distribution. This measure on the unit sphere in M-dimensions, in principle completely general, can be determined empirically by looking at extreme events. If it can be observed and determined, it will prove to be of importance for scenario generation in portfolio risk management.

Measures of precision for dissimilarity-based multivariate analysis of ecological communities

PubMed Central

Anderson, Marti J; Santana-Garcon, Julia

2015-01-01

Ecological studies require key decisions regarding the appropriate size and number of sampling units. No methods currently exist to measure precision for multivariate assemblage data when dissimilarity-based analyses are intended to follow. Here, we propose a pseudo multivariate dissimilarity-based standard error (MultSE) as a useful quantity for assessing sample-size adequacy in studies of ecological communities. Based on sums of squared dissimilarities, MultSE measures variability in the position of the centroid in the space of a chosen dissimilarity measure under repeated sampling for a given sample size. We describe a novel double resampling method to quantify uncertainty in MultSE values with increasing sample size. For more complex designs, values of MultSE can be calculated from the pseudo residual mean square of a permanova model, with the double resampling done within appropriate cells in the design. R code functions for implementing these techniques, along with ecological examples, are provided. PMID:25438826

Copula-based analysis of rhythm

NASA Astrophysics Data System (ADS)

García, J. E.; González-López, V. A.; Viola, M. L. Lanfredi

2016-06-01

In this paper we establish stochastic profiles of the rhythm for three languages: English, Japanese and Spanish. We model the increase or decrease of the acoustical energy, collected into three bands coming from the acoustic signal. The number of parameters needed to specify a discrete multivariate Markov chain grows exponentially with the order and dimension of the chain. In this case the size of the database is not large enough for a consistent estimation of the model. We apply a strategy to estimate a multivariate process with an order greater than the order achieved using standard procedures. The new strategy consist on obtaining a partition of the state space which is constructed from a combination of the partitions corresponding to the three marginal processes, one for each band of energy, and the partition coming from to the multivariate Markov chain. Then, all the partitions are linked using a copula, in order to estimate the transition probabilities.

¹H NMR and multivariate data analysis of the relationship between the age and quality of duck meat.

PubMed

Liu, Chunli; Pan, Daodong; Ye, Yangfang; Cao, Jinxuan

2013-11-15

To contribute to a better understanding of the factors affecting meat quality, we investigated the influence of age on the chemical composition of duck meat. Aging probably affects the quality of meat through changes in metabolism. Therefore, we studied the metabolic composition of duck meat using (1)H nuclear magnetic resonance (NMR) spectroscopy. Comprehensive multivariate data analysis showed significant differences between extracts from ducks that had been aged for four different time periods. Although lactate and anserine increased with age, fumarate, betaine, taurine, inosine and alkyl-substituted free amino acids decreased. These results contribute to a better understanding of changes in duck meat metabolism as meat ages, which could be used to help assess the quality of duck meat as a food. Copyright © 2013 Elsevier Ltd. All rights reserved.

Trends in Fatalities From Distracted Driving in the United States, 1999 to 2008

PubMed Central

Stimpson, Jim P.

2010-01-01

Objectives. We examined trends in distracted driving fatalities and their relation to cell phone use and texting volume. Methods. The Fatality Analysis Reporting System (FARS) records data on all road fatalities that occurred on public roads in the United States from 1999 to 2008. We studied trends in distracted driving fatalities, driver and crash characteristics, and trends in cell phone use and texting volume. We used multivariate regression analysis to estimate the relation between state-level distracted driving fatalities and texting volumes. Results. After declining from 1999 to 2005, fatalities from distracted driving increased 28% after 2005, rising from 4572 fatalities to 5870 in 2008. Crashes increasingly involved male drivers driving alone in collisions with roadside obstructions in urban areas. By use of multivariate analyses, we predicted that increasing texting volumes resulted in more than 16 000 additional road fatalities from 2001 to 2007. Conclusions. Distracted driving is a growing public safety hazard. Specifically, the dramatic rise in texting volume since 2005 appeared to be contributing to an alarming rise in distracted driving fatalities. Legislation enacting texting bans should be paired with effective enforcement to deter drivers from using cell phones while driving. PMID:20864709

Trends in fatalities from distracted driving in the United States, 1999 to 2008.

PubMed

Wilson, Fernando A; Stimpson, Jim P

2010-11-01

We examined trends in distracted driving fatalities and their relation to cell phone use and texting volume. The Fatality Analysis Reporting System (FARS) records data on all road fatalities that occurred on public roads in the United States from 1999 to 2008. We studied trends in distracted driving fatalities, driver and crash characteristics, and trends in cell phone use and texting volume. We used multivariate regression analysis to estimate the relation between state-level distracted driving fatalities and texting volumes. After declining from 1999 to 2005, fatalities from distracted driving increased 28% after 2005, rising from 4572 fatalities to 5870 in 2008. Crashes increasingly involved male drivers driving alone in collisions with roadside obstructions in urban areas. By use of multivariate analyses, we predicted that increasing texting volumes resulted in more than 16,000 additional road fatalities from 2001 to 2007. Distracted driving is a growing public safety hazard. Specifically, the dramatic rise in texting volume since 2005 appeared to be contributing to an alarming rise in distracted driving fatalities. Legislation enacting texting bans should be paired with effective enforcement to deter drivers from using cell phones while driving.

The Potential of Multivariate Analysis in Assessing Students' Attitude to Curriculum Subjects

ERIC Educational Resources Information Center

Gaotlhobogwe, Michael; Laugharne, Janet; Durance, Isabelle

2011-01-01

Background: Understanding student attitudes to curriculum subjects is central to providing evidence-based options to policy makers in education. Purpose: We illustrate how quantitative approaches used in the social sciences and based on multivariate analysis (categorical Principal Components Analysis, Clustering Analysis and General Linear…

A non-iterative extension of the multivariate random effects meta-analysis.

PubMed

Makambi, Kepher H; Seung, Hyunuk

2015-01-01

Multivariate methods in meta-analysis are becoming popular and more accepted in biomedical research despite computational issues in some of the techniques. A number of approaches, both iterative and non-iterative, have been proposed including the multivariate DerSimonian and Laird method by Jackson et al. (2010), which is non-iterative. In this study, we propose an extension of the method by Hartung and Makambi (2002) and Makambi (2001) to multivariate situations. A comparison of the bias and mean square error from a simulation study indicates that, in some circumstances, the proposed approach perform better than the multivariate DerSimonian-Laird approach. An example is presented to demonstrate the application of the proposed approach.

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

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.

An improved method for bivariate meta-analysis when within-study correlations are unknown.

PubMed

Hong, Chuan; D Riley, Richard; Chen, Yong

2018-03-01

Multivariate meta-analysis, which jointly analyzes multiple and possibly correlated outcomes in a single analysis, is becoming increasingly popular in recent years. An attractive feature of the multivariate meta-analysis is its ability to account for the dependence between multiple estimates from the same study. However, standard inference procedures for multivariate meta-analysis require the knowledge of within-study correlations, which are usually unavailable. This limits standard inference approaches in practice. Riley et al proposed a working model and an overall synthesis correlation parameter to account for the marginal correlation between outcomes, where the only data needed are those required for a separate univariate random-effects meta-analysis. As within-study correlations are not required, the Riley method is applicable to a wide variety of evidence synthesis situations. However, the standard variance estimator of the Riley method is not entirely correct under many important settings. As a consequence, the coverage of a function of pooled estimates may not reach the nominal level even when the number of studies in the multivariate meta-analysis is large. In this paper, we improve the Riley method by proposing a robust variance estimator, which is asymptotically correct even when the model is misspecified (ie, when the likelihood function is incorrect). Simulation studies of a bivariate meta-analysis, in a variety of settings, show a function of pooled estimates has improved performance when using the proposed robust variance estimator. In terms of individual pooled estimates themselves, the standard variance estimator and robust variance estimator give similar results to the original method, with appropriate coverage. The proposed robust variance estimator performs well when the number of studies is relatively large. Therefore, we recommend the use of the robust method for meta-analyses with a relatively large number of studies (eg, m≥50). When the sample size is relatively small, we recommend the use of the robust method under the working independence assumption. We illustrate the proposed method through 2 meta-analyses. Copyright © 2017 John Wiley & Sons, Ltd.

Multivariate missing data in hydrology - Review and applications

NASA Astrophysics Data System (ADS)

Ben Aissia, Mohamed-Aymen; Chebana, Fateh; Ouarda, Taha B. M. J.

2017-12-01

Water resources planning and management require complete data sets of a number of hydrological variables, such as flood peaks and volumes. However, hydrologists are often faced with the problem of missing data (MD) in hydrological databases. Several methods are used to deal with the imputation of MD. During the last decade, multivariate approaches have gained popularity in the field of hydrology, especially in hydrological frequency analysis (HFA). However, treating the MD remains neglected in the multivariate HFA literature whereas the focus has been mainly on the modeling component. For a complete analysis and in order to optimize the use of data, MD should also be treated in the multivariate setting prior to modeling and inference. Imputation of MD in the multivariate hydrological framework can have direct implications on the quality of the estimation. Indeed, the dependence between the series represents important additional information that can be included in the imputation process. The objective of the present paper is to highlight the importance of treating MD in multivariate hydrological frequency analysis by reviewing and applying multivariate imputation methods and by comparing univariate and multivariate imputation methods. An application is carried out for multiple flood attributes on three sites in order to evaluate the performance of the different methods based on the leave-one-out procedure. The results indicate that, the performance of imputation methods can be improved by adopting the multivariate setting, compared to mean substitution and interpolation methods, especially when using the copula-based approach.

Development of Pattern Recognition Techniques for the Evaluation of Toxicant Impacts to Multispecies Systems

DTIC Science & Technology

1993-06-18

the exception. In the Standardized Aquatic Microcosm and the Mixed Flask Culture (MFC) microcosms, multivariate analysis and clustering methods...rule rather than the exception. In the Standardized Aquatic Microcosm and the Mixed Flask Culture (MFC) microcosms, multivariate analysis and...experiments using two microcosm protocols. We use nonmetric clustering, a multivariate pattern recognition technique developed by Matthews and Heame (1991

Multivariate analysis for scanning tunneling spectroscopy data

NASA Astrophysics Data System (ADS)

Yamanishi, Junsuke; Iwase, Shigeru; Ishida, Nobuyuki; Fujita, Daisuke

2018-01-01

We applied principal component analysis (PCA) to two-dimensional tunneling spectroscopy (2DTS) data obtained on a Si(111)-(7 × 7) surface to explore the effectiveness of multivariate analysis for interpreting 2DTS data. We demonstrated that several components that originated mainly from specific atoms at the Si(111)-(7 × 7) surface can be extracted by PCA. Furthermore, we showed that hidden components in the tunneling spectra can be decomposed (peak separation), which is difficult to achieve with normal 2DTS analysis without the support of theoretical calculations. Our analysis showed that multivariate analysis can be an additional powerful way to analyze 2DTS data and extract hidden information from a large amount of spectroscopic data.

Exploring image data assimilation in the prospect of high-resolution satellite oceanic observations

NASA Astrophysics Data System (ADS)

Durán Moro, Marina; Brankart, Jean-Michel; Brasseur, Pierre; Verron, Jacques

2017-07-01

Satellite sensors increasingly provide high-resolution (HR) observations of the ocean. They supply observations of sea surface height (SSH) and of tracers of the dynamics such as sea surface salinity (SSS) and sea surface temperature (SST). In particular, the Surface Water Ocean Topography (SWOT) mission will provide measurements of the surface ocean topography at very high-resolution (HR) delivering unprecedented information on the meso-scale and submeso-scale dynamics. This study investigates the feasibility to use these measurements to reconstruct meso-scale features simulated by numerical models, in particular on the vertical dimension. A methodology to reconstruct three-dimensional (3D) multivariate meso-scale scenes is developed by using a HR numerical model of the Solomon Sea region. An inverse problem is defined in the framework of a twin experiment where synthetic observations are used. A true state is chosen among the 3D multivariate states which is considered as a reference state. In order to correct a first guess of this true state, a two-step analysis is carried out. A probability distribution of the first guess is defined and updated at each step of the analysis: (i) the first step applies the analysis scheme of a reduced-order Kalman filter to update the first guess probability distribution using SSH observation; (ii) the second step minimizes a cost function using observations of HR image structure and a new probability distribution is estimated. The analysis is extended to the vertical dimension using 3D multivariate empirical orthogonal functions (EOFs) and the probabilistic approach allows the update of the probability distribution through the two-step analysis. Experiments show that the proposed technique succeeds in correcting a multivariate state using meso-scale and submeso-scale information contained in HR SSH and image structure observations. It also demonstrates how the surface information can be used to reconstruct the ocean state below the surface.

A Multivariate Genome-Wide Association Analysis of 10 LDL Subfractions, and Their Response to Statin Treatment, in 1868 Caucasians

PubMed Central

Shim, Heejung; Chasman, Daniel I.; Smith, Joshua D.; Mora, Samia; Ridker, Paul M.; Nickerson, Deborah A.; Krauss, Ronald M.; Stephens, Matthew

2015-01-01

We conducted a genome-wide association analysis of 7 subfractions of low density lipoproteins (LDLs) and 3 subfractions of intermediate density lipoproteins (IDLs) measured by gradient gel electrophoresis, and their response to statin treatment, in 1868 individuals of European ancestry from the Pharmacogenomics and Risk of Cardiovascular Disease study. Our analyses identified four previously-implicated loci (SORT1, APOE, LPA, and CETP) as containing variants that are very strongly associated with lipoprotein subfractions (log10Bayes Factor > 15). Subsequent conditional analyses suggest that three of these (APOE, LPA and CETP) likely harbor multiple independently associated SNPs. Further, while different variants typically showed different characteristic patterns of association with combinations of subfractions, the two SNPs in CETP show strikingly similar patterns - both in our original data and in a replication cohort - consistent with a common underlying molecular mechanism. Notably, the CETP variants are very strongly associated with LDL subfractions, despite showing no association with total LDLs in our study, illustrating the potential value of the more detailed phenotypic measurements. In contrast with these strong subfraction associations, genetic association analysis of subfraction response to statins showed much weaker signals (none exceeding log10Bayes Factor of 6). However, two SNPs (in APOE and LPA) previously-reported to be associated with LDL statin response do show some modest evidence for association in our data, and the subfraction response proles at the LPA SNP are consistent with the LPA association, with response likely being due primarily to resistance of Lp(a) particles to statin therapy. An additional important feature of our analysis is that, unlike most previous analyses of multiple related phenotypes, we analyzed the subfractions jointly, rather than one at a time. Comparisons of our multivariate analyses with standard univariate analyses demonstrate that multivariate analyses can substantially increase power to detect associations. Software implementing our multivariate analysis methods is available at http://stephenslab.uchicago.edu/software.html. PMID:25898129

Kidney Transplant Outcomes in the Super Obese: A National Study From the UNOS Dataset.

PubMed

Kanthawar, Pooja; Mei, Xiaonan; Daily, Michael F; Chandarana, Jyotin; Shah, Malay; Berger, Jonathan; Castellanos, Ana Lia; Marti, Francesc; Gedaly, Roberto

2016-11-01

We evaluated outcomes of super-obese patients (BMI > 50) undergoing kidney transplantation in the US. We performed a review of 190 super-obese patients undergoing kidney transplantation from 1988 through 2013 using the UNOS dataset. Super-obese patients had a mean age of 45.7 years (21-75 years) and 111 (58.4 %) were female. The mean BMI of the super-obese group was 56 (range 50.0-74.2). A subgroup analysis demonstrated that patients with BMI > 50 had worse survival compared to any other BMI class. The 30-day perioperative mortality and length of stay was 3.7 % and 10.09 days compared to 0.8 % and 7.34 days in nonsuper-obese group. On multivariable analysis, BMI > 50 was an independent predictor of 30-day mortality, with a 4.6-fold increased risk of perioperative death. BMI > 50 increased the risk of delayed graft function and the length of stay by twofold. The multivariable analysis of survival showed a 78 % increased risk of death in this group. Overall patient survival for super-obese transplant recipients at 1, 3, and 5 years was 88, 82, and 76 %, compared to 96, 91, 86 % on patients transplanted with BMI < 50. A propensity score adjusted analysis further demonstrates significant worse survival rates in super-obese patients undergoing kidney transplantation. Super-obese patients had prolonged LOS and worse DGF rates. Perioperative mortality was increased 4.6-fold compared to patients with BMI < 50. In a subgroup analysis, super-obese patients who underwent kidney transplantation had significantly worse graft and patient survival compared to underweight, normal weight, and obesity class I, II, and III (BMI 40-50) patients.

Multivariate Analysis of Schools and Educational Policy.

ERIC Educational Resources Information Center

Kiesling, Herbert J.

This report describes a multivariate analysis technique that approaches the problems of educational production function analysis by (1) using comparable measures of output across large experiments, (2) accounting systematically for differences in socioeconomic background, and (3) treating the school as a complete system in which different…

The impact of maternal body mass index on external cephalic version success.

PubMed

Chaudhary, Shahrukh; Contag, Stephen; Yao, Ruofan

2018-01-21

The purpose of this study is to determine the association between body mass index (BMI) and success of ECV. This is a cross-sectional analysis of singleton live births in the USA from 2010 to 2014 using birth certificate data. Patients were assigned a BMI category according to standard WHO classification. Comparisons of success of ECV between the BMI categories were made using chi-square analysis with normal BMI as the reference group. Cochran-Armitage test was performed to look for a trend of decreasing success of ECV as BMI increased. The odds for successful ECV were estimated using multivariate logistic regression analysis, adjusting for possible confounders. A total of 51,002 patients with documented ECV were available for analysis. There was a decreased success rate for ECV as BMI increased (p < .01). Women with a BMI of 40 kg/m 2 or greater had a 58.5% success rate of ECV; women with a normal BMI had 65.0% success rate of ECV. Multivariate analyses demonstrated significant decrease in success of ECV in women with BMI of 40 kg/m 2 or greater (OR 0.621, CI 0.542-0.712). Among women with BMI of 40 kg/m 2 or greater with successful ECV, 59.5% delivered vaginally. In contrast, 81.0% of women with normal BMI and successful ECV delivered vaginally. Morbidly obese women have decreased success rate of ECV as BMI increases and decreased vaginal delivery rates after successful ECV.

Hope, Meaning, and Growth Following the September 11, 2001, Terrorist Attacks

ERIC Educational Resources Information Center

Ai, Amy; Cascio, Toni; Santangelo, Linda K.; Evans-Campbell, Teresa

2005-01-01

Positive psychologists found the increase of seven character strengths that encompass the so-called theological virtues, including hope and spirituality, in Americans after the September 11, 2001, attacks. Little is known about how they may affect post-September 11, 2001, mental health. Using multivariate analysis, this study investigated the…

Prediction of Gestational Diabetes through NMR Metabolomics of Maternal Blood.

PubMed

Pinto, Joana; Almeida, Lara M; Martins, Ana S; Duarte, Daniela; Barros, António S; Galhano, Eulália; Pita, Cristina; Almeida, Maria do Céu; Carreira, Isabel M; Gil, Ana M

2015-06-05

Metabolic biomarkers of pre- and postdiagnosis gestational diabetes mellitus (GDM) were sought, using nuclear magnetic resonance (NMR) metabolomics of maternal plasma and corresponding lipid extracts. Metabolite differences between controls and disease were identified through multivariate analysis of variable selected (1)H NMR spectra. For postdiagnosis GDM, partial least squares regression identified metabolites with higher dependence on normal gestational age evolution. Variable selection of NMR spectra produced good classification models for both pre- and postdiagnostic GDM. Prediagnosis GDM was accompanied by cholesterol increase and minor increases in lipoproteins (plasma), fatty acids, and triglycerides (extracts). Small metabolite changes comprised variations in glucose (up regulated), amino acids, betaine, urea, creatine, and metabolites related to gut microflora. Most changes were enhanced upon GDM diagnosis, in addition to newly observed changes in low-Mw compounds. GDM prediction seems possible exploiting multivariate profile changes rather than a set of univariate changes. Postdiagnosis GDM is successfully classified using a 26-resonance plasma biomarker. Plasma and extracts display comparable classification performance, the former enabling direct and more rapid analysis. Results and putative biochemical hypotheses require further confirmation in larger cohorts of distinct ethnicities.

Computed tomography findings associated with the risk for emergency ventral hernia repair.

PubMed

Mueck, Krislynn M; Holihan, Julie L; Mo, Jiandi; Flores-Gonzales, Juan R; Ko, Tien C; Kao, Lillian S; Liang, Mike K

2017-07-01

Conventional wisdom teaches that small hernia defects are more likely to incarcerate. We aim to identify radiographic features of ventral hernias associated with increased risk of bowel incarceration. We assessed all patients who underwent emergent ventral hernia repair for bowel complications from 2009 to 2015. Cases were matched 1:3 with elective controls. Computed tomography scans were reviewed to determine hernia characteristics. Univariate and multivariable analyses were performed to identify variables associated with emergent surgery. The cohort consisted of 88 patients and 264 controls. On univariate analysis, older age, higher ASA score, elevated BMI, ascites, larger hernias, small angle, and taller hernias were associated with emergent surgery. On multivariable analysis, morbid obesity, ascites, smaller angle, and taller hernias were independently associated with emergent surgery. The teaching that large defects do not incarcerate is inaccurate; bowel compromise occurs with ventral hernias of all sizes. Instead, taller height and smaller angle are associated with the need for emergent repair. Early elective repair should be considered for patients with hernia features concerning for increased risk of bowel compromise. Copyright © 2016 Elsevier Inc. All rights reserved.

Prognostic impact of intestinal wall thickening in hospitalized patients with heart failure.

PubMed

Ikeda, Yuki; Ishii, Shunsuke; Fujita, Teppei; Iida, Yuichiro; Kaida, Toyoji; Nabeta, Takeru; Maekawa, Emi; Yanagisawa, Tomoyoshi; Koitabashi, Toshimi; Takeuchi, Ichiro; Inomata, Takayuki; Ako, Junya

2017-03-01

Intestine-cardiovascular relationship has been increasingly recognized as a key factor in patients with heart disease. We aimed to identify the relationships among intestinal wall edema, cardiac function, and adverse clinical events in hospitalized heart failure (HF) patients. Abdominal computed tomographic images of 168 hospitalized HF patients were retrospectively investigated for identification of average colon wall thickness (CWT) from the ascending to sigmoid colon. Relationships between average CWT and echocardiographic parameters, blood sampling data, and primary outcomes including readmission for deteriorated HF and all-cause mortality were evaluated. Among the echocardiographic parameters, lower left ventricular diastolic function was correlated with higher average CWT. In multivariate analysis, higher logarithmic C-reactive protein level, lower estimated glomerular filtration rate, lower peripheral blood lymphocyte count, higher E/E' ratio, and extremely higher/lower defecation frequency were independently correlated with higher average CWT. Multivariate Cox-hazard analysis demonstrated that higher average CWT was independently related to higher incidence of primary outcomes. In hospitalized HF patients, increased CWT was associated with lower cardiac performance, and predicted poorer long-term clinical outcomes. Copyright © 2016. Published by Elsevier B.V.

Multivariate statistical analysis: Principles and applications to coorbital streams of meteorite falls

NASA Technical Reports Server (NTRS)

Wolf, S. F.; Lipschutz, M. E.

1993-01-01

Multivariate statistical analysis techniques (linear discriminant analysis and logistic regression) can provide powerful discrimination tools which are generally unfamiliar to the planetary science community. Fall parameters were used to identify a group of 17 H chondrites (Cluster 1) that were part of a coorbital stream which intersected Earth's orbit in May, from 1855 - 1895, and can be distinguished from all other H chondrite falls. Using multivariate statistical techniques, it was demonstrated that a totally different criterion, labile trace element contents - hence thermal histories - or 13 Cluster 1 meteorites are distinguishable from those of 45 non-Cluster 1 H chondrites. Here, we focus upon the principles of multivariate statistical techniques and illustrate their application using non-meteoritic and meteoritic examples.

Association of the Aspartate Aminotransferase to Alanine Aminotransferase Ratio with BNP Level and Cardiovascular Mortality in the General Population: The Yamagata Study 10-Year Follow-Up

PubMed Central

Yokoyama, Miyuki; Otaki, Yoichiro; Takahashi, Hiroki; Arimoto, Takanori; Shishido, Tetsuro; Miyamoto, Takuya; Konta, Tsuneo; Shibata, Yoko; Daimon, Makoto; Kayama, Takamasa; Kubota, Isao

2016-01-01

Background. Early identification of high risk subjects for cardiovascular disease in health check-up is still unmet medical need. Cardiovascular disease is characterized by the superior increase in aspartate aminotransferase (AST) to alanine aminotransferase (ALT). However, the association of AST/ALT ratio with brain natriuretic peptide (BNP) levels and cardiovascular mortality remains unclear in the general population. Methods and Results. This longitudinal cohort study included 3,494 Japanese subjects who participated in a community-based health check-up, with a 10-year follow-up. The AST/ALT ratio increased with increasing BNP levels. And multivariate logistic analysis showed that the AST/ALT ratio was significantly associated with a high BNP (≥100 pg/mL). There were 250 all-cause deaths including 79 cardiovascular deaths. Multivariate Cox proportional hazard regression analysis revealed that a high AST/ALT ratio (>90 percentile) was an independent predictor of all-cause and cardiovascular mortality after adjustment for confounding factors. Kaplan-Meier analysis demonstrated that cardiovascular mortality was higher in subjects with a high AST/ALT ratio than in those without. Conclusions. The AST/ALT ratio was associated with an increase in BNP and was predictive of cardiovascular mortality in a general population. Measuring the AST/ALT ratio during routine health check-ups may be a simple and cost-effective marker for cardiovascular mortality. PMID:27872510

Cardiovascular reactivity patterns and pathways to hypertension: a multivariate cluster analysis.

PubMed

Brindle, R C; Ginty, A T; Jones, A; Phillips, A C; Roseboom, T J; Carroll, D; Painter, R C; de Rooij, S R

2016-12-01

Substantial evidence links exaggerated mental stress induced blood pressure reactivity to future hypertension, but the results for heart rate reactivity are less clear. For this reason multivariate cluster analysis was carried out to examine the relationship between heart rate and blood pressure reactivity patterns and hypertension in a large prospective cohort (age range 55-60 years). Four clusters emerged with statistically different systolic and diastolic blood pressure and heart rate reactivity patterns. Cluster 1 was characterised by a relatively exaggerated blood pressure and heart rate response while the blood pressure and heart rate responses of cluster 2 were relatively modest and in line with the sample mean. Cluster 3 was characterised by blunted cardiovascular stress reactivity across all variables and cluster 4, by an exaggerated blood pressure response and modest heart rate response. Membership to cluster 4 conferred an increased risk of hypertension at 5-year follow-up (hazard ratio=2.98 (95% CI: 1.50-5.90), P<0.01) that survived adjustment for a host of potential confounding variables. These results suggest that the cardiac reactivity plays a potentially important role in the link between blood pressure reactivity and hypertension and support the use of multivariate approaches to stress psychophysiology.

Experimental analysis of multivariate female choice in gray treefrogs (Hyla versicolor): evidence for directional and stabilizing selection.

PubMed

Gerhardt, H Carl; Brooks, Robert

2009-10-01

Even simple biological signals vary in several measurable dimensions. Understanding their evolution requires, therefore, a multivariate understanding of selection, including how different properties interact to determine the effectiveness of the signal. We combined experimental manipulation with multivariate selection analysis to assess female mate choice on the simple trilled calls of male gray treefrogs. We independently and randomly varied five behaviorally relevant acoustic properties in 154 synthetic calls. We compared response times of each of 154 females to one of these calls with its response to a standard call that had mean values of the five properties. We found directional and quadratic selection on two properties indicative of the amount of signaling, pulse number, and call rate. Canonical rotation of the fitness surface showed that these properties, along with pulse rate, contributed heavily to a major axis of stabilizing selection, a result consistent with univariate studies showing diminishing effects of increasing pulse number well beyond the mean. Spectral properties contributed to a second major axis of stabilizing selection. The single major axis of disruptive selection suggested that a combination of two temporal and two spectral properties with values differing from the mean should be especially attractive.

[Temporary employment and health: a multivariate analysis of occupational injury risk by job tenure].

PubMed

Bena, Antonella; Giraudo, Massimiliano

2013-01-01

To study the relationship between job tenure and injury risk, controlling for individual factors and company characteristics. Analysis of incidence and injury risk by job tenure, controlling for gender, age, nationality, economic activity, firm size. Sample of 7% of Italian workers registered in the INPS (National Institute of Social Insurance) database. Private sector employees who worked as blue collars or apprentices. First-time occupational injuries, all occupational injuries, serious occupational injuries. Our findings show an increase in injury risk among those who start a new job and an inverse relationship between job tenure and injury risk. Multivariate analysis confirm these results. Recommendations for improving this situation include the adoption of organizational models that provide periods of mentoring from colleagues already in the company and the assignment to simple and not much hazardous tasks. The economic crisis may exacerbate this problem: it is important for Italy to improve the systems of monitoring relations between temporary employment and health.

Adverse effect of splenectomy on recurrence in total gastrectomy cancer patients with perioperative transfusion.

PubMed

Shen, Jian Guo; Cheong, Jae Ho; Hyung, Woo Jin; Kim, Junuk; Choi, Seung Ho; Noh, Sung Hoon

2006-09-01

To investigate the interactions between splenectomy and perioperative transfusion in gastric cancer patients. Medical records of 449 gastric cancer patients who had undergone total gastrectomies for curative intent between 1991 and 1995 were reviewed. The influence of splenectomy on tumor recurrence and survival both in the transfused and nontransfused patients were evaluated by univariate and multivariate analysis. The recurrence rate in the splenectomy group was 48.1% as compared with 22.6% in the spleen-preserved group among transfused patients (P=.001); it was 40.7% compared with 26.5% among nontransfused patients (P=.086). There was no significant difference in the mean survival between the splenectomy group and the spleen-preserved group in a subgroup analysis by stage. Multivariate analysis identified splenectomy as an independent risk factor for recurrence but not as a predictor for survival among transfused patients. Splenectomy does not appear to abrogate the adverse effect of perioperative transfusion on prognosis in gastric cancer patients. Moreover, it may increase postoperative recurrence in transfused patients.

Hypothyroidism among SLE patients: Case-control study.

PubMed

Watad, Abdulla; Mahroum, Naim; Whitby, Aaron; Gertel, Smadar; Comaneshter, Doron; Cohen, Arnon D; Amital, Howard

2016-05-01

The prevalence of hypothyroidism in SLE patients varies considerably and early reports were mainly based on small cohorts. To investigate the association between SLE and hypothyroidism. Patients with SLE were compared with age and sex-matched controls regarding the proportion of hypothyroidism in a case-control study. Chi-square and t-tests were used for univariate analysis and a logistic regression model was used for multivariate analysis. The study was performed utilizing the medical database of Clalit Health Services. The study included 5018 patients with SLE and 25,090 age and sex-matched controls. The proportion of hypothyroidism in patients with SLE was increased compared with the prevalence in controls (15.58% and 5.75%, respectively, P<0.001). In a multivariate analysis, SLE was associated with hypothyroidism (odds ratio 2.644, 95% confidence interval 2.405-2.908). Patients with SLE have a greater proportion of hypothyroidism than matched controls. Therefore, physicians treating patients with SLE should be aware of the possibility of thyroid dysfunction. Copyright © 2016 Elsevier B.V. All rights reserved.

The impact of multiple endpoint dependency on Q and I(2) in meta-analysis.

PubMed

Thompson, Christopher Glen; Becker, Betsy Jane

2014-09-01

A common assumption in meta-analysis is that effect sizes are independent. When correlated effect sizes are analyzed using traditional univariate techniques, this assumption is violated. This research assesses the impact of dependence arising from treatment-control studies with multiple endpoints on homogeneity measures Q and I(2) in scenarios using the unbiased standardized-mean-difference effect size. Univariate and multivariate meta-analysis methods are examined. Conditions included different overall outcome effects, study sample sizes, numbers of studies, between-outcomes correlations, dependency structures, and ways of computing the correlation. The univariate approach used typical fixed-effects analyses whereas the multivariate approach used generalized least-squares (GLS) estimates of a fixed-effects model, weighted by the inverse variance-covariance matrix. Increased dependence among effect sizes led to increased Type I error rates from univariate models. When effect sizes were strongly dependent, error rates were drastically higher than nominal levels regardless of study sample size and number of studies. In contrast, using GLS estimation to account for multiple-endpoint dependency maintained error rates within nominal levels. Conversely, mean I(2) values were not greatly affected by increased amounts of dependency. Last, we point out that the between-outcomes correlation should be estimated as a pooled within-groups correlation rather than using a full-sample estimator that does not consider treatment/control group membership. Copyright © 2014 John Wiley & Sons, Ltd.

Factors Influencing the Appearance of Oxaliplatin-Induced Allergy.

PubMed

Nishihara, Masayuki; Nishikura, Kyoko; Morikawa, Norimichi; Yokoyama, Shota

2017-01-01

Several studies reported that the administration of oxaliplatin often induced allergy, but few studies have analyzed the pathogenesis. In this study, we examined the relationship between the incidence of allergy and status of oxaliplatin administration, patient background, laboratory data, or combined drugs. The subjects were 144 patients with colorectal or gastric cancer in whom oxaliplatin administration was started and completed between 2010 and 2016. They were divided into 2 groups: allergy and non-allergy groups. We extracted important factors influencing its appearance using multivariate analysis, and analyzed items of which the influence was suggested, using receiver operating characteristic (ROC) analysis. In 11 patients (7.6%), allergy appeared. The median frequency of appearance was 9 times (range: 5-13), being similar to that previously reported. On multivariate analysis, albumin (Alb) was extracted as an important factor. The cut-off value of Alb for the risk of allergy was 4.1 g/dL. An increase in the number of protein conjugates may have increased the risk of functioning as a hapten. Furthermore, the results suggested that the more frequency of oxaliplatin administration might increase the incidence of allergy, although it was not extracted as an important factor. In addition to young and female patients, as previously indicated, careful follow-up may be necessary for those with an Alb level of ≥4.1 g/dL especially after the 6th course.

Folate Deficiency, Atopy, and Severe Asthma Exacerbations in Puerto Rican Children

PubMed Central

Blatter, Joshua; Brehm, John M.; Sordillo, Joanne; Forno, Erick; Boutaoui, Nadia; Acosta-Pérez, Edna; Alvarez, María; Colón-Semidey, Angel; Weiss, Scott T.; Litonjua, Augusto A.; Canino, Glorisa

2016-01-01

Background: Little is known about folate and atopy or severe asthma exacerbations. We examined whether folate deficiency is associated with number of positive skin tests to allergens or severe asthma exacerbations in a high-risk population and further assessed whether such association is explained or modified by vitamin D status. Methods: Cross-sectional study of 582 children aged 6 to 14 years with (n = 304) and without (n = 278) asthma in San Juan, Puerto Rico. Folate deficiency was defined as plasma folate less than or equal to 20 ng/ml. Our outcomes were the number of positive skin tests to allergens (range, 0–15) in all children and (in children with asthma) one or more severe exacerbations in the previous year. Logistic and negative binomial regression models were used for the multivariate analysis. All multivariate models were adjusted for age, sex, household income, residential proximity to a major road, and (for atopy) case/control status; those for severe exacerbations were also adjusted for use of inhaled corticosteroids and vitamin D insufficiency (a plasma 25[OH]D < 30 ng/ml). Measurements and Main Results: In a multivariate analysis, folate deficiency was significantly associated with an increased degree of atopy and 2.2 times increased odds of at least one severe asthma exacerbation (95% confidence interval for odds ratio, 1.1–4.6). Compared with children who had normal levels of both folate and vitamin D, those with both folate deficiency and vitamin D insufficiency had nearly eightfold increased odds of one or more severe asthma exacerbation (95% confidence interval for adjusted odds ratio, 2.7–21.6). Conclusions: Folate deficiency is associated with increased degree of atopy and severe asthma exacerbations in school-aged Puerto Ricans. Vitamin D insufficiency may further increase detrimental effects of folate deficiency on severe asthma exacerbations. PMID:26561879

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.

Multivariate statistical process control (MSPC) using Raman spectroscopy for in-line culture cell monitoring considering time-varying batches synchronized with correlation optimized warping (COW).

PubMed

Liu, Ya-Juan; André, Silvère; Saint Cristau, Lydia; Lagresle, Sylvain; Hannas, Zahia; Calvosa, Éric; Devos, Olivier; Duponchel, Ludovic

2017-02-01

Multivariate statistical process control (MSPC) is increasingly popular as the challenge provided by large multivariate datasets from analytical instruments such as Raman spectroscopy for the monitoring of complex cell cultures in the biopharmaceutical industry. However, Raman spectroscopy for in-line monitoring often produces unsynchronized data sets, resulting in time-varying batches. Moreover, unsynchronized data sets are common for cell culture monitoring because spectroscopic measurements are generally recorded in an alternate way, with more than one optical probe parallelly connecting to the same spectrometer. Synchronized batches are prerequisite for the application of multivariate analysis such as multi-way principal component analysis (MPCA) for the MSPC monitoring. Correlation optimized warping (COW) is a popular method for data alignment with satisfactory performance; however, it has never been applied to synchronize acquisition time of spectroscopic datasets in MSPC application before. In this paper we propose, for the first time, to use the method of COW to synchronize batches with varying durations analyzed with Raman spectroscopy. In a second step, we developed MPCA models at different time intervals based on the normal operation condition (NOC) batches synchronized by COW. New batches are finally projected considering the corresponding MPCA model. We monitored the evolution of the batches using two multivariate control charts based on Hotelling's T 2 and Q. As illustrated with results, the MSPC model was able to identify abnormal operation condition including contaminated batches which is of prime importance in cell culture monitoring We proved that Raman-based MSPC monitoring can be used to diagnose batches deviating from the normal condition, with higher efficacy than traditional diagnosis, which would save time and money in the biopharmaceutical industry. Copyright © 2016 Elsevier B.V. All rights reserved.

Expression of Tissue Factor in Epithelial Ovarian Carcinoma Is Involved in the Development of Venous Thromboembolism.

PubMed

Sakurai, Manabu; Matsumoto, Koji; Gosho, Masahiko; Sakata, Akiko; Hosokawa, Yoshihiko; Tenjimbayashi, Yuri; Katoh, Takashi; Shikama, Ayumi; Komiya, Haruna; Michikami, Hiroo; Tasaka, Nobutaka; Akiyama-Abe, Azusa; Nakao, Sari; Ochi, Hiroyuki; Onuki, Mamiko; Minaguchi, Takeo; Yoshikawa, Hiroyuki; Satoh, Toyomi

2017-01-01

Our 2007 study of 32 patients with ovarian cancer reported the possible involvement of tissue factor (TF) in the development of venous thromboembolism (VTE) before treatment, especially in clear cell carcinoma (CCC). This follow-up study further investigated this possibility in a larger cohort. We investigated the intensity of TF expression (ITFE) and other variables for associations with VTE using univariate and multivariate analyses in 128 patients with epithelial ovarian cancer initially treated between November 2004 and December 2010, none of whom had received neoadjuvant chemotherapy. Before starting treatment, all patients were ultrasonographically screened for VTE. The ITFE was graded based on immunostaining of surgical specimens. Histological types were serous carcinoma (n = 42), CCC (n = 12), endometrioid carcinoma (n = 15), mucinous carcinoma (n = 53), and undifferentiated carcinoma (n = 6). The prevalence of VTE was significantly higher in CCC (34%) than in non-CCC (17%, P = 0.03). As ITFE increased, the frequencies of CCC and VTE increased significantly (P < 0.001 and P = 0.014, respectively). Multivariate analysis identified TF expression and pretreatment dimerized plasmin fragment D level as significant independent risk factors for VTE development. These factors showed particularly strong impacts on advanced-stage disease (P = 0.021). The 2007 cohort was small, preventing multivariate analysis. This study of a larger cohort yielded stronger evidence that the development of VTE in epithelial ovarian cancer may involve TF expression in cancer tissues.

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.

Concomitant Mediastinoscopy Increases the Risk of Postoperative Pneumonia After Pulmonary Lobectomy.

PubMed

Yendamuri, Sai; Battoo, Athar; Attwood, Kris; Dhillon, Samjot Singh; Dy, Grace K; Hennon, Mark; Picone, Anthony; Nwogu, Chukwumere; Demmy, Todd; Dexter, Elisabeth

2018-05-01

Mediastinoscopy is considered the gold standard for preresectional staging of lung cancer. We sought to examine the effect of concomitant mediastinoscopy on postoperative pneumonia (POP) in patients undergoing lobectomy. All patients in our institutional database (2008-2015) undergoing lobectomy who did not receive neoadjuvant therapy were included in our study. The relationship between mediastinoscopy and POP was examined using univariate (Chi square) and multivariate analyses (binary logistic regression). In order to validate our institutional findings, lobectomy data in the National Surgical Quality Improvement Program (NSQIP) from 2005 to 2014 were analyzed for these associations. Of 810 patients who underwent a lobectomy at our institution, 741 (91.5%) surgeries were performed by video-assisted thoracic surgery (VATS) and 487 (60.1%) patients underwent concomitant mediastinoscopy. Univariate analysis demonstrated an association between mediastinoscopy and POP in patients undergoing VATS [odds ratio (OR) 1.80; p = 0.003], but not open lobectomy. Multivariate analysis retained mediastinoscopy as a variable, although the relationship showed only a trend (OR 1.64; p = 0.1). In the NSQIP cohort (N = 12,562), concomitant mediastinoscopy was performed in 9.0% of patients, with 44.5% of all the lobectomies performed by VATS. Mediastinoscopy was associated with POP in patients having both open (OR1.69; p < 0.001) and VATS lobectomy (OR 1.72; p = 0.002). This effect remained in multivariate analysis in both the open and VATS lobectomy groups (OR 1.46, p = 0.003; and 1.53, p = 0.02, respectively). Mediastinoscopy may be associated with an increased risk of POP after pulmonary lobectomy. This observation should be examined in other datasets as it potentially impacts preresectional staging algorithms for patients with lung cancer.

Conducting Privacy-Preserving Multivariable Propensity Score Analysis When Patient Covariate Information Is Stored in Separate Locations.

PubMed

Bohn, Justin; Eddings, Wesley; Schneeweiss, Sebastian

2017-03-15

Distributed networks of health-care data sources are increasingly being utilized to conduct pharmacoepidemiologic database studies. Such networks may contain data that are not physically pooled but instead are distributed horizontally (separate patients within each data source) or vertically (separate measures within each data source) in order to preserve patient privacy. While multivariable methods for the analysis of horizontally distributed data are frequently employed, few practical approaches have been put forth to deal with vertically distributed health-care databases. In this paper, we propose 2 propensity score-based approaches to vertically distributed data analysis and test their performance using 5 example studies. We found that these approaches produced point estimates close to what could be achieved without partitioning. We further found a performance benefit (i.e., lower mean squared error) for sequentially passing a propensity score through each data domain (called the "sequential approach") as compared with fitting separate domain-specific propensity scores (called the "parallel approach"). These results were validated in a small simulation study. This proof-of-concept study suggests a new multivariable analysis approach to vertically distributed health-care databases that is practical, preserves patient privacy, and warrants further investigation for use in clinical research applications that rely on health-care databases. © The Author 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Is there a relationship between periodontal conditions and number of medications among the elderly?

PubMed

Natto, Zuhair S; Aladmawy, Majdi; Alshaeri, Heba K; Alasqah, Mohammed; Papas, Athena

2016-03-01

To investigate possible correlations of clinical attachment level and pocket depth with number of medications in elderly individuals. Intra-oral examinations for 139 patients visiting Tufts dental clinic were done. Periodontal assessments were performed with a manual UNC-15 periodontal probe to measure probing depth (PD) and clinical attachment level (CAL) at 6 sites. Complete lists of patients' medications were obtained during the examinations. Statistical analysis involved Kruskal-Wallis, chi square and multivariate logistic regression analyses. Age and health status attained statistical significance (p< 0.05), in contingency table analysis with number of medications. Number of medications had an effect on CAL: increased attachment loss was observed when 4 or more medications were being taken by the patient. Number of medications did not have any effect on periodontal PD. In multivariate logistic regression analysis, 6 or more medications had a higher risk of attachment loss (>3mm) when compared to the no-medication group, in crude OR (1.20, 95% CI:0.22-6.64), and age adjusted (OR=1.16, 95% CI:0.21-6.45), but not with the multivariate model (OR=0.71, 95% CI:0.11-4.39). CAL seems to be more sensitive to the number of medications taken, when compared to PD. However, it is not possible to discriminate at exactly what number of drug combinations the breakdown in CAL will happen. We need to do further analysis, including more subjects, to understand the possible synergistic mechanisms for different drug and periodontal responses.

Cytologic separation of branchial cleft cyst from metastatic cystic squamous cell carcinoma: A multivariate analysis of nineteen cytomorphologic features.

PubMed

Layfield, Lester J; Esebua, Magda; Schmidt, Robert L

2016-07-01

The separation of branchial cleft cysts from metastatic cystic squamous cell carcinomas in adults can be clinically and cytologically challenging. Diagnostic accuracy for separation is reported to be as low as 75% prompting some authors to recommend frozen section evaluation of suspected branchial cleft cysts before resection. We evaluated 19 cytologic features to determine which were useful in this distinction. Thirty-three cases (21 squamous carcinoma and 12 branchial cysts) of histologically confirmed cystic lesions of the lateral neck were graded for the presence or absence of 19 cytologic features by two cytopathologists. The cytologic features were analyzed for agreement between observers and underwent multivariate analysis for correlation with the diagnosis of carcinoma. Interobserver agreement was greatest for increased nuclear/cytoplasmic (N/C) ratio, pyknotic nuclei, and irregular nuclear membranes. Recursive partitioning analysis showed increased N/C ratio, small clusters of cells, and irregular nuclear membranes were the best discriminators. The distinction of branchial cleft cysts from cystic squamous cell carcinoma is cytologically difficult. Both digital image analysis and p16 testing have been suggested as aids in this separation, but analysis of cytologic features remains the main method for diagnosis. In an analysis of 19 cytologic features, we found that high nuclear cytoplasmic ratio, irregular nuclear membranes, and small cell clusters were most helpful in their distinction. Diagn. Cytopathol. 2016;44:561-567. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Instrumental Neutron Activation Analysis and Multivariate Statistics for Pottery Provenance

NASA Astrophysics Data System (ADS)

Glascock, M. D.; Neff, H.; Vaughn, K. J.

2004-06-01

The application of instrumental neutron activation analysis and multivariate statistics to archaeological studies of ceramics and clays is described. A small pottery data set from the Nasca culture in southern Peru is presented for illustration.

A Study of Effects of MultiCollinearity in the Multivariable Analysis

PubMed Central

Yoo, Wonsuk; Mayberry, Robert; Bae, Sejong; Singh, Karan; (Peter) He, Qinghua; Lillard, James W.

2015-01-01

A multivariable analysis is the most popular approach when investigating associations between risk factors and disease. However, efficiency of multivariable analysis highly depends on correlation structure among predictive variables. When the covariates in the model are not independent one another, collinearity/multicollinearity problems arise in the analysis, which leads to biased estimation. This work aims to perform a simulation study with various scenarios of different collinearity structures to investigate the effects of collinearity under various correlation structures amongst predictive and explanatory variables and to compare these results with existing guidelines to decide harmful collinearity. Three correlation scenarios among predictor variables are considered: (1) bivariate collinear structure as the most simple collinearity case, (2) multivariate collinear structure where an explanatory variable is correlated with two other covariates, (3) a more realistic scenario when an independent variable can be expressed by various functions including the other variables. PMID:25664257

A Study of Effects of MultiCollinearity in the Multivariable Analysis.

PubMed

Yoo, Wonsuk; Mayberry, Robert; Bae, Sejong; Singh, Karan; Peter He, Qinghua; Lillard, James W

2014-10-01

A multivariable analysis is the most popular approach when investigating associations between risk factors and disease. However, efficiency of multivariable analysis highly depends on correlation structure among predictive variables. When the covariates in the model are not independent one another, collinearity/multicollinearity problems arise in the analysis, which leads to biased estimation. This work aims to perform a simulation study with various scenarios of different collinearity structures to investigate the effects of collinearity under various correlation structures amongst predictive and explanatory variables and to compare these results with existing guidelines to decide harmful collinearity. Three correlation scenarios among predictor variables are considered: (1) bivariate collinear structure as the most simple collinearity case, (2) multivariate collinear structure where an explanatory variable is correlated with two other covariates, (3) a more realistic scenario when an independent variable can be expressed by various functions including the other variables.

Time-varying correlations in global real estate markets: A multivariate GARCH with spatial effects approach

NASA Astrophysics Data System (ADS)

Gu, Huaying; Liu, Zhixue; Weng, Yingliang

2017-04-01

The present study applies the multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) with spatial effects approach for the analysis of the time-varying conditional correlations and contagion effects among global real estate markets. A distinguishing feature of the proposed model is that it can simultaneously capture the spatial interactions and the dynamic conditional correlations compared with the traditional MGARCH models. Results reveal that the estimated dynamic conditional correlations have exhibited significant increases during the global financial crisis from 2007 to 2009, thereby suggesting contagion effects among global real estate markets. The analysis further indicates that the returns of the regional real estate markets that are in close geographic and economic proximities exhibit strong co-movement. In addition, evidence of significantly positive leverage effects in global real estate markets is also determined. The findings have significant implications on global portfolio diversification opportunities and risk management practices.

Geographically Sourcing Cocaine's Origin - Delineation of the Nineteen Major Coca Growing Regions in South America.

PubMed

Mallette, Jennifer R; Casale, John F; Jordan, James; Morello, David R; Beyer, Paul M

2016-03-23

Previously, geo-sourcing to five major coca growing regions within South America was accomplished. However, the expansion of coca cultivation throughout South America made sub-regional origin determinations increasingly difficult. The former methodology was recently enhanced with additional stable isotope analyses ((2)H and (18)O) to fully characterize cocaine due to the varying environmental conditions in which the coca was grown. An improved data analysis method was implemented with the combination of machine learning and multivariate statistical analysis methods to provide further partitioning between growing regions. Here, we show how the combination of trace cocaine alkaloids, stable isotopes, and multivariate statistical analyses can be used to classify illicit cocaine as originating from one of 19 growing regions within South America. The data obtained through this approach can be used to describe current coca cultivation and production trends, highlight trafficking routes, as well as identify new coca growing regions.

Geographically Sourcing Cocaine’s Origin - Delineation of the Nineteen Major Coca Growing Regions in South America

NASA Astrophysics Data System (ADS)

Mallette, Jennifer R.; Casale, John F.; Jordan, James; Morello, David R.; Beyer, Paul M.

2016-03-01

Previously, geo-sourcing to five major coca growing regions within South America was accomplished. However, the expansion of coca cultivation throughout South America made sub-regional origin determinations increasingly difficult. The former methodology was recently enhanced with additional stable isotope analyses (2H and 18O) to fully characterize cocaine due to the varying environmental conditions in which the coca was grown. An improved data analysis method was implemented with the combination of machine learning and multivariate statistical analysis methods to provide further partitioning between growing regions. Here, we show how the combination of trace cocaine alkaloids, stable isotopes, and multivariate statistical analyses can be used to classify illicit cocaine as originating from one of 19 growing regions within South America. The data obtained through this approach can be used to describe current coca cultivation and production trends, highlight trafficking routes, as well as identify new coca growing regions.

Quantifying uncertainty in high-resolution coupled hydrodynamic-ecosystem models

NASA Astrophysics Data System (ADS)

Allen, J. I.; Somerfield, P. J.; Gilbert, F. J.

