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.

The use of multivariate statistics in studies of wildlife habitat

Treesearch

David E. Capen

1981-01-01

This report contains edited and reviewed versions of papers presented at a workshop held at the University of Vermont in April 1980. Topics include sampling avian habitats, multivariate methods, applications, examples, and new approaches to analysis and interpretation.

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.

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

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.

Network meta-analysis of multiple outcome measures accounting for borrowing of information across outcomes

PubMed Central

2014-01-01

Background Network meta-analysis (NMA) enables simultaneous comparison of multiple treatments while preserving randomisation. When summarising evidence to inform an economic evaluation, it is important that the analysis accurately reflects the dependency structure within the data, as correlations between outcomes may have implication for estimating the net benefit associated with treatment. A multivariate NMA offers a framework for evaluating multiple treatments across multiple outcome measures while accounting for the correlation structure between outcomes. Methods The standard NMA model is extended to multiple outcome settings in two stages. In the first stage, information is borrowed across outcomes as well across studies through modelling the within-study and between-study correlation structure. In the second stage, we make use of the additional assumption that intervention effects are exchangeable between outcomes to predict effect estimates for all outcomes, including effect estimates on outcomes where evidence is either sparse or the treatment had not been considered by any one of the studies included in the analysis. We apply the methods to binary outcome data from a systematic review evaluating the effectiveness of nine home safety interventions on uptake of three poisoning prevention practices (safe storage of medicines, safe storage of other household products, and possession of poison centre control telephone number) in households with children. Analyses are conducted in WinBUGS using Markov Chain Monte Carlo (MCMC) simulations. Results Univariate and the first stage multivariate models produced broadly similar point estimates of intervention effects but the uncertainty around the multivariate estimates varied depending on the prior distribution specified for the between-study covariance structure. The second stage multivariate analyses produced more precise effect estimates while enabling intervention effects to be predicted for all outcomes, including intervention effects on outcomes not directly considered by the studies included in the analysis. Conclusions Accounting for the dependency between outcomes in a multivariate meta-analysis may or may not improve the precision of effect estimates from a network meta-analysis compared to analysing each outcome separately. PMID:25047164

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.

Using sperm morphometry and multivariate analysis to differentiate species of gray Mazama

PubMed Central

Duarte, José Maurício Barbanti

2016-01-01

There is genetic evidence that the two species of Brazilian gray Mazama, Mazama gouazoubira and Mazama nemorivaga, belong to different genera. This study identified significant differences that separated them into distinct groups, based on characteristics of the spermatozoa and ejaculate of both species. The characteristics that most clearly differentiated between the species were ejaculate colour, white for M. gouazoubira and reddish for M. nemorivaga, and sperm head dimensions. Multivariate analysis of sperm head dimension and format data accurately discriminated three groups for species with total percentage of misclassified of 0.71. The individual analysis, by animal, and the multivariate analysis have also discriminated correctly all five animals (total percentage of misclassified of 13.95%), and the canonical plot has shown three different clusters: Cluster 1, including individuals of M. nemorivaga; Cluster 2, including two individuals of M. gouazoubira; and Cluster 3, including a single individual of M. gouazoubira. The results obtained in this work corroborate the hypothesis of the formation of new genera and species for gray Mazama. Moreover, the easily applied method described herein can be used as an auxiliary tool to identify sibling species of other taxonomic groups. PMID:28018612

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.

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 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.

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.

The choice of prior distribution for a covariance matrix in multivariate meta-analysis: a simulation study.

PubMed

Hurtado Rúa, Sandra M; Mazumdar, Madhu; Strawderman, Robert L

2015-12-30

Bayesian meta-analysis is an increasingly important component of clinical research, with multivariate meta-analysis a promising tool for studies with multiple endpoints. Model assumptions, including the choice of priors, are crucial aspects of multivariate Bayesian meta-analysis (MBMA) models. In a given model, two different prior distributions can lead to different inferences about a particular parameter. A simulation study was performed in which the impact of families of prior distributions for the covariance matrix of a multivariate normal random effects MBMA model was analyzed. Inferences about effect sizes were not particularly sensitive to prior choice, but the related covariance estimates were. A few families of prior distributions with small relative biases, tight mean squared errors, and close to nominal coverage for the effect size estimates were identified. Our results demonstrate the need for sensitivity analysis and suggest some guidelines for choosing prior distributions in this class of problems. The MBMA models proposed here are illustrated in a small meta-analysis example from the periodontal field and a medium meta-analysis from the study of stroke. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

Causal diagrams and multivariate analysis I: a quiver full of arrows.

PubMed

Jupiter, Daniel C

2014-01-01

How do we know which variables we should include in our multivariate analyses? What role does each variable play in our understanding of the analysis? In this article I begin a discussion of these issues and describe 2 different types of studies for which this problem must be handled in different ways. Copyright © 2014 American College of Foot and Ankle Surgeons. Published by Elsevier Inc. All rights reserved.

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.

Multivariate analysis of fatty acid and biochemical constitutes of seaweeds to characterize their potential as bioresource for biofuel and fine chemicals.

PubMed

Verma, Priyanka; Kumar, Manoj; Mishra, Girish; Sahoo, Dinabandhu

2017-02-01

In the present study bio prospecting of thirty seaweeds from Indian coasts was analyzed for their biochemical components including pigments, fatty acid and ash content. Multivariate analysis of biochemical components and fatty acids was done using Principal Component Analysis (PCA) and Agglomerative hierarchical clustering (AHC) to manifest chemotaxonomic relationship among various seaweeds. The overall analysis suggests that these seaweeds have multi-functional properties and can be utilized as promising bioresource for proteins, lipids, pigments and carbohydrates for the food/feed and biofuel industry. Copyright © 2016. Published by Elsevier Ltd.

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.

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.

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.

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.

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 Analysis, Retrieval, and Storage System (MARS). Volume 1: MARS System and Analysis Techniques

NASA Technical Reports Server (NTRS)

Hague, D. S.; Vanderberg, J. D.; Woodbury, N. W.

1974-01-01

A method for rapidly examining the probable applicability of weight estimating formulae to a specific aerospace vehicle design is presented. The Multivariate Analysis Retrieval and Storage System (MARS) is comprised of three computer programs which sequentially operate on the weight and geometry characteristics of past aerospace vehicles designs. Weight and geometric characteristics are stored in a set of data bases which are fully computerized. Additional data bases are readily added to the MARS system and/or the existing data bases may be easily expanded to include additional vehicles or vehicle characteristics.

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.

Statistical analysis of multivariate atmospheric variables. [cloud cover

NASA Technical Reports Server (NTRS)

Tubbs, J. D.

1979-01-01

Topics covered include: (1) estimation in discrete multivariate distributions; (2) a procedure to predict cloud cover frequencies in the bivariate case; (3) a program to compute conditional bivariate normal parameters; (4) the transformation of nonnormal multivariate to near-normal; (5) test of fit for the extreme value distribution based upon the generalized minimum chi-square; (6) test of fit for continuous distributions based upon the generalized minimum chi-square; (7) effect of correlated observations on confidence sets based upon chi-square statistics; and (8) generation of random variates from specified distributions.

Weighing of risk factors for penetrating keratoplasty graft failure: application of Risk Score System.

PubMed

Tourkmani, Abdo Karim; Sánchez-Huerta, Valeria; De Wit, Guillermo; Martínez, Jaime D; Mingo, David; Mahillo-Fernández, Ignacio; Jiménez-Alfaro, Ignacio

2017-01-01

To analyze the relationship between the score obtained in the Risk Score System (RSS) proposed by Hicks et al with penetrating keratoplasty (PKP) graft failure at 1y postoperatively and among each factor in the RSS with the risk of PKP graft failure using univariate and multivariate analysis. The retrospective cohort study had 152 PKPs from 152 patients. Eighteen cases were excluded from our study due to primary failure (10 cases), incomplete medical notes (5 cases) and follow-up less than 1y (3 cases). We included 134 PKPs from 134 patients stratified by preoperative risk score. Spearman coefficient was calculated for the relationship between the score obtained and risk of failure at 1y. Univariate and multivariate analysis were calculated for the impact of every single risk factor included in the RSS over graft failure at 1y. Spearman coefficient showed statistically significant correlation between the score in the RSS and graft failure ( P <0.05). Multivariate logistic regression analysis showed no statistically significant relationship ( P >0.05) between diagnosis and lens status with graft failure. The relationship between the other risk factors studied and graft failure was significant ( P <0.05), although the results for previous grafts and graft failure was unreliable. None of our patients had previous blood transfusion, thus, it had no impact. After the application of multivariate analysis techniques, some risk factors do not show the expected impact over graft failure at 1y.

Weighing of risk factors for penetrating keratoplasty graft failure: application of Risk Score System

PubMed Central

Tourkmani, Abdo Karim; Sánchez-Huerta, Valeria; De Wit, Guillermo; Martínez, Jaime D.; Mingo, David; Mahillo-Fernández, Ignacio; Jiménez-Alfaro, Ignacio

2017-01-01

AIM To analyze the relationship between the score obtained in the Risk Score System (RSS) proposed by Hicks et al with penetrating keratoplasty (PKP) graft failure at 1y postoperatively and among each factor in the RSS with the risk of PKP graft failure using univariate and multivariate analysis. METHODS The retrospective cohort study had 152 PKPs from 152 patients. Eighteen cases were excluded from our study due to primary failure (10 cases), incomplete medical notes (5 cases) and follow-up less than 1y (3 cases). We included 134 PKPs from 134 patients stratified by preoperative risk score. Spearman coefficient was calculated for the relationship between the score obtained and risk of failure at 1y. Univariate and multivariate analysis were calculated for the impact of every single risk factor included in the RSS over graft failure at 1y. RESULTS Spearman coefficient showed statistically significant correlation between the score in the RSS and graft failure (P<0.05). Multivariate logistic regression analysis showed no statistically significant relationship (P>0.05) between diagnosis and lens status with graft failure. The relationship between the other risk factors studied and graft failure was significant (P<0.05), although the results for previous grafts and graft failure was unreliable. None of our patients had previous blood transfusion, thus, it had no impact. CONCLUSION After the application of multivariate analysis techniques, some risk factors do not show the expected impact over graft failure at 1y. PMID:28393027

POLO2: a user's guide to multiple Probit Or LOgit analysis

Treesearch

Robert M. Russell; N. E. Savin; Jacqueline L. Robertson

1981-01-01

This guide provides instructions for the use of POLO2, a computer program for multivariate probit or logic analysis of quantal response data. As many as 3000 test subjects may be included in a single analysis. Including the constant term, up to nine explanatory variables may be used. Examples illustrating input, output, and uses of the program's special features...

Apparatus and system for multivariate spectral analysis

DOEpatents

Keenan, Michael R.; Kotula, Paul G.

2003-06-24

An apparatus and system for determining the properties of a sample from measured spectral data collected from the sample by performing a method of multivariate spectral analysis. The method can include: generating a two-dimensional matrix A containing measured spectral data; providing a weighted spectral data matrix D by performing a weighting operation on matrix A; factoring D into the product of two matrices, C and S.sup.T, by performing a constrained alternating least-squares analysis of D=CS.sup.T, where C is a concentration intensity matrix and S is a spectral shapes matrix; unweighting C and S by applying the inverse of the weighting used previously; and determining the properties of the sample by inspecting C and S. This method can be used by a spectrum analyzer to process X-ray spectral data generated by a spectral analysis system that can include a Scanning Electron Microscope (SEM) with an Energy Dispersive Detector and Pulse Height Analyzer.

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.

Physical vs. photolithographic patterning of plasma polymers: an investigation by ToF-SSIMS and multivariate analysis

PubMed Central

Mishra, Gautam; Easton, Christopher D.; McArthur, Sally L.

2009-01-01

Physical and photolithographic techniques are commonly used to create chemical patterns for a range of technologies including cell culture studies, bioarrays and other biomedical applications. In this paper, we describe the fabrication of chemical micropatterns from commonly used plasma polymers. Atomic force microcopy (AFM) imaging, Time-of-Flight Static Secondary Ion Mass Spectrometry (ToF-SSIMS) imaging and multivariate analysis have been employed to visualize the chemical boundaries created by these patterning techniques and assess the spatial and chemical resolution of the patterns. ToF-SSIMS analysis demonstrated that well defined chemical and spatial boundaries were obtained from photolithographic patterning, while the resolution of physical patterning via a transmission electron microscopy (TEM) grid varied depending on the properties of the plasma system including the substrate material. In general, physical masking allowed diffusion of the plasma species below the mask and bleeding of the surface chemistries. Multivariate analysis techniques including Principal Component Analysis (PCA) and Region of Interest (ROI) assessment were used to investigate the ToF-SSIMS images of a range of different plasma polymer patterns. In the most challenging case, where two strongly reacting polymers, allylamine and acrylic acid were deposited, PCA confirmed the fabrication of micropatterns with defined spatial resolution. ROI analysis allowed for the identification of an interface between the two plasma polymers for patterns fabricated using the photolithographic technique which has been previously overlooked. This study clearly demonstrated the versatility of photolithographic patterning for the production of multichemistry plasma polymer arrays and highlighted the need for complimentary characterization and analytical techniques during the fabrication plasma polymer micropatterns. PMID:19950941

On-line evaluation of multiloop digital controller performance

NASA Technical Reports Server (NTRS)

Wieseman, Carol D.

1993-01-01

The purpose of this presentation is to inform the Guidance and Control community of capabilities which were developed by the Aeroservoelasticity Branch to evaluate the performance of multivariable control laws, on-line, during wind-tunnel testing. The capabilities are generic enough to be useful for all kinds of on-line analyses involving multivariable control in experimental testing. Consequently, it was decided to present this material at this workshop even though it has been presented elsewhere. Topics covered include: essential on-line analysis requirements; on-line analysis capabilities; on-line analysis software; frequency domain procedures; controller performance evaluation frequency-domain flutter suppression; and plant determination.

Assessing signal-to-noise in quantitative proteomics: multivariate statistical analysis in DIGE experiments.

PubMed

Friedman, David B

2012-01-01

All quantitative proteomics experiments measure variation between samples. When performing large-scale experiments that involve multiple conditions or treatments, the experimental design should include the appropriate number of individual biological replicates from each condition to enable the distinction between a relevant biological signal from technical noise. Multivariate statistical analyses, such as principal component analysis (PCA), provide a global perspective on experimental variation, thereby enabling the assessment of whether the variation describes the expected biological signal or the unanticipated technical/biological noise inherent in the system. Examples will be shown from high-resolution multivariable DIGE experiments where PCA was instrumental in demonstrating biologically significant variation as well as sample outliers, fouled samples, and overriding technical variation that would not be readily observed using standard univariate tests.

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 analysis of molecular and morphological diversity in fig (Ficus carica L.)

USDA-ARS?s Scientific Manuscript database

Genetic polymorphism across 15 microsatellite loci among 194 fig accessions including Common, Smyrna, San Pedro, and Caprifig were analyzed using a cluster analysis (CA) and the principal components analysis (PCA). The collection was moderately variable with observed number of alleles per locus rang...

Multivariate analysis of prognostic factors for idiopathic sudden sensorineural hearing loss treated with adjuvant hyperbaric oxygen therapy.

PubMed

Xie, Shaobing; Qiang, Qingfen; Mei, Lingyun; He, Chufeng; Feng, Yong; Sun, Hong; Wu, Xuewen

2018-01-01

The objective of this study is to evaluate possible prognostic factors of idiopathic sudden sensorineural hearing loss (ISSNHL) treated with adjuvant hyperbaric oxygen therapy (HBOT) using univariate and multivariate analyses. From January 2008 to October 2016, records of 178 ISSNHL patients treated with auxiliary hyperbaric oxygen therapy were reviewed to assess hearing recovery and evaluate associated prognostic factors (gender, age, localization, initial hearing threshold, presence of tinnitus, vertigo, ear fullness, hypertension, diabetes, onset of HBOT, number of HBOT, and audiogram), by using univariate and multivariate analyses. The overall recovery rate was 37.1%, including complete recovery (19.7%) and partial recovery (17.4%). According to multivariate analysis, later onset of HBOT and higher initial hearing threshold were associated with a poor prognosis in ISSNHL patients treated with HBOT. HBOT is a safe and beneficial adjuvant therapy for ISSNHL patients. 20 sessions of HBOT is possibly enough to show its therapeutic effect. Earlier HBOT onset and lower initial hearing threshold is associated with favorable hearing recovery.

Natural history definition and a suggested clinical approach to Buerger's disease: a case-control study with survival analysis.

PubMed

Fazeli, Bahare; Ravari, Hassan; Assadi, Reza

2012-08-01

The aim of this study was first to describe the natural history of Buerger's disease (BD) and then to discuss a clinical approach to this disease based on multivariate analysis. One hundred eight patients who corresponded with Shionoya's criteria were selected from 2000 to 2007 for this study. Major amputation was considered the ultimate adverse event. Survival analyses were performed by Kaplan-Meier curves. Independent variables including gender, duration of smoking, number of cigarettes smoked per day, minor amputation events and type of treatments, were determined by multivariate Cox regression analysis. The recorded data demonstrated that BD may present in four forms, including relapsing-remitting (75%), secondary progressive (4.6%), primary progressive (14.2%) and benign BD (6.2%). Most of the amputations occurred due to relapses within the six years after diagnosis of BD. In multivariate analysis, duration of smoking of more than 20 years had a significant relationship with further major amputation among patients with BD. Smoking cessation programs with experienced psychotherapists are strongly recommended for those areas in which Buerger's disease is common. Patients who have smoked for more than 20 years should be encouraged to quit smoking, but should also be recommended for more advanced treatment for limb salvage.

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

PubMed

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

2017-12-30

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

Antioxidant and metabolite profiling of North American and neotropical blueberries using LC-TOF-MS and multivariate analyses.

PubMed

Ma, Chunhui; Dastmalchi, Keyvan; Flores, Gema; Wu, Shi-Biao; Pedraza-Peñalosa, Paola; Long, Chunlin; Kennelly, Edward J

2013-04-10

There are many neotropical blueberries, and recent studies have shown that some have even stronger antioxidant activity than the well-known edible North American blueberries. Antioxidant marker compounds were predicted by applying multivariate statistics to data from LC-TOF-MS analysis and antioxidant assays of 3 North American blueberry species (Vaccinium corymbosum, Vaccinium angustifolium, and a defined mixture of Vaccinium virgatum with V. corymbosum) and 12 neotropical blueberry species (Anthopterus wardii, Cavendishia grandifolia, Cavendishia isernii, Ceratostema silvicola, Disterigma rimbachii, Macleania coccoloboides, Macleania cordifolia, Macleania rupestris, Satyria boliviana, Sphyrospermum buxifolium, Sphyrospermum cordifolium, and Sphyrospermum ellipticum). Fourteen antioxidant markers were detected, and 12 of these, including 7 anthocyanins, 3 flavonols, 1 hydroxycinnamic acid, and 1 iridoid glycoside, were identified. This application of multivariate analysis to bioactivity and mass data can be used for identification of pharmacologically active natural products and may help to determine which neotropical blueberry species will be prioritized for agricultural development. Also, the compositional differences between North American and neotropical blueberries were determined by chemometric analysis, and 44 marker compounds including 16 anthocyanins, 15 flavonoids, 7 hydroxycinnamic acid derivatives, 5 triterpene glycosides, and 1 iridoid glycoside were identified.

MULTIVARIATE ANALYSIS OF DRINKING BEHAVIOUR IN A RURAL POPULATION

PubMed Central

Mathrubootham, N.; Bashyam, V.S.P.; Shahjahan

1997-01-01

This study was carried out to find out the drinking pattern in a rural population, using multivariate techniques. 386 current users identified in a community were assessed with regard to their drinking behaviours using a structured interview. For purposes of the study the questions were condensed into 46 meaningful variables. In bivariate analysis, 14 variables including dependent variables such as dependence, MAST & CAGE (measuring alcoholic status), Q.F. Index and troubled drinking were found to be significant. Taking these variables and other multivariate techniques too such as ANOVA, correlation, regression analysis and factor analysis were done using both SPSS PC + and HCL magnum mainframe computer with FOCUS package and UNIX systems. Results revealed that number of factors such as drinking style, duration of drinking, pattern of abuse, Q.F. Index and various problems influenced drinking and some of them set up a vicious circle. Factor analysis revealed mainly 3 factors, abuse, dependence and social drinking factors. Dependence could be divided into low/moderate dependence. The implications and practical applications of these tests are also discussed. PMID:21584077

External Validation of the European Hernia Society Classification for Postoperative Complications after Incisional Hernia Repair: A Cohort Study of 2,191 Patients.

PubMed

Kroese, Leonard F; Kleinrensink, Gert-Jan; Lange, Johan F; Gillion, Jean-Francois

2018-03-01

Incisional hernia is a frequent complication after midline laparotomy. Surgical hernia repair is associated with complications, but no clear predictive risk factors have been identified. The European Hernia Society (EHS) classification offers a structured framework to describe hernias and to analyze postoperative complications. Because of its structured nature, it might prove to be useful for preoperative patient or treatment classification. The objective of this study was to investigate the EHS classification as a predictor for postoperative complications after incisional hernia surgery. An analysis was performed using a registry-based, large-scale, prospective cohort study, including all patients undergoing incisional hernia surgery between September 1, 2011 and February 29, 2016. Univariate analyses and multivariable logistic regression analysis were performed to identify risk factors for postoperative complications. A total of 2,191 patients were included, of whom 323 (15%) had 1 or more complications. Factors associated with complications in univariate analyses (p < 0.20) and clinically relevant factors were included in the multivariable analysis. In the multivariable analysis, EHS width class, incarceration, open surgery, duration of surgery, Altemeier wound class, and therapeutic antibiotic treatment were independent risk factors for postoperative complications. Third recurrence and emergency surgery were associated with fewer complications. Incisional hernia repair is associated with a 15% complication rate. The EHS width classification is associated with postoperative complications. To identify patients at risk for complications, the EHS classification is useful. Copyright © 2017. Published by Elsevier Inc.

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 Statistical Approach Applied to Sediment Source Tracking Through Quantification and Mineral Identification, Cheyenne River, South Dakota

NASA Astrophysics Data System (ADS)

Valder, J.; Kenner, S.; Long, A.

2008-12-01

Portions of the Cheyenne River are characterized as impaired by the U.S. Environmental Protection Agency because of water-quality exceedences. The Cheyenne River watershed includes the Black Hills National Forest and part of the Badlands National Park. Preliminary analysis indicates that the Badlands National Park is a major contributor to the exceedances of the water-quality constituents for total dissolved solids and total suspended solids. Water-quality data have been collected continuously since 2007, and in the second year of collection (2008), monthly grab and passive sediment samplers are being used to collect total suspended sediment and total dissolved solids in both base-flow and runoff-event conditions. In addition, sediment samples from the river channel, including bed, bank, and floodplain, have been collected. These samples are being analyzed at the South Dakota School of Mines and Technology's X-Ray Diffraction Lab to quantify the mineralogy of the sediments. A multivariate statistical approach (including principal components, least squares, and maximum likelihood techniques) is applied to the mineral percentages that were characterized for each site to identify the contributing source areas that are causing exceedances of sediment transport in the Cheyenne River watershed. Results of the multivariate analysis demonstrate the likely sources of solids found in the Cheyenne River samples. A further refinement of the methods is in progress that utilizes a conceptual model which, when applied with the multivariate statistical approach, provides a better estimate for sediment sources.

Multivariate time series clustering on geophysical data recorded at Mt. Etna from 1996 to 2003

NASA Astrophysics Data System (ADS)

Di Salvo, Roberto; Montalto, Placido; Nunnari, Giuseppe; Neri, Marco; Puglisi, Giuseppe

2013-02-01

Time series clustering is an important task in data analysis issues in order to extract implicit, previously unknown, and potentially useful information from a large collection of data. Finding useful similar trends in multivariate time series represents a challenge in several areas including geophysics environment research. While traditional time series analysis methods deal only with univariate time series, multivariate time series analysis is a more suitable approach in the field of research where different kinds of data are available. Moreover, the conventional time series clustering techniques do not provide desired results for geophysical datasets due to the huge amount of data whose sampling rate is different according to the nature of signal. In this paper, a novel approach concerning geophysical multivariate time series clustering is proposed using dynamic time series segmentation and Self Organizing Maps techniques. This method allows finding coupling among trends of different geophysical data recorded from monitoring networks at Mt. Etna spanning from 1996 to 2003, when the transition from summit eruptions to flank eruptions occurred. This information can be used to carry out a more careful evaluation of the state of volcano and to define potential hazard assessment at Mt. Etna.

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.

Using SPSS to Analyze Book Collection Data.

ERIC Educational Resources Information Center

Townley, Charles T.

1981-01-01

Describes and illustrates Statistical Package for the Social Sciences (SPSS) procedures appropriate for book collection data analysis. Several different procedures for univariate, bivariate, and multivariate analysis are discussed, and applications of procedures for book collection studies are presented. Included are 24 tables illustrating output…

Multivariate analysis of risk factors for long-term urethroplasty outcome.

PubMed

Breyer, Benjamin N; McAninch, Jack W; Whitson, Jared M; Eisenberg, Michael L; Mehdizadeh, Jennifer F; Myers, Jeremy B; Voelzke, Bryan B

2010-02-01

We studied the patient risk factors that promote urethroplasty failure. Records of patients who underwent urethroplasty at the University of California, San Francisco Medical Center between 1995 and 2004 were reviewed. Cox proportional hazards regression analysis was used to identify multivariate predictors of urethroplasty outcome. Between 1995 and 2004, 443 patients of 495 who underwent urethroplasty had complete comorbidity data and were included in analysis. Median patient age was 41 years (range 18 to 90). Median followup was 5.8 years (range 1 month to 10 years). Stricture recurred in 93 patients (21%). Primary estimated stricture-free survival at 1, 3 and 5 years was 88%, 82% and 79%. After multivariate analysis smoking (HR 1.8, 95% CI 1.0-3.1, p = 0.05), prior direct vision internal urethrotomy (HR 1.7, 95% CI 1.0-3.0, p = 0.04) and prior urethroplasty (HR 1.8, 95% CI 1.1-3.1, p = 0.03) were predictive of treatment failure. On multivariate analysis diabetes mellitus showed a trend toward prediction of urethroplasty failure (HR 2.0, 95% CI 0.8-4.9, p = 0.14). Length of urethral stricture (greater than 4 cm), prior urethroplasty and failed endoscopic therapy are predictive of failure after urethroplasty. Smoking and diabetes mellitus also may predict failure potentially secondary to microvascular damage. Copyright 2010 American Urological Association. Published by Elsevier Inc. All rights reserved.

Defining critical habitats of threatened and endemic reef fishes with a multivariate approach.

PubMed

Purcell, Steven W; Clarke, K Robert; Rushworth, Kelvin; Dalton, Steven J

2014-12-01

Understanding critical habitats of threatened and endemic animals is essential for mitigating extinction risks, developing recovery plans, and siting reserves, but assessment methods are generally lacking. We evaluated critical habitats of 8 threatened or endemic fish species on coral and rocky reefs of subtropical eastern Australia, by measuring physical and substratum-type variables of habitats at fish sightings. We used nonmetric and metric multidimensional scaling (nMDS, mMDS), Analysis of similarities (ANOSIM), similarity percentages analysis (SIMPER), permutational analysis of multivariate dispersions (PERMDISP), and other multivariate tools to distinguish critical habitats. Niche breadth was widest for 2 endemic wrasses, and reef inclination was important for several species, often found in relatively deep microhabitats. Critical habitats of mainland reef species included small caves or habitat-forming hosts such as gorgonian corals and black coral trees. Hard corals appeared important for reef fishes at Lord Howe Island, and red algae for mainland reef fishes. A wide range of habitat variables are required to assess critical habitats owing to varied affinities of species to different habitat features. We advocate assessments of critical habitats matched to the spatial scale used by the animals and a combination of multivariate methods. Our multivariate approach furnishes a general template for assessing the critical habitats of species, understanding how these vary among species, and determining differences in the degree of habitat specificity. © 2014 Society for Conservation Biology.

Multivariate Bayesian analysis of Gaussian, right censored Gaussian, ordered categorical and binary traits using Gibbs sampling

PubMed Central

Korsgaard, Inge Riis; Lund, Mogens Sandø; Sorensen, Daniel; Gianola, Daniel; Madsen, Per; Jensen, Just

2003-01-01

A fully Bayesian analysis using Gibbs sampling and data augmentation in a multivariate model of Gaussian, right censored, and grouped Gaussian traits is described. The grouped Gaussian traits are either ordered categorical traits (with more than two categories) or binary traits, where the grouping is determined via thresholds on the underlying Gaussian scale, the liability scale. Allowances are made for unequal models, unknown covariance matrices and missing data. Having outlined the theory, strategies for implementation are reviewed. These include joint sampling of location parameters; efficient sampling from the fully conditional posterior distribution of augmented data, a multivariate truncated normal distribution; and sampling from the conditional inverse Wishart distribution, the fully conditional posterior distribution of the residual covariance matrix. Finally, a simulated dataset was analysed to illustrate the methodology. This paper concentrates on a model where residuals associated with liabilities of the binary traits are assumed to be independent. A Bayesian analysis using Gibbs sampling is outlined for the model where this assumption is relaxed. PMID:12633531

Multivariate Copula Analysis Toolbox (MvCAT): Describing dependence and underlying uncertainty using a Bayesian framework

NASA Astrophysics Data System (ADS)

Sadegh, Mojtaba; Ragno, Elisa; AghaKouchak, Amir

2017-06-01

We present a newly developed Multivariate Copula Analysis Toolbox (MvCAT) which includes a wide range of copula families with different levels of complexity. MvCAT employs a Bayesian framework with a residual-based Gaussian likelihood function for inferring copula parameters and estimating the underlying uncertainties. The contribution of this paper is threefold: (a) providing a Bayesian framework to approximate the predictive uncertainties of fitted copulas, (b) introducing a hybrid-evolution Markov Chain Monte Carlo (MCMC) approach designed for numerical estimation of the posterior distribution of copula parameters, and (c) enabling the community to explore a wide range of copulas and evaluate them relative to the fitting uncertainties. We show that the commonly used local optimization methods for copula parameter estimation often get trapped in local minima. The proposed method, however, addresses this limitation and improves describing the dependence structure. MvCAT also enables evaluation of uncertainties relative to the length of record, which is fundamental to a wide range of applications such as multivariate frequency analysis.

Optimal Multicomponent Analysis Using the Generalized Standard Addition Method.

ERIC Educational Resources Information Center

Raymond, Margaret; And Others

1983-01-01

Describes an experiment on the simultaneous determination of chromium and magnesium by spectophotometry modified to include the Generalized Standard Addition Method computer program, a multivariate calibration method that provides optimal multicomponent analysis in the presence of interference and matrix effects. Provides instructions for…

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.

Multivariate analysis in thoracic research.