2007-01-01

Marine ecosystem models are becoming increasingly complex and sophisticated, and are being used to estimate the effects of future changes in the earth system with a view to informing important policy decisions. Despite their potential importance, far too little attention has been, and is generally, paid to model errors and the extent to which model outputs actually relate to real-world processes. With the increasing complexity of the models themselves comes an increasing complexity among model results. If we are to develop useful modelling tools for the marine environment we need to be able to understand and quantify the uncertainties inherent in the simulations. Analysing errors within highly multivariate model outputs, and relating them to even more complex and multivariate observational data, are not trivial tasks. Here we describe the application of a series of techniques, including a 2-stage self-organising map (SOM), non-parametric multivariate analysis, and error statistics, to a complex spatio-temporal model run for the period 1988-1989 in the Southern North Sea, coinciding with the North Sea Project which collected a wealth of observational data. We use model output, large spatio-temporally resolved data sets and a combination of methodologies (SOM, MDS, uncertainty metrics) to simplify the problem and to provide tractable information on model performance. The use of a SOM as a clustering tool allows us to simplify the dimensions of the problem while the use of MDS on independent data grouped according to the SOM classification allows us to validate the SOM. The combination of classification and uncertainty metrics allows us to pinpoint the variables and associated processes which require attention in each region. We recommend the use of this combination of techniques for simplifying complex comparisons of model outputs with real data, and analysis of error distributions.

Sparse multivariate factor analysis regression models and its applications to integrative genomics analysis.

PubMed

Zhou, Yan; Wang, Pei; Wang, Xianlong; Zhu, Ji; Song, Peter X-K

2017-01-01

The multivariate regression model is a useful tool to explore complex associations between two kinds of molecular markers, which enables the understanding of the biological pathways underlying disease etiology. For a set of correlated response variables, accounting for such dependency can increase statistical power. Motivated by integrative genomic data analyses, we propose a new methodology-sparse multivariate factor analysis regression model (smFARM), in which correlations of response variables are assumed to follow a factor analysis model with latent factors. This proposed method not only allows us to address the challenge that the number of association parameters is larger than the sample size, but also to adjust for unobserved genetic and/or nongenetic factors that potentially conceal the underlying response-predictor associations. The proposed smFARM is implemented by the EM algorithm and the blockwise coordinate descent algorithm. The proposed methodology is evaluated and compared to the existing methods through extensive simulation studies. Our results show that accounting for latent factors through the proposed smFARM can improve sensitivity of signal detection and accuracy of sparse association map estimation. We illustrate smFARM by two integrative genomics analysis examples, a breast cancer dataset, and an ovarian cancer dataset, to assess the relationship between DNA copy numbers and gene expression arrays to understand genetic regulatory patterns relevant to the disease. We identify two trans-hub regions: one in cytoband 17q12 whose amplification influences the RNA expression levels of important breast cancer genes, and the other in cytoband 9q21.32-33, which is associated with chemoresistance in ovarian cancer. © 2016 WILEY PERIODICALS, INC.

Influence of Time-Series Normalization, Number of Nodes, Connectivity and Graph Measure Selection on Seizure-Onset Zone Localization from Intracranial EEG.

PubMed

van Mierlo, Pieter; Lie, Octavian; Staljanssens, Willeke; Coito, Ana; Vulliémoz, Serge

2018-04-26

We investigated the influence of processing steps in the estimation of multivariate directed functional connectivity during seizures recorded with intracranial EEG (iEEG) on seizure-onset zone (SOZ) localization. We studied the effect of (i) the number of nodes, (ii) time-series normalization, (iii) the choice of multivariate time-varying connectivity measure: Adaptive Directed Transfer Function (ADTF) or Adaptive Partial Directed Coherence (APDC) and (iv) graph theory measure: outdegree or shortest path length. First, simulations were performed to quantify the influence of the various processing steps on the accuracy to localize the SOZ. Afterwards, the SOZ was estimated from a 113-electrodes iEEG seizure recording and compared with the resection that rendered the patient seizure-free. The simulations revealed that ADTF is preferred over APDC to localize the SOZ from ictal iEEG recordings. Normalizing the time series before analysis resulted in an increase of 25-35% of correctly localized SOZ, while adding more nodes to the connectivity analysis led to a moderate decrease of 10%, when comparing 128 with 32 input nodes. The real-seizure connectivity estimates localized the SOZ inside the resection area using the ADTF coupled to outdegree or shortest path length. Our study showed that normalizing the time-series is an important pre-processing step, while adding nodes to the analysis did only marginally affect the SOZ localization. The study shows that directed multivariate Granger-based connectivity analysis is feasible with many input nodes (> 100) and that normalization of the time-series before connectivity analysis is preferred.

The International Scoring System (ISS) for multiple myeloma remains a robust prognostic tool independently of patients' renal function.

PubMed

Dimopoulos, M A; Kastritis, E; Michalis, E; Tsatalas, C; Michael, M; Pouli, A; Kartasis, Z; Delimpasi, S; Gika, D; Zomas, A; Roussou, M; Konstantopoulos, K; Parcharidou, A; Zervas, K; Terpos, E

2012-03-01

The International Staging System (ISS) is the most widely used staging system for patients with multiple myeloma (MM). However, serum β2-microglobulin increases in renal impairment (RI) and there have been concerns that ISS-3 stage may include 'up-staged' MM patients in whom elevated β2-microglobulin reflects the degree of renal dysfunction rather than tumor load. In order to assess the impact of RI on the prognostic value of ISS, we analyzed 1516 patients with symptomatic MM and the degree of RI was classified according to the Kidney Disease Outcomes Quality Initiative-Chronic Kidney Disease (CKD) criteria. Forty-eight percent patients had stages 3-5 CKD while 29% of patients had ISS-1, 38% had ISS-2 and 33% ISS-3. The frequency and severity of RI were more common in ISS-3 patients. RI was associated with inferior survival in univariate but not in multivariate analysis. When analyzed separately, ISS-1 and ISS-2 patients with RI had inferior survival in univariate but not in multivariate analysis. In ISS-3 MM patients, RI had no prognostic impact either in univariate or multivariate analysis. Results were similar, when we analyzed only patients with Bence-Jones >200 mg/day. ISS remains unaffected by the degree of RI, even in patients with ISS-3, which includes most patients with renal dysfunction.

A population-based study of the association of medical manpower with county trauma death rates in the United States.

PubMed Central

Rutledge, R; Fakhry, S M; Baker, C C; Weaver, N; Ramenofsky, M; Sheldon, G F; Meyer, A A

1994-01-01

OBJECTIVE: To determine the association between measures of medical manpower available to treat trauma patients and county trauma death rates in the United States. The primary hypothesis was that greater availability of medical manpower to treat trauma injury would be associated with lower trauma death rates. SUMMARY BACKGROUND DATA: When viewed from the standpoint of the number of productive years of life lost, trauma has a greater effect on health care and lost productivity in the United States than any disease. Allocation of health care manpower to treat injuries seems logical, but studies have not been done to determine its efficacy. The effect of medical manpower and hospital resource allocation on the outcome of injury in the United States has not been fully explored or adequately evaluated. METHODS: Data on trauma deaths in the United States were obtained from the National Center for Health Statistics. Data on the number of surgeons and emergency medicine physicians were obtained from the American Hospital Association and the American Medical Association. Data on physicians who have participated in the American College of Surgeons (ACS) Advanced Trauma Life Support Course (ATLS) were obtained from the ACS. Membership information for the American Association for Surgery of Trauma (AAST) was obtained from that organization. Demographic data were obtained from the United States Census Bureau. Multivariate stepwise linear regression and cluster analysis were used to model the county trauma death rates in the United States. The Statistical Analysis System (Cary, NC) for statistical analysis was used. RESULTS: Bivariate and multivariate analyses showed that a variety of medical manpower measures and demographic factors were associated with county trauma death rates in the United States. As in other studies, measures of low population density and high levels of poverty were found to be strongly associated with increased trauma death rates. After accounting for these variables, using multivariate analysis and cluster analysis, an increase in the following medical manpower measures were associated with decreased county trauma death rates: number of board-certified general surgeons, number of board-certified emergency medicine physicians, number of AAST members, and number of ATLS-trained physicians. CONCLUSIONS: This study confirms previous work that showed a strong relation among measures of poverty, rural setting, and increased county trauma death rates. It also found that counties with more board-certified surgeons per capita and with more surgeons with an increased interest (AAST membership) or increased training (ATLS) in trauma care have lower per-capita trauma death rates.(ABSTRACT TRUNCATED AT 400 WORDS) Images Figure 1. PMID:8185404

Multivariate frequency domain analysis of protein dynamics

NASA Astrophysics Data System (ADS)

Matsunaga, Yasuhiro; Fuchigami, Sotaro; Kidera, Akinori

2009-03-01

Multivariate frequency domain analysis (MFDA) is proposed to characterize collective vibrational dynamics of protein obtained by a molecular dynamics (MD) simulation. MFDA performs principal component analysis (PCA) for a bandpass filtered multivariate time series using the multitaper method of spectral estimation. By applying MFDA to MD trajectories of bovine pancreatic trypsin inhibitor, we determined the collective vibrational modes in the frequency domain, which were identified by their vibrational frequencies and eigenvectors. At near zero temperature, the vibrational modes determined by MFDA agreed well with those calculated by normal mode analysis. At 300 K, the vibrational modes exhibited characteristic features that were considerably different from the principal modes of the static distribution given by the standard PCA. The influences of aqueous environments were discussed based on two different sets of vibrational modes, one derived from a MD simulation in water and the other from a simulation in vacuum. Using the varimax rotation, an algorithm of the multivariate statistical analysis, the representative orthogonal set of eigenmodes was determined at each vibrational frequency.

Imaging of polysaccharides in the tomato cell wall with Raman microspectroscopy

PubMed Central

2014-01-01

Background The primary cell wall of fruits and vegetables is a structure mainly composed of polysaccharides (pectins, hemicelluloses, cellulose). Polysaccharides are assembled into a network and linked together. It is thought that the percentage of components and of plant cell wall has an important influence on mechanical properties of fruits and vegetables. Results In this study the Raman microspectroscopy technique was introduced to the visualization of the distribution of polysaccharides in cell wall of fruit. The methodology of the sample preparation, the measurement using Raman microscope and multivariate image analysis are discussed. Single band imaging (for preliminary analysis) and multivariate image analysis methods (principal component analysis and multivariate curve resolution) were used for the identification and localization of the components in the primary cell wall. Conclusions Raman microspectroscopy supported by multivariate image analysis methods is useful in distinguishing cellulose and pectins in the cell wall in tomatoes. It presents how the localization of biopolymers was possible with minimally prepared samples. PMID:24917885

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 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

Use of nitrates in ischemic heart disease.

PubMed

Giuseppe, Cocco; Paul, Jerie; Hans-Ulrich, Iselin

2015-01-01

Short-acting nitrates are beneficial in acute myocardial ischemia. However, many unresolved questions remain about the use of long-acting nitrates in stable ischemic heart disease. The use of long-acting nitrates is weakened by the development of endothelial dysfunction and tolerance. Also, we currently ignore whether lower doses of transdermal nitroglycerin would be better than those presently used. Multivariate analysis data from large nonrandomized studies suggested that long-acting nitrates increase the incidence of acute coronary syndromes, while data from another multivariate study indicate that they have positive effects. Because of methodological differences and open questions, the two studies cannot be compared. A study in Japanese patients with vasospastic angina has shown that, when compared with calcium antagonists, long-acting nitrates do not improve long-term prognosis and that the risk for cardiac adverse events increases with the combined therapy. We have many unanswered questions.

Changes in the proportion of facility-based deliveries and related maternal health services among the poor in rural Jhang, Pakistan: results from a demand-side financing intervention.

PubMed

Agha, Sohail

2011-11-30

Demand-side financing projects are now being implemented in many developing countries, yet evidence showing that they reach the poor is scanty. A maternal health voucher scheme provided voucher-paid services in Jhang, a predominantly rural district of Pakistan, during 2010. A pre-test/post-test quasi-experimental design was used to assess the changes in the proportion of facility-based deliveries and related maternal health services among the poor. Household interviews were conducted with randomly selected women in the intervention and control union councils, before and after the intervention.A strong outreach model was used. Voucher promoters were given basic training in identification of poor women using the Poverty Scorecard for Pakistan, in the types of problems women could face during delivery, and in the promotion of antenatal care (ANC), institutional delivery and postnatal care (PNC). Voucher booklets valued at Rs. 4,000 ($48), including three ANC visits, a PNC visit, an institutional delivery, and a postnatal family planning visit, were sold for Rs. 100 ($1.2) to low-income women targeted by project outreach workers. Women suffering from complications were referred to emergency obstetric care services.Analysis was conducted at the bivariate and the multivariate levels. At the multivariate level, logistic regression analysis was conducted to determine whether the increase in institutional delivery was greater among poor women (defined for this study as women in the fourth or fifth quintiles) relative to non-poor women (defined for this study as women in the first quintile) in the intervention union councils compared to the control union councils. Bivariate analysis showed significant increases in the institutional delivery rate among women in the fourth or fifth wealth quintiles in the intervention union councils but no significant changes in this indicator among women in the same wealth quintiles in the control union councils. Multivariate analysis showed that the increase in institutional delivery among poor women relative to non-poor women was significantly greater in the intervention compared to the control union councils. Demand-side financing projects using vouchers can be an effective way of reducing inequities in institutional delivery.

Utilization of surgical treatment for local and locoregional esophageal cancer: Analysis of the National Cancer Data Base.

PubMed

Taylor, Lauren J; Greenberg, Caprice C; Lidor, Anne O; Leverson, Glen E; Maloney, James D; Macke, Ryan A

2017-02-01

Previous studies have suggested that esophagectomy is severely underused for patients with resectable esophageal cancer. The recent expansion of endoscopic local therapies, advances in surgical techniques, and improved postoperative outcomes have changed the therapeutic landscape. The impact of these developments and evolving treatment guidelines on national practice patterns is unknown. Patients diagnosed with clinical stage 0 to III esophageal cancer were identified from the National Cancer Database (2004-2013). The receipt of potentially curative surgical treatment over time was analyzed, and multivariate logistic regression was used to identify factors associated with surgical treatment. The analysis included 52,122 patients. From 2004 to 2013, the overall rate of potentially curative surgical treatment increased from 36.4% to 47.4% (P < .001). For stage 0 disease, the receipt of esophagectomy decreased from 23.8% to 17.9% (P < .001), whereas the use of local therapies increased from 34.3% to 58.8% (P < .001). The use of surgical treatment increased from 43.4% to 61.8% (P < .001), from 36.1% to 45.0% (P < .001), and from 30.8% to 38.6% (P < .001) for patients with stage I, II, and III disease, respectively. In the multivariate analysis, divergent practice patterns and adherence to national guidelines were noted between academic and community facilities. The use of potentially curative surgical treatment has increased for patients with stage 0 to III esophageal cancer. The expansion of local therapies has driven increased rates of surgical treatment for early-stage disease. Although the increased use of esophagectomy for more advanced disease is encouraging, significant variation persists at the patient and facility levels. Cancer 2017;123:410-419. © 2016 American Cancer Society. © 2016 American Cancer Society.

Job Performance as Multivariate Dynamic Criteria: Experience Sampling and Multiway Component Analysis

ERIC Educational Resources Information Center

Spain, Seth M.; Miner, Andrew G.; Kroonenberg, Pieter M.; Drasgow, Fritz

2010-01-01

Questions about the dynamic processes that drive behavior at work have been the focus of increasing attention in recent years. Models describing behavior at work and research on momentary behavior indicate that substantial variation exists within individuals. This article examines the rationale behind this body of work and explores a method of…

A Baseline for the Multivariate Comparison of Resting-State Networks

PubMed Central

Allen, Elena A.; Erhardt, Erik B.; Damaraju, Eswar; Gruner, William; Segall, Judith M.; Silva, Rogers F.; Havlicek, Martin; Rachakonda, Srinivas; Fries, Jill; Kalyanam, Ravi; Michael, Andrew M.; Caprihan, Arvind; Turner, Jessica A.; Eichele, Tom; Adelsheim, Steven; Bryan, Angela D.; Bustillo, Juan; Clark, Vincent P.; Feldstein Ewing, Sarah W.; Filbey, Francesca; Ford, Corey C.; Hutchison, Kent; Jung, Rex E.; Kiehl, Kent A.; Kodituwakku, Piyadasa; Komesu, Yuko M.; Mayer, Andrew R.; Pearlson, Godfrey D.; Phillips, John P.; Sadek, Joseph R.; Stevens, Michael; Teuscher, Ursina; Thoma, Robert J.; Calhoun, Vince D.

2011-01-01

As the size of functional and structural MRI datasets expands, it becomes increasingly important to establish a baseline from which diagnostic relevance may be determined, a processing strategy that efficiently prepares data for analysis, and a statistical approach that identifies important effects in a manner that is both robust and reproducible. In this paper, we introduce a multivariate analytic approach that optimizes sensitivity and reduces unnecessary testing. We demonstrate the utility of this mega-analytic approach by identifying the effects of age and gender on the resting-state networks (RSNs) of 603 healthy adolescents and adults (mean age: 23.4 years, range: 12–71 years). Data were collected on the same scanner, preprocessed using an automated analysis pipeline based in SPM, and studied using group independent component analysis. RSNs were identified and evaluated in terms of three primary outcome measures: time course spectral power, spatial map intensity, and functional network connectivity. Results revealed robust effects of age on all three outcome measures, largely indicating decreases in network coherence and connectivity with increasing age. Gender effects were of smaller magnitude but suggested stronger intra-network connectivity in females and more inter-network connectivity in males, particularly with regard to sensorimotor networks. These findings, along with the analysis approach and statistical framework described here, provide a useful baseline for future investigations of brain networks in health and disease. PMID:21442040

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.

Pleiotropy Analysis of Quantitative Traits at Gene Level by Multivariate Functional Linear Models

PubMed Central

Wang, Yifan; Liu, Aiyi; Mills, James L.; Boehnke, Michael; Wilson, Alexander F.; Bailey-Wilson, Joan E.; Xiong, Momiao; Wu, Colin O.; Fan, Ruzong

2015-01-01

In genetics, pleiotropy describes the genetic effect of a single gene on multiple phenotypic traits. A common approach is to analyze the phenotypic traits separately using univariate analyses and combine the test results through multiple comparisons. This approach may lead to low power. Multivariate functional linear models are developed to connect genetic variant data to multiple quantitative traits adjusting for covariates for a unified analysis. Three types of approximate F-distribution tests 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 in one genetic region. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and optimal sequence kernel association test (SKAT-O). Extensive simulations were performed to evaluate the false positive rates and power performance of the proposed models and tests. We show that the approximate F-distribution tests control the type I error rates very well. Overall, simultaneous analysis of multiple traits can increase power performance compared to an individual test of each trait. The proposed methods were applied to analyze (1) four lipid traits in eight European cohorts, and (2) three biochemical traits in the Trinity Students Study. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and SKAT-O for the three biochemical traits. The approximate F-distribution tests of the proposed functional linear models are more sensitive than those of the traditional multivariate linear models that in turn are more sensitive than SKAT-O in the univariate case. The analysis of the four lipid traits and the three biochemical traits detects more association than SKAT-O in the univariate case. PMID:25809955

Pleiotropy analysis of quantitative traits at gene level by multivariate functional linear models.

PubMed

Wang, Yifan; Liu, Aiyi; Mills, James L; Boehnke, Michael; Wilson, Alexander F; Bailey-Wilson, Joan E; Xiong, Momiao; Wu, Colin O; Fan, Ruzong

2015-05-01

In genetics, pleiotropy describes the genetic effect of a single gene on multiple phenotypic traits. A common approach is to analyze the phenotypic traits separately using univariate analyses and combine the test results through multiple comparisons. This approach may lead to low power. Multivariate functional linear models are developed to connect genetic variant data to multiple quantitative traits adjusting for covariates for a unified analysis. Three types of approximate F-distribution tests 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 in one genetic region. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and optimal sequence kernel association test (SKAT-O). Extensive simulations were performed to evaluate the false positive rates and power performance of the proposed models and tests. We show that the approximate F-distribution tests control the type I error rates very well. Overall, simultaneous analysis of multiple traits can increase power performance compared to an individual test of each trait. The proposed methods were applied to analyze (1) four lipid traits in eight European cohorts, and (2) three biochemical traits in the Trinity Students Study. The approximate F-distribution tests provide much more significant results than those of F-tests of univariate analysis and SKAT-O for the three biochemical traits. The approximate F-distribution tests of the proposed functional linear models are more sensitive than those of the traditional multivariate linear models that in turn are more sensitive than SKAT-O in the univariate case. The analysis of the four lipid traits and the three biochemical traits detects more association than SKAT-O in the univariate case. © 2015 WILEY PERIODICALS, INC.

Factors associated with sealant outcome in 2 pediatric dental clinics: a multivariate hierarchical analysis.

PubMed

West, Nathan G; Ilief-Ala, Melina A; Douglass, Joanna M; Hagadorn, James I

2011-01-01

This study's purpose was to determine whether one-time sealants placed by pediatric dental residents vs dental students have different outcomes. The effect of isolation technique, behavior, duration of follow-up, and caries history was also examined. Records from 2 inner-city pediatric dental clinics were audited for 6- to 10-year-old patients with a permanent first molar sealant with at least 2 years of follow-up. A successful sealant was a one-time sealant that received no further treatment and was sealed or unsealed but not carious or restored at the final audit. Charts from 203 children with 481 sealants were audited. Of these, 281 sealants were failures. Univariate analysis revealed longer follow-up and younger age were associated with sealant failure. Operator type, child behavior, and isolation technique were not associated with sealant failure. After adjusting for follow-up duration, increased age at treatment reduced the odds of sealant failure while a history of caries reduced the protective effect of increased age. After adjusting for these factors, practitioner type, behavior, and type of isolation were not associated with sealant outcome in multivariate analysis. Age at sealant placement, history of caries prior to placement, and longer duration of follow-up are associated with sealant failure.

National reimbursement listing determinants of new cancer drugs: a retrospective analysis of 58 cancer treatment appraisals in 2007-2016 in South Korea.

PubMed

Kim, Eun-Sook; Kim, Jung-Ae; Lee, Eui-Kyung

2017-08-01

Since the positive-list system was introduced, concerns have been raised over restricting access to new cancer drugs in Korea. Policy changes in the decision-making process, such as risk-sharing agreement and the waiver of pharmacoeconomic data submission, were implemented to improve access to oncology medicines, and other factors are also involved in the reimbursement for cancer drugs. The aim of this study is to investigate the reimbursement listing determinants of new cancer drugs in Korea. All cancer treatment appraisals of Health Insurance Review and Assessment during 2007-2016 were analyzed based on 13 independent variables (comparative effectiveness, cost-effectiveness, drug-price comparison, oncology-specific policy, and innovation such as new mode of action). Univariate and multivariate logistic analyses were conducted. Of 58 analyzed submissions, 40% were listed in the national reimbursement formulary. In univariate analysis, four variables were related to listing: comparative effectiveness, drug-price comparison, new mode of action, and risk-sharing agreement. In multivariate logistic analysis, three variables significantly increased the likelihood of listing: clinical improvement, below alternative's price, and risk-sharing arrangement. Cancer drug's listing increased from 17% to 47% after risk-sharing agreement implementation. Clinical improvement, cost-effectiveness, and RSA application are critical to successful national reimbursement listing.

Prevention of bleeding after islet transplantation: lessons learned from a multivariate analysis of 132 cases at a single institution.

PubMed

Villiger, P; Ryan, E A; Owen, R; O'Kelly, K; Oberholzer, J; Al Saif, F; Kin, T; Wang, H; Larsen, I; Blitz, S L; Menon, V; Senior, P; Bigam, D L; Paty, B; Kneteman, N M; Lakey, J R T; Shapiro, A M James

2005-12-01

Islet transplantation is being offered increasingly for selected patients with unstable type 1 diabetes. Percutaneous transhepatic portal access avoids a need for surgery, but is associated with potential risk of bleeding. Between 1999 and 2005, we performed 132 percutaneous transhepatic islet transplants in 67 patients. We encountered bleeding in 18/132 cases (13.6%). In univariate analysis, the risk of bleeding in the absence of effective track ablation was associated with an increasing number of procedures (2nd and 3rd procedures with an odds ratio (OR) of 9.5 and 20.9, respectively), platelets count <150,000 (OR 4.4), elevated portal pressure (OR 1.1 per mm Hg rise), heparin dose > or =45 U/kg (OR 9.8) and pre-transplant aspirin (81 mg per day) (OR 2.6, p = 0.05). A multivariate analysis further confirmed the cumulative transplant procedure number (p < 0.001) and heparin dose > or =45 U/kg (p = 0.02) as independent risk factors for bleeding. Effective mechanical sealing of the intrahepatic portal catheter tract with thrombostatic coils and tissue fibrin glue completely prevented bleeding in all subsequent procedures (n = 26, p = 0.02). We conclude that bleeding after percutaneous islet implantation is an avoidable complication provided the intraparenchymal liver tract is sealed effectively.

Data analysis techniques

NASA Technical Reports Server (NTRS)

Park, Steve

1990-01-01

A large and diverse number of computational techniques are routinely used to process and analyze remotely sensed data. These techniques include: univariate statistics; multivariate statistics; principal component analysis; pattern recognition and classification; other multivariate techniques; geometric correction; registration and resampling; radiometric correction; enhancement; restoration; Fourier analysis; and filtering. Each of these techniques will be considered, in order.

Chemical structure of wood charcoal by infrared spectroscopy and multivariate analysis

Treesearch

Nicole Labbe; David Harper; Timothy Rials; Thomas Elder

2006-01-01

In this work, the effect of temperature on charcoal structure and chemical composition is investigated for four tree species. Wood charcoal carbonized at various temperatures is analyzed by mid infrared spectroscopy coupled with multivariate analysis and by thermogravimetric analysis to characterize the chemical composition during the carbonization process. The...

Root Cause Analysis of Quality Defects Using HPLC-MS Fingerprint Knowledgebase for Batch-to-batch Quality Control of Herbal Drugs.

PubMed

Yan, Binjun; Fang, Zhonghua; Shen, Lijuan; Qu, Haibin

2015-01-01

The batch-to-batch quality consistency of herbal drugs has always been an important issue. To propose a methodology for batch-to-batch quality control based on HPLC-MS fingerprints and process knowledgebase. The extraction process of Compound E-jiao Oral Liquid was taken as a case study. After establishing the HPLC-MS fingerprint analysis method, the fingerprints of the extract solutions produced under normal and abnormal operation conditions were obtained. Multivariate statistical models were built for fault detection and a discriminant analysis model was built using the probabilistic discriminant partial-least-squares method for fault diagnosis. Based on multivariate statistical analysis, process knowledge was acquired and the cause-effect relationship between process deviations and quality defects was revealed. The quality defects were detected successfully by multivariate statistical control charts and the type of process deviations were diagnosed correctly by discriminant analysis. This work has demonstrated the benefits of combining HPLC-MS fingerprints, process knowledge and multivariate analysis for the quality control of herbal drugs. Copyright © 2015 John Wiley & Sons, Ltd.

Alcohol Intake and Risk of Incident Melanoma: A Pooled Analysis of Three Prospective Studies in the U.S

PubMed Central

Rivera, Andrew; Nan, Hongmei; Li, Tricia; Qureshi, Abrar; Cho, Eunyoung

2016-01-01

Background Alcohol consumption is associated with increased risk of numerous cancers, but existing evidence for an association with melanoma is equivocal. No study has evaluated the association with different anatomic locations of melanoma. Methods We used data from three large prospective cohort studies to investigate whether alcohol intake was associated with risk of melanoma. Alcohol intake was assessed repeatedly by food-frequency questionnaires. A Cox proportional hazards model was used to calculate multivariate-adjusted hazard ratios (HRs). Results A total of 1,374 cases of invasive melanoma were documented during 3,855,706 person-years of follow-up. There was an association between higher alcohol intake and incidence of invasive melanoma (pooled multivariate HR 1.14; 95% confidence interval [CI]: 1.00–1.29] per drink/d, p trend = 0.04). Among alcoholic beverages, white wine consumption was associated with an increased risk of melanoma (pooled multivariate HR 1.13 [95% CI: 1.04–1.24] per drink/d, p trend <0.01) after adjusting for other alcoholic beverages. The association between alcohol consumption and melanoma risk was stronger for melanoma in relatively UV-spared sites (trunk) versus more UV-exposed sites (head, neck, or extremities). Compared to non-drinkers, the pooled multivariate-adjusted HRs for ≥20g/d of alcohol were 1.02 (95% CI: 0.64–1.62; P trend =0.25) for melanomas of the head, neck, and extremities and 1.73 (95% CI: 1.25–2.38; P trend =0.02) for melanomas of the trunk. Conclusions Alcohol intake was associated with a modest increase in the risk of melanoma, particularly in UV-protected sites. Impact These findings further support American Cancer Society Guidelines for Cancer Prevention to limit alcohol intake. PMID:27909090

Multivariate analysis: greater insights into complex systems

USDA-ARS?s Scientific Manuscript database

Many agronomic researchers measure and collect multiple response variables in an effort to understand the more complex nature of the system being studied. Multivariate (MV) statistical methods encompass the simultaneous analysis of all random variables (RV) measured on each experimental or sampling ...

Assessing the impacts of socio-economic and hydrological factors on urban water demand: A multivariate statistical approach

NASA Astrophysics Data System (ADS)

Panagopoulos, George P.

2014-10-01

The multivariate statistical techniques conducted on quarterly water consumption data in Mytilene reveal valuable tools that could help the local authorities in assigning strategies aimed at the sustainable development of urban water resources. The proposed methodology is an innovative approach, applied for the first time in the international literature, to handling urban water consumption data in order to analyze statistically the interrelationships among the determinants of urban water use. Factor analysis of demographic, socio-economic and hydrological variables shows that total water consumption in Mytilene is the combined result of increases in (a) income, (b) population, (c) connections and (d) climate parameters. On the other hand, the per connection water demand is influenced by variations in water prices but with different consequences in each consumption class. Increases in water prices are faced by large consumers; they then reduce their consumption rates and transfer to lower consumption blocks. These shifts are responsible for the increase in the average consumption values in the lower blocks despite the increase in the marginal prices.

Multivariate analysis of progressive thermal desorption coupled gas chromatography-mass spectrometry.

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

Van Benthem, Mark Hilary; Mowry, Curtis Dale; Kotula, Paul Gabriel

Thermal decomposition of poly dimethyl siloxane compounds, Sylgard{reg_sign} 184 and 186, were examined using thermal desorption coupled gas chromatography-mass spectrometry (TD/GC-MS) and multivariate analysis. This work describes a method of producing multiway data using a stepped thermal desorption. The technique involves sequentially heating a sample of the material of interest with subsequent analysis in a commercial GC/MS system. The decomposition chromatograms were analyzed using multivariate analysis tools including principal component analysis (PCA), factor rotation employing the varimax criterion, and multivariate curve resolution. The results of the analysis show seven components related to offgassing of various fractions of siloxanes that varymore » as a function of temperature. Thermal desorption coupled with gas chromatography-mass spectrometry (TD/GC-MS) is a powerful analytical technique for analyzing chemical mixtures. It has great potential in numerous analytic areas including materials analysis, sports medicine, in the detection of designer drugs; and biological research for metabolomics. Data analysis is complicated, far from automated and can result in high false positive or false negative rates. We have demonstrated a step-wise TD/GC-MS technique that removes more volatile compounds from a sample before extracting the less volatile compounds. This creates an additional dimension of separation before the GC column, while simultaneously generating three-way data. Sandia's proven multivariate analysis methods, when applied to these data, have several advantages over current commercial options. It also has demonstrated potential for success in finding and enabling identification of trace compounds. Several challenges remain, however, including understanding the sources of noise in the data, outlier detection, improving the data pretreatment and analysis methods, developing a software tool for ease of use by the chemist, and demonstrating our belief that this multivariate analysis will enable superior differentiation capabilities. In addition, noise and system artifacts challenge the analysis of GC-MS data collected on lower cost equipment, ubiquitous in commercial laboratories. This research has the potential to affect many areas of analytical chemistry including materials analysis, medical testing, and environmental surveillance. It could also provide a method to measure adsorption parameters for chemical interactions on various surfaces by measuring desorption as a function of temperature for mixtures. We have presented results of a novel method for examining offgas products of a common PDMS material. Our method involves utilizing a stepped TD/GC-MS data acquisition scheme that may be almost totally automated, coupled with multivariate analysis schemes. This method of data generation and analysis can be applied to a number of materials aging and thermal degradation studies.« less

The prognostic significance of nonsentinel lymph node metastasis in melanoma.

PubMed

Brown, Russell E; Ross, Merrick I; Edwards, Michael J; Noyes, R Dirk; Reintgen, Douglas S; Hagendoorn, Lee J; Stromberg, Arnold J; Martin, Robert C G; McMasters, Kelly M; Scoggins, Charles R

2010-12-01

We hypothesized that metastasis beyond the sentinel lymph nodes (SLN) to the nonsentinel nodes (NSN) is an important predictor of survival. Analysis was performed of a prospective multi-institutional study that included patients with melanoma ≥ 1.0 mm in Breslow thickness. All patients underwent SLN biopsy; completion lymphadenectomy was performed for all SLN metastases. Disease-free survival (DFS) and overall survival (OS) were computed by Kaplan-Meier analysis; univariate and multivariate analyses were performed to identify factors associated with differences in survival among groups. A total of 2335 patients were analyzed over a median follow-up of 68 months. We compared 3 groups: SLN negative (n = 1988), SLN-only positive (n = 296), and both SLN and NSN positive (n = 51). The 5-year DFS rates were 85.5, 64.8, and 42.6% for groups 1, 2, and 3, respectively (P < 0.001). The 5-year OS rates were 85.5, 64.9, and 49.4%, respectively (P < 0.001). On univariate analysis, predictors of decreased OS included: SLN metastasis, NSN metastasis, increased total number of positive LN, increased ratio of positive LN to total LN, increased age, male gender, increased Breslow thickness, presence of ulceration, Clark level ≥ IV, and axial primary site (in all cases, P < 0.01). When the total number of positive LN and NSN status were evaluated using multivariate analysis, NSN status remained statistically significant (P < 0.01), while the total number of positive LN and LN ratio did not. NSN melanoma metastasis is an independent prognostic factor for DFS and OS, which is distinct from the number of positive lymph nodes or the lymph node ratio.

Regular sugar-sweetened beverage consumption between meals increases risk of overweight among preschool-aged children.

PubMed

Dubois, Lise; Farmer, Anna; Girard, Manon; Peterson, Kelly

2007-06-01

To examine the relationship between consumption of sugar-sweetened beverages (eg, nondiet carbonated drinks and fruit drinks) and the prevalence of overweight among preschool-aged children living in Canada. Data come from the Longitudinal Study of Child Development in Québec (1998-2002). A representative sample (n=2,103) of children born in 1998 in Québec, Canada. A total of 1,944 children (still representative of the same-age children in this population) remaining at 4 to 5 years in 2002 participated in the nutrition study. Data were collected via 24-hour dietary recall interview. Frequency of sugar-sweetened beverage consumption between meals at age 2.5, 3.5, and 4.5 years was recorded and children's height and weight were measured. Multivariate regression analysis was done with Statistical Analysis System software. Weighted data were adjusted for within-child variability and significance level was set at 5%. Overall, 6.9% of children who were nonconsumers of sugar-sweetened beverages between meals between the ages of 2.5 to 4.5 years were overweight at 4.5 years, compared to 15.4% of regular consumers (four to six times or more per week) at ages 2.5 years, 3.5 years, and 4.5 years. According to multivariate analysis, sugar-sweetened beverage consumption between meals more than doubles the odds of being overweight when other important factors are considered in multivariate analysis. Children from families with insufficient income who consume sugar-sweetened beverages regularly between the ages of 2.5 and 4.5 years are more than three times more likely to be overweight at age 4.5 years compared to nonconsuming children from sufficient income households. Regular sugar-sweetened beverage consumption between meals may put some young children at a greater risk for overweight. Parents should limit the quantity of sweetened beverages consumed during preschool years because it may increase propensity to gain weight.

Identification of adverse events that have a negative impact on quality of life in a clinical trial comparing docetaxel versus S-1 with cisplatin in lung cancer.

PubMed

Aotani, Eriko; Hamano, Tetsutaro; Gemma, Akihiko; Takeuchi, Masahiro; Takebayashi, Toru; Kobayashi, Kunihiko

2016-10-01

In the CATS (Cisplatin And TS-1) randomized trial comparing cisplatin plus either docetaxel (DP arm) or TS-1 (SP arm) in lung cancer, efficacy was found to be equivalent but the global quality of life (QOL) score was higher in the SP arm. The purpose of the current study was to identify which of the adverse events (AEs) contributed to the deterioration of QOL. QOL and AE data from the CATS trial were used to quantitatively analyze the relationship between deterioration of QOL score and occurrence of AEs. Subtracted values of the QOL score from post-chemotherapy to pre-chemotherapy were fully compared between patients with or without each AE (Student's t test, significance level = 0.001). Multivariate linear regression analysis was also performed. Analysis of variance was performed to identify whether grade of AE(s) might be significantly correlated with the deterioration of the QOL score (significance level of 0.05). As expected, gastrointestinal (GI) toxicities were associated with worsening of a variety of QOL items in both trial arms, detected by both univariate and multivariate analysis (p < 0.001 and p < 0.0001, respectively). Multivariate analysis unpredictably indicated that an increase in serum bilirubin level was the only AE that was uniquely associated with worsening of physical functioning (p = 0.0002), cognitive functioning (p < 0.0001), and financial problems (p = 0.0005) in the DP arm, although not in the SP arm. GI toxicities tended to be prolonged in the SP arm. An increase in serum bilirubin level may contribute to the worse global QOL of subjects in the DP arm in the CATS trial. The method we used here may be a unique approach to identify unpredictable AE(s) that worsen the QOL of patients treated by chemotherapy.

Comprehensive analysis of Polygoni Multiflori Radix of different geographical origins using ultra-high-performance liquid chromatography fingerprints and multivariate chemometric methods.

PubMed

Sun, Li-Li; Wang, Meng; Zhang, Hui-Jie; Liu, Ya-Nan; Ren, Xiao-Liang; Deng, Yan-Ru; Qi, Ai-Di

2018-01-01

Polygoni Multiflori Radix (PMR) is increasingly being used not just as a traditional herbal medicine but also as a popular functional food. In this study, multivariate chemometric methods and mass spectrometry were combined to analyze the ultra-high-performance liquid chromatograph (UPLC) fingerprints of PMR from six different geographical origins. A chemometric strategy based on multivariate curve resolution-alternating least squares (MCR-ALS) and three classification methods is proposed to analyze the UPLC fingerprints obtained. Common chromatographic problems, including the background contribution, baseline contribution, and peak overlap, were handled by the established MCR-ALS model. A total of 22 components were resolved. Moreover, relative species concentrations were obtained from the MCR-ALS model, which was used for multivariate classification analysis. Principal component analysis (PCA) and Ward's method have been applied to classify 72 PMR samples from six different geographical regions. The PCA score plot showed that the PMR samples fell into four clusters, which related to the geographical location and climate of the source areas. The results were then corroborated by Ward's method. In addition, according to the variance-weighted distance between cluster centers obtained from Ward's method, five components were identified as the most significant variables (chemical markers) for cluster discrimination. A counter-propagation artificial neural network has been applied to confirm and predict the effects of chemical markers on different samples. Finally, the five chemical markers were identified by UPLC-quadrupole time-of-flight mass spectrometer. Components 3, 12, 16, 18, and 19 were identified as 2,3,5,4'-tetrahydroxy-stilbene-2-O-β-d-glucoside, emodin-8-O-β-d-glucopyranoside, emodin-8-O-(6'-O-acetyl)-β-d-glucopyranoside, emodin, and physcion, respectively. In conclusion, the proposed method can be applied for the comprehensive analysis of natural samples. Copyright © 2016. Published by Elsevier B.V.

Near-infrared confocal micro-Raman spectroscopy combined with PCA-LDA multivariate analysis for detection of esophageal cancer

NASA Astrophysics Data System (ADS)

Chen, Long; Wang, Yue; Liu, Nenrong; Lin, Duo; Weng, Cuncheng; Zhang, Jixue; Zhu, Lihuan; Chen, Weisheng; Chen, Rong; Feng, Shangyuan

2013-06-01

The diagnostic capability of using tissue intrinsic micro-Raman signals to obtain biochemical information from human esophageal tissue is presented in this paper. Near-infrared micro-Raman spectroscopy combined with multivariate analysis was applied for discrimination of esophageal cancer tissue from normal tissue samples. Micro-Raman spectroscopy measurements were performed on 54 esophageal cancer tissues and 55 normal tissues in the 400-1750 cm-1 range. The mean Raman spectra showed significant differences between the two groups. Tentative assignments of the Raman bands in the measured tissue spectra suggested some changes in protein structure, a decrease in the relative amount of lactose, and increases in the percentages of tryptophan, collagen and phenylalanine content in esophageal cancer tissue as compared to those of a normal subject. The diagnostic algorithms based on principal component analysis (PCA) and linear discriminate analysis (LDA) achieved a diagnostic sensitivity of 87.0% and specificity of 70.9% for separating cancer from normal esophageal tissue samples. The result demonstrated that near-infrared micro-Raman spectroscopy combined with PCA-LDA analysis could be an effective and sensitive tool for identification of esophageal cancer.

Association of pretreatment neutrophil-lymphocyte ratio and outcome in emergency colorectal cancer care.

PubMed

Palin, R P; Devine, A T; Hicks, G; Burke, D

2018-04-01

Introduction The association between the neutrophil-lymphocyte ratio (NLR) and outcome in elective colorectal cancer surgery is well established; the relationship between NLR and the emergency colorectal cancer patient is, as yet, unexplored. This paper evaluates the predictive quality of the NLR for outcome in the emergency colorectal cancer patient. Materials and Methods A total of 187 consecutive patients who underwent emergency surgery for colorectal cancer were included in the study. NLR was calculated from the haematological tests done on admission. Receiver operating characteristic analyses were used to determine the most suitable cut-off for NLR. Outcomes were assessed by mortality at 30 and 90 days using stepwise Cox proportional hazards regression. Results An NLR cut-off of 5 was found to have the highest sensitivity and specificity. At 30 days, age and time from admission to surgery were associated with increased mortality; a high NLR was associated with an increased risk of mortality in univariate but not multivariate analysis. At 90 days, age, NLR, time from admission to surgery and nodal status were all significantly associated with increased mortality on multivariate analysis. Conclusions Pre-operative NLR is a cheap, easily performed and useful clinical tool to aid prediction of outcome in the emergency colorectal cancer patient.

Body height as risk factor for emphysema in COPD

PubMed Central

Miniati, Massimo; Bottai, Matteo; Pavlickova, Ivana; Monti, Simonetta

2016-01-01

Pulmonary emphysema is a phenotypic component of chronic obstructive pulmonary disease (COPD) which carries substantial morbidity and mortality. We explored the association between emphysema and body height in 726 patients with COPD using computed tomography as the reference diagnostic standard for emphysema. We applied univariate analysis to look for differences between patients with emphysema and those without, and multivariate logistic regression to identify significant predictors of the risk of emphysema. As covariates we included age, sex, body height, body mass index, pack-years of smoking, and forced expiratory volume in one second (FEV1) as percent predicted. The overall prevalence of emphysema was 52%. Emphysemic patients were significantly taller and thinner than non-emphysemic ones, and featured significantly higher pack-years of smoking and lower FEV1 (P < 0.001). The prevalence of emphysema rose linearly by 10-cm increase in body height (r2 = 0.96). In multivariate analysis, the odds of emphysema increased by 5% (95% confidence interval, 3 to 7%) along with one-centimeter increase in body height, and remained unchanged after adjusting for all the potential confounders considered (P < 0.001). The odds of emphysema were not statistically different between males and females. In conclusion, body height is a strong, independent risk factor for emphysema in COPD. PMID:27874046

Comparison of connectivity analyses for resting state EEG data

NASA Astrophysics Data System (ADS)

Olejarczyk, Elzbieta; Marzetti, Laura; Pizzella, Vittorio; Zappasodi, Filippo

2017-06-01

Objective. In the present work, a nonlinear measure (transfer entropy, TE) was used in a multivariate approach for the analysis of effective connectivity in high density resting state EEG data in eyes open and eyes closed. Advantages of the multivariate approach in comparison to the bivariate one were tested. Moreover, the multivariate TE was compared to an effective linear measure, i.e. directed transfer function (DTF). Finally, the existence of a relationship between the information transfer and the level of brain synchronization as measured by phase synchronization value (PLV) was investigated. Approach. The comparison between the connectivity measures, i.e. bivariate versus multivariate TE, TE versus DTF, TE versus PLV, was performed by means of statistical analysis of indexes based on graph theory. Main results. The multivariate approach is less sensitive to false indirect connections with respect to the bivariate estimates. The multivariate TE differentiated better between eyes closed and eyes open conditions compared to DTF. Moreover, the multivariate TE evidenced non-linear phenomena in information transfer, which are not evidenced by the use of DTF. We also showed that the target of information flow, in particular the frontal region, is an area of greater brain synchronization. Significance. Comparison of different connectivity analysis methods pointed to the advantages of nonlinear methods, and indicated a relationship existing between the flow of information and the level of synchronization of the brain.

Measures of precision for dissimilarity-based multivariate analysis of ecological communities.

PubMed

Anderson, Marti J; Santana-Garcon, Julia

2015-01-01

Ecological studies require key decisions regarding the appropriate size and number of sampling units. No methods currently exist to measure precision for multivariate assemblage data when dissimilarity-based analyses are intended to follow. Here, we propose a pseudo multivariate dissimilarity-based standard error (MultSE) as a useful quantity for assessing sample-size adequacy in studies of ecological communities. Based on sums of squared dissimilarities, MultSE measures variability in the position of the centroid in the space of a chosen dissimilarity measure under repeated sampling for a given sample size. We describe a novel double resampling method to quantify uncertainty in MultSE values with increasing sample size. For more complex designs, values of MultSE can be calculated from the pseudo residual mean square of a permanova model, with the double resampling done within appropriate cells in the design. R code functions for implementing these techniques, along with ecological examples, are provided. © 2014 The Authors. Ecology Letters published by John Wiley & Sons Ltd and CNRS.

MULTIVARIATE CURVE RESOLUTION OF NMR SPECTROSCOPY METABONOMIC DATA

EPA Science Inventory

Sandia National Laboratories is working with the EPA to evaluate and develop mathematical tools for analysis of the collected NMR spectroscopy data. Initially, we have focused on the use of Multivariate Curve Resolution (MCR) also known as molecular factor analysis (MFA), a tech...

Plasma urate concentration and risk of coronary heart disease: a Mendelian randomisation analysis.

PubMed

White, Jon; Sofat, Reecha; Hemani, Gibran; Shah, Tina; Engmann, Jorgen; Dale, Caroline; Shah, Sonia; Kruger, Felix A; Giambartolomei, Claudia; Swerdlow, Daniel I; Palmer, Tom; McLachlan, Stela; Langenberg, Claudia; Zabaneh, Delilah; Lovering, Ruth; Cavadino, Alana; Jefferis, Barbara; Finan, Chris; Wong, Andrew; Amuzu, Antoinette; Ong, Ken; Gaunt, Tom R; Warren, Helen; Davies, Teri-Louise; Drenos, Fotios; Cooper, Jackie; Ebrahim, Shah; Lawlor, Debbie A; Talmud, Philippa J; Humphries, Steve E; Power, Christine; Hypponen, Elina; Richards, Marcus; Hardy, Rebecca; Kuh, Diana; Wareham, Nicholas; Ben-Shlomo, Yoav; Day, Ian N; Whincup, Peter; Morris, Richard; Strachan, Mark W J; Price, Jacqueline; Kumari, Meena; Kivimaki, Mika; Plagnol, Vincent; Whittaker, John C; Smith, George Davey; Dudbridge, Frank; Casas, Juan P; Holmes, Michael V; Hingorani, Aroon D

2016-04-01

Increased circulating plasma urate concentration is associated with an increased risk of coronary heart disease, but the extent of any causative effect of urate on risk of coronary heart disease is still unclear. In this study, we aimed to clarify any causal role of urate on coronary heart disease risk using Mendelian randomisation analysis. We first did a fixed-effects meta-analysis of the observational association of plasma urate and risk of coronary heart disease. We then used a conventional Mendelian randomisation approach to investigate the causal relevance using a genetic instrument based on 31 urate-associated single nucleotide polymorphisms (SNPs). To account for potential pleiotropic associations of certain SNPs with risk factors other than urate, we additionally did both a multivariable Mendelian randomisation analysis, in which the genetic associations of SNPs with systolic and diastolic blood pressure, HDL cholesterol, and triglycerides were included as covariates, and an Egger Mendelian randomisation (MR-Egger) analysis to estimate a causal effect accounting for unmeasured pleiotropy. In the meta-analysis of 17 prospective observational studies (166 486 individuals; 9784 coronary heart disease events) a 1 SD higher urate concentration was associated with an odds ratio (OR) for coronary heart disease of 1·07 (95% CI 1·04-1·10). The corresponding OR estimates from the conventional, multivariable adjusted, and Egger Mendelian randomisation analysis (58 studies; 198 598 individuals; 65 877 events) were 1·18 (95% CI 1·08-1·29), 1·10 (1·00-1·22), and 1·05 (0·92-1·20), respectively, per 1 SD increment in plasma urate. Conventional and multivariate Mendelian randomisation analysis implicates a causal role for urate in the development of coronary heart disease, but these estimates might be inflated by hidden pleiotropy. Egger Mendelian randomisation analysis, which accounts for pleiotropy but has less statistical power, suggests there might be no causal effect. These results might help investigators to determine the priority of trials of urate lowering for the prevention of coronary heart disease compared with other potential interventions. UK National Institute for Health Research, British Heart Foundation, and UK Medical Research Council. Copyright © 2016 White et al. Open Access article distributed under the terms of CC BY. Published by Elsevier Ltd.. All rights reserved.

Plasma urate concentration and risk of coronary heart disease: a Mendelian randomisation analysis

PubMed Central

White, Jon; Sofat, Reecha; Hemani, Gibran; Shah, Tina; Engmann, Jorgen; Dale, Caroline; Shah, Sonia; Kruger, Felix A; Giambartolomei, Claudia; Swerdlow, Daniel I; Palmer, Tom; McLachlan, Stela; Langenberg, Claudia; Zabaneh, Delilah; Lovering, Ruth; Cavadino, Alana; Jefferis, Barbara; Finan, Chris; Wong, Andrew; Amuzu, Antoinette; Ong, Ken; Gaunt, Tom R; Warren, Helen; Davies, Teri-Louise; Drenos, Fotios; Cooper, Jackie; Ebrahim, Shah; Lawlor, Debbie A; Talmud, Philippa J; Humphries, Steve E; Power, Christine; Hypponen, Elina; Richards, Marcus; Hardy, Rebecca; Kuh, Diana; Wareham, Nicholas; Ben-Shlomo, Yoav; Day, Ian N; Whincup, Peter; Morris, Richard; Strachan, Mark W J; Price, Jacqueline; Kumari, Meena; Kivimaki, Mika; Plagnol, Vincent; Whittaker, John C; Smith, George Davey; Dudbridge, Frank; Casas, Juan P; Holmes, Michael V; Hingorani, Aroon D

2016-01-01

Summary Background Increased circulating plasma urate concentration is associated with an increased risk of coronary heart disease, but the extent of any causative effect of urate on risk of coronary heart disease is still unclear. In this study, we aimed to clarify any causal role of urate on coronary heart disease risk using Mendelian randomisation analysis. Methods We first did a fixed-effects meta-analysis of the observational association of plasma urate and risk of coronary heart disease. We then used a conventional Mendelian randomisation approach to investigate the causal relevance using a genetic instrument based on 31 urate-associated single nucleotide polymorphisms (SNPs). To account for potential pleiotropic associations of certain SNPs with risk factors other than urate, we additionally did both a multivariable Mendelian randomisation analysis, in which the genetic associations of SNPs with systolic and diastolic blood pressure, HDL cholesterol, and triglycerides were included as covariates, and an Egger Mendelian randomisation (MR-Egger) analysis to estimate a causal effect accounting for unmeasured pleiotropy. Findings In the meta-analysis of 17 prospective observational studies (166 486 individuals; 9784 coronary heart disease events) a 1 SD higher urate concentration was associated with an odds ratio (OR) for coronary heart disease of 1·07 (95% CI 1·04–1·10). The corresponding OR estimates from the conventional, multivariable adjusted, and Egger Mendelian randomisation analysis (58 studies; 198 598 individuals; 65 877 events) were 1·18 (95% CI 1·08–1·29), 1·10 (1·00–1·22), and 1·05 (0·92–1·20), respectively, per 1 SD increment in plasma urate. Interpretation Conventional and multivariate Mendelian randomisation analysis implicates a causal role for urate in the development of coronary heart disease, but these estimates might be inflated by hidden pleiotropy. Egger Mendelian randomisation analysis, which accounts for pleiotropy but has less statistical power, suggests there might be no causal effect. These results might help investigators to determine the priority of trials of urate lowering for the prevention of coronary heart disease compared with other potential interventions. Funding UK National Institute for Health Research, British Heart Foundation, and UK Medical Research Council. PMID:26781229

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

Drunk driving detection based on classification of multivariate time series.