PubMed

Mengual-Macenlle, Noemí; Marcos, Pedro J; Golpe, Rafael; González-Rivas, Diego

2015-03-01

Multivariate analysis is based in observation and analysis of more than one statistical outcome variable at a time. In design and analysis, the technique is used to perform trade studies across multiple dimensions while taking into account the effects of all variables on the responses of interest. The development of multivariate methods emerged to analyze large databases and increasingly complex data. Since the best way to represent the knowledge of reality is the modeling, we should use multivariate statistical methods. Multivariate methods are designed to simultaneously analyze data sets, i.e., the analysis of different variables for each person or object studied. Keep in mind at all times that all variables must be treated accurately reflect the reality of the problem addressed. There are different types of multivariate analysis and each one should be employed according to the type of variables to analyze: dependent, interdependence and structural methods. In conclusion, multivariate methods are ideal for the analysis of large data sets and to find the cause and effect relationships between variables; there is a wide range of analysis types that we can use.

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

PubMed

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

2014-11-01

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

Risk factors for superficial surgical site infection after elective rectal cancer resection: a multivariate analysis of 8880 patients from the American College of Surgeons National Surgical Quality Improvement Program database.

PubMed

Sutton, Elie; Miyagaki, Hiromichi; Bellini, Geoffrey; Shantha Kumara, H M C; Yan, Xiaohong; Howe, Brett; Feigel, Amanda; Whelan, Richard L

2017-01-01

Superficial surgical site infection (sSSI) is one of the most common complications after colorectal resection. The goal of this study was to determine the comorbidities and operative characteristics that place patients at risk for sSSI in patients who underwent rectal cancer resection. The American College of Surgeons National Surgical Quality Improvement Program database was queried (via diagnosis and Current Procedural Terminology codes) for patients with rectal cancer who underwent elective resection between 2005 and 2012. Patients for whom data concerning 27 demographic factors, comorbidities, and operative characteristics were available were eligible. A univariate and multivariate analysis was performed to identify possible risk factors for sSSI. A total of 8880 patients met the entry criteria and were included. sSSIs were diagnosed in 861 (9.7%) patients. Univariate analysis found 14 patients statistically significant risk factors for sSSI. Multivariate analysis revealed the following risk factors: male gender, body mass index (BMI) >30, current smoking, history of chronic obstructive pulmonary disease (COPD), American Society of Anesthesiologists III/IV, abdominoperineal resection (APR), stoma formation, open surgery (versus laparoscopic), and operative time >217 min. The greatest difference in sSSI rates was noted in patients with COPD (18.9 versus 9.5%). Of note, 54.2% of sSSIs was noted after hospital discharge. With regard to the timing of presentation, univariate analysis revealed a statistically significant delay in sSSI presentation in patients with the following factors and/or characteristics: BMI <30, previous radiation therapy (RT), APR, minimally invasive surgery, and stoma formation. Multivariate analysis suggested that only laparoscopic surgery (versus open) and preoperative RT were risk factors for delay. Rectal cancer resections are associated with a high incidence of sSSIs, over half of which are noted after discharge. Nine patient and operative characteristics, including smoking, BMI, COPD, APR, and open surgery were found to be significant risk factors for SSI on multivariate analysis. Furthermore, sSSI presentation in patients who had laparoscopic surgery and those who had preoperative RT is significantly delayed for unclear reasons. Copyright © 2016 Elsevier Inc. All rights reserved.

Regional magnetic resonance imaging measures for multivariate analysis in Alzheimer's disease and mild cognitive impairment.

PubMed

Westman, Eric; Aguilar, Carlos; Muehlboeck, J-Sebastian; Simmons, Andrew

2013-01-01

Automated structural magnetic resonance imaging (MRI) processing pipelines are gaining popularity for Alzheimer's disease (AD) research. They generate regional volumes, cortical thickness measures and other measures, which can be used as input for multivariate analysis. It is not clear which combination of measures and normalization approach are most useful for AD classification and to predict mild cognitive impairment (MCI) conversion. The current study includes MRI scans from 699 subjects [AD, MCI and controls (CTL)] from the Alzheimer's disease Neuroimaging Initiative (ADNI). The Freesurfer pipeline was used to generate regional volume, cortical thickness, gray matter volume, surface area, mean curvature, gaussian curvature, folding index and curvature index measures. 259 variables were used for orthogonal partial least square to latent structures (OPLS) multivariate analysis. Normalisation approaches were explored and the optimal combination of measures determined. Results indicate that cortical thickness measures should not be normalized, while volumes should probably be normalized by intracranial volume (ICV). Combining regional cortical thickness measures (not normalized) with cortical and subcortical volumes (normalized with ICV) using OPLS gave a prediction accuracy of 91.5 % when distinguishing AD versus CTL. This model prospectively predicted future decline from MCI to AD with 75.9 % of converters correctly classified. Normalization strategy did not have a significant effect on the accuracies of multivariate models containing multiple MRI measures for this large dataset. The appropriate choice of input for multivariate analysis in AD and MCI is of great importance. The results support the use of un-normalised cortical thickness measures and volumes normalised by ICV.

The classification of gunshot residue using laser electrospray mass spectrometry and offline multivariate statistical analysis

USDA-ARS?s Scientific Manuscript database

Nonresonant laser vaporization combined with high-resolution electrospray time-of-flight mass spectrometry enables analysis of a casing after discharge of a firearm revealing organic signature molecules including methyl centralite (MC), diphenylamine (DPA), N-nitrosodiphenylamine (N-NO-DPA), 4-nitro...

An Analysis of Methods Used to Examine Gender Differences in Computer-Related Behavior.

ERIC Educational Resources Information Center

Kay, Robin

1992-01-01

Review of research investigating gender differences in computer-related behavior examines statistical and methodological flaws. Issues addressed include sample selection, sample size, scale development, scale quality, the use of univariate and multivariate analyses, regressional analysis, construct definition, construct testing, and the…

qFeature

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

2015-09-14

This package contains statistical routines for extracting features from multivariate time-series data which can then be used for subsequent multivariate statistical analysis to identify patterns and anomalous behavior. It calculates local linear or quadratic regression model fits to moving windows for each series and then summarizes the model coefficients across user-defined time intervals for each series. These methods are domain agnostic-but they have been successfully applied to a variety of domains, including commercial aviation and electric power grid data.

Multivariable Parametric Cost Model for Ground Optical Telescope Assembly

NASA Technical Reports Server (NTRS)

Stahl, H. Philip; Rowell, Ginger Holmes; Reese, Gayle; Byberg, Alicia

2005-01-01

A parametric cost model for ground-based telescopes is developed using multivariable statistical analysis of both engineering and performance parameters. While diameter continues to be the dominant cost driver, diffraction-limited wavelength is found to be a secondary driver. Other parameters such as radius of curvature are examined. The model includes an explicit factor for primary mirror segmentation and/or duplication (i.e., multi-telescope phased-array systems). Additionally, single variable models Based on aperture diameter are derived.

Proceedings of the Third Annual Symposium on Mathematical Pattern Recognition and Image Analysis

NASA Technical Reports Server (NTRS)

Guseman, L. F., Jr.

1985-01-01

Topics addressed include: multivariate spline method; normal mixture analysis applied to remote sensing; image data analysis; classifications in spatially correlated environments; probability density functions; graphical nonparametric methods; subpixel registration analysis; hypothesis integration in image understanding systems; rectification of satellite scanner imagery; spatial variation in remotely sensed images; smooth multidimensional interpolation; and optimal frequency domain textural edge detection filters.

HEPDOOP: High-Energy Physics Analysis using Hadoop

NASA Astrophysics Data System (ADS)

Bhimji, W.; Bristow, T.; Washbrook, A.

2014-06-01

We perform a LHC data analysis workflow using tools and data formats that are commonly used in the "Big Data" community outside High Energy Physics (HEP). These include Apache Avro for serialisation to binary files, Pig and Hadoop for mass data processing and Python Scikit-Learn for multi-variate analysis. Comparison is made with the same analysis performed with current HEP tools in ROOT.

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

PubMed

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

2018-02-27

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

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

PubMed Central

Ye, Lanhan; Song, Kunlin; Shen, Tingting

2018-01-01

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

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.

Clinical Trials With Large Numbers of Variables: Important Advantages of Canonical Analysis.

PubMed

Cleophas, Ton J

2016-01-01

Canonical analysis assesses the combined effects of a set of predictor variables on a set of outcome variables, but it is little used in clinical trials despite the omnipresence of multiple variables. The aim of this study was to assess the performance of canonical analysis as compared with traditional multivariate methods using multivariate analysis of covariance (MANCOVA). As an example, a simulated data file with 12 gene expression levels and 4 drug efficacy scores was used. The correlation coefficient between the 12 predictor and 4 outcome variables was 0.87 (P = 0.0001) meaning that 76% of the variability in the outcome variables was explained by the 12 covariates. Repeated testing after the removal of 5 unimportant predictor and 1 outcome variable produced virtually the same overall result. The MANCOVA identified identical unimportant variables, but it was unable to provide overall statistics. (1) Canonical analysis is remarkable, because it can handle many more variables than traditional multivariate methods such as MANCOVA can. (2) At the same time, it accounts for the relative importance of the separate variables, their interactions and differences in units. (3) Canonical analysis provides overall statistics of the effects of sets of variables, whereas traditional multivariate methods only provide the statistics of the separate variables. (4) Unlike other methods for combining the effects of multiple variables such as factor analysis/partial least squares, canonical analysis is scientifically entirely rigorous. (5) Limitations include that it is less flexible than factor analysis/partial least squares, because only 2 sets of variables are used and because multiple solutions instead of one is offered. We do hope that this article will stimulate clinical investigators to start using this remarkable method.

Longitudinal assessment of treatment effects on pulmonary ventilation using 1H/3He MRI multivariate templates

NASA Astrophysics Data System (ADS)

Tustison, Nicholas J.; Contrella, Benjamin; Altes, Talissa A.; Avants, Brian B.; de Lange, Eduard E.; Mugler, John P.

2013-03-01

The utitlity of pulmonary functional imaging techniques, such as hyperpolarized 3He MRI, has encouraged their inclusion in research studies for longitudinal assessment of disease progression and the study of treatment effects. We present methodology for performing voxelwise statistical analysis of ventilation maps derived from hyper­ polarized 3He MRI which incorporates multivariate template construction using simultaneous acquisition of IH and 3He images. Additional processing steps include intensity normalization, bias correction, 4-D longitudinal segmentation, and generation of expected ventilation maps prior to voxelwise regression analysis. Analysis is demonstrated on a cohort of eight individuals with diagnosed cystic fibrosis (CF) undergoing treatment imaged five times every two weeks with a prescribed treatment schedule.

Spectroscopic analysis and control

DOEpatents

Tate; , James D.; Reed, Christopher J.; Domke, Christopher H.; Le, Linh; Seasholtz, Mary Beth; Weber, Andy; Lipp, Charles

2017-04-18

Apparatus for spectroscopic analysis which includes a tunable diode laser spectrometer having a digital output signal and a digital computer for receiving the digital output signal from the spectrometer, the digital computer programmed to process the digital output signal using a multivariate regression algorithm. In addition, a spectroscopic method of analysis using such apparatus. Finally, a method for controlling an ethylene cracker hydrogenator.

A simple prognostic model for overall survival in metastatic renal cell carcinoma.

PubMed

Assi, Hazem I; Patenaude, Francois; Toumishey, Ethan; Ross, Laura; Abdelsalam, Mahmoud; Reiman, Tony

2016-01-01

The primary purpose of this study was to develop a simpler prognostic model to predict overall survival for patients treated for metastatic renal cell carcinoma (mRCC) by examining variables shown in the literature to be associated with survival. We conducted a retrospective analysis of patients treated for mRCC at two Canadian centres. All patients who started first-line treatment were included in the analysis. A multivariate Cox proportional hazards regression model was constructed using a stepwise procedure. Patients were assigned to risk groups depending on how many of the three risk factors from the final multivariate model they had. There were three risk factors in the final multivariate model: hemoglobin, prior nephrectomy, and time from diagnosis to treatment. Patients in the high-risk group (two or three risk factors) had a median survival of 5.9 months, while those in the intermediate-risk group (one risk factor) had a median survival of 16.2 months, and those in the low-risk group (no risk factors) had a median survival of 50.6 months. In multivariate analysis, shorter survival times were associated with hemoglobin below the lower limit of normal, absence of prior nephrectomy, and initiation of treatment within one year of diagnosis.

A simple prognostic model for overall survival in metastatic renal cell carcinoma

PubMed Central

Assi, Hazem I.; Patenaude, Francois; Toumishey, Ethan; Ross, Laura; Abdelsalam, Mahmoud; Reiman, Tony

2016-01-01

Introduction: The primary purpose of this study was to develop a simpler prognostic model to predict overall survival for patients treated for metastatic renal cell carcinoma (mRCC) by examining variables shown in the literature to be associated with survival. Methods: We conducted a retrospective analysis of patients treated for mRCC at two Canadian centres. All patients who started first-line treatment were included in the analysis. A multivariate Cox proportional hazards regression model was constructed using a stepwise procedure. Patients were assigned to risk groups depending on how many of the three risk factors from the final multivariate model they had. Results: There were three risk factors in the final multivariate model: hemoglobin, prior nephrectomy, and time from diagnosis to treatment. Patients in the high-risk group (two or three risk factors) had a median survival of 5.9 months, while those in the intermediate-risk group (one risk factor) had a median survival of 16.2 months, and those in the low-risk group (no risk factors) had a median survival of 50.6 months. Conclusions: In multivariate analysis, shorter survival times were associated with hemoglobin below the lower limit of normal, absence of prior nephrectomy, and initiation of treatment within one year of diagnosis. PMID:27217858

Quantitative investigation of inappropriate regression model construction and the importance of medical statistics experts in observational medical research: a cross-sectional study.

PubMed

Nojima, Masanori; Tokunaga, Mutsumi; Nagamura, Fumitaka

2018-05-05

To investigate under what circumstances inappropriate use of 'multivariate analysis' is likely to occur and to identify the population that needs more support with medical statistics. The frequency of inappropriate regression model construction in multivariate analysis and related factors were investigated in observational medical research publications. The inappropriate algorithm of using only variables that were significant in univariate analysis was estimated to occur at 6.4% (95% CI 4.8% to 8.5%). This was observed in 1.1% of the publications with a medical statistics expert (hereinafter 'expert') as the first author, 3.5% if an expert was included as coauthor and in 12.2% if experts were not involved. In the publications where the number of cases was 50 or less and the study did not include experts, inappropriate algorithm usage was observed with a high proportion of 20.2%. The OR of the involvement of experts for this outcome was 0.28 (95% CI 0.15 to 0.53). A further, nation-level, analysis showed that the involvement of experts and the implementation of unfavourable multivariate analysis are associated at the nation-level analysis (R=-0.652). Based on the results of this study, the benefit of participation of medical statistics experts is obvious. Experts should be involved for proper confounding adjustment and interpretation of statistical models. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Multivariate normative comparisons using an aggregated database

PubMed Central

Murre, Jaap M. J.; Huizenga, Hilde M.

2017-01-01

In multivariate normative comparisons, a patient’s profile of test scores is compared to those in a normative sample. Recently, it has been shown that these multivariate normative comparisons enhance the sensitivity of neuropsychological assessment. However, multivariate normative comparisons require multivariate normative data, which are often unavailable. In this paper, we show how a multivariate normative database can be constructed by combining healthy control group data from published neuropsychological studies. We show that three issues should be addressed to construct a multivariate normative database. First, the database may have a multilevel structure, with participants nested within studies. Second, not all tests are administered in every study, so many data may be missing. Third, a patient should be compared to controls of similar age, gender and educational background rather than to the entire normative sample. To address these issues, we propose a multilevel approach for multivariate normative comparisons that accounts for missing data and includes covariates for age, gender and educational background. Simulations show that this approach controls the number of false positives and has high sensitivity to detect genuine deviations from the norm. An empirical example is provided. Implications for other domains than neuropsychology are also discussed. To facilitate broader adoption of these methods, we provide code implementing the entire analysis in the open source software package R. PMID:28267796

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.

Multivariable Parametric Cost Model for Ground Optical: Telescope Assembly

NASA Technical Reports Server (NTRS)

Stahl, H. Philip; Rowell, Ginger Holmes; Reese, Gayle; Byberg, Alicia

2004-01-01

A parametric cost model for ground-based telescopes is developed using multi-variable statistical analysis of both engineering and performance parameters. While diameter continues to be the dominant cost driver, diffraction limited wavelength is found to be a secondary driver. Other parameters such as radius of curvature were examined. The model includes an explicit factor for primary mirror segmentation and/or duplication (i.e. multi-telescope phased-array systems). Additionally, single variable models based on aperture diameter were derived.

Independent Prognostic Factors for Acute Organophosphorus Pesticide Poisoning.

PubMed

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

2016-07-01

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

A Unified Framework for Association Analysis with Multiple Related Phenotypes

PubMed Central

Stephens, Matthew

2013-01-01

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

Multivariate 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…

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.

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

Differentiation of benign and malignant ampullary obstruction by multi-row detector CT.

PubMed

Angthong, Wirana; Jiarakoop, Kran; Tangtiang, Kaan

2018-05-21

To determine useful CT parameters to differentiate ampullary carcinomas from benign ampullary obstruction. This study included 93 patients who underwent abdominal CT, 31 patients with ampullary carcinomas, and 62 patients with benign ampullary obstruction. Two radiologists independently evaluated CT parameters then reached consensus decisions. Statistically significant CT parameters were identified through univariate and multivariate analyses. In univariate analysis, the presence of ampullary mass, asymmetric, abrupt narrowing of distal common bile duct (CBD), dilated intrahepatic bile duct (IHD), dilated pancreatic duct (PD), peripancreatic lymphadenopathy, duodenal wall thickening, and delayed enhancement were more frequently in ampullary carcinomas observed (P < 0.05). Multivariate logistic regression analysis using significant CT parameters and clinical data from univariate analysis, and clinical symptom with jaundice (P = 0.005) was an independent predictor of ampullary carcinomas. For multivariate analysis using only significant CT parameters, abrupt narrowing of distal CBD was an independent predictor of ampullary carcinomas (P = 0.019). Among various CT criteria, abrupt narrowing of distal CBD and dilated IHD had highest sensitivity (77.4%) and highest accuracy (90.3%). The abrupt narrowing of distal CBD and dilated IHD is useful for differentiation of ampullary carcinomas from benign entity in patients without the presence of mass.

Multivariate data analysis and machine learning in Alzheimer's disease with a focus on structural magnetic resonance imaging.

PubMed

Falahati, Farshad; Westman, Eric; Simmons, Andrew

2014-01-01

Machine learning algorithms and multivariate data analysis methods have been widely utilized in the field of Alzheimer's disease (AD) research in recent years. Advances in medical imaging and medical image analysis have provided a means to generate and extract valuable neuroimaging information. Automatic classification techniques provide tools to analyze this information and observe inherent disease-related patterns in the data. In particular, these classifiers have been used to discriminate AD patients from healthy control subjects and to predict conversion from mild cognitive impairment to AD. In this paper, recent studies are reviewed that have used machine learning and multivariate analysis in the field of AD research. The main focus is on studies that used structural magnetic resonance imaging (MRI), but studies that included positron emission tomography and cerebrospinal fluid biomarkers in addition to MRI are also considered. A wide variety of materials and methods has been employed in different studies, resulting in a range of different outcomes. Influential factors such as classifiers, feature extraction algorithms, feature selection methods, validation approaches, and cohort properties are reviewed, as well as key MRI-based and multi-modal based studies. Current and future trends are discussed.

Multivariate Genetic Analysis of Learning and Early Reading Development

ERIC Educational Resources Information Center

Byrne, Brian; Wadsworth, Sally; Boehme, Kristi; Talk, Andrew C.; Coventry, William L.; Olson, Richard K.; Samuelsson, Stefan; Corley, Robin

2013-01-01

The genetic factor structure of a range of learning measures was explored in twin children, recruited in preschool and followed to Grade 2 ("N"?=?2,084). Measures of orthographic learning and word reading were included in the analyses to determine how these patterned with the learning processes. An exploratory factor analysis of the…

Toward exploratory analysis of diversity unified across fields of study: an information visualization approach

Treesearch

Tuan Pham; Julia Jones; Ronald Metoyer; Frederick Colwell

2014-01-01

The study of the diversity of multivariate objects shares common characteristics and goals across disciplines, including ecology and organizational management. Nevertheless, subject-matter experts have adopted somewhat separate diversity concepts and analysis techniques, limiting the potential for sharing and comparing across disciplines. Moreover, while large and...

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

Predictive factors in patients with hepatocellular carcinoma receiving sorafenib therapy using time-dependent receiver operating characteristic analysis.

PubMed

Nishikawa, Hiroki; Nishijima, Norihiro; Enomoto, Hirayuki; Sakamoto, Azusa; Nasu, Akihiro; Komekado, Hideyuki; Nishimura, Takashi; Kita, Ryuichi; Kimura, Toru; Iijima, Hiroko; Nishiguchi, Shuhei; Osaki, Yukio

2017-01-01

To investigate variables before sorafenib therapy on the clinical outcomes in hepatocellular carcinoma (HCC) patients receiving sorafenib and to further assess and compare the predictive performance of continuous parameters using time-dependent receiver operating characteristics (ROC) analysis. A total of 225 HCC patients were analyzed. We retrospectively examined factors related to overall survival (OS) and progression free survival (PFS) using univariate and multivariate analyses. Subsequently, we performed time-dependent ROC analysis of continuous parameters which were significant in the multivariate analysis in terms of OS and PFS. Total sum of area under the ROC in all time points (defined as TAAT score) in each case was calculated. Our cohort included 175 male and 50 female patients (median age, 72 years) and included 158 Child-Pugh A and 67 Child-Pugh B patients. The median OS time was 0.68 years, while the median PFS time was 0.24 years. On multivariate analysis, gender, body mass index (BMI), Child-Pugh classification, extrahepatic metastases, tumor burden, aspartate aminotransferase (AST) and alpha-fetoprotein (AFP) were identified as significant predictors of OS and ECOG-performance status, Child-Pugh classification and extrahepatic metastases were identified as significant predictors of PFS. Among three continuous variables (i.e., BMI, AST and AFP), AFP had the highest TAAT score for the entire cohort. In subgroup analyses, AFP had the highest TAAT score except for Child-Pugh B and female among three continuous variables. In continuous variables, AFP could have higher predictive accuracy for survival in HCC patients undergoing sorafenib therapy.

Psychosocial distress in patients with thyroid cancer.

PubMed

Buchmann, Luke; Ashby, Shaelene; Cannon, Richard B; Hunt, Jason P

2015-04-01

The purpose of this study is to evaluate levels of psychosocial distress in thyroid cancer patients. An analysis of factors contributing to levels of distress is included. Individual retrospective cohort study. Head and neck cancer clinic at the Huntsman Cancer Institute. A total of 118 newly diagnosed thyroid cancer patients were included in the study. Univariate and multivariate analyses evaluated levels of and factors contributing to distress. Almost half (43.3%) of patients had significant distress. Those with self-reported psychiatric history, use of antidepressant medication, and history of radiation treatment had higher levels of distress. On multivariate analysis, patient endorsement of emotional issues predicted a higher distress level. Thyroid cancer patients have high distress levels. Identification of thyroid cancer patients with high distress levels is important to offer additional support during cancer therapy. © American Academy of Otolaryngology—Head and Neck Surgery Foundation 2015.

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

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.

Multivariate analysis of the influences of oceanic and meteorological processes on suspended particulate matter distributions in Mississippi coastal waters

NASA Astrophysics Data System (ADS)

O'Brien, S. J.; Fitzpatrick, P. J.; Dzwonkowski, B.; Dykstra, S. L.; Wallace, D. J.; Church, I.; Wiggert, J. D.

2016-02-01

The Mississippi Sound is influenced by a high volume of sediment discharge from the Biloxi River, Mobile Bay via Pas aux Herons, Pascagoula River, Pearl River, Wolf River, and Lake Pontchartrain through the Rigolets. The river discharge, variable wind speed, wind direction and tides have a significant impact on the turbidity and transport of sediments in the Sound. Level 1 Moderate Resolution Imaging Spectroradiometer (MODIS) data is processed to extract the remote sensing reflectance at the wavelength of 645 nm and binned into an 8-day composite at a resolution of 500 m. The study uses a regional ocean color algorithm to compute suspended particulate matter (SPM) concentration based on these 8-day composite images. Multivariate analysis is applied between the SPM and time series of tides, wind, turbidity and river discharge measured at federal and academic institutions' stations and moorings. The multivariate analysis also includes in situ measurements of suspended sediment concentration and advective exchanges through the Mississippi Sound's tidal inlets between the coastal shelf and the nearshore estuarine waters. Mechanisms underlying the observed spatiotemporal distribution of SPM, including material exchange between the Sound and adjacent shelf waters, will be explored. The results of this study will contribute to current understanding of exchange mechanisms and pathways with the Mississippi Bight via the Mississippi Sound's tidal inlets.

Predictors of persistent pain after total knee arthroplasty: a systematic review and meta-analysis.

PubMed

Lewis, G N; Rice, D A; McNair, P J; Kluger, M

2015-04-01

Several studies have identified clinical, psychosocial, patient characteristic, and perioperative variables that are associated with persistent postsurgical pain; however, the relative effect of these variables has yet to be quantified. The aim of the study was to provide a systematic review and meta-analysis of predictor variables associated with persistent pain after total knee arthroplasty (TKA). Included studies were required to measure predictor variables prior to or at the time of surgery, include a pain outcome measure at least 3 months post-TKA, and include a statistical analysis of the effect of the predictor variable(s) on the outcome measure. Counts were undertaken of the number of times each predictor was analysed and the number of times it was found to have a significant relationship with persistent pain. Separate meta-analyses were performed to determine the effect size of each predictor on persistent pain. Outcomes from studies implementing uni- and multivariable statistical models were analysed separately. Thirty-two studies involving almost 30 000 patients were included in the review. Preoperative pain was the predictor that most commonly demonstrated a significant relationship with persistent pain across uni- and multivariable analyses. In the meta-analyses of data from univariate models, the largest effect sizes were found for: other pain sites, catastrophizing, and depression. For data from multivariate models, significant effects were evident for: catastrophizing, preoperative pain, mental health, and comorbidities. Catastrophizing, mental health, preoperative knee pain, and pain at other sites are the strongest independent predictors of persistent pain after TKA. © The Author 2014. Published by Oxford University Press on behalf of the British Journal of Anaesthesia. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

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.

Risk factors for baclofen pump infection in children: a multivariate analysis.

PubMed

Spader, Heather S; Bollo, Robert J; Bowers, Christian A; Riva-Cambrin, Jay

2016-06-01

OBJECTIVE Intrathecal baclofen infusion systems to manage severe spasticity and dystonia are associated with higher infection rates in children than in adults. Factors unique to this population, such as poor nutrition and physical limitations for pump placement, have been hypothesized as the reasons for this disparity. The authors assessed potential risk factors for infection in a multivariate analysis. METHODS Patients who underwent implantation of a programmable pump and intrathecal catheter for baclofen infusion at a single center between January 1, 2000, and March 1, 2012, were identified in this retrospective cohort study. The primary end point was infection. Potential risk factors investigated included preoperative (i.e., demographics, body mass index [BMI], gastrostomy tube, tracheostomy, previous spinal fusion), intraoperative (i.e., surgeon, antibiotics, pump size, catheter location), and postoperative (i.e., wound dehiscence, CSF leak, and number of revisions) factors. Univariate analysis was performed, and a multivariate logistic regression model was created to identify independent risk factors for infection. RESULTS A total of 254 patients were evaluated. The overall infection rate was 9.8%. Univariate analysis identified young age, shorter height, lower weight, dehiscence, CSF leak, and number of revisions within 6 months of pump placement as significantly associated with infection. Multivariate analysis identified young age, dehiscence, and number of revisions as independent risk factors for infection. CONCLUSIONS Young age, wound dehiscence, and number of revisions were independent risk factors for infection in this pediatric cohort. A low BMI and the presence of either a gastrostomy or tracheostomy were not associated with infection and may not be contraindications for this procedure.

Multivariate analysis of fears in dental phobic patients according to a reduced FSS-II scale.

PubMed

Hakeberg, M; Gustafsson, J E; Berggren, U; Carlsson, S G

1995-10-01

This study analyzed and assessed dimensions of a questionnaire developed to measure general fears and phobias. A previous factor analysis among 109 dental phobics had revealed a five-factor structure with 22 items and an explained total variance of 54%. The present study analyzed the same material using a multivariate statistical procedure (LISREL) to reveal structural latent variables. The LISREL analysis, based on the correlation matrix, yielded a chi-square of 216.6 with 195 degrees of freedom (P = 0.138) and showed a model with seven latent variables. One was a general fear factor correlated to all 22 items. The other six factors concerned "Illness & Death" (5 items), "Failures & Embarrassment" (5 items), "Social situations" (5 items), "Physical injuries" (4 items), "Animals & Natural phenomena" (4 items). One item (opposite sex) was included in both "Failures & Embarrassment" and "Social situations". The last factor, "Social interaction", combined all the items in "Failures & Embarrassment" and "Social situations" (9 items). In conclusion, this multivariate statistical analysis (LISREL) revealed and confirmed a factor structure similar to our previous study, but added two important dimensions not shown with a traditional factor analysis. This reduced FSS-II version measures general fears and phobias and may be used on a routine clinical basis as well as in dental phobia research.

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.

Fresh Biomass Estimation in Heterogeneous Grassland Using Hyperspectral Measurements and Multivariate Statistical Analysis

NASA Astrophysics Data System (ADS)

Darvishzadeh, R.; Skidmore, A. K.; Mirzaie, M.; Atzberger, C.; Schlerf, M.

2014-12-01

Accurate estimation of grassland biomass at their peak productivity can provide crucial information regarding the functioning and productivity of the rangelands. Hyperspectral remote sensing has proved to be valuable for estimation of vegetation biophysical parameters such as biomass using different statistical techniques. However, in statistical analysis of hyperspectral data, multicollinearity is a common problem due to large amount of correlated hyper-spectral reflectance measurements. The aim of this study was to examine the prospect of above ground biomass estimation in a heterogeneous Mediterranean rangeland employing multivariate calibration methods. Canopy spectral measurements were made in the field using a GER 3700 spectroradiometer, along with concomitant in situ measurements of above ground biomass for 170 sample plots. Multivariate calibrations including partial least squares regression (PLSR), principal component regression (PCR), and Least-Squared Support Vector Machine (LS-SVM) were used to estimate the above ground biomass. The prediction accuracy of the multivariate calibration methods were assessed using cross validated R2 and RMSE. The best model performance was obtained using LS_SVM and then PLSR both calibrated with first derivative reflectance dataset with R2cv = 0.88 & 0.86 and RMSEcv= 1.15 & 1.07 respectively. The weakest prediction accuracy was appeared when PCR were used (R2cv = 0.31 and RMSEcv= 2.48). The obtained results highlight the importance of multivariate calibration methods for biomass estimation when hyperspectral data are used.

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.

Linear, multivariable robust control with a mu perspective

NASA Technical Reports Server (NTRS)

Packard, Andy; Doyle, John; Balas, Gary

1993-01-01

The structured singular value is a linear algebra tool developed to study a particular class of matrix perturbation problems arising in robust feedback control of multivariable systems. These perturbations are called linear fractional, and are a natural way to model many types of uncertainty in linear systems, including state-space parameter uncertainty, multiplicative and additive unmodeled dynamics uncertainty, and coprime factor and gap metric uncertainty. The structured singular value theory provides a natural extension of classical SISO robustness measures and concepts to MIMO systems. The structured singular value analysis, coupled with approximate synthesis methods, make it possible to study the tradeoff between performance and uncertainty that occurs in all feedback systems. In MIMO systems, the complexity of the spatial interactions in the loop gains make it difficult to heuristically quantify the tradeoffs that must occur. This paper examines the role played by the structured singular value (and its computable bounds) in answering these questions, as well as its role in the general robust, multivariable control analysis and design problem.

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

Research Update: Spatially resolved mapping of electronic structure on atomic level by multivariate statistical analysis

NASA Astrophysics Data System (ADS)

Belianinov, Alex; Ganesh, Panchapakesan; Lin, Wenzhi; Sales, Brian C.; Sefat, Athena S.; Jesse, Stephen; Pan, Minghu; Kalinin, Sergei V.

2014-12-01

Atomic level spatial variability of electronic structure in Fe-based superconductor FeTe0.55Se0.45 (Tc = 15 K) is explored using current-imaging tunneling-spectroscopy. Multivariate statistical analysis of the data differentiates regions of dissimilar electronic behavior that can be identified with the segregation of chalcogen atoms, as well as boundaries between terminations and near neighbor interactions. Subsequent clustering analysis allows identification of the spatial localization of these dissimilar regions. Similar statistical analysis of modeled calculated density of states of chemically inhomogeneous FeTe1-xSex structures further confirms that the two types of chalcogens, i.e., Te and Se, can be identified by their electronic signature and differentiated by their local chemical environment. This approach allows detailed chemical discrimination of the scanning tunneling microscopy data including separation of atomic identities, proximity, and local configuration effects and can be universally applicable to chemically and electronically inhomogeneous surfaces.

Classification of adulterated honeys by multivariate analysis.

PubMed

Amiry, Saber; Esmaiili, Mohsen; Alizadeh, Mohammad

2017-06-01

In this research, honey samples were adulterated with date syrup (DS) and invert sugar syrup (IS) at three concentrations (7%, 15% and 30%). 102 adulterated samples were prepared in six batches with 17 replications for each batch. For each sample, 32 parameters including color indices, rheological, physical, and chemical parameters were determined. To classify the samples, based on type and concentrations of adulterant, a multivariate analysis was applied using principal component analysis (PCA) followed by a linear discriminant analysis (LDA). Then, 21 principal components (PCs) were selected in five sets. Approximately two-thirds were identified correctly using color indices (62.75%) or rheological properties (67.65%). A power discrimination was obtained using physical properties (97.06%), and the best separations were achieved using two sets of chemical properties (set 1: lactone, diastase activity, sucrose - 100%) (set 2: free acidity, HMF, ash - 95%). Copyright © 2016 Elsevier Ltd. All rights reserved.

Stature and jumping height are required in female volleyball, but motor coordination is a key factor for future elite success.

PubMed

Pion, Johan A; Fransen, Job; Deprez, Dieter N; Segers, Veerle I; Vaeyens, Roel; Philippaerts, Renaat M; Lenoir, Matthieu

2015-06-01

It was hypothesized that differences in anthropometry, physical performance, and motor coordination would be found between Belgian elite and sub-elite level female volleyball players using a retrospective analysis of test results gathered over a 5-year period. The test sample in this study consisted of 21 young female volleyball players (15.3 ± 1.5 years) who were selected to train at the Flemish Top Sports Academy for Volleyball in 2008. All players (elite, n = 13; sub-elite, n = 8) were included in the same talent development program, and the elite-level athletes were of a high to very high performance levels according to European competition level in 2013. Five multivariate analyses of variance were used. There was no significant effect of playing level on measures of anthropometry (F = 0.455, p = 0.718, (Equation is included in full-text article.)= 0.07), flexibility (F = 1.861, p = 0.188, (Equation is included in full-text article.)= 0.19), strength (F = 1.218, p = 0.355, (Equation is included in full-text article.)= 0.32); and speed and agility (F = 1.176, p = 0.350, (Equation is included in full-text article.)= 0.18). Multivariate analyses of variance revealed significant multivariate effects between playing levels for motor coordination (F = 3.470, p = 0.036, (Equation is included in full-text article.)= 0.59). A Mann-Whitney U test and a sequential discriminant analysis confirmed these results. Previous research revealed that stature and jump height are prerequisites for talent identification in female volleyball. In addition, the results show that motor coordination is an important factor in determining inclusion into the elite level in female volleyball.

Extracting galactic structure parameters from multivariated density estimation

NASA Technical Reports Server (NTRS)

Chen, B.; Creze, M.; Robin, A.; Bienayme, O.

1992-01-01

Multivariate statistical analysis, including includes cluster analysis (unsupervised classification), discriminant analysis (supervised classification) and principle component analysis (dimensionlity reduction method), and nonparameter density estimation have been successfully used to search for meaningful associations in the 5-dimensional space of observables between observed points and the sets of simulated points generated from a synthetic approach of galaxy modelling. These methodologies can be applied as the new tools to obtain information about hidden structure otherwise unrecognizable, and place important constraints on the space distribution of various stellar populations in the Milky Way. In this paper, we concentrate on illustrating how to use nonparameter density estimation to substitute for the true densities in both of the simulating sample and real sample in the five-dimensional space. In order to fit model predicted densities to reality, we derive a set of equations which include n lines (where n is the total number of observed points) and m (where m: the numbers of predefined groups) unknown parameters. A least-square estimation will allow us to determine the density law of different groups and components in the Galaxy. The output from our software, which can be used in many research fields, will also give out the systematic error between the model and the observation by a Bayes rule.

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.

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.

Ground-Based Telescope Parametric Cost Model

NASA Technical Reports Server (NTRS)

Stahl, H. Philip; Rowell, Ginger Holmes

2004-01-01

A parametric cost model for ground-based telescopes is developed using multi-variable statistical analysis, The model includes both engineering and performance parameters. While diameter continues to be the dominant cost driver, other significant factors include primary mirror radius of curvature and diffraction limited wavelength. The model includes an explicit factor for primary mirror segmentation and/or duplication (i.e.. multi-telescope phased-array systems). Additionally, single variable models based on aperture diameter are derived. This analysis indicates that recent mirror technology advances have indeed reduced the historical telescope cost curve.

Method of multivariate spectral analysis

DOEpatents

Keenan, Michael R.; Kotula, Paul G.

2004-01-06

A method of determining the properties of a sample from measured spectral data collected from the sample by performing a multivariate spectral analysis. The method can include: generating a two-dimensional matrix A containing measured spectral data; providing a weighted spectral data matrix D by performing a weighting operation on matrix A; factoring D into the product of two matrices, C and S.sup.T, by performing a constrained alternating least-squares analysis of D=CS.sup.T, where C is a concentration intensity matrix and S is a spectral shapes matrix; unweighting C and S by applying the inverse of the weighting used previously; and determining the properties of the sample by inspecting C and S. This method can be used to analyze X-ray spectral data generated by operating a Scanning Electron Microscope (SEM) with an attached Energy Dispersive Spectrometer (EDS).

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

Multivariable polynomial fitting of controlled single-phase nonlinear load of input current total harmonic distortion

NASA Astrophysics Data System (ADS)

Sikora, Roman; Markiewicz, Przemysław; Pabjańczyk, Wiesława

2018-04-01

The power systems usually include a number of nonlinear receivers. Nonlinear receivers are the source of disturbances generated to the power system in the form of higher harmonics. The level of these disturbances describes the total harmonic distortion coefficient THD. Its value depends on many factors. One of them are the deformation and change in RMS value of supply voltage. A modern LED luminaire is a nonlinear receiver as well. The paper presents the results of the analysis of the influence of change in RMS value of supply voltage and the level of dimming of the tested luminaire on the value of the current THD. The analysis was made using a mathematical model based on multivariable polynomial fitting.

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 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…

Site 765: Sedimentology

USGS Publications Warehouse

,

1990-01-01

Various techniques were used to decipher the sedimentation history of Site 765, including Markov chain analysis of facies transitions, XRD analysis of clay and other minerals, and multivariate analysis of smear-slide data, in addition to the standard descriptive procedures employed by the shipboard sedimentologist. This chapter presents brief summaries of methodology and major findings of these three techniques, a summary of the sedimentation history, and a discussion of trends in sedimentation through time.

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.

Multivariate analysis of the impacts of the turbine fuel JP-4 in a microcosm toxicity test with implications for the evaluation of ecosystem dynamics and risk assessment.

PubMed

Landis, W G; Matthews, R A; Markiewicz, A J; Matthews, G B

1993-12-01

Turbine fuels are often the only aviation fuel available in most of the world. Turbine fuels consist of numerous constituents with varying water solubilities, volatilities and toxicities. This study investigates the toxicity of the water soluble fraction (WSF) of JP-4 using the Standard Aquatic Microcosm (SAM). Multivariate analysis of the complex data, including the relatively new method of nonmetric clustering, was used and compared to more traditional analyses. Particular emphasis is placed on ecosystem dynamics in multivariate space.The WSF is prepared by vigorously mixing the fuel and the SAM microcosm media in a separatory funnel. The water phase, which contains the water-soluble fraction of JP-4 is then collected. The SAM experiment was conducted using concentrations of 0.0, 1.5 and 15% WSF. The WSF is added on day 7 of the experiments by removing 450 ml from each microcosm including the controls, then adding the appropriate amount of toxicant solution and finally bringing the final volume to 3 L with microcosm media. Analysis of the WSF was performed by purge and trap gas chromatography. The organic constituents of the WSF were not recoverable from the water column within several days of the addition of the toxicant. However, the impact of the WSF on the microcosm was apparent. In the highest initial concentration treatment group an algal bloom ensued, generated by the apparent toxicity of the WSF of JP-4 to the daphnids. As the daphnid populations recovered the algal populations decreased to control values. Multivariate methods clearly demonstrated this initial impact along with an additional oscillation seperating the four treatment groups in the latter segment of the experiment. Apparent recovery may be an artifact of the projections used to describe the multivariate data. The variables that were most important in distinguishing the four groups shifted during the course of the 63 day experiment. Even this simple microcosm exhibited a variety of dynamics, with implications for biomonitoring schemes and ecological risk assessments.

Multivariate meta-analysis of prognostic factor studies with multiple cut-points and/or methods of measurement.

PubMed

Riley, Richard D; Elia, Eleni G; Malin, Gemma; Hemming, Karla; Price, Malcolm P

2015-07-30

A prognostic factor is any measure that is associated with the risk of future health outcomes in those with existing disease. Often, the prognostic ability of a factor is evaluated in multiple studies. However, meta-analysis is difficult because primary studies often use different methods of measurement and/or different cut-points to dichotomise continuous factors into 'high' and 'low' groups; selective reporting is also common. We illustrate how multivariate random effects meta-analysis models can accommodate multiple prognostic effect estimates from the same study, relating to multiple cut-points and/or methods of measurement. The models account for within-study and between-study correlations, which utilises more information and reduces the impact of unreported cut-points and/or measurement methods in some studies. The applicability of the approach is improved with individual participant data and by assuming a functional relationship between prognostic effect and cut-point to reduce the number of unknown parameters. The models provide important inferential results for each cut-point and method of measurement, including the summary prognostic effect, the between-study variance and a 95% prediction interval for the prognostic effect in new populations. Two applications are presented. The first reveals that, in a multivariate meta-analysis using published results, the Apgar score is prognostic of neonatal mortality but effect sizes are smaller at most cut-points than previously thought. In the second, a multivariate meta-analysis of two methods of measurement provides weak evidence that microvessel density is prognostic of mortality in lung cancer, even when individual participant data are available so that a continuous prognostic trend is examined (rather than cut-points). © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

LSST Astroinformatics And Astrostatistics: Data-oriented Astronomical Research

NASA Astrophysics Data System (ADS)

Borne, Kirk D.; Stassun, K.; Brunner, R. J.; Djorgovski, S. G.; Graham, M.; Hakkila, J.; Mahabal, A.; Paegert, M.; Pesenson, M.; Ptak, A.; Scargle, J.; Informatics, LSST; Statistics Team

2011-01-01

The LSST Informatics and Statistics Science Collaboration (ISSC) focuses on research and scientific discovery challenges posed by the very large and complex data collection that LSST will generate. Application areas include astroinformatics, machine learning, data mining, astrostatistics, visualization, scientific data semantics, time series analysis, and advanced signal processing. Research problems to be addressed with these methodologies include transient event characterization and classification, rare class discovery, correlation mining, outlier/anomaly/surprise detection, improved estimators (e.g., for photometric redshift or early onset supernova classification), exploration of highly dimensional (multivariate) data catalogs, and more. We present sample science results from these data-oriented approaches to large-data astronomical research. We present results from LSST ISSC team members, including the EB (Eclipsing Binary) Factory, the environmental variations in the fundamental plane of elliptical galaxies, and outlier detection in multivariate catalogs.

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.

Chemotaxonomy of Hawaiian Anthurium cultivars based on multivariate analysis of phenolic metabolites

USDA-ARS?s Scientific Manuscript database

Thirty-six anthurium spathes, sampled from species and commercial cultivars, were extracted and profiled using liquid-chromatography-mass spectrometry (LC-MS). 315 compounds, including anthocyanins, flavonoid glycosides, and flavanols, were detected from these extracts and used in chemotaxonomic ana...

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

Genetic association of impulsivity in young adults: a multivariate study

PubMed Central

Khadka, S; Narayanan, B; Meda, S A; Gelernter, J; Han, S; Sawyer, B; Aslanzadeh, F; Stevens, M C; Hawkins, K A; Anticevic, A; Potenza, M N; Pearlson, G D

2014-01-01

Impulsivity is a heritable, multifaceted construct with clinically relevant links to multiple psychopathologies. We assessed impulsivity in young adult (N~2100) participants in a longitudinal study, using self-report questionnaires and computer-based behavioral tasks. Analysis was restricted to the subset (N=426) who underwent genotyping. Multivariate association between impulsivity measures and single-nucleotide polymorphism data was implemented using parallel independent component analysis (Para-ICA). Pathways associated with multiple genes in components that correlated significantly with impulsivity phenotypes were then identified using a pathway enrichment analysis. Para-ICA revealed two significantly correlated genotype–phenotype component pairs. One impulsivity component included the reward responsiveness subscale and behavioral inhibition scale of the Behavioral-Inhibition System/Behavioral-Activation System scale, and the second impulsivity component included the non-planning subscale of the Barratt Impulsiveness Scale and the Experiential Discounting Task. Pathway analysis identified processes related to neurogenesis, nervous system signal generation/amplification, neurotransmission and immune response. We identified various genes and gene regulatory pathways associated with empirically derived impulsivity components. Our study suggests that gene networks implicated previously in brain development, neurotransmission and immune response are related to impulsive tendencies and behaviors. PMID:25268255

Reporting and Methodology of Multivariable Analyses in Prognostic Observational Studies Published in 4 Anesthesiology Journals: A Methodological Descriptive Review.

PubMed

Guglielminotti, Jean; Dechartres, Agnès; Mentré, France; Montravers, Philippe; Longrois, Dan; Laouénan, Cedric

2015-10-01

Prognostic research studies in anesthesiology aim to identify risk factors for an outcome (explanatory studies) or calculate the risk of this outcome on the basis of patients' risk factors (predictive studies). Multivariable models express the relationship between predictors and an outcome and are used in both explanatory and predictive studies. Model development demands a strict methodology and a clear reporting to assess its reliability. In this methodological descriptive review, we critically assessed the reporting and methodology of multivariable analysis used in observational prognostic studies published in anesthesiology journals. A systematic search was conducted on Medline through Web of Knowledge, PubMed, and journal websites to identify observational prognostic studies with multivariable analysis published in Anesthesiology, Anesthesia & Analgesia, British Journal of Anaesthesia, and Anaesthesia in 2010 and 2011. Data were extracted by 2 independent readers. First, studies were analyzed with respect to reporting of outcomes, design, size, methods of analysis, model performance (discrimination and calibration), model validation, clinical usefulness, and STROBE (i.e., Strengthening the Reporting of Observational Studies in Epidemiology) checklist. A reporting rate was calculated on the basis of 21 items of the aforementioned points. Second, they were analyzed with respect to some predefined methodological points. Eighty-six studies were included: 87.2% were explanatory and 80.2% investigated a postoperative event. The reporting was fairly good, with a median reporting rate of 79% (75% in explanatory studies and 100% in predictive studies). Six items had a reporting rate <36% (i.e., the 25th percentile), with some of them not identified in the STROBE checklist: blinded evaluation of the outcome (11.9%), reason for sample size (15.1%), handling of missing data (36.0%), assessment of colinearity (17.4%), assessment of interactions (13.9%), and calibration (34.9%). When reported, a few methodological shortcomings were observed, both in explanatory and predictive studies, such as an insufficient number of events of the outcome (44.6%), exclusion of cases with missing data (93.6%), or categorization of continuous variables (65.1%.). The reporting of multivariable analysis was fairly good and could be further improved by checking reporting guidelines and EQUATOR Network website. Limiting the number of candidate variables, including cases with missing data, and not arbitrarily categorizing continuous variables should be encouraged.

Application of multivariate statistical techniques in microbial ecology

PubMed Central

Paliy, O.; Shankar, V.

2016-01-01

Recent advances in high-throughput methods of molecular analyses have led to an explosion of studies generating large scale ecological datasets. Especially noticeable effect has been attained in the field of microbial ecology, where new experimental approaches provided in-depth assessments of the composition, functions, and dynamic changes of complex microbial communities. Because even a single high-throughput experiment produces large amounts of data, powerful statistical techniques of multivariate analysis are well suited to analyze and interpret these datasets. Many different multivariate techniques are available, and often it is not clear which method should be applied to a particular dataset. In this review we describe and compare the most widely used multivariate statistical techniques including exploratory, interpretive, and discriminatory procedures. We consider several important limitations and assumptions of these methods, and we present examples of how these approaches have been utilized in recent studies to provide insight into the ecology of the microbial world. Finally, we offer suggestions for the selection of appropriate methods based on the research question and dataset structure. PMID:26786791

Multivariate Analysis and Prediction of Dioxin-Furan ...

EPA Pesticide Factsheets

Peer Review Draft of Regional Methods Initiative Final Report Dioxins, which are bioaccumulative and environmentally persistent, pose an ongoing risk to human and ecosystem health. Fish constitute a significant source of dioxin exposure for humans and fish-eating wildlife. Current dioxin analytical methods are costly, time-consuming, and produce hazardous by-products. A Danish team developed a novel, multivariate statistical methodology based on the covariance of dioxin-furan congener Toxic Equivalences (TEQs) and fatty acid methyl esters (FAMEs) and applied it to North Atlantic Ocean fishmeal samples. The goal of the current study was to attempt to extend this Danish methodology to 77 whole and composite fish samples from three trophic groups: predator (whole largemouth bass), benthic (whole flathead and channel catfish) and forage fish (composite bluegill, pumpkinseed and green sunfish) from two dioxin contaminated rivers (Pocatalico R. and Kanawha R.) in West Virginia, USA. Multivariate statistical analyses, including, Principal Components Analysis (PCA), Hierarchical Clustering, and Partial Least Squares Regression (PLS), were used to assess the relationship between the FAMEs and TEQs in these dioxin contaminated freshwater fish from the Kanawha and Pocatalico Rivers. These three multivariate statistical methods all confirm that the pattern of Fatty Acid Methyl Esters (FAMEs) in these freshwater fish covaries with and is predictive of the WHO TE

Clinical Factors and Viral Load Influencing Severity of Acute Hepatitis A.

PubMed

Lee, Hyun Woong; Chang, Dong-Yeop; Moon, Hong Ju; Chang, Hye Young; Shin, Eui-Cheol; Lee, June Sung; Kim, Kyung-Ah; Kim, Hyung Joon

2015-01-01

Clinical manifestations of hepatitis A virus (HAV) infection vary from mild to fulminant hepatic failure (FHF) in adults. We investigated the relationship between laboratory findings, including viral load, and clinical outcomes in patients with acute hepatitis A (AHA) and evaluated predictive factors for severe acute hepatitis (s-AH). We analyzed the clinical manifestations of AHA in 770 patients. Patients with a prothrombin time (PT) of less than 40% of normal were classified as s-AH and included 4 patients with FHF, 11 patients with acute renal failure, and 3 patients with prolonged jaundice (n = 128). Other patients were defined as mild acute hepatitis (m-AH) (n = 642). Serum samples were obtained from 48 patients with acute hepatitis A. Among them, 20 with s-AH, and 28 with m-AH, were tested for HAV RNA titer. In a multivariate analysis, age (HR = 1.042, P = 0.041), peak creatinine (HR = 4.014, P = 0.001), bilirubin (HR = 1.153, P = 0.003), alanine aminotransferase (ALT) (HR = 1.001, P < 0.001), initial lactate dehydrogenase (LDH) (HR = 1.000, P = 0.045) and total cholesterol (HR = 0.978, P < 0.001) were independent factors for s-AH. Serum HAV RNA was detected in 20/20 (100%) patients with s-AH and 22/28 (78.6%) patients with m-AH. In a multivariate analysis of the 48 patients who were tested for HAV RNA, peak ALT (HR = 1.001, P = 0.004) and HAV RNA titer (HR = 2.076, P = 0.012) were independent factors for s-AH. Clinical factors including age, peak creatinine, bilirubin, ALT, initial LDH and total cholesterol were independent factors for s-AH in a multivariate analysis. In particular, HAV load strongly correlated with the severity of hepatitis A.

Clinical Factors and Viral Load Influencing Severity of Acute Hepatitis A

PubMed Central

Lee, Hyun Woong; Chang, Dong-Yeop; Moon, Hong Ju; Chang, Hye Young; Shin, Eui-Cheol; Lee, June Sung; Kim, Kyung-Ah; Kim, Hyung Joon

2015-01-01

Background and Aims Clinical manifestations of hepatitis A virus (HAV) infection vary from mild to fulminant hepatic failure (FHF) in adults. We investigated the relationship between laboratory findings, including viral load, and clinical outcomes in patients with acute hepatitis A (AHA) and evaluated predictive factors for severe acute hepatitis (s-AH). Methods We analyzed the clinical manifestations of AHA in 770 patients. Patients with a prothrombin time (PT) of less than 40% of normal were classified as s-AH and included 4 patients with FHF, 11 patients with acute renal failure, and 3 patients with prolonged jaundice (n = 128). Other patients were defined as mild acute hepatitis (m-AH) (n = 642). Serum samples were obtained from 48 patients with acute hepatitis A. Among them, 20 with s-AH, and 28 with m-AH, were tested for HAV RNA titer. Results In a multivariate analysis, age (HR = 1.042, P = 0.041), peak creatinine (HR = 4.014, P = 0.001), bilirubin (HR = 1.153, P = 0.003), alanine aminotransferase (ALT) (HR = 1.001, P<0.001), initial lactate dehydrogenase (LDH) (HR = 1.000, P = 0.045) and total cholesterol (HR = 0.978, P<0.001) were independent factors for s-AH. Serum HAV RNA was detected in 20/20 (100%) patients with s-AH and 22/28 (78.6%) patients with m-AH. In a multivariate analysis of the 48 patients who were tested for HAV RNA, peak ALT (HR = 1.001, P = 0.004) and HAV RNA titer (HR = 2.076, P = 0.012) were independent factors for s-AH. Conclusions Clinical factors including age, peak creatinine, bilirubin, ALT, initial LDH and total cholesterol were independent factors for s-AH in a multivariate analysis. In particular, HAV load strongly correlated with the severity of hepatitis A. PMID:26090677

Experiments with a three-dimensional statistical objective analysis scheme using FGGE data

NASA Technical Reports Server (NTRS)

Baker, Wayman E.; Bloom, Stephen C.; Woollen, John S.; Nestler, Mark S.; Brin, Eugenia

1987-01-01

A three-dimensional (3D), multivariate, statistical objective analysis scheme (referred to as optimum interpolation or OI) has been developed for use in numerical weather prediction studies with the FGGE data. Some novel aspects of the present scheme include: (1) a multivariate surface analysis over the oceans, which employs an Ekman balance instead of the usual geostrophic relationship, to model the pressure-wind error cross correlations, and (2) the capability to use an error correlation function which is geographically dependent. A series of 4-day data assimilation experiments are conducted to examine the importance of some of the key features of the OI in terms of their effects on forecast skill, as well as to compare the forecast skill using the OI with that utilizing a successive correction method (SCM) of analysis developed earlier. For the three cases examined, the forecast skill is found to be rather insensitive to varying the error correlation function geographically. However, significant differences are noted between forecasts from a two-dimensional (2D) version of the OI and those from the 3D OI, with the 3D OI forecasts exhibiting better forecast skill. The 3D OI forecasts are also more accurate than those from the SCM initial conditions. The 3D OI with the multivariate oceanic surface analysis was found to produce forecasts which were slightly more accurate, on the average, than a univariate version.

EVENT-LEVEL ANALYSIS OF ANAL SEX ROLES AND SEX DRUG USE AMONG GAY AND BISEXUAL MEN IN VANCOUVER, BRITISH COLUMBIA, CANADA

PubMed Central

Rich, Ashleigh J; Lachowsky, Nathan J; Cui, Zishan; Sereda, Paul; Lal, Allan; Moore, David M; Hogg, Robert S; Roth, Eric A

2015-01-01

This study analyzed event-level partnership data from a computer-assisted survey of 719 gay and bisexual men (GBM) enrolled in the Momentum Health Study to delineate potential linkages between anal sex roles and so-called “sex drugs”, i.e. erectile dysfunction drugs (EDD), poppers and crystal methamphetamine. Univariable and multivariable analyses using generalized linear mixed models with logit link function with sexual encounters (n=2,514) as the unit of analysis tested four hypotheses: 1) EDD are significantly associated with insertive anal sex roles, 2) poppers are significantly associated with receptive anal sex, 3) both poppers and EDD are significantly associated with anal sexual versatility and, 4) crystal methamphetamine is significantly associated with all anal sex roles. Data for survey respondents and their sexual partners allowed testing these hypotheses for both anal sex partners in the same encounter. Multivariable results supported the first three hypotheses. Crystal methamphetamine was significantly associated with all anal sex roles in the univariable models, but not significant in any multivariable ones. Other multivariable significant variables included attending group sex events, venue where first met, and self-described sexual orientation. Results indicate that GBM sex-drug use behavior features rational decision-making strategies linked to anal sex roles. They also suggest that more research on anal sex roles, particularly versatility, is needed, and that sexual behavior research can benefit from partnership analysis. PMID:26525571

Event-Level Analysis of Anal Sex Roles and Sex Drug Use Among Gay and Bisexual Men in Vancouver, British Columbia, Canada.

PubMed

Rich, Ashleigh J; Lachowsky, Nathan J; Cui, Zishan; Sereda, Paul; Lal, Allan; Moore, David M; Hogg, Robert S; Roth, Eric A

2016-08-01

This study analyzed event-level partnership data from a computer-assisted survey of 719 gay and bisexual men (GBM) enrolled in the Momentum Health Study to delineate potential linkages between anal sex roles and the so-called "sex drugs," i.e., erectile dysfunction drugs (EDD), poppers, and crystal methamphetamine. Univariable and multivariable analyses using generalized linear mixed models with logit link function with sexual encounters (n = 2514) as the unit of analysis tested four hypotheses: (1) EDD are significantly associated with insertive anal sex roles, (2) poppers are significantly associated with receptive anal sex, (3) both poppers and EDD are significantly associated with anal sexual versatility, and (4) crystal methamphetamine is significantly associated with all anal sex roles. Data for survey respondents and their sexual partners allowed testing these hypotheses for both anal sex partners in the same encounter. Multivariable results supported the first three hypotheses. Crystal methamphetamine was significantly associated with all anal sex roles in the univariable models, but not significant in any multivariable ones. Other multivariable significant variables included attending group sex events, venue where first met, and self-described sexual orientation. Results indicate that GBM sex-drug use behavior features rational decision-making strategies linked to anal sex roles. They also suggest that more research on anal sex roles, particularly versatility, is needed, and that sexual behavior research can benefit from partnership analysis.

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…

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

PubMed

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

2016-01-01

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

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

PubMed

Grapov, Dmitry; Newman, John W

2012-09-01

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

A multivariate analysis of sex offender recidivism.

PubMed

Scalora, Mario J; Garbin, Calvin

2003-06-01

Sex offender recidivism risk is a multifaceted phenomenon requiring consideration across multiple risk factor domains. The impact of treatment involvement and subsequent recidivism is given limited attention in comparison to other forensic mental health issues. The present analysis is a retrospective study of sex offenders treated at a secure facility utilizing a cognitive-behavioral program matched with an untreated correctional sample. Variables studied included demographic, criminal history, offense related, and treatment progress. Recidivism was assessed through arrest data. Multivariate analysis suggests that recidivism is significantly related to quality of treatment involvement, offender demographics, offense characteristics, and criminal history. Successfully treated offenders were significantly less likely to subsequently reoffend. Recidivists were also significantly younger, less likely married, had engaged in more victim grooming or less violent offending behavior, and had significantly more prior property charges. The authors discuss the clinical and policy implications of the interrelationship between treatment involvement and recidivism.

Simultaneous quantitative analysis of olmesartan, amlodipine and hydrochlorothiazide in their combined dosage form utilizing classical and alternating least squares based chemometric methods.

PubMed

Darwish, Hany W; Bakheit, Ahmed H; Abdelhameed, Ali S

2016-03-01

Simultaneous spectrophotometric analysis of a multi-component dosage form of olmesartan, amlodipine and hydrochlorothiazide used for the treatment of hypertension has been carried out using various chemometric methods. Multivariate calibration methods include classical least squares (CLS) executed by net analyte processing (NAP-CLS), orthogonal signal correction (OSC-CLS) and direct orthogonal signal correction (DOSC-CLS) in addition to multivariate curve resolution-alternating least squares (MCR-ALS). Results demonstrated the efficiency of the proposed methods as quantitative tools of analysis as well as their qualitative capability. The three analytes were determined precisely using the aforementioned methods in an external data set and in a dosage form after optimization of experimental conditions. Finally, the efficiency of the models was validated via comparison with the partial least squares (PLS) method in terms of accuracy and precision.

Multivariate Analysis and Machine Learning in Cerebral Palsy Research

PubMed Central

Zhang, Jing

2017-01-01

Cerebral palsy (CP), a common pediatric movement disorder, causes the most severe physical disability in children. Early diagnosis in high-risk infants is critical for early intervention and possible early recovery. In recent years, multivariate analytic and machine learning (ML) approaches have been increasingly used in CP research. This paper aims to identify such multivariate studies and provide an overview of this relatively young field. Studies reviewed in this paper have demonstrated that multivariate analytic methods are useful in identification of risk factors, detection of CP, movement assessment for CP prediction, and outcome assessment, and ML approaches have made it possible to automatically identify movement impairments in high-risk infants. In addition, outcome predictors for surgical treatments have been identified by multivariate outcome studies. To make the multivariate and ML approaches useful in clinical settings, further research with large samples is needed to verify and improve these multivariate methods in risk factor identification, CP detection, movement assessment, and outcome evaluation or prediction. As multivariate analysis, ML and data processing technologies advance in the era of Big Data of this century, it is expected that multivariate analysis and ML will play a bigger role in improving the diagnosis and treatment of CP to reduce mortality and morbidity rates, and enhance patient care for children with CP. PMID:29312134

Multivariate Analysis and Machine Learning in Cerebral Palsy Research.