PubMed

Li, Zhenlong; Jin, Xue; Zhao, Xiaohua

2015-09-01

This paper addresses the problem of detecting drunk driving based on classification of multivariate time series. First, driving performance measures were collected from a test in a driving simulator located in the Traffic Research Center, Beijing University of Technology. Lateral position and steering angle were used to detect drunk driving. Second, multivariate time series analysis was performed to extract the features. A piecewise linear representation was used to represent multivariate time series. A bottom-up algorithm was then employed to separate multivariate time series. The slope and time interval of each segment were extracted as the features for classification. Third, a support vector machine classifier was used to classify driver's state into two classes (normal or drunk) according to the extracted features. The proposed approach achieved an accuracy of 80.0%. Drunk driving detection based on the analysis of multivariate time series is feasible and effective. The approach has implications for drunk driving detection. Copyright © 2015 Elsevier Ltd and National Safety Council. All rights reserved.

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

PubMed

Henrard, S; Speybroeck, N; Hermans, C

2015-11-01

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

The Decoding Toolbox (TDT): a versatile software package for multivariate analyses of functional imaging data

PubMed Central

Hebart, Martin N.; Görgen, Kai; Haynes, John-Dylan

2015-01-01

The multivariate analysis of brain signals has recently sparked a great amount of interest, yet accessible and versatile tools to carry out decoding analyses are scarce. Here we introduce The Decoding Toolbox (TDT) which represents a user-friendly, powerful and flexible package for multivariate analysis of functional brain imaging data. TDT is written in Matlab and equipped with an interface to the widely used brain data analysis package SPM. The toolbox allows running fast whole-brain analyses, region-of-interest analyses and searchlight analyses, using machine learning classifiers, pattern correlation analysis, or representational similarity analysis. It offers automatic creation and visualization of diverse cross-validation schemes, feature scaling, nested parameter selection, a variety of feature selection methods, multiclass capabilities, and pattern reconstruction from classifier weights. While basic users can implement a generic analysis in one line of code, advanced users can extend the toolbox to their needs or exploit the structure to combine it with external high-performance classification toolboxes. The toolbox comes with an example data set which can be used to try out the various analysis methods. Taken together, TDT offers a promising option for researchers who want to employ multivariate analyses of brain activity patterns. PMID:25610393

Application of two tests of multivariate discordancy to fisheries data sets

USGS Publications Warehouse

Stapanian, M.A.; Kocovsky, P.M.; Garner, F.C.

2008-01-01

The generalized (Mahalanobis) distance and multivariate kurtosis are two powerful tests of multivariate discordancies (outliers). Unlike the generalized distance test, the multivariate kurtosis test has not been applied as a test of discordancy to fisheries data heretofore. We applied both tests, along with published algorithms for identifying suspected causal variable(s) of discordant observations, to two fisheries data sets from Lake Erie: total length, mass, and age from 1,234 burbot, Lota lota; and 22 combinations of unique subsets of 10 morphometrics taken from 119 yellow perch, Perca flavescens. For the burbot data set, the generalized distance test identified six discordant observations and the multivariate kurtosis test identified 24 discordant observations. In contrast with the multivariate tests, the univariate generalized distance test identified no discordancies when applied separately to each variable. Removing discordancies had a substantial effect on length-versus-mass regression equations. For 500-mm burbot, the percent difference in estimated mass after removing discordancies in our study was greater than the percent difference in masses estimated for burbot of the same length in lakes that differed substantially in productivity. The number of discordant yellow perch detected ranged from 0 to 2 with the multivariate generalized distance test and from 6 to 11 with the multivariate kurtosis test. With the kurtosis test, 108 yellow perch (90.7%) were identified as discordant in zero to two combinations, and five (4.2%) were identified as discordant in either all or 21 of the 22 combinations. The relationship among the variables included in each combination determined which variables were identified as causal. The generalized distance test identified between zero and six discordancies when applied separately to each variable. Removing the discordancies found in at least one-half of the combinations (k=5) had a marked effect on a principal components analysis. In particular, the percent of the total variation explained by second and third principal components, which explain shape, increased by 52 and 44% respectively when the discordancies were removed. Multivariate applications of the tests have numerous ecological advantages over univariate applications, including improved management of fish stocks and interpretation of multivariate morphometric data. ?? 2007 Springer Science+Business Media B.V.

Application of multivariable statistical techniques in plant-wide WWTP control strategies analysis.

PubMed

Flores, X; Comas, J; Roda, I R; Jiménez, L; Gernaey, K V

2007-01-01

The main objective of this paper is to present the application of selected multivariable statistical techniques in plant-wide wastewater treatment plant (WWTP) control strategies analysis. In this study, cluster analysis (CA), principal component analysis/factor analysis (PCA/FA) and discriminant analysis (DA) are applied to the evaluation matrix data set obtained by simulation of several control strategies applied to the plant-wide IWA Benchmark Simulation Model No 2 (BSM2). These techniques allow i) to determine natural groups or clusters of control strategies with a similar behaviour, ii) to find and interpret hidden, complex and casual relation features in the data set and iii) to identify important discriminant variables within the groups found by the cluster analysis. This study illustrates the usefulness of multivariable statistical techniques for both analysis and interpretation of the complex multicriteria data sets and allows an improved use of information for effective evaluation of control strategies.

Moving beyond Univariate Post-Hoc Testing in Exercise Science: A Primer on Descriptive Discriminate Analysis

ERIC Educational Resources Information Center

Barton, Mitch; Yeatts, Paul E.; Henson, Robin K.; Martin, Scott B.

2016-01-01

There has been a recent call to improve data reporting in kinesiology journals, including the appropriate use of univariate and multivariate analysis techniques. For example, a multivariate analysis of variance (MANOVA) with univariate post hocs and a Bonferroni correction is frequently used to investigate group differences on multiple dependent…

Shielding of the Hip Prosthesis During Radiation Therapy for Heterotopic Ossification is Associated with Increased Failure of Prophylaxis

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

Balboni, Tracy A.; Gaccione, Peter; Gobezie, Reuben

2007-04-01

Purpose: Radiation therapy (RT) is frequently administered to prevent heterotopic ossification (HO) after total hip arthroplasty (THA). The purpose of this study was to determine if there is an increased risk of HO after RT prophylaxis with shielding of the THA components. Methods and Materials: This is a retrospective analysis of THA patients undergoing RT prophylaxis of HO at Brigham and Women's Hospital between June 1994 and February 2004. Univariate and multivariate logistic regressions were used to assess the relationships of all variables to failure of RT prophylaxis. Results: A total of 137 patients were identified and 84 were eligiblemore » for analysis (61%). The median RT dose was 750 cGy in one fraction, and the median follow-up was 24 months. Eight of 40 unshielded patients (20%) developed any progression of HO compared with 21 of 44 shielded patients (48%) (p = 0.009). Brooker Grade III-IV HO developed in 5% of unshielded and 18% of shielded patients (p 0.08). Multivariate analysis revealed shielding (p = 0.02) and THA for prosthesis infection (p = 0.03) to be significant predictors of RT failure, with a trend toward an increasing risk of HO progression with age (p = 0.07). There was no significant difference in the prosthesis failure rates between shielded and unshielded patients. Conclusions: A significantly increased risk of failure of RT prophylaxis for HO was noted in those receiving shielding of the hip prosthesis. Shielding did not appear to reduce the risk of prosthesis failure.« less

Influence of shifting cultivation practices on soil-plant-beetle interactions.

PubMed

Ibrahim, Kalibulla Syed; Momin, Marcy D; Lalrotluanga, R; Rosangliana, David; Ghatak, Souvik; Zothansanga, R; Kumar, Nachimuthu Senthil; Gurusubramanian, Guruswami

2016-08-01

Shifting cultivation (jhum) is a major land use practice in Mizoram. It was considered as an eco-friendly and efficient method when the cycle duration was long (15-30 years), but it poses the problem of land degradation and threat to ecology when shortened (4-5 years) due to increased intensification of farming systems. Studying beetle community structure is very helpful in understanding how shifting cultivation affects the biodiversity features compared to natural forest system. The present study examines the beetle species diversity and estimates the effects of shifting cultivation practices on the beetle assemblages in relation to change in tree species composition and soil nutrients. Scarabaeidae and Carabidae were observed to be the dominant families in the land use systems studied. Shifting cultivation practice significantly (P < 0.05) affected the beetle and tree species diversity as well as the soil nutrients as shown by univariate (one-way analysis of variance (ANOVA), correlation and regression, diversity indices) and multivariate (cluster analysis, principal component analysis (PCA), detrended correspondence analysis (DCA), canonical variate analysis (CVA), permutational multivariate analysis of variance (PERMANOVA), permutational multivariate analysis of dispersion (PERMDISP)) statistical analyses. Besides changing the tree species composition and affecting the soil fertility, shifting cultivation provides less suitable habitat conditions for the beetle species. Bioindicator analysis categorized the beetle species into forest specialists, anthropogenic specialists (shifting cultivation habitat specialist), and habitat generalists. Molecular analysis of bioindicator beetle species was done using mitochondrial cytochrome oxidase subunit I (COI) marker to validate the beetle species and describe genetic variation among them in relation to heterogeneity, transition/transversion bias, codon usage bias, evolutionary distance, and substitution pattern. The present study revealed the fact that shifting cultivation practice significantly affects the beetle species in terms of biodiversity pattern as well as evolutionary features. Spatiotemporal assessment of soil-plant-beetle interactions in shifting cultivation system and their influence in land degradation and ecology will be helpful in making biodiversity conservation decisions in the near future.

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 Statistical Consulting Center for Astronomy (SCCA)

NASA Technical Reports Server (NTRS)

Akritas, Michael

2001-01-01

The process by which raw astronomical data acquisition is transformed into scientifically meaningful results and interpretation typically involves many statistical steps. Traditional astronomy limits itself to a narrow range of old and familiar statistical methods: means and standard deviations; least-squares methods like chi(sup 2) minimization; and simple nonparametric procedures such as the Kolmogorov-Smirnov tests. These tools are often inadequate for the complex problems and datasets under investigations, and recent years have witnessed an increased usage of maximum-likelihood, survival analysis, multivariate analysis, wavelet and advanced time-series methods. The Statistical Consulting Center for Astronomy (SCCA) assisted astronomers with the use of sophisticated tools, and to match these tools with specific problems. The SCCA operated with two professors of statistics and a professor of astronomy working together. Questions were received by e-mail, and were discussed in detail with the questioner. Summaries of those questions and answers leading to new approaches were posted on the Web (www.state.psu.edu/ mga/SCCA). In addition to serving individual astronomers, the SCCA established a Web site for general use that provides hypertext links to selected on-line public-domain statistical software and services. The StatCodes site (www.astro.psu.edu/statcodes) provides over 200 links in the areas of: Bayesian statistics; censored and truncated data; correlation and regression, density estimation and smoothing, general statistics packages and information; image analysis; interactive Web tools; multivariate analysis; multivariate clustering and classification; nonparametric analysis; software written by astronomers; spatial statistics; statistical distributions; time series analysis; and visualization tools. StatCodes has received a remarkable high and constant hit rate of 250 hits/week (over 10,000/year) since its inception in mid-1997. It is of interest to scientists both within and outside of astronomy. The most popular sections are multivariate techniques, image analysis, and time series analysis. Hundreds of copies of the ASURV, SLOPES and CENS-TAU codes developed by SCCA scientists were also downloaded from the StatCodes site. In addition to formal SCCA duties, SCCA scientists continued a variety of related activities in astrostatistics, including refereeing of statistically oriented papers submitted to the Astrophysical Journal, talks in meetings including Feigelson's talk to science journalists entitled "The reemergence of astrostatistics" at the American Association for the Advancement of Science meeting, and published papers of astrostatistical content.

Multivariate meta-analysis using individual participant data

PubMed Central

Riley, R. D.; Price, M. J.; Jackson, D.; Wardle, M.; Gueyffier, F.; Wang, J.; Staessen, J. A.; White, I. R.

2016-01-01

When combining results across related studies, a multivariate meta-analysis allows the joint synthesis of correlated effect estimates from multiple outcomes. Joint synthesis can improve efficiency over separate univariate syntheses, may reduce selective outcome reporting biases, and enables joint inferences across the outcomes. A common issue is that within-study correlations needed to fit the multivariate model are unknown from published reports. However, provision of individual participant data (IPD) allows them to be calculated directly. Here, we illustrate how to use IPD to estimate within-study correlations, using a joint linear regression for multiple continuous outcomes and bootstrapping methods for binary, survival and mixed outcomes. In a meta-analysis of 10 hypertension trials, we then show how these methods enable multivariate meta-analysis to address novel clinical questions about continuous, survival and binary outcomes; treatment–covariate interactions; adjusted risk/prognostic factor effects; longitudinal data; prognostic and multiparameter models; and multiple treatment comparisons. Both frequentist and Bayesian approaches are applied, with example software code provided to derive within-study correlations and to fit the models. PMID:26099484

Robotic approach mitigates perioperative morbidity in obese patients following pancreaticoduodenectomy.

PubMed

Girgis, Mark D; Zenati, Mazen S; Steve, Jennifer; Bartlett, David L; Zureikat, Amer; Zeh, Herbert J; Hogg, Melissa E

2017-02-01

The aim was to evaluate the impact of obesity on perioperative outcomes in patients undergoing robotic pancreaticoduodenectomy (RPD) compared to open pancreaticoduodenectomy (OPD). A retrospective review of all pancreaticoduodenectomies from 9/2011 to 4/2015 was performed. Obesity was defined as body mass index (BMI) > 30 kg/m 2 . Of 474 pancreaticoduodenectomies performed: RPD = 213 (45%) and OPD = 261 (55%). A total of 145 (31%) patients were obese (70 RPD, 75 OPD). Obese patients had increased EBL (p = 0.03), pancreatic fistula (B&C; p = 0.077), and wound infection (p = 0.068) compared to the non-obese. For obese patients, RPD had decreased OR time (p = 0.0003), EBL (p < 0.001), and wound infection (p = 0.001) with no difference in Clavien ≥3 complications, margins, LOS or 30-day mortality compared with OPD. In multivariate analysis, obesity was the strongest predictor of Clavien ≥3 (OR 1.6; p = 0.041) and wound infection if BMI > 35 (OR 2.6; p = 0.03). The robotic approach was protective of Clavien ≥3 (OR 0.6; p = 0.03) on univariate analysis and wound infection (OR 0.3; p < 0.001) and grade B/C pancreatic fistula (OR 0.34; p < 0.001) on multivariate analysis. Obese patients are at risk for increased postoperative complications regardless of approach. However, the robotic approach mitigates some of the increased complication rate, while preserving other perioperative outcomes. Published by Elsevier Ltd.

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.

Applications of Multivariate Statistical Techniques for Computer Performance Evaluation.

DTIC Science & Technology

1983-12-01

parameters has on another parameter. VII-f1 *T-. . . . . . . -,z X 71 .7 . V - AFIT/GCS/EE/83D-4 CHAPTER VIII CLUSTER ANALYSIS In data analysis the study...their highest, with bnchmk being 50% greater than the overall average of . 318 seconds and nuprocs being 147% greater than its overall average of 30.8...overall average of . 318 seconds and nuprocs being 147% greater than its overall average of 30.8. These increased values of bnchmk indicate that during

An integrated phenomic approach to multivariate allelic association

PubMed Central

Medland, Sarah Elizabeth; Neale, Michael Churton

2010-01-01

The increased feasibility of genome-wide association has resulted in association becoming the primary method used to localize genetic variants that cause phenotypic variation. Much attention has been focused on the vast multiple testing problems arising from analyzing large numbers of single nucleotide polymorphisms. However, the inflation of experiment-wise type I error rates through testing numerous phenotypes has received less attention. Multivariate analyses can be used to detect both pleiotropic effects that influence a latent common factor, and monotropic effects that operate at a variable-specific levels, whilst controlling for non-independence between phenotypes. In this study, we present a maximum likelihood approach, which combines both latent and variable-specific tests and which may be used with either individual or family data. Simulation results indicate that in the presence of factor-level association, the combined multivariate (CMV) analysis approach performs well with a minimal loss of power as compared with a univariate analysis of a factor or sum score (SS). As the deviation between the pattern of allelic effects and the factor loadings increases, the power of univariate analyses of both factor and SSs decreases dramatically, whereas the power of the CMV approach is maintained. We show the utility of the approach by examining the association between dopamine receptor D2 TaqIA and the initiation of marijuana, tranquilizers and stimulants in data from the Add Health Study. Perl scripts that takes ped and dat files as input and produces Mx scripts and data for running the CMV approach can be downloaded from www.vipbg.vcu.edu/~sarahme/WriteMx. PMID:19707246

The prevalence of anxiety and depression in patients with or without hyperhidrosis (HH).

PubMed

Bahar, Rayeheh; Zhou, Pingyu; Liu, Yudan; Huang, Yuanshen; Phillips, Arlie; Lee, Tim K; Su, Mingwan; Yang, Sen; Kalia, Sunil; Zhang, Xuejun; Zhou, Youwen

2016-12-01

There are conflicting data about the correlation between hyperhidrosis (HH) and anxiety and depression. We sought to determine the prevalence of anxiety and depression in patients with or without HH. We examined 2017 consecutive dermatology outpatients from Vancouver, British Columbia, Canada, and Shanghai, China, using Patient Health Questionnaire-9 and Generalized Anxiety Disorder-7 scales for anxiety and depression assessments. Multivariable logistic regression analysis was performed to evaluate if the impact of HH on anxiety and depression is dependent on demographic factors and diagnoses of the patients' presenting skin conditions. The prevalence of anxiety and depression was 21.3% and 27.2% in patients with HH, respectively, and 7.5% and 9.7% in patients without HH, respectively (P value <.001 for both). There were positive correlations between HH severity and the prevalence of anxiety and depression. Multivariable analysis showed that HH-associated increase in anxiety and depression prevalence is independent of demographic factors and presenting skin conditions. The data from the questionnaires relied on the accuracy of patients' self-reports. Both single variant and multivariable analyses showed a significant association between HH and the prevalence of anxiety and depression in a HH severity-dependent manner. Copyright © 2016 American Academy of Dermatology, Inc. Published by Elsevier Inc. All rights reserved.

Molecular monitoring of epithelial-to-mesenchymal transition in breast cancer cells by means of Raman spectroscopy.

PubMed

Marro, M; Nieva, C; Sanz-Pamplona, R; Sierra, A

2014-09-01

In breast cancer the presence of cells undergoing the epithelial-to-mesenchymal transition is indicative of metastasis progression. Since metabolic features of breast tumour cells are critical in cancer progression and drug resistance, we hypothesized that the lipid content of malignant cells might be a useful indirect measure of cancer progression. In this study Multivariate Curve Resolution was applied to cellular Raman spectra to assess the metabolic composition of breast cancer cells undergoing the epithelial to mesenchymal transition. Multivariate Curve Resolution analysis led to the conclusion that this transition affects the lipid profile of cells, increasing tryptophan but maintaining a low fatty acid content in comparison with highly metastatic cells. Supporting those results, a Partial Least Square-Discriminant analysis was performed to test the ability of Raman spectroscopy to discriminate the initial steps of epithelial to mesenchymal transition in breast cancer cells. We achieved a high level of sensitivity and specificity, 94% and 100%, respectively. In conclusion, Raman microspectroscopy coupled with Multivariate Curve Resolution enables deconvolution and tracking of the molecular content of cancer cells during a biochemical process, being a powerful, rapid, reagent-free and non-invasive tool for identifying metabolic features of breast cancer cell aggressiveness at first stages of malignancy. Copyright © 2014 Elsevier B.V. All rights reserved.

Sarcopenia predicts 1-year mortality in elderly patients undergoing curative gastrectomy for gastric cancer: a prospective study.

PubMed

Huang, Dong-Dong; Chen, Xiao-Xi; Chen, Xi-Yi; Wang, Su-Lin; Shen, Xian; Chen, Xiao-Lei; Yu, Zhen; Zhuang, Cheng-Le

2016-11-01

One-year mortality is vital for elderly oncologic patients undergoing surgery. Recent studies have demonstrated that sarcopenia can predict outcomes after major abdominal surgeries, but the association of sarcopenia and 1-year mortality has never been investigated in a prospective study. We conducted a prospective study of elderly patients (≥65 years) who underwent curative gastrectomy for gastric cancer from July 2014 to July 2015. Sarcopenia was determined by the measurements of muscle mass, handgrip strength, and gait speed. Univariate and multivariate analyses were used to identify the risk factors associated with 1-year mortality. A total of 173 patients were included, in which 52 (30.1 %) patients were identified as having sarcopenia. Twenty-four (13.9 %) patients died within 1 year of surgery. Multivariate analysis showed that sarcopenia was an independent risk factor for 1-year mortality. Area under the receiver operating characteristic curve demonstrated an increased predictive power for 1-year mortality with the inclusion of sarcopenia, from 0.835 to 0.868. Solely low muscle mass was not predictive of 1-year mortality in the multivariate analysis. Sarcopenia is predictive of 1-year mortality in elderly patients undergoing gastric cancer surgery. The measurement of muscle function is important for sarcopenia as a preoperative assessment tool.

Title VII funding and physician practice in rural or low-income areas.

PubMed

Krist, Alex H; Johnson, Robert E; Callahan, David; Woolf, Steven H; Marsland, David

2005-01-01

Whether Title VII funding enhances physician supply in underserved areas has not clearly been established. To determine the relation between Title VII funding in medical school, residency, or both, and the number of family physicians practicing in rural or low-income communities. A retrospective cross sectional analysis was carried out using the 2000 American Academy of Family Physicians physician database, Title VII funding records, and 1990 U.S. Census data. Included were 9,107 family physicians practicing in 9 nationally representative states in the year 2000. Physicians exposed to Title VII funding through medical school and residency were more likely to have their current practice in low-income communities (11.9% vs 9.9%, P< or =.02) and rural areas (24.5% vs 21.8%, P< or =.02). Physicians were more likely to practice in rural communities if they attended medical schools (24.2% vs 21.4%; P =.009) and residencies (24.0% vs 20.3%; P =.011) after the school or program had at least 5 years of Title VII funding vs before. Similar increases were not observed for practice in low-income communities. In a multivariate analysis, exposure to funding and attending an institution with more years of funding independently increased the odds of practicing in rural or low-income communities. Title VII funding is associated with an increase in the family physician workforce in rural and low-income communities. This effect is temporally related to initiation of funding and independently associated with effect in a multivariate analysis, suggesting a potential causal relationship. Whereas the absolute 2% increase in family physicians in these underserved communities may seem modest, it can represent a substantial increase in access to health care for community members.

Hyperferritinemia increases the risk of hyperuricemia in HFE-hereditary hemochromatosis.

PubMed

Flais, Jérémy; Bardou-Jacquet, Edouard; Deugnier, Yves; Coiffier, Guillaume; Perdriger, Aleth; Chalès, Gérard; Ropert, Martine; Loréal, Olivier; Guggenbuhl, Pascal

2017-05-01

Hyperuricemia is becoming increasingly frequent in the population, and is known to be sometimes the cause of gout. The impact of uric acid is still not clearly understood, however. The iron metabolism may interact with the uric acid metabolism. The aim of this study was to examine the relationship between the serum uric acid and serum ferritin levels in a cohort of hemochromatosis patients who were homozygous for the HFE p.Cys282Tyr mutation. 738 patients with the HFE gene mutation Cys282Tyr in the homozygous state were included in the study. The variables measured during the initial evaluation were compared in univariate analysis by Student's t test. In multivariate analysis, linear stepwise regression was used. In the group of hyperuricemic patients, ferritinemia was significantly higher than in the group of non-hyperuricemic patients (1576.7±1387.4μg/l vs. 1095.63±1319.24μg/l, P<0.005). With multivariate analysis, only ferritin and BMI independently explained the uricemia (R 2 =0.258) after adjustment for age, glycemia and CRP. The correlation between uricemia and log(ferritin) with partial regression correlation coefficients was 0.307 (P<0.01). The increase in uricemia is associated with the increase in ferritin in a population of patients who were homozygous for the HFE gene mutation p.Cys282Tyr and this independently of factors commonly associated with hyperuricemia. The increase in uric acid associated with hyperferritinemia, could be a response to the visceral toxicity of excess non-transferrin bound iron linked to oxidative stress via the antioxidant properties of uric acid. Copyright © 2016 Société française de rhumatologie. Published by Elsevier SAS. All rights reserved.

Association of regional body composition with bone mineral density in HIV-infected and HIV-uninfected women: women's interagency HIV study.

PubMed

Sharma, Anjali; Tian, Fang; Yin, Michael T; Keller, Marla J; Cohen, Mardge; Tien, Phyllis C

2012-12-01

To understand how regional body composition affects bone mineral density (BMD) in HIV-infected and HIV-uninfected women. Dual energy x-ray absorptiometry was used to measure regional lean and fat mass and BMD at lumbar spine (LS), total hip (TH), and femoral neck (FN) in 318 HIV-infected and 122 HIV-uninfected Women's Interagency HIV Study participants at baseline and 2 and 5 years later. Total lean and fat mass were measured using bioimpedance analysis. Multivariate marginal linear regression models assessed the association of HIV status and body composition on BMD change. Compared with HIV-uninfected women, HIV-infected women were older (44 vs. 37 years), more likely to be Hepatitis C virus-infected (32% vs. 14%), and postmenopausal (26% vs. 3%) and had lower baseline total fat mass, trunk fat, and leg fat. In multivariate models, increased total lean mass was independently associated with increased BMD at LS, TH, and FN, and total fat mass was associated with increased BMD at TH and FN (all P < 0.05). When total fat was replaced in multivariate models with trunk fat and leg fat, increased trunk fat (and not leg fat) was associated with increased TH and FN BMD (P < 0.001). Total fat and lean mass are strong independent predictors of TH and FN BMD, and lean mass was associated with greater LS BMD. Regardless of HIV status, greater trunk fat (and not leg fat) was associated with increased TH and FN BMD, suggesting that weight-bearing fat may be a more important predictor of BMD in the hip.

Hybrid least squares multivariate spectral analysis methods

DOEpatents

Haaland, David M.

2002-01-01

A set of hybrid least squares multivariate spectral analysis methods in which spectral shapes of components or effects not present in the original calibration step are added in a following estimation or calibration step to improve the accuracy of the estimation of the amount of the original components in the sampled mixture. The "hybrid" method herein means a combination of an initial classical least squares analysis calibration step with subsequent analysis by an inverse multivariate analysis method. A "spectral shape" herein means normally the spectral shape of a non-calibrated chemical component in the sample mixture but can also mean the spectral shapes of other sources of spectral variation, including temperature drift, shifts between spectrometers, spectrometer drift, etc. The "shape" can be continuous, discontinuous, or even discrete points illustrative of the particular effect.

Multivariate generalized multifactor dimensionality reduction to detect gene-gene interactions

PubMed Central

2013-01-01

Background Recently, one of the greatest challenges in genome-wide association studies is to detect gene-gene and/or gene-environment interactions for common complex human diseases. Ritchie et al. (2001) proposed multifactor dimensionality reduction (MDR) method for interaction analysis. MDR is a combinatorial approach to reduce multi-locus genotypes into high-risk and low-risk groups. Although MDR has been widely used for case-control studies with binary phenotypes, several extensions have been proposed. One of these methods, a generalized MDR (GMDR) proposed by Lou et al. (2007), allows adjusting for covariates and applying to both dichotomous and continuous phenotypes. GMDR uses the residual score of a generalized linear model of phenotypes to assign either high-risk or low-risk group, while MDR uses the ratio of cases to controls. Methods In this study, we propose multivariate GMDR, an extension of GMDR for multivariate phenotypes. Jointly analysing correlated multivariate phenotypes may have more power to detect susceptible genes and gene-gene interactions. We construct generalized estimating equations (GEE) with multivariate phenotypes to extend generalized linear models. Using the score vectors from GEE we discriminate high-risk from low-risk groups. We applied the multivariate GMDR method to the blood pressure data of the 7,546 subjects from the Korean Association Resource study: systolic blood pressure (SBP) and diastolic blood pressure (DBP). We compare the results of multivariate GMDR for SBP and DBP to the results from separate univariate GMDR for SBP and DBP, respectively. We also applied the multivariate GMDR method to the repeatedly measured hypertension status from 5,466 subjects and compared its result with those of univariate GMDR at each time point. Results Results from the univariate GMDR and multivariate GMDR in two-locus model with both blood pressures and hypertension phenotypes indicate best combinations of SNPs whose interaction has significant association with risk for high blood pressures or hypertension. Although the test balanced accuracy (BA) of multivariate analysis was not always greater than that of univariate analysis, the multivariate BAs were more stable with smaller standard deviations. Conclusions In this study, we have developed multivariate GMDR method using GEE approach. It is useful to use multivariate GMDR with correlated multiple phenotypes of interests. PMID:24565370

Power analysis for multivariate and repeated measures designs: a flexible approach using the SPSS MANOVA procedure.

PubMed

D'Amico, E J; Neilands, T B; Zambarano, R

2001-11-01

Although power analysis is an important component in the planning and implementation of research designs, it is often ignored. Computer programs for performing power analysis are available, but most have limitations, particularly for complex multivariate designs. An SPSS procedure is presented that can be used for calculating power for univariate, multivariate, and repeated measures models with and without time-varying and time-constant covariates. Three examples provide a framework for calculating power via this method: an ANCOVA, a MANOVA, and a repeated measures ANOVA with two or more groups. The benefits and limitations of this procedure are discussed.

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.

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.

A Statistical Approach for Testing Cross-Phenotype Effects of Rare Variants

PubMed Central

Broadaway, K. Alaine; Cutler, David J.; Duncan, Richard; Moore, Jacob L.; Ware, Erin B.; Jhun, Min A.; Bielak, Lawrence F.; Zhao, Wei; Smith, Jennifer A.; Peyser, Patricia A.; Kardia, Sharon L.R.; Ghosh, Debashis; Epstein, Michael P.

2016-01-01

Increasing empirical evidence suggests that many genetic variants influence multiple distinct phenotypes. When cross-phenotype effects exist, multivariate association methods that consider pleiotropy are often more powerful than univariate methods that model each phenotype separately. Although several statistical approaches exist for testing cross-phenotype effects for common variants, there is a lack of similar tests for gene-based analysis of rare variants. In order to fill this important gap, we introduce a statistical method for cross-phenotype analysis of rare variants using a nonparametric distance-covariance approach that compares similarity in multivariate phenotypes to similarity in rare-variant genotypes across a gene. The approach can accommodate both binary and continuous phenotypes and further can adjust for covariates. Our approach yields a closed-form test whose significance can be evaluated analytically, thereby improving computational efficiency and permitting application on a genome-wide scale. We use simulated data to demonstrate that our method, which we refer to as the Gene Association with Multiple Traits (GAMuT) test, provides increased power over competing approaches. We also illustrate our approach using exome-chip data from the Genetic Epidemiology Network of Arteriopathy. PMID:26942286

Comparison of univariate and multivariate calibration for the determination of micronutrients in pellets of plant materials by laser induced breakdown spectrometry

NASA Astrophysics Data System (ADS)

Braga, Jez Willian Batista; Trevizan, Lilian Cristina; Nunes, Lidiane Cristina; Rufini, Iolanda Aparecida; Santos, Dário, Jr.; Krug, Francisco José

2010-01-01

The application of laser induced breakdown spectrometry (LIBS) aiming the direct analysis of plant materials is a great challenge that still needs efforts for its development and validation. In this way, a series of experimental approaches has been carried out in order to show that LIBS can be used as an alternative method to wet acid digestions based methods for analysis of agricultural and environmental samples. The large amount of information provided by LIBS spectra for these complex samples increases the difficulties for selecting the most appropriated wavelengths for each analyte. Some applications have suggested that improvements in both accuracy and precision can be achieved by the application of multivariate calibration in LIBS data when compared to the univariate regression developed with line emission intensities. In the present work, the performance of univariate and multivariate calibration, based on partial least squares regression (PLSR), was compared for analysis of pellets of plant materials made from an appropriate mixture of cryogenically ground samples with cellulose as the binding agent. The development of a specific PLSR model for each analyte and the selection of spectral regions containing only lines of the analyte of interest were the best conditions for the analysis. In this particular application, these models showed a similar performance, but PLSR seemed to be more robust due to a lower occurrence of outliers in comparison to the univariate method. Data suggests that efforts dealing with sample presentation and fitness of standards for LIBS analysis must be done in order to fulfill the boundary conditions for matrix independent development and validation.

The structural equation analysis of childhood abuse, adult stressful life events, and temperaments in major depressive disorders and their influence on refractoriness

PubMed Central

Toda, Hiroyuki; Inoue, Takeshi; Tsunoda, Tomoya; Nakai, Yukiei; Tanichi, Masaaki; Tanaka, Teppei; Hashimoto, Naoki; Nakato, Yasuya; Nakagawa, Shin; Kitaichi, Yuji; Mitsui, Nobuyuki; Boku, Shuken; Tanabe, Hajime; Nibuya, Masashi; Yoshino, Aihide; Kusumi, Ichiro

2015-01-01

Background Previous studies have shown the interaction between heredity and childhood stress or life events on the pathogenesis of a major depressive disorder (MDD). In this study, we tested our hypothesis that childhood abuse, affective temperaments, and adult stressful life events interact and influence the diagnosis of MDD. Patients and methods A total of 170 healthy controls and 98 MDD patients were studied using the following self-administered questionnaire surveys: the Patient Health Questionnaire-9 (PHQ-9), the Life Experiences Survey, the Temperament Evaluation of the Memphis, Pisa, Paris, and San Diego Autoquestionnaire, and the Child Abuse and Trauma Scale (CATS). The data were analyzed with univariate analysis, multivariable analysis, and structural equation modeling. Results The neglect scores of the CATS indirectly predicted the diagnosis of MDD through cyclothymic and anxious temperament scores of the Temperament Evaluation of the Memphis, Pisa, Paris, and San Diego Autoquestionnaire in the structural equation modeling. Two temperaments – cyclothymic and anxious – directly predicted the diagnosis of MDD. The validity of this result was supported by the results of the stepwise multivariate logistic regression analysis as follows: three factors – neglect, cyclothymic, and anxious temperaments – were significant predictors of MDD. Neglect and the total CATS scores were also predictors of remission vs treatment-resistance in MDD patients independently of depressive symptoms. Limitations The sample size was small for the comparison between the remission and treatment-resistant groups in MDD patients in multivariable analysis. Conclusion This study suggests that childhood abuse, especially neglect, indirectly predicted the diagnosis of MDD through increased affective temperaments. The important role as a mediator of affective temperaments in the effect of childhood abuse on MDD was suggested. PMID:26316754

Multi-Sample Cluster Analysis Using Akaike’s Information Criterion.

DTIC Science & Technology

1982-12-20

of Likelihood Criteria for I)fferent Hypotheses," in P. A. Krishnaiah (Ed.), Multivariate Analysis-Il, New York: Academic Press. [5] Fisher, R. A...Methods of Simultaneous Inference in MANOVA," in P. R. Krishnaiah (Ed.), rultivariate Analysis-Il, New York: Academic Press. [8) Kendall, M. G. (1966...1982), Applied Multivariate Statisti- cal-Analysis, Englewood Cliffs: Prentice-Mall, Inc. [1U] Krishnaiah , P. R. (1969), "Simultaneous Test

Docking and multivariate methods to explore HIV-1 drug-resistance: a comparative analysis

NASA Astrophysics Data System (ADS)

Almerico, Anna Maria; Tutone, Marco; Lauria, Antonino

2008-05-01

In this paper we describe a comparative analysis between multivariate and docking methods in the study of the drug resistance to the reverse transcriptase and the protease inhibitors. In our early papers we developed a simple but efficient method to evaluate the features of compounds that are less likely to trigger resistance or are effective against mutant HIV strains, using the multivariate statistical procedures PCA and DA. In the attempt to create a more solid background for the prediction of susceptibility or resistance, we carried out a comparative analysis between our previous multivariate approach and molecular docking study. The intent of this paper is not only to find further support to the results obtained by the combined use of PCA and DA, but also to evidence the structural features, in terms of molecular descriptors, similarity, and energetic contributions, derived from docking, which can account for the arising of drug-resistance against mutant strains.

SUGGESTIONS FOR OPTIMIZED PLANNING OF MULTIVARIATE MONITORING OF ATMOSPHERIC POLLUTION

EPA Science Inventory

Recent work in factor analysis of multivariate data sets has shown that variables with little signal should not be included in the factor analysis. Work also shows that rotational ambiguity is reduced if sources impacting a receptor have both large and small contributions. Thes...

Multivariate Meta-Analysis Using Individual Participant Data

ERIC Educational Resources Information Center

Riley, R. D.; Price, M. J.; Jackson, D.; Wardle, M.; Gueyffier, F.; Wang, J.; Staessen, J. A.; White, I. R.

2015-01-01

When combining results across related studies, a multivariate meta-analysis allows the joint synthesis of correlated effect estimates from multiple outcomes. Joint synthesis can improve efficiency over separate univariate syntheses, may reduce selective outcome reporting biases, and enables joint inferences across the outcomes. A common issue is…

Marital status independently predicts testis cancer survival--an analysis of the SEER database.

PubMed

Abern, Michael R; Dude, Annie M; Coogan, Christopher L

2012-01-01

Previous reports have shown that married men with malignancies have improved 10-year survival over unmarried men. We sought to investigate the effect of marital status on 10-year survival in a U.S. population-based cohort of men with testis cancer. We examined 30,789 cases of testis cancer reported to the Surveillance, Epidemiology, and End Results (SEER 17) database between 1973 and 2005. All staging were converted to the 1997 AJCC TNM system. Patients less than 18 years of age at time of diagnosis were excluded. A subgroup analysis of patients with stages I or II non-seminomatous germ cell tumors (NSGCT) was performed. Univariate analysis using t-tests and χ(2) tests compared characteristics of patients separated by marital status. Multivariate analysis was performed using a Cox proportional hazard model to generate Kaplan-Meier survival curves, with all-cause and cancer-specific mortality as the primary endpoints. 20,245 cases met the inclusion criteria. Married men were more likely to be older (38.9 vs. 31.4 years), Caucasian (94.4% vs. 92.1%), stage I (73.1% vs. 61.4%), and have seminoma as the tumor histology (57.3% vs. 43.4%). On multivariate analysis, married status (HR 0.58, P < 0.001) and Caucasian race (HR 0.66, P < 0.001) independently predicted improved overall survival, while increased age (HR 1.05, P < 0.001), increased stage (HR 1.53-6.59, P < 0.001), and lymphoid (HR 4.05, P < 0.001), or NSGCT (HR 1.89, P < 0.001) histology independently predicted death. Similarly, on multivariate analysis, married status (HR 0.60, P < 0.001) and Caucasian race (HR 0.57, P < 0.001) independently predicted improved testis cancer-specific survival, while increased age (HR 1.03, P < 0.001), increased stage (HR 2.51-15.67, P < 0.001), and NSGCT (HR 2.54, P < 0.001) histology independently predicted testis cancer-specific death. A subgroup analysis of men with stages I or II NSGCT revealed similar predictors of all-cause survival as the overall cohort, with retroperitoneal lymph node dissection (RPLND) as an additional independent predictor of overall survival (HR 0.59, P = 0.001), despite equal rates of the treatment between married and unmarried men (44.8% vs. 43.4%, P = 0.33). Marital status is an independent predictor of improved overall and cancer-specific survival in men with testis cancer. In men with stages I or II NSGCT, RPLND is an additional predictor of improved overall survival. Marital status does not appear to influence whether men undergo RPLND. Copyright © 2012 Elsevier Inc. All rights reserved.

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.

A multivariable model for predicting the frictional behaviour and hydration of the human skin.

PubMed

Veijgen, N K; van der Heide, E; Masen, M A

2013-08-01

The frictional characteristics of skin-object interactions are important when handling objects, in the assessment of perception and comfort of products and materials and in the origins and prevention of skin injuries. In this study, based on statistical methods, a quantitative model is developed that describes the friction behaviour of human skin as a function of the subject characteristics, contact conditions, the properties of the counter material as well as environmental conditions. Although the frictional behaviour of human skin is a multivariable problem, in literature the variables that are associated with skin friction have been studied using univariable methods. In this work, multivariable models for the static and dynamic coefficients of friction as well as for the hydration of the skin are presented. A total of 634 skin-friction measurements were performed using a recently developed tribometer. Using a statistical analysis, previously defined potential influential variables were linked to the static and dynamic coefficient of friction and to the hydration of the skin, resulting in three predictive quantitative models that descibe the friction behaviour and the hydration of human skin respectively. Increased dynamic coefficients of friction were obtained from older subjects, on the index finger, with materials with a higher surface energy at higher room temperatures, whereas lower dynamic coefficients of friction were obtained at lower skin temperatures, on the temple with rougher contact materials. The static coefficient of friction increased with higher skin hydration, increasing age, on the index finger, with materials with a higher surface energy and at higher ambient temperatures. The hydration of the skin was associated with the skin temperature, anatomical location, presence of hair on the skin and the relative air humidity. Predictive models have been derived for the static and dynamic coefficient of friction using a multivariable approach. These two coefficients of friction show a strong correlation. Consequently the two multivariable models resemble, with the static coefficient of friction being on average 18% lower than the dynamic coefficient of friction. The multivariable models in this study can be used to describe the data set that was the basis for this study. Care should be taken when generalising these results. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Stochastic modelling of temperatures affecting the in situ performance of a solar-assisted heat pump: The multivariate approach and physical interpretation

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

Loveday, D.L.; Craggs, C.

Box-Jenkins-based multivariate stochastic modeling is carried out using data recorded from a domestic heating system. The system comprises an air-source heat pump sited in the roof space of a house, solar assistance being provided by the conventional tile roof acting as a radiation absorber. Multivariate models are presented which illustrate the time-dependent relationships between three air temperatures - at external ambient, at entry to, and at exit from, the heat pump evaporator. Using a deterministic modeling approach, physical interpretations are placed on the results of the multivariate technique. It is concluded that the multivariate Box-Jenkins approach is a suitable techniquemore » for building thermal analysis. Application to multivariate Box-Jenkins approach is a suitable technique for building thermal analysis. Application to multivariate model-based control is discussed, with particular reference to building energy management systems. It is further concluded that stochastic modeling of data drawn from a short monitoring period offers a means of retrofitting an advanced model-based control system in existing buildings, which could be used to optimize energy savings. An approach to system simulation is suggested.« less

Recent applications of multivariate data analysis methods in the authentication of rice and the most analyzed parameters: A review.

PubMed

Maione, Camila; Barbosa, Rommel Melgaço

2018-01-24

Rice is one of the most important staple foods around the world. Authentication of rice is one of the most addressed concerns in the present literature, which includes recognition of its geographical origin and variety, certification of organic rice and many other issues. Good results have been achieved by multivariate data analysis and data mining techniques when combined with specific parameters for ascertaining authenticity and many other useful characteristics of rice, such as quality, yield and others. This paper brings a review of the recent research projects on discrimination and authentication of rice using multivariate data analysis and data mining techniques. We found that data obtained from image processing, molecular and atomic spectroscopy, elemental fingerprinting, genetic markers, molecular content and others are promising sources of information regarding geographical origin, variety and other aspects of rice, being widely used combined with multivariate data analysis techniques. Principal component analysis and linear discriminant analysis are the preferred methods, but several other data classification techniques such as support vector machines, artificial neural networks and others are also frequently present in some studies and show high performance for discrimination of rice.

MicroRNA-34c-5p is related to recurrence in laryngeal squamous cell carcinoma.

PubMed

Re, Massimo; Çeka, Artan; Rubini, Corrado; Ferrante, Luigi; Zizzi, Antonio; Gioacchini, Federico M; Tulli, Michele; Spazzafumo, Liana; Sellari-Franceschini, Stefano; Procopio, Antonio D; Olivieri, Fabiola

2015-09-01

Altered microRNA expression has been found in many cancer types, including laryngeal squamous cell carcinoma (LSCC). We investigated the association of LSCC-related miR-34c-5p with disease-free survival and overall survival. Retrospective cohort study. Expression levels of miR-34c-5p were detected in 90 LSCC formalin-fixed paraffin-embedded tissues by reverse-transcription quantitative polymerase chain reaction. Overall survival and disease-free survival were evaluated using the Kaplan-Meier method, and multivariate analysis was performed using Cox proportional hazard analysis. A downregulation of miR-34c-5p expression significantly correlated with worse disease-free and overall survival. In the multivariate analysis, low miR-34c-5p expression was associated with an increased risk of recurrence. A downregulation of miR-34c-5p in LSCC is independently associated with unfavorable disease-free survival, suggesting that miR-34c-5p might be a promising marker for evaluating the risk of recurrences. NA. © 2015 The American Laryngological, Rhinological and Otological Society, Inc.

Detection and discrimination of microorganisms on various substrates with quantum cascade laser spectroscopy

NASA Astrophysics Data System (ADS)

Padilla-Jiménez, Amira C.; Ortiz-Rivera, William; Rios-Velazquez, Carlos; Vazquez-Ayala, Iris; Hernández-Rivera, Samuel P.

2014-06-01

Investigations focusing on devising rapid and accurate methods for developing signatures for microorganisms that could be used as biological warfare agents' detection, identification, and discrimination have recently increased significantly. Quantum cascade laser (QCL)-based spectroscopic systems have revolutionized many areas of defense and security including this area of research. In this contribution, infrared spectroscopy detection based on QCL was used to obtain the mid-infrared (MIR) spectral signatures of Bacillus thuringiensis, Escherichia coli, and Staphylococcus epidermidis. These bacteria were used as microorganisms that simulate biothreats (biosimulants) very truthfully. The experiments were conducted in reflection mode with biosimulants deposited on various substrates including cardboard, glass, travel bags, wood, and stainless steel. Chemometrics multivariate statistical routines, such as principal component analysis regression and partial least squares coupled to discriminant analysis, were used to analyze the MIR spectra. Overall, the investigated infrared vibrational techniques were useful for detecting target microorganisms on the studied substrates, and the multivariate data analysis techniques proved to be very efficient for classifying the bacteria and discriminating them in the presence of highly IR-interfering media.

The Japanese Histologic Classification and T-score in the Oxford Classification system could predict renal outcome in Japanese IgA nephropathy patients.

PubMed

Kaihan, Ahmad Baseer; Yasuda, Yoshinari; Katsuno, Takayuki; Kato, Sawako; Imaizumi, Takahiro; Ozeki, Takaya; Hishida, Manabu; Nagata, Takanobu; Ando, Masahiko; Tsuboi, Naotake; Maruyama, Shoichi

2017-12-01

The Oxford Classification is utilized globally, but has not been fully validated. In this study, we conducted a comparative analysis between the Oxford Classification and Japanese Histologic Classification (JHC) to predict renal outcome in Japanese patients with IgA nephropathy (IgAN). A retrospective cohort study including 86 adult IgAN patients was conducted. The Oxford Classification and the JHC were evaluated by 7 independent specialists. The JHC, MEST score in the Oxford Classification, and crescents were analyzed in association with renal outcome, defined as a 50% increase in serum creatinine. In multivariate analysis without the JHC, only the T score was significantly associated with renal outcome. While, a significant association was revealed only in the JHC on multivariate analysis with JHC. The JHC and T score in the Oxford Classification were associated with renal outcome among Japanese patients with IgAN. Superiority of the JHC as a predictive index should be validated with larger study population and cohort studies in different ethnicities.

Opportunities for multivariate analysis of open spatial datasets to characterize urban flooding risks

NASA Astrophysics Data System (ADS)

Gaitan, S.; ten Veldhuis, J. A. E.

2015-06-01

Cities worldwide are challenged by increasing urban flood risks. Precise and realistic measures are required to reduce flooding impacts. However, currently implemented sewer and topographic models do not provide realistic predictions of local flooding occurrence during heavy rain events. Assessing other factors such as spatially distributed rainfall, socioeconomic characteristics, and social sensing, may help to explain probability and impacts of urban flooding. Several spatial datasets have been recently made available in the Netherlands, including rainfall-related incident reports made by citizens, spatially distributed rain depths, semidistributed socioeconomic information, and buildings age. Inspecting the potential of this data to explain the occurrence of rainfall related incidents has not been done yet. Multivariate analysis tools for describing communities and environmental patterns have been previously developed and used in the field of study of ecology. The objective of this paper is to outline opportunities for these tools to explore urban flooding risks patterns in the mentioned datasets. To that end, a cluster analysis is performed. Results indicate that incidence of rainfall-related impacts is higher in areas characterized by older infrastructure and higher population density.

Longitudinal costs of caring for people with Alzheimer's disease.

PubMed

Gillespie, Paddy; O'Shea, Eamon; Cullinan, John; Buchanan, Jacqui; Bobula, Joel; Lacey, Loretto; Gallagher, Damien; Mhaolain, Aine Ni; Lawlor, Brian

2015-05-01

There has been an increasing interest in the relationship between severity of disease and costs in the care of people with dementia. Much of the current evidence is based on cross-sectional data, suggesting the need to examine trends over time for this important and growing cohort of the population. This paper estimates resource use and costs of care based on longitudinal data for 72 people with dementia in Ireland. Data were collected from the Enhancing Care in Alzheimer's Disease (ECAD) study at two time points: baseline and follow-up, two years later. Patients' dependence on others was measured using the Dependence Scale (DS), while patient function was measured using the Disability Assessment for Dementia (DAD) scale. Univariate and multivariate analysis were used to explore the effects of a range of variables on formal and informal care costs. Total costs of formal and informal care over six months rose from €9,266 (Standard Deviation (SD): 12,947) per patient at baseline to €21,266 (SD: 26,883) at follow-up, two years later. This constituted a statistically significant (p = 0.0014) increase in costs over time, driven primarily by an increase in estimated informal care costs. In the multivariate analysis, a one-point increase in the DS score, that is a one-unit increase in patient's dependence on others, was associated with a 19% increase in total costs (p = 0.0610). Higher levels of dependence in people with Alzheimer's disease are significantly associated with increased costs of informal care as the disease progresses. Formal care services did not respond to increased dependence in people with dementia, leaving it to families to fill the caring gap, mainly through increased supervision with the progress of disease.

Factors predictive of increased influenza and pneumococcal vaccination coverage in long-term care facilities: the CMS-CDC standing orders program Project.