PubMed

Zhang, Jing

2017-01-01

Cerebral palsy (CP), a common pediatric movement disorder, causes the most severe physical disability in children. Early diagnosis in high-risk infants is critical for early intervention and possible early recovery. In recent years, multivariate analytic and machine learning (ML) approaches have been increasingly used in CP research. This paper aims to identify such multivariate studies and provide an overview of this relatively young field. Studies reviewed in this paper have demonstrated that multivariate analytic methods are useful in identification of risk factors, detection of CP, movement assessment for CP prediction, and outcome assessment, and ML approaches have made it possible to automatically identify movement impairments in high-risk infants. In addition, outcome predictors for surgical treatments have been identified by multivariate outcome studies. To make the multivariate and ML approaches useful in clinical settings, further research with large samples is needed to verify and improve these multivariate methods in risk factor identification, CP detection, movement assessment, and outcome evaluation or prediction. As multivariate analysis, ML and data processing technologies advance in the era of Big Data of this century, it is expected that multivariate analysis and ML will play a bigger role in improving the diagnosis and treatment of CP to reduce mortality and morbidity rates, and enhance patient care for children with CP.

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.

Multivariate curve resolution of incomplete fused multiset data from chromatographic and spectrophotometric analyses for drug photostability studies.

PubMed

De Luca, Michele; Ragno, Gaetano; Ioele, Giuseppina; Tauler, Romà

2014-07-21

An advanced and powerful chemometric approach is proposed for the analysis of incomplete multiset data obtained by fusion of hyphenated liquid chromatographic DAD/MS data with UV spectrophotometric data from acid-base titration and kinetic degradation experiments. Column- and row-wise augmented data blocks were combined and simultaneously processed by means of a new version of the multivariate curve resolution-alternating least squares (MCR-ALS) technique, including the simultaneous analysis of incomplete multiset data from different instrumental techniques. The proposed procedure was applied to the detailed study of the kinetic photodegradation process of the amiloride (AML) drug. All chemical species involved in the degradation and equilibrium reactions were resolved and the pH dependent kinetic pathway described. Copyright © 2014 Elsevier B.V. All rights reserved.

A FORTRAN program for multivariate survival analysis on the personal computer.

PubMed

Mulder, P G

1988-01-01

In this paper a FORTRAN program is presented for multivariate survival or life table regression analysis in a competing risks' situation. The relevant failure rate (for example, a particular disease or mortality rate) is modelled as a log-linear function of a vector of (possibly time-dependent) explanatory variables. The explanatory variables may also include the variable time itself, which is useful for parameterizing piecewise exponential time-to-failure distributions in a Gompertz-like or Weibull-like way as a more efficient alternative to Cox's proportional hazards model. Maximum likelihood estimates of the coefficients of the log-linear relationship are obtained from the iterative Newton-Raphson method. The program runs on a personal computer under DOS; running time is quite acceptable, even for large samples.

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.

Simple and Multivariate Relationships Between Spiritual Intelligence with General Health and Happiness.

PubMed

Amirian, Mohammad-Elyas; Fazilat-Pour, Masoud

2016-08-01

The present study examined simple and multivariate relationships of spiritual intelligence with general health and happiness. The employed method was descriptive and correlational. King's Spiritual Quotient scales, GHQ-28 and Oxford Happiness Inventory, are filled out by a sample consisted of 384 students, which were selected using stratified random sampling from the students of Shahid Bahonar University of Kerman. Data are subjected to descriptive and inferential statistics including correlations and multivariate regressions. Bivariate correlations support positive and significant predictive value of spiritual intelligence toward general health and happiness. Further analysis showed that among the Spiritual Intelligence' subscales, Existential Critical Thinking Predicted General Health and Happiness, reversely. In addition, happiness was positively predicted by generation of personal meaning and transcendental awareness. The findings are discussed in line with the previous studies and the relevant theoretical background.

Generating Virtual Patients by Multivariate and Discrete Re-Sampling Techniques.

PubMed

Teutonico, D; Musuamba, F; Maas, H J; Facius, A; Yang, S; Danhof, M; Della Pasqua, O

2015-10-01

Clinical Trial Simulations (CTS) are a valuable tool for decision-making during drug development. However, to obtain realistic simulation scenarios, the patients included in the CTS must be representative of the target population. This is particularly important when covariate effects exist that may affect the outcome of a trial. The objective of our investigation was to evaluate and compare CTS results using re-sampling from a population pool and multivariate distributions to simulate patient covariates. COPD was selected as paradigm disease for the purposes of our analysis, FEV1 was used as response measure and the effects of a hypothetical intervention were evaluated in different populations in order to assess the predictive performance of the two methods. Our results show that the multivariate distribution method produces realistic covariate correlations, comparable to the real population. Moreover, it allows simulation of patient characteristics beyond the limits of inclusion and exclusion criteria in historical protocols. Both methods, discrete resampling and multivariate distribution generate realistic pools of virtual patients. However the use of a multivariate distribution enable more flexible simulation scenarios since it is not necessarily bound to the existing covariate combinations in the available clinical data sets.

Refusal of postoperative radiotherapy and its association with survival in head and neck cancer.

PubMed

Schwam, Zachary G; Husain, Zain; Judson, Benjamin L

2015-11-01

Administering postoperative radiotherapy (PORT) is associated with improved survival and slower disease progression in select head and neck cancer patients. Predictive factors for PORT refusal have not been described in this population. Retrospective analysis of 6127 head and neck cancer patients who received or refused PORT in the National Cancer Database (2003-2006) was performed. Statistical analysis included Chi-square, multivariable logistic regression, Kaplan-Meier, and Cox proportional hazards analysis. In total, 247 patients (4.0%) refused PORT. Three-year overall survival was 62.8% versus 53.4% for those who received and refused PORT, respectively. PORT refusers were more likely to have negative nodes than those who underwent PORT (37.4% versus 20.1%, p<.001). In multivariate analysis, predictive factors for refusing PORT included living far from the treatment facility (OR 1.92), having negative nodes (OR 2.14), and Charlson score of ⩾ 2 (OR 2.14) (all p ⩽.001). PORT refusal was associated with increased mortality (hazard ratio 1.20, p=.044). A significant proportion of head and neck cancer patients refused PORT; this was associated with compromised overall survival. Predictive factors for PORT refusal included socioeconomic, demographic, and pathologic variables. Elucidating root causes of refusal may lead to interventions that improve long-term outcomes. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

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 influence of dose distribution on treatment outcome in the SCOPE 1 oesophageal cancer trial.

PubMed

Carrington, Rhys; Spezi, Emiliano; Gwynne, Sarah; Dutton, Peter; Hurt, Chris; Staffurth, John; Crosby, Thomas

2016-02-06

The first aim of this study was to assess plan quality using a conformity index (CI) and analyse its influence on patient outcome. The second aim was to identify whether clinical and technological factors including planning treatment volume (PTV) volume and treatment delivery method could be related to the CI value. By extending the original concept of the mean distance to conformity (MDC) index, the OverMDC and UnderMDC of the 95 % isodose line (50Gy prescribed dose) to the PTV was calculated for 97 patients from the UK SCOPE 1 trial (ISCRT47718479). Data preparation was carried out in CERR, with Kaplan-Meier and multivariate analysis undertaken in EUCLID and further tests in Microsoft Excel and IBM's SPSS. A statistically significant breakpoint in the overall survival data, independent of cetuximab, was found with OverMDC (4.4 mm, p < 0.05). This was not the case with UnderMDC. There was a statistically significant difference in PTV volume either side of the OverMDC breakpoint (Mann Whitney p < 0.001) and in OverMDC value dependent on the treatment delivery method (mean IMRT = 2.1 mm, mean 3D-CRT = 4.1 mm Mann Whitney p < 0.001). Re-planning the worst performing patients according to OverMDC from 3D-CRT to VMAT resulted in a mean reduction in OverMDC of 2.8 mm (1.6-4.0 mm). OverMDC was not significant in multivariate analysis that included age, sex, staging, tumour type, and position. Although not significant when included in multivariate analysis, we have shown in univariate analysis that a patient's OverMDC is correlated with overall survival. OverMDC is strongly related to IMRT and to a lesser extent with PTV volume. We recommend that VMAT planning should be used for oesophageal planning when available and that attention should be paid to the conformity of the 95 % to the PTV.

Passive Fully Polarimetric W-Band Millimeter-Wave Imaging

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

Bernacki, Bruce E.; Kelly, James F.; Sheen, David M.

2012-04-01

We present the theory, design, and experimental results obtained from a scanning passive W-band fully polarimetric imager. Passive millimeter-wave imaging offers persistent day/nighttime imaging and the ability to penetrate dust, clouds and other obscurants, including clothing and dry soil. The single-pixel scanning imager includes both far-field and near-field fore-optics for investigation of polarization phenomena. Using both fore-optics, a variety of scenes including natural and man-made objects was imaged and these results are presented showing the utility of polarimetric imaging for anomaly detection. Analysis includes conventional Stokes-parameter based approaches as well as multivariate image analysis methods.

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

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.

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.

Different MR features for differentiation of intrahepatic mass-forming cholangiocarcinoma from hepatocellular carcinoma according to tumor size.

PubMed

Ni, Ting; Shang, Xiao-Sha; Wang, Wen-Tao; Hu, Xin-Xing; Zeng, Meng-Su; Rao, Sheng-Xiang

2018-06-05

To identify reliable magnetic resonance (MR) features for distinguishing mass-forming type of intrahepatic cholangiocarcinoma (IMCC) from hepatocellular carcinoma (HCC) based on tumor size. This retrospective study included 395 patients with pathologically confirmed IMCCs (n = 180) and HCCs (n = 215) who underwent pre-operative contrast-enhanced MRI including diffusion-weighted imaging (DWI). MR features were evaluated and clinical data were also recorded. All the characteristics were compared in small (≤3 cm) and large tumor (>3 cm) groups by univariate analysis and subsequently calculated by multivariable logistic regression analysis. Multivariable analysis revealed that rim arterial phase hyperenhancement [odds ratios (ORs) = 13.16], biliary dilation (OR = 23.42) and CA19-9 (OR = 21.45) were significant predictors of large IMCCs (n = 138), and washout appearance (OR = 0.036), enhancing capsule appearance (OR = 0.039), fat in mass (OR = 0.057), chronic liver disease (OR = 0.088) and alpha fetoprotein (OR = 0.019) were more frequently found in large HCCs (n = 143). For small IMCCs (n = 42) and HCCs (n = 72), rim arterial phase hyperenhancement (OR = 9.68), target appearance at DWI (OR = 12.51), alpha fetoprotein (OR = 0.12) and sex (OR = 0.20) were independent predictors in multivariate analysis. Valuable MR features and clinical factors varied for differential diagnosis of IMCCs and HCCs according to tumor size. Advances in knowledge: MR features for differential diagnosis of large IMCC and HCC (>3 cm) are in keeping with that recommended by LI-RADS. However, for small IMCCs and HCCs (≤3 cm), only rim enhancement on arterial phase and target appearance at DWI are reliable predictors.

Research Update: Spatially resolved mapping of electronic structure on atomic level by multivariate statistical analysis

DOE PAGES

Belianinov, Alex; Panchapakesan, G.; Lin, Wenzhi; ...

2014-12-02

Atomic level spatial variability of electronic structure in Fe-based superconductor FeTe0.55Se0.45 (Tc = 15 K) is explored using current-imaging tunneling-spectroscopy. Multivariate statistical analysis of the data differentiates regions of dissimilar electronic behavior that can be identified with the segregation of chalcogen atoms, as well as boundaries between terminations and near neighbor interactions. Subsequent clustering analysis allows identification of the spatial localization of these dissimilar regions. Similar statistical analysis of modeled calculated density of states of chemically inhomogeneous FeTe1 x Sex structures further confirms that the two types of chalcogens, i.e., Te and Se, can be identified by their electronic signaturemore » and differentiated by their local chemical environment. This approach allows detailed chemical discrimination of the scanning tunneling microscopy data including separation of atomic identities, proximity, and local configuration effects and can be universally applicable to chemically and electronically inhomogeneous surfaces.« less

Research Update: Spatially resolved mapping of electronic structure on atomic level by multivariate statistical analysis

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

Belianinov, Alex, E-mail: belianinova@ornl.gov; Ganesh, Panchapakesan; Lin, Wenzhi

2014-12-01

Atomic level spatial variability of electronic structure in Fe-based superconductor FeTe{sub 0.55}Se{sub 0.45} (T{sub c} = 15 K) is explored using current-imaging tunneling-spectroscopy. Multivariate statistical analysis of the data differentiates regions of dissimilar electronic behavior that can be identified with the segregation of chalcogen atoms, as well as boundaries between terminations and near neighbor interactions. Subsequent clustering analysis allows identification of the spatial localization of these dissimilar regions. Similar statistical analysis of modeled calculated density of states of chemically inhomogeneous FeTe{sub 1−x}Se{sub x} structures further confirms that the two types of chalcogens, i.e., Te and Se, can be identified bymore » their electronic signature and differentiated by their local chemical environment. This approach allows detailed chemical discrimination of the scanning tunneling microscopy data including separation of atomic identities, proximity, and local configuration effects and can be universally applicable to chemically and electronically inhomogeneous surfaces.« less

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.

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.

Multi-country health surveys: are the analyses misleading?

PubMed

Masood, Mohd; Reidpath, Daniel D

2014-05-01

The aim of this paper was to review the types of approaches currently utilized in the analysis of multi-country survey data, specifically focusing on design and modeling issues with a focus on analyses of significant multi-country surveys published in 2010. A systematic search strategy was used to identify the 10 multi-country surveys and the articles published from them in 2010. The surveys were selected to reflect diverse topics and foci; and provide an insight into analytic approaches across research themes. The search identified 159 articles appropriate for full text review and data extraction. The analyses adopted in the multi-country surveys can be broadly classified as: univariate/bivariate analyses, and multivariate/multivariable analyses. Multivariate/multivariable analyses may be further divided into design- and model-based analyses. Of the 159 articles reviewed, 129 articles used model-based analysis, 30 articles used design-based analyses. Similar patterns could be seen in all the individual surveys. While there is general agreement among survey statisticians that complex surveys are most appropriately analyzed using design-based analyses, most researchers continued to use the more common model-based approaches. Recent developments in design-based multi-level analysis may be one approach to include all the survey design characteristics. This is a relatively new area, however, and there remains statistical, as well as applied analytic research required. An important limitation of this study relates to the selection of the surveys used and the choice of year for the analysis, i.e., year 2010 only. There is, however, no strong reason to believe that analytic strategies have changed radically in the past few years, and 2010 provides a credible snapshot of current practice.

Identification of complex metabolic states in critically injured patients using bioinformatic cluster analysis.

PubMed

Cohen, Mitchell J; Grossman, Adam D; Morabito, Diane; Knudson, M Margaret; Butte, Atul J; Manley, Geoffrey T

2010-01-01

Advances in technology have made extensive monitoring of patient physiology the standard of care in intensive care units (ICUs). While many systems exist to compile these data, there has been no systematic multivariate analysis and categorization across patient physiological data. The sheer volume and complexity of these data make pattern recognition or identification of patient state difficult. Hierarchical cluster analysis allows visualization of high dimensional data and enables pattern recognition and identification of physiologic patient states. We hypothesized that processing of multivariate data using hierarchical clustering techniques would allow identification of otherwise hidden patient physiologic patterns that would be predictive of outcome. Multivariate physiologic and ventilator data were collected continuously using a multimodal bioinformatics system in the surgical ICU at San Francisco General Hospital. These data were incorporated with non-continuous data and stored on a server in the ICU. A hierarchical clustering algorithm grouped each minute of data into 1 of 10 clusters. Clusters were correlated with outcome measures including incidence of infection, multiple organ failure (MOF), and mortality. We identified 10 clusters, which we defined as distinct patient states. While patients transitioned between states, they spent significant amounts of time in each. Clusters were enriched for our outcome measures: 2 of the 10 states were enriched for infection, 6 of 10 were enriched for MOF, and 3 of 10 were enriched for death. Further analysis of correlations between pairs of variables within each cluster reveals significant differences in physiology between clusters. Here we show for the first time the feasibility of clustering physiological measurements to identify clinically relevant patient states after trauma. These results demonstrate that hierarchical clustering techniques can be useful for visualizing complex multivariate data and may provide new insights for the care of critically injured patients.

Causes of death in long-term lung cancer survivors: a SEER database analysis.

PubMed

Abdel-Rahman, Omar

2017-07-01

Long-term (>5 years) lung cancer survivors represent a small but distinct subgroup of lung cancer patients and information about the causes of death of this subgroup is scarce. The Surveillance, Epidemiology and End Results (SEER) database (1988-2008) was utilized to determine the causes of death of long-term survivors of lung cancer. Survival analysis was conducted using Kaplan-Meier analysis and multivariate analysis was conducted using a Cox proportional hazard model. Clinicopathological characteristics and survival outcomes were assessed for the whole cohort. A total of 78,701 lung cancer patients with >5 years survival were identified. This cohort included 54,488 patients surviving 5-10 years and 24,213 patients surviving >10 years. Among patients surviving 5-10 years, 21.8% were dead because of primary lung cancer, 10.2% were dead because of other cancers, 6.8% were dead because of cardiac disease and 5.3% were dead because of non-malignant pulmonary disease. Among patients surviving >10 years, 12% were dead because of primary lung cancer, 6% were dead because of other cancers, 6.9% were dead because of cardiac disease and 5.6% were dead because of non-malignant pulmonary disease. On multivariate analysis, factors associated with longer cardiac-disease-specific survival in multivariate analysis include younger age at diagnosis (p < .0001), white race (vs. African American race) (p = .005), female gender (p < .0001), right-sided disease (p = .003), adenocarcinoma (vs. large cell or small cell carcinoma), histology and receiving local treatment by surgery rather than radiotherapy (p < .0001). The probability of death from primary lung cancer is still significant among other causes of death even 20 years after diagnosis of lung cancer. Moreover, cardiac as well as non-malignant pulmonary causes contribute a considerable proportion of deaths in long-term lung cancer survivors.

Fibroblast Growth Factor 2-A Predictor of Outcome for Patients Irradiated for Stage II-III Non-Small-Cell Lung Cancer

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

Rades, Dirk, E-mail: Rades.Dirk@gmx.net; Setter, Cornelia; Dahl, Olav

2012-01-01

Purpose: The prognostic value of the tumor cell expression of the fibroblast growth factor 2 (FGF-2) in patients with non-small-cell lung cancer (NSCLC) is unclear. The present study investigated the effect of tumor cell expression of FGF-2 on the outcome of 60 patients irradiated for Stage II-III NSCLC. Methods and Materials: The effect of FGF-2 expression and 13 additional factors on locoregional control (LRC), metastasis-free survival (MFS), and overall survival (OS) were retrospectively evaluated. These additional factors included age, gender, Karnofsky performance status, histologic type, histologic grade, T and N category, American Joint Committee on Cancer stage, surgery, chemotherapy, pack-years,more » smoking during radiotherapy, and hemoglobin during radiotherapy. Locoregional failure was identified by endoscopy or computed tomography. Univariate analyses were performed with the Kaplan-Meier method and the Wilcoxon test and multivariate analyses with the Cox proportional hazard model. Results: On univariate analysis, improved LRC was associated with surgery (p = .017), greater hemoglobin levels (p = .036), and FGF-2 negativity (p <.001). On multivariate analysis of LRC, surgery (relative risk [RR], 2.44; p = .037), and FGF-2 expression (RR, 5.06; p <.001) maintained significance. On univariate analysis, improved MFS was associated with squamous cell carcinoma (p = .020), greater hemoglobin levels (p = .007), and FGF-2 negativity (p = .001). On multivariate analysis of MFS, the hemoglobin levels (RR, 2.65; p = .019) and FGF-2 expression (RR, 3.05; p = .004) were significant. On univariate analysis, improved OS was associated with a lower N category (p = .048), greater hemoglobin levels (p <.001), and FGF-2 negativity (p <.001). On multivariate analysis of OS, greater hemoglobin levels (RR, 4.62; p = .002) and FGF-2 expression (RR, 3.25; p = .002) maintained significance. Conclusions: Tumor cell expression of FGF-2 appeared to be an independent negative predictor of LRC, MFS, and OS.« less

Evaluating the Significance of CDK2-PELP1 Axis in Tumorigenesis and Hormone Therapy Resistance

DTIC Science & Technology

2010-02-01

them into the cell cycle. Analysis of cell lysates on a 4-12% gradient gel revealed that the phospho PELP1 antibody recognized both forms (Fig 3D...Hulin,M., Lidereau,R. and Bieche,I. Expression analysis of estrogen receptor alpha coregulators in breast carcinoma: evidence that NCOR1 expression...Pohl et al, 2003). This trial included 512 randomized patients wherein multivariate analysis revealed decreased p27 expression to be correlated

Spatial characterization of dissolved trace elements and heavy metals in the upper Han River (China) using multivariate statistical techniques.

PubMed

Li, Siyue; Zhang, Quanfa

2010-04-15

A data matrix (4032 observations), obtained during a 2-year monitoring period (2005-2006) from 42 sites in the upper Han River is subjected to various multivariate statistical techniques including cluster analysis, principal component analysis (PCA), factor analysis (FA), correlation analysis and analysis of variance to determine the spatial characterization of dissolved trace elements and heavy metals. Our results indicate that waters in the upper Han River are primarily polluted by Al, As, Cd, Pb, Sb and Se, and the potential pollutants include Ba, Cr, Hg, Mn and Ni. Spatial distribution of trace metals indicates the polluted sections mainly concentrate in the Danjiang, Danjiangkou Reservoir catchment and Hanzhong Plain, and the most contaminated river is in the Hanzhong Plain. Q-model clustering depends on geographical location of sampling sites and groups the 42 sampling sites into four clusters, i.e., Danjiang, Danjiangkou Reservoir region (lower catchment), upper catchment and one river in headwaters pertaining to water quality. The headwaters, Danjiang and lower catchment, and upper catchment correspond to very high polluted, moderate polluted and relatively low polluted regions, respectively. Additionally, PCA/FA and correlation analysis demonstrates that Al, Cd, Mn, Ni, Fe, Si and Sr are controlled by natural sources, whereas the other metals appear to be primarily controlled by anthropogenic origins though geogenic source contributing to them. 2009 Elsevier B.V. All rights reserved.

Influence of Pelvicaliceal Anatomy on Stone Clearance After Flexible Ureteroscopy and Holmium Laser Lithotripsy for Large Renal Stones.

PubMed

Inoue, Takaaki; Murota, Takashi; Okada, Shinsuke; Hamamoto, Shuzo; Muguruma, Kouei; Kinoshita, Hidefumi; Matsuda, Tadashi

2015-09-01

This study was performed to evaluate the impact of pelvicaliceal anatomy on stone clearance in patients with remnant fragments in the lower pole after flexible ureteroscopy and holmium laser lithotripsy (fURSL) for renal stones >15 mm. This retrospective study included 67 patients with radiopaque residual fragments (>2 mm) in the lower pole after fURSL for large renal stones (>15 mm). The preoperative infundibular length (IL), infundibular width (IW), infundibulopelvic angle (IPA), and caliceal pelvic height (CPH) were measured using intravenous urography. Multivariate analysis was performed to determine whether any of these measurements affected stone clearance. Of the 67 patients, 55 (82.1%) were stone free (SF) 3 months after fURSL. The anatomic factors significantly favorable for an SF status were a short IL, broad IW, wide IPA, and low CPH. On multivariate analysis, the IPA had a significant influence on an SF status after fURSL (p=0.010). An IPA <30° was a negative risk factor (p=0.019). Postoperative complications occurred in nine patients (13.4%), including Clavien grade I complications in two patients (2.9%), grade II in six patients (8.9%), and grade IIIa in one patient (1.8%). Almost all complications were minor. An IPA <30° is the only negative risk factor for stone clearance after fURSL for large renal stones according to our multivariate analysis. Additional studies are required to further evaluate the characteristics of the pelvicaliceal anatomy influencing stone clearance.

Analysis models for the estimation of oceanic fields

NASA Technical Reports Server (NTRS)

Carter, E. F.; Robinson, A. R.

1987-01-01

A general model for statistically optimal estimates is presented for dealing with scalar, vector and multivariate datasets. The method deals with anisotropic fields and treats space and time dependence equivalently. Problems addressed include the analysis, or the production of synoptic time series of regularly gridded fields from irregular and gappy datasets, and the estimate of fields by compositing observations from several different instruments and sampling schemes. Technical issues are discussed, including the convergence of statistical estimates, the choice of representation of the correlations, the influential domain of an observation, and the efficiency of numerical computations.

Turkish Student Teachers' Concerns about Teaching

ERIC Educational Resources Information Center

Boz, Yezdan

2008-01-01

The purpose of this study was to examine the teaching concerns of Turkish student teachers and how these concerns differ among year groups within the teacher education programme. Data were collected from 339 student teachers using the Teacher Concerns Checklist. Analysis of the data, including both descriptive statistics and multivariate analysis…

Individualism-Collectivism: Links to Occupational Plans and Work Values

ERIC Educational Resources Information Center

Hartung, Paul J.; Fouad, Nadya A.; Leong, Frederick T. L.; Hardin, Erin E.

2010-01-01

Individualism-collectivism (IC) constitutes a cultural variable thought to influence a wide variety of variables including career planning and decision making. To examine this possibility, college students (216 women, 106 men, 64% racial-ethnic minorities) responded to measures of IC, occupational plans, and work values. Multivariate analysis of…

Clinical characterisation of pneumonia caused by atypical pathogens combining classic and novel predictors.

PubMed

Masiá, M; Gutiérrez, F; Padilla, S; Soldán, B; Mirete, C; Shum, C; Hernández, I; Royo, G; Martin-Hidalgo, A

2007-02-01

The aim of this study was to characterise community-acquired pneumonia (CAP) caused by atypical pathogens by combining distinctive clinical and epidemiological features and novel biological markers. A population-based prospective study of consecutive patients with CAP included investigation of biomarkers of bacterial infection, e.g., procalcitonin, C-reactive protein and lipopolysaccharide-binding protein (LBP) levels. Clinical, radiological and laboratory data for patients with CAP caused by atypical pathogens were compared by univariate and multivariate analysis with data for patients with typical pathogens and patients from whom no organisms were identified. Two predictive scoring models were developed with the most discriminatory variables from multivariate analysis. Of 493 patients, 94 had CAP caused by atypical pathogens. According to multivariate analysis, patients with atypical pneumonia were more likely to have normal white blood cell counts, have repetitive air-conditioning exposure, be aged <65 years, have elevated aspartate aminotransferase levels, have been exposed to birds, and have lower serum levels of LBP. Two different scoring systems were developed that predicted atypical pathogens with sensitivities of 35.2% and 48.8%, and specificities of 93% and 91%, respectively. The combination of selected patient characteristics and laboratory data identified up to half of the cases of atypical pneumonia with high specificity, which should help clinicians to optimise initial empirical therapy for CAP.

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.

metaCCA: summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis.

PubMed

Cichonska, Anna; Rousu, Juho; Marttinen, Pekka; Kangas, Antti J; Soininen, Pasi; Lehtimäki, Terho; Raitakari, Olli T; Järvelin, Marjo-Riitta; Salomaa, Veikko; Ala-Korpela, Mika; Ripatti, Samuli; Pirinen, Matti

2016-07-01

A dominant approach to genetic association studies is to perform univariate tests between genotype-phenotype pairs. However, analyzing related traits together increases statistical power, and certain complex associations become detectable only when several variants are tested jointly. Currently, modest sample sizes of individual cohorts, and restricted availability of individual-level genotype-phenotype data across the cohorts limit conducting multivariate tests. We introduce metaCCA, a computational framework for summary statistics-based analysis of a single or multiple studies that allows multivariate representation of both genotype and phenotype. It extends the statistical technique of canonical correlation analysis to the setting where original individual-level records are not available, and employs a covariance shrinkage algorithm to achieve robustness.Multivariate meta-analysis of two Finnish studies of nuclear magnetic resonance metabolomics by metaCCA, using standard univariate output from the program SNPTEST, shows an excellent agreement with the pooled individual-level analysis of original data. Motivated by strong multivariate signals in the lipid genes tested, we envision that multivariate association testing using metaCCA has a great potential to provide novel insights from already published summary statistics from high-throughput phenotyping technologies. Code is available at https://github.com/aalto-ics-kepaco anna.cichonska@helsinki.fi or matti.pirinen@helsinki.fi Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

metaCCA: summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis

PubMed Central

Cichonska, Anna; Rousu, Juho; Marttinen, Pekka; Kangas, Antti J.; Soininen, Pasi; Lehtimäki, Terho; Raitakari, Olli T.; Järvelin, Marjo-Riitta; Salomaa, Veikko; Ala-Korpela, Mika; Ripatti, Samuli; Pirinen, Matti

2016-01-01

Motivation: A dominant approach to genetic association studies is to perform univariate tests between genotype-phenotype pairs. However, analyzing related traits together increases statistical power, and certain complex associations become detectable only when several variants are tested jointly. Currently, modest sample sizes of individual cohorts, and restricted availability of individual-level genotype-phenotype data across the cohorts limit conducting multivariate tests. Results: We introduce metaCCA, a computational framework for summary statistics-based analysis of a single or multiple studies that allows multivariate representation of both genotype and phenotype. It extends the statistical technique of canonical correlation analysis to the setting where original individual-level records are not available, and employs a covariance shrinkage algorithm to achieve robustness. Multivariate meta-analysis of two Finnish studies of nuclear magnetic resonance metabolomics by metaCCA, using standard univariate output from the program SNPTEST, shows an excellent agreement with the pooled individual-level analysis of original data. Motivated by strong multivariate signals in the lipid genes tested, we envision that multivariate association testing using metaCCA has a great potential to provide novel insights from already published summary statistics from high-throughput phenotyping technologies. Availability and implementation: Code is available at https://github.com/aalto-ics-kepaco Contacts: anna.cichonska@helsinki.fi or matti.pirinen@helsinki.fi Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27153689

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

Multivariate Tensor-based Morphometry on Surfaces: Application to Mapping Ventricular Abnormalities in HIV/AIDS

PubMed Central

Wang, Yalin; Zhang, Jie; Gutman, Boris; Chan, Tony F.; Becker, James T.; Aizenstein, Howard J.; Lopez, Oscar L.; Tamburo, Robert J.; Toga, Arthur W.; Thompson, Paul M.