PubMed

Bardenheier, Barbara H; Shefer, Abigail; McKibben, Linda; Roberts, Henry; Rhew, David; Bratzler, Dale

2005-01-01

Between 1999 and 2002, a multistate demonstration project was conducted in long-term care facilities (LTCFs) to encourage implementation of standing orders programs (SOP) as evidence-based vaccine delivery strategies to increase influenza and pneumococcal vaccination coverage in LTCFs. Examine predictors of increase in influenza and pneumococcal vaccination coverage in LTCFs. Intervention study. Self-administered surveys of LTCFs merged with data from OSCAR (On-line Survey Certification and Reporting System) and immunization coverage was abstracted from residents' medical charts in LTCFs. Twenty LTCFs were sampled from 9 intervention and 5 control states in the 2000 to 2001 influenza season for baseline and during the 2001 to 2002 influenza season for postintervention. Each state's quality improvement organization (QIO) promoted the use of standing orders for immunizations as well as other strategies to increase immunization coverage among LTCF residents. Multivariate analysis included Poisson regression to determine independent predictors of at least a 10 percentage-point increase in facility influenza and pneumococcal vaccination coverage. Forty-two (20%) and 59 (28%) of the facilities had at least a 10 percentage-point increase in influenza and pneumococcal immunizations, respectively. In the multivariate analysis, predictors associated with increase in influenza vaccination coverage included adoption of requirement in written immunization protocol to document refusals, less-demanding consent requirements, lower baseline influenza coverage, and small facility size. Factors associated with increase in pneumococcal vaccination coverage included adoption of recording pneumococcal immunizations in a consistent place, affiliation with a multifacility chain, and provision of resource materials. To improve the health of LTCF residents, strategies should be considered that increase immunization coverage, including written protocol for immunizations and documentation of refusals, documenting vaccination status in a consistent place in medical records, and minimal consent requirements for vaccinations.

Determinants of Adiponectin Levels in Patients with Chronic Systolic Heart Failure

PubMed Central

Biolo, Andreia; Shibata, Rei; Ouchi, Noriyuki; Kihara, Shinji; Sonoda, Mina; Walsh, Kenneth; Sam, Flora

2010-01-01

Adiponectin, an adipocytokine, is secreted by adipocytes and mediates anti-hypertrophic and anti-inflammatory effects in the heart. Plasma concentrations of adiponectin are decreased in obesity, insulin resistance and obesity-associated conditions such as hypertension and coronary heart disease. However, a paradoxical increase in adiponectin levels is observed in human systolic heart failure (HF). We sought to investigate the determinants of adiponectin levels in patients with chronic systolic HF. Total adiponectin levels were measured in 99 patients with stable HF and left ventricular (LV) ejection fraction (EF) <40%. Determinants of adiponectin levels by univariate analysis were included in a multivariate linear regression model. At baseline patients were 62% black, 63% male, mean age of 60±13 years, LVEF of 21±9% and a body mass index (BMI) of 30.6±6.7kg/m2. Mean adiponectin levels were 15.8±15µg/ml. Beta-blocker use, BMI, and blood urea nitrogen (BUN) were significant determinants of adiponectin levels by multivariate analysis. LV mass, structure, and LVEF were not related to adiponectin levels by multivariate analysis. Interestingly, the effect of beta-blocker therapy was most marked in non-obese patients with BMI < 30kg/m2. In conclusion, in chronic systolic HF patients, beta-blocker therapy is correlated with lower adiponectin levels, especially in non-obese patients. This relation should be taken into account when studying the complex role of adiponectin in chronic systolic HF. PMID:20381668

Fermentanomics: Relating quality attributes of a monoclonal antibody to cell culture process variables and raw materials using multivariate data analysis.

PubMed

Rathore, Anurag S; Kumar Singh, Sumit; Pathak, Mili; Read, Erik K; Brorson, Kurt A; Agarabi, Cyrus D; Khan, Mansoor

2015-01-01

Fermentanomics is an emerging field of research and involves understanding the underlying controlled process variables and their effect on process yield and product quality. Although major advancements have occurred in process analytics over the past two decades, accurate real-time measurement of significant quality attributes for a biotech product during production culture is still not feasible. Researchers have used an amalgam of process models and analytical measurements for monitoring and process control during production. This article focuses on using multivariate data analysis as a tool for monitoring the internal bioreactor dynamics, the metabolic state of the cell, and interactions among them during culture. Quality attributes of the monoclonal antibody product that were monitored include glycosylation profile of the final product along with process attributes, such as viable cell density and level of antibody expression. These were related to process variables, raw materials components of the chemically defined hybridoma media, concentration of metabolites formed during the course of the culture, aeration-related parameters, and supplemented raw materials such as glucose, methionine, threonine, tryptophan, and tyrosine. This article demonstrates the utility of multivariate data analysis for correlating the product quality attributes (especially glycosylation) to process variables and raw materials (especially amino acid supplements in cell culture media). The proposed approach can be applied for process optimization to increase product expression, improve consistency of product quality, and target the desired quality attribute profile. © 2015 American Institute of Chemical Engineers.

NCA-LDAS land analysis: Development and performance of a multisensory, multivariate land data assimilation for the National Climate Assessment

NASA Astrophysics Data System (ADS)

Kumar, S.; Jasinski, M. F.; Mocko, D. M.; Rodell, M.; Borak, J.; Li, B.; Beaudoing, H. K.; Peters-Lidard, C. D.

2017-12-01

This presentation will describe one of the first successful examples of multisensor, multivariate land data assimilation, encompassing a large suite of soil moisture, snow depth, snow cover and irrigation intensity environmental data records (EDRs) from Scanning Multi-channel Microwave Radiometer (SMMR), the Special Sensor Microwave Imager (SSM/I), the Advanced Scatterometer (ASCAT), the Moderate-Resolution Imaging Spectroradiometer (MODIS), the Advanced Microwave Scanning Radiometer (AMSR-E and AMSR2), the Soil Moisture Ocean Salinity (SMOS) mission and the Soil Moisture Active Passive (SMAP) mission. The analysis is performed using the NASA Land Information System (LIS) as an enabling tool for the U.S. National Climate Assessment (NCA). The performance of NCA Land Data Assimilation System (NCA-LDAS) is evaluated by comparing to a number of hydrological reference data products. Results indicate that multivariate assimilation provides systematic improvements in simulated soil moisture and snow depth, with marginal effects on the accuracy of simulated streamflow and ET. An important conclusion is that across all evaluated variables, assimilation of data from increasingly more modern sensors (e.g. SMOS, SMAP, AMSR2, ASCAT) produces more skillful results than assimilation of data from older sensors (e.g. SMMR, SSM/I, AMSR-E). The evaluation also indicates high skill of NCA-LDAS when compared with other land analysis products. Further, drought indicators based on NCA-LDAS output suggest a trend of longer and more severe droughts over parts of Western U.S. during 1979-2015, particularly in the Southwestern U.S.

Micro vs. macrodiscectomy: Does use of the microscope reduce complication rates?

PubMed

Murphy, Meghan E; Hakim, Jeffrey S; Kerezoudis, Panagiotis; Alvi, Mohammed Ali; Ubl, Daniel S; Habermann, Elizabeth B; Bydon, Mohamad

2017-01-01

A single level discectomy is one of the most common procedures performed by spine surgeons. While some practitioners utilize the microscope, others do not. We postulate improved visualization with an intraoperative microscope decreases complications and inferior outcomes. A multicenter surgical registry was utilized for this retrospective cohort analysis. Patients with degenerative spinal diagnoses undergoing elective single level discectomies from 2010 to 2014 were included. Univariate analysis was performed comparing demographics, patient characteristics, operative data, and outcomes for discectomies performed with and without a microscope. Multivariable logistic regression analysis was then applied to compare outcomes of micro- and macrodiscectomies. Query of the registry yielded 23,583 patients meeting inclusion criteria. On univariate analysis the microscope was used in a greater proportion of the oldest age group as well as Hispanic white patients. Patients with any functional dependency, history of congestive heart failure, chronic corticosteroid use, or anemia (hematocrit<35%) also had greater proportions of microdiscectomies. Thoracic region discectomies more frequently involved use of the microscope than cervical or lumbar discectomies (25.0% vs. 16.4% and 13.0%, respectively, p<0.001). Median operative time (IQR) was increased in microscope cases [80min (60, 108) vs. 74min (54, 102), p<0.001]. Of the patients that required reoperation within 30days, 2.5% of them had undergone a microdiscectomy compared to 1.9% who had undergone a macrodiscectomy, p=0.044. On multivariable analysis, microdiscectomies were more likely to have an operative time in the top quartile of discectomy operative times, ≥103min (OR 1.256, 95% CI 1.151-1.371, p<0.001). In regards to other multivariable outcome models for any complication, surgical site infection, dural tears, reoperation, and readmission, no significant association with microdiscectomy was found. The use of the microscope was found to significantly increase the odds of longer operative time, but not influence rates of postoperative complications. Thus, without evidence from this study that the microscope decreases complications, the use of the microscope should be at the surgeon's discretion, validating the use of both macro and micro approaches to discectomy as acceptable standards of care. Copyright © 2016 Elsevier B.V. All rights reserved.

Nutritional Intervention: A Secondary Analysis of Its Effect on Malnourished Colombian Pre-Schoolers.

ERIC Educational Resources Information Center

Bejar, Isaac I.

1981-01-01

Effects of nutritional supplementation on physical development of malnourished children was analyzed by univariate and multivariate methods for the analysis of repeated measures. Results showed that the nutritional treatment was successful, but it was necessary to resort to the multivariate approach. (Author/GK)

A Multivariate Descriptive Model of Motivation for Orthodontic Treatment.

ERIC Educational Resources Information Center

Hackett, Paul M. W.; And Others

1993-01-01

Motivation for receiving orthodontic treatment was studied among 109 young adults, and a multivariate model of the process is proposed. The combination of smallest scale analysis and Partial Order Scalogram Analysis by base Coordinates (POSAC) illustrates an interesting methodology for health treatment studies and explores motivation for dental…

Exploring Pattern of Socialisation Conditions and Human Development by Nonlinear Multivariate Analysis.

ERIC Educational Resources Information Center

Grundmann, Matthias

Following the assumptions of ecological socialization research, adequate analysis of socialization conditions must take into account the multilevel and multivariate structure of social factors that impact on human development. This statement implies that complex models of family configurations or of socialization factors are needed to explain the…

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…

Applied Statistics: From Bivariate through Multivariate Techniques [with CD-ROM

ERIC Educational Resources Information Center

Warner, Rebecca M.

2007-01-01

This book provides a clear introduction to widely used topics in bivariate and multivariate statistics, including multiple regression, discriminant analysis, MANOVA, factor analysis, and binary logistic regression. The approach is applied and does not require formal mathematics; equations are accompanied by verbal explanations. Students are asked…

Evaluation of Meterorite Amono Acid Analysis Data Using Multivariate Techniques

NASA Technical Reports Server (NTRS)

McDonald, G.; Storrie-Lombardi, M.; Nealson, K.

1999-01-01

The amino acid distributions in the Murchison carbonaceous chondrite, Mars meteorite ALH84001, and ice from the Allan Hills region of Antarctica are shown, using a multivariate technique known as Principal Component Analysis (PCA), to be statistically distinct from the average amino acid compostion of 101 terrestrial protein superfamilies.

MULTIVARIATE ANALYSIS ON LEVELS OF SELECTED METALS, PARTICULATE MATTER, VOC, AND HOUSEHOLD CHARACTERISTICS AND ACTIVITIES FROM THE MIDWESTERN STATES NHEXAS

EPA Science Inventory

Microenvironmental and biological/personal monitoring information were collected during the National Human Exposure Assessment Survey (NHEXAS), conducted in the six states comprising U.S. EPA Region Five. They have been analyzed by multivariate analysis techniques with general ...

A Review of Multivariate Methods for Multimodal Fusion of Brain Imaging Data

PubMed Central

Adali, Tülay; Yu, Qingbao; Calhoun, Vince D.

2011-01-01

The development of various neuroimaging techniques is rapidly improving the measurements of brain function/structure. However, despite improvements in individual modalities, it is becoming increasingly clear that the most effective research approaches will utilize multi-modal fusion, which takes advantage of the fact that each modality provides a limited view of the brain. The goal of multimodal fusion is to capitalize on the strength of each modality in a joint analysis, rather than a separate analysis of each. This is a more complicated endeavor that must be approached more carefully and efficient methods should be developed to draw generalized and valid conclusions from high dimensional data with a limited number of subjects. Numerous research efforts have been reported in the field based on various statistical approaches, e.g. independent component analysis (ICA), canonical correlation analysis (CCA) and partial least squares (PLS). In this review paper, we survey a number of multivariate methods appearing in previous reports, which are performed with or without prior information and may have utility for identifying potential brain illness biomarkers. We also discuss the possible strengths and limitations of each method, and review their applications to brain imaging data. PMID:22108139

Militarism and mortality. An international analysis of arms spending and infant death rates.

PubMed

Woolhandler, S; Himmelstein, D U

1985-06-15

Examination of data from 141 countries showed that infant mortality rates for 1979 were positively correlated with the proportion of gross national product devoted to military spending (r = 0.23, p less than 0.01) and negatively correlated with indicators of economic development, health resources, and social spending. In a multivariate analysis controlling for per caput gross national product, arms spending remained a significant positive predictor of infant mortality rate (p less than 0.0001), while the proportion of the population with access to clean water, the number of teachers per head, and caloric consumption per head were negative predictors. The multivariate model accounted for much of the observed variance in infant mortality rate (R2 = 0.78, p less than 0.0001), and showed good fit to similar data for the year 1972 (R2 = 0.80, p less than 0.0001). The model was also predictive of infant mortality rates in subgroup analysis of underdeveloped, middle developed, and developed nations. Analysis of time trends confirmed that an increase in military spending presages a poor record of improvement in infant mortality rate. These findings support the hypothesis that arms spending is causally related to infant mortality.

A diagnostic analysis of the VVP single-doppler retrieval technique

NASA Technical Reports Server (NTRS)

Boccippio, Dennis J.

1995-01-01

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

Implementation of physicochemical and sensory analysis in conjunction with multivariate analysis towards assessing olive oil authentication/adulteration.

PubMed

Arvanitoyannis, Ioannis S; Vlachos, Antonios

2007-01-01

The authenticity of products labeled as olive oils, and in particular as virgin olive oils, stands for a very important issue both in terms of its health and commercial aspects. In view of the continuously increasing interest in virgin olive oil therapeutic properties, the traditional methods of characterization and physical and sensory analysis were further enriched with more advanced and sophisticated methods such as HPLC-MS, HPLC-GC/C/IRMS, RPLC-GC, DEPT, and CSIA among others. The results of both traditional and "novel" methods were treated both by means of classical multivariate analysis (cluster, principal component, correspondence, canonical, and discriminant) and artificial intelligence methods showing that nowadays the adulteration of virgin olive oil with seed oil is detectable at very low percentages, sometimes even at less than 1%. Furthermore, the detection of geographical origin of olive oil is equally feasible and much more accurate in countries like Italy and Spain where databases of physical/chemical properties exist. However, this geographical origin classification can also be accomplished in the absence of such databases provided that an adequate number of oil samples are used and the parameters studied have "discriminating power."

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.

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

Classical least squares multivariate spectral analysis

DOEpatents

Haaland, David M.

2002-01-01

An improved classical least squares multivariate spectral analysis method that adds spectral shapes describing non-calibrated components and system effects (other than baseline corrections) present in the analyzed mixture to the prediction phase of the method. These improvements decrease or eliminate many of the restrictions to the CLS-type methods and greatly extend their capabilities, accuracy, and precision. One new application of PACLS includes the ability to accurately predict unknown sample concentrations when new unmodeled spectral components are present in the unknown samples. Other applications of PACLS include the incorporation of spectrometer drift into the quantitative multivariate model and the maintenance of a calibration on a drifting spectrometer. Finally, the ability of PACLS to transfer a multivariate model between spectrometers is demonstrated.

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.

Differences in Brain Glucose Metabolism During Preparation for 131I Ablation in Thyroid Cancer Patients: Thyroid Hormone Withdrawal Versus Recombinant Human Thyrotropin.

PubMed

Jeong, Hyeonseok S; Choi, Eun Kyoung; Song, In-Uk; Chung, Yong-An; Park, Jong-Sik; Oh, Jin Kyoung

2017-01-01

In preparation for 131 I ablation, temporary withdrawal of thyroid hormone is commonly used in patients with thyroid cancer after total thyroidectomy. The current study aimed to investigate brain glucose metabolism and its relationships with mood or cognitive function in these patients using 18 F-fluoro-2-deoxyglucose positron emission tomography ( 18 F-FDG-PET). A total of 40 consecutive adult patients with thyroid carcinoma who had undergone total thyroidectomy were recruited for this cross-sectional study. At the time of assessment, 20 patients were hypothyroid after two weeks of thyroid hormone withdrawal, while 20 received thyroid hormone replacement therapy and were euthyroid. All participants underwent brain 18 F-FDG-PET scans and completed mood questionnaires and cognitive tests. Multivariate spatial covariance analysis and univariate voxel-wise analysis were applied for the image data. The hypothyroid patients were more anxious and depressed than the euthyroid participants. The multivariate covariance analysis showed increases in glucose metabolism primarily in the bilateral insula and surrounding areas and concomitant decreases in the parieto-occipital regions in the hypothyroid group. The level of thyrotropin was positively associated with the individual expression of the covariance pattern. The decreased 18 F-FDG uptake in the right cuneus cluster from the univariate analysis was correlated with the increased thyrotropin level and greater depressive symptoms in the hypothyroid group. These results suggest that temporary hypothyroidism, even for a short period, may induce impairment in glucose metabolism and related affective symptoms.

Waddell non-organic signs: new evidence suggests somatic amplification among outpatient chronic pain patients.

PubMed

Wygant, Dustin B; Arbisi, Paul A; Bianchini, Kevin J; Umlauf, Robert L

2017-04-01

Waddell et al. identified a set of eight non-organic signs in 1980. There has been controversy about their meaning, particularly with respect to their use as validity indicators. The current study examined the Waddell signs in relation to measures of somatic amplification or over-reporting in a sample of outpatient chronic pain patients. We examined the degree to which these signs were associated with measures of over-reporting. This study examined scores on the Waddell signs in relation to over-reporting indicators in an outpatient chronic pain sample. We examined 230 chronic pain patients treated at a multidisciplinary pain clinic. The majority of these patients presented with primary back or spinal injuries. The outcome measures used in the study were Waddell signs, Modified Somatic Perception Questionnaire, Pain Disability Index, and the Minnesota Multiphasic Personality Inventory-2 Restructured Form. We examined Waddell signs using multivariate analysis of variance (MANOVA) and analysis of variance (ANOVA), receiver operating characteristic analysis, classification accuracy, and relative risk ratios. Multivariate analysis of variance and ANOVA showed a significant association between Waddell signs and somatic amplification. Classification analyses showed increased odds of somatic amplification at a Waddell score of 2 or 3. Our results found significant evidence of an association between Waddell signs and somatic over-reporting. Elevated scores on the Waddell signs (particularly scores higher than 2 and 3) were associated with increased odds of exhibiting somatic over-reporting. Copyright © 2016 Elsevier Inc. All rights reserved.

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

Genomic Analysis of Complex Microbial Communities in Wounds

DTIC Science & Technology

2012-01-01

thoroughly in the ecology literature. Permutation Multivariate Analysis of Variance ( PerMANOVA ). We used PerMANOVA to test the null-hypothesis of no...difference between the bacterial communities found within a single wound compared to those from different patients (α = 0.05). PerMANOVA is a...permutation-based version of the multivariate analysis of variance (MANOVA). PerMANOVA uses the distances between samples to partition variance and

The relationships between breast volume, breast dense volume and volumetric breast density with body mass index, body fat mass and ethnicity

NASA Astrophysics Data System (ADS)

Zakariyah, N.; Pathy, N. B.; Taib, N. A. M.; Rahmat, K.; Judy, C. W.; Fadzil, F.; Lau, S.; Ng, K. H.

2016-03-01

It has been shown that breast density and obesity are related to breast cancer risk. The aim of this study is to investigate the relationships of breast volume, breast dense volume and volumetric breast density (VBD) with body mass index (BMI) and body fat mass (BFM) for the three ethnic groups (Chinese, Malay and Indian) in Malaysia. We collected raw digital mammograms from 2450 women acquired on three digital mammography systems. The mammograms were analysed using Volpara software to obtain breast volume, breast dense volume and VBD. Body weight, BMI and BFM of the women were measured using a body composition analyser. Multivariable logistic regression was used to determine the independent predictors of increased overall breast volume, breast dense volume and VBD. Indians have highest breast volume and breast dense volume followed by Malays and Chinese. While Chinese are highest in VBD, followed by Malay and Indian. Multivariable analysis showed that increasing BMI and BFM were independent predictors of increased overall breast volume and dense volume. Moreover, BMI and BFM were independently and inversely related to VBD.

An anthropometric study of Serbian metal industry workers.

PubMed

Omić, S; Brkić, V K Spasojevic; Golubović, T A; Brkić, A D; Klarin, M M

2017-01-01

There are recent studies using new industrial workers' anthropometric data in different countries, but for Serbia such data are not available. This study is the first anthropometric study of Serbian metal industry workers in the country, whose labor force is increasingly employed both on local and international markets. The metal industry is one of Serbia's most important economic sectors. To this end, we collected the basic static anthropometric dimensions of 122 industrial workers and used principal components analysis (PCA) to obtain multivariate anthropometric models. To confirm the results, the dimensions of an additional 50 workers were collected. The PCA methodology was also compared with the percentile method. Comparing both data samples, we found that 96% of the participants are within the tolerance ellipsoid. According to this study, multivariate modeling covers a larger extent of the intended population proportion compared to percentiles. The results of this research are useful for the designers of metal industry workstations. This information can be used in dimensioning the workplace, thus increasing job satisfaction, reducing the risk of injuries and fatalities, and consequently increasing productivity and safety.

Geographically Sourcing Cocaine’s Origin – Delineation of the Nineteen Major Coca Growing Regions in South America

PubMed Central

Mallette, Jennifer R.; Casale, John F.; Jordan, James; Morello, David R.; Beyer, Paul M.

2016-01-01

Previously, geo-sourcing to five major coca growing regions within South America was accomplished. However, the expansion of coca cultivation throughout South America made sub-regional origin determinations increasingly difficult. The former methodology was recently enhanced with additional stable isotope analyses (2H and 18O) to fully characterize cocaine due to the varying environmental conditions in which the coca was grown. An improved data analysis method was implemented with the combination of machine learning and multivariate statistical analysis methods to provide further partitioning between growing regions. Here, we show how the combination of trace cocaine alkaloids, stable isotopes, and multivariate statistical analyses can be used to classify illicit cocaine as originating from one of 19 growing regions within South America. The data obtained through this approach can be used to describe current coca cultivation and production trends, highlight trafficking routes, as well as identify new coca growing regions. PMID:27006288

[Methods of the multivariate statistical analysis of so-called polyetiological diseases using the example of coronary heart disease].

PubMed

Lifshits, A M

1979-01-01

General characteristics of the multivariate statistical analysis (MSA) is given. Methodical premises and criteria for the selection of an adequate MSA method applicable to pathoanatomic investigations of the epidemiology of multicausal diseases are presented. The experience of using MSA with computors and standard computing programs in studies of coronary arteries aterosclerosis on the materials of 2060 autopsies is described. The combined use of 4 MSA methods: sequential, correlational, regressional, and discriminant permitted to quantitate the contribution of each of the 8 examined risk factors in the development of aterosclerosis. The most important factors were found to be the age, arterial hypertension, and heredity. Occupational hypodynamia and increased fatness were more important in men, whereas diabetes melitus--in women. The registration of this combination of risk factors by MSA methods provides for more reliable prognosis of the likelihood of coronary heart disease with a fatal outcome than prognosis of the degree of coronary aterosclerosis.

The impact of operative time on complications after plastic surgery: a multivariate regression analysis of 1753 cases.

PubMed

Hardy, Krista L; Davis, Kathryn E; Constantine, Ryan S; Chen, Mo; Hein, Rachel; Jewell, James L; Dirisala, Karunakar; Lysikowski, Jerzy; Reed, Gary; Kenkel, Jeffrey M

2014-05-01

Little evidence within plastic surgery literature supports the precept that longer operative times lead to greater morbidity. The authors investigate surgery duration as a determinant of morbidity, with the goal of defining a clinically relevant time for increased risk. A retrospective chart review was conducted of patients who underwent a broad range of complex plastic surgical procedures (n = 1801 procedures) at UT Southwestern Medical Center in Dallas, Texas, from January 1, 2008 to January 31, 2012. Adjusting for possible confounders, multivariate logistic regression assessed surgery duration as an independent predictor of morbidity. To define a cutoff for increased risk, incidence of complications was compared among quintiles of surgery duration. Stratification by type of surgery controlled for procedural complexity. A total of 1753 cases were included in multivariate analyses with an overall complication rate of 27.8%. Most operations were combined (75.8%), averaging 4.9 concurrent procedures. Each hour increase in surgery duration was associated with a 21% rise in odds of morbidity (P < .0001). Compared with the first quintile of operative time (<2.0 hours), there was no change in complications until after 3.1 hours of surgery (odds ratio, 1.6; P = .017), with progressively greater odds increases of 3.1 times after 4.5 hours (P < .0001) and 4.7 times after 6.8 hours (P < .0001). When stratified by type of surgery, longer operations continued to be associated with greater morbidity. Surgery duration is an independent predictor of complications, with a significantly increased risk above 3 hours. Although procedural complexity undoubtedly affects morbidity, operative time should factor into surgical decision making.

Multivariate moment closure techniques for stochastic kinetic models

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

Lakatos, Eszter, E-mail: e.lakatos13@imperial.ac.uk; Ale, Angelique; Kirk, Paul D. W.

2015-09-07

Stochastic effects dominate many chemical and biochemical processes. Their analysis, however, can be computationally prohibitively expensive and a range of approximation schemes have been proposed to lighten the computational burden. These, notably the increasingly popular linear noise approximation and the more general moment expansion methods, perform well for many dynamical regimes, especially linear systems. At higher levels of nonlinearity, it comes to an interplay between the nonlinearities and the stochastic dynamics, which is much harder to capture correctly by such approximations to the true stochastic processes. Moment-closure approaches promise to address this problem by capturing higher-order terms of the temporallymore » evolving probability distribution. Here, we develop a set of multivariate moment-closures that allows us to describe the stochastic dynamics of nonlinear systems. Multivariate closure captures the way that correlations between different molecular species, induced by the reaction dynamics, interact with stochastic effects. We use multivariate Gaussian, gamma, and lognormal closure and illustrate their use in the context of two models that have proved challenging to the previous attempts at approximating stochastic dynamics: oscillations in p53 and Hes1. In addition, we consider a larger system, Erk-mediated mitogen-activated protein kinases signalling, where conventional stochastic simulation approaches incur unacceptably high computational costs.« less

Determinants of elevated healthcare utilization in patients with COPD.

PubMed

Simon-Tuval, Tzahit; Scharf, Steven M; Maimon, Nimrod; Bernhard-Scharf, Barbara J; Reuveni, Haim; Tarasiuk, Ariel

2011-01-13

Chronic obstructive pulmonary disease (COPD) imparts a substantial economic burden on western health systems. Our objective was to analyze the determinants of elevated healthcare utilization among patients with COPD in a single-payer health system. Three-hundred eighty-nine adults with COPD were matched 1:3 to controls by age, gender and area of residency. Total healthcare cost 5 years prior recruitment and presence of comorbidities were obtained from a computerized database. Health related quality of life (HRQoL) indices were obtained using validated questionnaires among a subsample of 177 patients. Healthcare utilization was 3.4-fold higher among COPD patients compared with controls (p < 0.001). The "most-costly" upper 25% of COPD patients (n = 98) consumed 63% of all costs. Multivariate analysis revealed that independent determinants of being in the "most costly" group were (OR; 95% CI): age-adjusted Charlson Comorbidity Index (1.09; 1.01-1.2), history of: myocardial infarct (2.87; 1.5-5.5), congestive heart failure (3.52; 1.9-6.4), mild liver disease (3.83; 1.3-11.2) and diabetes (2.02; 1.1-3.6). Bivariate analysis revealed that cost increased as HRQoL declined and severity of airflow obstruction increased but these were not independent determinants in a multivariate analysis. Comorbidity burden determines elevated utilization for COPD patients. Decision makers should prioritize scarce health care resources to a better care management of the "most costly" patients.

Households with young children and use of freely distributed bednets in rural Madagascar.

PubMed

Krezanoski, Paul J; Comfort, Alison B; Tsai, Alexander C; Bangsberg, David R

2014-03-01

Malaria infections are the leading cause of death for children in Madagascar. Insecticide-treated bednets offer effective prevention, but it is unclear how well free bednet distribution programs reach young children. We conducted a secondary analysis of a free bednet distribution program in Madagascar from 2007-2008. Interviews were performed at baseline and 6 months. Principal components analysis was used to construct a wealth and malaria knowledge index. Coverage efficiency was calculated as coverage of children per bednet owned. Univariable and multivariable regressions were used to determine predictors of bednet use. Bednet use, among the 560 households in the study, increased from 6 to 91% after 6 months. Coverage efficiency increased from 1.29 to 1.56 children covered per bednet owned. In multivariable analysis, having a child under 5 years of age was the only variable associated with bednet use (OR 9.10; p=0.001), yielding a 99% likelihood of using a bednet (95% CI 96.4 to 99.9%) versus 82% (95% CI 72.2 to 88.4%) in households without young children. This free bednet distribution program achieved high levels of adherence after 6 months. Household presence of children was associated with bednet use, but not household income or education, suggesting that distribution to priority groups may help overcome traditional barriers to adoption in some settings.

Gastroduodenal Ulcers and ABO Blood Group: the Japan Nurses' Health Study (JNHS).

PubMed

Alkebsi, Lobna; Ideno, Yuki; Lee, Jung-Su; Suzuki, Shosuke; Nakajima-Shimada, Junko; Ohnishi, Hiroshi; Sato, Yasunori; Hayashi, Kunihiko

2018-01-05

Although several studies have shown that blood type O is associated with increased risk of peptic ulcer, few studies have investigated these associations in Japan. We sought to investigate the association between the ABO blood group and risk of gastroduodenal ulcers (GDU) using combined analysis of both retrospective and prospective data from a large cohort study of Japanese women, the Japan Nurses' Health Study (JNHS; n = 15,019). The impact of the ABO blood group on GDU risk was examined using Cox regression analysis to estimate hazard ratios (HRs) and 95% confidence intervals (CI), with adjustment for potential confounders. Compared with women with non-O blood types (A, B, and AB), women with blood type O had a significantly increased risk of GDU from birth (multivariable-adjusted HR 1.18; 95% CI, 1.04-1.34). Moreover, the highest cumulative incidence of GDU was observed in women born pre-1956 with blood type O. In a subgroup analysis stratified by birth year (pre-1956 or post-1955), the multivariable-adjusted HR of women with blood type O was 1.22 (95% CI, 1.00-1.49) and 1.15 (95% CI, 0.98-1.35) in the pre-1956 and post-1955 groups, respectively. In this large, combined, ambispective cohort study of Japanese women, older women with blood type O had a higher risk of developing GDU than those with other blood types.

Risk factors in laparoscopic cholecystectomy: a multivariate analysis.

PubMed

Kanakala, Venkatesh; Borowski, David W; Pellen, Michael G C; Dronamraju, Shridhar S; Woodcock, Sean A A; Seymour, Keith; Attwood, Stephen E A; Horgan, Liam F

2011-01-01

Laparoscopic cholecystectomy (LC) is the operation of choice in the treatment of symptomatic gallstone disease. The aim of this study is to identify risk factors for LC, outcomes include operating time, length of stay, conversion rate, morbidity and mortality. All patients undergoing LC between 1998 and 2007 in a single district general hospital. Risk factors were examined using uni- and multivariate analysis. 2117 patients underwent LC, with 1706 (80.6%) patients operated on electively. Male patients were older, had more co-morbidity and more emergency surgery than females. The median post-operative hospital stay was one day, and was positively correlated with the complexity of surgery. Conversion rates were higher in male patients (OR 1.47, p = 0.047) than in females, and increased with co-morbidity. Emergency surgery (OR 1.75, p = 0.005), male gender (OR 1.68, p = 0.005), increasing co-morbidity and complexity of surgery were all positively associated with the incidence of complications (153/2117 [7.2%]), whereas only male gender was significantly associated with mortality (OR 5.71, p = 0.025). Adverse outcome from LC is particularly associated with male gender, but also the patient's co-morbidity, complexity and urgency of surgery. Risk-adjusted outcome analysis is desirable to ensure an informed consent process. Copyright © 2011 Surgical Associates Ltd. Published by Elsevier Ltd. All rights reserved.

In situ X-ray diffraction analysis of (CF x) n batteries: signal extraction by multivariate analysis

DOE PAGES

Rodriguez, Mark A.; Keenan, Michael R.; Nagasubramanian, Ganesan

2007-11-10

In this study, (CF x) n cathode reaction during discharge has been investigated using in situ X-ray diffraction (XRD). Mathematical treatment of the in situ XRD data set was performed using multivariate curve resolution with alternating least squares (MCR–ALS), a technique of multivariate analysis. MCR–ALS analysis successfully separated the relatively weak XRD signal intensity due to the chemical reaction from the other inert cell component signals. The resulting dynamic reaction component revealed the loss of (CF x) n cathode signal together with the simultaneous appearance of LiF by-product intensity. Careful examination of the XRD data set revealed an additional dynamicmore » component which may be associated with the formation of an intermediate compound during the discharge process.« less

Hybrid least squares multivariate spectral analysis methods

DOEpatents

Haaland, David M.

2004-03-23

A set of hybrid least squares multivariate spectral analysis methods in which spectral shapes of components or effects not present in the original calibration step are added in a following prediction or calibration step to improve the accuracy of the estimation of the amount of the original components in the sampled mixture. The hybrid method herein means a combination of an initial calibration step with subsequent analysis by an inverse multivariate analysis method. A spectral shape herein means normally the spectral shape of a non-calibrated chemical component in the sample mixture but can also mean the spectral shapes of other sources of spectral variation, including temperature drift, shifts between spectrometers, spectrometer drift, etc. The shape can be continuous, discontinuous, or even discrete points illustrative of the particular effect.

A functional U-statistic method for association analysis of sequencing data.

PubMed

Jadhav, Sneha; Tong, Xiaoran; Lu, Qing

2017-11-01

Although sequencing studies hold great promise for uncovering novel variants predisposing to human diseases, the high dimensionality of the sequencing data brings tremendous challenges to data analysis. Moreover, for many complex diseases (e.g., psychiatric disorders) multiple related phenotypes are collected. These phenotypes can be different measurements of an underlying disease, or measurements characterizing multiple related diseases for studying common genetic mechanism. Although jointly analyzing these phenotypes could potentially increase the power of identifying disease-associated genes, the different types of phenotypes pose challenges for association analysis. To address these challenges, we propose a nonparametric method, functional U-statistic method (FU), for multivariate analysis of sequencing data. It first constructs smooth functions from individuals' sequencing data, and then tests the association of these functions with multiple phenotypes by using a U-statistic. The method provides a general framework for analyzing various types of phenotypes (e.g., binary and continuous phenotypes) with unknown distributions. Fitting the genetic variants within a gene using a smoothing function also allows us to capture complexities of gene structure (e.g., linkage disequilibrium, LD), which could potentially increase the power of association analysis. Through simulations, we compared our method to the multivariate outcome score test (MOST), and found that our test attained better performance than MOST. In a real data application, we apply our method to the sequencing data from Minnesota Twin Study (MTS) and found potential associations of several nicotine receptor subunit (CHRN) genes, including CHRNB3, associated with nicotine dependence and/or alcohol dependence. © 2017 WILEY PERIODICALS, INC.

Multivariate Generalizability Analysis of Automated Scoring for Short Answer Items of Social Studies in Large-Scale Assessment

ERIC Educational Resources Information Center

Sung, Kyung Hee; Noh, Eun Hee; Chon, Kyong Hee

2017-01-01

With increased use of constructed response items in large scale assessments, the cost of scoring has been a major consideration (Noh et al. in KICE Report RRE 2012-6, 2012; Wainer and Thissen in "Applied Measurement in Education" 6:103-118, 1993). In response to the scoring cost issues, various forms of automated system for scoring…

Association between breastfeeding duration, non-nutritive sucking habits and dental arch dimensions in deciduous dentition: a cross-sectional study.

PubMed

Agarwal, Shiv Shankar; Nehra, Karan; Sharma, Mohit; Jayan, Balakrishna; Poonia, Anish; Bhattal, Hiteshwar

2014-10-31

This cross-sectional retrospective study was conducted to determine association between breastfeeding duration, non-nutritive sucking habits, dental arch transverse diameters, posterior crossbite and anterior open bite in deciduous dentition. 415 children (228 males and 187 females), 4 to 6 years old, from a mixed Indian population were clinically examined. Based on written questionnaire answered by parents, children were divided into two groups: group 1 (breastfed for <6 months (n = 158)) and group 2 (breastfed for ≥6 months (n = 257)). The associations were analysed using chi-square test (P < 0.05 taken as statistically significant). Odds ratio (OR) was calculated to determine the strength of associations tested. Multivariate logistic regression analysis was done for obtaining independent predictors of posterior crossbite and maxillary and mandibular IMD (Inter-molar distance) and ICD (Inter-canine distance). Non-nutritive sucking (NNS) was present in 15.18% children (20.3% in group 1 as compared to 12.1% in group 2 (P = 0.024)). The average ICD and IMD in maxilla and average IMD in mandible were significantly higher among group 2 as compared to group 1 (P < 0.01). In mandible, average ICD did not differ significantly between the two groups (P = 0.342). The distribution of anterior open bite did not differ significantly between the two groups (P = 0.865). The distribution of posterior crossbite was significantly different between the two groups (P = 0.001). OR assessment (OR = 1.852) revealed that group 1 had almost twofold higher prevalence of NNS habits than group 2. Multivariate logistic regression analysis revealed that the first group had independently fourfold increased risk of developing crossbite compared to the second group (OR = 4.3). Multivariate linear regression analysis also revealed that age and breastfeeding duration were the most significant determinants of ICD and IMD. An increased prevalence of NNS in the first group suggests that NNS is a dominant variable in the association between breastfeeding duration and reduced intra-arch transverse diameters which leads to increased prevalence of posterior crossbites as seen in our study. Mandibular inter-canine width is however unaffected due to a lowered tongue posture seen in these children.

Bivariate and multivariate analyses of the influence of blood variables of patients submitted to Roux-en-Y gastric bypass on the stability of erythrocyte membrane against the chaotropic action of ethanol.

PubMed

de Arvelos, Leticia Ramos; Rocha, Vanessa Custódio Afonso; Felix, Gabriela Pereira; da Cunha, Cleine Chagas; Bernardino Neto, Morun; da Silva Garrote Filho, Mario; de Fátima Pinheiro, Conceição; Resende, Elmiro Santos; Penha-Silva, Nilson

2013-03-01

The stability of the erythrocyte membrane, which is essential for the maintenance of cell functions, occurs in a critical region of fluidity, which depends largely on its composition and the composition and characteristics of the medium. As the composition of the erythrocyte membrane is influenced by several blood variables, the stability of the erythrocyte membrane must have relations with them. The present study aimed to evaluate, by bivariate and multivariate statistical analyses, the correlations and causal relationships between hematologic and biochemical variables and the stability of the erythrocyte membrane against the chaotropic action of ethanol. The validity of this type of analysis depends on the homogeneity of the population and on the variability of the studied parameters, conditions that can be filled by patients who undergo bariatric surgery by the technique of Roux-en-Y gastric bypass since they will suffer feeding restrictions that have great impact on their blood composition. Pathway analysis revealed that an increase in hemoglobin leads to decreased stability of the cell, probably through a process mediated by an increase in mean corpuscular volume. Furthermore, an increase in the mean corpuscular hemoglobin (MCH) leads to an increase in erythrocyte membrane stability, probably because higher values of MCH are associated with smaller quantities of red blood cells and a larger contact area between the cell membrane and ethanol present in the medium.

The Influence of Total Nodes Examined, Number of Positive Nodes, and Lymph Node Ratio on Survival After Surgical Resection and Adjuvant Chemoradiation for Pancreatic Cancer: A Secondary Analysis of RTOG 9704

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

Showalter, Timothy N.; Winter, Kathryn A.; Berger, Adam C., E-mail: adam.berger@jefferson.edu

2011-12-01

Purpose: Lymph node status is an important predictor of survival in pancreatic cancer. We performed a secondary analysis of Radiation Therapy Oncology Group (RTOG) 9704, an adjuvant chemotherapy and chemoradiation trial, to determine the influence of lymph node factors-number of positive nodes (NPN), total nodes examined (TNE), and lymph node ratio (LNR ratio of NPN to TNE)-on OS and disease-free survival (DFS). Patient and Methods: Eligible patients from RTOG 9704 form the basis of this secondary analysis of lymph node parameters. Actuarial estimates for OS and DFS were calculated using Kaplan-Meier methods. Cox proportional hazards models were performed to evaluatemore » associations of NPN, TNE, and LNR with OS and DFS. Multivariate Cox proportional hazards models were also performed. Results: There were 538 patients enrolled in the RTOG 9704 trial. Of these, 445 patients were eligible with lymph nodes removed. Overall median NPN was 1 (min-max, 0-18). Increased NPN was associated with worse OS (HR = 1.06, p = 0.001) and DFS (HR = 1.05, p = 0.01). In multivariate analyses, both NPN and TNE were associated with OS and DFS. TNE > 12, and >15 were associated with increased OS for all patients, but not for node-negative patients (n = 142). Increased LNR was associated with worse OS (HR = 1.01, p < 0.0001) and DFS (HR = 1.006, p = 0.002). Conclusion: In patients who undergo surgical resection followed by adjuvant chemoradiation, TNE, NPN, and LNR are associated with OS and DFS. This secondary analysis of a prospective, cooperative group trial supports the influence of these lymph node parameters on outcomes after surgery and adjuvant therapy using contemporary techniques.« less

FAILURE OF RADIOACTIVE IODINE IN TREATMENT OF HYPERTHYROIDISM

PubMed Central

Schneider, David F.; Sonderman, Philip E.; Jones, Michaela F.; Ojomo, Kristin A.; Chen, Herbert; Jaume, Juan C.; Elson, Diane F.; Perlman, Scott B.; Sippel, Rebecca S.

2015-01-01

Introduction Persistent or recurrent hyperthyroidism after treatment with radioactive iodine (RAI) is common, and many patients require either additional doses or surgery before they are cured. The purpose of this study was to identify patterns and predictors of failure of RAI in patients with hyperthyroidism. Methods We conducted a retrospective review of patients treated with RAI from 2007–2010. Failure of RAI was defined as receipt of additional dose(s) and/or total thyroidectomy. Using a Cox proportional hazards model, we conducted univariate analysis to identify factors associated with failure of RAI. A final multivariate model was then constructed with significant (p < 0.05) variables from the univariate analysis. Results Of the 325 patients analyzed, 74 patients (22.8%) failed initial RAI treatment. 53 (71.6%) received additional RAI, 13 (17.6%) received additional RAI followed by surgery, and the remaining 8 (10.8%) were cured after thyroidectomy. The percentage of patients who failed decreased in a step-wise fashion as RAI dose increased. Similarly, the incidence of failure increased as the presenting T3 level increased. Sensitivity analysis revealed that RAI doses < 12.5 mCi were associated with failure while initial T3 and free T4 levels of at least 4.5 pg/mL and 2.3 ng/dL, respectively, were associated with failure. In the final multivariate analysis, higher T4 (HR 1.13, 95% CI 1.02–1.26, p=0.02) and methimazole treatment (HR 2.55, 95% CI 1.22–5.33, p=0.01) were associated with failure. Conclusions Laboratory values at presentation can predict which patients with hyperthyroidism are at risk for failing RAI treatment. Higher doses of RAI or surgical referral may prevent the need for repeat RAI in selected patients. PMID:25001092

Failure of radioactive iodine in the treatment of hyperthyroidism.

PubMed

Schneider, David F; Sonderman, Philip E; Jones, Michaela F; Ojomo, Kristin A; Chen, Herbert; Jaume, Juan C; Elson, Diane F; Perlman, Scott B; Sippel, Rebecca S

2014-12-01

Persistent or recurrent hyperthyroidism after treatment with radioactive iodine (RAI) is common and many patients require either additional doses or surgery before they are cured. The purpose of this study was to identify patterns and predictors of failure of RAI in patients with hyperthyroidism. We conducted a retrospective review of patients treated with RAI from 2007 to 2010. Failure of RAI was defined as receipt of additional dose(s) and/or total thyroidectomy. Using a Cox proportional hazards model, we conducted univariate analysis to identify factors associated with failure of RAI. A final multivariate model was then constructed with significant (p < 0.05) variables from the univariate analysis. Of the 325 patients analyzed, 74 patients (22.8 %) failed initial RAI treatment, 53 (71.6 %) received additional RAI, 13 (17.6 %) received additional RAI followed by surgery, and the remaining 8 (10.8 %) were cured after thyroidectomy. The percentage of patients who failed decreased in a stepwise fashion as RAI dose increased. Similarly, the incidence of failure increased as the presenting T3 level increased. Sensitivity analysis revealed that RAI doses <12.5 mCi were associated with failure while initial T3 and free T4 levels of at least 4.5 pg/mL and 2.3 ng/dL, respectively, were associated with failure. In the final multivariate analysis, higher T4 (hazard ratio [HR] 1.13; 95 % confidence interval [CI] 1.02-1.26; p = 0.02) and methimazole treatment (HR 2.55; 95 % CI 1.22-5.33; p = 0.01) were associated with failure. Laboratory values at presentation can predict which patients with hyperthyroidism are at risk for failing RAI treatment. Higher doses of RAI or surgical referral may prevent the need for repeat RAI in selected patients.

Bacteriuria and urinary tract infection after female urodynamic studies: risk factors and microbiological analysis.

PubMed

Nóbrega, Mônica Martins; Auge, Antonio Pedro Flores; de Toledo, Luis Gustavo Morato; da Silva Carramão, Sílvia; Frade, Armando Brites; Salles, Mauro José Costa

2015-10-01

This study was conducted to determine risk factors for infectious complications after urodynamic study (UDS) in women, which can assist clinicians in identifying high-risk subjects who would benefit from antibiotic prophylaxis before UDS. In this prospective cohort study, we studied 232 women who underwent UDS at Santa Casa de São Paulo School of Medical Sciences between June 2013 and June 2014. Women ranging in age from 26 to 84 years who had urinary incontinence, pelvic organ prolapse, or voiding dysfunction were required to collect urine samples at 7 days before, on the day of, and 3-5 days after UDS. Urine cultures with >100,000 CFU/mL were considered positive. Risk factors associated with bacteriuria and urinary tract infection (UTI) after UDS were evaluated using multivariate analysis with multiple logistic regression. Two hundred thirty-two out of 257 women were subjected to further analysis. The incidence of bacteriuria, transient bacteriuria, and UTI after UDS was 11.6%, 7.3%, and 4.3%, respectively. On multivariate analysis, hypothyroidism (P = .04), body mass index (BMI) >30 (P = .025), and advanced pelvic organ prolapse (P = .021) were associated with a significantly increased risk of bacteriuria; however, only BMI >30 (P = .02) was associated with an increased risk for UTI. The rate of infectious complications after UDS was low, and advanced pelvic organ prolapse and hypothyroidism increased the risk for bacteriuria. However, only BMI >30 was associated with bacteriuria and UTI after UDS. Copyright © 2015 Association for Professionals in Infection Control and Epidemiology, Inc. Published by Elsevier Inc. All rights reserved.

Multivariate geomorphic analysis of forest streams: Implications for assessment of land use impacts on channel condition

Treesearch

Richard. D. Wood-Smith; John M. Buffington

1996-01-01

Multivariate statistical analyses of geomorphic variables from 23 forest stream reaches in southeast Alaska result in successful discrimination between pristine streams and those disturbed by land management, specifically timber harvesting and associated road building. Results of discriminant function analysis indicate that a three-variable model discriminates 10...

Modeling Associations among Multivariate Longitudinal Categorical Variables in Survey Data: A Semiparametric Bayesian Approach

ERIC Educational Resources Information Center

Tchumtchoua, Sylvie; Dey, Dipak K.

2012-01-01

This paper proposes a semiparametric Bayesian framework for the analysis of associations among multivariate longitudinal categorical variables in high-dimensional data settings. This type of data is frequent, especially in the social and behavioral sciences. A semiparametric hierarchical factor analysis model is developed in which the…

Use of Multivariate Linkage Analysis for Dissection of a Complex Cognitive Trait

PubMed Central

Marlow, Angela J.; Fisher, Simon E.; Francks, Clyde; MacPhie, I. Laurence; Cherny, Stacey S.; Richardson, Alex J.; Talcott, Joel B.; Stein, John F.; Monaco, Anthony P.; Cardon, Lon R.

2003-01-01

Replication of linkage results for complex traits has been exceedingly difficult, owing in part to the inability to measure the precise underlying phenotype, small sample sizes, genetic heterogeneity, and statistical methods employed in analysis. Often, in any particular study, multiple correlated traits have been collected, yet these have been analyzed independently or, at most, in bivariate analyses. Theoretical arguments suggest that full multivariate analysis of all available traits should offer more power to detect linkage; however, this has not yet been evaluated on a genomewide scale. Here, we conduct multivariate genomewide analyses of quantitative-trait loci that influence reading- and language-related measures in families affected with developmental dyslexia. The results of these analyses are substantially clearer than those of previous univariate analyses of the same data set, helping to resolve a number of key issues. These outcomes highlight the relevance of multivariate analysis for complex disorders for dissection of linkage results in correlated traits. The approach employed here may aid positional cloning of susceptibility genes in a wide spectrum of complex traits. PMID:12587094

Multivariate meta-analysis using individual participant data.

PubMed

Riley, R D; Price, M J; Jackson, D; Wardle, M; Gueyffier, F; Wang, J; Staessen, J A; White, I R

2015-06-01

When combining results across related studies, a multivariate meta-analysis allows the joint synthesis of correlated effect estimates from multiple outcomes. Joint synthesis can improve efficiency over separate univariate syntheses, may reduce selective outcome reporting biases, and enables joint inferences across the outcomes. A common issue is that within-study correlations needed to fit the multivariate model are unknown from published reports. However, provision of individual participant data (IPD) allows them to be calculated directly. Here, we illustrate how to use IPD to estimate within-study correlations, using a joint linear regression for multiple continuous outcomes and bootstrapping methods for binary, survival and mixed outcomes. In a meta-analysis of 10 hypertension trials, we then show how these methods enable multivariate meta-analysis to address novel clinical questions about continuous, survival and binary outcomes; treatment-covariate interactions; adjusted risk/prognostic factor effects; longitudinal data; prognostic and multiparameter models; and multiple treatment comparisons. Both frequentist and Bayesian approaches are applied, with example software code provided to derive within-study correlations and to fit the models. © 2014 The Authors. Research Synthesis Methods published by John Wiley & Sons, Ltd.

An analysis of fracture trace patterns in areas of flat-lying sedimentary rocks for the detection of buried geologic structure. [Kansas and Texas

NASA Technical Reports Server (NTRS)

Podwysocki, M. H.

1974-01-01

Two study areas in a cratonic platform underlain by flat-lying sedimentary rocks were analyzed to determine if a quantitative relationship exists between fracture trace patterns and their frequency distributions and subsurface structural closures which might contain petroleum. Fracture trace lengths and frequency (number of fracture traces per unit area) were analyzed by trend surface analysis and length frequency distributions also were compared to a standard Gaussian distribution. Composite rose diagrams of fracture traces were analyzed using a multivariate analysis method which grouped or clustered the rose diagrams and their respective areas on the basis of the behavior of the rays of the rose diagram. Analysis indicates that the lengths of fracture traces are log-normally distributed according to the mapping technique used. Fracture trace frequency appeared higher on the flanks of active structures and lower around passive reef structures. Fracture trace log-mean lengths were shorter over several types of structures, perhaps due to increased fracturing and subsequent erosion. Analysis of rose diagrams using a multivariate technique indicated lithology as the primary control for the lower grouping levels. Groupings at higher levels indicated that areas overlying active structures may be isolated from their neighbors by this technique while passive structures showed no differences which could be isolated.

Kidney donation after circulatory death in a country with a high number of brain dead donors: 10-year experience in Belgium.

PubMed

Jochmans, Ina; Darius, Tom; Kuypers, Dirk; Monbaliu, Diethard; Goffin, Eric; Mourad, Michel; Ledinh, Hieu; Weekers, Laurent; Peeters, Patrick; Randon, Caren; Bosmans, Jean-Louis; Roeyen, Geert; Abramowicz, Daniel; Hoang, Anh-Dung; De Pauw, Luc; Rahmel, Axel; Squifflet, Jean-Paul; Pirenne, Jacques

2012-08-01

Worldwide shortage of standard brain dead donors (DBD) has revived the use of kidneys donated after circulatory death (DCD). We reviewed the Belgian DCD kidney transplant (KT) experience since its reintroduction in 2000. Risk factors for delayed graft function (DGF) were identified using multivariate analysis. Five-year patient/graft survival was assessed using Kaplan-Meier curves. The evolution of the kidney donor type and the impact of DCDs on the total KT activity in Belgium were compared with the Netherlands. Between 2000 and 2009, 287 DCD KT were performed. Primary nonfunction occurred in 1% and DGF in 31%. Five-year patient and death-censored graft survival were 93% and 95%, respectively. In multivariate analysis, cold storage (versus machine perfusion), cold ischemic time, and histidine-tryptophan-ketoglutarate solution were independent risk factors for the development of DGF. Despite an increased number of DCD donations and transplantations, the total number of deceased KT did not increase significantly. This could suggest a shift from DBDs to DCDs. To increase KT activity, Belgium should further expand controlled DCD programs while simultaneously improve the identification of all potential DBDs and avoid their referral for donation as DCDs before brain death occurs. Furthermore, living donation remains underused. © 2012 The Authors. Transplant International © 2012 European Society for Organ Transplantation.

Diagnostic value of history and physical examination in patients suspected of lumbosacral nerve root compression

PubMed Central

Vroomen, P; de Krom, M C T F M; Wilmink, J; Kester, A; Knottnerus, J

2002-01-01

Objective: To evaluate patient characteristics, symptoms, and examination findings in the clinical diagnosis of lumbosacral nerve root compression causing sciatica. Methods: The study involved 274 patients with pain radiating into the leg. All had a standardised clinical assessment and magnetic resonance (MR) imaging. The associations between patient characteristics, clinical findings, and lumbosacral nerve root compression on MR imaging were analysed. Results: Nerve root compression was associated with three patient characteristics, three symptoms, and four physical examination findings (paresis, absence of tendon reflexes, a positive straight leg raising test, and increased finger-floor distance). Multivariate analysis, analysing the independent diagnostic value of the tests, showed that nerve root compression was predicted by two patient characteristics, four symptoms, and two signs (increased finger-floor distance and paresis). The straight leg raise test was not predictive. The area under the curve of the receiver-operating characteristic was 0.80 for the history items. It increased to 0.83 when the physical examination items were added. Conclusions: Various clinical findings were found to be associated with nerve root compression on MR imaging. While this set of findings agrees well with those commonly used in daily practice, the tests tended to have lower sensitivity and specificity than previously reported. Stepwise multivariate analysis showed that most of the diagnostic information revealed by physical examination findings had already been revealed by the history items. PMID:11971050

Increase in the Risk of Respiratory Disorders in Adults and Children Related to Crop-Growing in Niger.

PubMed

Mamane, Ali; Tessier, Jean-François; Bouvier, Ghislaine; Salamon, Roger; Lebailly, Pierre; Raherison, Chantal; Baldi, Isabelle

2016-01-01

Background and Objective. Environmental factors are an increasing concern for respiratory health in developing countries. The objective of this study was to investigate whether Nigerien people living in cultivated areas have more respiratory symptoms than those living in pastoral areas. Method. A cross-sectional study was conducted in 2013 in two populations during the rainy season when land is cultivated. Environmental factors including pesticide use and respiratory symptoms were collected in adults and children during face-to-face interviews. Multivariate analysis between exposures and symptoms was performed in children and in adults separately. Results. The study included 471 adults and 229 children. Overall, none of the households reported the use of pesticides for agricultural purposes. However, 87.2% reported the use of insecticides at home. Multivariate analysis showed that people living in agricultural areas compared to those in pastoral areas had an increased risk of respiratory symptoms in adults (wheezing, dyspnea, sudden shortness of breath, and cough without fever) and in children (cough without fever). The use of insecticides showed no effect on respiratory symptoms after adjustment. Conclusion. This first epidemiological study on the environment and respiratory health conducted in Niger demonstrates a significant relationship between respiratory manifestations and the agricultural characteristics of the living area. However only the effect of insecticides in the home on respiratory health was observed.

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.

Multivariate analysis of longitudinal rates of change.

PubMed

Bryan, Matthew; Heagerty, Patrick J

2016-12-10

Longitudinal data allow direct comparison of the change in patient outcomes associated with treatment or exposure. Frequently, several longitudinal measures are collected that either reflect a common underlying health status, or characterize processes that are influenced in a similar way by covariates such as exposure or demographic characteristics. Statistical methods that can combine multivariate response variables into common measures of covariate effects have been proposed in the literature. Current methods for characterizing the relationship between covariates and the rate of change in multivariate outcomes are limited to select models. For example, 'accelerated time' methods have been developed which assume that covariates rescale time in longitudinal models for disease progression. In this manuscript, we detail an alternative multivariate model formulation that directly structures longitudinal rates of change and that permits a common covariate effect across multiple outcomes. We detail maximum likelihood estimation for a multivariate longitudinal mixed model. We show via asymptotic calculations the potential gain in power that may be achieved with a common analysis of multiple outcomes. We apply the proposed methods to the analysis of a trivariate outcome for infant growth and compare rates of change for HIV infected and uninfected infants. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Bicycle Use and Cyclist Safety Following Boston’s Bicycle Infrastructure Expansion, 2009–2012

PubMed Central

Angriman, Federico; Bellows, Alexandra L.; Taylor, Kathryn

2016-01-01

Objectives. To evaluate changes in bicycle use and cyclist safety in Boston, Massachusetts, following the rapid expansion of its bicycle infrastructure between 2007 and 2014. Methods. We measured bicycle lane mileage, a surrogate for bicycle infrastructure expansion, and quantified total estimated number of commuters. In addition, we calculated the number of reported bicycle accidents from 2009 to 2012. Bicycle accident and injury trends over time were assessed via generalized linear models. Multivariable logistic regression was used to examine factors associated with bicycle injuries. Results. Boston increased its total bicycle lane mileage from 0.034 miles in 2007 to 92.2 miles in 2014 (P < .001). The percentage of bicycle commuters increased from 0.9% in 2005 to 2.4% in 2014 (P = .002) and the total percentage of bicycle accidents involving injuries diminished significantly, from 82.7% in 2009 to 74.6% in 2012. The multivariable logistic regression analysis showed that for every 1-year increase in time from 2009 to 2012, there was a 14% reduction in the odds of being injured in an accident. Conclusions. The expansion of Boston’s bicycle infrastructure was associated with increases in both bicycle use and cyclist safety. PMID:27736203

Worsening renal function in patients admitted with acute decompensated heart failure: incidence, risk factors and prognostic implications.

PubMed

Belziti, César A; Bagnati, Rodrigo; Ledesma, Paola; Vulcano, Norberto; Fernández, Sandra

2010-03-01

Acute decompensated heart failure (ADHF) is a common cause of hospital admission and is associated with an increased risk of worsening renal function (WRF). The aims of this study were to investigate the incidence and predictors of WRF in patients admitted for ADHF and to assess the prognostic significance of WRF at 1 year. A retrospective analysis of data on 200 consecutive patients admitted with ADHF was carried out. By definition, WRF occurred when the serum creatinine level increased during hospitalization by 0.3 mg/dL and by > or =25% from admission. Overall, 23% of patients developed WRF. On multivariate analysis, age >80 years (odds ratio [OR]=2.72; 95% confidence interval [CI], 1.86-3.42), admission glomerular filtration rate <60 mL/min per 1.73 m2 (OR=2.05; 95% CI, 1.53-2.27) and admission systolic pressure <90 mmHg (OR=1.61, 95% CI, 1.17-3.22) were independently associated with WRF. The rate of mortality or readmission for heart failure (HF) at 1 year was higher in the WRF group (P< .01 by log-rank test). The median hospital stay was 9 days for patients with WRF and 4 days for those without (P< .05). On multivariate analysis, WRF remained independently associated with mortality or HF rehospitalization (hazard ratio=1.65; 95% CI, 1.12-2.67; P=.003). In patients admitted for ADHF, WRF was a common complication and was associated with a longer hospital stay and an increased risk of mortality or HF hospitalization. Clinical characteristics at admission can help identify patients at an increased risk of WRF.

TGFbeta1 (Leu10Pro), p53 (Arg72Pro) can predict for increased risk for breast cancer in south Indian women and TGFbeta1 Pro (Leu10Pro) allele predicts response to neo-adjuvant chemo-radiotherapy.

PubMed

Rajkumar, Thangarajan; Samson, Mani; Rama, Ranganathan; Sridevi, Veluswami; Mahji, Urmila; Swaminathan, Rajaraman; Nancy, Nirmala K

2008-11-01

The breast cancer incidence has been increasing in the south Indian women. A case (n=250)-control (n=500) study was undertaken to investigate the role of Single Nucleotide Polymorphisms (SNP's) in GSTM1 (Present/Null); GSTP1 (Ile105Val), p53 (Arg72Pro), TGFbeta1 (Leu10Pro), c-erbB2 (Ile655Val), and GSTT1 (Null/Present) in breast cancer. In addition, the value of the SNP's in predicting primary tumor's pathologic response following neo-adjuvant chemo-radiotherapy was assessed. Genotyping was done using PCR (GSTM1, GSTT1), Taqman Allelic discrimination assay (GSTP1, c-erbB2) and PCR-CTPP (p53 and TGFbeta1). None of the gene SNP's studied were associated with a statistically significant increased risk for the breast cancer. However, combined analysis of the SNP's showed that p53 (Arg/Arg and Arg/Pro) with TGFbeta1 (Pro/Pro and Leu/Pro) were associated with greater than 2 fold increased risk for breast cancer in Univariate (P=0.01) and Multivariate (P=0.003) analysis. There was no statistically significant association for the GST family members with the breast cancer risk. TGFbeta1 (Pro/Pro) allele was found to predict complete pathologic response in the primary tumour following neo-adjuvant chemo-radiotherapy (OR=6.53 and 10.53 in Univariate and Multivariate analysis respectively) (P=0.004) and was independent of stage. This study suggests that SNP's can help predict breast cancer risk in south Indian women and that TGFbeta1 (Pro/Pro) allele is associated with a better pCR in the primary tumour.

Comparison of pure laparoscopic versus open left hemihepatectomy by multivariate analysis: a retrospective cohort study.

PubMed

Cho, Hwui-Dong; Kim, Ki-Hun; Hwang, Shin; Ahn, Chul-Soo; Moon, Deok-Bog; Ha, Tae-Yong; Song, Gi-Won; Jung, Dong-Hwan; Park, Gil-Chun; Lee, Sung-Gyu

2018-02-01

To compare the outcomes of pure laparoscopic left hemihepatectomy (LLH) versus open left hemihepatectomy (OLH) for benign and malignant conditions using multivariate analysis. All consecutive cases of LLH and OLH between October 2007 and December 2013 in a tertiary referral hospital were enrolled in this retrospective cohort study. All surgical procedures were performed by one surgeon. The LLH and OLH groups were compared in terms of patient demographics, preoperative data, clinical perioperative outcomes, and tumor characteristics in patients with malignancy. Multivariate analysis of the prognostic factors associated with severe complications was then performed. The LLH group (n = 62) had a significantly shorter postoperative hospital stay than the OLH group (n = 118) (9.53 ± 3.30 vs 14.88 ± 11.36 days, p < 0.001). Multivariate analysis revealed that the OLH group had >4 times the risk of the LLH group in terms of developing severe complications (Clavien-Dindo grade ≥III) (odds ratio 4.294, 95% confidence intervals 1.165-15.832, p = 0.029). LLH was a safe and feasible procedure for selected patients. LLH required shorter hospital stay and resulted in less operative blood loss. Multivariate analysis revealed that LLH was associated with a lower risk of severe complications compared to OLH. The authors suggest that LLH could be a reasonable treatment option for selected patients.

Use of electronic nicotine delivery systems and recent initiation of smoking among US youth.

PubMed

Cardenas, Victor M; Evans, Victoria L; Balamurugan, Appathurai; Faramawi, Mohammed F; Delongchamp, Robert R; Wheeler, J Gary

2016-03-01

We assessed whether the prevalence of recent (within a year) initiation of cigarette smoking was associated with reports of ever using electronic delivery systems (ENDS) in the National Youth Tobacco Survey (NYTS) and whether the association varied by age. Weighted cross-sectional analysis of use of ENDS, cigarette smoking, age at interview and age at initiation of smoking collected systematically through the 2011-2013 NYTS cycles. In multivariate analyses those who ever used ENDS were twice as likely as nonusers of ENDS to have tried cigarette smoking in the last year (multivariate PR: 2.3; 95 % CI 1.9, 2.7). This average hid significant variations by age: a 4.1-fold increase (95 %; 2.6, 6.4) among those 11-13 years of age, compared to a smaller increase among those 16-18 years: 1.4-fold (95 % CI 1.1, 1.8). Use of ENDS by adolescents was associated with initiation of cigarette smoking in the last year. This association was stronger in younger adolescents.

Health-state utilities in a prisoner population: a cross-sectional survey

PubMed Central

Chong, Christopher AKY; Li, Sicong; Nguyen, Geoffrey C; Sutton, Andrew; Levy, Michael H; Butler, Tony; Krahn, Murray D; Thein, Hla-Hla

2009-01-01

Background Health-state utilities for prisoners have not been described. Methods We used data from a 1996 cross-sectional survey of Australian prisoners (n = 734). Respondent-level SF-36 data was transformed into utility scores by both the SF-6D and Nichol's method. Socio-demographic and clinical predictors of SF-6D utility were assessed in univariate analyses and a multivariate general linear model. Results The overall mean SF-6D utility was 0.725 (SD 0.119). When subdivided by various medical conditions, prisoner SF-6D utilities ranged from 0.620 for angina to 0.764 for those with none/mild depressive symptoms. Utilities derived by the Nichol's method were higher than SF-6D scores, often by more than 0.1. In multivariate analysis, significant independent predictors of worse utility included female gender, increasing age, increasing number of comorbidities and more severe depressive symptoms. Conclusion The utilities presented may prove useful for future economic and decision models evaluating prison-based health programs. PMID:19715571

Retinal vessel diameter and estimated cerebrospinal fluid pressure in arterial hypertension: the Beijing Eye Study.

PubMed

Jonas, Jost B; Wang, Ningli; Wang, Shuang; Wang, Ya Xing; You, Qi Sheng; Yang, Diya; Wei, Wen Bin; Xu, Liang

2014-09-01

Hypertensive retinal microvascular abnormalities include an increased retinal vein-to-artery diameter ratio. Because central retinal vein pressure depends on cerebrospinal fluid pressure (CSFP), we examined whether the retinal vein-to-artery diameter ratio and other retinal hypertensive signs are associated with CSFP. Participants of the population-based Beijing Eye Study (n = 1,574 subjects) underwent measurement of the temporal inferior and superior retinal artery and vein diameter. CSFP was calculated as 0.44 × body mass index (kg/m(2)) + 0.16 × diastolic blood pressure (mm Hg) - 0.18 × age (years) - 1.91. Larger retinal vein diameters and higher vein-to-artery diameter ratios were significantly associated with higher estimated CSFP (P = 0.001) in multivariable analysis. In contrast, temporal inferior retinal arterial diameter was marginally associated (P = 0.03) with estimated CSFP, and temporal superior artery diameter was not significantly associated (P = 0.10) with estimated CSFP; other microvascular abnormalities, such as arteriovenous crossing signs, were also not significantly associated with estimated CSFP. In a reverse manner, higher estimated CSFP as a dependent variable in the multivariable analysis was associated with wider retinal veins and higher vein-to-artery diameter ratio. In the same model, estimated CSFP was not significantly correlated with retinal artery diameters or other retinal microvascular abnormalities. Correspondingly, arterial hypertension was associated with retinal microvascular abnormalities such as arteriovenous crossing signs (P = 0.003), thinner temporal retinal arteries (P < 0.001), higher CSFP (P < 0.001), and wider retinal veins (P = 0.001) or, as a corollary, with a higher vein-to-artery diameter ratio in multivariable analysis. Wider retinal vein diameters are associated with higher estimated CSFP and vice versa. In arterial hypertension, an increased retinal vein-to-artery diameter ratio depends on elevated CSFP, which is correlated with blood pressure. © American Journal of Hypertension, Ltd 2014. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Risk Factors for Urinary Tract Infections in Cardiac Surgical Patients

PubMed Central

Gillen, Jacob R.; Isbell, James M.; Michaels, Alex D.; Lau, Christine L.

2015-01-01

Abstract Background: Risk factors for catheter-associated urinary tract infections (CAUTIs) in patients undergoing non-cardiac surgical procedures have been well documented. However, the variables associated with CAUTIs in the cardiac surgical population have not been clearly defined. Therefore, the purpose of this study was to investigate risk factors associated with CAUTIs in patients undergoing cardiac procedures. Methods: All patients undergoing cardiac surgery at a single institution from 2006 through 2012 (4,883 patients) were reviewed. Patients with U.S. Centers for Disease Control (CDC) criteria for CAUTI were identified from the hospital's Quality Assessment database. Pre-operative, operative, and post-operative patient factors were evaluated. Univariate and multivariable analyses were used to identify significant correlations between perioperative characteristics and CAUTIs. Results: There were 55 (1.1%) documented CAUTIs in the study population. On univariate analysis, older age, female gender, diabetes mellitus, cardiogenic shock, urgent or emergent operation, packed red blood cell (PRBC) units transfused, and intensive care unit length of stay (ICU LOS) were all significantly associated with CAUTI [p<0.05]. On multivariable logistic regression, older age, female gender, diabetes mellitus, and ICU LOS remained significantly associated with CAUTI. Additionally, there was a significant association between CAUTI and 30-d mortality on univariate analysis. However, when controlling for common predictors of operative mortality on multivariable analysis, CAUTI was no longer associated with mortality. Conclusions: There are several identifiable risk factors for CAUTI in patients undergoing cardiac procedures. CAUTI is not independently associated with increased mortality, but it does serve as a marker of sicker patients more likely to die from other comorbidities or complications. Therefore, awareness of the high-risk nature of these patients should lead to increased diligence and may help to improve peri-operative outcomes. Recognizing patients at high risk for CAUTI may lead to improved measures to decrease CAUTI rates within this population. PMID:26115336

Risk Factors for Urinary Tract Infections in Cardiac Surgical Patients.

PubMed

Gillen, Jacob R; Isbell, James M; Michaels, Alex D; Lau, Christine L; Sawyer, Robert G

2015-10-01

Risk factors for catheter-associated urinary tract infections (CAUTIs) in patients undergoing non-cardiac surgical procedures have been well documented. However, the variables associated with CAUTIs in the cardiac surgical population have not been clearly defined. Therefore, the purpose of this study was to investigate risk factors associated with CAUTIs in patients undergoing cardiac procedures. All patients undergoing cardiac surgery at a single institution from 2006 through 2012 (4,883 patients) were reviewed. Patients with U.S. Centers for Disease Control (CDC) criteria for CAUTI were identified from the hospital's Quality Assessment database. Pre-operative, operative, and post-operative patient factors were evaluated. Univariate and multivariable analyses were used to identify significant correlations between perioperative characteristics and CAUTIs. There were 55 (1.1%) documented CAUTIs in the study population. On univariate analysis, older age, female gender, diabetes mellitus, cardiogenic shock, urgent or emergent operation, packed red blood cell (PRBC) units transfused, and intensive care unit length of stay (ICU LOS) were all significantly associated with CAUTI [p<0.05]. On multivariable logistic regression, older age, female gender, diabetes mellitus, and ICU LOS remained significantly associated with CAUTI. Additionally, there was a significant association between CAUTI and 30-d mortality on univariate analysis. However, when controlling for common predictors of operative mortality on multivariable analysis, CAUTI was no longer associated with mortality. There are several identifiable risk factors for CAUTI in patients undergoing cardiac procedures. CAUTI is not independently associated with increased mortality, but it does serve as a marker of sicker patients more likely to die from other comorbidities or complications. Therefore, awareness of the high-risk nature of these patients should lead to increased diligence and may help to improve peri-operative outcomes. Recognizing patients at high risk for CAUTI may lead to improved measures to decrease CAUTI rates within this population.

HIV infection and awareness among men who have sex with men-20 cities, United States, 2008 and 2011.

PubMed

Wejnert, Cyprian; Le, Binh; Rose, Charles E; Oster, Alexandra M; Smith, Amanda J; Zhu, Julia

2013-01-01

Over half of HIV infections in the United States occur among men who have sex with men (MSM). Awareness of infection is a necessary precursor to antiretroviral treatment and risk reduction among HIV-infected persons. We report data on prevalence and awareness of HIV infection among MSM in 2008 and 2011, using data from 20 cities participating in the 2008 and 2011 National HIV Behavioral Surveillance System (NHBS) among MSM. Venue-based, time-space sampling was used to recruit men for interview and HIV testing. We analyzed data for men who reported ≥ 1 male sex partner in the past 12 months. Participants who tested positive were considered to be aware of their infection if they reported a prior positive HIV test. We used multivariable analysis to examine differences between results from 2011 vs. 2008. HIV prevalence was 19% in 2008 and 18% in 2011 (p = 0.14). In both years, HIV prevalence was highest among older age groups, blacks, and men with lower education and income. In multivariable analysis, HIV prevalence did not change significantly from 2008 to 2011 overall (p = 0.51) or in any age or racial/ethnic category (p>0.15 in each category). Among those testing positive, a greater proportion was aware of their infection in 2011 (66%) than in 2008 (56%) (p<0.001). In both years, HIV awareness was higher for older age groups, whites, and men with higher education and income. In multivariable analysis, HIV awareness increased from 2008 to 2011 overall (p<0.001) and for all age and racial/ethnic categories (p<0.01 in each category). In both years, black MSM had the highest HIV prevalence and the lowest awareness among racial/ethnic groups. These findings suggest that HIV-positive MSM are increasingly aware of their infections.

Univariate and multivariate skewness and kurtosis for measuring nonnormality: Prevalence, influence and estimation.

PubMed

Cain, Meghan K; Zhang, Zhiyong; Yuan, Ke-Hai

2017-10-01

Nonnormality of univariate data has been extensively examined previously (Blanca et al., Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, 9(2), 78-84, 2013; Miceeri, Psychological Bulletin, 105(1), 156, 1989). However, less is known of the potential nonnormality of multivariate data although multivariate analysis is commonly used in psychological and educational research. Using univariate and multivariate skewness and kurtosis as measures of nonnormality, this study examined 1,567 univariate distriubtions and 254 multivariate distributions collected from authors of articles published in Psychological Science and the American Education Research Journal. We found that 74 % of univariate distributions and 68 % multivariate distributions deviated from normal distributions. In a simulation study using typical values of skewness and kurtosis that we collected, we found that the resulting type I error rates were 17 % in a t-test and 30 % in a factor analysis under some conditions. Hence, we argue that it is time to routinely report skewness and kurtosis along with other summary statistics such as means and variances. To facilitate future report of skewness and kurtosis, we provide a tutorial on how to compute univariate and multivariate skewness and kurtosis by SAS, SPSS, R and a newly developed Web application.

Predictive factors for rebleeding and death in alcoholic cirrhotic patients with acute variceal bleeding: a multivariate analysis.

PubMed

Krige, Jake E J; Kotze, Urda K; Distiller, Greg; Shaw, John M; Bornman, Philippus C

2009-10-01

Bleeding from esophageal varices is a leading cause of death in alcoholic cirrhotic patients. The aim of the present single-center study was to identify risk factors predictive of variceal rebleeding and death within 6 weeks of initial treatment. Univariate and multivariate analyses were performed on 310 prospectively documented alcoholic cirrhotic patients with acute variceal hemorrhage (AVH) who underwent 786 endoscopic variceal injection treatments between January 1984 and December 2006. All injections were administered during the first 6 weeks after the patients were treated for their first variceal bleed. Seventy-five (24.2%) patients experienced a rebleed, 38 within 5 days of the initial treatment and 37 within 6 weeks of their initial treatment. Of the 15 variables studied and included in a multivariate analysis using a logistic regression model, a bilirubin level >51 mmol/l and transfusion of >6 units of blood during the initial hospital admission were predictors of variceal rebleeding within the first 6 weeks. Seventy-seven (24.8%) patients died, 29 (9.3%) within 5 days and 48 (15.4%) between 6 and 42 days after the initial treatment. Stepwise multivariate logistic regression analysis showed that six variables were predictors of death within the first 6 weeks: encephalopathy, ascites, bilirubin level >51 mmol/l, international normalized ratio (INR) >2.3, albumin <25 g/l, and the need for balloon tube tamponade. Survival was influenced by the severity of liver failure, with most deaths occurring in Child-Pugh grade C patients. Patients with AVH and encephalopathy, ascites, bilirubin levels >51 mmol/l, INR >2.3, albumin <25 g/l and who require balloon tube tamponade are at increased risk of dying within the first 6 weeks. Bilirubin levels >51 mmol/l and transfusion of >6 units of blood were predictors of variceal rebleeding.

Survival outcomes and prognostic factors in mycosis fungoides/Sézary syndrome: validation of the revised International Society for Cutaneous Lymphomas/European Organisation for Research and Treatment of Cancer staging proposal.

PubMed

Agar, Nita Sally; Wedgeworth, Emma; Crichton, Siobhan; Mitchell, Tracey J; Cox, Michael; Ferreira, Silvia; Robson, Alistair; Calonje, Eduardo; Stefanato, Catherine M; Wain, Elizabeth Mary; Wilkins, Bridget; Fields, Paul A; Dean, Alan; Webb, Katherine; Scarisbrick, Julia; Morris, Stephen; Whittaker, Sean J

2010-11-01

We have analyzed the outcome of mycosis fungoides (MF) and Sézary syndrome (SS) patients using the recent International Society for Cutaneous Lymphomas (ISCL)/European Organisation for Research and Treatment of Cancer (EORTC) revised staging proposal. Overall survival (OS), disease-specific survival (DSS), and risk of disease progression (RDP) were calculated for a cohort of 1,502 patients using univariate and multivariate models. The mean age at diagnosis was 54 years, and 71% of patients presented with early-stage disease. Disease progression occurred in 34%, and 26% of patients died due to MF/SS. A significant difference in survival and progression was noted for patients with early-stage disease having patches alone (T1a/T2a) compared with those having patches and plaques (T1b/T2b). Univariate analysis established that (1) advanced skin and overall clinical stage, increased age, male sex, increased lactate dehydrogenase (LDH), and large-cell transformation were associated with reduced survival and increased RDP; (2) hypopigmented MF, MF with lymphomatoid papulosis, and poikilodermatous MF were associated with improved survival and reduced RDP; and (3) folliculotropic MF was associated with an increased RDP. Multivariate analysis established that (1) advanced skin (T) stage, the presence in peripheral blood of the tumor clone without Sézary cells (B0b), increased LDH, and folliculotropic MF were independent predictors of poor survival and increased RDP; (2) large-cell transformation and tumor distribution were independent predictors of increased RDP only; and (3) N, M, and B stages; age; male sex; and poikilodermatous MF were only significant for survival. This study has validated the recently proposed ISCL/EORTC staging system and identified new prognostic factors.

A Statistical Discrimination Experiment for Eurasian Events Using a Twenty-Seven-Station Network

DTIC Science & Technology

1980-07-08

to test the effectiveness of a multivariate method of analysis for distinguishing earthquakes from explosions. The data base for the experiment...to test the effectiveness of a multivariate method of analysis for distinguishing earthquakes from explosions. The data base for the experiment...the weight assigned to each variable whenever a new one is added. Jennrich, R. I. (1977). Stepwise discriminant analysis , in Statistical Methods for

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

Multivariate optimum interpolation of surface pressure and winds over oceans

NASA Technical Reports Server (NTRS)

Bloom, S. C.

1984-01-01

The observations of surface pressure are quite sparse over oceanic areas. An effort to improve the analysis of surface pressure over oceans through the development of a multivariate surface analysis scheme which makes use of surface pressure and wind data is discussed. Although the present research used ship winds, future versions of this analysis scheme could utilize winds from additional sources, such as satellite scatterometer data.

Nonlinear multivariate and time series analysis by neural network methods

NASA Astrophysics Data System (ADS)

Hsieh, William W.

2004-03-01

Methods in multivariate statistical analysis are essential for working with large amounts of geophysical data, data from observational arrays, from satellites, or from numerical model output. In classical multivariate statistical analysis, there is a hierarchy of methods, starting with linear regression at the base, followed by principal component analysis (PCA) and finally canonical correlation analysis (CCA). A multivariate time series method, the singular spectrum analysis (SSA), has been a fruitful extension of the PCA technique. The common drawback of these classical methods is that only linear structures can be correctly extracted from the data. Since the late 1980s, neural network methods have become popular for performing nonlinear regression and classification. More recently, neural network methods have been extended to perform nonlinear PCA (NLPCA), nonlinear CCA (NLCCA), and nonlinear SSA (NLSSA). This paper presents a unified view of the NLPCA, NLCCA, and NLSSA techniques and their applications to various data sets of the atmosphere and the ocean (especially for the El Niño-Southern Oscillation and the stratospheric quasi-biennial oscillation). These data sets reveal that the linear methods are often too simplistic to describe real-world systems, with a tendency to scatter a single oscillatory phenomenon into numerous unphysical modes or higher harmonics, which can be largely alleviated in the new nonlinear paradigm.

Ecological prediction with nonlinear multivariate time-frequency functional data models

USGS Publications Warehouse

Yang, Wen-Hsi; Wikle, Christopher K.; Holan, Scott H.; Wildhaber, Mark L.

2013-01-01

Time-frequency analysis has become a fundamental component of many scientific inquiries. Due to improvements in technology, the amount of high-frequency signals that are collected for ecological and other scientific processes is increasing at a dramatic rate. In order to facilitate the use of these data in ecological prediction, we introduce a class of nonlinear multivariate time-frequency functional models that can identify important features of each signal as well as the interaction of signals corresponding to the response variable of interest. Our methodology is of independent interest and utilizes stochastic search variable selection to improve model selection and performs model averaging to enhance prediction. We illustrate the effectiveness of our approach through simulation and by application to predicting spawning success of shovelnose sturgeon in the Lower Missouri River.

Forecasting electric vehicles sales with univariate and multivariate time series models: The case of China.

PubMed

Zhang, Yong; Zhong, Miner; Geng, Nana; Jiang, Yunjian

2017-01-01

The market demand for electric vehicles (EVs) has increased in recent years. Suitable models are necessary to understand and forecast EV sales. This study presents a singular spectrum analysis (SSA) as a univariate time-series model and vector autoregressive model (VAR) as a multivariate model. Empirical results suggest that SSA satisfactorily indicates the evolving trend and provides reasonable results. The VAR model, which comprised exogenous parameters related to the market on a monthly basis, can significantly improve the prediction accuracy. The EV sales in China, which are categorized into battery and plug-in EVs, are predicted in both short term (up to December 2017) and long term (up to 2020), as statistical proofs of the growth of the Chinese EV industry.

Forecasting electric vehicles sales with univariate and multivariate time series models: The case of China

PubMed Central

Zhang, Yong; Zhong, Miner; Geng, Nana; Jiang, Yunjian

2017-01-01

The market demand for electric vehicles (EVs) has increased in recent years. Suitable models are necessary to understand and forecast EV sales. This study presents a singular spectrum analysis (SSA) as a univariate time-series model and vector autoregressive model (VAR) as a multivariate model. Empirical results suggest that SSA satisfactorily indicates the evolving trend and provides reasonable results. The VAR model, which comprised exogenous parameters related to the market on a monthly basis, can significantly improve the prediction accuracy. The EV sales in China, which are categorized into battery and plug-in EVs, are predicted in both short term (up to December 2017) and long term (up to 2020), as statistical proofs of the growth of the Chinese EV industry. PMID:28459872

Analysis and assessment on heavy metal sources in the coastal soils developed from alluvial deposits using multivariate statistical methods.

PubMed

Li, Jinling; He, Ming; Han, Wei; Gu, Yifan

2009-05-30

An investigation on heavy metal sources, i.e., Cu, Zn, Ni, Pb, Cr, and Cd in the coastal soils of Shanghai, China, was conducted using multivariate statistical methods (principal component analysis, clustering analysis, and correlation analysis). All the results of the multivariate analysis showed that: (i) Cu, Ni, Pb, and Cd had anthropogenic sources (e.g., overuse of chemical fertilizers and pesticides, industrial and municipal discharges, animal wastes, sewage irrigation, etc.); (ii) Zn and Cr were associated with parent materials and therefore had natural sources (e.g., the weathering process of parent materials and subsequent pedo-genesis due to the alluvial deposits). The effect of heavy metals in the soils was greatly affected by soil formation, atmospheric deposition, and human activities. These findings provided essential information on the possible sources of heavy metals, which would contribute to the monitoring and assessment process of agricultural soils in worldwide regions.

Application of multivariate statistical techniques for differentiation of ripe banana flour based on the composition of elements.

PubMed

Alkarkhi, Abbas F M; Ramli, Saifullah Bin; Easa, Azhar Mat

2009-01-01

Major (sodium, potassium, calcium, magnesium) and minor elements (iron, copper, zinc, manganese) and one heavy metal (lead) of Cavendish banana flour and Dream banana flour were determined, and data were analyzed using multivariate statistical techniques of factor analysis and discriminant analysis. Factor analysis yielded four factors explaining more than 81% of the total variance: the first factor explained 28.73%, comprising magnesium, sodium, and iron; the second factor explained 21.47%, comprising only manganese and copper; the third factor explained 15.66%, comprising zinc and lead; while the fourth factor explained 15.50%, comprising potassium. Discriminant analysis showed that magnesium and sodium exhibited a strong contribution in discriminating the two types of banana flour, affording 100% correct assignation. This study presents the usefulness of multivariate statistical techniques for analysis and interpretation of complex mineral content data from banana flour of different varieties.

Is Food Insufficiency Associated with Health Status and Health Care Utilization Among Adults with Diabetes?

PubMed Central

Nelson, Karin; Cunningham, William; Andersen, Ron; Harrison, Gail; Gelberg, Lillian

2001-01-01

OBJECTIVES Preliminary studies have shown that among adults with diabetes, food insufficiency has adverse health consequences, including hypoglycemic episodes and increased need for health care services. The purpose of this study was to determine the prevalence of food insufficiency and to describe the association of food insufficiency with health status and health care utilization in a national sample of adults with diabetes. METHODS We analyzed data from adults with diabetes (n = 1,503) interviewed in the Third National Health and Nutrition Examination Survey. Bivariate and multivariate analyses were used to examine the relationship of food insufficiency to self-reported health status and health care utilization. RESULTS Six percent of adults with diabetes reported food insufficiency, representing more than 568,600 persons nationally (95% confidence interval, 368,400 to 768,800). Food insufficiency was more common among those with incomes below the federal poverty level (17% vs 4%, P≤.001). Adults with diabetes who were food insufficient were more likely to report fair or poor health status than those who were not (63% vs 43%; odds ratio, 2.2; P =.05). In a multivariate analysis, fair or poor health status was independently associated with poverty, nonwhite race, low educational achievement, and number of chronic diseases, but not with food insufficiency. Diabetic adults who were food insufficient reported more physician encounters, either in clinic or by phone, than those who were food secure (12 vs 7, P <.05). In a multivariate linear regression, food insufficiency remained independently associated with increased physician utilization among adults with diabetes. There was no association between food insufficiency and hospitalization in bivariate analysis. CONCLUSIONS Food insufficiency is relatively common among low-income adults with diabetes and was associated with higher physician utilization. PMID:11422638

OGLE II Eclipsing Binaries In The LMC: Analysis With Class

NASA Astrophysics Data System (ADS)

Devinney, Edward J.; Prsa, A.; Guinan, E. F.; DeGeorge, M.

2011-01-01

The Eclipsing Binaries (EBs) via Artificial Intelligence (EBAI) Project is applying machine learning techniques to elucidate the nature of EBs. Previously, Prsa, et al. applied artificial neural networks (ANNs) trained on physically-realistic Wilson-Devinney models to solve the light curves of the 1882 detached EBs in the LMC discovered by the OGLE II Project (Wyrzykowski, et al.) fully automatically, bypassing the need for manually-derived starting solutions. A curious result is the non-monotonic distribution of the temperature ratio parameter T2/T1, featuring a subsidiary peak noted previously by Mazeh, et al. in an independent analysis using the EBOP EB solution code (Tamuz, et al.). To explore this and to gain a fuller understanding of the multivariate EBAI LMC observational plus solutions data, we have employed automatic clustering and advanced visualization (CAV) techniques. Clustering the OGLE II data aggregates objects that are similar with respect to many parameter dimensions. Measures of similarity for example, could include the multidimensional Euclidean Distance between data objects, although other measures may be appropriate. Applying clustering, we find good evidence that the T2/T1 subsidiary peak is due to evolved binaries, in support of Mazeh et al.'s speculation. Further, clustering suggests that the LMC detached EBs occupying the main sequence region belong to two distinct classes. Also identified as a separate cluster in the multivariate data are stars having a Period-I band relation. Derekas et al. had previously found a Period-K band relation for LMC EBs discovered by the MACHO Project (Alcock, et al.). We suggest such CAV techniques will prove increasingly useful for understanding the large, multivariate datasets increasingly being produced in astronomy. We are grateful for the support of this research from NSF/RUI Grant AST-05-75042 f.

Comparison of the prognostic value of pretreatment measurements of systemic inflammatory response in patients undergoing curative resection of clear cell renal cell carcinoma.

PubMed

Lucca, Ilaria; de Martino, Michela; Hofbauer, Sebastian L; Zamani, Nura; Shariat, Shahrokh F; Klatte, Tobias

2015-12-01

Pretreatment measurements of systemic inflammatory response, including the Glasgow prognostic score (GPS), the neutrophil-to-lymphocyte ratio (NLR), the monocyte-to-lymphocyte ratio (MLR), the platelet-to-lymphocyte ratio (PLR) and the prognostic nutritional index (PNI) have been recognized as prognostic factors in clear cell renal cell carcinoma (CCRCC), but there is at present no study that compared these markers. We evaluated the pretreatment GPS, NLR, MLR, PLR and PNI in 430 patients, who underwent surgery for clinically localized CCRCC (pT1-3N0M0). Associations with disease-free survival were assessed with Cox models. Discrimination was measured with the C-index, and a decision curve analysis was used to evaluate the clinical net benefit. On multivariable analyses, all measures of systemic inflammatory response were significant prognostic factors. The increase in discrimination compared with the stage, size, grade and necrosis (SSIGN) score alone was 5.8 % for the GPS, 1.1-1.4 % for the NLR, 2.9-3.4 % for the MLR, 2.0-3.3 % for the PLR and 1.4-3.0 % for the PNI. On the simultaneous multivariable analysis of all candidate measures, the final multivariable model contained the SSIGN score (HR 1.40, P < 0.001), the GPS (HR 2.32, P < 0.001) and the MLR (HR 5.78, P = 0.003) as significant variables. Adding both the GPS and the MLR increased the discrimination of the SSIGN score by 6.2 % and improved the clinical net benefit. In patients with clinically localized CCRCC, the GPS and the MLR appear to be the most relevant prognostic measures of systemic inflammatory response. They may be used as an adjunct for patient counseling, tailoring management and clinical trial design.

Hydration status measured by BCM: A potential modifiable risk factor for peritonitis in patients on peritoneal dialysis.

PubMed

Santhakumaran, Territa; Samad, Nasreen; Fan, Stanley L

2016-05-01

Peritoneal dialysis peritonitis and fluid overhydration (OH) are frequent problems in peritoneal dialysis. The latter can cause gut wall oedema or be associated with malnutrition. Both may lead to increased peritonitis risk. We wished to determine if OH is an independent risk factor for peritonitis (caused by enteric organisms). Retrospectively study of patients with >2 bioimpedance assessments (Body Composition Monitor). We compared peritonitis rates of patients with above or below the median time-averaged hydration parameter (OH/extracellular water, OH/ECW). Multivariate analysis was performed to determine independent risk factors for peritonitis by enteric organism. We studied 580 patients. Peritonitis was experienced by 28% patients (followed up for an average of 17 months). The overall peritonitis rate was 1:34 patient months. Patients with low OH/ECW values had significantly lower rates of peritonitis from enteric organisms than overhydrated patients (incident rate ratio 1.53, 95% confidence interval 1.38-1.70, P < 0.001). Hydration remained an independent predictor of peritonitis from enteric organisms when multivariate model included demographic parameters (odds ratio for a 1% increment of OH/ECW was 1.05; 95% confidence interval 1.01-1.10, P < 0.02). However, including biochemical parameters of malnutrition reduced the predictive power of overhydration. We found an association between overhydration and increased rates of peritonitis. While this may partly be due to the high co-morbidity of patients (advanced age and diabetes), on multivariate analysis, only inclusion of nutritional parameters reduced this association. It remains to be determined if overhydration will prove to be a modifiable risk factor for peritonitis or whether malnutrition will prove to be more important. © 2015 Asian Pacific Society of Nephrology.

HIV mono-infection is associated with FIB-4 - A noninvasive index of liver fibrosis - in women.

PubMed

Blackard, Jason T; Welge, Jeffrey A; Taylor, Lynn E; Mayer, Kenneth H; Klein, Robert S; Celentano, David D; Jamieson, Denise J; Gardner, Lytt; Sherman, Kenneth E

2011-03-01

FIB-4 represents a noninvasive, composite index that is a validated measure of hepatic fibrosis, which is an important indicator of liver disease. To date, there are limited data regarding hepatic fibrosis in women. FIB-4 was evaluated in a cohort of 1227 women, and associations were evaluated in univariate and multivariate regression models among 4 groups of subjects classified by their human immunodeficiency virus (HIV) and hepatitis C virus (HCV) infection status. The median FIB-4 scores were 0.60 in HIV-/HCV- women, 0.83 in HIV-/HCV+ women, 0.86 in HIV+/HCV- women, and 1.30 in HIV+/HCV+ women. In the HIV/HCV co-infected group, multivariate analysis showed that CD4(+) cell count and albumin level were negatively associated with FIB-4 (P <.0001), whereas antiretroviral therapy (ART) was positively associated with FIB-4 score (P =.0008). For the HIV mono-infected group, multivariate analysis showed that CD4(+) cell count (P <.0001) and albumin level (P =.0019) were negatively correlated with FIB-4 score, ART was positively associated with FIB-4 score (P =.0008), and plasma HIV RNA level was marginally associated with FIB-4 score (P =.080). In 72 HIV mono-infected women who were also hepatitis B surface antigen negative, ART naive, and reported no recent alcohol intake, plasma HIV RNA level was associated with increased FIB-4 score (P =.030). HIV RNA level was associated with increased FIB-4 score in the absence of hepatitis B, hepatitis C, ART, or alcohol use, suggesting a potential relationship between HIV infection and hepatic fibrosis in vivo. A better understanding of the various demographic and virologic variables that contribute to hepatic fibrosis may lead to more effective treatment of HIV infection and its co-morbid conditions.

The association between temperature and mortality in tropical middle income Thailand from 1999 to 2008

NASA Astrophysics Data System (ADS)

Tawatsupa, Benjawan; Dear, Keith; Kjellstrom, Tord; Sleigh, Adrian

2014-03-01

We have investigated the association between tropical weather condition and age-sex adjusted death rates (ADR) in Thailand over a 10-year period from 1999 to 2008. Population, mortality, weather and air pollution data were obtained from four national databases. Alternating multivariable fractional polynomial (MFP) regression and stepwise multivariable linear regression analysis were used to sequentially build models of the associations between temperature variable and deaths, adjusted for the effects and interactions of age, sex, weather (6 variables), and air pollution (10 variables). The associations are explored and compared among three seasons (cold, hot and wet months) and four weather zones of Thailand (the North, Northeast, Central, and South regions). We found statistically significant associations between temperature and mortality in Thailand. The maximum temperature is the most important variable in predicting mortality. Overall, the association is nonlinear U-shape and 31 °C is the minimum-mortality temperature in Thailand. The death rates increase when maximum temperature increase with the highest rates in the North and Central during hot months. The final equation used in this study allowed estimation of the impact of a 4 °C increase in temperature as projected for Thailand by 2100; this analysis revealed that the heat-related deaths will increase more than the cold-related deaths avoided in the hot and wet months, and overall the net increase in expected mortality by region ranges from 5 to 13 % unless preventive measures were adopted. Overall, these results are useful for health impact assessment for the present situation and future public health implication of global climate change for tropical Thailand.

Dietary Sodium Consumption Predicts Future Blood Pressure and Incident Hypertension in the Japanese Normotensive General Population

PubMed Central

Takase, Hiroyuki; Sugiura, Tomonori; Kimura, Genjiro; Ohte, Nobuyuki; Dohi, Yasuaki

2015-01-01

Background Although there is a close relationship between dietary sodium and hypertension, the concept that persons with relatively high dietary sodium are at increased risk of developing hypertension compared with those with relatively low dietary sodium has not been studied intensively in a cohort. Methods and Results We conducted an observational study to investigate whether dietary sodium intake predicts future blood pressure and the onset of hypertension in the general population. Individual sodium intake was estimated by calculating 24-hour urinary sodium excretion from spot urine in 4523 normotensive participants who visited our hospital for a health checkup. After a baseline examination, they were followed for a median of 1143 days, with the end point being development of hypertension. During the follow-up period, hypertension developed in 1027 participants (22.7%). The risk of developing hypertension was higher in those with higher rather than lower sodium intake (hazard ratio 1.25, 95% CI 1.04 to 1.50). In multivariate Cox proportional hazards regression analysis, baseline sodium intake and the yearly change in sodium intake during the follow-up period (as continuous variables) correlated with the incidence of hypertension. Furthermore, both the yearly increase in sodium intake and baseline sodium intake showed significant correlations with the yearly increase in systolic blood pressure in multivariate regression analysis after adjustment for possible risk factors. Conclusions Both relatively high levels of dietary sodium intake and gradual increases in dietary sodium are associated with future increases in blood pressure and the incidence of hypertension in the Japanese general population. PMID:26224048

Placebo group improvement in trials of pharmacotherapies for alcohol use disorders: a multivariate meta-analysis examining change over time.

PubMed

Del Re, A C; Maisel, Natalya; Blodgett, Janet C; Wilbourne, Paula; Finney, John W

2013-10-01

Placebo group improvement in pharmacotherapy trials has been increasing over time across several pharmacological treatment areas. However, it is unknown to what degree increasing improvement has occurred in pharmacotherapy trials for alcohol use disorders or what factors may account for placebo group improvement. This meta-analysis of 47 alcohol pharmacotherapy trials evaluated (1) the magnitude of placebo group improvement, (2) the extent to which placebo group improvement has been increasing over time, and (3) several potential moderators that might account for variation in placebo group improvement. Random-effects univariate and multivariate analyses were conducted that examined the magnitude of placebo group improvement in the 47 studies and several potential moderators of improvement: (a) publication year, (b) country in which the study was conducted, (c) outcome data source/type, (d) number of placebo administrations, (e) overall severity of study participants, and (f) additional psychosocial treatment. Substantial placebo group improvement was found overall and improvement was larger in more recent studies. Greater improvement was found on moderately subjective outcomes, with more frequent administrations of the placebo, and in studies with greater participant severity of illness. However, even after controlling for these moderators, placebo group improvement remained significant, as did placebo group improvement over time. Similar to previous pharmacotherapy placebo research, substantial pretest to posttest placebo group improvement has occurred in alcohol pharmacotherapy trials, an effect that has been increasing over time. However, several plausible moderator variables were not able to explain why placebo group improvement has been increasing over time.

Positron emission tomography–computed tomography predictors of progression after DA-R-EPOCH for PMBCL

PubMed Central

Ng, Andrea K.; Dabaja, Bouthaina S.; Milgrom, Sarah A.; Gunther, Jillian R.; Fuller, C. David; Smith, Grace L.; Abou Yehia, Zeinab; Qiao, Wei; Wogan, Christine F.; Akhtari, Mani; Mawlawi, Osama; Medeiros, L. Jeffrey; Chuang, Hubert H.; Martin-Doyle, William; Armand, Philippe; LaCasce, Ann S.; Oki, Yasuhiro; Fanale, Michelle; Westin, Jason; Neelapu, Sattva; Nastoupil, Loretta

2018-01-01

Dose-adjusted rituximab plus etoposide, prednisone, vincristine, cyclophosphamide, and doxorubicin (DA-R-EPOCH) has produced good outcomes in primary mediastinal B-cell lymphoma (PMBCL), but predictors of resistance to this treatment are unclear. We investigated whether [18F]fluorodeoxyglucose positron emission tomography–computed tomography (PET-CT) findings could identify patients with PMBCL who would not respond completely to DA-R-EPOCH. We performed a retrospective analysis of 65 patients with newly diagnosed stage I to IV PMBCL treated at 2 tertiary cancer centers who had PET-CT scans available before and after frontline therapy with DA-R-EPOCH. Pretreatment variables assessed included metabolic tumor volume (MTV) and total lesion glycolysis (TLG). Optimal cutoff points for progression-free survival (PFS) were determined by a machine learning approach. Univariate and multivariable models were constructed to assess associations between radiographic variables and PFS. At a median follow-up of 36.6 months (95% confidence interval, 28.1-45.1), 2-year PFS and overall survival rates for the 65 patients were 81.4% and 98.4%, respectively. Machine learning–derived thresholds for baseline MTV and TLG were associated with inferior PFS (elevated MTV: hazard ratio [HR], 11.5; P = .019; elevated TLG: HR, 8.99; P = .005); other pretreatment clinical factors, including International Prognostic Index and bulky (>10 cm) disease, were not. On multivariable analysis, only TLG retained statistical significance (P = .049). Univariate analysis of posttreatment variables revealed that residual CT tumor volume, maximum standardized uptake value, and Deauville score were associated with PFS; a Deauville score of 5 remained significant on multivariable analysis (P = .006). A model combining baseline TLG and end-of-therapy Deauville score identified patients at increased risk of progression. PMID:29895624

Valuing the visual impact of wind farms: A calculus method for synthesizing choice experiments studies.

PubMed

Wen, Cheng; Dallimer, Martin; Carver, Steve; Ziv, Guy

2018-05-06

Despite the great potential of mitigating carbon emission, development of wind farms is often opposed by local communities due to the visual impact on landscape. A growing number of studies have applied nonmarket valuation methods like Choice Experiments (CE) to value the visual impact by eliciting respondents' willingness to pay (WTP) or willingness to accept (WTA) for hypothetical wind farms through survey questions. Several meta-analyses have been found in the literature to synthesize results from different valuation studies, but they have various limitations related to the use of the prevailing multivariate meta-regression analysis. In this paper, we propose a new meta-analysis method to establish general functions for the relationships between the estimated WTP or WTA and three wind farm attributes, namely the distance to residential/coastal areas, the number of turbines and turbine height. This method involves establishing WTA or WTP functions for individual studies, fitting the average derivative functions and deriving the general integral functions of WTP or WTA against wind farm attributes. Results indicate that respondents in different studies consistently showed increasing WTP for moving wind farms to greater distances, which can be fitted by non-linear (natural logarithm) functions. However, divergent preferences for the number of turbines and turbine height were found in different studies. We argue that the new analysis method proposed in this paper is an alternative to the mainstream multivariate meta-regression analysis for synthesizing CE studies and the general integral functions of WTP or WTA against wind farm attributes are useful for future spatial modelling and benefit transfer studies. We also suggest that future multivariate meta-analyses should include non-linear components in the regression functions. Copyright © 2018. Published by Elsevier B.V.