2010-01-01

Here we developed a new method, called multivariate tensor-based surface morphometry (TBM), and applied it to study lateral ventricular surface differences associated with HIV/AIDS. Using concepts from differential geometry and the theory of differential forms, we created mathematical structures known as holomorphic one-forms, to obtain an efficient and accurate conformal parameterization of the lateral ventricular surfaces in the brain. The new meshing approach also provides a natural way to register anatomical surfaces across subjects, and improves on prior methods as it handles surfaces that branch and join at complex 3D junctions. To analyze anatomical differences, we computed new statistics from the Riemannian surface metrics - these retain multivariate information on local surface geometry. We applied this framework to analyze lateral ventricular surface morphometry in 3D MRI data from 11 subjects with HIV/AIDS and 8 healthy controls. Our method detected a 3D profile of surface abnormalities even in this small sample. Multivariate statistics on the local tensors gave better effect sizes for detecting group differences, relative to other TBM-based methods including analysis of the Jacobian determinant, the largest and smallest eigenvalues of the surface metric, and the pair of eigenvalues of the Jacobian matrix. The resulting analysis pipeline may improve the power of surface-based morphometry studies of the brain. PMID:19900560

Pressure ulcer incidence and Braden subscales: Retrospective cohort analysis in general wards of a Portuguese hospital.

PubMed

Sardo, Pedro Miguel Garcez; Guedes, Jenifer Adriana Domingues; Alvarelhão, José Joaquim Marques; Machado, Paulo Alexandre Puga; Melo, Elsa Maria Oliveira Pinheiro

2018-05-01

To study the influence of Braden subscales scores (at the first pressure ulcer risk assessment) on pressure ulcer incidence using a univariate and a multivariate time to event analysis. Retrospective cohort analysis of electronic health record database from adult patients admitted without pressure ulcer(s) to medical and surgical wards of a Portuguese hospital during 2012. The hazard ratio of developing a pressure ulcer during the length of inpatient stay was calculated by univariate Cox regression for each variable of interest and by multivariate Cox regression for the Braden subscales that were statistically significant. This study included a sample of 6552 participants. During the length of stay, 153 participants developed (at least) one pressure ulcer, giving a pressure ulcer incidence of 2.3%. The univariate time to event analysis showed that all Braden subscales, except "nutrition", were associated with the development of pressure ulcer. By multivariate analysis the scores for "mobility" and "activity" were independently predictive of the development of pressure ulcer(s) for all participants. (Im)"mobility" (the lack of ability to change and control body position) and (in)"activity" (the limited degree of physical activity) were the major risk factors assessed by Braden Scale for pressure ulcer development during the length of inpatient stay. Thus, the greatest efforts in managing pressure ulcer risk should be on "mobility" and "activity", independently of the total Braden Scale score. Copyright © 2018 Tissue Viability Society. Published by Elsevier Ltd. All rights reserved.

Use of Diuretics is not associated with mortality in patients admitted to the emergency department: results from a cross-sectional study.

PubMed

Haider, Dominik G; Lindner, Gregor; Wolzt, Michael; Leichtle, Alexander Benedikt; Fiedler, Georg-Martin; Sauter, Thomas C; Fuhrmann, Valentin; Exadaktylos, Aristomenis K

2016-02-01

Patients with diuretic therapy are at risk for drug-induced adverse reactions. It is unknown if presence of diuretic therapy at hospital emergency room admission is associated with mortality. In this cross sectional analysis, all emergency room patients 2010 and 2011 at the Inselspital Bern, Switzerland were included. A multivariable logistic regression model was performed to assess the association between pre-existing diuretic medication and 28 day mortality. Twenty-two thousand two hundred thirty-nine subjects were included in the analysis. A total of 8.5%, 2.5%, and 0.4% of patients used one, two, or three or more diuretics. In univariate analysis spironolactone, torasemide and chlortalidone use were associated with 28 day mortality (all p < 0.05). In a multivariate cox regression model no association with mortality was detectable (p > 0.05). No difference existed between patients with or without diuretic therapy (P > 0.05). Age and creatinine were independent risk factors for mortaliy (both p < 0.05). Use of diuretics is not associated with mortality in an unselected cohort of patients presenting in an emergency room.

Determinants of children's use of and time spent in fast-food and full-service restaurants.

PubMed

McIntosh, Alex; Kubena, Karen S; Tolle, Glen; Dean, Wesley; Kim, Mi-Jeong; Jan, Jie-Sheng; Anding, Jenna

2011-01-01

Identify parental and children's determinants of children's use of and time spent in fast-food (FF) and full-service (FS) restaurants. Analysis of cross-sectional data. Parents were interviewed by phone; children were interviewed in their homes. Parents and children ages 9-11 or 13-15 from 312 families were obtained via random-digit dialing. Dependent variables were the use of and the time spent in FF and FS restaurants by children. Determinants included parental work schedules, parenting style, and family meal ritual perceptions. Logistic regression was used for multivariate analysis of use of restaurants. Least squares regression was used for multivariate analysis of time spent in restaurants. Significance set at P < .05. Factors related to use of and time spent in FF and FS restaurants included parental work schedules, fathers' use of such restaurants, and children's time spent in the family automobile. Parenting style, parental work, parental eating habits and perceptions of family meals, and children's other uses of their time influence children's use of and time spent in FF and FS restaurants. Copyright © 2011 Society for Nutrition Education. Published by Elsevier Inc. All rights reserved.

Predictors of readmission after successful electroconvulsive therapy for depression: a chart review study.

PubMed

Uchida, Takahito; Kishimoto, Taishiro; Koreki, Akihiro; Nakao, Shigetsugu; Owada, Ai; Koizumi, Teruki; Saito, Atsuyuki; Sato, Minako; Sawada, Shinya; Matsuzaki, Ryuta; Petrides, Georgios; Mimura, Masaru

2016-11-01

The study aimed to identify the predictors for readmission after a successful electroconvulsive therapy (ECT) course. Medical charts of patients who received ECT for major depressive episodes were reviewed. Patients' demographic characteristics and treatment parameters, such as ECT charge, seizure duration, the number of ECT sessions and pharmacotherapy, were extracted. We compared differences between those who were readmitted after successful ECT within 6 and 12 months, versus those not readmitted. We also conducted a multivariate logistic regression analysis to identify the predictors for readmission. Out of 51 patients who were discharged after ECT, 27 patients met the inclusion criteria and were included in the analysis. Eight patients were readmitted within 6 months after discharge, and four more patients were readmitted during the next 6-month follow up. Comparing patients who were and were not readmitted, we found no significant differences between groups, including ECT parameters such as the number of ECT sessions, average charge and final charge. No predictors for readmission were found through multivariate analysis. Although patients who require higher ECT charge and more sessions seem to be prone to readmission, our dataset suggested that none of these types of ECT parameters were risk factors for readmission.

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…

Gas-water two-phase flow characterization with Electrical Resistance Tomography and Multivariate Multiscale Entropy analysis.

PubMed

Tan, Chao; Zhao, Jia; Dong, Feng

2015-03-01

Flow behavior characterization is important to understand gas-liquid two-phase flow mechanics and further establish its description model. An Electrical Resistance Tomography (ERT) provides information regarding flow conditions at different directions where the sensing electrodes implemented. We extracted the multivariate sample entropy (MSampEn) by treating ERT data as a multivariate time series. The dynamic experimental results indicate that the MSampEn is sensitive to complexity change of flow patterns including bubbly flow, stratified flow, plug flow and slug flow. MSampEn can characterize the flow behavior at different direction of two-phase flow, and reveal the transition between flow patterns when flow velocity changes. The proposed method is effective to analyze two-phase flow pattern transition by incorporating information of different scales and different spatial directions. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Application of multivariate statistical techniques in microbial ecology.

PubMed

Paliy, O; Shankar, V

2016-03-01

Recent advances in high-throughput methods of molecular analyses have led to an explosion of studies generating large-scale ecological data sets. In particular, noticeable effect has been attained in the field of microbial ecology, where new experimental approaches provided in-depth assessments of the composition, functions and dynamic changes of complex microbial communities. Because even a single high-throughput experiment produces large amount of data, powerful statistical techniques of multivariate analysis are well suited to analyse and interpret these data sets. Many different multivariate techniques are available, and often it is not clear which method should be applied to a particular data set. In this review, we describe and compare the most widely used multivariate statistical techniques including exploratory, interpretive and discriminatory procedures. We consider several important limitations and assumptions of these methods, and we present examples of how these approaches have been utilized in recent studies to provide insight into the ecology of the microbial world. Finally, we offer suggestions for the selection of appropriate methods based on the research question and data set structure. © 2016 John Wiley & Sons Ltd.

Multivariate analysis of the geochemistry and mineralogy of soils along two continental-scale transects in North America

USGS Publications Warehouse

Drew, L.J.; Grunsky, E.C.; Sutphin, D.M.; Woodruff, L.G.

2010-01-01

Soils collected in 2004 along two North American continental-scale transects were subjected to geochemical and mineralogical analyses. In previous interpretations of these analyses, data were expressed in weight percent and parts per million, and thus were subject to the effect of the constant-sum phenomenon. In a new approach to the data, this effect was removed by using centered log-ratio transformations to 'open' the mineralogical and geochemical arrays. Multivariate analyses, including principal component and linear discriminant analyses, of the centered log-ratio data reveal the effects of soil-forming processes, including soil parent material, weathering, and soil age, at the continental-scale of the data arrays that were not readily apparent in the more conventionally presented data. Linear discriminant analysis of the data arrays indicates that the majority of the soil samples collected along the transects can be more successfully classified with Level 1 ecological regional-scale classification by the soil geochemistry than soil mineralogy. A primary objective of this study is to discover and describe, in a parsimonious way, geochemical processes that are both independent and inter-dependent and manifested through compositional data including estimates of the elements and corresponding mineralogy. ?? 2010.

A power analysis for multivariate tests of temporal trend in species composition.

PubMed

Irvine, Kathryn M; Dinger, Eric C; Sarr, Daniel

2011-10-01

Long-term monitoring programs emphasize power analysis as a tool to determine the sampling effort necessary to effectively document ecologically significant changes in ecosystems. Programs that monitor entire multispecies assemblages require a method for determining the power of multivariate statistical models to detect trend. We provide a method to simulate presence-absence species assemblage data that are consistent with increasing or decreasing directional change in species composition within multiple sites. This step is the foundation for using Monte Carlo methods to approximate the power of any multivariate method for detecting temporal trends. We focus on comparing the power of the Mantel test, permutational multivariate analysis of variance, and constrained analysis of principal coordinates. We find that the power of the various methods we investigate is sensitive to the number of species in the community, univariate species patterns, and the number of sites sampled over time. For increasing directional change scenarios, constrained analysis of principal coordinates was as or more powerful than permutational multivariate analysis of variance, the Mantel test was the least powerful. However, in our investigation of decreasing directional change, the Mantel test was typically as or more powerful than the other models.

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.

Unsupervised pattern recognition methods in ciders profiling based on GCE voltammetric signals.

PubMed

Jakubowska, Małgorzata; Sordoń, Wanda; Ciepiela, Filip

2016-07-15

This work presents a complete methodology of distinguishing between different brands of cider and ageing degrees, based on voltammetric signals, utilizing dedicated data preprocessing procedures and unsupervised multivariate analysis. It was demonstrated that voltammograms recorded on glassy carbon electrode in Britton-Robinson buffer at pH 2 are reproducible for each brand. By application of clustering algorithms and principal component analysis visible homogenous clusters were obtained. Advanced signal processing strategy which included automatic baseline correction, interval scaling and continuous wavelet transform with dedicated mother wavelet, was a key step in the correct recognition of the objects. The results show that voltammetry combined with optimized univariate and multivariate data processing is a sufficient tool to distinguish between ciders from various brands and to evaluate their freshness. Copyright © 2016 Elsevier Ltd. All rights reserved.

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

Multivariate modelling of endophenotypes associated with the metabolic syndrome in Chinese twins.

PubMed

Pang, Z; Zhang, D; Li, S; Duan, H; Hjelmborg, J; Kruse, T A; Kyvik, K O; Christensen, K; Tan, Q

2010-12-01

The common genetic and environmental effects on endophenotypes related to the metabolic syndrome have been investigated using bivariate and multivariate twin models. This paper extends the pairwise analysis approach by introducing independent and common pathway models to Chinese twin data. The aim was to explore the common genetic architecture in the development of these phenotypes in the Chinese population. Three multivariate models including the full saturated Cholesky decomposition model, the common factor independent pathway model and the common factor common pathway model were fitted to 695 pairs of Chinese twins representing six phenotypes including BMI, total cholesterol, total triacylglycerol, fasting glucose, HDL and LDL. Performances of the nested models were compared with that of the full Cholesky model. Cross-phenotype correlation coefficients gave clear indication of common genetic or environmental backgrounds in the phenotypes. Decomposition of phenotypic correlation by the Cholesky model revealed that the observed phenotypic correlation among lipid phenotypes had genetic and unique environmental backgrounds. Both pathway models suggest a common genetic architecture for lipid phenotypes, which is distinct from that of the non-lipid phenotypes. The declining performance with model restriction indicates biological heterogeneity in development among some of these phenotypes. Our multivariate analyses revealed common genetic and environmental backgrounds for the studied lipid phenotypes in Chinese twins. Model performance showed that physiologically distinct endophenotypes may follow different genetic regulations.

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…

Two-sample tests and one-way MANOVA for multivariate biomarker data with nondetects.

PubMed

Thulin, M

2016-09-10

Testing whether the mean vector of a multivariate set of biomarkers differs between several populations is an increasingly common problem in medical research. Biomarker data is often left censored because some measurements fall below the laboratory's detection limit. We investigate how such censoring affects multivariate two-sample and one-way multivariate analysis of variance tests. Type I error rates, power and robustness to increasing censoring are studied, under both normality and non-normality. Parametric tests are found to perform better than non-parametric alternatives, indicating that the current recommendations for analysis of censored multivariate data may have to be revised. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

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.

Differentiation between borderline and benign ovarian tumors: combined analysis of MRI with tumor markers for large cystic masses (≥5 cm).

PubMed

Park, Sung Yoon; Oh, Young Taik; Jung, Dae Chul

2016-05-01

There is overlap in imaging features between borderline and benign ovarian tumors. To analyze diagnostic performance of magnetic resonance imaging (MRI) combined with tumor markers for differentiating borderline from benign ovarian tumor. Ninety-nine patient with MRI and surgically confirmed ovarian tumors 5 cm or larger (borderline, n = 37; benign, n = 62) were included. On MRI, tumor size, septal number (0; 1-4; 5 or more), and presence of solid portion such as papillary projection or septal thickening 0.5 cm or larger were investigated. Serum tumor markers (carbohydrate antigen 125 [CA 125] and CA 19-9) were recorded. Multivariate analysis was conducted for assessing whether combined MRI with tumor markers could differentiate borderline from benign tumor. The diagnostic performance was also analyzed. Incidence of solid portion was 67.6% (25/37) in borderline and 3.2% (2/62) in benign tumors (P < 0.05). In all patients, without combined analysis of MRI with tumor markers, multivariate analysis revealed solid portion (P < 0.001) and CA 125 (P = 0.039) were significant for predicting borderline tumors. When combined analysis of MRI with CA 125 ((i) the presence of solid portion or (ii) CA 125 > 44.1 U/mL with septal number ≥5 for borderline tumor) is incorporated to multivariate analysis, it was only significant (P = 0.001). The sensitivity, specificity, PPV, NPV, and accuracy of combined analysis of MRI with CA 125 were 89.1%, 91.9%, 86.8%, 93.4, and 90.9%, respectively. Combined analysis of MRI with CA 125 may allow better differentiation between borderline and benign ovarian tumor compared with MRI alone. © The Foundation Acta Radiologica 2015.

Particle analysis using laser ablation mass spectroscopy

DOEpatents

Parker, Eric P.; Rosenthal, Stephen E.; Trahan, Michael W.; Wagner, John S.

2003-09-09

The present invention provides a method of quickly identifying bioaerosols by class, even if the subject bioaerosol has not been previously encountered. The method begins by collecting laser ablation mass spectra from known particles. The spectra are correlated with the known particles, including the species of particle and the classification (e.g., bacteria). The spectra can then be used to train a neural network, for example using genetic algorithm-based training, to recognize each spectra and to recognize characteristics of the classifications. The spectra can also be used in a multivariate patch algorithm. Laser ablation mass specta from unknown particles can be presented as inputs to the trained neural net for identification as to classification. The description below first describes suitable intelligent algorithms and multivariate patch algorithms, then presents an example of the present invention including results.

Multivariate analysis of the volatile components in tobacco based on infrared-assisted extraction coupled to headspace solid-phase microextraction and gas chromatography-mass spectrometry.

PubMed

Yang, Yanqin; Pan, Yuanjiang; Zhou, Guojun; Chu, Guohai; Jiang, Jian; Yuan, Kailong; Xia, Qian; Cheng, Changhe

2016-11-01

A novel infrared-assisted extraction coupled to headspace solid-phase microextraction followed by gas chromatography with mass spectrometry method has been developed for the rapid determination of the volatile components in tobacco. The optimal extraction conditions for maximizing the extraction efficiency were as follows: 65 μm polydimethylsiloxane-divinylbenzene fiber, extraction time of 20 min, infrared power of 175 W, and distance between the infrared lamp and the headspace vial of 2 cm. Under the optimum conditions, 50 components were found to exist in all ten tobacco samples from different geographical origins. Compared with conventional water-bath heating and nonheating extraction methods, the extraction efficiency of infrared-assisted extraction was greatly improved. Furthermore, multivariate analysis including principal component analysis, hierarchical cluster analysis, and similarity analysis were performed to evaluate the chemical information of these samples and divided them into three classifications, including rich, moderate, and fresh flavors. The above-mentioned classification results were consistent with the sensory evaluation, which was pivotal and meaningful for tobacco discrimination. As a simple, fast, cost-effective, and highly efficient method, the infrared-assisted extraction coupled to headspace solid-phase microextraction technique is powerful and promising for distinguishing the geographical origins of the tobacco samples coupled to suitable chemometrics. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Risk factors associated with gastric cancer in Mexico: education, breakfast and chili.

PubMed

Trujillo Rivera, Alejandro; Sampieri, Clara Luz; Morales Romero, Jaime; Montero, Hilda; Acosta Mesa, Héctor Gabriel; Cruz Ramírez, Nicandro; Novoa Del Toro, Elva María; León Córdoba, Kenneth

2018-06-01

the aim of the study was to use a validated questionnaire to identify factors associated with the development of gastric cancer (GC) in the Mexican population. the study included cases and controls that were paired by sex and ± 10 years of age at diagnosis. In relation to cases, 46 patients with a confirmed histopathological diagnosis of adenocarcinoma-type GC, as reported in the hospital records, were selected, and 46 blood bank donors from the same hospital were included as controls. The previously validated Questionnaire to Find Factors Associated with Gastric Cancer (QUFA-GC©) was used to collect data. Odds ratio (OR) and 95% confidence interval (IC) were estimated via univariate analysis (paired OR). Multivariate analysis was performed by logistic regression. A decision tree was constructed using the J48 algorithm. an association was found by univariate analysis between GC risk and a lack of formal education, having smoked for ≥ 10 years, eating rapidly, consuming very hot food and drinks, a non-suitable breakfast within two hours of waking, pickled food and capsaicin. In contrast, a protective association against GC was found with taking recreational exercise and consuming fresh fruit and vegetables. No association was found between the development of GC and having an income that reflected poverty, using a refrigerator, perception of the omission of breakfast and time period of alcoholism. In the final multivariate analysis model, having no formal education (OR = 17.47, 95% CI = 5.17-76.69), consuming a non-suitable breakfast within two hours of waking (OR = 8.99, 95% CI = 2.85-35.50) and the consumption of capsaicin ˃ 29.9 mg capsaicin per day (OR = 3.77, 95% CI = 1.21-13.11) were factors associated with GC. an association was found by multivariate analysis between the presence of GC and education, type of breakfast and the consumption of capsaicin. These variables are susceptible to intervention and can be identified via the QUFA-GC ©.

Fast Genome-Wide QTL Association Mapping on Pedigree and Population Data.

PubMed

Zhou, Hua; Blangero, John; Dyer, Thomas D; Chan, Kei-Hang K; Lange, Kenneth; Sobel, Eric M

2017-04-01

Since most analysis software for genome-wide association studies (GWAS) currently exploit only unrelated individuals, there is a need for efficient applications that can handle general pedigree data or mixtures of both population and pedigree data. Even datasets thought to consist of only unrelated individuals may include cryptic relationships that can lead to false positives if not discovered and controlled for. In addition, family designs possess compelling advantages. They are better equipped to detect rare variants, control for population stratification, and facilitate the study of parent-of-origin effects. Pedigrees selected for extreme trait values often segregate a single gene with strong effect. Finally, many pedigrees are available as an important legacy from the era of linkage analysis. Unfortunately, pedigree likelihoods are notoriously hard to compute. In this paper, we reexamine the computational bottlenecks and implement ultra-fast pedigree-based GWAS analysis. Kinship coefficients can either be based on explicitly provided pedigrees or automatically estimated from dense markers. Our strategy (a) works for random sample data, pedigree data, or a mix of both; (b) entails no loss of power; (c) allows for any number of covariate adjustments, including correction for population stratification; (d) allows for testing SNPs under additive, dominant, and recessive models; and (e) accommodates both univariate and multivariate quantitative traits. On a typical personal computer (six CPU cores at 2.67 GHz), analyzing a univariate HDL (high-density lipoprotein) trait from the San Antonio Family Heart Study (935,392 SNPs on 1,388 individuals in 124 pedigrees) takes less than 2 min and 1.5 GB of memory. Complete multivariate QTL analysis of the three time-points of the longitudinal HDL multivariate trait takes less than 5 min and 1.5 GB of memory. The algorithm is implemented as the Ped-GWAS Analysis (Option 29) in the Mendel statistical genetics package, which is freely available for Macintosh, Linux, and Windows platforms from http://genetics.ucla.edu/software/mendel. © 2016 WILEY PERIODICALS, INC.

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

High Ki-67 Immunohistochemical Reactivity Correlates With Poor Prognosis in Bladder Carcinoma

PubMed Central

Luo, Yihuan; Zhang, Xin; Mo, Meile; Tan, Zhong; Huang, Lanshan; Zhou, Hong; Wang, Chunqin; Wei, Fanglin; Qiu, Xiaohui; He, Rongquan; Chen, Gang

2016-01-01

Abstract Ki-67 is considered as one of prime biomarkers to reflect cell proliferation and immunohistochemical Ki-67 staining has been widely applied in clinical pathology. To solve the widespread controversy whether Ki-67 reactivity significantly predicts clinical prognosis of bladder carcinoma (BC), we performed a comprehensive meta-analysis by combining results from different literature. A comprehensive search was conducted in the Chinese databases of WanFang, China National Knowledge Infrastructure and Chinese VIP as well as English databases of PubMed, ISI web of science, EMBASE, Science Direct, and Wiley online library. Independent studies linking Ki-67 to cancer-specific survival (CSS), disease-free survival (DFS), overall survival (OS), progression-free survival (PFS), and recurrence-free survival (RFS) were included in our meta-analysis. With the cut-off values literature provided, hazard ratio (HR) values between the survival distributions were extracted and later combined with STATA 12.0. In total, 76 studies (n = 13,053 patients) were eligible for the meta-analysis. It was indicated in either univariate or multivariate analysis for survival that high Ki-67 reactivity significantly predicted poor prognosis. In the univariate analysis, the combined HR for CSS, DFS, OS, PFS, and RFS were 2.588 (95% confidence interval [CI]: 1.623–4.127, P < 0.001), 2.697 (95%CI: 1.874–3.883, P < 0.001), 2.649 (95%CI: 1.632–4.300, P < 0.001), 3.506 (95%CI: 2.231–5.508, P < 0.001), and 1.792 (95%CI: 1.409–2.279, P < 0.001), respectively. The pooled HR of multivariate analysis for CSS, DFS, OS, PFS, and RFS were 1.868 (95%CI: 1.343–2.597, P < 0.001), 2.626 (95%CI: 2.089–3.301, P < 0.001), 1.104 (95%CI: 1.008–1.209, P = 0.032), 1.518 (95%CI: 1.299–1.773, P < 0.001), and 1.294 (95%CI: 1.203–1.392, P < 0.001), respectively. Subgroup analysis of univariate analysis by origin showed that Ki-67 reactivity significantly correlated with all 5 clinical outcome in Asian and European-American patients (P < 0.05). For multivariate analysis, however, the pooled results were only significant for DFS, OS, and RFS in Asian patients, for CSS, DFS, PFS, and RFS in European-American patients (P < 0.05). In the subgroup with low cut-off value (<20%), our meta-analysis indicated that high Ki-67 reactivity was significantly correlated with worsened CSS, DFS, OS, PFS, and RFS on univariate analysis (P < 0.05). For multivariate analysis, the meta-analysis of literature with low cut-off value (<20%) demonstrated that high Ki-67 reactivity predicted shorter DFS, PFS, and RFS in BC patients (P < 0.05). In the subgroup analysis of high cut-off value (≥20%), our meta-analysis indicated that high Ki-67 reactivity, in either univariate or multivariate analysis, significantly correlated with all five clinical outcomes in BC patients (P < 0.05). The meta-analysis indicates that high Ki-67 reactivity significantly correlates with deteriorated clinical outcomes in BC patients and that Ki-67 can be considered as an independent indicator for the prognosis by the meta-analyses of multivariate analysis. PMID:27082587

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.

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…

Multivariate analysis of morphological diversity among closely related Daucus species and subspecies in Tunisia

USDA-ARS?s Scientific Manuscript database

The genus Daucus includes about 20-25 species worldwide. Northern Africa represents a major center of diversity of Daucus, with Tunisia thought to contain 11 species and seven subspecies. We assessed morphological diversity from a Daucus germplasm collection of 103 accessions at the National Gene Ba...

Examining a Comprehensive Model of Disaster-Related Posttraumatic Stress Disorder in Systematically Studied Survivors of 10 Disasters

PubMed Central

Oliver, Julianne; Pandya, Anand

2012-01-01

Objectives. Using a comprehensive disaster model, we examined predictors of posttraumatic stress disorder (PTSD) in combined data from 10 different disasters. Methods. The combined sample included data from 811 directly exposed survivors of 10 disasters between 1987 and 1995. We used consistent methods across all 10 disaster samples, including full diagnostic assessment. Results. In multivariate analyses, predictors of PTSD were female gender, younger age, Hispanic ethnicity, less education, ever-married status, predisaster psychopathology, disaster injury, and witnessing injury or death; exposure through death or injury to friends or family members and witnessing the disaster aftermath did not confer additional PTSD risk. Intentionally caused disasters associated with PTSD in bivariate analysis did not independently predict PTSD in multivariate analysis. Avoidance and numbing symptoms represented a PTSD marker. Conclusions. Despite confirming some previous research findings, we found no associations between PTSD and disaster typology. Prospective research is needed to determine whether early avoidance and numbing symptoms identify individuals likely to develop PTSD later. Our findings may help identify at-risk populations for treatment research. PMID:22897543

Utility of Inflammatory Marker- and Nutritional Status-based Prognostic Factors for Predicting the Prognosis of Stage IV Gastric Cancer Patients Undergoing Non-curative Surgery.

PubMed

Mimatsu, Kenji; Fukino, Nobutada; Ogasawara, Yasuo; Saino, Yoko; Oida, Takatsugu

2017-08-01

The present study aimed to compare the utility of various inflammatory marker- and nutritional status-based prognostic factors, including many previous established prognostic factors, for predicting the prognosis of stage IV gastric cancer patients undergoing non-curative surgery. A total of 33 patients with stage IV gastric cancer who had undergone palliative gastrectomy and gastrojejunostomy were included in the study. Univariate and multivariate analyses were performed to evaluate the relationships between the mGPS, PNI, NLR, PLR, the CONUT, various clinicopathological factors and cancer-specific survival (CS). Among patients who received non-curative surgery, univariate analysis of CS identified the following significant risk factors: chemotherapy, mGPS and NLR, and multivariate analysis revealed that the mGPS was independently associated with CS. The mGPS was a more useful prognostic factor than the PNI, NLR, PLR and CONUT in patients undergoing non-curative surgery for stage IV gastric cancer. Copyright© 2017, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

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…

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.

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.

Plasma metabolic profiling analysis of nephrotoxicity induced by acyclovir using metabonomics coupled with multivariate data analysis.

PubMed

Zhang, Xiuxiu; Li, Yubo; Zhou, Huifang; Fan, Simiao; Zhang, Zhenzhu; Wang, Lei; Zhang, Yanjun

2014-08-01

Acyclovir (ACV) is an antiviral agent. However, its use is limited by adverse side effect, particularly by its nephrotoxicity. Metabonomics technology can provide essential information on the metabolic profiles of biofluids and organs upon drug administration. Therefore, in this study, mass spectrometry-based metabonomics coupled with multivariate data analysis was used to identify the plasma metabolites and metabolic pathways related to nephrotoxicity caused by intraperitoneal injection of low (50mg/kg) and high (100mg/kg) doses of acyclovir. Sixteen biomarkers were identified by metabonomics and nephrotoxicity results revealed the dose-dependent effect of acyclovir on kidney tissues. The present study showed that the top four metabolic pathways interrupted by acyclovir included the metabolisms of arachidonic acid, tryptophan, arginine and proline, and glycerophospholipid. This research proves the established metabonomic approach can provide information on changes in metabolites and metabolic pathways, which can be applied to in-depth research on the mechanism of acyclovir-induced kidney injury. Copyright © 2014 Elsevier B.V. All rights reserved.

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

PubMed Central

Grapov, Dmitry; Newman, John W.

2012-01-01

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

Bioinformatics Identification of Modules of Transcription Factor Binding Sites in Alzheimer's Disease-Related Genes by In Silico Promoter Analysis and Microarrays

PubMed Central

Augustin, Regina; Lichtenthaler, Stefan F.; Greeff, Michael; Hansen, Jens; Wurst, Wolfgang; Trümbach, Dietrich

2011-01-01

The molecular mechanisms and genetic risk factors underlying Alzheimer's disease (AD) pathogenesis are only partly understood. To identify new factors, which may contribute to AD, different approaches are taken including proteomics, genetics, and functional genomics. Here, we used a bioinformatics approach and found that distinct AD-related genes share modules of transcription factor binding sites, suggesting a transcriptional coregulation. To detect additional coregulated genes, which may potentially contribute to AD, we established a new bioinformatics workflow with known multivariate methods like support vector machines, biclustering, and predicted transcription factor binding site modules by using in silico analysis and over 400 expression arrays from human and mouse. Two significant modules are composed of three transcription factor families: CTCF, SP1F, and EGRF/ZBPF, which are conserved between human and mouse APP promoter sequences. The specific combination of in silico promoter and multivariate analysis can identify regulation mechanisms of genes involved in multifactorial diseases. PMID:21559189

[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.

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.

Blackberry wines mineral and heavy metal content determination after dry ashing: multivariate data analysis as a tool for fruit wine quality control.

PubMed

Amidžić Klarić, Daniela; Klarić, Ilija; Mornar, Ana; Velić, Darko; Velić, Natalija

2015-08-01

This study brings out the data on the content of 21 mineral and heavy metal in 15 blackberry wines made of conventionally and organically grown blackberries. The objective of this study was to classify the blackberry wine samples based on their mineral composition and the applied cultivation method of the starting raw material by using chemometric analysis. The metal content of Croatian blackberry wine samples was determined by AAS after dry ashing. The comparison between an organic and conventional group of investigated blackberry wines showed statistically significant difference in concentrations of Si and Li, where the organic group contained higher concentrations of these compounds. According to multivariate data analysis, the model based on the original metal content data set finally included seven original variables (K, Fe, Mn, Cu, Ba, Cd and Cr) and gave a satisfactory separation of two applied cultivation methods of the starting raw material.

Multivariate Strategies in Functional Magnetic Resonance Imaging

ERIC Educational Resources Information Center

Hansen, Lars Kai

2007-01-01

We discuss aspects of multivariate fMRI modeling, including the statistical evaluation of multivariate models and means for dimensional reduction. In a case study we analyze linear and non-linear dimensional reduction tools in the context of a "mind reading" predictive multivariate fMRI model.

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.

Cement Leakage in Percutaneous Vertebral Augmentation for Osteoporotic Vertebral Compression Fractures: Analysis of Risk Factors.

PubMed

Xie, Weixing; Jin, Daxiang; Ma, Hui; Ding, Jinyong; Xu, Jixi; Zhang, Shuncong; Liang, De

2016-05-01

The risk factors for cement leakage were retrospectively reviewed in 192 patients who underwent percutaneous vertebral augmentation (PVA). To discuss the factors related to the cement leakage in PVA procedure for the treatment of osteoporotic vertebral compression fractures. PVA is widely applied for the treatment of osteoporotic vertebral fractures. Cement leakage is a major complication of this procedure. The risk factors for cement leakage were controversial. A retrospective review of 192 patients who underwent PVA was conducted. The following data were recorded: age, sex, bone density, number of fractured vertebrae before surgery, number of treated vertebrae, severity of the treated vertebrae, operative approach, volume of injected bone cement, preoperative vertebral compression ratio, preoperative local kyphosis angle, intraosseous clefts, preoperative vertebral cortical bone defect, and ratio and type of cement leakage. To study the correlation between each factor and cement leakage ratio, bivariate regression analysis was employed to perform univariate analysis, whereas multivariate linear regression analysis was employed to perform multivariate analysis. The study included 192 patients (282 treated vertebrae), and cement leakage occurred in 100 vertebrae (35.46%). The vertebrae with preoperative cortical bone defects generally exhibited higher cement leakage ratio, and the leakage is typically type C. Vertebrae with intact cortical bones before the procedure tend to experience type S leakage. Univariate analysis showed that patient age, bone density, number of fractured vertebrae before surgery, and vertebral cortical bone were associated with cement leakage ratio (P<0.05). Multivariate analysis showed that the main factors influencing bone cement leakage are bone density and vertebral cortical bone defect, with standardized partial regression coefficients of -0.085 and 0.144, respectively. High bone density and vertebral cortical bone defect are independent risk factors associated with bone cement leakage.

Interaction of Soil Heavy Metal Pollution with Industrialisation and the Landscape Pattern in Taiyuan City, China

PubMed Central

Liu, Yong; Su, Chao; Zhang, Hong; Li, Xiaoting; Pei, Jingfei

2014-01-01

Many studies indicated that industrialization and urbanization caused serious soil heavy metal pollution from industrialized age. However, fewer previous studies have conducted a combined analysis of the landscape pattern, urbanization, industrialization, and heavy metal pollution. This paper was aimed at exploring the relationships of heavy metals in the soil (Pb, Cu, Ni, As, Cd, Cr, Hg, and Zn) with landscape pattern, industrialisation, urbanisation in Taiyuan city using multivariate analysis. The multivariate analysis included correlation analysis, analysis of variance (ANOVA), independent-sample T test, and principal component analysis (PCA). Geographic information system (GIS) was also applied to determine the spatial distribution of the heavy metals. The spatial distribution maps showed that the heavy metal pollution of the soil was more serious in the centre of the study area. The results of the multivariate analysis indicated that the correlations among heavy metals were significant, and industrialisation could significantly affect the concentrations of some heavy metals. Landscape diversity showed a significant negative correlation with the heavy metal concentrations. The PCA showed that a two-factor model for heavy metal pollution, industrialisation, and the landscape pattern could effectively demonstrate the relationships between these variables. The model explained 86.71% of the total variance of the data. Moreover, the first factor was mainly loaded with the comprehensive pollution index (P), and the second factor was primarily loaded with landscape diversity and dominance (H and D). An ordination of 80 samples could show the pollution pattern of all the samples. The results revealed that local industrialisation caused heavy metal pollution of the soil, but such pollution could respond negatively to the landscape pattern. The results of the study could provide a basis for agricultural, suburban, and urban planning. PMID:25251460

Interaction of soil heavy metal pollution with industrialisation and the landscape pattern in Taiyuan city, China.

PubMed

Liu, Yong; Su, Chao; Zhang, Hong; Li, Xiaoting; Pei, Jingfei

2014-01-01

Many studies indicated that industrialization and urbanization caused serious soil heavy metal pollution from industrialized age. However, fewer previous studies have conducted a combined analysis of the landscape pattern, urbanization, industrialization, and heavy metal pollution. This paper was aimed at exploring the relationships of heavy metals in the soil (Pb, Cu, Ni, As, Cd, Cr, Hg, and Zn) with landscape pattern, industrialisation, urbanisation in Taiyuan city using multivariate analysis. The multivariate analysis included correlation analysis, analysis of variance (ANOVA), independent-sample T test, and principal component analysis (PCA). Geographic information system (GIS) was also applied to determine the spatial distribution of the heavy metals. The spatial distribution maps showed that the heavy metal pollution of the soil was more serious in the centre of the study area. The results of the multivariate analysis indicated that the correlations among heavy metals were significant, and industrialisation could significantly affect the concentrations of some heavy metals. Landscape diversity showed a significant negative correlation with the heavy metal concentrations. The PCA showed that a two-factor model for heavy metal pollution, industrialisation, and the landscape pattern could effectively demonstrate the relationships between these variables. The model explained 86.71% of the total variance of the data. Moreover, the first factor was mainly loaded with the comprehensive pollution index (P), and the second factor was primarily loaded with landscape diversity and dominance (H and D). An ordination of 80 samples could show the pollution pattern of all the samples. The results revealed that local industrialisation caused heavy metal pollution of the soil, but such pollution could respond negatively to the landscape pattern. The results of the study could provide a basis for agricultural, suburban, and urban planning.

Effects of intranasal oxytocin on symptoms of schizophrenia: A multivariate Bayesian meta-analysis.

PubMed

Williams, Donald R; Bürkner, Paul-Christian

2017-01-01

Schizophrenia is a heterogeneous disorder in which psychiatric symptoms are classified into two general subgroups-positive and negative symptoms. Current antipsychotic drugs are effective for treating positive symptoms, whereas negative symptoms are less responsive. Since the neuropeptide oxytocin (OT) has been shown to mediate social behavior in animals and humans, it has been used as an experimental therapeutic for treating schizophrenia and in particular negative symptoms which includes social deficits. Through eight randomized controlled trials (RCTs) and three meta-analyses, evidence for an effect of intranasal OT (IN-OT) has been inconsistent. We therefore conducted an updated meta-analysis that offers several advantages when compared to those done previously: (1) We used a multivariate analysis which allows for comparisons between symptoms and accounts for correlations between symptoms; (2) We controlled for baseline scores; (3) We used a fully Bayesian framework that allows for assessment of evidence in favor of the null hypothesis using Bayes factors; and (4) We addressed inconsistencies in the primary studies and previous meta-analyses. Eight RCTs (n=238) were included in the present study and we found that oxytocin did not improve any aspect of symptomology in schizophrenic patients and there was moderate evidence in favor of the null (no effect of oxytocin) for negative symptoms. Multivariate comparisons between symptom types revealed that oxytocin was not especially beneficial for treating negative symptoms. The effect size estimates were not moderated, publication bias was absent, and our estimates were robust to sensitivity analyses. These results suggest that IN-OT is not an effective therapeutic for schizophrenia. Copyright © 2016 Elsevier Ltd. All rights reserved.

Predicting microbiologically defined infection in febrile neutropenic episodes in children: global individual participant data multivariable meta-analysis

PubMed Central

Phillips, Robert S; Sung, Lillian; Amman, Roland A; Riley, Richard D; Castagnola, Elio; Haeusler, Gabrielle M; Klaassen, Robert; Tissing, Wim J E; Lehrnbecher, Thomas; Chisholm, Julia; Hakim, Hana; Ranasinghe, Neil; Paesmans, Marianne; Hann, Ian M; Stewart, Lesley A

2016-01-01

Background: Risk-stratified management of fever with neutropenia (FN), allows intensive management of high-risk cases and early discharge of low-risk cases. No single, internationally validated, prediction model of the risk of adverse outcomes exists for children and young people. An individual patient data (IPD) meta-analysis was undertaken to devise one. Methods: The ‘Predicting Infectious Complications in Children with Cancer' (PICNICC) collaboration was formed by parent representatives, international clinical and methodological experts. Univariable and multivariable analyses, using random effects logistic regression, were undertaken to derive and internally validate a risk-prediction model for outcomes of episodes of FN based on clinical and laboratory data at presentation. Results: Data came from 22 different study groups from 15 countries, of 5127 episodes of FN in 3504 patients. There were 1070 episodes in 616 patients from seven studies available for multivariable analysis. Univariable analyses showed associations with microbiologically defined infection (MDI) in many items, including higher temperature, lower white cell counts and acute myeloid leukaemia, but not age. Patients with osteosarcoma/Ewings sarcoma and those with more severe mucositis were associated with a decreased risk of MDI. The predictive model included: malignancy type, temperature, clinically ‘severely unwell', haemoglobin, white cell count and absolute monocyte count. It showed moderate discrimination (AUROC 0.723, 95% confidence interval 0.711–0.759) and good calibration (calibration slope 0.95). The model was robust to bootstrap and cross-validation sensitivity analyses. Conclusions: This new prediction model for risk of MDI appears accurate. It requires prospective studies assessing implementation to assist clinicians and parents/patients in individualised decision making. PMID:26954719

Multivariate analysis in the pharmaceutical industry: enabling process understanding and improvement in the PAT and QbD era.

PubMed

Ferreira, Ana P; Tobyn, Mike

2015-01-01

In the pharmaceutical industry, chemometrics is rapidly establishing itself as a tool that can be used at every step of product development and beyond: from early development to commercialization. This set of multivariate analysis methods allows the extraction of information contained in large, complex data sets thus contributing to increase product and process understanding which is at the core of the Food and Drug Administration's Process Analytical Tools (PAT) Guidance for Industry and the International Conference on Harmonisation's Pharmaceutical Development guideline (Q8). This review is aimed at providing pharmaceutical industry professionals an introduction to multivariate analysis and how it is being adopted and implemented by companies in the transition from "quality-by-testing" to "quality-by-design". It starts with an introduction to multivariate analysis and the two methods most commonly used: principal component analysis and partial least squares regression, their advantages, common pitfalls and requirements for their effective use. That is followed with an overview of the diverse areas of application of multivariate analysis in the pharmaceutical industry: from the development of real-time analytical methods to definition of the design space and control strategy, from formulation optimization during development to the application of quality-by-design principles to improve manufacture of existing commercial products.

Men and women show similar survival outcome in stage IV breast cancer.

PubMed

Wu, San-Gang; Zhang, Wen-Wen; Liao, Xu-Lin; Sun, Jia-Yuan; Li, Feng-Yan; Su, Jing-Jun; He, Zhen-Yu

2017-08-01

To evaluate the clinicopathological features, patterns of distant metastases, and survival outcome between stage IV male breast cancer (MBC) and female breast cancer (FBC). Patients diagnosed with stage IV MBC and FBC between 2010 and 2013 were included using the Surveillance, Epidemiology, and End Results program. Univariate and multivariate Cox regression analyses were used to analyze risk factors for overall survival (OS). A total of 4997 patients were identified, including 60 MBC and 4937 FBC. Compared with FBC, patients with MBC were associated with a significantly higher rate of estrogen receptor-positive, progesterone receptor-positive, unmarried, lung metastases, and a lower frequency of liver metastases. Univariate and multivariate analyses showed no significant difference in OS between MBC and FBC. In the propensity score-matched population, there was also no difference in survival between MBC and FBC. Multivariate analysis of MBC showed that OS was longer for patients aged 50-69 years and with estrogen receptor-positive disease. There was no significant difference in survival outcome between stage IV MBC and FBC, but significant differences in clinicopathological features and patterns of metastases between the genders. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

PubMed

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

2016-11-30

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

Quantitative Outline-based Shape Analysis and Classification of Planetary Craterforms using Supervised Learning Models

NASA Astrophysics Data System (ADS)

Slezak, Thomas Joseph; Radebaugh, Jani; Christiansen, Eric

2017-10-01

The shapes of craterform morphology on planetary surfaces provides rich information about their origins and evolution. While morphologic information provides rich visual clues to geologic processes and properties, the ability to quantitatively communicate this information is less easily accomplished. This study examines the morphology of craterforms using the quantitative outline-based shape methods of geometric morphometrics, commonly used in biology and paleontology. We examine and compare landforms on planetary surfaces using shape, a property of morphology that is invariant to translation, rotation, and size. We quantify the shapes of paterae on Io, martian calderas, terrestrial basaltic shield calderas, terrestrial ash-flow calderas, and lunar impact craters using elliptic Fourier analysis (EFA) and the Zahn and Roskies (Z-R) shape function, or tangent angle approach to produce multivariate shape descriptors. These shape descriptors are subjected to multivariate statistical analysis including canonical variate analysis (CVA), a multiple-comparison variant of discriminant analysis, to investigate the link between craterform shape and classification. Paterae on Io are most similar in shape to terrestrial ash-flow calderas and the shapes of terrestrial basaltic shield volcanoes are most similar to martian calderas. The shapes of lunar impact craters, including simple, transitional, and complex morphology, are classified with a 100% rate of success in all models. Multiple CVA models effectively predict and classify different craterforms using shape-based identification and demonstrate significant potential for use in the analysis of planetary surfaces.

Multivariate analysis of volatile compounds detected by headspace solid-phase microextraction/gas chromatography: A tool for sensory classification of cork stoppers.

PubMed

Prat, Chantal; Besalú, Emili; Bañeras, Lluís; Anticó, Enriqueta

2011-06-15

The volatile fraction of aqueous cork macerates of tainted and non-tainted agglomerate cork stoppers was analysed by headspace solid-phase microextraction (HS-SPME)/gas chromatography. Twenty compounds containing terpenoids, aliphatic alcohols, lignin-related compounds and others were selected and analysed in individual corks. Cork stoppers were previously classified in six different classes according to sensory descriptions including, 2,4,6-trichloroanisole taint and other frequent, non-characteristic odours found in cork. A multivariate analysis of the chromatographic data of 20 selected chemical compounds using linear discriminant analysis models helped in the differentiation of the a priori made groups. The discriminant model selected five compounds as the best combination. Selected compounds appear in the model in the following order; 2,4,6 TCA, fenchyl alcohol, 1-octen-3-ol, benzyl alcohol and benzothiazole. Unfortunately, not all six a priori differentiated sensory classes were clearly discriminated in the model, probably indicating that no measurable differences exist in the chromatographic data for some categories. The predictive analyses of a refined model in which two sensory classes were fused together resulted in a good classification. Prediction rates of control (non-tainted), TCA, musty-earthy-vegetative, vegetative and chemical descriptions were 100%, 100%, 85%, 67.3% and 100%, respectively, when the modified model was used. The multivariate analysis of chromatographic data will help in the classification of stoppers and provide a perfect complement to sensory analyses. Copyright © 2010 Elsevier Ltd. All rights reserved.

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.

Fast and simultaneously determination of light and heavy rare earth elements in monazite using combination of ultraviolet-visible spectrophotometry and multivariate analysis

NASA Astrophysics Data System (ADS)

Anggraeni, Anni; Arianto, Fernando; Mutalib, Abdul; Pratomo, Uji; Bahti, Husein H.

2017-05-01

Rare Earth Elements (REE) are elements that a lot of function for life, such as metallurgy, optical devices, and manufacture of electronic devices. Sources of REE is present in the mineral, in which each element has similar properties. Currently, to determining the content of REE is used instruments such as ICP-OES, ICP-MS, XRF, and HPLC. But in each instruments, there are still have some weaknesses. Therefore we need an alternative analytical method for the determination of rare earth metal content, one of them is by a combination of UV-Visible spectrophotometry and multivariate analysis, including Principal Component Analysis (PCA), Principal Component Regression (PCR), and Partial Least Square Regression (PLS). The purpose of this experiment is to determine the content of light and medium rare earth elements in the mineral monazite without chemical separation by using a combination of multivariate analysis and UV-Visible spectrophotometric methods. Training set created 22 variations of concentration and absorbance was measured using a UV-Vis spectrophotometer, then the data is processed by PCA, PCR, and PLSR. The results were compared and validated to obtain the mathematical equation with the smallest percent error. From this experiment, mathematical equation used PLS methods was better than PCR after validated, which has RMSE value for La, Ce, Pr, Nd, Gd, Sm, Eu, and Tb respectively 0.095; 0.573; 0.538; 0.440; 3.387; 1.240; 1.870; and 0.639.

Anthropometric profile of combat athletes via multivariate analysis.

PubMed

Burdukiewicz, Anna; Pietraszewska, Jadwiga; Stachoń, Aleksandra; Andrzejewska, Justyna

2017-11-07

Athletic success is a complex phenotype influenced by multiple factors, from sport-specific skills to anthropometric characteristics. Considering the latter, the literature has repeatedly indicated that athletes possess distinct physical characteristics depending on the practiced discipline. The aim of the present study was to apply univariate and multivariate methods to assess a wide range of morphometric and somatotypic characteristics in male combat athletes. Biometric data were obtained from 206 male university-level practitioners of judo, jiu-jitsu, karate, kickboxing, taekwondo, and wrestling. Measures included height- and length-based variables, breadths, circumferences, and skinfolds. Body proportions and somatotype, using Sheldon's method of somatotopy as modified by Heath and Carter, were then determined. Body fat percentage was assessed by bioelectrical impedance analysis using tetrapolar hand-to-foot electrodes. Data were subjected to a wide array of statistical analysis. The results show between-group differences in the magnitudes of the analyzed characteristics. While mesomorphy was the dominant component of each group somatotype, enhanced ectomorphy was observed in those disciplines that require a high level of agility. Principal component analysis reduced the multivariate dimensionality of the data to three components (characterizing body size, height-based measures, and the anthropometric structure of the upper extremities) that explained the majority of data variance. The development of a sport-specific anthropometric profile via height- and mass-based and morphometric and somatotypic variables can aid in the design of training protocols and the identification of athlete markers as well as serve as a diagnostic criterion in predicting combat athlete performance.

Source Evaluation and Trace Metal Contamination in Benthic Sediments from Equatorial Ecosystems Using Multivariate Statistical Techniques

PubMed Central

Benson, Nsikak U.; Asuquo, Francis E.; Williams, Akan B.; Essien, Joseph P.; Ekong, Cyril I.; Akpabio, Otobong; Olajire, Abaas A.

2016-01-01

Trace metals (Cd, Cr, Cu, Ni and Pb) concentrations in benthic sediments were analyzed through multi-step fractionation scheme to assess the levels and sources of contamination in estuarine, riverine and freshwater ecosystems in Niger Delta (Nigeria). The degree of contamination was assessed using the individual contamination factors (ICF) and global contamination factor (GCF). Multivariate statistical approaches including principal component analysis (PCA), cluster analysis and correlation test were employed to evaluate the interrelationships and associated sources of contamination. The spatial distribution of metal concentrations followed the pattern Pb>Cu>Cr>Cd>Ni. Ecological risk index by ICF showed significant potential mobility and bioavailability for Cu, Cu and Ni. The ICF contamination trend in the benthic sediments at all studied sites was Cu>Cr>Ni>Cd>Pb. The principal component and agglomerative clustering analyses indicate that trace metals contamination in the ecosystems was influenced by multiple pollution sources. PMID:27257934

Pneumonia in multiple injured patients: a prospective controlled trial on early prediction using clinical and immunological parameters.

PubMed

Andermahr, J; Greb, A; Hensler, T; Helling, H J; Bouillon, B; Sauerland, S; Rehm, K E; Neugebauer, E

2002-05-01

In a prospective trial 266 multiple injured patients were included to evaluate clinical risk factors and immune parameters related to pneumonia. Clinical and humoral parameters were assessed and multivariate analysis performed. The multivariate analysis (odds ratio with 95% confidence interval (CI)) revealed male gender (3.65), traumatic brain injury (TBI) (2.52), thorax trauma (AIS(thorax) > or = 3) (2.05), antibiotic prophylaxis (1.30), injury severity score (ISS) (1.03 per ISS point) and the age (1.02 per year) as risk factors for pneumonia. The main pathogens were Acinetobacter Baumannii (40%) and Staphylococcus aureus (25%). A tendency towards higher Procalcitonin (PCT) and Interleukin (IL)-6 levels two days after trauma was observed for pneumonia patients. The immune parameters (PCT, IL-6, IL-10, soluble tumor necrosis factor p-55 and p-75) could not confirm the diagnosis of pneumonia earlier than the clinical parameters.

High prevalence of suicide risk in people living with HIV: who is at higher risk?

PubMed

Passos, Susane Müller Klug; Souza, Luciano Dias de Mattos; Spessato, Bárbara Coiro

2014-01-01

A cross-sectional study was developed to evaluate suicide risk and associated factors in HIV/AIDS patients at a regional reference center for the treatment of HIV/AIDS in southern Brazil. We assessed 211 patients in regard to suicide risk, clinical and sociodemographic characteristics, drug use, depression, and anxiety. Suicide risk was assessed with Mini International Neuropsychiatric Interview, Module C. Multivariate analysis was performed using Poisson regression. Of the total sample, 34.1% were at risk of suicide. In the multivariate analysis, the following variables were independently associated with suicide risk: female gender; age up to 47 years; unemployment; indicative of anxiety; indicative of depression; and abuse or addiction on psychoactive substances. Suicide risk is high in this population. Psychosocial factors should be included in the physical and clinical evaluation, given their strong association with suicide risk.

Career and lifestyle satisfaction among surgeons: what really matters? The National Lifestyles in Surgery Today Survey.

PubMed

Troppmann, Kathrin M; Palis, Bryan E; Goodnight, James E; Ho, Hung S; Troppmann, Christoph

2009-08-01

Optimizing recruitment of the next surgical generation is paramount. Unfortunately, many nonsurgeons perceive surgeons' lifestyle as undesirable. It is unknown, however, whether the surgeons-important opinion makers about their profession-are indeed dissatisfied. We analyzed responses to a survey mailed to all surgeons who were certified by the American Board of Surgery in 1988, 1992, 1996, 2000, and 2004. We performed multivariate analyses to study career dissatisfaction and inability to achieve work-life balance, while adjusting for practice characteristics, demographics, and satisfaction with reimbursement. A total of 895 (25.5%) surgeons responded: mean age was 46 years; 80% were men; 88% were married; 86% had children; 45% were general surgeons; 72% were in urban practice; and 83% were in nonuniversity practice. Surgeons worked 64 hours per week; ideally, they would prefer to work 50 hours per week (median). Fifteen percent were dissatisfied with their careers. On multivariate analysis, significant (p < 0.05) risk factors were nonuniversity practice (odds ratio [OR] 3.3) and dissatisfaction with reimbursement (OR 5.9). Forty percent would not recommend a surgical career to their own children. On multivariate analysis, significant risk factors were nonuniversity practice (OR 2.5) and dissatisfaction with reimbursement (OR 3.4). In all, 33.5% did not achieve work-life balance. On multivariate analysis, dissatisfaction with reimbursement (OR 3.0) was a significant risk factor. Respondents' lives could be improved by "limiting emergency call" (77%), "diminishing litigation" (92%), and "improving reimbursement" (94%). Most surgeons are satisfied with their careers. Areas in need of improvement, particularly for nonuniversity surgeons, include reimbursement, work hours, and litigation. Strong local and national advocacy may not only improve career satisfaction, but could also render the profession more attractive for those contemplating a surgical career.

Obstructive urination problems after high-dose-rate brachytherapy boost treatment for prostate cancer are avoidable.

PubMed

Kragelj, Borut

2016-03-01

Aiming at improving treatment individualization in patients with prostate cancer treated with combination of external beam radiotherapy and high-dose-rate brachytherapy to boost the dose to prostate (HDRB-B), the objective was to evaluate factors that have potential impact on obstructive urination problems (OUP) after HDRB-B. In the follow-up study 88 patients consecutively treated with HDRB-B at the Institute of Oncology Ljubljana in the period 2006-2011 were included. The observed outcome was deterioration of OUP (DOUP) during the follow-up period longer than 1 year. Univariate and multivariate relationship analysis between DOUP and potential risk factors (treatment factors, patients' characteristics) was carried out by using binary logistic regression. ROC curve was constructed on predicted values and the area under the curve (AUC) calculated to assess the performance of the multivariate model. Analysis was carried out on 71 patients who completed 3 years of follow-up. DOUP was noted in 13/71 (18.3%) of them. The results of multivariate analysis showed statistically significant relationship between DOUP and anti-coagulation treatment (OR 4.86, 95% C.I. limits: 1.21-19.61, p = 0.026). Also minimal dose received by 90% of the urethra volume was close to statistical significance (OR = 1.23; 95% C.I. limits: 0.98-1.07, p = 0.099). The value of AUC was 0.755. The study emphasized the relationship between DOUP and anticoagulation treatment, and suggested the multivariate model with fair predictive performance. This model potentially enables a reduction of DOUP after HDRB-B. It supports the belief that further research should be focused on urethral sphincter as a critical structure for OUP.

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.

Multivariate pattern recognition for diagnosis and prognosis in clinical neuroimaging: state of the art, current challenges and future trends.

PubMed

Haller, Sven; Lovblad, Karl-Olof; Giannakopoulos, Panteleimon; Van De Ville, Dimitri

2014-05-01

Many diseases are associated with systematic modifications in brain morphometry and function. These alterations may be subtle, in particular at early stages of the disease progress, and thus not evident by visual inspection alone. Group-level statistical comparisons have dominated neuroimaging studies for many years, proving fascinating insight into brain regions involved in various diseases. However, such group-level results do not warrant diagnostic value for individual patients. Recently, pattern recognition approaches have led to a fundamental shift in paradigm, bringing multivariate analysis and predictive results, notably for the early diagnosis of individual patients. We review the state-of-the-art fundamentals of pattern recognition including feature selection, cross-validation and classification techniques, as well as limitations including inter-individual variation in normal brain anatomy and neurocognitive reserve. We conclude with the discussion of future trends including multi-modal pattern recognition, multi-center approaches with data-sharing and cloud-computing.

A multiplexed analysis approach identifies new association of inflammatory proteins in patients with overactive bladder

PubMed Central

Ma, Emily; Vetter, Joel; Bliss, Laura; Lai, H. Henry; Mysorekar, Indira U.

2016-01-01

Overactive bladder (OAB) is a common debilitating bladder condition with unknown etiology and limited diagnostic modalities. Here, we explored a novel high-throughput and unbiased multiplex approach with cellular and molecular components in a well-characterized patient cohort to identify biomarkers that could be reliably used to distinguish OAB from controls or provide insights into underlying etiology. As a secondary analysis, we determined whether this method could discriminate between OAB and other chronic bladder conditions. We analyzed plasma samples from healthy volunteers (n = 19) and patients diagnosed with OAB, interstitial cystitis/bladder pain syndrome (IC/BPS), or urinary tract infections (UTI; n = 51) for proinflammatory, chemokine, cytokine, angiogenesis, and vascular injury factors using Meso Scale Discovery (MSD) analysis and urinary cytological analysis. Wilcoxon rank-sum tests were used to perform univariate and multivariate comparisons between patient groups (controls, OAB, IC/BPS, and UTI). Multivariate logistic regression models were fit for each MSD analyte on 1) OAB patients and controls, 2) OAB and IC/BPS patients, and 3) OAB and UTI patients. Age, race, and sex were included as independent variables in all multivariate analysis. Receiver operating characteristic (ROC) curves were generated to determine the diagnostic potential of a given analyte. Our findings demonstrate that five analytes, i.e., interleukin 4, TNF-α, macrophage inflammatory protein-1β, serum amyloid A, and Tie2 can reliably differentiate OAB relative to controls and can be used to distinguish OAB from the other conditions. Together, our pilot study suggests a molecular imbalance in inflammatory proteins may contribute to OAB pathogenesis. PMID:27029431

Predictors of Major Depression and Posttraumatic Stress Disorder Following Traumatic Brain Injury: A Systematic Review and Meta-Analysis.

PubMed

Cnossen, Maryse C; Scholten, Annemieke C; Lingsma, Hester F; Synnot, Anneliese; Haagsma, Juanita; Steyerberg, Prof Ewout W; Polinder, Suzanne

2017-01-01

Although major depressive disorder (MDD) and posttraumatic stress disorder (PTSD) are prevalent after traumatic brain injury (TBI), little is known about which patients are at risk for developing them. The authors systematically reviewed the literature on predictors and multivariable models for MDD and PTSD after TBI. The authors included 26 observational studies. MDD was associated with female gender, preinjury depression, postinjury unemployment, and lower brain volume, whereas PTSD was related to shorter posttraumatic amnesia, memory of the traumatic event, and early posttraumatic symptoms. Risk of bias ratings for most studies were acceptable, although studies that developed a multivariable model suffered from methodological shortcomings.

Localization of genes involved in the metabolic syndrome using multivariate linkage analysis.

PubMed

Olswold, Curtis; de Andrade, Mariza

2003-12-31

There are no well accepted criteria for the diagnosis of the metabolic syndrome. However, the metabolic syndrome is identified clinically by the presence of three or more of these five variables: larger waist circumference, higher triglyceride levels, lower HDL-cholesterol concentrations, hypertension, and impaired fasting glucose. We use sets of two or three variables, which are available in the Framingham Heart Study data set, to localize genes responsible for this syndrome using multivariate quantitative linkage analysis. This analysis demonstrates the applicability of using multivariate linkage analysis and how its use increases the power to detect linkage when genes are involved in the same disease mechanism.

Pattern of tumour growth of the primary colon cancer predicts long-term outcome after resection of liver metastases.

PubMed

Spelt, Lidewij; Sasor, Agata; Ansari, Daniel; Andersson, Roland

2016-10-01

To identify significant predictive factors for overall survival (OS) and disease-free survival (DFS) after liver resection for colon cancer metastases, with special focus on features of the primary colon cancer, such as lymph node ratio (LNR), vascular invasion, and perineural invasion. Patients operated for colonic cancer liver metastases between 2006 and 2014 were included. Details on patient characteristics, the primary colon cancer operation and metastatic disease were collected. Multivariate analysis was performed to select predictive variables for OS and DFS. Median OS and DFS were 67 and 20 months, respectively. 1-, 3- and 5-year OS were 97, 76, and 52%. 1-, 3- and 5-year DFS were 65, 42, and 37%. Multivariate analysis showed LNR to be an independent predictive factor for DFS but not for OS. Other identified predictive factors were vascular and perineural invasion of the primary colon cancer, size of the largest metastasis and severe complications after liver surgery for OS, and perineural invasion, number of liver metastases and preoperative CEA-level for DFS. Traditional N-stage was also considered to be an independent predictive factor for DFS in a separate multivariate analysis. LNR and perineural invasion of the primary colon cancer can be used as a prognostic variable for DFS after a concomitant liver resection for colon cancer metastases. Vascular and perineural invasion of the primary colon cancer are predictive for OS.