Confocal Raman microscopy and multivariate statistical analysis for determination of different penetration abilities of caffeine and propylene glycol applied simultaneously in a mixture on porcine skin ex vivo.

PubMed

Mujica Ascencio, Saul; Choe, ChunSik; Meinke, Martina C; Müller, Rainer H; Maksimov, George V; Wigger-Alberti, Walter; Lademann, Juergen; Darvin, Maxim E

2016-07-01

Propylene glycol is one of the known substances added in cosmetic formulations as a penetration enhancer. Recently, nanocrystals have been employed also to increase the skin penetration of active components. Caffeine is a component with many applications and its penetration into the epidermis is controversially discussed in the literature. In the present study, the penetration ability of two components - caffeine nanocrystals and propylene glycol, applied topically on porcine ear skin in the form of a gel, was investigated ex vivo using two confocal Raman microscopes operated at different excitation wavelengths (785nm and 633nm). Several depth profiles were acquired in the fingerprint region and different spectral ranges, i.e., 526-600cm(-1) and 810-880cm(-1) were chosen for independent analysis of caffeine and propylene glycol penetration into the skin, respectively. Multivariate statistical methods such as principal component analysis (PCA) and linear discriminant analysis (LDA) combined with Student's t-test were employed to calculate the maximum penetration depths of each substance (caffeine and propylene glycol). The results show that propylene glycol penetrates significantly deeper than caffeine (20.7-22.0μm versus 12.3-13.0μm) without any penetration enhancement effect on caffeine. The results confirm that different substances, even if applied onto the skin as a mixture, can penetrate differently. The penetration depths of caffeine and propylene glycol obtained using two different confocal Raman microscopes are comparable showing that both types of microscopes are well suited for such investigations and that multivariate statistical PCA-LDA methods combined with Student's t-test are very useful for analyzing the penetration of different substances into the skin. Copyright © 2016 Elsevier B.V. All rights reserved.

Factors influencing pre-operative urinary calcium excretion in primary hyperparathyroidism.

PubMed

Kaderli, Reto M; Riss, Philipp; Geroldinger, Angelika; Selberherr, Andreas; Scheuba, Christian; Niederle, Bruno

2017-07-01

Normal or elevated 24-hour urinary calcium (Ca) excretion is a diagnostic marker in primary hyperparathyroidism (PHPT). It is used to distinguish familial hypocalciuric hypercalcaemia (FHH) from PHPT by calculating the Ca/creatinine clearance ratio (CCCR). The variance of CCCR in patients with PHPT is considerable. The aim of this study was to analyse the parameters affecting CCCR in patients with PHPT. The data were collected prospectively. Patients with sporadic PHPT undergoing successful surgery were included in a retrospective analysis. The analysis covered 381 patients with pre-operative workup 2 days before removal of a solitary parathyroid adenoma. The impact of serum Ca and 25-hydroxyvitamin D3 (25-OH D3) on CCCR. The coefficient of determination (R 2 ) in the multivariable model for CCCR consisting of age, Ca, 25-OH D3, 1,25-dihydroxyvitamin D3 (1,25-(OH)2 D3), testosterone (separately for males and females), intact parathyroid hormone (iPTH) and osteocalcin was 25.8%. The only significant parameters in the multivariable analysis were 1,25-(OH)2 D3 and osteocalcin with a drop in R 2 of 15.4% (P<.001) and 2.4% (P=.006), respectively. Bone mineral densities at the lumbar spine, distal radius and left femoral neck were not associated with CCCR (r=-.08, r=-.10 and r=-0.09). In multivariable analysis, 1,25-(OH)2 D3 and osteocalcin were the only factors correlating with CCCR. Vitamin D3 replacement may therefore impair the diagnostic value of CCCR and increase the importance of close monitoring of urinary Ca excretion during treatment. © 2017 John Wiley & Sons Ltd.

Positron emission tomography-computed tomography predictors of progression after DA-R-EPOCH for PMBCL.

PubMed

Pinnix, Chelsea C; Ng, Andrea K; Dabaja, Bouthaina S; Milgrom, Sarah A; Gunther, Jillian R; Fuller, C David; Smith, Grace L; Abou Yehia, Zeinab; Qiao, Wei; Wogan, Christine F; Akhtari, Mani; Mawlawi, Osama; Medeiros, L Jeffrey; Chuang, Hubert H; Martin-Doyle, William; Armand, Philippe; LaCasce, Ann S; Oki, Yasuhiro; Fanale, Michelle; Westin, Jason; Neelapu, Sattva; Nastoupil, Loretta

2018-06-12

Dose-adjusted rituximab plus etoposide, prednisone, vincristine, cyclophosphamide, and doxorubicin (DA-R-EPOCH) has produced good outcomes in primary mediastinal B-cell lymphoma (PMBCL), but predictors of resistance to this treatment are unclear. We investigated whether [ 18 F]fluorodeoxyglucose positron emission tomography-computed tomography (PET-CT) findings could identify patients with PMBCL who would not respond completely to DA-R-EPOCH. We performed a retrospective analysis of 65 patients with newly diagnosed stage I to IV PMBCL treated at 2 tertiary cancer centers who had PET-CT scans available before and after frontline therapy with DA-R-EPOCH. Pretreatment variables assessed included metabolic tumor volume (MTV) and total lesion glycolysis (TLG). Optimal cutoff points for progression-free survival (PFS) were determined by a machine learning approach. Univariate and multivariable models were constructed to assess associations between radiographic variables and PFS. At a median follow-up of 36.6 months (95% confidence interval, 28.1-45.1), 2-year PFS and overall survival rates for the 65 patients were 81.4% and 98.4%, respectively. Machine learning-derived thresholds for baseline MTV and TLG were associated with inferior PFS (elevated MTV: hazard ratio [HR], 11.5; P = .019; elevated TLG: HR, 8.99; P = .005); other pretreatment clinical factors, including International Prognostic Index and bulky (>10 cm) disease, were not. On multivariable analysis, only TLG retained statistical significance ( P = .049). Univariate analysis of posttreatment variables revealed that residual CT tumor volume, maximum standardized uptake value, and Deauville score were associated with PFS; a Deauville score of 5 remained significant on multivariable analysis ( P = .006). A model combining baseline TLG and end-of-therapy Deauville score identified patients at increased risk of progression. © 2018 by The American Society of Hematology.

Clinical Usefulness of Measuring Red Blood Cell Distribution Width in Patients with Hepatitis B Virus-Related Acute-On-Chronic Liver Failure.

PubMed

Jin, Lei; Gao, Yufeng; Ye, Jun; Zou, Guizhou; Li, Xu

2017-09-01

The red blood cell distribution width (RDW) is increased in chronic liver disease, but its clinical significance in hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) is still unclear. The aim of the present study was to investigate the clinical significance of RDW in HBV-ACLF patients. The medical records of HBV-ACLF patients who were admitted to The Second Affiliated Hospital of Anhui Medical University between April 2012 and December 2015 were retrospectively reviewed. Correlations between RDW, neutrophil lymphocyte ratio (NLR), and the model for end-stage liver disease (MELD) scores were analyzed using the Spearman's approach. Multivariable stepwise logistic regression test was used to evaluate independent clinical parameters predicting 3-month mortality of HBV-ACLF patients. The association between RDW and hospitalization outcome was estimated by receiver operating curve (ROC) analysis. Patient survival was estimated by Kaplan-Meier analysis and subsequently compared by log-rank test. Sixty-two HBV-ACLF patients and sixty CHB patients were enrolled. RDW were increased in HBVACLF patients and positively correlated with the NLR as well as MELD scores. Multivariate analysis demonstrated that RDW value was an independent predictor for mortality. RDW had an area under the ROC of 0.799 in predicting 3-month mortality of HBV-ACLF patients. Patients with HBV-ACLF who had RDW > 17% showed significantly poorer survival than those who had RDW ≤ 17%. RDW values are significantly increased in patients with HBV-ACLF. Moreover, RDW values are an independent predicting factor for an in-hospital mortality in patients with HBV-ACLF.

Location in the right hemi-colon is an independent risk factor for delayed post-polypectomy hemorrhage: a multi-center case-control study.

PubMed

Buddingh, K Tim; Herngreen, Thomas; Haringsma, Jelle; van der Zwet, Wil C; Vleggaar, Frank P; Breumelhof, Ronald; Ter Borg, Frank

2011-06-01

Delayed hemorrhage is an infrequent, but serious complication of colonoscopic polypectomy. Large size is the only polyp-related factor that has been unequivocally proven to increase the risk of delayed bleeding. It has been suggested that location in the right hemi-colon is also a risk factor. The objective of this study was to determine whether polyp location is an independent risk factor for delayed post-polypectomy hemorrhage. A retrospective case-control study was conducted in two university hospitals and two community hospitals. Thirty-nine cases and 117 controls were identified. In multivariate analysis, size and location were found to be independent polyp-related risk factors for delayed type hemorrhage. The risk increased by 13% for every 1 mm increase in polyp diameter (odds ratio (OR) 1.13, 95% confidence interval (CI) 1.05-1.20, P<0.001). Polyps located in the right hemi-colon had an OR of 4.67 (1.88-11.61, P=0.001) for delayed hemorrhage. Polyps in the cecum seemed to be especially at high risk in univariate analysis (OR 13.82, 95% CI 2.66-71.73), but this could not be assessed in multivariate analysis as the number of cases was too small. Polyp type (sessile or pedunculated) was not a risk factor. Polyp location in the right hemi-colon seems to be an independent and substantial risk factor for delayed post-polypectomy hemorrhage. A low threshold for preventive hemostatic measures is advised when removing polyps from this region.

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).

Multivariate longitudinal data analysis with censored and intermittent missing responses.

PubMed

Lin, Tsung-I; Lachos, Victor H; Wang, Wan-Lun

2018-05-08

The multivariate linear mixed model (MLMM) has emerged as an important analytical tool for longitudinal data with multiple outcomes. However, the analysis of multivariate longitudinal data could be complicated by the presence of censored measurements because of a detection limit of the assay in combination with unavoidable missing values arising when subjects miss some of their scheduled visits intermittently. This paper presents a generalization of the MLMM approach, called the MLMM-CM, for a joint analysis of the multivariate longitudinal data with censored and intermittent missing responses. A computationally feasible expectation maximization-based procedure is developed to carry out maximum likelihood estimation within the MLMM-CM framework. Moreover, the asymptotic standard errors of fixed effects are explicitly obtained via the information-based method. We illustrate our methodology by using simulated data and a case study from an AIDS clinical trial. Experimental results reveal that the proposed method is able to provide more satisfactory performance as compared with the traditional MLMM approach. Copyright © 2018 John Wiley & Sons, Ltd.

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

A framework for multivariate data-based at-site flood frequency analysis: Essentiality of the conjugal application of parametric and nonparametric approaches

NASA Astrophysics Data System (ADS)

Vittal, H.; Singh, Jitendra; Kumar, Pankaj; Karmakar, Subhankar

2015-06-01

In watershed management, flood frequency analysis (FFA) is performed to quantify the risk of flooding at different spatial locations and also to provide guidelines for determining the design periods of flood control structures. The traditional FFA was extensively performed by considering univariate scenario for both at-site and regional estimation of return periods. However, due to inherent mutual dependence of the flood variables or characteristics [i.e., peak flow (P), flood volume (V) and flood duration (D), which are random in nature], analysis has been further extended to multivariate scenario, with some restrictive assumptions. To overcome the assumption of same family of marginal density function for all flood variables, the concept of copula has been introduced. Although, the advancement from univariate to multivariate analyses drew formidable attention to the FFA research community, the basic limitation was that the analyses were performed with the implementation of only parametric family of distributions. The aim of the current study is to emphasize the importance of nonparametric approaches in the field of multivariate FFA; however, the nonparametric distribution may not always be a good-fit and capable of replacing well-implemented multivariate parametric and multivariate copula-based applications. Nevertheless, the potential of obtaining best-fit using nonparametric distributions might be improved because such distributions reproduce the sample's characteristics, resulting in more accurate estimations of the multivariate return period. Hence, the current study shows the importance of conjugating multivariate nonparametric approach with multivariate parametric and copula-based approaches, thereby results in a comprehensive framework for complete at-site FFA. Although the proposed framework is designed for at-site FFA, this approach can also be applied to regional FFA because regional estimations ideally include at-site estimations. The framework is based on the following steps: (i) comprehensive trend analysis to assess nonstationarity in the observed data; (ii) selection of the best-fit univariate marginal distribution with a comprehensive set of parametric and nonparametric distributions for the flood variables; (iii) multivariate frequency analyses with parametric, copula-based and nonparametric approaches; and (iv) estimation of joint and various conditional return periods. The proposed framework for frequency analysis is demonstrated using 110 years of observed data from Allegheny River at Salamanca, New York, USA. The results show that for both univariate and multivariate cases, the nonparametric Gaussian kernel provides the best estimate. Further, we perform FFA for twenty major rivers over continental USA, which shows for seven rivers, all the flood variables followed nonparametric Gaussian kernel; whereas for other rivers, parametric distributions provide the best-fit either for one or two flood variables. Thus the summary of results shows that the nonparametric method cannot substitute the parametric and copula-based approaches, but should be considered during any at-site FFA to provide the broadest choices for best estimation of the flood return periods.

Hispanic ethnicity is associated with increased costs after carotid endarterectomy and carotid stenting in the United States.

PubMed

Propper, Brandon; Black, James H; Schneider, Eric B; Lum, Ying Wei; Malas, Mahmoud B; Arnold, Margaret W; Abularrage, Christopher J

2013-09-01

We have previously demonstrated an adverse impact of black race and Hispanic ethnicity on the outcomes of carotid endarterectomy (CEA) and carotid artery stenting (CAS). The current study was undertaken to examine the influence of race and ethnicity on the cost of CEA and CAS. The Nationwide Inpatient Sample (2005-2009) was queried using ICD-9 codes for CEA and CAS in patients with carotid artery stenosis. The primary outcome was total hospital charges. Multivariate analysis was performed using a generalized linear model adjusting for age, sex, race, comorbidities (Charlson index), high-risk status, procedure type, symptomatic status, year, insurance type, and surgeon and hospital operative volumes and characteristics. Hispanic and black patients were more likely to have a symptomatic presentation, and were more likely to undergo either CEA or CAS by low-volume surgeons at low-volume hospitals (P < 0.05, all). They were also less likely to have private insurance or Medicare (P < 0.001). Overall, CEA was less expensive than CAS over the 4-y study period ($29,502 ± $104 versus $46,713 ± $409, P < 0.001). Total hospital charges after CEA were increased in both blacks ($39,562 ± $843) and Hispanics ($45,325 ± $735) compared with whites on univariate analysis ($28,403 ± $101, P < 0.001). After CAS, total hospital charges were similarly increased in both blacks ($51,770 ± $2085) and Hispanics ($63,637 ± $2766) compared with whites on univariate analysis ($45,550 ± $412, P < 0.001). On multivariable analysis, however, only Hispanic ethnicity remained independently associated with increased charges after both CEA (exponentiated coefficient 1.18; 95% CI [1.15-1.20]; P < 0.001) and CAS (exponentiated coefficient 1.17; 95% CI [1.09-1.24]; P < 0.001). Hispanic ethnicity was independently associated with increased hospital charges after both CEA and CAS. The increased charges seen in black patients were explained, in part, by decreased surgeon operative volume and increased postoperative complications. Further efforts are warranted to contain costs in minorities undergoing carotid revascularization. Copyright © 2013 Elsevier Inc. All rights reserved.

[Factors affecting residual stones after percutaneous nephrolithotomy in patients with renal calculus].

PubMed

Qiao, Mingzhou; Zhang, Haifang; Zhou, Chenlong

2015-11-24

To explore the factors affecting the residual stones after percutaneous nephrolithotomy (PCNL) in patients with renal calculus. A retrospective analysis was performed for 1 200 patients who were affected by renal calculus and treated with PCNL between Jan 2008 and May 2014 in People's Hospital of Anyang City. Among those patients, 16 were diagnosed as bilateral renal stone and had two successive operations. The size, location and number of stones, previous history of surgery, the degree of hydronephrosis, urinary infection were included in the univariate analysis. Significant factors in univariate analysis were included in the multivariate analysis to determine factors affecting stone residual. A total of 385 cases developed stone residual after surgery. The overall residual rate was 31.7%. In univariate analysis, renal pelvis combined with caliceal calculus (P=0.006), stone size larger than 4 cm (P=0.005), stone number more than 4 (P=0.002), the amount of bleeding more than 200 ml (P=0.025), operation time longer than 120 minutes (P=0.028) were associated with an increased rate of stone residual. When subjected to the Cox multivariate analysis, the independent risk factors for residual stones were renal pelvis combined with caliceal calculus (P=0.049), stone size larger than 4 cm (P=0.038) and stone number more than 4 (P=0.018). Factors affecting the incidence of residual stones after PCNL are the size, location and number of stones. Larger size stone and the presence of renal pelvis combined with caliceal calculus are significantly associated with residual stones. Nevertheless, stone number less than 4 indicates an increased stone clearance rate.

Metabolomics Coupled with Multivariate Data and Pathway Analysis on Potential Biomarkers in Gastric Ulcer and Intervention Effects of Corydalis yanhusuo Alkaloid

PubMed Central

Shuai, Wang; Yongrui, Bao; Shanshan, Guan; Bo, Liu; Lu, Chen; Lei, Wang; Xiaorong, Ran

2014-01-01

Metabolomics, the systematic analysis of potential metabolites in a biological specimen, has been increasingly applied to discovering biomarkers, identifying perturbed pathways, measuring therapeutic targets, and discovering new drugs. By analyzing and verifying the significant difference in metabolic profiles and changes of metabolite biomarkers, metabolomics enables us to better understand substance metabolic pathways which can clarify the mechanism of Traditional Chinese Medicines (TCM). Corydalis yanhusuo alkaloid (CA) is a major component of Qizhiweitong (QZWT) prescription which has been used for treating gastric ulcer for centuries and its mechanism remains unclear completely. Metabolite profiling was performed by high-performance liquid chromatography combined with time-of-flight mass spectrometry (HPLC/ESI-TOF-MS) and in conjunction with multivariate data analysis and pathway analysis. The statistic software Mass Profiller Prossional (MPP) and statistic method including ANOVA and principal component analysis (PCA) were used for discovering novel potential biomarkers to clarify mechanism of CA in treating acid injected rats with gastric ulcer. The changes in metabolic profiling were restored to their base-line values after CA treatment according to the PCA score plots. Ten different potential biomarkers and seven key metabolic pathways contributing to the treatment of gastric ulcer were discovered and identified. Among the pathways, sphingophospholipid metabolism and fatty acid metabolism related network were acutely perturbed. Quantitative real time polymerase chain reaction (RT-PCR) analysis were performed to evaluate the expression of genes related to the two pathways for verifying the above results. The results show that changed biomarkers and pathways may provide evidence to insight into drug action mechanisms and enable us to increase research productivity toward metabolomics drug discovery. PMID:24454691

Association of educational status with cardiovascular disease: Teheran Lipid and Glucose Study.

PubMed

Hajsheikholeslami, Farhad; Hatami, Masumeh; Hadaegh, Farzad; Ghanbarian, Arash; Azizi, Fereidoun

2011-06-01

The aim of this study was to evaluate the associations between educational level and cardiovascular disease (CVD) in an older Iranian population. To estimate the odds ratio (OR) of educational level in a cross-sectional study, logistic regression analysis was used on 1,788 men and 2,204 women (222 men and 204 women positive based on their CVD status) aged ≥ 45 years. In men, educational levels of college degree and literacy level below diploma were inversely associated with CVD in the multivariate model [0.52 (0.28-0.94), 0.61 (0.40-0.92), respectively], but diploma level did not show any significant association with CVD, neither in the crude model nor in the multivariate model. In women, increase in educational level was inversely associated with risk of CVD in the crude model, but in the multivariate adjusted model, literacy level below diploma decreased risk of CVD by 39%, compared with illiteracy. Our findings support those of developed countries that, along with other CVD risk factors, educational status has an inverse association with CVD among a representative Iranian population of older men and women.

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.

Multivariate geometry as an approach to algal community analysis

USGS Publications Warehouse

Allen, T.F.H.; Skagen, S.

1973-01-01

Multivariate analyses are put in the context of more usual approaches to phycological investigations. The intuitive common-sense involved in methods of ordination, classification and discrimination are emphasised by simple geometric accounts which avoid jargon and matrix algebra. Warnings are given that artifacts result from technique abuses by the naive or over-enthusiastic. An analysis of a simple periphyton data set is presented as an example of the approach. Suggestions are made as to situations in phycological investigations, where the techniques could be appropriate. The discipline is reprimanded for its neglect of the multivariate approach.

Network Analysis of Associations between Serum Interferon Alpha Activity, Autoantibodies, and Clinical Features in Systemic Lupus Erythematosus

PubMed Central

Weckerle, Corinna E.; Franek, Beverly S.; Kelly, Jennifer A.; Kumabe, Marissa; Mikolaitis, Rachel A.; Green, Stephanie L.; Utset, Tammy O.; Jolly, Meenakshi; James, Judith A.; Harley, John B.; Niewold, Timothy B.

2010-01-01

Background Interferon-alpha (IFN-α) is a primary pathogenic factor in systemic lupus erythematosus (SLE), and high IFN-α levels may be associated with particular clinical manifestations. The prevalence of individual clinical and serologic features differs significantly by ancestry. We used multivariate and network analyses to detect associations between clinical and serologic disease manifestations and serum IFN-α activity in a large diverse SLE cohort. Methods 1089 SLE patients were studied (387 African-American, 186 Hispanic-American, and 516 European-American). Presence or absence of ACR clinical criteria for SLE, autoantibodies, and serum IFN-α activity data were analyzed in univariate and multivariate models. Iterative multivariate logistic regression was performed in each background separately to establish the network of associations between variables that were independently significant following Bonferroni correction. Results In all ancestral backgrounds, high IFN-α activity was associated with anti-Ro and anti-dsDNA antibodies (p-values 4.6×10−18 and 2.9 × 10−16 respectively). Younger age, non-European ancestry, and anti-RNP were also independently associated with increased serum IFN-α activity (p≤6.7×10−4). We found 14 unique associations between variables in network analysis, and only 7 of these associations were shared by more than one ancestral background. Associations between clinical criteria were different in different ancestral backgrounds, while autoantibody-IFN-α relationships were similar across backgrounds. IFN-α activity and autoantibodies were not associated with ACR clinical features in multivariate models. Conclusions Serum IFN-α activity was strongly and consistently associated with autoantibodies, and not independently associated with clinical features in SLE. IFN-α may be more relevant to humoral tolerance and initial pathogenesis than later clinical disease manifestations. PMID:21162028

Comparison of Optimum Interpolation and Cressman Analyses

NASA Technical Reports Server (NTRS)

Baker, W. E.; Bloom, S. C.; Nestler, M. S.

1984-01-01

The objective of this investigation is to develop a state-of-the-art optimum interpolation (O/I) objective analysis procedure for use in numerical weather prediction studies. A three-dimensional multivariate O/I analysis scheme has been developed. Some characteristics of the GLAS O/I compared with those of the NMC and ECMWF systems are summarized. Some recent enhancements of the GLAS scheme include a univariate analysis of water vapor mixing ratio, a geographically dependent model prediction error correlation function and a multivariate oceanic surface analysis.

Comparison of Optimum Interpolation and Cressman Analyses

NASA Technical Reports Server (NTRS)

Baker, W. E.; Bloom, S. C.; Nestler, M. S.

1985-01-01

The development of a state of the art optimum interpolation (O/I) objective analysis procedure for use in numerical weather prediction studies was investigated. A three dimensional multivariate O/I analysis scheme was developed. Some characteristics of the GLAS O/I compared with those of the NMC and ECMWF systems are summarized. Some recent enhancements of the GLAS scheme include a univariate analysis of water vapor mixing ratio, a geographically dependent model prediction error correlation function and a multivariate oceanic surface analysis.

Socio-economic Correlates of Malnutrition among Married Women in Bangladesh.

PubMed

Mostafa Kamal, S M; Md Aynul, Islam

2010-12-01

This paper examines the prevalence and socio-economic correlates of malnutrition among ever married non-pregnant women of reproductive age of Bangladesh using a nationally representative weighted sample of 10,145. Body mass index was used to measure nutritional status. Both bivariate and multivariate statistical analyses were employed to assess the relationship between socio-economic characteristics and women's nutritional status. Overall, 28.5% of the women were found to be underweight. The fixed effect multivariate binary logistic regression analysis yielded significantly increased risk of underweight for the young, currently working, non-Muslim, rural residents, widowed, divorced or separated women. Significant wide variations of malnourishment prevailed in the administrative regions of the country. Wealth index and women's education were the most important determinants of underweight. The multivariate logistic regression analysis revealed that the risk of being underweight was almost seven times higher (OR=6.76, 95% CI=5.20-8.80) among women with no formal education as compared to those with higher education and the likelihood of underweight was significantly (p<0.001) 5.2 times (OR=5.23, 95% CI=4.51-6.07) in the poorest as compared to their richest counterparts. Poverty alleviation programmes should be strengthened targeting the poor. Effective policies, information and health education programmes for women are required to ensure adequate access to health services and for them to understand the components of a healthy diet.

Tracking Problem Solving by Multivariate Pattern Analysis and Hidden Markov Model Algorithms

ERIC Educational Resources Information Center

Anderson, John R.

2012-01-01

Multivariate pattern analysis can be combined with Hidden Markov Model algorithms to track the second-by-second thinking as people solve complex problems. Two applications of this methodology are illustrated with a data set taken from children as they interacted with an intelligent tutoring system for algebra. The first "mind reading" application…

Functional Path Analysis as a Multivariate Technique in Developing a Theory of Participation in Adult Education.

ERIC Educational Resources Information Center

Martin, James L.

This paper reports on attempts by the author to construct a theoretical framework of adult education participation using a theory development process and the corresponding multivariate statistical techniques. Two problems are identified: the lack of theoretical framework in studying problems, and the limiting of statistical analysis to univariate…

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…

Web-Based Tools for Modelling and Analysis of Multivariate Data: California Ozone Pollution Activity

ERIC Educational Resources Information Center

Dinov, Ivo D.; Christou, Nicolas

2011-01-01

This article presents a hands-on web-based activity motivated by the relation between human health and ozone pollution in California. This case study is based on multivariate data collected monthly at 20 locations in California between 1980 and 2006. Several strategies and tools for data interrogation and exploratory data analysis, model fitting…

Bias and Precision of Measures of Association for a Fixed-Effect Multivariate Analysis of Variance Model

ERIC Educational Resources Information Center

Kim, Soyoung; Olejnik, Stephen

2005-01-01

The sampling distributions of five popular measures of association with and without two bias adjusting methods were examined for the single factor fixed-effects multivariate analysis of variance model. The number of groups, sample sizes, number of outcomes, and the strength of association were manipulated. The results indicate that all five…

Multivariate analysis of climate along the southern coast of Alaska—some forestry implications.

Treesearch

Wilbur A. Farr; John S. Hard

1987-01-01

A multivariate analysis of climate was used to delineate 10 significantly different groups of climatic stations along the southern coast of Alaska based on latitude, longitude, seasonal temperatures and precipitation, frost-free periods, and total number of growing degree days. The climatic stations were too few to delineate this rugged, mountainous region into...

Use of Stereotactic Radiosurgery in Elderly and Very Elderly Patients With Brain Metastases to Limit Toxicity Associated With Whole Brain Radiation Therapy.

PubMed

Chen, Linda; Shen, Colette; Redmond, Kristin J; Page, Brandi R; Kummerlowe, Megan; Mcnutt, Todd; Bettegowda, Chetan; Rigamonti, Daniele; Lim, Michael; Kleinberg, Lawrence

2017-07-15

We evaluated the toxicity associated with stereotactic radiosurgery (SRS) and whole brain radiation therapy (WBRT) in elderly and very elderly patients with brain metastases, as the role of SRS in geriatric patients who would traditionally receive WBRT is unclear. We conducted a retrospective review of elderly patients (aged 70-79 years) and very elderly patients (aged ≥80 years) with brain metastases who underwent RT from 2010 to 2015 at Johns Hopkins Hospital. Patients received either upfront WBRT or SRS for metastatic solid malignancies, excluding small cell lung cancer. Acute central nervous system toxicity within 3 months of RT was graded using the Radiation Therapy Oncology Group acute radiation central nervous system morbidity scale. The toxicity data between age groups and treatment modalities were analyzed using Fisher's exact test and multivariate logistic regression analysis. Kaplan-Meier curves were used to estimate the median overall survival, and the Cox proportion hazard model was used for multivariate analysis. A total of 811 brain metastases received RT in 119 geriatric patients. The median overall survival from the diagnosis of brain metastases was 4.3 months for the patients undergoing WBRT and 14.4 months for the patients undergoing SRS. On multivariate analysis, WBRT was associated with worse overall survival in this cohort of geriatric patients (odds ratio [OR] 3.7, 95% confidence interval [CI] 1.9-7.0, P<.0001) and age ≥80 years was not. WBRT was associated with significantly greater rates of any grade 1 to 4 toxicity (OR 7.5, 95% CI 1.6-33.3, P=.009) and grade 2 to 4 toxicity (OR 2.8, 95% CI 1.0-8.1, P=.047) on multivariate analysis. Elderly and very elderly patients did not have significantly different statistically acute toxicity rates when stratified by age. WBRT was associated with increased toxicity compared with SRS in elderly and very elderly patients with brain metastases. SRS, rather than WBRT, should be prospectively evaluated in geriatric patients with the goal of minimizing treatment-related toxicity. Copyright © 2017. Published by Elsevier Inc.

Multivariate Meta-Analysis of Genetic Association Studies: A Simulation Study

PubMed Central

Neupane, Binod; Beyene, Joseph

2015-01-01

In a meta-analysis with multiple end points of interests that are correlated between or within studies, multivariate approach to meta-analysis has a potential to produce more precise estimates of effects by exploiting the correlation structure between end points. However, under random-effects assumption the multivariate estimation is more complex (as it involves estimation of more parameters simultaneously) than univariate estimation, and sometimes can produce unrealistic parameter estimates. Usefulness of multivariate approach to meta-analysis of the effects of a genetic variant on two or more correlated traits is not well understood in the area of genetic association studies. In such studies, genetic variants are expected to roughly maintain Hardy-Weinberg equilibrium within studies, and also their effects on complex traits are generally very small to modest and could be heterogeneous across studies for genuine reasons. We carried out extensive simulation to explore the comparative performance of multivariate approach with most commonly used univariate inverse-variance weighted approach under random-effects assumption in various realistic meta-analytic scenarios of genetic association studies of correlated end points. We evaluated the performance with respect to relative mean bias percentage, and root mean square error (RMSE) of the estimate and coverage probability of corresponding 95% confidence interval of the effect for each end point. Our simulation results suggest that multivariate approach performs similarly or better than univariate method when correlations between end points within or between studies are at least moderate and between-study variation is similar or larger than average within-study variation for meta-analyses of 10 or more genetic studies. Multivariate approach produces estimates with smaller bias and RMSE especially for the end point that has randomly or informatively missing summary data in some individual studies, when the missing data in the endpoint are imputed with null effects and quite large variance. PMID:26196398

Multivariate Meta-Analysis of Genetic Association Studies: A Simulation Study.

PubMed

Neupane, Binod; Beyene, Joseph

2015-01-01

In a meta-analysis with multiple end points of interests that are correlated between or within studies, multivariate approach to meta-analysis has a potential to produce more precise estimates of effects by exploiting the correlation structure between end points. However, under random-effects assumption the multivariate estimation is more complex (as it involves estimation of more parameters simultaneously) than univariate estimation, and sometimes can produce unrealistic parameter estimates. Usefulness of multivariate approach to meta-analysis of the effects of a genetic variant on two or more correlated traits is not well understood in the area of genetic association studies. In such studies, genetic variants are expected to roughly maintain Hardy-Weinberg equilibrium within studies, and also their effects on complex traits are generally very small to modest and could be heterogeneous across studies for genuine reasons. We carried out extensive simulation to explore the comparative performance of multivariate approach with most commonly used univariate inverse-variance weighted approach under random-effects assumption in various realistic meta-analytic scenarios of genetic association studies of correlated end points. We evaluated the performance with respect to relative mean bias percentage, and root mean square error (RMSE) of the estimate and coverage probability of corresponding 95% confidence interval of the effect for each end point. Our simulation results suggest that multivariate approach performs similarly or better than univariate method when correlations between end points within or between studies are at least moderate and between-study variation is similar or larger than average within-study variation for meta-analyses of 10 or more genetic studies. Multivariate approach produces estimates with smaller bias and RMSE especially for the end point that has randomly or informatively missing summary data in some individual studies, when the missing data in the endpoint are imputed with null effects and quite large variance.

MULTIVARIATE ANALYSES (CONONICAL CORRELATION AND PARTIAL LEAST SQUARE, PLS) TO MODEL AND ASSESS THE ASSOCIATION OF LANDSCAPE METRICS TO SURFACE WATER CHEMICAL AND BIOLOGICAL PROPERTIES USING SAVANNAH RIVER BASIN DATA.

EPA Science Inventory

Many multivariate methods are used in describing and predicting relation; each has its unique usage of categorical and non-categorical data. In multivariate analysis of variance (MANOVA), many response variables (y's) are related to many independent variables that are categorical...

Multivariate Density Estimation and Remote Sensing

NASA Technical Reports Server (NTRS)

Scott, D. W.

1983-01-01

Current efforts to develop methods and computer algorithms to effectively represent multivariate data commonly encountered in remote sensing applications are described. While this may involve scatter diagrams, multivariate representations of nonparametric probability density estimates are emphasized. The density function provides a useful graphical tool for looking at data and a useful theoretical tool for classification. This approach is called a thunderstorm data analysis.

Comprehensive drought characteristics analysis based on a nonlinear multivariate drought index

NASA Astrophysics Data System (ADS)

Yang, Jie; Chang, Jianxia; Wang, Yimin; Li, Yunyun; Hu, Hui; Chen, Yutong; Huang, Qiang; Yao, Jun

2018-02-01

It is vital to identify drought events and to evaluate multivariate drought characteristics based on a composite drought index for better drought risk assessment and sustainable development of water resources. However, most composite drought indices are constructed by the linear combination, principal component analysis and entropy weight method assuming a linear relationship among different drought indices. In this study, the multidimensional copulas function was applied to construct a nonlinear multivariate drought index (NMDI) to solve the complicated and nonlinear relationship due to its dependence structure and flexibility. The NMDI was constructed by combining meteorological, hydrological, and agricultural variables (precipitation, runoff, and soil moisture) to better reflect the multivariate variables simultaneously. Based on the constructed NMDI and runs theory, drought events for a particular area regarding three drought characteristics: duration, peak, and severity were identified. Finally, multivariate drought risk was analyzed as a tool for providing reliable support in drought decision-making. The results indicate that: (1) multidimensional copulas can effectively solve the complicated and nonlinear relationship among multivariate variables; (2) compared with single and other composite drought indices, the NMDI is slightly more sensitive in capturing recorded drought events; and (3) drought risk shows a spatial variation; out of the five partitions studied, the Jing River Basin as well as the upstream and midstream of the Wei River Basin are characterized by a higher multivariate drought risk. In general, multidimensional copulas provides a reliable way to solve the nonlinear relationship when constructing a comprehensive drought index and evaluating multivariate drought characteristics.

Association of Discharge Home with Home Health Care and 30-day Readmission after Pancreatectomy

PubMed Central

Sanford, Dominic E; Olsen, Margaret A; Bommarito, Kerry M; Shah, Manish; Fields, Ryan C; Hawkins, William G; Jaques, David P; Linehan, David C

2014-01-01

Background We sought to determine if discharge home with home health care (HHC) is an independent predictor of increased readmission following pancreatectomy. Study Design We examined 30-day readmissions in patients undergoing pancreatectomy using the Healthcare Cost and Utilization Project State Inpatient Database for California from 2009 to 2011. Readmissions were categorized as severe or non-severe using the Modified Accordion Severity Grading System. Multivariable logistic regression models were used to examine the association of discharge home with HHC and 30-day readmission using discharge home without HHC as the reference group. Propensity score matching was used as an additional analysis to compare the rate of 30-day readmission between patients discharged home with HHC to patients discharged home without HHC. Results 3,573 patients underwent pancreatectomy and 752 (21.0%) were readmitted within 30 days of discharge. In a multivariable logistic regression model, discharge home with HHC was an independent predictor of increased 30-day readmission (OR=1.37; 95%CI=1.11-1.69, p=0.004). Using propensity score matching, patients who received HHC had a significantly increased rate of 30-day readmission compared to patients discharged home without HHC (24.3% vs 19.8%, p<0.001). Patients discharged home with HHC had a significantly increased rate of non-severe readmission compared to those discharged home without HHC by univariate comparison (19.2% vs 13.9%, p<0.001), but not severe readmission (6.4% vs 4.7%, p= 0.08). In multivariable logistic regression models, excluding patients discharged to facilities, discharge home with HHC was an independent predictor of increased non-severe readmissions (OR=1.41; 95%CI=1.11-1.79, p=0.005), but not severe readmissions (OR=1.31; 95%CI=0.88-1.93, p=0.18). Conclusions Discharge home with HHC following pancreatectomy is an independent predictor of increased 30-day readmission; specifically, these services are associated with increased non-severe readmissions, but not severe readmissions. PMID:25440026

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

Clustangles: An Open Library for Clustering Angular Data.

PubMed

Sargsyan, Karen; Hua, Yun Hao; Lim, Carmay

2015-08-24

Dihedral angles are good descriptors of the numerous conformations visited by large, flexible systems, but their analysis requires directional statistics. A single package including the various multivariate statistical methods for angular data that accounts for the distinct topology of such data does not exist. Here, we present a lightweight standalone, operating-system independent package called Clustangles to fill this gap. Clustangles will be useful in analyzing the ever-increasing number of structures in the Protein Data Bank and clustering the copious conformations from increasingly long molecular dynamics simulations.

Data extraction for complex meta-analysis (DECiMAL) guide.

PubMed

Pedder, Hugo; Sarri, Grammati; Keeney, Edna; Nunes, Vanessa; Dias, Sofia

2016-12-13

As more complex meta-analytical techniques such as network and multivariate meta-analyses become increasingly common, further pressures are placed on reviewers to extract data in a systematic and consistent manner. Failing to do this appropriately wastes time, resources and jeopardises accuracy. This guide (data extraction for complex meta-analysis (DECiMAL)) suggests a number of points to consider when collecting data, primarily aimed at systematic reviewers preparing data for meta-analysis. Network meta-analysis (NMA), multiple outcomes analysis and analysis combining different types of data are considered in a manner that can be useful across a range of data collection programmes. The guide has been shown to be both easy to learn and useful in a small pilot study.

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

Impact of age on outcome after colorectal cancer surgery in the elderly - a developing country perspective.

PubMed

Khan, Muhammad Rizwan; Bari, Hassaan; Zafar, Syed Nabeel; Raza, Syed Ahsan

2011-08-17

Colorectal cancer (CRC) is a major source of morbidity and mortality in the elderly population and surgery is often the only definitive management option. The suitability of surgical candidates based on age alone has traditionally been a source of controversy. Surgical resection may be considered detrimental in the elderly solely on the basis of advanced age. Based on recent evidence suggesting that age alone is not a predictor of outcomes, Western societies are increasingly performing definitive procedures on the elderly. Such evidence is not available from our region. We aimed to determine whether age has an independent effect on complications after surgery for colorectal cancer in our population. A retrospective review of all patients who underwent surgery for pathologically confirmed colorectal cancer at Aga Khan University Hospital, Karachi between January 1999 and December 2008 was conducted. Using a cut-off of 70 years, patients were divided into two groups. Patient demographics, tumor characteristics and postoperative complications and 30-day mortality were compared. Multivariate logistic regression analysis was performed with clinically relevant variables to determine whether age had an independent and significant association with the outcome. A total of 271 files were reviewed, of which 56 belonged to elderly patients (≥ 70 years). The gender ratio was equal in both groups. Elderly patients had a significantly higher comorbidity status, Charlson score and American society of anesthesiologists (ASA) class (all p < 0.001). Upon multivariate analysis, factors associated with more complications were ASA status (95% CI = 1.30-6.25), preoperative perforation (95% CI = 1.94-48.0) and rectal tumors (95% CI = 1.21-5.34). Old age was significantly associated with systemic complications upon univariate analysis (p = 0.05), however, this association vanished upon multivariate analysis (p = 0.36). Older patients have more co-morbid conditions and higher ASA scores, but increasing age itself is not independently associated with complications after surgery for CRC. Therefore patient selection should focus on the clinical status and ASA class of the patient rather than age.

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.

A Statistical Portrait of Working at Home in the U.K.: Evidence from the Labour Force Survey. Working Paper.

ERIC Educational Resources Information Center

Felstead, Alan; Jewson, Nick; Phizacklea, Annie; Walters, Sally

The patterns, extent, and problems of working at home in the United Kingdom were examined through a multivariate analysis of data from the Labour Force Survey, which has questioned respondents about the location of their workplace since 1992. The numbers of people working "mainly" at home increased from 345,920 (1.5%) in 1981 to 680,612…

Initial Principal Readiness to Interconnect Positive Behavioral Interventions and Supports and School Mental Health: A Sequential Multivariate Exploratory Analysis

ERIC Educational Resources Information Center

Ecker, Andrew Joseph

2017-01-01

Approximately 20% of youth in the U.S. are experiencing a mental health challenge; a rate that is said to increase by more than 50% by 2020. Schools are the largest provider of mental health services to youth, yet two of schools' most efficacious evidence-based systems, Positive Behavioral Interventions and Supports (PBIS) and school mental health…

A Framework for Establishing Standard Reference Scale of Texture by Multivariate Statistical Analysis Based on Instrumental Measurement and Sensory Evaluation.

PubMed

Zhi, Ruicong; Zhao, Lei; Xie, Nan; Wang, Houyin; Shi, Bolin; Shi, Jingye

2016-01-13

A framework of establishing standard reference scale (texture) is proposed by multivariate statistical analysis according to instrumental measurement and sensory evaluation. Multivariate statistical analysis is conducted to rapidly select typical reference samples with characteristics of universality, representativeness, stability, substitutability, and traceability. The reasonableness of the framework method is verified by establishing standard reference scale of texture attribute (hardness) with Chinese well-known food. More than 100 food products in 16 categories were tested using instrumental measurement (TPA test), and the result was analyzed with clustering analysis, principal component analysis, relative standard deviation, and analysis of variance. As a result, nine kinds of foods were determined to construct the hardness standard reference scale. The results indicate that the regression coefficient between the estimated sensory value and the instrumentally measured value is significant (R(2) = 0.9765), which fits well with Stevens's theory. The research provides reliable a theoretical basis and practical guide for quantitative standard reference scale establishment on food texture characteristics.

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

Using video and theater to increase knowledge and change attitudes-Why are gorillas important to the world and to Congo?

PubMed

Breuer, Thomas; Mavinga, Franck Barrel; Evans, Ron; Lukas, Kristen E

2017-10-01

Applying environmental education in primate range countries is an important long-term activity to stimulate pro-conservation behavior. Within captive settings, mega-charismatic species, such as great apes are often used to increase knowledge and positively influence attitudes of visitors. Here, we evaluate the effectiveness of a short-term video and theater program developed for a Western audience and adapted to rural people living in two villages around Nouabalé-Ndoki National Park, Republic of Congo. We assessed the knowledge gain and attitude change using oral evaluation in the local language (N = 111). Overall pre-program knowledge about Western gorillas (Gorilla gorilla) was high. Detailed multivariate analysis of pre-program knowledge revealed differences in knowledge between two villages and people with different jobs while attitudes largely were similar between groups. The short-term education program was successful in raising knowledge, particularly of those people with less pre-program knowledge. We also noted an overall significant attitude improvement. Our data indicate short-term education programs are useful in quickly raising knowledge as well improving attitudes. Furthermore, education messages need to be clearly adapted to the daily livelihood realities of the audience, and multi-variate analysis can help to identify potential target groups for education programs. © 2017 Wiley Periodicals, Inc.

Racial Differences in Circulating Natriuretic Peptide Levels: The Atherosclerosis Risk in Communities Study

PubMed Central

Gupta, Deepak K; Claggett, Brian; Wells, Quinn; Cheng, Susan; Li, Man; Maruthur, Nisa; Selvin, Elizabeth; Coresh, Josef; Konety, Suma; Butler, Kenneth R; Mosley, Thomas; Boerwinkle, Eric; Hoogeveen, Ron; Ballantyne, Christie M; Solomon, Scott D

2015-01-01

Background Natriuretic peptides promote natriuresis, diuresis, and vasodilation. Experimental deficiency of natriuretic peptides leads to hypertension (HTN) and cardiac hypertrophy, conditions more common among African Americans. Hospital-based studies suggest that African Americans may have reduced circulating natriuretic peptides, as compared to Caucasians, but definitive data from community-based cohorts are lacking. Methods and Results We examined plasma N-terminal pro B-type natriuretic peptide (NTproBNP) levels according to race in 9137 Atherosclerosis Risk in Communities (ARIC) Study participants (22% African American) without prevalent cardiovascular disease at visit 4 (1996–1998). Multivariable linear and logistic regression analyses were performed adjusting for clinical covariates. Among African Americans, percent European ancestry was determined from genetic ancestry informative markers and then examined in relation to NTproBNP levels in multivariable linear regression analysis. NTproBNP levels were significantly lower in African Americans (median, 43 pg/mL; interquartile range [IQR], 18, 88) than Caucasians (median, 68 pg/mL; IQR, 36, 124; P<0.0001). In multivariable models, adjusted log NTproBNP levels were 40% lower (95% confidence interval [CI], −43, −36) in African Americans, compared to Caucasians, which was consistent across subgroups of age, gender, HTN, diabetes, insulin resistance, and obesity. African-American race was also significantly associated with having nondetectable NTproBNP (adjusted OR, 5.74; 95% CI, 4.22, 7.80). In multivariable analyses in African Americans, a 10% increase in genetic European ancestry was associated with a 7% (95% CI, 1, 13) increase in adjusted log NTproBNP. Conclusions African Americans have lower levels of plasma NTproBNP than Caucasians, which may be partially owing to genetic variation. Low natriuretic peptide levels in African Americans may contribute to the greater risk for HTN and its sequalae in this population. PMID:25999400

Physical function in older men with hyperkyphosis.

PubMed

Katzman, Wendy B; Harrison, Stephanie L; Fink, Howard A; Marshall, Lynn M; Orwoll, Eric; Barrett-Connor, Elizabeth; Cawthon, Peggy M; Kado, Deborah M

2015-05-01

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. 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. 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. Hyperkyphosis is associated with impaired lower extremity physical function in older men. Further studies are needed to determine the direction of causality. © The Author 2014. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Will the clinicians support the researchers and teachers? Results of a salary satisfaction survey of 947 academic surgeons.

PubMed

Scarborough, John E; Bennett, Kyla M; Schroeder, Rebecca A; Swedish, Tristan B; Jacobs, Danny O; Kuo, Paul C

2009-09-01

To determine whether academic surgeons are satisfied with their salaries, and if they are willing to forego some compensation to support departmental academic endeavors. Increasing financial constraints have led many academic surgery departments to rely on increasingly on clinical revenue generation for the cross-subsidization of research and teach missions. Members of 3 academic surgical societies (n = 3059) were surveyed on practice characteristics and attitudes about financial compensation. Univariate and multivariate logistic regression analyses were performed to identify determinants of salary satisfaction and willingness to forego compensation to support academic missions. One thousand thirty-eight (33.9%) surgeons responded to our survey, 947 of whom maintain an academic practice. Of these academic surgeons, 49.7% expressed satisfaction with their compensation. Length of career, administrative responsibility for compensation and membership in the American Surgical Association or the Society of University Surgeons were predictive of salary satisfaction on univariate analysis. Frequent emergency call duty, increased clinical activity, and greater perceived difference between academic and private practice compensation were predictive of salary dissatisfaction. On multivariate analysis, increased clinical activity was inversely associated with both salary satisfaction (adjusted odds ratio [AOR], 0.77; [95% CI: 0.64, 0.94]; P = 0.009) and amount of compensation willingly killed for an academic practice (AOR, 0.71; [0.61, 0.83]; P < 0.0005). Increasing reliance on clinical revenue to subsidize nonclinical academic missions is disaffecting many academic surgeons. Redefined mission priorities, enhanced nonfinancial rewards, utilization of nonclinical revenue sources (eg, philanthropy, grants), increased efficiency of business practices and/or redesign of fund flows may be necessary to sustain recruitment and retention of young academic surgeons.

Effects of pre-pregnancy body mass index and gestational weight gain on neonatal birth weight.

PubMed

Du, Meng-Kai; Ge, Li-Ya; Zhou, Meng-Lin; Ying, Jun; Qu, Fan; Dong, Min-Yue; Chen, Dan-Qing

To evaluate the effects of maternal pre-pregnancy body mass index (pre-BMI) and gestational weight gain (GWG) on neonatal birth weight (NBW) in the population of Chinese healthy pregnant women, attempting to guide weight control in pregnancy. A retrospective cohort study of 3772 Chinese women was conducted. The population was stratified by maternal pre-BMI categories as underweight (<18.5 kg/m 2 ), normal weight (18.5-23.9 kg/m 2 ), overweight (24.0-27.9 kg/m 2 ), and obesity (≥28.0 kg/m 2 ). The NBW differences were tested among the four groups, and then deeper associations among maternal pre-BMI, GWG, and NBW were investigated by multivariate analysis. NBW increased significantly with the increase of maternal pre-BMI level (P<0.05), except overweight to obesity (P>0.05). The multivariate analysis showed that both pre-BMI and GWG were positively correlated with NBW (P<0.05). Compared with normal pre-BMI, underweight predicted an increased odds ratio of small-for-gestational-age (SGA) and decreased odds ratio for macrosomia and large-for-gestational-age (LGA), and the results were opposite for overweight. With the increase of GWG, the risk of SGA decreased and the risks of macrosomia and LGA increased. In addition, in different pre-BMI categories, the effects of weight gain in the first trimester on NBW were different (P<0.05). NBW is positively affected by both maternal pre-BMI and GWG, extreme pre-BMI and GWG are both associated with increased risks of abnormal birth weight, and maternal pre-BMI may modify the effect of weight gain in each trimester on NBW. A valid GWG guideline for Chinese women is an urgent requirement, whereas existing recommendations seem to be not very suitable for the Chinese.

A Course in... Multivariable Control Methods.

ERIC Educational Resources Information Center

Deshpande, Pradeep B.

1988-01-01

Describes an engineering course for graduate study in process control. Lists four major topics: interaction analysis, multiloop controller design, decoupling, and multivariable control strategies. Suggests a course outline and gives information about each topic. (MVL)

Increased risk for complications following removal of hardware in patients with liver disease, pilon or pelvic fractures: A regression analysis.

PubMed

Brown, Bryan D; Steinert, Justin N; Stelzer, John W; Yoon, Richard S; Langford, Joshua R; Koval, Kenneth J

2017-12-01

Indications for removing orthopedic hardware on an elective basis varies widely. Although viewed as a relatively benign procedure, there is a lack of data regarding overall complication rates after fracture fixation. The purpose of this study is to determine the overall short-term complication rate for elective removal of orthopedic hardware after fracture fixation and to identify associated risk factors. Adult patients indicated for elective hardware removal after fracture fixation between July 2012 and July 2016 were screened for inclusion. Inclusion criteria included patients with hardware related pain and/or impaired cosmesis with complete medical and radiographic records and at least 3-month follow-up. Exclusion criteria were those patients indicated for hardware removal for a diagnosis of malunion, non-union, and/or infection. Data collected included patient age, gender, anatomic location of hardware removed, body mass index, ASA score, and comorbidities. Overall complications, as well as complications requiring revision surgery were recorded. Statistical analysis was performed with SPSS 20.0, and included univariate and multivariate regression analysis. 391 patients (418 procedures) were included for analysis. Overall complication rates were 8.4%, with a 3.6% revision surgery rate. Univariate regression analysis revealed that patients who had liver disease were at significant risk for complication (p=0.001) and revision surgery (p=0.036). Multivariate regression analysis showed that: 1) patients who had liver disease were at significant risk of overall complication (p=0.001) and revision surgery (p=0.039); 2) Removal of hardware following fixation for a pilon had significantly increased risk for complication (p=0.012), but not revision surgery (p=0.43); and 3) Removal of hardware for pelvic fixation had a significantly increased risk for revision surgery (p=0.017). Removal of hardware following fracture fixation is not a risk-free procedure. Patients with liver disease are at increased risk for complications, including increased risk for needing revision surgery following hardware removal. Patients having hardware removed following fixation for pilon fractures also are at increased risk for complication, although they may not require a return trip to the operating room. Finally, removal of pelvic hardware is associated with a higher return to the operating room. Copyright © 2017 Elsevier Ltd. All rights reserved.