Genetic association between milk yield, stayability, and mastitis in Holstein cows under tropical conditions.

PubMed

Irano, Natalia; Bignardi, Annaiza Braga; El Faro, Lenira; Santana, Mário Luiz; Cardoso, Vera Lúcia; Albuquerque, Lucia Galvão

2014-03-01

The objective of this study was to estimate genetic parameters for milk yield, stayability, and the occurrence of clinical mastitis in Holstein cows, as well as studying the genetic relationship between them, in order to provide subsidies for the genetic evaluation of these traits. Records from 5,090 Holstein cows with calving varying from 1991 to 2010, were used in the analysis. Two standard multivariate analyses were carried out, one containing the trait of accumulated 305-day milk yields in the first lactation (MY1), stayability (STAY) until the third lactation, and clinical mastitis (CM), as well as the other traits, considering accumulated 305-day milk yields (Y305), STAY, and CM, including the first three lactations as repeated measures for Y305 and CM. The covariance components were obtained by a Bayesian approach. The heritability estimates obtained by multivariate analysis with MY1 were 0.19, 0.28, and 0.13 for MY1, STAY, and CM, respectively, whereas using the multivariate analysis with the Y305, the estimates were 0.19, 0.31, and 0.14, respectively. The genetic correlations between MY1 and STAY, MY1 and CM, and STAY and CM, respectively, were 0.38, 0.12, and -0.49. The genetic correlations between Y305 and STAY, Y305 and CM, and STAY and CM, respectively, were 0.66, -0.25, and -0.52.

Hepatic Progression-free and Overall Survival After Regional Therapy to the Liver for Metastatic Melanoma.

PubMed

Abbott, Andrea M; Doepker, Matthew P; Kim, Youngchul; Perez, Matthew C; Gandle, Cassandra; Thomas, Kerry L; Choi, Junsung; Shridhar, Ravi; Zager, Jonathan S

2017-01-04

Regional therapy for metastatic melanoma to the liver represents an alternative to systemic therapy. Hepatic progression-free survival (HPFS), progression-free survival (PFS), and overall survival (OS) were evaluated. A retrospective review of patients with liver metastases from cutaneous or uveal melanoma treated with yttrium-90 (Y90), chemoembolization (CE), or percutaneous hepatic perfusion (PHP) was conducted. Thirty patients (6 Y90, 10 PHP, 12 CE, 1 PHP then Y90, 1 CE then PHP) were included. Multivariate analysis showed improved HPFS for PHP versus Y90 (P=0.004), PHP versus CE (P=0.02) but not for CE versus Y90. PFS was also significantly different: Y90 (54 d), CE (52 d), PHP (245 d), P=0.03. PHP treatment and lower tumor burden were significant predictors of prolonged PFS on multivariate analysis. Median OS from time of treatment was longest, but not significant, for PHP at 608 days versus Y90 (295 d) and CE (265 d), P=0.24. Only PHP treatment versus Y90 and lower tumor burden had improved OS on multivariate analysis (P=0.03, 0.03, respectively). HPFS and PFS were significantly prolonged in patients treated with PHP versus CE or Y90. Median OS in PHP patients was over double that seen in Y90 or CE patients but was significant only between PHP and Y90.

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

Determinants of Budget Allocations to Academic Departments: A Case Study. ASHE 1987 Annual Meeting Paper.

ERIC Educational Resources Information Center

Winans, Glen T.

General fund budgetary determinants in 27 academic departments at the University of California Santa Barbara were studied for the period from 1977/78 through 1983/84. The focus was resource allocation and utilization within departments of the College of Letters and Science. The research design included a pooled multivariate regression analysis of…

An Analysis of Secondary Teachers' Reasoning with Participatory Sensing Data

ERIC Educational Resources Information Center

Gould, Robert; Bargagliotti, Anna; Johnson, Terri

2017-01-01

Participatory sensing is a data collection method in which communities of people collect and share data to investigate large-scale processes. These data have many features often associated with the big data paradigm: they are rich and multivariate, include non-numeric data, and are collected as determined by an algorithm rather than by traditional…

In-Between Fatalism and Leverage: The Different Effects of Socioeconomic Variables on Students' Civic and Political Experiences and Literacy

ERIC Educational Resources Information Center

Malafaia, Carla; Neves, Tiago; Menezes, Isabel

2017-01-01

Purpose: This article explores the classical relationship between socioeconomic status and political domains, and the need to include different variables (contextual and individual) to measure the effect of economic and cultural capitals on youth participation and knowledge. Method: A multivariate analysis of covariance was performed on a sample…

A Multivariate Analysis of Personality, Values and Expectations as Correlates of Career Aspirations of Final Year Medical Students

ERIC Educational Resources Information Center

Rogers, Mary E.; Searle, Judy; Creed, Peter A.; Ng, Shu-Kay

2010-01-01

This study reports on the career intentions of 179 final year medical students who completed an online survey that included measures of personality, values, professional and lifestyle expectations, and well-being. Logistic regression analyses identified the determinants of preferred medical specialty, practice location and hours of work.…

Evaluation of stapler hepatectomy during a laparoscopic liver resection

PubMed Central

Buell, Joseph F; Gayet, Brice; Han, Ho-Seong; Wakabayashi, Go; Kim, Ki-Hun; Belli, Giulio; Cannon, Robert; Saggi, Bob; Keneko, Hiro; Koffron, Alan; Brock, Guy; Dagher, Ibrahim

2013-01-01

Methods An international database of 1499 laparoscopic liver resections was analysed using multivariate and Kaplan–Meier analysis. Results In total, 764 stapler hepatectomies (SH) were compared with 735 electrosurgical resections (ER). SH was employed in larger tumours (4.5 versus 3.8 cm; P < 0.003) with decreased operative times (2.6 versus 3.1 h; P < 0.001), blood loss (100 versus 200 cc; P < 0.001) and length of stay (3.0 versus 7.0 days; P < 0.001). SH incurred a trend towards higher complications (16% versus 13%; P = 0.057) including bile leaks (26/764, 3.4% versus 16/735, 2.2%: P = 0.091). To address group homogeneity, a subset analysis of lobar resections confirmed the benefits of SH. Kaplan–Meier analysis in non-cirrhotic and cirrhotic patients confirmed equivalent patient (P = 0.290 and 0.118) and disease-free survival (P = 0.120 and 0.268). Multivariate analysis confirmed the parenchymal transection technique did not increase the risk of cancer recurrence, whereas tumour size, the presence of cirrhosis and concomitant operations did. Conclusions A SH provides several advantages including: diminished blood loss, transfusion requirements and shorter operative times. In spite of the smaller surgical margins in the SH group, equivalent recurrence and survival rates were observed when matched for parenchyma and extent of resection. PMID:23458439

Universal Pressure Ulcer Prevention Bundle With WOC Nurse Support.

PubMed

Anderson, Megan; Finch Guthrie, Patricia; Kraft, Wendy; Reicks, Patty; Skay, Carol; Beal, Alan L

2015-01-01

This study examined the effectiveness of a universal pressure ulcer prevention bundle (UPUPB) applied to intensive care unit (ICU) patients combined with proactive, semiweekly WOC nurse rounds. The UPUBP was compared to a standard guideline with referral-based WOC nurse involvement measuring adherence to 5 evidence-based prevention interventions and incidence of pressure ulcers. The study used a quasi-experimental, pre-, and postintervention design in which each phase included different subjects. Descriptive methods assisted in exploring the content of WOC nurse rounds. One hundred eighty-one pre- and 146 postintervention subjects who met inclusion criteria and were admitted to ICU for more than 24 hours participated in the study. The research setting was 3 ICUs located at North Memorial Medical Center in Minneapolis, Minnesota. Data collection included admission/discharge skin assessments, chart reviews for 5 evidence-based interventions and patient characteristics, and WOC nurse rounding logs. Study subjects with intact skin on admission identified with an initial skin assessment were enrolled in which prephase subjects received standard care and postphase subjects received the UPUPB. Skin assessments on ICU discharge and chart reviews throughout the stay determined the presence of unit-acquired pressure ulcers and skin care received. Analysis included description of WOC nurse rounds, t-tests for guideline adherence, and multivariate analysis for intervention effect on pressure ulcer incidence. Unit assignment, Braden Scale score, and ICU length of stay were covariates for a multivariate model based on bivariate logistic regression screening. The incidence of unit-acquired pressure ulcers decreased from 15.5% to 2.1%. WOC nurses logged 204 rounds over 6 months, focusing primarily on early detection of pressure sources. Data analysis revealed significantly increased adherence to heel elevation (t = -3.905, df = 325, P < .001) and repositioning (t = -2.441, df = 325, P < .015). Multivariate logistic regression modeling showed a significant reduction in unit-acquired pressure ulcers (P < .001). The intervention increased the Nagelkerke R-Square value by 0.099 (P < .001) more than 0.297 (P < .001) when including only covariates, for a final model value of 0.396 (P < .001). The UPUPB with WOC nurse rounds resulted in a statistically significant and clinically relevant reduction in the incidence of pressure ulcers.

An analysis of prognostic factors after percutaneous endoscopic gastrostomy placement in Japanese patients with amyotrophic lateral sclerosis.

PubMed

Nagashima, Kazuaki; Furuta, Natsumi; Makioka, Kouki; Fujita, Yukio; Ikeda, Masaki; Ikeda, Yoshio

2017-05-15

A percutaneous endoscopic gastrostomy (PEG) is an useful intervention for feeding of amyotrophic lateral sclerosis (ALS) patients who have lost oral intake function. The aim of this study was to investigate the risk factors for early death and the survival after PEG placement. A total of 102 ALS patients who underwent PEG placement were enrolled in this study. Patients were divided into two groups; the poor prognosis group included patients who died or needed permanent mechanical ventilation within 30days after PEG placement, and the good prognosis group included patients who did not meet the criteria of the poor prognosis group. Clinical characteristics, respiratory function, and nutritional parameters were compared for the two groups to assess the correlations between clinical and laboratory variables and early death after PEG placement. Multivariate analysis between two groups revealed that higher arterial carbon dioxide pressure (PaCO 2 ) and aphagia before PEG placement were significantly associated with the poor prognosis group. Multivariate analysis for survival also revealed that higher PaCO 2 and shorter duration from onset to PEG placement were significantly associated with shorter survival after PEG placement. In conclusion, respiratory and nutritional parameters are revealed to be important prognostic factors for ALS patients who undergo PEG placement. Copyright © 2017 Elsevier B.V. All rights reserved.

Nationwide analysis of adrenocortical carcinoma reveals higher perioperative morbidity in functional tumors.

PubMed

Parikh, Punam P; Rubio, Gustavo A; Farra, Josefina C; Lew, John I

2017-08-25

Current adrenalectomy outcomes for functional adrenocortical carcinoma (ACC) remain unclear. This study examines nationwide in-hospital post-adrenalectomy outcomes for ACC. A retrospective analysis of the Nationwide Inpatient Sample database (2006-2011) to identify unilateral adrenalectomy patients for functional or nonfunctional ACC was performed. Patient demographics, comorbidities and postoperative outcomes were evaluated by t-test, Chi-square and multivariate regression. Of 2199 patients who underwent adrenalectomy, 87% had nonfunctional and 13% had functional ACC (86% hypercortisolism, 16% hyperaldosteronism, 4% hyperandrogenism). Functional ACC patients had significantly more comorbidities, and experienced certain postoperative complications more frequently including wound issues, adrenocortical insufficiency and acute kidney injury with longer hospital stay compared to nonfunctional ACC (P < 0.01). On multivariate analysis, functional ACC was an independent prognosticator for wound complications (28.1, 95%CI 4.59-176.6). Patients with functional ACC manifest significant comorbidities with certain in-hospital complications. Such high-risk patients require appropriate preoperative medical optimization prior to adrenalectomy. Patients with functional adrenocortical carcinoma (ACC) have significant preoperative comorbidities and experience higher rates of certain postoperative complications including wound complications, hematoma formation, adrenal insufficiency, pulmonary embolism and acute kidney injury. Functional ACC patients also necessitate longer hospitalizations. These patients should undergo appropriate preoperative counseling in preparation for adrenalectomy. Copyright © 2017 Elsevier Inc. All rights reserved.

Modeling the predictive value of pain intensity on costs and resources utilization in patients with peripheral neuropathic pain.

PubMed

Pérez, Concepción; Navarro, Ana; Saldaña, María T; Wilson, Koo; Rejas, Javier

2015-03-01

The aim of the present analysis was to model the association and predictive value of pain intensity on cost and resource utilization in patients with chronic peripheral neuropathic pain (PNP) treated in routine clinical practice settings in Spain. We performed a secondary economic analysis based on data from a multicenter, observational, and prospective cost-of-illness study in patients with chronic PNP that is refractory to prior treatment. Pain intensity was measured using the Short-Form McGill Pain Questionnaire. Univariate and multivariate linear regression models were fitted to identify independent predictors of cost and health care/non-health care resource utilization. A total of 1703 patients were included in the current analysis. Pain intensity was an independent predictor of total costs ([total costs]=35.6 [pain intensity]+214.5; coefficient of determination [R(2)]=0.19, P<0.001), direct costs ([direct costs]=10.8 [pain intensity]+257.7; R=0.06, P<0.001), and indirect costs ([indirect costs]=24.8 [pain intensity]-43.4; R(2)=0.20, P<0.001) related to chronic PNP in the univariate analysis. Pain intensity remains significantly associated with total costs, direct costs, and indirect costs after adjustment by other covariates in the multivariate analysis (P<0.001). None of the other variables considered in the multivariate analysis were predictors of resource utilization. Pain intensity predicts the health care and non-health care resource utilization, and costs related to chronic PNP. Management of patients with drugs associated with a higher reduction of pain intensity may have a greater impact on the economic burden of that condition.

Impact of smoking history on the outcomes of women with early-stage breast cancer: a secondary analysis of a randomized study.

PubMed

Abdel-Rahman, Omar; Cheung, Winson Y

2018-04-11

To assess the impact of smoking history on the outcomes of early-stage breast cancer patients treated with sequential anthracyclines-taxanes in a randomized study. This is a secondary analysis of patient-level data of 1242 breast cancer patients referred for adjuvant chemotherapy in the BCIRG005 clinical trial. Overall survival was assessed according to smoking history through Kaplan-Meier analysis. Univariate and multivariate Cox regression analyses of factors affecting overall and relapse-free survival were subsequently conducted. Factors that were evaluated included: age, performance status, number of chemotherapy cycles, T stage, lymph node ratio, estrogen receptor status, adjuvant radiotherapy and smoking history. Kaplan-Meier analysis of overall survival according to smoking status (ever smoker vs. never smoker) was conducted. There was a trend toward a better overall survival among never smokers compared to ever smokers; however, it was not statistically significant (P = 0.098). The following factors were associated with better overall survival in multivariate analysis: older age (P = 0.011), complete chemotherapy course (P = 0.002), lower T stage (P < 0.0001), lower lymph node ratio (P < 0.0001) and positive estrogen receptor status (P = 0.006). Otherwise, the following factors were associated with better relapse-free survival in multivariate analysis: older age (P = 0.001), never smoking status (P = 0.021), lower T stage (P = 0.028), lower lymph node ratio (P < 0.0001) and positive estrogen receptor status (P < 0.0001). Early-stage breast cancer patients with a positive smoking history experienced worse relapse-free survival compared to never smokers. Physicians managing breast cancer patients should prioritize discussion about the benefits of smoking cessation when counseling their patients.

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.

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.

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.

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 ...

Decoding Dynamic Brain Patterns from Evoked Responses: A Tutorial on Multivariate Pattern Analysis Applied to Time Series Neuroimaging Data.

PubMed

Grootswagers, Tijl; Wardle, Susan G; Carlson, Thomas A

2017-04-01

Multivariate pattern analysis (MVPA) or brain decoding methods have become standard practice in analyzing fMRI data. Although decoding methods have been extensively applied in brain-computer interfaces, these methods have only recently been applied to time series neuroimaging data such as MEG and EEG to address experimental questions in cognitive neuroscience. In a tutorial style review, we describe a broad set of options to inform future time series decoding studies from a cognitive neuroscience perspective. Using example MEG data, we illustrate the effects that different options in the decoding analysis pipeline can have on experimental results where the aim is to "decode" different perceptual stimuli or cognitive states over time from dynamic brain activation patterns. We show that decisions made at both preprocessing (e.g., dimensionality reduction, subsampling, trial averaging) and decoding (e.g., classifier selection, cross-validation design) stages of the analysis can significantly affect the results. In addition to standard decoding, we describe extensions to MVPA for time-varying neuroimaging data including representational similarity analysis, temporal generalization, and the interpretation of classifier weight maps. Finally, we outline important caveats in the design and interpretation of time series decoding experiments.

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.

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

PubMed

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

2016-01-01

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

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...

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.

A Multivariate Analysis of Galaxy Cluster Properties

NASA Astrophysics Data System (ADS)

Ogle, P. M.; Djorgovski, S.

1993-05-01

We have assembled from the literature a data base on on 394 clusters of galaxies, with up to 16 parameters per cluster. They include optical and x-ray luminosities, x-ray temperatures, galaxy velocity dispersions, central galaxy and particle densities, optical and x-ray core radii and ellipticities, etc. In addition, derived quantities, such as the mass-to-light ratios and x-ray gas masses are included. Doubtful measurements have been identified, and deleted from the data base. Our goal is to explore the correlations between these parameters, and interpret them in the framework of our understanding of evolution of clusters and large-scale structure, such as the Gott-Rees scaling hierarchy. Among the simple, monovariate correlations we found, the most significant include those between the optical and x-ray luminosities, x-ray temperatures, cluster velocity dispersions, and central galaxy densities, in various mutual combinations. While some of these correlations have been discussed previously in the literature, generally smaller samples of objects have been used. We will also present the results of a multivariate statistical analysis of the data, including a principal component analysis (PCA). Such an approach has not been used previously for studies of cluster properties, even though it is much more powerful and complete than the simple monovariate techniques which are commonly employed. The observed correlations may lead to powerful constraints for theoretical models of formation and evolution of galaxy clusters. P.M.O. was supported by a Caltech graduate fellowship. S.D. acknowledges a partial support from the NASA contract NAS5-31348 and the NSF PYI award AST-9157412.

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 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.

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.

Comparison of 20% sulfur hexafluoride with air for intraocular tamponade in Descemet membrane endothelial keratoplasty (DMEK).

PubMed

Botsford, Benjamin; Vedana, Gustavo; Cope, Leslie; Yiu, Samuel C; Jun, Albert S

2016-01-01

To compare the effect of 20% sulfur hexafluoride (SF6) with that of air on graft detachment rates for intraocular tamponade in Descemet membrane endothelial keratoplasty (DMEK). Forty-two eyes of patients who underwent DMEK by a single surgeon (A.S.J.) at Wilmer Eye Institute between January 2012 and 2014 were identified; 21 received air for intraocular tamponade and the next consecutive 21 received SF6. The main outcome measure was the graft detachment rate; univariate and multivariate analyses were performed. The graft detachment rate was 67% in the air group and 19% in the SF6 group (p<0.05). No complete graft detachments occurred, and all partial detachments underwent intervention with injection of intraocular air. The percentages of eyes with 20/25 or better vision were not different between the groups (67% vs. 71%). Univariate analysis showed significantly higher detachment rates with air tamponade (OR, 8.50; p<0.005) and larger donor graft size (OR, 14.96; p<0.05). Multivariate analysis with gas but not graft size included showed that gas was an independent statistically significant predictor of outcome (OR, 6.65; p<0.05). When graft size was included as a covariate, gas was no longer a statistically significant predictor of detachment but maintained OR of 7.81 (p=0.063) similar to the results of univariate and multivariate analyses without graft size. In comparison with air, graft detachment rates for intraocular tamponade in DMEK were significantly reduced by 20% SF6.

Sex hormones and quantitative ultrasound parameters at the heel in men and women from the general population.

PubMed

Pätzug, Konrad; Friedrich, Nele; Kische, Hanna; Hannemann, Anke; Völzke, Henry; Nauck, Matthias; Keevil, Brian G; Haring, Robin

2017-12-01

The present study investigates potential associations between liquid chromatography-mass spectrometry (LC-MS) measured sex hormones, dehydroepiandrosterone sulphate, sex hormone-binding globulin (SHBG) and bone ultrasound parameters at the heel in men and women from the general population. Data from 502 women and 425 men from the population-based Study of Health in Pomerania (SHIP-TREND) were used. Cross-sectional associations of sex hormones including testosterone (TT), calculated free testosterone (FT), dehydroepiandrosterone sulphate (DHEAS), androstenedione (ASD), estrone (E1) and SHBG with quantitative ultrasound (QUS) parameters at the heel, including broadband ultrasound attenuation (BUA), speed of sound (SOS) and stiffness index (SI) were examined by analysis of variance (ANOVA) and multivariable quantile regression models. Multivariable regression analysis showed a sex-specific inverse association of DHEAS with SI in men (Beta per SI unit = - 3.08, standard error (SE) = 0.88), but not in women (Beta = - 0.01, SE = 2.09). Furthermore, FT was positively associated with BUA in men (Beta per BUA unit = 29.0, SE = 10.1). None of the other sex hormones (ASD, E1) or SHBG was associated with QUS parameters after multivariable adjustment. This cross-sectional population-based study revealed independent associations of DHEAS and FT with QUS parameters in men, suggesting a potential influence on male bone metabolism. The predictive role of DHEAS and FT as a marker for osteoporosis in men warrants further investigation in clinical trials and large-scale observational studies.

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

Augmented classical least squares multivariate spectral analysis

DOEpatents

Haaland, David M.; Melgaard, David K.

2004-02-03

A method of multivariate spectral analysis, termed augmented classical least squares (ACLS), provides an improved CLS calibration model when unmodeled sources of spectral variation are contained in a calibration sample set. The ACLS methods use information derived from component or spectral residuals during the CLS calibration to provide an improved calibration-augmented CLS model. The ACLS methods are based on CLS so that they retain the qualitative benefits of CLS, yet they have the flexibility of PLS and other hybrid techniques in that they can define a prediction model even with unmodeled sources of spectral variation that are not explicitly included in the calibration model. The unmodeled sources of spectral variation may be unknown constituents, constituents with unknown concentrations, nonlinear responses, non-uniform and correlated errors, or other sources of spectral variation that are present in the calibration sample spectra. Also, since the various ACLS methods are based on CLS, they can incorporate the new prediction-augmented CLS (PACLS) method of updating the prediction model for new sources of spectral variation contained in the prediction sample set without having to return to the calibration process. The ACLS methods can also be applied to alternating least squares models. The ACLS methods can be applied to all types of multivariate data.

Augmented Classical Least Squares Multivariate Spectral Analysis

DOEpatents

Haaland, David M.; Melgaard, David K.

2005-07-26

A method of multivariate spectral analysis, termed augmented classical least squares (ACLS), provides an improved CLS calibration model when unmodeled sources of spectral variation are contained in a calibration sample set. The ACLS methods use information derived from component or spectral residuals during the CLS calibration to provide an improved calibration-augmented CLS model. The ACLS methods are based on CLS so that they retain the qualitative benefits of CLS, yet they have the flexibility of PLS and other hybrid techniques in that they can define a prediction model even with unmodeled sources of spectral variation that are not explicitly included in the calibration model. The unmodeled sources of spectral variation may be unknown constituents, constituents with unknown concentrations, nonlinear responses, non-uniform and correlated errors, or other sources of spectral variation that are present in the calibration sample spectra. Also, since the various ACLS methods are based on CLS, they can incorporate the new prediction-augmented CLS (PACLS) method of updating the prediction model for new sources of spectral variation contained in the prediction sample set without having to return to the calibration process. The ACLS methods can also be applied to alternating least squares models. The ACLS methods can be applied to all types of multivariate data.

Augmented Classical Least Squares Multivariate Spectral Analysis

DOEpatents

Haaland, David M.; Melgaard, David K.

2005-01-11

A method of multivariate spectral analysis, termed augmented classical least squares (ACLS), provides an improved CLS calibration model when unmodeled sources of spectral variation are contained in a calibration sample set. The ACLS methods use information derived from component or spectral residuals during the CLS calibration to provide an improved calibration-augmented CLS model. The ACLS methods are based on CLS so that they retain the qualitative benefits of CLS, yet they have the flexibility of PLS and other hybrid techniques in that they can define a prediction model even with unmodeled sources of spectral variation that are not explicitly included in the calibration model. The unmodeled sources of spectral variation may be unknown constituents, constituents with unknown concentrations, nonlinear responses, non-uniform and correlated errors, or other sources of spectral variation that are present in the calibration sample spectra. Also, since the various ACLS methods are based on CLS, they can incorporate the new prediction-augmented CLS (PACLS) method of updating the prediction model for new sources of spectral variation contained in the prediction sample set without having to return to the calibration process. The ACLS methods can also be applied to alternating least squares models. The ACLS methods can be applied to all types of multivariate data.

Extraction, isolation, and purification of analytes from samples of marine origin--a multivariate task.

PubMed

Liguori, Lucia; Bjørsvik, Hans-René

2012-12-01

The development of a multivariate study for a quantitative analysis of six different polybrominated diphenyl ethers (PBDEs) in tissue of Atlantic Salmo salar L. is reported. An extraction, isolation, and purification process based on an accelerated solvent extraction system was designed, investigated, and optimized by means of statistical experimental design and multivariate data analysis and regression. An accompanying gas chromatography-mass spectrometry analytical method was developed for the identification and quantification of the analytes, BDE 28, BDE 47, BDE 99, BDE 100, BDE 153, and BDE 154. These PBDEs have been used in commercial blends that were used as flame-retardants for a variety of materials, including electronic devices, synthetic polymers and textiles. The present study revealed that an extracting solvent mixture composed of hexane and CH₂Cl₂ (10:90) provided excellent recoveries of all of the six PBDEs studied herein. A somewhat lower polarity in the extracting solvent, hexane and CH₂Cl₂ (40:60) decreased the analyte %-recoveries, which still remain acceptable and satisfactory. The study demonstrates the necessity to perform an intimately investigation of the extraction and purification process in order to achieve quantitative isolation of the analytes from the specific matrix. Copyright © 2012 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.

Are prostatic calculi independent predictive factors of lower urinary tract symptoms?

PubMed

Park, Sung-Woo; Nam, Jong-Kil; Lee, Sang-Don; Chung, Moon-Kee

2010-03-01

We determined the correlation between prostatic calculi and lower urinary tract symptoms (LUTS), as well as the predisposing factors of prostatic calculi. Of the 1 527 patients who presented at our clinic for LUTS, 802 underwent complete evaluations, including transrectal ultrasonography, voided bladder-3 specimen and international prostatic symptoms score (IPSS). A total of 335 patients with prostatic calculi and 467 patients without prostatic calculi were divided into calculi and no calculi groups, respectively. Predictive factors of severe LUTS and prostatic calculi were determined using uni/multivariate analysis. The overall IPSS score was 15.7 +/- 9.2 and 14.1 +/- 9.2 in the calculi and no calculi group, respectively (P = 0.013). The maximum flow rate was 12.1 +/- 6.9 and 14.2 +/- 8.2 mL s(-1) in the calculi and no calculi group, respectively (P = 0.003). On univariate analysis for predicting factors of severe LUTS, differences on age (P = 0.042), prostatic calculi (P = 0.048) and prostatitis (P = 0.018) were statistically significant. However, on multivariate analysis, no factor was significant. On multivariate analysis for predisposing factors of prostatic calculi, differences on age (P < 0.001) and prostate volume (P = 0.001) were significant. To our knowledge, patients who have prostatic calculi complain of more severe LUTS. However, prostatic calculi are not an independent predictive factor of severe LUTS. Therefore, men with prostatic calculi have more severe LUTS not only because of prostatic calculi but also because of age and other factors. In addition, old age and large prostate volume are independent predisposing factors for prostatic calculi.

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.

On a Family of Multivariate Modified Humbert Polynomials

PubMed Central

Aktaş, Rabia; Erkuş-Duman, Esra

2013-01-01

This paper attempts to present a multivariable extension of generalized Humbert polynomials. The results obtained here include various families of multilinear and multilateral generating functions, miscellaneous properties, and also some special cases for these multivariable polynomials. PMID:23935411

Prevalence of gestational diabetes mellitus in Europe: A meta-analysis.

PubMed

Eades, Claire E; Cameron, Dawn M; Evans, Josie M M

2017-07-01

Estimates of the prevalence of gestational diabetes vary widely. It is important to have a clear understanding of the prevalence of this condition to be able to plan interventions and health care provision. This paper describes a meta-analysis of primary research data reporting the prevalence of gestational diabetes mellitus in the general pregnant population of developed countries in Europe. Four electronic databases were systematically searched in May 2016. English language articles reporting gestational diabetes mellitus prevalence using universal screening in general pregnant population samples from developed countries in Europe were included. All papers identified by the search were screened by one author, and then half screened independently by a second author and half by a third author. Data were extracted by one author. Values for the measures of interest were combined using a random effects model and analysis of the effects of moderator variables was carried out. A total of 3258 abstracts were screened, with 40 studies included in the review. Overall prevalence of gestational diabetes mellitus was 5.4% (3.8-7.8). Maternal age, year of data collection, country, area of Europe, week of gestation at testing, and diagnostic criteria were found to have a significant univariate effect on GDM prevalence, and area, week of gestation at testing and year of data collection remained statistically significant in multivariate analysis. Quality category was significant in multivariate but not univariate analysis. This meta-analysis shows prevalence of GDM that is at the upper end of previous estimates in Europe. Copyright © 2017 Elsevier B.V. All rights reserved.

Using the Rural-Urban Continuum to Explore Adolescent Alcohol, Tobacco, and Other Drug Use in Montana

ERIC Educational Resources Information Center

Hanson, Carl L.; Novilla, M. Lelinneth L. B.; Barnes, Michael D.; Eggett, Dennis; McKell, Chelsea; Reichman, Peter; Havens, Mike

2009-01-01

The purpose of the study was to compare 30-day prevalence of alcohol, tobacco, and other drug use among twelfth-grade students in Montana across a rural-urban continuum during 2000, 2002, and 2004. The methods include an analysis of the Montana Prevention Needs Assessment (N = 15,372) using multivariable logistic regression adjusting for risk…

VEMAP phase 2 bioclimatic database. I. Gridded historical (20th century) climate for modeling ecosystem dynamics across the conterminous USA

Treesearch

Timothy G.F. Kittel; Nan. A. Rosenbloom; J.A. Royle; C. Daly; W.P. Gibson; H.H. Fisher; P. Thornton; D.N. Yates; S. Aulenbach; C. Kaufman; R. McKeown; Dominque Bachelet; David S. Schimel

2004-01-01

Analysis and simulation of biospheric responses to historical forcing require surface climate data that capture those aspects of climate that control ecological processes, including key spatial gradients and modes of temporal variability. We developed a multivariate, gridded historical climate dataset for the conterminous USA as a common input database for the...

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.

The role of the human leukocyte antigen system in retinopathy of prematurity: a pilot study.