Cost of bariatric surgery and factors associated with increased cost: an analysis of national inpatient sample.

PubMed

Khorgami, Zhamak; Aminian, Ali; Shoar, Saeed; Andalib, Amin; Saber, Alan A; Schauer, Philip R; Brethauer, Stacy A; Sclabas, Guido M

2017-08-01

In the current healthcare environment, bariatric surgery centers need to be cost-effective while maintaining quality. The aim of this study was to evaluate national cost of bariatric surgery to identify the factors associated with a higher cost. A retrospective analysis of 2012-2013 Healthcare Cost and Utilization Project - Nationwide Inpatient Sample (HCUP-NIS). We included all patients with a diagnosis of morbid obesity (ICD9 278.01) and a Diagnosis Related Group code related to procedures for obesity, who underwent Roux-en-Y gastric bypass (RYGB), sleeve gastrectomy (SG), or adjustable gastric banding (AGB) as their primary procedure. We converted "hospital charges" to "cost," using hospital specific cost-to-charge ratio. Inflation was adjusted using the annual consumer price index. Increased cost was defined as the top 20th percentile of the expenditure and its associated factors were analyzed using the logistic regression multivariate analysis. A total of 45,219 patients (20,966 RYGBs, 22,380 SGs, and 1,873 AGBs) were included. The median (interquartile range) calculated costs for RYGB, SG, and AGB were $12,543 ($9,970-$15,857), $10,531 ($8,248-$13,527), and $9,219 ($7,545-$12,106), respectively (P<.001). Robotic-assisted procedures had the highest impact on the cost (odds ratio 3.6, 95% confidence interval 3.2-4). Hospital cost of RYGB and SG increased linearly with the length of hospital stay and almost doubled after 7 days. Furthermore, multivariate analysis showed that certain co-morbidities and concurrent procedures were associated with an increased cost. Factors contributing to the cost variation of bariatric procedures include co-morbidities, robotic platform, complexity of surgery, and hospital length of stay. Copyright © 2017 American Society for Bariatric Surgery. Published by Elsevier Inc. All rights reserved.

A systematic review of the relationship factor between women and health professionals within the multivariant analysis of maternal satisfaction.

PubMed

Macpherson, Ignacio; Roqué-Sánchez, María V; Legget Bn, Finola O; Fuertes, Ferran; Segarra, Ignacio

2016-10-01

personalised support provided to women by health professionals is one of the prime factors attaining women's satisfaction during pregnancy and childbirth. However the multifactorial nature of 'satisfaction' makes difficult to assess it. Statistical multivariate analysis may be an effective technique to obtain in depth quantitative evidence of the importance of this factor and its interaction with the other factors involved. This technique allows us to estimate the importance of overall satisfaction in its context and suggest actions for healthcare services. systematic review of studies that quantitatively measure the personal relationship between women and healthcare professionals (gynecologists, obstetricians, nurse, midwifes, etc.) regarding maternity care satisfaction. The literature search focused on studies carried out between 1970 and 2014 that used multivariate analyses and included the woman-caregiver relationship as a factor of their analysis. twenty-four studies which applied various multivariate analysis tools to different periods of maternity care (antenatal, perinatal, post partum) were selected. The studies included discrete scale scores and questionnaires from women with low-risk pregnancies. The "personal relationship" factor appeared under various names: care received, personalised treatment, professional support, amongst others. The most common multivariate techniques used to assess the percentage of variance explained and the odds ratio of each factor were principal component analysis and logistic regression. the data, variables and factor analysis suggest that continuous, personalised care provided by the usual midwife and delivered within a family or a specialised setting, generates the highest level of satisfaction. In addition, these factors foster the woman's psychological and physiological recovery, often surpassing clinical action (e.g. medicalization and hospital organization) and/or physiological determinants (e.g. pain, pathologies, etc.). Copyright © 2016 Elsevier Ltd. All rights reserved.

Association between driving pressure and development of postoperative pulmonary complications in patients undergoing mechanical ventilation for general anaesthesia: a meta-analysis of individual patient data.

PubMed

Neto, Ary Serpa; Hemmes, Sabrine N T; Barbas, Carmen S V; Beiderlinden, Martin; Fernandez-Bustamante, Ana; Futier, Emmanuel; Gajic, Ognjen; El-Tahan, Mohamed R; Ghamdi, Abdulmohsin A Al; Günay, Ersin; Jaber, Samir; Kokulu, Serdar; Kozian, Alf; Licker, Marc; Lin, Wen-Qian; Maslow, Andrew D; Memtsoudis, Stavros G; Reis Miranda, Dinis; Moine, Pierre; Ng, Thomas; Paparella, Domenico; Ranieri, V Marco; Scavonetto, Federica; Schilling, Thomas; Selmo, Gabriele; Severgnini, Paolo; Sprung, Juraj; Sundar, Sugantha; Talmor, Daniel; Treschan, Tanja; Unzueta, Carmen; Weingarten, Toby N; Wolthuis, Esther K; Wrigge, Hermann; Amato, Marcelo B P; Costa, Eduardo L V; de Abreu, Marcelo Gama; Pelosi, Paolo; Schultz, Marcus J

2016-04-01

Protective mechanical ventilation strategies using low tidal volume or high levels of positive end-expiratory pressure (PEEP) improve outcomes for patients who have had surgery. The role of the driving pressure, which is the difference between the plateau pressure and the level of positive end-expiratory pressure is not known. We investigated the association of tidal volume, the level of PEEP, and driving pressure during intraoperative ventilation with the development of postoperative pulmonary complications. We did a meta-analysis of individual patient data from randomised controlled trials of protective ventilation during general anesthaesia for surgery published up to July 30, 2015. The main outcome was development of postoperative pulmonary complications (postoperative lung injury, pulmonary infection, or barotrauma). We included data from 17 randomised controlled trials, including 2250 patients. Multivariate analysis suggested that driving pressure was associated with the development of postoperative pulmonary complications (odds ratio [OR] for one unit increase of driving pressure 1·16, 95% CI 1·13-1·19; p<0·0001), whereas we detected no association for tidal volume (1·05, 0·98-1·13; p=0·179). PEEP did not have a large enough effect in univariate analysis to warrant inclusion in the multivariate analysis. In a mediator analysis, driving pressure was the only significant mediator of the effects of protective ventilation on development of pulmonary complications (p=0·027). In two studies that compared low with high PEEP during low tidal volume ventilation, an increase in the level of PEEP that resulted in an increase in driving pressure was associated with more postoperative pulmonary complications (OR 3·11, 95% CI 1·39-6·96; p=0·006). In patients having surgery, intraoperative high driving pressure and changes in the level of PEEP that result in an increase of driving pressure are associated with more postoperative pulmonary complications. However, a randomised controlled trial comparing ventilation based on driving pressure with usual care is needed to confirm these findings. None. Copyright © 2016 Elsevier Ltd. All rights reserved.

Independent Predictors of Prognosis Based on Oral Cavity Squamous Cell Carcinoma Surgical Margins.

PubMed

Buchakjian, Marisa R; Ginader, Timothy; Tasche, Kendall K; Pagedar, Nitin A; Smith, Brian J; Sperry, Steven M

2018-05-01

Objective To conduct a multivariate analysis of a large cohort of oral cavity squamous cell carcinoma (OCSCC) cases for independent predictors of local recurrence (LR) and overall survival (OS), with emphasis on the relationship between (1) prognosis and (2) main specimen permanent margins and intraoperative tumor bed frozen margins. Study Design Retrospective cohort study. Setting Tertiary academic head and neck cancer program. Subjects and Methods This study included 426 patients treated with OCSCC resection between 2005 and 2014 at University of Iowa Hospitals and Clinics. Patients underwent excision of OCSCC with intraoperative tumor bed frozen margin sampling and main specimen permanent margin assessment. Multivariate analysis of the data set to predict LR and OS was performed. Results Independent predictors of LR included nodal involvement, histologic grade, and main specimen permanent margin status. Specifically, the presence of a positive margin (odds ratio, 6.21; 95% CI, 3.3-11.9) or <1-mm/carcinoma in situ margin (odds ratio, 2.41; 95% CI, 1.19-4.87) on the main specimen was an independent predictor of LR, whereas intraoperative tumor bed margins were not predictive of LR on multivariate analysis. Similarly, independent predictors of OS on multivariate analysis included nodal involvement, extracapsular extension, and a positive main specimen margin. Tumor bed margins did not independently predict OS. Conclusion The main specimen margin is a strong independent predictor of LR and OS on multivariate analysis. Intraoperative tumor bed frozen margins do not independently predict prognosis. We conclude that emphasis should be placed on evaluating the main specimen margins when estimating prognosis after OCSCC resection.

Prognostic factors in patients with advanced cholangiocarcinoma: Role of surgery, chemotherapy and body mass index

PubMed Central

Farhat, Mirna H; Shamseddine, Ali I; Tawil, Ayman N; Berjawi, Ghina; Sidani, Charif; Shamseddeen, Wael; Barada, Kassem A

2008-01-01

AIM: To study the factors that may affect survival of cholangiocarcinoma in Lebanon. METHODS: A retrospective review of the medical records of 55 patients diagnosed with cholangio-carcinoma at the American University of Beirut between 1990 and 2005 was conducted. Univariate and multivariate analyses were performed to determine the impact of surgery, chemotherapy, body mass index, bilirubin level and other factors on survival. RESULTS: The median survival of all patients was 8.57 mo (0.03-105.2). Univariate analysis showed that low bilirubin level (< 10 mg/dL), radical surgery and chemotherapy administration were significantly associated with better survival (P = 0.012, 0.038 and 0.038, respectively). In subgroup analysis on patients who had no surgery, chemotherapy administration prolonged median survival significantly (17.0 mo vs 3.5 mo, P = 0.001). Multivariate analysis identified only low bilirubin level < 10 mg/dL and chemotherapy administration as independent predictors associated with better survival (P < 0.05). CONCLUSION: Our data show that palliative and postoperative chemotherapy as well as a bilirubin level < 10 mg/dL are independent predictors of a significant increase in survival in patients with cholangiocarcinoma. PMID:18506930

Personality traits in the differentiation of major depressive disorder and bipolar disorder during a depressive episode.

PubMed

Araujo, Jaciana Marlova Gonçalves; dos Passos, Miguel Bezerra; Molina, Mariane Lopez; da Silva, Ricardo Azevedo; Souza, Luciano Dias de Mattos

2016-02-28

The aim of this study was to determine the differences in personality traits between individuals with Major Depressive Disorder (MDD) and Bipolar Disorder (BD) during a depressive episode, when it can be hard to differentiate them. Data on personality traits (NEO-FFI), mental disorders (Mini International Neuropsychiatric Interview Plus) and socioeconomic variables were collected from 245 respondents who were in a depressive episode. Individuals with MDD (183) and BD (62) diagnosis were compared concerning personality traits, clinical aspects and socioeconomic variables through bivariate analyses (chi-square and ANOVA) and multivariate analysis (logistic regression). There were no differences in the prevalence of the disorders between socioeconomic and clinical variables. As for the personality traits, only the difference in Agreeableness was statistically significant. Considering the control of suicide risk, gender and anxiety comorbidity in the multivariate analysis, the only variable that remained associated was Agreeableness, with an increase in MDD cases. The brief version of the NEO inventories (NEO-FFI) does not allow for the analysis of personality facets. During a depressive episode, high levels of Agreeableness can indicate that MDD is a more likely diagnosis than BD. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

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.

Groundwater flow and hydrogeochemical evolution in the Jianghan Plain, central China

NASA Astrophysics Data System (ADS)

Gan, Yiqun; Zhao, Ke; Deng, Yamin; Liang, Xing; Ma, Teng; Wang, Yanxin

2018-05-01

Hydrogeochemical analysis and multivariate statistics were applied to identify flow patterns and major processes controlling the hydrogeochemistry of groundwater in the Jianghan Plain, which is located in central Yangtze River Basin (central China) and characterized by intensive surface-water/groundwater interaction. Although HCO3-Ca-(Mg) type water predominated in the study area, the 457 (21 surface water and 436 groundwater) samples were effectively classified into five clusters by hierarchical cluster analysis. The hydrochemical variations among these clusters were governed by three factors from factor analysis. Major components (e.g., Ca, Mg and HCO3) in surface water and groundwater originated from carbonate and silicate weathering (factor 1). Redox conditions (factor 2) influenced the geogenic Fe and As contamination in shallow confined groundwater. Anthropogenic activities (factor 3) primarily caused high levels of Cl and SO4 in surface water and phreatic groundwater. Furthermore, the factor score 1 of samples in the shallow confined aquifer gradually increased along the flow paths. This study demonstrates that enhanced information on hydrochemistry in complex groundwater flow systems, by multivariate statistical methods, improves the understanding of groundwater flow and hydrogeochemical evolution due to natural and anthropogenic impacts.

Hyperthyroidism association with SLE, lessons from real-life data--A case-control study.

PubMed

Watad, Abdulla; Cohen, Arnon D; Comaneshter, Doron; Tekes-Manova, Dorit; Amital, Howard

2016-01-01

Despite the frequently encountered association between thyroid disease and systemic lupus erythematosus (SLE) is well known, it is of surprise that only several reports compromised of small population size support this observation. To investigate the association of comorbid SLE and hyperthyroidism. Using the database of the largest health maintenance organization (HMO) in Israel, the Clalit Health Services, we searched for the co-existence of SLE and hyperthyroidism. Patients with SLE were compared with age- and sex-matched controls regarding the prevalence of hyperthyroidism in a case-control study. Chi-square and t-tests were used for univariate analysis and a logistic regression model was used for multivariate analysis. The study included 5018 patients with SLE and 25,090 age- and sex- matched controls. The prevalence of hyperthyroidism in patients with SLE was increased compared with the prevalence in controls (2.59% and 0.91%, respectively, p < 0.001). In a multivariate analysis, SLE was associated with hyperthyroidism (odds ratio 2.52, 95% confidence interval 2.028-3.137). Patients with SLE have a greater prevalence of hyperthyroidism than matched controls. Therefore, physicians treating patients with SLE should be aware of this possibility of this thyroid dysfunction.

Copula Multivariate analysis of Gross primary production and its hydro-environmental driver; A BIOME-BGC model applied to the Antisana páramos

NASA Astrophysics Data System (ADS)

Minaya, Veronica; Corzo, Gerald; van der Kwast, Johannes; Galarraga, Remigio; Mynett, Arthur

2014-05-01

Simulations of carbon cycling are prone to uncertainties from different sources, which in general are related to input data, parameters and the model representation capacities itself. The gross carbon uptake in the cycle is represented by the gross primary production (GPP), which deals with the spatio-temporal variability of the precipitation and the soil moisture dynamics. This variability associated with uncertainty of the parameters can be modelled by multivariate probabilistic distributions. Our study presents a novel methodology that uses multivariate Copulas analysis to assess the GPP. Multi-species and elevations variables are included in a first scenario of the analysis. Hydro-meteorological conditions that might generate a change in the next 50 or more years are included in a second scenario of this analysis. The biogeochemical model BIOME-BGC was applied in the Ecuadorian Andean region in elevations greater than 4000 masl with the presence of typical vegetation of páramo. The change of GPP over time is crucial for climate scenarios of the carbon cycling in this type of ecosystem. The results help to improve our understanding of the ecosystem function and clarify the dynamics and the relationship with the change of climate variables. Keywords: multivariate analysis, Copula, BIOME-BGC, NPP, páramos

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.

Characterization of Interfacial Chemistry of Adhesive/Dentin Bond Using FTIR Chemical Imaging With Univariate and Multivariate Data Processing

PubMed Central

Wang, Yong; Yao, Xiaomei; Parthasarathy, Ranganathan

2008-01-01

Fourier transform infrared (FTIR) chemical imaging can be used to investigate molecular chemical features of the adhesive/dentin interfaces. However, the information is not straightforward, and is not easily extracted. The objective of this study was to use multivariate analysis methods, principal component analysis and fuzzy c-means clustering, to analyze spectral data in comparison with univariate analysis. The spectral imaging data collected from both the adhesive/healthy dentin and adhesive/caries-affected dentin specimens were used and compared. The univariate statistical methods such as mapping of intensities of specific functional group do not always accurately identify functional group locations and concentrations due to more or less band overlapping in adhesive and dentin. Apart from the ease with which information can be extracted, multivariate methods highlight subtle and often important changes in the spectra that are difficult to observe using univariate methods. The results showed that the multivariate methods gave more satisfactory, interpretable results than univariate methods and were conclusive in showing that they can discriminate and classify differences between healthy dentin and caries-affected dentin within the interfacial regions. It is demonstrated that the multivariate FTIR imaging approaches can be used in the rapid characterization of heterogeneous, complex structure. PMID:18980198

Multivariate Analysis of Longitudinal Rates of Change

PubMed Central

Bryan, Matthew; Heagerty, Patrick J.

2016-01-01

Longitudinal data allow direct comparison of the change in patient outcomes associated with treatment or exposure. Frequently, several longitudinal measures are collected that either reflect a common underlying health status, or characterize processes that are influenced in a similar way by covariates such as exposure or demographic characteristics. Statistical methods that can combine multivariate response variables into common measures of covariate effects have been proposed by Roy and Lin [1]; Proust-Lima, Letenneur and Jacqmin-Gadda [2]; and Gray and Brookmeyer [3] among others. Current methods for characterizing the relationship between covariates and the rate of change in multivariate outcomes are limited to select models. For example, Gray and Brookmeyer [3] introduce an “accelerated time” method which assumes that covariates rescale time in longitudinal models for disease progression. In this manuscript we detail an alternative multivariate model formulation that directly structures longitudinal rates of change, and that permits a common covariate effect across multiple outcomes. We detail maximum likelihood estimation for a multivariate longitudinal mixed model. We show via asymptotic calculations the potential gain in power that may be achieved with a common analysis of multiple outcomes. We apply the proposed methods to the analysis of a trivariate outcome for infant growth and compare rates of change for HIV infected and uninfected infants. PMID:27417129

Exploring the Structure of Library and Information Science Web Space Based on Multivariate Analysis of Social Tags

ERIC Educational Resources Information Center

Joo, Soohyung; Kipp, Margaret E. I.

2015-01-01

Introduction: This study examines the structure of Web space in the field of library and information science using multivariate analysis of social tags from the Website, Delicious.com. A few studies have examined mathematical modelling of tags, mainly examining tagging in terms of tripartite graphs, pattern tracing and descriptive statistics. This…

Multivariate Analysis of High Through-Put Adhesively Bonded Single Lap Joints: Experimental and Workflow Protocols

DTIC Science & Technology

2016-06-01

unlimited. v List of Tables Table 1 Single-lap-joint experimental parameters ..............................................7 Table 2 Survey ...Joints: Experimental and Workflow Protocols by Robert E Jensen, Daniel C DeSchepper, and David P Flanagan Approved for...TR-7696 ● JUNE 2016 US Army Research Laboratory Multivariate Analysis of High Through-Put Adhesively Bonded Single Lap Joints: Experimental

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…

Atomic-scale phase composition through multivariate statistical analysis of atom probe tomography data.

PubMed

Keenan, Michael R; Smentkowski, Vincent S; Ulfig, Robert M; Oltman, Edward; Larson, David J; Kelly, Thomas F

2011-06-01

We demonstrate for the first time that multivariate statistical analysis techniques can be applied to atom probe tomography data to estimate the chemical composition of a sample at the full spatial resolution of the atom probe in three dimensions. Whereas the raw atom probe data provide the specific identity of an atom at a precise location, the multivariate results can be interpreted in terms of the probabilities that an atom representing a particular chemical phase is situated there. When aggregated to the size scale of a single atom (∼0.2 nm), atom probe spectral-image datasets are huge and extremely sparse. In fact, the average spectrum will have somewhat less than one total count per spectrum due to imperfect detection efficiency. These conditions, under which the variance in the data is completely dominated by counting noise, test the limits of multivariate analysis, and an extensive discussion of how to extract the chemical information is presented. Efficient numerical approaches to performing principal component analysis (PCA) on these datasets, which may number hundreds of millions of individual spectra, are put forward, and it is shown that PCA can be computed in a few seconds on a typical laptop computer.

Evaluation of the microscopic distribution of florfenicol in feed pellets for salmon by Fourier Transform infrared imaging and multivariate analysis.

PubMed

Bastidas, Camila Y; von Plessing, Carlos; Troncoso, José; Del P Castillo, Rosario

2018-04-15

Fourier Transform infrared imaging and multivariate analysis were used to identify, at the microscopic level, the presence of florfenicol (FF), a heavily-used antibiotic in the salmon industry, supplied to fishes in feed pellets for the treatment of salmonid rickettsial septicemia (SRS). The FF distribution was evaluated using Principal Component Analysis (PCA) and Augmented Multivariate Curve Resolution with Alternating Least Squares (augmented MCR-ALS) on the spectra obtained from images with pixel sizes of 6.25 μm × 6.25 μm and 1.56 μm × 1.56 μm, in different zones of feed pellets. Since the concentration of the drug was 3.44 mg FF/g pellet, this is the first report showing the powerful ability of the used of spectroscopic techniques and multivariate analysis, especially the augmented MCR-ALS, to describe the FF distribution in both the surface and inner parts of feed pellets at low concentration, in a complex matrix and at the microscopic level. The results allow monitoring the incorporation of the drug into the feed pellets. Copyright © 2018 Elsevier B.V. All rights reserved.

Risk factors for incidental durotomy during lumbar surgery: a retrospective study by multivariate analysis.

PubMed

Chen, Zhixiang; Shao, Peng; Sun, Qizhao; Zhao, Dong

2015-03-01

The purpose of the present study was to use a prospectively collected data to evaluate the rate of incidental durotomy (ID) during lumbar surgery and determine the associated risk factors by using univariate and multivariate analysis. We retrospectively reviewed 2184 patients who underwent lumbar surgery from January 1, 2009 to December 31, 2011 at a single hospital. Patients with ID (n=97) were compared with the patients without ID (n=2019). The influences of several potential risk factors that might affect the occurrence of ID were assessed using univariate and multivariate analyses. The overall incidence of ID was 4.62%. Univariate analysis demonstrated that older age, diabetes, lumbar central stenosis, posterior approach, revision surgery, prior lumber surgery and minimal invasive surgery are risk factors for ID during lumbar surgery. However, multivariate analysis identified older age, prior lumber surgery, revision surgery, and minimally invasive surgery as independent risk factors. Older age, prior lumber surgery, revision surgery, and minimal invasive surgery were independent risk factors for ID during lumbar surgery. These findings may guide clinicians making future surgical decisions regarding ID and aid in the patient counseling process to alleviate risks and complications. Copyright © 2015 Elsevier B.V. All rights reserved.

Directly observed treatment is associated with reduced default among foreign tuberculosis patients in Thailand.

PubMed

Kapella, B K; Anuwatnonthakate, A; Komsakorn, S; Moolphate, S; Charusuntonsri, P; Limsomboon, P; Wattanaamornkiat, W; Nateniyom, S; Varma, J K

2009-02-01

Thailand's Tuberculosis (TB) Active Surveillance Network in four provinces in Thailand. As treatment default is common in mobile and foreign populations, we evaluated risk factors for default among non-Thai TB patients in Thailand. Observational cohort study using TB program data. Analysis was restricted to patients with an outcome categorized as cured, completed, failure or default. We used multivariate analysis to identify factors associated with default, including propensity score analysis, to adjust for factors associated with receiving directly observed treatment (DOT). During October 2004-September 2006, we recorded data for 14359 TB patients, of whom 995 (7%) were non-Thais. Of the 791 patients analyzed, 313 (40%) defaulted. In multivariate analysis, age>or=45 years (RR 1.47, 95%CI 1.25-1.74), mobility (RR 2.36, 95%CI 1.77-3.14) and lack of DOT (RR 2.29, 95%CI 1.45-3.61) were found to be significantly associated with default among non-Thais. When controlling for propensity to be assigned DOT, the risk of default remained increased in those not assigned DOT (RR 1.99, 95%CI 1.03-3.85). In non-Thai TB patients, DOT was the only modifiable factor associated with default. Using DOT may help improve TB treatment outcomes in non-Thai TB patients.

Multivariate statistical analysis of heavy metal concentration in soils of Yelagiri Hills, Tamilnadu, India--spectroscopical approach.

PubMed

Chandrasekaran, A; Ravisankar, R; Harikrishnan, N; Satapathy, K K; Prasad, M V R; Kanagasabapathy, K V

2015-02-25

Anthropogenic activities increase the accumulation of heavy metals in the soil environment. Soil pollution significantly reduces environmental quality and affects the human health. In the present study soil samples were collected at different locations of Yelagiri Hills, Tamilnadu, India for heavy metal analysis. The samples were analyzed for twelve selected heavy metals (Mg, Al, K, Ca, Ti, Fe, V, Cr, Mn, Co, Ni and Zn) using energy dispersive X-ray fluorescence (EDXRF) spectroscopy. Heavy metals concentration in soil were investigated using enrichment factor (EF), geo-accumulation index (Igeo), contamination factor (CF) and pollution load index (PLI) to determine metal accumulation, distribution and its pollution status. Heavy metal toxicity risk was assessed using soil quality guidelines (SQGs) given by target and intervention values of Dutch soil standards. The concentration of Ni, Co, Zn, Cr, Mn, Fe, Ti, K, Al, Mg were mainly controlled by natural sources. Multivariate statistical methods such as correlation matrix, principal component analysis and cluster analysis were applied for the identification of heavy metal sources (anthropogenic/natural origin). Geo-statistical methods such as kirging identified hot spots of metal contamination in road areas influenced mainly by presence of natural rocks. Copyright © 2014 Elsevier B.V. All rights reserved.

An efficient approach to identify different chemical markers between fibrous root and rhizome of Anemarrhena asphodeloides by ultra high-performance liquid chromatography quadrupole time-of-flight tandem mass spectrometry with multivariate statistical analysis.

PubMed

Wang, Fang-Xu; Yuan, Jian-Chao; Kang, Li-Ping; Pang, Xu; Yan, Ren-Yi; Zhao, Yang; Zhang, Jie; Sun, Xin-Guang; Ma, Bai-Ping

2016-09-10

An ultra high-performance liquid chromatography quadrupole time-of-flight tandem mass spectrometry approach coupled with multivariate statistical analysis was established and applied to rapidly distinguish the chemical differences between fibrous root and rhizome of Anemarrhena asphodeloides. The datasets of tR-m/z pairs, ion intensity and sample code were processed by principal component analysis and orthogonal partial least squares discriminant analysis. Chemical markers could be identified based on their exact mass data, fragmentation characteristics, and retention times. And the new compounds among chemical markers could be isolated rapidly guided by the ultra high-performance liquid chromatography quadrupole time-of-flight tandem mass spectrometry and their definitive structures would be further elucidated by NMR spectra. Using this approach, twenty-four markers were identified on line including nine new saponins and five new steroidal saponins of them were obtained in pure form. The study validated this proposed approach as a suitable method for identification of the chemical differences between various medicinal parts in order to expand medicinal parts and increase the utilization rate of resources. Copyright © 2016 Elsevier B.V. All rights reserved.

Linear models of coregionalization for multivariate lattice data: Order-dependent and order-free cMCARs.

PubMed

MacNab, Ying C

2016-08-01

This paper concerns with multivariate conditional autoregressive models defined by linear combination of independent or correlated underlying spatial processes. Known as linear models of coregionalization, the method offers a systematic and unified approach for formulating multivariate extensions to a broad range of univariate conditional autoregressive models. The resulting multivariate spatial models represent classes of coregionalized multivariate conditional autoregressive models that enable flexible modelling of multivariate spatial interactions, yielding coregionalization models with symmetric or asymmetric cross-covariances of different spatial variation and smoothness. In the context of multivariate disease mapping, for example, they facilitate borrowing strength both over space and cross variables, allowing for more flexible multivariate spatial smoothing. Specifically, we present a broadened coregionalization framework to include order-dependent, order-free, and order-robust multivariate models; a new class of order-free coregionalized multivariate conditional autoregressives is introduced. We tackle computational challenges and present solutions that are integral for Bayesian analysis of these models. We also discuss two ways of computing deviance information criterion for comparison among competing hierarchical models with or without unidentifiable prior parameters. The models and related methodology are developed in the broad context of modelling multivariate data on spatial lattice and illustrated in the context of multivariate disease mapping. The coregionalization framework and related methods also present a general approach for building spatially structured cross-covariance functions for multivariate geostatistics. © The Author(s) 2016.

Multivariate reference technique for quantitative analysis of fiber-optic tissue Raman spectroscopy.

PubMed

Bergholt, Mads Sylvest; Duraipandian, Shiyamala; Zheng, Wei; Huang, Zhiwei

2013-12-03

We report a novel method making use of multivariate reference signals of fused silica and sapphire Raman signals generated from a ball-lens fiber-optic Raman probe for quantitative analysis of in vivo tissue Raman measurements in real time. Partial least-squares (PLS) regression modeling is applied to extract the characteristic internal reference Raman signals (e.g., shoulder of the prominent fused silica boson peak (~130 cm(-1)); distinct sapphire ball-lens peaks (380, 417, 646, and 751 cm(-1))) from the ball-lens fiber-optic Raman probe for quantitative analysis of fiber-optic Raman spectroscopy. To evaluate the analytical value of this novel multivariate reference technique, a rapid Raman spectroscopy system coupled with a ball-lens fiber-optic Raman probe is used for in vivo oral tissue Raman measurements (n = 25 subjects) under 785 nm laser excitation powers ranging from 5 to 65 mW. An accurate linear relationship (R(2) = 0.981) with a root-mean-square error of cross validation (RMSECV) of 2.5 mW can be obtained for predicting the laser excitation power changes based on a leave-one-subject-out cross-validation, which is superior to the normal univariate reference method (RMSE = 6.2 mW). A root-mean-square error of prediction (RMSEP) of 2.4 mW (R(2) = 0.985) can also be achieved for laser power prediction in real time when we applied the multivariate method independently on the five new subjects (n = 166 spectra). We further apply the multivariate reference technique for quantitative analysis of gelatin tissue phantoms that gives rise to an RMSEP of ~2.0% (R(2) = 0.998) independent of laser excitation power variations. This work demonstrates that multivariate reference technique can be advantageously used to monitor and correct the variations of laser excitation power and fiber coupling efficiency in situ for standardizing the tissue Raman intensity to realize quantitative analysis of tissue Raman measurements in vivo, which is particularly appealing in challenging Raman endoscopic applications.

Inhospital and Post-discharge Changes in Renal Function After Transcatheter Aortic Valve Replacement.

PubMed

Blair, John E A; Brummel, Kent; Friedman, Julie L; Atri, Prashant; Sweis, Ranya N; Russell, Hyde; Ricciardi, Mark J; Malaisrie, S Chris; Davidson, Charles J; Flaherty, James D

2016-02-15

The aim of this study was to determine the influence of inhospital and post-discharge worsening renal function (WRF) on prognosis after transcatheter aortic valve replacement (TAVR). Severe chronic kidney disease and inhospital WRF are both associated with poor outcomes after TAVR. There are no data available on post-discharge WRF and outcomes. This was a single-center study evaluating all TAVR from June 1, 2008, to June 31, 2014. WRF was defined as an increase in serum creatinine of ≥0.3 mg/dl. Inhospital WRF was measured from day 0 until discharge or day 7 if the hospitalization was >7 days. Post-discharge WRF was measured at 30 days after discharge. Descriptive statistics, Kaplan-Meier time-to-event analysis, and multivariate logistic regression were used. In a series of 208 patients who underwent TAVR, 204 with complete renal function data were used in the inhospital analysis and 168 who returned for the 30-day follow-up were used in the post-discharge analysis. Inhospital WRF was seen in 28%, whereas post-discharge WRF in 12%. Inhospital and post-discharge WRF were associated with lower rates of survival; however, after multivariate analysis, only post-discharge WRF remained a predictor of 1-year mortality (hazard ratio 1.18, p = 0.030 for every 1 mg/dl increase in serum creatinine). In conclusion, the rate of inhospital WRF is higher than the rate of post-discharge WRF after TAVR, and post-discharge WRF is more predictive of mortality than inhospital WRF. Copyright © 2016 Elsevier Inc. All rights reserved.

Predictors of mistimed, and unwanted pregnancies among women of childbearing age in Rufiji, Kilombero, and Ulanga districts of Tanzania.

PubMed

Exavery, Amon; Kanté, Almamy Malick; Njozi, Mustafa; Tani, Kassimu; Doctor, Henry V; Hingora, Ahmed; Phillips, James F

2014-08-08

While unintended pregnancies pose a serious threat to the health and well-being of families globally, characteristics of Tanzanian women who conceive unintentionally are rarely documented. This analysis identifies factors associated with unintended pregnancies-both mistimed and unwanted-in three rural districts of Tanzania. A cross-sectional survey of 2,183 random households was conducted in three Tanzanian districts of Rufiji, Kilombero, and Ulanga in 2011 to assess women's health behavior and service utilization patterns. These households produced 3,127 women age 15+ years from which 2,199 gravid women aged 15-49 were selected for the current analysis. Unintended pregnancies were identified as either mistimed (wanted later) or unwanted (not wanted at all). Correlates of mistimed, and unwanted pregnancies were identified through Chi-squared tests to assess associations and multinomial logistic regression for multivariate analysis. Mean age of the participants was 32.1 years. While 54.1% of the participants reported that their most recent pregnancy was intended, 32.5% indicated their most recent pregnancy as mistimed and 13.4% as unwanted. Multivariate analysis revealed that young age (<20 years), and single marital status were significant predictors of both mistimed and unwanted pregnancies. Lack of inter-partner communication about family planning increased the risk of mistimed pregnancy significantly, and multi-gravidity was shown to significantly increase the risk of unwanted pregnancy. About one half of women in Rufiji, Kilombero, and Ulanga districts of Tanzania conceive unintentionally. Women, especially the most vulnerable should be empowered to avoid pregnancy at their own will and discretion.

Infection following Anterior Cruciate Ligament Reconstruction: An Analysis of 6,389 Cases.

PubMed

Westermann, Robert; Anthony, Chris A; Duchman, Kyle R; Gao, Yubo; Pugely, Andrew J; Hettrich, Carolyn M; Amendola, Ned; Wolf, Brian R

2017-07-01

Infection following anterior cruciate ligament reconstruction (ACLR) is rare. Previous authors have concluded that diabetes, tobacco use, and previous knee surgery may influence infection rates following ACLR. The purpose of this study was to identify a cohort of patients undergoing ACLR and define (1) the incidence of infection after ACLR from a large multicenter database and (2) the risk factors for infection after ACLR. We identified patients undergoing elective ACLRs in the American College of Surgeons National Surgical Quality Improvement Program database between 2007 and 2013. The primary outcome was any surgical site infection within 30 days of surgery. We performed univariate and multivariate analyses comparing infected and noninfected cases to identify risk factors for infection. In total, 6,398 ACLRs were available for analysis of which 39 (0.61%) were diagnosed with a postoperative infection. Univariate analysis identified preoperative dyspnea, low hematocrit, operative time > 1 hour, and hospital admission following surgery as predictors of postoperative infection. Diabetes, tobacco use, age, and body mass index (BMI) were not associated with infection ( p  > 0.05). After multivariate analysis, the only independent predictor of postoperative infection was hospital admission following surgery (odds ratio, 2.67; 95% confidence interval, 1.02-6.96; p  = 0.04). Hospital admission following surgery was associated with an increased incidence of infection in this large, multicenter cohort. Smoking, elevated BMI, and diabetes did not increase the risk infection in the present study. Surgeons should optimize outpatient operating systems and practices to aid in same-day discharges following ACLR. Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

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.

Multivariate optimum interpolation of surface pressure and surface wind over oceans

NASA Technical Reports Server (NTRS)

Bloom, S. C.; Baker, W. E.; Nestler, M. S.

1984-01-01

The present multivariate analysis method for surface pressure and winds incorporates ship wind observations into the analysis of surface pressure. For the specific case of 0000 GMT, on February 3, 1979, the additional data resulted in a global rms difference of 0.6 mb; individual maxima as larse as 5 mb occurred over the North Atlantic and East Pacific Oceans. These differences are noted to be smaller than the analysis increments to the first-guess fields.

Multiscale Characterization of PM2.5 in Southern Taiwan based on Noise-assisted Multivariate Empirical Mode Decomposition and Time-dependent Intrinsic Correlation

NASA Astrophysics Data System (ADS)

Hsiao, Y. R.; Tsai, C.

2017-12-01

As the WHO Air Quality Guideline indicates, ambient air pollution exposes world populations under threat of fatal symptoms (e.g. heart disease, lung cancer, asthma etc.), raising concerns of air pollution sources and relative factors. This study presents a novel approach to investigating the multiscale variations of PM2.5 in southern Taiwan over the past decade, with four meteorological influencing factors (Temperature, relative humidity, precipitation and wind speed),based on Noise-assisted Multivariate Empirical Mode Decomposition(NAMEMD) algorithm, Hilbert Spectral Analysis(HSA) and Time-dependent Intrinsic Correlation(TDIC) method. NAMEMD algorithm is a fully data-driven approach designed for nonlinear and nonstationary multivariate signals, and is performed to decompose multivariate signals into a collection of channels of Intrinsic Mode Functions (IMFs). TDIC method is an EMD-based method using a set of sliding window sizes to quantify localized correlation coefficients for multiscale signals. With the alignment property and quasi-dyadic filter bank of NAMEMD algorithm, one is able to produce same number of IMFs for all variables and estimates the cross correlation in a more accurate way. The performance of spectral representation of NAMEMD-HSA method is compared with Complementary Empirical Mode Decomposition/ Hilbert Spectral Analysis (CEEMD-HSA) and Wavelet Analysis. The nature of NAMAMD-based TDICC analysis is then compared with CEEMD-based TDIC analysis and the traditional correlation analysis.

Analysis/forecast experiments with a multivariate statistical analysis scheme using FGGE data

NASA Technical Reports Server (NTRS)

Baker, W. E.; Bloom, S. C.; Nestler, M. S.

1985-01-01

A three-dimensional, multivariate, statistical analysis method, optimal interpolation (OI) is described for modeling meteorological data from widely dispersed sites. The model was developed to analyze FGGE data at the NASA-Goddard Laboratory of Atmospherics. The model features a multivariate surface analysis over the oceans, including maintenance of the Ekman balance and a geographically dependent correlation function. Preliminary comparisons are made between the OI model and similar schemes employed at the European Center for Medium Range Weather Forecasts and the National Meteorological Center. The OI scheme is used to provide input to a GCM, and model error correlations are calculated for forecasts of 500 mb vertical water mixing ratios and the wind profiles. Comparisons are made between the predictions and measured data. The model is shown to be as accurate as a successive corrections model out to 4.5 days.

Visualizing frequent patterns in large multivariate time series

NASA Astrophysics Data System (ADS)

Hao, M.; Marwah, M.; Janetzko, H.; Sharma, R.; Keim, D. A.; Dayal, U.; Patnaik, D.; Ramakrishnan, N.

2011-01-01

The detection of previously unknown, frequently occurring patterns in time series, often called motifs, has been recognized as an important task. However, it is difficult to discover and visualize these motifs as their numbers increase, especially in large multivariate time series. To find frequent motifs, we use several temporal data mining and event encoding techniques to cluster and convert a multivariate time series to a sequence of events. Then we quantify the efficiency of the discovered motifs by linking them with a performance metric. To visualize frequent patterns in a large time series with potentially hundreds of nested motifs on a single display, we introduce three novel visual analytics methods: (1) motif layout, using colored rectangles for visualizing the occurrences and hierarchical relationships of motifs in a multivariate time series, (2) motif distortion, for enlarging or shrinking motifs as appropriate for easy analysis and (3) motif merging, to combine a number of identical adjacent motif instances without cluttering the display. Analysts can interactively optimize the degree of distortion and merging to get the best possible view. A specific motif (e.g., the most efficient or least efficient motif) can be quickly detected from a large time series for further investigation. We have applied these methods to two real-world data sets: data center cooling and oil well production. The results provide important new insights into the recurring patterns.

An evaluation of the impact of donor BMI on survival and post-transplant obesity in pediatric liver transplant recipients

PubMed Central

Perito, Emily Rothbaum; Rhee, Sue; Glidden, Dave; Roberts, John Paul; Rosenthal, Philip

2012-01-01

Introduction In adult liver transplant recipients, donor BMI is associated with post-transplant obesity but not graft or patient survival. Given the U.S. obesity epidemic and already-limited supply of liver donors, clarifying whether donor BMI affects pediatric outcomes is important. Methods UNOS data on pediatric U.S. liver transplants 1990-2010 was evaluated. Data on transplants 2004-2010 (n=3788) was used for survival analysis with Kaplan-Meier and Cox proportional hazards models and for post-transplant obesity analysis with generalized estimating equations. Results For children receiving adult donor livers, donor BMI 25-35 kg/m2 was not associated with graft or patient survival in univariate or multivariate analyses. Donor BMI>35 kg/m2 increased the risk of graft loss (HR 2.54, 95%CI 1.29-5.01, p=0.007) and death (HR 3.56, 95%CI 1.64-7.72, p=0.001). For pediatric donors, donor BMI was not associated with graft loss or mortality in univariate or multivariate analysis. Donor overweight/obesity was not a risk factor for post-transplant obesity. Conclusions Overweight/obesity is common among liver transplant donors. This analysis suggests that for adult donors, BMI 25-35 should not by itself be a contraindication to liver donation. Severe obesity (BMI>35) in adult donors increased the risk of graft loss and mortality, even after adjustment for recipient, donor, and transplant risk factors. Post-transplant obesity was not associated with donor BMI in this analysis. Further research is needed to clarify the impact of donor obesity on pediatric liver transplant recipients. PMID:22467594

Predictors of recurrence of prolapse after procedure for prolapse and haemorrhoids.

PubMed

Festen, S; Molthof, H; van Geloven, A A W; Luchters, S; Gerhards, M F

2012-08-01

The procedure for prolapse and haemorrhoids (PPH) is an effective surgical therapy for symptomatic haemorrhoids. Compared with haemorrhoidectomy, meta-analysis has shown PPH to be less painful, with higher patient satisfaction and a quicker return to work, but at the cost of higher prolapse recurrence rates. This is the first report describing predictors of prolapse recurrence after PPH. A cohort of patients with symptomatic haemorrhoids, treated with PPH in our hospital between 2002 and 2009, was retrospectively analysed. Multivariate analysis was performed to identify patient-related and perioperative predictors associated with persisting prolapse and prolapse recurrence. In total, 159 consecutively enrolled patients were analysed. Persistence and recurrence of prolapse was observed in 16% of the patients. Increased surgical experience showed a trend towards lower recurrence rates. Multivariate analysis identified female gender, long duration of PPH surgery and the absence of muscle tissue in the resected specimen as independent predictors of postoperative persistence of prolapse of haemorrhoids. The absence of prior treatment with rubber band ligation (RBL) as well as increased PPH experience at the hospital showed a trend towards a higher rate of prolapse recurrence. In order to reduce recurrence of prolapse, PPH should be performed by a surgeon with adequate PPH experience, patients should be treated with RBL prior to PPH and a resection of mucosa with underlying muscle fibres should be strived for. © 2011 The Authors. Colorectal Disease © 2011 The Association of Coloproctology of Great Britain and Ireland.

The relationship between hypophosphataemia and outcomes during low-intensity and high-intensity continuous renal replacement therapy.

PubMed

Bellomo, Rinaldo; Cass, Alan; Cole, Louise; Finfer, Simon; Gallagher, Martin; Kim, Inbyung; Lee, Joanne; Lo, Serigne; McArthur, Colin; McGuinness, Shay; McGuiness, Shay; Norton, Robyn; Myburgh, John; Scheinkestel, Carlos

2014-03-01

To identify risk factors for development of hypophosphataemia in patients treated with two different intensities of continuous renal replacement therapy (CRRT) and to assess the independent association of hypophosphataemia with major clinical outcomes. We performed secondary analysis of data collected from 1441 patients during a large, multicentre randomised controlled trial of CRRT intensity. We allocated patients to two different intensities of CRRT (25mL/kg/hour vs 40 mL/kg/hour of effluent generation) and obtained daily measurement of serum phosphate levels. We obtained 14 115 phosphate measurements and identified 462 patients (32.1%) with hypophosphataemia, with peak incidence on Day 2 and Day 3. With lower intensity CRRT, there were 58 episodes of hypophosphataemia/1000 patient days, compared with 112 episodes/1000 patient days with higher intensity CRRT (P < 0.001). On multivariable logistic regression analysis, higher intensity CRRT, female sex, higher Acute Physiology and Chronic Health Evaluation score and hypokalaemia were independently associated with an increased odds ratio (OR) for hypophosphataemia. On multivariable models, hypophosphataemia was associated with better clinical outcomes, but when analysis was confined to patients alive at 96 hours, hypophosphataemia was not independently associated with clinical outcomes. Hypophosphataemia is common during CRRT and its incidence increases with greater CRRT intensity. Hypophosphataemia is not a robust independent predictor of mortality. Its greater incidence in the higher intensity CRRT arm of the Randomised Evaluation of Normal vs Augmented Level trial does not explain the lack of improved outcomes with such treatment.

Prediction of Postoperative Mortality in Liver Transplantation in the Era of MELD-Based Liver Allocation: A Multivariate Analysis

PubMed Central

Schultze, Daniel; Hillebrand, Norbert; Hinz, Ulf; Büchler, Markus W.; Schemmer, Peter

2014-01-01

Background and Aims Liver transplantation is the only curative treatment for end-stage liver disease. While waiting list mortality can be predicted by the MELD-score, reliable scoring systems for the postoperative period do not exist. This study's objective was to identify risk factors that contribute to postoperative mortality. Methods Between December 2006 and March 2011, 429 patients underwent liver transplantation in our department. Risk factors for postoperative mortality in 266 consecutive liver transplantations were identified using univariate and multivariate analyses. Patients who were <18 years, HU-listings, and split-, living related, combined or re-transplantations were excluded from the analysis. The correlation between number of risk factors and mortality was analyzed. Results A labMELD ≥20, female sex, coronary heart disease, donor risk index >1.5 and donor Na+>145 mmol/L were identified to be independent predictive factors for postoperative mortality. With increasing number of these risk-factors, postoperative 90-day and 1-year mortality increased (0–1: 0 and 0%; 2: 2.9 and 17.4%; 3: 5.6 and 16.8%; 4: 22.2 and 33.3%; 5–6: 60.9 and 66.2%). Conclusions In this analysis, a simple score was derived that adequately identified patients at risk after liver transplantation. Opening a discussion on the inclusion of these parameters in the process of organ allocation may be a worthwhile venture. PMID:24905210

HbA1c is significantly associated with arterial stiffness but not with carotid atherosclerosis in a community-based population without type 2 diabetes: The Dong-gu study.

PubMed

Lee, Young-Hoon; Shin, Min-Ho; Choi, Jin-Su; Rhee, Jung-Ae; Nam, Hae-Sung; Jeong, Seul-Ki; Park, Kyeong-Soo; Ryu, So-Yeon; Choi, Seong-Woo; Kim, Bok-Hee; Oh, Gyung-Jae; Kweon, Sun-Seog

2016-04-01

We examined the associations between HbA1c levels and various atherosclerotic vascular parameters among adults without diabetes from the general population. A total of 6500 community-dwelling adults, who were free of type 2 diabetes and ≥50 years of age, were included. High-resolution B-mode ultrasound was used to evaluate carotid artery structure, including intima-media thickness (IMT), plaque, and luminal diameter. Brachial-ankle pulse wave velocity (baPWV), which is a useful indicator of systemic arterial stiffness, was determined using an automatic waveform analysis device. No significant associations were observed between HbA1c, carotid IMT, plaque, or luminal diameter in a fully adjusted model. However, the odds ratio (95% confidence interval) for high baPWV (defined as the highest quartile) increased by 1.43 (1.19-1.71) per 1% HbA1c increase after adjusting for conventional risk factors in a multivariate logistic regression analysis. In addition, HbA1c was independently associated with baPWV in a multivariate linear regression analysis. High-normal HbA1c level was independently associated with arterial stiffness, but not with carotid atherosclerotic parameters, in the general population without diabetes. Our results suggest that the functional atherosclerotic process may already be accelerated according to HbA1c level, even at a level below the diagnostic threshold for diabetes. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Gastroduodenal Ulcers and ABO Blood Group: the Japan Nurses’ Health Study (JNHS)

PubMed Central

Ideno, Yuki; Lee, Jung-Su; Suzuki, Shosuke; Nakajima-Shimada, Junko; Ohnishi, Hiroshi; Sato, Yasunori; Hayashi, Kunihiko

2018-01-01

Background Although several studies have shown that blood type O is associated with increased risk of peptic ulcer, few studies have investigated these associations in Japan. We sought to investigate the association between the ABO blood group and risk of gastroduodenal ulcers (GDU) using combined analysis of both retrospective and prospective data from a large cohort study of Japanese women, the Japan Nurses’ Health Study (JNHS; n = 15,019). Methods The impact of the ABO blood group on GDU risk was examined using Cox regression analysis to estimate hazard ratios (HRs) and 95% confidence intervals (CI), with adjustment for potential confounders. Results Compared with women with non-O blood types (A, B, and AB), women with blood type O had a significantly increased risk of GDU from birth (multivariable-adjusted HR 1.18; 95% CI, 1.04–1.34). Moreover, the highest cumulative incidence of GDU was observed in women born pre-1956 with blood type O. In a subgroup analysis stratified by birth year (pre-1956 or post-1955), the multivariable-adjusted HR of women with blood type O was 1.22 (95% CI, 1.00–1.49) and 1.15 (95% CI, 0.98–1.35) in the pre-1956 and post-1955 groups, respectively. Conclusion In this large, combined, ambispective cohort study of Japanese women, older women with blood type O had a higher risk of developing GDU than those with other blood types. PMID:29093357

Brain galanin system genes interact with life stresses in depression-related phenotypes

PubMed Central

Juhasz, Gabriella; Hullam, Gabor; Eszlari, Nora; Gonda, Xenia; Antal, Peter; Anderson, Ian Muir; Hökfelt, Tomas G. M.; Deakin, J. F. William; Bagdy, Gyorgy

2014-01-01

Galanin is a stress-inducible neuropeptide and cotransmitter in serotonin and norepinephrine neurons with a possible role in stress-related disorders. Here we report that variants in genes for galanin (GAL) and its receptors (GALR1, GALR2, GALR3), despite their disparate genomic loci, conferred increased risk of depression and anxiety in people who experienced childhood adversity or recent negative life events in a European white population cohort totaling 2,361 from Manchester, United Kingdom and Budapest, Hungary. Bayesian multivariate analysis revealed a greater relevance of galanin system genes in highly stressed subjects compared with subjects with moderate or low life stress. Using the same method, the effect of the galanin system genes was stronger than the effect of the well-studied 5-HTTLPR polymorphism in the serotonin transporter gene (SLC6A4). Conventional multivariate analysis using general linear models demonstrated that interaction of galanin system genes with life stressors explained more variance (1.7%, P = 0.005) than the life stress-only model. This effect replicated in independent analysis of the Manchester and Budapest subpopulations, and in males and females. The results suggest that the galanin pathway plays an important role in the pathogenesis of depression in humans by increasing the vulnerability to early and recent psychosocial stress. Correcting abnormal galanin function in depression could prove to be a novel target for drug development. The findings further emphasize the importance of modeling environmental interaction in finding new genes for depression. PMID:24706871

Predictors of mortality in patients with emphysema and severe airflow obstruction.

PubMed

Martinez, Fernando J; Foster, Gregory; Curtis, Jeffrey L; Criner, Gerard; Weinmann, Gail; Fishman, Alfred; DeCamp, Malcolm M; Benditt, Joshua; Sciurba, Frank; Make, Barry; Mohsenifar, Zab; Diaz, Philip; Hoffman, Eric; Wise, Robert

2006-06-15

Limited data exist describing risk factors for mortality in patients having predominantly emphysema. A total of 609 patients with severe emphysema (ages 40-83 yr; 64.2% male) randomized to the medical therapy arm of the National Emphysema Treatment Trial formed the study group. Cox proportional hazards regression analysis was used to investigate risk factors for all-cause mortality. Risk factors examined included demographics, body mass index, physiologic data, quality of life, dyspnea, oxygen utilization, hemoglobin, smoking history, quantitative emphysema markers on computed tomography, and a modification of a recently described multifunctional index (modified BODE). Overall, high mortality was seen in this cohort (12.7 deaths per 100 person-years; 292 total deaths). In multivariate analyses, increasing age (p=0.001), oxygen utilization (p=0.04), lower total lung capacity % predicted (p=0.05), higher residual volume % predicted (p=0.04), lower maximal cardiopulmonary exercise testing workload (p=0.002), greater proportion of emphysema in the lower lung zone versus the upper lung zone (p=0.005), and lower upper-to-lower-lung perfusion ratio (p=0.007), and modified BODE (p=0.02) were predictive of mortality. FEV1 was a significant predictor of mortality in univariate analysis (p=0.005), but not in multivariate analysis (p=0.21). Although patients with advanced emphysema experience significant mortality, subgroups based on age, oxygen utilization, physiologic measures, exercise capacity, and emphysema distribution identify those at increased risk of death.

The Changes of Lipid Metabolism in Advanced Renal Cell Carcinoma Patients Treated with Everolimus: A New Pharmacodynamic Marker?

PubMed Central

Pantano, Francesco; Santoni, Matteo; Procopio, Giuseppe; Rizzo, Mimma; Iacovelli, Roberto; Porta, Camillo; Conti, Alessandro; Lugini, Antonio; Milella, Michele; Galli, Luca; Ortega, Cinzia; Guida, Francesco Maria; Silletta, Marianna; Schinzari, Giovanni; Verzoni, Elena; Modica, Daniela; Crucitti, Pierfilippo; Rauco, Annamaria; Felici, Alessandra; Ballatore, Valentina; Cascinu, Stefano; Tonini, Giuseppe; Carteni, Giacomo; Russo, Antonio; Santini, Daniele

2015-01-01

Background Everolimus is a mammalian target of rapamycin (mTOR) inhibitor approved for the treatment of metastatic renal cell carcinoma (mRCC). We aimed to assess the association between the baseline values and treatmentrelated modifications of total serum cholesterol (C), triglycerides (T), body mass index (BMI), fasting blood glucose level (FBG) and blood pressure (BP) levels and the outcome of patients treated with everolimus for mRCC. Methods 177 patients were included in this retrospective analysis. Time to progression (TTP), clinical benefit (CB) and overall survival (OS) were evaluated. Results Basal BMI was significantly higher in patients who experienced a CB (p=0,0145). C,T and C+T raises were significantly associated with baseline BMI (p=0.0412, 0.0283 and 0.0001). Median TTP was significantly longer in patients with T raise compared to patients without T (10 vs 6, p=0.030), C (8 vs 5, p=0.042) and C+T raise (10.9 vs 5.0, p=0.003). At the multivariate analysis, only C+T increase was associated with improved TTP (p=0.005). T raise (21.0 vs 14.0, p=0.002) and C+T increase (21.0 vs 14.0, p=0.006) were correlated with improved OS but were not significant at multivariate analysis. Conclusion C+T raise is an early predictor for everolimus efficacy for patients with mRCC. PMID:25885920

Spectral compression algorithms for the analysis of very large multivariate images

DOEpatents

Keenan, Michael R.

2007-10-16

A method for spectrally compressing data sets enables the efficient analysis of very large multivariate images. The spectral compression algorithm uses a factored representation of the data that can be obtained from Principal Components Analysis or other factorization technique. Furthermore, a block algorithm can be used for performing common operations more efficiently. An image analysis can be performed on the factored representation of the data, using only the most significant factors. The spectral compression algorithm can be combined with a spatial compression algorithm to provide further computational efficiencies.

Changing epidemiology of pediatric Staphylococcus aureus bacteremia in Denmark from 1971 through 2000.

PubMed

Frederiksen, Marianne Sjølin; Espersen, Frank; Frimodt-Møller, Niels; Jensen, Allan Garlik; Larsen, Anders Rhod; Pallesen, Lars Villiam; Skov, Robert; Westh, Henrik; Skinhøj, Peter; Benfield, Thomas

2007-05-01

Staphylococcus aureus is known to be a leading cause of bacteremia in childhood, and is associated with severe morbidity and increased mortality. To determine developments in incidence and mortality rates, as well as risk factors associated with outcome, we analyzed data from 1971 through 2000. Nationwide registration of S. aureus bacteremia (SAB) among children and adolescents from birth to 20 years of age was performed. Data on age, sex, source of bacteremia, comorbidity and outcome were extracted from discharge records. Rates were population adjusted and risk factors for death were assessed by multivariate logistic regression analysis. During the 30-year study period, 2648 cases of SAB were reported. Incidence increased from 4.6 to 8.4 cases per 100,000 population and case-mortality rates decreased from 19.6% to 2.5% (P = 0.0001). Incidence in the infant age group (<1 year) were 10- to 17-fold greater compared with that in the other age strata and mortality rate was twice as high. Hospital-acquired infections dominated the infant group, accounting for 73.9%-91.0% versus 39.2%-50.5% in the other age groups. By multivariate analysis, pulmonary infection and endocarditis for all age groups, comorbidity for the older than 1 year, and hospital-acquired infections for the oldest group were independently associated with an increased risk of death. Mortality rates associated with SAB decreased significantly in the past 3 decades, possibly because of new and improved treatment modalities. However, incidence rates have increased significantly in the same period, underscoring that S. aureus remains an important invasive pathogen.

Esophageal cancer detection based on tissue surface-enhanced Raman spectroscopy and multivariate analysis

NASA Astrophysics Data System (ADS)

Feng, Shangyuan; Lin, Juqiang; Huang, Zufang; Chen, Guannan; Chen, Weisheng; Wang, Yue; Chen, Rong; Zeng, Haishan

2013-01-01

The capability of using silver nanoparticle based near-infrared surface enhanced Raman scattering (SERS) spectroscopy combined with principal component analysis (PCA) and linear discriminate analysis (LDA) to differentiate esophageal cancer tissue from normal tissue was presented. Significant differences in Raman intensities of prominent SERS bands were observed between normal and cancer tissues. PCA-LDA multivariate analysis of the measured tissue SERS spectra achieved diagnostic sensitivity of 90.9% and specificity of 97.8%. This exploratory study demonstrated great potential for developing label-free tissue SERS analysis into a clinical tool for esophageal cancer detection.

New multivariable capabilities of the INCA program

NASA Technical Reports Server (NTRS)

Bauer, Frank H.; Downing, John P.; Thorpe, Christopher J.

1989-01-01

The INteractive Controls Analysis (INCA) program was developed at NASA's Goddard Space Flight Center to provide a user friendly, efficient environment for the design and analysis of control systems, specifically spacecraft control systems. Since its inception, INCA has found extensive use in the design, development, and analysis of control systems for spacecraft, instruments, robotics, and pointing systems. The (INCA) program was initially developed as a comprehensive classical design analysis tool for small and large order control systems. The latest version of INCA, expected to be released in February of 1990, was expanded to include the capability to perform multivariable controls analysis and design.

Testing Mean Differences among Groups: Multivariate and Repeated Measures Analysis with Minimal Assumptions

PubMed Central

Bathke, Arne C.; Friedrich, Sarah; Pauly, Markus; Konietschke, Frank; Staffen, Wolfgang; Strobl, Nicolas; Höller, Yvonne

2018-01-01

ABSTRACT To date, there is a lack of satisfactory inferential techniques for the analysis of multivariate data in factorial designs, when only minimal assumptions on the data can be made. Presently available methods are limited to very particular study designs or assume either multivariate normality or equal covariance matrices across groups, or they do not allow for an assessment of the interaction effects across within-subjects and between-subjects variables. We propose and methodologically validate a parametric bootstrap approach that does not suffer from any of the above limitations, and thus provides a rather general and comprehensive methodological route to inference for multivariate and repeated measures data. As an example application, we consider data from two different Alzheimer’s disease (AD) examination modalities that may be used for precise and early diagnosis, namely, single-photon emission computed tomography (SPECT) and electroencephalogram (EEG). These data violate the assumptions of classical multivariate methods, and indeed classical methods would not have yielded the same conclusions with regards to some of the factors involved. PMID:29565679

Can a bank crisis break your heart?

PubMed Central

Stuckler, David; Meissner, Christopher M; King, Lawrence P

2008-01-01

Background To assess whether a banking system crisis increases short-term population cardiovascular mortality rates. Methods International, longitudinal multivariate regression analysis of cardiovascular disease mortality data from 1960 to 2002 Results A system-wide banking crisis increases population heart disease mortality rates by 6.4% (95% CI: 2.5% to 10.2%, p < 0.01) in high income countries, after controlling for economic change, macroeconomic instability, and population age and social distribution. The estimated effect is nearly four times as large in low income countries. Conclusion Banking crises are a significant determinant of short-term increases in heart disease mortality rates, and may have more severe consequences for developing countries. PMID:18197979

Increasing time to treatment initiation for head and neck cancer: an analysis of the National Cancer Database.

PubMed

Murphy, Colin T; Galloway, Thomas J; Handorf, Elizabeth A; Wang, Lora; Mehra, Ranee; Flieder, Douglas B; Ridge, John A

2015-04-15

The objective of this study was to identify trends and predictors of the time to treatment initiation (TTI) for patients with head and neck squamous cell carcinoma (HNSCC). The National Cancer Database (NCDB) was reviewed for the following head and neck cancer sites: oral tongue, oropharynx, larynx, and hypopharynx. TTI was defined as the number of days from diagnosis to the initiation of definitive treatment and was measured according to covariates. Significant differences in the median TTI across each covariate were measured using the Kruskal-Wallis test, and the Spearman test was used to measure trends within covariates. For multivariate analysis, a zero-inflated, negative, binomial regression model was used to estimate the expected TTI, which was expressed in the predicted number of days; and the Vuong test was used to identify the predictors of TTI. In total, 274,630 patients were included. Between 1998 and 2011, the median TTI for all patients was 26 days, and it increased from 19 days to 30 days (P < .0001). Treatment with chemoradiation (CRT) (P < .0001), treatment at academic facilities (P < .0001), and stage IV disease (P < .0001) were associated with increased TTI. TTI significantly increased for each disease stage (P < .0001), treatment modality (P < .0001), and facility type (P < .0001) over time. In addition, patients became more likely to transition care between facilities after diagnosis for treatment initiation (P < .0001) over time. On multivariate analysis, treatment at academic facilities (33 days), transitioning care (37 days), and receipt of CRT (39 days) predicted for a longer TTI. TTI is rising for patients with HNSCC. Those who have advanced-stage disease, receive treatment with CRT, are treated at academic facilities, and who have a transition in care realized the greatest increases in TTI. © 2014 American Cancer Society.

Development of multivariate exposure and fatal accident involvement rates for 1977

DOT National Transportation Integrated Search

1985-10-01

The need for multivariate accident involvement rates is often encounted in : accident analysis. The FARS (Fatal Accident Reporting System) files contain : records of fatal involvements characterized by many variables while NPTS : (National Personal T...

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

A Novel Approach to Detect Accelerated Aged and Surface-Mediated Degradation in Explosives by UPLC-ESI-MS.

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

Beppler, Christina L

2015-12-01

A new approach was created for studying energetic material degradation. This approach involved detecting and tentatively identifying non-volatile chemical species by liquid chromatography-mass spectrometry (LC-MS) with multivariate statistical data analysis that form as the CL-20 energetic material thermally degraded. Multivariate data analysis showed clear separation and clustering of samples based on sample group: either pristine or aged material. Further analysis showed counter-clockwise trends in the principal components analysis (PCA), a type of multivariate data analysis, Scores plots. These trends may indicate that there was a discrete shift in the chemical markers as the went from pristine to aged material, andmore » then again when the aged CL-20 mixed with a potentially incompatible material was thermally aged for 4, 6, or 9 months. This new approach to studying energetic material degradation should provide greater knowledge of potential degradation markers in these materials.« less

Asian American Women in California: A Pooled Analysis of Predictors for Breast and Cervical Cancer Screening

PubMed Central

Breen, Nancy; Liu, Benmei; Lee, Richard; Kagawa-Singer, Marjorie

2015-01-01

Objectives. We examined patterns of cervical and breast cancer screening among Asian American women in California and assessed their screening trends over time. Methods. We pooled weighted data from 5 cycles of the California Health Interview Survey (2001, 2003, 2005, 2007, 2009) to examine breast and cervical cancer screening trends and predictors among 6 Asian nationalities. We calculated descriptive statistics, bivariate associations, multivariate logistic regressions, predictive margins, and 95% confidence intervals. Results. Multivariate analyses indicated that Papanicolaou test rates did not significantly change over time (77.9% in 2001 vs 81.2% in 2007), but mammography receipt increased among Asian American women overall (75.6% in 2001 vs 81.8% in 2009). Length of time in the United States was associated with increased breast and cervical cancer screening among all nationalities. Sociodemographic and health care access factors had varied effects, with education and insurance coverage significantly predicting screening for certain groups. Overall, we observed striking variation by nationality. Conclusions. Our results underscore the need for intervention and policy efforts that are targeted to specific Asian nationalities, recent immigrants, and individuals without health care access to increase screening rates among Asian women in California. PMID:25521898

Complex numbers in chemometrics: examples from multivariate impedance measurements on lipid monolayers.

PubMed

Geladi, Paul; Nelson, Andrew; Lindholm-Sethson, Britta

2007-07-09

Electrical impedance gives multivariate complex number data as results. Two examples of multivariate electrical impedance data measured on lipid monolayers in different solutions give rise to matrices (16x50 and 38x50) of complex numbers. Multivariate data analysis by principal component analysis (PCA) or singular value decomposition (SVD) can be used for complex data and the necessary equations are given. The scores and loadings obtained are vectors of complex numbers. It is shown that the complex number PCA and SVD are better at concentrating information in a few components than the naïve juxtaposition method and that Argand diagrams can replace score and loading plots. Different concentrations of Magainin and Gramicidin A give different responses and also the role of the electrolyte medium can be studied. An interaction of Gramicidin A in the solution with the monolayer over time can be observed.

A Multivariate Methodological Workflow for the Analysis of FTIR Chemical Mapping Applied on Historic Paint Stratigraphies

PubMed Central

Sciutto, Giorgia; Oliveri, Paolo; Catelli, Emilio; Bonacini, Irene

2017-01-01

In the field of applied researches in heritage science, the use of multivariate approach is still quite limited and often chemometric results obtained are often underinterpreted. Within this scenario, the present paper is aimed at disseminating the use of suitable multivariate methodologies and proposes a procedural workflow applied on a representative group of case studies, of considerable importance for conservation purposes, as a sort of guideline on the processing and on the interpretation of this FTIR data. Initially, principal component analysis (PCA) is performed and the score values are converted into chemical maps. Successively, the brushing approach is applied, demonstrating its usefulness for a deep understanding of the relationships between the multivariate map and PC score space, as well as for the identification of the spectral bands mainly involved in the definition of each area localised within the score maps. PMID:29333162

Risk Factors for Central Serous Chorioretinopathy: Multivariate Approach in a Case-Control Study.

PubMed

Chatziralli, Irini; Kabanarou, Stamatina A; Parikakis, Efstratios; Chatzirallis, Alexandros; Xirou, Tina; Mitropoulos, Panagiotis

2017-07-01

The purpose of this prospective study was to investigate the potential risk factors associated independently with central serous retinopathy (CSR) in a Greek population, using multivariate approach. Participants in the study were 183 consecutive patients diagnosed with CSR and 183 controls, matched for age. All participants underwent complete ophthalmological examination and information regarding their sociodemographic, clinical, medical and ophthalmological history were recorded, so as to assess potential risk factors for CSR. Univariate and multivariate analysis was performed. Univariate analysis showed that male sex, high educational status, high income, alcohol consumption, smoking, hypertension, coronary heart disease, obstructive sleep apnea, autoimmune disorders, H. pylori infection, type A personality and stress, steroid use, pregnancy and hyperopia were associated with CSR, while myopia was found to protect from CSR. In multivariate analysis, alcohol consumption, hypertension, coronary heart disease and autoimmune disorders lost their significance, while the remaining factors were all independently associated with CSR. It is important to take into account the various risk factors for CSR, so as to define vulnerable groups and to shed light into the pathogenesis of the disease.

Can we discover double Higgs production at the LHC?

NASA Astrophysics Data System (ADS)

Alves, Alexandre; Ghosh, Tathagata; Sinha, Kuver

2017-08-01

We explore double Higgs production via gluon fusion in the b b ¯γ γ channel at the high-luminosity LHC using machine learning tools. We first propose a Bayesian optimization approach to select cuts on kinematic variables, obtaining a 30%-50% increase in the significance compared to current results in the literature. We show that this improvement persists once systematic uncertainties are taken into account. We next use boosted decision trees (BDT) to further discriminate signal and background events. Our analysis shows that a joint optimization of kinematic cuts and BDT hyperparameters results in an appreciable improvement in the significance. Finally, we perform a multivariate analysis of the output scores of the BDT. We find that assuming a very low level of systematics, the techniques proposed here will be able to confirm the production of a pair of standard model Higgs bosons at 5 σ level with 3 ab-1 of data. Assuming a more realistic projection of the level of systematics, around 10%, the optimization of cuts to train BDTs combined with a multivariate analysis delivers a respectable significance of 4.6 σ . Even assuming large systematics of 20%, our analysis predicts a 3.6 σ significance, which represents at least strong evidence in favor of double Higgs production. We carefully incorporate background contributions coming from light flavor jets or c jets being misidentified as b jets and jets being misidentified as photons in our analysis.

Correlation between increased urinary sodium excretion and decreased left ventricular diastolic function in patients with type 2 diabetes mellitus.

PubMed

Kagiyama, Shuntaro; Koga, Tokushi; Kaseda, Shigeru; Ishihara, Shiro; Kawazoe, Nobuyuki; Sadoshima, Seizo; Matsumura, Kiyoshi; Takata, Yutaka; Tsuchihashi, Takuya; Iida, Mitsuo

2009-10-01

Increased salt intake may induce hypertension, lead to cardiac hypertrophy, and exacerbate heart failure. When elderly patients develop heart failure, diastolic dysfunction is often observed, although the ejection fraction has decreased. Diabetes mellitus (DM) is an established risk factor for heart failure. However, little is known about the relationship between cardiac function and urinary sodium excretion (U-Na) in patients with DM. We measured 24-hour U-Na; cardiac function was evaluated directly during coronary catheterization in type 2 DM (n = 46) or non-DM (n = 55) patients with preserved cardiac systolic function (ejection fraction > or = 60%). Cardiac diastolic and systolic function was evaluated as - dp/dt and + dp/dt, respectively. The average of U-Na was 166.6 +/- 61.2 mEq/24 hour (mean +/- SD). In all patients, stepwise multivariate regression analysis revealed that - dp/dt had a negative correlation with serum B-type natriuretic peptide (BNP; beta = - 0.23, P = .021) and U-Na (beta = - 0.24, P = .013). On the other hand, + dp/dt negatively correlated with BNP (beta = - 0.30, P < .001), but did not relate to U-Na. In the DM-patients, stepwise multivariate regression analysis showed that - dp/dt still had a negative correlation with U-Na (beta = - 0.33, P = .025). The results indicated that increased urinary sodium excretion is associated with an impairment of cardiac diastolic function, especially in patients with DM, suggesting that a reduction of salt intake may improve cardiac diastolic function.

Prognostic value of inflammation-based markers in patients with pancreatic cancer administered gemcitabine and erlotinib.

PubMed

Lee, Jae Min; Lee, Hong Sik; Hyun, Jong Jin; Choi, Hyuk Soon; Kim, Eun Sun; Keum, Bora; Seo, Yeon Seok; Jeen, Yoon Tae; Chun, Hoon Jai; Um, Soon Ho; Kim, Chang Duck

2016-07-15

To evaluate the value of systemic inflammation-based markers as prognostic factors for advanced pancreatic cancer (PC). Data from 82 patients who underwent combination chemotherapy with gemcitabine and erlotinib for PC from 2011 to 2014 were collected retrospectively. Data that included the neutrophil-to-lymphocyte ratio (NLR), the platelet-to-lymphocyte ratio, and the C-reactive protein (CRP)-to-albumin (CRP/Alb) ratio were analyzed. Kaplan-Meier curves, and univariate and multivariate Cox proportional hazards regression analyses were used to identify the prognostic factors associated with progression-free survival (PFS) and overall survival (OS). The univariate analysis demonstrated the prognostic value of the NLR (P = 0.049) and the CRP/Alb ratio (P = 0.047) in relation to PFS, and a positive relationship between an increase in inflammation-based markers and a poor prognosis in relation to OS. The multivariate analysis determined that an increased NLR (hazard ratio = 2.76, 95%CI: 1.33-5.75, P = 0.007) is an independent prognostic factor for poor OS. There was no association between the PLR and the patients' prognoses in those who had received chemotherapy that comprised gemcitabine and erlotinib in combination. The Kaplan-Meier method and the log-rank test determined significantly worse outcomes in relation to PFS and OS in patients with an NLR > 5 or a CRP/Alb ratio > 5. Systemic inflammation-based markers, including increases in the NLR and the CRP/Alb ratio, may be useful for predicting PC prognoses.

Normal tissue complication probability (NTCP) models for late rectal bleeding, stool frequency and fecal incontinence after radiotherapy in prostate cancer patients.

PubMed

Schaake, Wouter; van der Schaaf, Arjen; van Dijk, Lisanne V; Bongaerts, Alfons H H; van den Bergh, Alfons C M; Langendijk, Johannes A

2016-06-01

Curative radiotherapy for prostate cancer may lead to anorectal side effects, including rectal bleeding, fecal incontinence, increased stool frequency and rectal pain. The main objective of this study was to develop multivariable NTCP models for these side effects. The study sample was composed of 262 patients with localized or locally advanced prostate cancer (stage T1-3). Anorectal toxicity was prospectively assessed using a standardized follow-up program. Different anatomical subregions within and around the anorectum were delineated. A LASSO logistic regression analysis was used to analyze dose volume effects on toxicity. In the univariable analysis, rectal bleeding, increase in stool frequency and fecal incontinence were significantly associated with a large number of dosimetric parameters. The collinearity between these predictors was high (VIF>5). In the multivariable model, rectal bleeding was associated with the anorectum (V70) and anticoagulant use, fecal incontinence was associated with the external sphincter (V15) and the iliococcygeal muscle (V55). Finally, increase in stool frequency was associated with the iliococcygeal muscle (V45) and the levator ani (V40). No significant associations were found for rectal pain. Different anorectal side effects are associated with different anatomical substructures within and around the anorectum. The dosimetric variables associated with these side effects can be used to optimize radiotherapy treatment planning aiming at prevention of specific side effects and to estimate the benefit of new radiation technologies. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Relationship between neighborhood poverty rate and bloodstream infections in the critically ill.

PubMed

Mendu, Mallika L; Zager, Sam; Gibbons, Fiona K; Christopher, Kenneth B

2012-05-01

Poverty is associated with increased risk of chronic illness, but its contribution to bloodstream infections is not well-defined. We performed a multicenter observational study of 14,657 patients, aged 18 yrs or older, who received critical care and had blood cultures drawn between 1997 and 2007 in two hospitals in Boston, Massachusetts. Data sources included 1990 U.S. Census and hospital administrative data. Census tracts were used as the geographic units of analysis. The exposure of interest was neighborhood poverty rate categorized as <5%, 5%-10%, 10%-20%, 20%-40%, and >40%. Neighborhood poverty rate is the percentage of residents with income below the federal poverty line. The primary end point was bloodstream infection occurring 48 hrs before critical care initiation to 48 hrs after. Associations between neighborhood poverty rate and bloodstream infection were estimated by logistic regression models. Adjusted odds ratios were estimated by multivariable logistic regression models. Two thousand four-hundred thirty-five patients had bloodstream infections. Neighborhood poverty rate was a strong predictor of risk of bloodstream infection, with a significant risk gradient across neighborhood poverty rate quintiles. After multivariable analysis, neighborhood poverty rate in the highest quintiles (20%-40% and >40%) were associated with a 26% and 49% increase in bloodstream infection risk, respectively, relative to patients with neighborhood poverty rate of <5%. Within the limitations of our study design, increased neighborhood poverty rate, a proxy for decreased socioeconomic status, appears to be associated with risk of bloodstream infection among patients who receive critical care.

Shifting chronic disease management from hospitals to primary care in Estonian health system: analysis of national panel data.

PubMed

Atun, Rifat; Gurol-Urganci, Ipek; Hone, Thomas; Pell, Lisa; Stokes, Jonathan; Habicht, Triin; Lukka, Kaija; Raaper, Elin; Habicht, Jarno

2016-12-01

Following independence from the Soviet Union in 1991, Estonia introduced a national insurance system, consolidated the number of health care providers, and introduced family medicine centred primary health care (PHC) to strengthen the health system. Using routinely collected health billing records for 2005-2012, we examine health system utilisation for seven ambulatory care sensitive conditions (ACSCs) (asthma, chronic obstructive pulmonary disease [COPD], depression, Type 2 diabetes, heart failure, hypertension, and ischemic heart disease [IHD]), and by patient characteristics (gender, age, and number of co-morbidities). The data set contained 552 822 individuals. We use patient level data to test the significance of trends, and employ multivariate regression analysis to evaluate the probability of inpatient admission while controlling for patient characteristics, health system supply-side variables, and PHC use. Over the study period, utilisation of PHC increased, whilst inpatient admissions fell. Service mix in PHC changed with increases in phone, email, nurse, and follow-up (vs initial) consultations. Healthcare utilisation for diabetes, depression, IHD and hypertension shifted to PHC, whilst for COPD, heart failure and asthma utilisation in outpatient and inpatient settings increased. Multivariate regression indicates higher probability of inpatient admission for males, older patient and especially those with multimorbidity, but protective effect for PHC, with significantly lower hospital admission for those utilising PHC services. Our findings suggest health system reforms in Estonia have influenced the shift of ACSCs from secondary to primary care, with PHC having a protective effect in reducing hospital admissions.

Impaired left ventricular systolic function and increased brachial-ankle pulse-wave velocity are independently associated with rapid renal function progression.

PubMed

Chen, Szu-Chia; Lin, Tsung-Hsien; Hsu, Po-Chao; Chang, Jer-Ming; Lee, Chee-Siong; Tsai, Wei-Chung; Su, Ho-Ming; Voon, Wen-Chol; Chen, Hung-Chun

2011-09-01

Heart failure and increased arterial stiffness are associated with declining renal function. Few studies have evaluated the association between left ventricular ejection fraction (LVEF) and brachial-ankle pulse-wave velocity (baPWV) and renal function progression. The aim of this study was to assess whether LVEF<40% and baPWV are associated with a decline in the estimated glomerular filtration rate (eGFR) and the progression to a renal end point of ≥25% decline in eGFR. This longitudinal study included 167 patients. The baPWV was measured with an ankle-brachial index-form device. The change in renal function was estimated by eGFR slope. The renal end point was defined as ≥25% decline in eGFR. Clinical and echocardiographic parameters were compared and analyzed. After a multivariate analysis, serum hematocrit was positively associated with eGFR slope, and diabetes mellitus, baPWV (P=0.031) and LVEF<40% (P=0.001) were negatively associated with eGFR slope. Forty patients reached the renal end point. Multivariate, forward Cox regression analysis found that lower serum albumin and hematocrit levels, higher triglyceride levels, higher baPWV (P=0.039) and LVEF<40% (P<0.001) were independently associated with progression to the renal end point. Our results show that LVEF<40% and increased baPWV are independently associated with renal function decline and progression to the renal end point.

Social Cognitive and Planned Behavior Variables Associated with Stages of Change for Physical Activity in Spinal Cord Injury: A Multivariate Analysis

ERIC Educational Resources Information Center

Keegan, John; Ditchman, Nicole; Dutta, Alo; Chiu, Chung-Yi; Muller, Veronica; Chan, Fong; Kundu, Madan

2016-01-01

Purpose: To apply the constructs of social cognitive theory (SCT) and the theory of planned behavior (TPB) to understand the stages of change (SOC) for physical activities among individuals with a spinal cord injury (SCI). Method: Ex post facto design using multivariate analysis of variance (MANOVA). The participants were 144 individuals with SCI…

To See the World in a Grain of Sand: Recognizing the Origin of Sand Specimens by Diffuse Reflectance Infrared Fourier Transform Spectroscopy and Multivariate Exploratory Data Analysis

ERIC Educational Resources Information Center

Pezzolo, Alessandra De Lorenzi

2011-01-01

The diffuse reflectance infrared Fourier transform (DRIFT) spectra of sand samples exhibit features reflecting their composition. Basic multivariate analysis (MVA) can be used to effectively sort subsets of homogeneous specimens collected from nearby locations, as well as pointing out similarities in composition among sands of different origins.…

Testing key predictions of the associative account of mirror neurons in humans using multivariate pattern analysis.

PubMed

Oosterhof, Nikolaas N; Wiggett, Alison J; Cross, Emily S

2014-04-01

Cook et al. overstate the evidence supporting their associative account of mirror neurons in humans: most studies do not address a key property, action-specificity that generalizes across the visual and motor domains. Multivariate pattern analysis (MVPA) of neuroimaging data can address this concern, and we illustrate how MVPA can be used to test key predictions of their account.

Multivariate Quantitative Chemical Analysis

NASA Technical Reports Server (NTRS)

Kinchen, David G.; Capezza, Mary

1995-01-01

Technique of multivariate quantitative chemical analysis devised for use in determining relative proportions of two components mixed and sprayed together onto object to form thermally insulating foam. Potentially adaptable to other materials, especially in process-monitoring applications in which necessary to know and control critical properties of products via quantitative chemical analyses of products. In addition to chemical composition, also used to determine such physical properties as densities and strengths.

Multivariate statistical analysis of low-voltage EDS spectrum images

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

Anderson, I.M.

1998-03-01

Whereas energy-dispersive X-ray spectrometry (EDS) has been used for compositional analysis in the scanning electron microscope for 30 years, the benefits of using low operating voltages for such analyses have been explored only during the last few years. This paper couples low-voltage EDS with two other emerging areas of characterization: spectrum imaging and multivariate statistical analysis. The specimen analyzed for this study was a finished Intel Pentium processor, with the polyimide protective coating stripped off to expose the final active layers.

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.

Association of Socioeconomic Status Measured by Education and Risk Factors for Carotid Atherosclerosis: Cross-sectional Study

PubMed Central

Maksimović, Miloš Ž.; Vlajinac, Hristina D.; Radak, Đorđe J.; Maksimović, Jadranka M.; Marinković, Jelena M.; Jorga, Jagoda B.

2008-01-01

Aim To investigate the association between socioeconomic status and metabolic syndrome, lifestyle, clinical and biochemical characteristics, and inflammatory markers as risk factors for carotid atherosclerotic disease. Methods This cross-sectional study, involving 657 consecutive patients with verified carotid atherosclerotic disease, was performed in Belgrade, Serbia, during the period 2006-2007. Formal education level was used as a proxy for socioeconomic status. Anthropometric parameters and data on cardiovascular risk factors were analyzed in participants with different levels of education – low (≤primary school), medium (secondary school), and high (university education). In the analysis, univariate and multivariate logistic regressions were used. Results Multivariate analysis showed that low education was significantly positively associated with female sex (odds ratio [OR], 2.38; 95% confidence interval [CI], 1.45-3.81), increased triglycerides (OR, 1.79; 95% CI, 1.12-2.78), increased high-sensitivity C-reactive protein (hsCRP) (OR, 3.53; 95% CI, 2.17-5.88), and physical inactivity (OR, 4.24; 95% CI, 1.82-9.86) and negatively associated with former smoking (OR, 0.42; 95% CI, 0.23-0.75). Medium education was significantly positively associated with increased triglycerides (OR, 1.73; 95% CI, 1.14-2.62) and increased hsCRP (OR, 2.17; 95% CI, 1.37-3.41), and negatively with age (OR, 0.97; 95% CI, 0.94-0.99). Conclusion Increased triglycerides and hsCRP in people with low and medium education, and high prevalence of metabolic syndrome, its components and inflammatory markers in all study participants, suggest that regular health check-up, especially for those with lower education, may be useful in early detection and treatment of any abnormality that can be associated with cardiovascular disease. PMID:19090608

Dietary Sodium Consumption Predicts Future Blood Pressure and Incident Hypertension in the Japanese Normotensive General Population.

PubMed

Takase, Hiroyuki; Sugiura, Tomonori; Kimura, Genjiro; Ohte, Nobuyuki; Dohi, Yasuaki

2015-07-29

Although there is a close relationship between dietary sodium and hypertension, the concept that persons with relatively high dietary sodium are at increased risk of developing hypertension compared with those with relatively low dietary sodium has not been studied intensively in a cohort. We conducted an observational study to investigate whether dietary sodium intake predicts future blood pressure and the onset of hypertension in the general population. Individual sodium intake was estimated by calculating 24-hour urinary sodium excretion from spot urine in 4523 normotensive participants who visited our hospital for a health checkup. After a baseline examination, they were followed for a median of 1143 days, with the end point being development of hypertension. During the follow-up period, hypertension developed in 1027 participants (22.7%). The risk of developing hypertension was higher in those with higher rather than lower sodium intake (hazard ratio 1.25, 95% CI 1.04 to 1.50). In multivariate Cox proportional hazards regression analysis, baseline sodium intake and the yearly change in sodium intake during the follow-up period (as continuous variables) correlated with the incidence of hypertension. Furthermore, both the yearly increase in sodium intake and baseline sodium intake showed significant correlations with the yearly increase in systolic blood pressure in multivariate regression analysis after adjustment for possible risk factors. Both relatively high levels of dietary sodium intake and gradual increases in dietary sodium are associated with future increases in blood pressure and the incidence of hypertension in the Japanese general population. © 2015 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell.

The association between temperature and mortality in tropical middle income Thailand from 1999 to 2008.

PubMed

Tawatsupa, Benjawan; Dear, Keith; Kjellstrom, Tord; Sleigh, Adrian

2014-03-01

We have investigated the association between tropical weather condition and age-sex adjusted death rates (ADR) in Thailand over a 10-year period from 1999 to 2008. Population, mortality, weather and air pollution data were obtained from four national databases. Alternating multivariable fractional polynomial (MFP) regression and stepwise multivariable linear regression analysis were used to sequentially build models of the associations between temperature variable and deaths, adjusted for the effects and interactions of age, sex, weather (6 variables), and air pollution (10 variables). The associations are explored and compared among three seasons (cold, hot and wet months) and four weather zones of Thailand (the North, Northeast, Central, and South regions). We found statistically significant associations between temperature and mortality in Thailand. The maximum temperature is the most important variable in predicting mortality. Overall, the association is nonlinear U-shape and 31 °C is the minimum-mortality temperature in Thailand. The death rates increase when maximum temperature increase with the highest rates in the North and Central during hot months. The final equation used in this study allowed estimation of the impact of a 4 °C increase in temperature as projected for Thailand by 2100; this analysis revealed that the heat-related deaths will increase more than the cold-related deaths avoided in the hot and wet months, and overall the net increase in expected mortality by region ranges from 5 to 13 % unless preventive measures were adopted. Overall, these results are useful for health impact assessment for the present situation and future public health implication of global climate change for tropical Thailand.

Prospective validation of immunological infiltrate for prediction of response to neoadjuvant chemotherapy in HER2-negative breast cancer--a substudy of the neoadjuvant GeparQuinto trial.

PubMed

Issa-Nummer, Yasmin; Darb-Esfahani, Silvia; Loibl, Sibylle; Kunz, Georg; Nekljudova, Valentina; Schrader, Iris; Sinn, Bruno Valentin; Ulmer, Hans-Ullrich; Kronenwett, Ralf; Just, Marianne; Kühn, Thorsten; Diebold, Kurt; Untch, Michael; Holms, Frank; Blohmer, Jens-Uwe; Habeck, Jörg-Olaf; Dietel, Manfred; Overkamp, Friedrich; Krabisch, Petra; von Minckwitz, Gunter; Denkert, Carsten

2013-01-01

We have recently described an increased lymphocytic infiltration rate in breast carcinoma tissue is a significant response predictor for anthracycline/taxane-based neoadjuvant chemotherapy (NACT). The aim of this study was to prospectively validate the tumor-associated lymphocyte infiltrate as predictive marker for response to anthracycline/taxane-based NACT. The immunological infiltrate was prospectively evaluated in a total of 313 core biopsies from HER2 negative patients of the multicenter PREDICT study, a substudy of the neoadjuvant GeparQuinto study. Intratumoral lymphocytes (iTuLy), stromal lymphocytes (strLy) as well as lymphocyte-predominant breast cancer (LPBC) were evaluated by histopathological assessment. Pathological complete response (pCR) rates were analyzed and compared between the defined subgroups using the exact test of Fisher. Patients with lymphocyte-predominant breast cancer (LPBC) had a significantly increased pCR rate of 36.6%, compared to non-LPBC patients (14.3%, p<0.001). LPBC and stromal lymphocytes were significantly independent predictors for pCR in multivariate analysis (LPBC: OR 2.7, p = 0.003, strLy: OR 1.2, p = 0.01). The amount of intratumoral lymphocytes was significantly predictive for pCR in univariate (OR 1.2, p = 0.01) but not in multivariate logistic regression analysis (OR 1.2, p = 0.11). Confirming previous investigations of our group, we have prospectively validated in an independent cohort that an increased immunological infiltrate in breast tumor tissue is predictive for response to anthracycline/taxane-based NACT. Patients with LPBC and increased stromal lymphocyte infiltration have significantly increased pCR rates. The lymphocytic infiltrate is a promising additional parameter for histopathological evaluation of breast cancer core biopsies.

Association of socioeconomic status measured by education and risk factors for carotid atherosclerosis: cross-sectional study.

PubMed

Maksimović, Milos Z; Vlajinac, Hristina D; Radak, Dorde J; Maksimović, Jadranka M; Marinković, Jelena M; Jorga, Jagoda B

2008-12-01

To investigate the association between socioeconomic status and metabolic syndrome, lifestyle, clinical and biochemical characteristics, and inflammatory markers as risk factors for carotid atherosclerotic disease. This cross-sectional study, involving 657 consecutive patients with verified carotid atherosclerotic disease, was performed in Belgrade, Serbia, during the period 2006-2007. Formal education level was used as a proxy for socioeconomic status. Anthropometric parameters and data on cardiovascular risk factors were analyzed in participants with different levels of education--low (< or = primary school), medium (secondary school), and high (university education). In the analysis, univariate and multivariate logistic regressions were used. Multivariate analysis showed that low education was significantly positively associated with female sex (odds ratio [OR], 2.38; 95% confidence interval [CI], 1.45-3.81), increased triglycerides (OR, 1.79; 95% CI, 1.12-2.78), increased high-sensitivity C-reactive protein (hsCRP) (OR, 3.53; 95% CI, 2.17-5.88), and physical inactivity (OR, 4.24; 95% CI, 1.82-9.86) and negatively associated with former smoking (OR, 0.42; 95% CI, 0.23-0.75). Medium education was significantly positively associated with increased triglycerides (OR, 1.73; 95% CI, 1.14-2.62) and increased hsCRP (OR, 2.17; 95% CI, 1.37-3.41), and negatively with age (OR, 0.97; 95% CI, 0.94-0.99). Increased triglycerides and hsCRP in people with low and medium education, and high prevalence of metabolic syndrome, its components and inflammatory markers in all study participants, suggest that regular health check-up, especially for those with lower education, may be useful in early detection and treatment of any abnormality that can be associated with cardiovascular disease.

Impact of Delayed Time to Advanced Imaging on Missed Appointments Across Different Demographic and Socioeconomic Factors.

PubMed

Daye, Dania; Carrodeguas, Emmanuel; Glover, McKinley; Guerrier, Claude Emmanuel; Harvey, H Benjamin; Flores, Efrén J

2018-05-01

The aim of this study was to investigate the impact of wait days (WDs) on missed outpatient MRI appointments across different demographic and socioeconomic factors. An institutional review board-approved retrospective study was conducted among adult patients scheduled for outpatient MRI during a 12-month period. Scheduling data and demographic information were obtained. Imaging missed appointments were defined as missed scheduled imaging encounters. WDs were defined as the number of days from study order to appointment. Multivariate logistic regression was applied to assess the contribution of race and socioeconomic factors to missed appointments. Linear regression was performed to assess the relationship between missed appointment rates and WDs stratified by race, income, and patient insurance groups with analysis of covariance statistics. A total of 42,727 patients met the inclusion criteria. Mean WDs were 7.95 days. Multivariate regression showed increased odds ratio for missed appointments for patients with increased WDs (7-21 days: odds ratio [OR], 1.39; >21 days: OR, 1.77), African American patients (OR, 1.71), Hispanic patients (OR, 1.30), patients with noncommercial insurance (OR, 2.00-2.55), and those with imaging performed at the main hospital campus (OR, 1.51). Missed appointment rate linearly increased with WDs, with analysis of covariance revealing underrepresented minorities and Medicaid insurance as significant effect modifiers. Increased WDs for advanced imaging significantly increases the likelihood of missed appointments. This effect is most pronounced among underrepresented minorities and patients with lower socioeconomic status. Efforts to reduce WDs may improve equity in access to and utilization of advanced diagnostic imaging for all patients. Copyright © 2018. Published by Elsevier Inc.

Examining the impacts of increased corn production on ...

EPA Pesticide Factsheets

This study demonstrates the value of a coupled chemical transport modeling system for investigating groundwater nitrate contamination responses associated with nitrogen (N) fertilizer application and increased corn production. The coupled Community Multiscale Air Quality Bidirectional and Environmental Policy Integrated Climate modeling system incorporates agricultural management practices and N exchange processes between the soil and atmosphere to estimate levels of N that may volatilize into the atmosphere, re-deposit, and seep or flow into surface and groundwater. Simulated values from this modeling system were used in a land-use regression model to examine associations between groundwater nitrate-N measurements and a suite of factors related to N fertilizer and groundwater nitrate contamination. Multi-variable modeling analysis revealed that the N-fertilizer rate (versus total) applied to irrigated (versus rainfed) grain corn (versus other crops) was the strongest N-related predictor variable of groundwater nitrate-N concentrations. Application of this multi-variable model considered groundwater nitrate-N concentration responses under two corn production scenarios. Findings suggest that increased corn production between 2002 and 2022 could result in 56% to 79% increase in areas vulnerable to groundwater nitrate-N concentrations ≥ 5 mg/L. These above-threshold areas occur on soils with a hydraulic conductivity 13% higher than the rest of the domain. Additio

Correlates of HIV knowledge and Sexual risk behaviors among Female Military Personnel

PubMed Central

Essien, E. James; Monjok, Emmanuel; Chen, Hua; Abughosh, Susan; Ekong, Ernest; Peters, Ronald J.; Holmes, Laurens; Holstad, Marcia M.; Mgbere, Osaro

2010-01-01

Objective Uniformed services personnel are at an increased risk of HIV infection. We examined the HIV/AIDS knowledge and sexual risk behaviors among female military personnel to determine the correlates of HIV risk behaviors in this population. Method The study used a cross-sectional design to examine HIV/AIDS knowledge and sexual risk behaviors in a sample of 346 females drawn from two military cantonments in Southwestern Nigeria. Data was collected between 2006 and 2008. Using bivariate analysis and multivariate logistic regression, HIV/AIDS knowledge and sexual behaviors were described in relation to socio-demographic characteristics of the participants. Results Multivariate logistic regression analysis revealed that level of education and knowing someone with HIV/AIDS were significant (p<0.05) predictors of HIV knowledge in this sample. HIV prevention self-efficacy was significantly (P<0.05) predicted by annual income and race/ethnicity. Condom use attitudes were also significantly (P<0.05) associated with number of children, annual income, and number of sexual partners. Conclusion Data indicates the importance of incorporating these predictor variables into intervention designs. PMID:20387111

Trends, Frequency, and Nature of Surgeon-Reported Conflicts of Interest in Plastic Surgery.

PubMed

Lopez, Joseph; Musavi, Leila; Quan, Amy; Calotta, Nicholas; Juan, Ilona; Park, Angela; Tufaro, Anthony P; May, James W; Dorafshar, Amir H

2017-10-01

The purpose of this study was to identify types and trends in industry sponsorship of plastic surgery research since the establishment of conflict-of-interest reporting policies in plastic surgery. The authors analyzed the frequency and types of self-reported conflicts of interest in the plastic surgery literature since the adoption of reporting policies in 2007. All original articles that met the authors' inclusion criteria and were published in the following three journals from 2008 to 2013 were included: Annals of Plastic Surgery, Plastic and Reconstructive Surgery, and Journal of Plastic, Reconstructive & Aesthetic Surgery. A multivariate regression analysis was performed to determine what study-specific variables were associated with conflict-of-interest disclosures. A total of 3722 articles were analyzed. The incidence of conflicts of interest increased from 14 percent in 2008 to 24 percent in 2009. However, thereafter, the incidence of conflicts of interest decreased steadily from 21 percent in 2010 to 9 percent in 2013. Furthermore, the authors' analysis revealed that from 2008 to 2013, industry decreased direct research support but steadily increased the rate of consultantships (p < 0.001). A multivariate regression analysis revealed that, after adjusting for potential confounders, self-reported conflicts of interest have decreased since 2008 (p = 0.03) and the prevalence of conflicts of interest differs by plastic surgery subspecialty (p < 0.0001), country of origin (p < 0.0001), and journal of publication (p = 0.05). If self-reporting of conflicts of interest is assumed to be accurate, the number of surgeon-reported conflicts of interest in plastic surgery declined overall. Although the absolute number of consultantships did not change, the rate of consultantships rather than direct research support increased over this period.

Better consenting for thyroidectomy: who has an increased risk of postoperative hypocalcaemia?

PubMed

Harris, Andrew S; Prades, Eduardo; Tkachuk, Olena; Zeitoun, Hisham

2016-12-01

Hypocalcaemia is the most common complication following thyroidectomy. This study aimed to establish the factors associated with increased risk of hypocalcaemia on day 1 following thyroidectomy. All patients who underwent thyroidectomy under a single consultant during a 5-year period were included. A multivariate analysis was undertaken to ascertain which variables had the most effect on the risk of hypocalcaemia. A prognosis table was constructed to allow risk to be predicted for individual patients based on these factors. Included in the analysis were 210 procedures and 194 patients. Eighty-two percent of patients had no calcium derangement postoperatively. Fourteen point nine percent were categorised as early hypocalcaemia, 1 % had protracted hypocalcaemia and 2.1 % had permanent hypocalcaemia. For hemi-thyroidectomies 2.8 % had postoperative hypocalcaemia and 0.9 % had permanent hypocalcaemia. The multivariate analysis revealed total thyroidectomy (risk ratio 26.5, p < 0.0001), diabetes (risk ratio 4.8, p = 0.07) and thyrotoxicosis (risk ratio 3.1, p = 0.04) as statistically significant variables for early postoperative hypocalcaemia. Gender as an isolated factor did not reach significance but was included in the model. The p value for the model was p < 1 × 10 -12 . Total thyroidectomy increases risk of early hypocalcaemia when compared to hemithyroidectomy. Gender, diabetes and thyrotoxicosis were also been found to influence the risk. All of these factors are available pre-operatively and can therefore be used to predict a more specific risk for individual patients. It is hoped that this can lead to better informed consent, prevention and better resource allocation.

Colorectal Specialization Increases Lymph Node Yield: Evidence from a National Database.

PubMed

Jeganathan, Arjun N; Shanmugan, Skandan; Bleier, Joshua I S; Hall, Glenn M; Paulson, Emily C

2016-07-01

Current guidelines recommend the evaluation of at least 12 lymph nodes (LNs) in the pathologic specimen following surgery for colorectal cancer (CRC). We sought to examine the role of colorectal specialization on nodal identification. We conducted a retrospective cohort study using SEER-Medicare data to examine the association between colorectal specialization and LN identification following surgery for colon and rectal adenocarcinoma between 2001 and 2009. Our dataset included patients >65 years who underwent surgical resection for CRC. We excluded patients with rectal cancer who had received neoadjuvant therapy. The primary outcome measure was the number of LNs identified in the pathologic specimen following surgery for CRC. Multivariate analysis was used to identify the association between surgical specialization and LN identification in the pathologic specimen. In multivariate analysis, odds of an adequate lymphadenectomy following surgery with a colorectal specialist were 1.32 and 1.41 times greater for colon and rectal cancer, respectively, than following surgery by a general surgeon (p < 0.001). These odds increased to 1.36 and 1.58, respectively, when analysis was limited to board-certified colorectal surgeons. Hospital factors associated with ≥12 LNs identified included high-volume CRC surgery (colon OR 1.84, p < 0.001; rectal OR 1.78, p < 0.001) and NCI-designated Cancer Centers (colon OR 1.75, p < 0.001; rectal OR 1.64; p = 0.007). Colorectal specialization and, in particular, board-certification in colorectal surgery, is significantly associated with increased LN identification following surgery for colon and rectal adenocarcinoma since the adoption of the 12-LN guideline in 2001.

Prognostic Significance of the Number of Positive Lymph Nodes in Women With T1-2N1 Breast Cancer Treated With Mastectomy: Should Patients With 1, 2, and 3 Positive Lymph Nodes Be Grouped Together?

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

Dai Kubicky, Charlotte, E-mail: charlottedai@gmail.com; Mongoue-Tchokote, Solange

2013-04-01

Purpose: To determine whether patients with 1, 2, or 3 positive lymph nodes (LNs) have similar survival outcomes. Methods and Materials: We analyzed the Surveillance, Epidemiology, and End Results registry of breast cancer patients diagnosed between 1990 and 2003. We identified 10,415 women with T1-2N1M0 breast cancer who were treated with mastectomy with no adjuvant radiation, with at least 10 LNs examined and 6 months of follow-up. The Kaplan-Meier method and log–rank test were used for survival analysis. Multivariate analysis was performed using the Cox proportional hazard model. Results: Median follow-up was 92 months. Ten-year overall survival (OS) and cause-specificmore » survival (CSS) were progressively worse with increasing number of positive LNs. Survival rates were 70%, 64%, and 60% (OS), and 82%, 76%, and 72% (CSS) for 1, 2, and 3 positive LNs, respectively. Pairwise log–rank test P values were <.001 (1 vs 2 positive LNs), <.001 (1 vs 3 positive LNs), and .002 (2 vs 3 positive LNs). Multivariate analysis showed that number of positive LNs was a significant predictor of OS and CSS. Hazard ratios increased with the number of positive LNs. In addition, age, primary tumor size, grade, estrogen receptor and progesterone receptor status, race, and year of diagnosis were significant prognostic factors. Conclusions: Our study suggests that patients with 1, 2, and 3 positive LNs have distinct survival outcomes, with increasing number of positive LNs associated with worse OS and CSS. The conventional grouping of 1-3 positive LNs needs to be reconsidered.« less