PubMed

Flor-de-Lima, Filipa; Rocha, Gustavo; Proença, Elisa; Tafulo, Sandra; Freitas, Fátima; Guimarães, Hercília

2013-12-01

To assess the association between the human leukocyte antigen system and retinopathy of prematurity. Neonates of <32 weeks of gestational age, born at two level III neonatal intensive care units from January 2000 to December 2001 and from January 2006 to June 2009, were included in the study. Demographic and clinical data were recorded, and retinopathy was classified according to the International Classification. Epithelial cells were collected from the oral cavity and the HLA were studied using the PCR/SSO method. Univariate and multivariate analyses were performed using SPSS® v.18. We evaluated 156 neonates, including 82 (52.6%) males. Median gestational age was 29 (23-31) weeks, and median birth weight was 1030 (525-1935) grams. Seventy (44.9%) of the neonates developed retinopathy. Alleles HLA-B*38, HLA-Cw*12, HLA-DRB1*09, HLA-DRB1*14 (univariate analysis) and HLA-A*68 and HLA-Cw*12 were associated to retinopathy (multivariate analysis). The results suggest that the HLA system may be associated with the development of retinopathy of prematurity. A large-scale population-based study should be performed to clarify this association. ©2013 Foundation Acta Paediatrica. Published by John Wiley & Sons Ltd.

[Predictive factors of complications during CT-guided transthoracic biopsy].

PubMed

Fontaine-Delaruelle, C; Souquet, P-J; Gamondes, D; Pradat, E; de Leusse, A; Ferretti, G R; Couraud, S

2017-04-01

CT-guided transthoracic core-needle biopsy (TTNB) is frequently used for the diagnosis of lung nodules. The aim of this study is to describe TTNBs' complications and to investigate predictive factors of complications. All consecutive TTNBs performed in three centers between 2006 and 2012 were included. Binary logistic regression was used for multivariate analysis. Overall, 970 TTNBs were performed in 929 patients. The complication rate was 34% (life-threatening complication in 6%). The most frequent complications were pneumothorax (29% included 4% which required chest-tube) and hemoptysis (5%). The mortality rate was 0.1% (n=1). In multivariate analysis, predictive factor for a complication was small target size (AOR=0.984; 95% CI [0.976-0.992]; P<0.001). This predictive factor was also found for occurrence of life-threatening complication (AOR=0.982; [0.965-0.999]; P=0.037), of pneumothorax (AOR=0.987; [0.978-0.995]; P=0.002) and of hemoptysis (AOR=0.973; [0.951-0.997]; P=0.024). One complication occurred in one-third of TTNBs. The proportion of life-threatening complication was 6%. A small lesion size was predictive of complication occurrence. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

Marginal analysis in assessing factors contributing time to physician in the Emergency Department using operations data.

PubMed

Pathan, Sameer A; Bhutta, Zain A; Moinudheen, Jibin; Jenkins, Dominic; Silva, Ashwin D; Sharma, Yogdutt; Saleh, Warda A; Khudabakhsh, Zeenat; Irfan, Furqan B; Thomas, Stephen H

2016-01-01

Background: Standard Emergency Department (ED) operations goals include minimization of the time interval (tMD) between patients' initial ED presentation and initial physician evaluation. This study assessed factors known (or suspected) to influence tMD with a two-step goal. The first step was generation of a multivariate model identifying parameters associated with prolongation of tMD at a single study center. The second step was the use of a study center-specific multivariate tMD model as a basis for predictive marginal probability analysis; the marginal model allowed for prediction of the degree of ED operations benefit that would be affected with specific ED operations improvements. Methods: The study was conducted using one month (May 2015) of data obtained from an ED administrative database (EDAD) in an urban academic tertiary ED with an annual census of approximately 500,000; during the study month, the ED saw 39,593 cases. The EDAD data were used to generate a multivariate linear regression model assessing the various demographic and operational covariates' effects on the dependent variable tMD. Predictive marginal probability analysis was used to calculate the relative contributions of key covariates as well as demonstrate the likely tMD impact on modifying those covariates with operational improvements. Analyses were conducted with Stata 14MP, with significance defined at p  < 0.05 and confidence intervals (CIs) reported at the 95% level. Results: In an acceptable linear regression model that accounted for just over half of the overall variance in tMD (adjusted r 2 0.51), important contributors to tMD included shift census ( p  = 0.008), shift time of day ( p  = 0.002), and physician coverage n ( p  = 0.004). These strong associations remained even after adjusting for each other and other covariates. Marginal predictive probability analysis was used to predict the overall tMD impact (improvement from 50 to 43 minutes, p  < 0.001) of consistent staffing with 22 physicians. Conclusions: The analysis identified expected variables contributing to tMD with regression demonstrating significance and effect magnitude of alterations in covariates including patient census, shift time of day, and number of physicians. Marginal analysis provided operationally useful demonstration of the need to adjust physician coverage numbers, prompting changes at the study ED. The methods used in this analysis may prove useful in other EDs wishing to analyze operations information with the goal of predicting which interventions may have the most benefit.

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

MAOA, MTHFR, and TNF-β genes polymorphisms and personality traits in the pathogenesis of migraine.

PubMed

Ishii, Masakazu; Shimizu, Shunichi; Sakairi, Yuki; Nagamine, Ayumu; Naito, Yuika; Hosaka, Yukiko; Naito, Yuko; Kurihara, Tatsuya; Onaya, Tomomi; Oyamada, Hideto; Imagawa, Atsuko; Shida, Kenji; Takahashi, Johji; Oguchi, Katsuji; Masuda, Yutaka; Hara, Hajime; Usami, Shino; Kiuchi, Yuji

2012-04-01

Migraine is a multifactorial disease with various factors, such as genetic polymorphisms and personality traits, but the contribution of those factors is not clear. To clarify the pathogenesis of migraine, the contributions of genetic polymorphisms and personality traits were simultaneously investigated using multivariate analysis. Ninety-one migraine patients and 119 non-headache healthy volunteers were enrolled. The 12 gene polymorphisms analysis and NEO-FFI personality test were performed. At first, the univariate analysis was performed to extract the contributing factors to pathogenesis of migraine. We then extracted the factors that independently contributed to the pathogenesis of migraine using multivariate stepwise logistic regression analysis. Using the multivariate analysis, three gene polymorphisms including monoamine oxidase A (MAOA) T941G, methylenetetrahydrofolate reductase (MTHFR) C677T, and tumor necrosis factor beta (TNF-β) G252Α, and the neuroticism and conscientiousness scores in NEO-FFI were selected as significant factors that independently contributed to the pathogenesis of migraine. Their odds ratios were 1.099 (per point of neuroticism score), 1.080 (per point of conscientiousness score), 2.272 (T and T/T or T/G vs G and G/G genotype of MAOA), 1.939 (C/T or T/T vs C/C genotype of MTHFR), and 2.748 (G/A or A/A vs G/G genotype of TNF-β), respectively. We suggested that multiple factors, such as gene polymorphisms and personality traits, contribute to the pathogenesis of migraine. The contribution of polymorphisms, such as MAOA T941G, MTHFR C677T, and TNF-β G252A, were more important than personality traits in the pathogenesis of migraine, a multifactorial disorder.

Metabolic phenotyping of urine for discriminating alcohol-dependent from social drinkers and alcohol-naive subjects.

PubMed

Mostafa, Hamza; Amin, Arwa M; Teh, Chin-Hoe; Murugaiyah, Vikneswaran; Arif, Nor Hayati; Ibrahim, Baharudin

2016-12-01

Alcohol-dependence (AD) is a ravaging public health and social problem. AD diagnosis depends on questionnaires and some biomarkers, which lack specificity and sensitivity, however, often leading to less precise diagnosis, as well as delaying treatment. This represents a great burden, not only on AD individuals but also on their families. Metabolomics using nuclear magnetic resonance spectroscopy (NMR) can provide novel techniques for the identification of novel biomarkers of AD. These putative biomarkers can facilitate early diagnosis of AD. To identify novel biomarkers able to discriminate between alcohol-dependent, non-AD alcohol drinkers and controls using metabolomics. Urine samples were collected from 30 alcohol-dependent persons who did not yet start AD treatment, 54 social drinkers and 60 controls, who were then analysed using NMR. Data analysis was done using multivariate analysis including principal component analysis (PCA) and orthogonal partial least square-discriminate analysis (OPLS-DA), followed by univariate and multivariate logistic regression to develop the discriminatory model. The reproducibility was done using intraclass correlation coefficient (ICC). The OPLS-DA revealed significant discrimination between AD and other groups with sensitivity 86.21%, specificity 97.25% and accuracy 94.93%. Six biomarkers were significantly associated with AD in the multivariate logistic regression model. These biomarkers were cis-aconitic acid, citric acid, alanine, lactic acid, 1,2-propanediol and 2-hydroxyisovaleric acid. The reproducibility of all biomarkers was excellent (0.81-1.0). This study revealed that metabolomics analysis of urine using NMR identified AD novel biomarkers which can discriminate AD from social drinkers and controls with high accuracy. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

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.

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

Enhancements of Bayesian Blocks; Application to Large Light Curve Databases

NASA Technical Reports Server (NTRS)

Scargle, Jeff

2015-01-01

Bayesian Blocks are optimal piecewise linear representations (step function fits) of light-curves. The simple algorithm implementing this idea, using dynamic programming, has been extended to include more data modes and fitness metrics, multivariate analysis, and data on the circle (Studies in Astronomical Time Series Analysis. VI. Bayesian Block Representations, Scargle, Norris, Jackson and Chiang 2013, ApJ, 764, 167), as well as new results on background subtraction and refinement of the procedure for precise timing of transient events in sparse data. Example demonstrations will include exploratory analysis of the Kepler light curve archive in a search for "star-tickling" signals from extraterrestrial civilizations. (The Cepheid Galactic Internet, Learned, Kudritzki, Pakvasa1, and Zee, 2008, arXiv: 0809.0339; Walkowicz et al., in progress).

Earth resources data analysis program, phase 2

NASA Technical Reports Server (NTRS)

1974-01-01

The efforts and findings of the Earth Resources Data Analysis Program are summarized. Results of a detailed study of the needs of EOD with respect to an applications development system (ADS) for the analysis of remotely sensed data, including an evaluation of four existing systems with respect to these needs are described. Recommendations as to possible courses for EOD to follow to obtain a viable ADS are presented. Algorithmic development comprised of several subtasks is discussed. These subtasks include the following: (1) two algorithms for multivariate density estimation; (2) a data smoothing algorithm; (3) a method for optimally estimating prior probabilities of unclassified data; and (4) further applications of the modified Cholesky decomposition in various calculations. Little effort was expended on task 3, however, two reports were reviewed.

Advances in metabolome information retrieval: turning chemistry into biology. Part II: biological information recovery.

PubMed

Tebani, Abdellah; Afonso, Carlos; Bekri, Soumeya

2018-05-01

This work reports the second part of a review intending to give the state of the art of major metabolic phenotyping strategies. It particularly deals with inherent advantages and limits regarding data analysis issues and biological information retrieval tools along with translational challenges. This Part starts with introducing the main data preprocessing strategies of the different metabolomics data. Then, it describes the main data analysis techniques including univariate and multivariate aspects. It also addresses the challenges related to metabolite annotation and characterization. Finally, functional analysis including pathway and network strategies are discussed. The last section of this review is devoted to practical considerations and current challenges and pathways to bring metabolomics into clinical environments.

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.

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…

Multivariate proteomic profiling identifies novel accessory proteins of coated vesicles

PubMed Central

Antrobus, Robin; Hirst, Jennifer; Bhumbra, Gary S.; Kozik, Patrycja; Jackson, Lauren P.; Sahlender, Daniela A.

2012-01-01

Despite recent advances in mass spectrometry, proteomic characterization of transport vesicles remains challenging. Here, we describe a multivariate proteomics approach to analyzing clathrin-coated vesicles (CCVs) from HeLa cells. siRNA knockdown of coat components and different fractionation protocols were used to obtain modified coated vesicle-enriched fractions, which were compared by stable isotope labeling of amino acids in cell culture (SILAC)-based quantitative mass spectrometry. 10 datasets were combined through principal component analysis into a “profiling” cluster analysis. Overall, 136 CCV-associated proteins were predicted, including 36 new proteins. The method identified >93% of established CCV coat proteins and assigned >91% correctly to intracellular or endocytic CCVs. Furthermore, the profiling analysis extends to less well characterized types of coated vesicles, and we identify and characterize the first AP-4 accessory protein, which we have named tepsin. Finally, our data explain how sequestration of TACC3 in cytosolic clathrin cages causes the severe mitotic defects observed in auxilin-depleted cells. The profiling approach can be adapted to address related cell and systems biological questions. PMID:22472443

Multivariate Analysis for Quantification of Plutonium(IV) in Nitric Acid Based on Absorption Spectra

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

Lines, Amanda M.; Adami, Susan R.; Sinkov, Sergey I.

Development of more effective, reliable, and fast methods for monitoring process streams is a growing opportunity for analytical applications. Many fields can benefit from on-line monitoring, including the nuclear fuel cycle where improved methods for monitoring radioactive materials will facilitate maintenance of proper safeguards and ensure safe and efficient processing of materials. On-line process monitoring with a focus on optical spectroscopy can provide a fast, non-destructive method for monitoring chemical species. However, identification and quantification of species can be hindered by the complexity of the solutions if bands overlap or show condition-dependent spectral features. Plutonium (IV) is one example ofmore » a species which displays significant spectral variation with changing nitric acid concentration. Single variate analysis (i.e. Beer’s Law) is difficult to apply to the quantification of Pu(IV) unless the nitric acid concentration is known and separate calibration curves have been made for all possible acid strengths. Multivariate, or chemometric, analysis is an approach that allows for the accurate quantification of Pu(IV) without a priori knowledge of nitric acid concentration.« less

Screening and analysis of aconitum alkaloids and their metabolites in rat urine after oral administration of aconite roots extract using LC-TOFMS-based metabolomics.

PubMed

Tan, Guangguo; Lou, Ziyang; Jing, Jing; Li, Wuhong; Zhu, Zhenyu; Zhao, Liang; Zhang, Guoqing; Chai, Yifeng

2011-12-01

Aconite roots are popularly used in herbal medicines in China. Many cases of accidental and intentional intoxication with this plant have been reported; some of these are fatal because the toxicity of aconitum is very high. It is thus important to detect and identify aconitum alkaloids in biofluids. In this work, an improved method employing LC-TOFMS with multivariate data analysis was developed for screening and analysis of major aconitum alkaloids and their metabolites in rat urine following oral administration of aconite roots extract. Thirty-four signals highlighted by multivariate statistical analyses including 24 parent components and 10 metabolites were screened out and further identified by adjustment of the fragmentor voltage to produce structure-relevant fragment ions. It is helpful for studying aconite roots in toxicology, pharmacology and forensic medicine. This work also confirmed that the metabolomic approach provides effective tools for screening multiple absorbed and metabolic components of Chinese herbal medicines in vivo. Copyright © 2011 John Wiley & Sons, Ltd.

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.

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.

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.

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

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

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.

Impact of novel histopathological factors on the outcomes of liver surgery for colorectal cancer metastases.

PubMed

Serrablo, A; Paliogiannis, P; Pulighe, F; Moro, S Saudi-Moro; Borrego-Estella, V; Attene, F; Scognamillo, F; Hörndler, C

2016-09-01

We evaluated the impacts of a series of novel histopathological factors on clinical-surgical outcomes and survival of patients who underwent surgery for colorectal cancer liver metastasis, with and without neoadjuvant chemotherapy. A prospective database including 150 consecutive patients who underwent 183 hepatic resections for metastatic colorectal cancer was evaluated. Among them, 74 (49.3%) received neoadjuvant chemotherapy before surgery. The histopathological factors studied were: a) microsatellitosis, b) type and pattern of tumour growth, c) nuclear grade and the number of mitoses/mm(2), d) perilesional pseudocapsule, e) intratumoural fibrosis, f) lesion cellularity, g) hypoxic-angiogenic perilesional growth pattern, and h) the tumour normal interface. Three or more metastatic lesions, R1 resection margins, and <50% tumour necrosis were prognostic factors for a worse OS, but only the former was confirmed to be an independent prognostic factor in the multivariate analysis. Furthermore, tumour fibrosis <40% and cellularity >10% were predictive of a worse neoadjuvant therapy response, but these findings were not confirmed in the multivariate analysis. Finally, tumour necrosis <50%, cellularity >10%, and TNI >0.5 mm were prognostic factors for a worse DFS and AS in the univariate but not in the multivariate analysis. Several factors seem to influence the outcomes of surgery for colorectal cancer liver metastasis, especially the number of the lesions, the margins of resection, the percentage of necrosis and fibrosis, as well as the cellularity and the TNI. Copyright © 2016 Elsevier Ltd. All rights reserved.

The impact of cavernosal nerve preservation on continence after robotic radical prostatectomy

PubMed Central

Pick, Donald L.; Osann, Kathryn; Skarecky, Douglas; Narula, Navneet; Finley, David S.; Ahlering, Thomas E.

2014-01-01

OBJECTIVE To evaluate associations between baseline characteristics, nerve-sparing (NS) status and return of continence, as a relationship may exist between return to continence and preservation of the neurovascular bundles for potency during radical prostatectomy (RP). PATIENTS AND METHODS The study included 592 consecutive robotic RPs completed between 2002 and 2007. All data were entered prospectively into an electronic database. Continence data (defined as zero pads) was collected using self-administered validated questionnaires. Baseline characteristics (age, International Index of Erectile Function [IIEF-5] score, American Urological Association symptom score, body mass index [BMI], clinical T-stage, Gleason score, and prostate-specific antigen level), NS status and learning curve were retrospectively evaluated for association with overall continence at 1, 3 and 12 months after RP using univariate and multivariable methods. Any patient taking preoperative phosphodiesterase inhibitors was excluded from the postoperative analysis. RESULTS Complete data were available for 537 of 592 patients (91%). Continence rates at 12 months after RP were 89.2%, 88.9% and 84.8% for bilateral NS, unilateral NS and non-NS respectively (P = 0.56). In multivariable analysis age, IIEF-5 score and BMI were significant independent predictors of continence. Cavernosal NS status did not significantly affect continence after adjusting for other co-variables. CONCLUSION After careful multivariable analysis of baseline characteristics age, IIEF-5 score and BMI affected continence in a statistically significant fashion. This suggests that baseline factors and not the physical preservation of the cavernosal nerves predict overall return to continence. PMID:21244602

Prematurity and fetal lung response after tracheal occlusion in fetuses with severe congenital diaphragmatic hernia.

PubMed

Sananes, Nicolas; Rodo, Carlota; Peiro, Jose Luis; Britto, Ingrid Schwach Werneck; Sangi-Haghpeykar, Haleh; Favre, Romain; Joal, Arnaud; Gaudineau, Adrien; Silva, Marcos Marques da; Tannuri, Uenis; Zugaib, Marcelo; Carreras, Elena; Ruano, Rodrigo

2016-09-01

To evaluate the independent association of fetal pulmonary response and prematurity to postnatal outcomes after fetal tracheal occlusion for congenital diaphragmatic hernia. Fetal pulmonary response, prematurity (<37 weeks at delivery) and extreme prematurity (<32 weeks at delivery) were evaluated and compared between survivors and non-survivors at 6 months of life. Multivariable analysis was conducted with generalized linear mixed models for variables significantly associated with survival in univariate analysis. Eighty-four infants were included, of whom 40 survived (47.6%) and 44 died (52.4%). Univariate analysis demonstrated that survival was associated with greater lung response (p=0.006), and the absence of extreme preterm delivery (p=0.044). In multivariable analysis, greater pulmonary response after FETO was an independent predictor of survival (aOR 1.87, 95% CI 1.08-3.33, p=0.023), whereas the presence of extreme prematurity was not statistically associated with mortality after controlling for fetal pulmonary response (aOR 0.52, 95% CI 0.12-2.30, p=0.367). Fetal pulmonary response after FETO is the most important factor associated with survival, independently from the gestational age at delivery.

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

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…

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 ...

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.

Predictive modeling in Clostridium acetobutylicum fermentations employing Raman spectroscopy and multivariate data analysis for real-time culture monitoring

NASA Astrophysics Data System (ADS)

Zu, Theresah N. K.; Liu, Sanchao; Germane, Katherine L.; Servinsky, Matthew D.; Gerlach, Elliot S.; Mackie, David M.; Sund, Christian J.

2016-05-01

The coupling of optical fibers with Raman instrumentation has proven to be effective for real-time monitoring of chemical reactions and fermentations when combined with multivariate statistical data analysis. Raman spectroscopy is relatively fast, with little interference from the water peak present in fermentation media. Medical research has explored this technique for analysis of mammalian cultures for potential diagnosis of some cancers. Other organisms studied via this route include Escherichia coli, Saccharomyces cerevisiae, and some Bacillus sp., though very little work has been performed on Clostridium acetobutylicum cultures. C. acetobutylicum is a gram-positive anaerobic bacterium, which is highly sought after due to its ability to use a broad spectrum of substrates and produce useful byproducts through the well-known Acetone-Butanol-Ethanol (ABE) fermentation. In this work, real-time Raman data was acquired from C. acetobutylicum cultures grown on glucose. Samples were collected concurrently for comparative off-line product analysis. Partial-least squares (PLS) models were built both for agitated cultures and for static cultures from both datasets. Media components and metabolites monitored include glucose, butyric acid, acetic acid, and butanol. Models were cross-validated with independent datasets. Experiments with agitation were more favorable for modeling with goodness of fit (QY) values of 0.99 and goodness of prediction (Q2Y) values of 0.98. Static experiments did not model as well as agitated experiments. Raman results showed the static experiments were chaotic, especially during and shortly after manual sampling.

Decreased tongue pressure is associated with sarcopenia and sarcopenic dysphagia in the elderly.

PubMed

Maeda, Keisuke; Akagi, Junji

2015-02-01

The aim of this study was to clarify the association between tongue pressure and factors related to sarcopenia such as aging, activities of daily living, nutritional state, and dysphagia. One-hundred-and-four patients without a history of treatment of stroke and without a diagnosis of neurodegenerative disease (36 men and 68 women), with a mean age of 84.1 ± 5.6 years, hospitalized from May 2013 to June 2013 were included in this study. Maximum voluntary tongue pressure against the palate (MTP) was measured by a device consisting of a disposable oral balloon probe. Nutritional and anthropometric parameters such as serum albumin concentration, Mini-Nutritional Assessment short form (MNA-SF), body mass index, arm muscle area (AMA), and others and presence of sarcopenia and dysphagia were analyzed to evaluate their relationships. Correlation analysis and univariate or multivariate analysis were performed. Simple correlation analysis showed that MTP correlated with Barthel index (BI), MNA-SF, serum albumin concentration, body mass index, and AMA. Univariate and multivariate analysis showed that sarcopenia, BI, MNA-SF, and age were the independent explanatory factors for decreased MTP, and the propensity score for dysphagia, including causes of primary or secondary sarcopenia, and the presence of sarcopenia were significantly associated with the presence of dysphagia. Decreased MTP and dysphagia were related to sarcopenia or the causes of sarcopenia in the studied population. Furthermore, the clinical condition of sarcopenic dysphagia may be partially interpreted as the presence of sarcopenia and causal factors for sarcopenia.

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.

A multivariate variational objective analysis-assimilation method. Part 1: Development of the basic model

NASA Technical Reports Server (NTRS)

Achtemeier, Gary L.; Ochs, Harry T., III

1988-01-01

The variational method of undetermined multipliers is used to derive a multivariate model for objective analysis. The model is intended for the assimilation of 3-D fields of rawinsonde height, temperature and wind, and mean level temperature observed by satellite into a dynamically consistent data set. Relative measurement errors are taken into account. The dynamic equations are the two nonlinear horizontal momentum equations, the hydrostatic equation, and an integrated continuity equation. The model Euler-Lagrange equations are eleven linear and/or nonlinear partial differential and/or algebraic equations. A cyclical solution sequence is described. Other model features include a nonlinear terrain-following vertical coordinate that eliminates truncation error in the pressure gradient terms of the horizontal momentum equations and easily accommodates satellite observed mean layer temperatures in the middle and upper troposphere. A projection of the pressure gradient onto equivalent pressure surfaces removes most of the adverse impacts of the lower coordinate surface on the variational adjustment.

NMR Spectroscopy Identifies Metabolites Translocated from Powdery Mildew Resistant Rootstocks to Susceptible Watermelon Scions.

PubMed

Mahmud, Iqbal; Kousik, Chandrasekar; Hassell, Richard; Chowdhury, Kamal; Boroujerdi, Arezue F

2015-09-16

Powdery mildew (PM) disease causes significant loss in watermelon. Due to the unavailability of a commercial watermelon variety that is resistant to PM, grafting susceptible cultivars on wild resistant rootstocks is being explored as a short-term management strategy to combat this disease. Nuclear magnetic resonance-based metabolic profiles of susceptible and resistant rootstocks of watermelon and their corresponding susceptible scions (Mickey Lee) were compared to screen for potential metabolites related to PM resistance using multivariate principal component analysis. Significant score plot differences between the susceptible and resistant groups were revealed through Mahalanobis distance analysis. Significantly different spectral buckets and their corresponding metabolites (including choline, fumarate, 5-hydroxyindole-3-acetate, and melatonin) have been identified quantitatively using multivariate loading plots and verified by volcano plot analyses. The data suggest that these metabolites were translocated from the powdery mildew resistant rootstocks to their corresponding powdery mildew susceptible scions and can be related to PM disease resistance.

Boosting Higgs pair production in the [Formula: see text] final state with multivariate techniques.

PubMed

Behr, J Katharina; Bortoletto, Daniela; Frost, James A; Hartland, Nathan P; Issever, Cigdem; Rojo, Juan

2016-01-01

The measurement of Higgs pair production will be a cornerstone of the LHC program in the coming years. Double Higgs production provides a crucial window upon the mechanism of electroweak symmetry breaking and has a unique sensitivity to the Higgs trilinear coupling. We study the feasibility of a measurement of Higgs pair production in the [Formula: see text] final state at the LHC. Our analysis is based on a combination of traditional cut-based methods with state-of-the-art multivariate techniques. We account for all relevant backgrounds, including the contributions from light and charm jet mis-identification, which are ultimately comparable in size to the irreducible 4 b QCD background. We demonstrate the robustness of our analysis strategy in a high pileup environment. For an integrated luminosity of [Formula: see text] ab[Formula: see text], a signal significance of [Formula: see text] is obtained, indicating that the [Formula: see text] final state alone could allow for the observation of double Higgs production at the High Luminosity LHC.

Prognostic predictors of patients with carcinoma of the gastric cardia.

PubMed

Zhang, Ming; Li, Zhigao; Ma, Yan; Zhu, Guanyu; Zhang, Hongfeng; Xue, Yingwei

2012-05-01

This study gives insight into survival predictors and clinicopathological features of carcinoma of the gastric cardia. The study included 233 patients who underwent operation for carcinoma of the gastric cardia. Clinicopathological prognostic variables were evaluated as predictors of long-term survival by univariate and multivariate analysis. Cox regression was used for multivariate analysis and survival curves were drawn by the Kaplan- Meier method. Carcinoma of the gastric cardia was characterized by positive lymph node metastasis (77.3%), serosal invasion (83.3%) and more stage III or IV tumors (72.5%). Overall 5-year survival rate was 21.9% and median survival period was 24 months. The 5-year survival rate was influenced by tumor size, depth on invasion, lymph node metastasis, extent of lymph node dissection, disease stage, operation methods and resection margin. The absent of serosal invasion and lymph node metastasis, curative resection should be considered to be the favourable predictors of long-term survival of patients with carcinoma of the gastric cardia.

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.

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

Phase angle as bioelectrical marker to identify elderly patients at risk of sarcopenia.

PubMed

Basile, Claudia; Della-Morte, David; Cacciatore, Francesco; Gargiulo, Gaetano; Galizia, Gianluigi; Roselli, Mario; Curcio, Francesco; Bonaduce, Domenico; Abete, Pasquale

2014-10-01

Several markers have been associated with sarcopenia in the elderly, including bioelectrical indices. Phase angle (PhA) is an impedance parameter and it has been suggested as an indicator of cellular death. Thus, the relationship between PhA and muscle mass and strength was investigated in 207 consecutively elderly participants (mean age 76.2±6.7years) admitted for multidimensional geriatric evaluation. Muscle strength by grip strength using a hand-held dynamometer and muscle mass was measured by bioimpedentiometer. PhA was calculated directly with its arctangent (resistance/reactance×180°/π). Linear relationship among muscular mass and strength and with clinical and biochemical parameters, including PhA at uni- and multivariate analysis were performed. Linear regression analysis demonstrated that lower level of PhA is associated with reduction in grip strength (y=3.16+0.08x; r=0.49; p<0.001), and even more, with muscle mass (y=3.04+0.25x; r=0.60; p<0001). Multivariate analysis confirms these relationships (grip strength β=0.245, p=0.031; muscular mass β=0.623, p<0.01). Thus, PhA is inversely related to muscle mass and strength in elderly subjects and it may be considered a good bioelectrical marker to identify elderly patients at risk of sarcopenia. Copyright © 2014 Elsevier Inc. All rights reserved.

An efficient swarm intelligence approach to feature selection based on invasive weed optimization: Application to multivariate calibration and classification using spectroscopic data

NASA Astrophysics Data System (ADS)

Sheykhizadeh, Saheleh; Naseri, Abdolhossein

2018-04-01

Variable selection plays a key role in classification and multivariate calibration. Variable selection methods are aimed at choosing a set of variables, from a large pool of available predictors, relevant to the analyte concentrations estimation, or to achieve better classification results. Many variable selection techniques have now been introduced among which, those which are based on the methodologies of swarm intelligence optimization have been more respected during a few last decades since they are mainly inspired by nature. In this work, a simple and new variable selection algorithm is proposed according to the invasive weed optimization (IWO) concept. IWO is considered a bio-inspired metaheuristic mimicking the weeds ecological behavior in colonizing as well as finding an appropriate place for growth and reproduction; it has been shown to be very adaptive and powerful to environmental changes. In this paper, the first application of IWO, as a very simple and powerful method, to variable selection is reported using different experimental datasets including FTIR and NIR data, so as to undertake classification and multivariate calibration tasks. Accordingly, invasive weed optimization - linear discrimination analysis (IWO-LDA) and invasive weed optimization- partial least squares (IWO-PLS) are introduced for multivariate classification and calibration, respectively.

An efficient swarm intelligence approach to feature selection based on invasive weed optimization: Application to multivariate calibration and classification using spectroscopic data.

PubMed

Sheykhizadeh, Saheleh; Naseri, Abdolhossein

2018-04-05

Variable selection plays a key role in classification and multivariate calibration. Variable selection methods are aimed at choosing a set of variables, from a large pool of available predictors, relevant to the analyte concentrations estimation, or to achieve better classification results. Many variable selection techniques have now been introduced among which, those which are based on the methodologies of swarm intelligence optimization have been more respected during a few last decades since they are mainly inspired by nature. In this work, a simple and new variable selection algorithm is proposed according to the invasive weed optimization (IWO) concept. IWO is considered a bio-inspired metaheuristic mimicking the weeds ecological behavior in colonizing as well as finding an appropriate place for growth and reproduction; it has been shown to be very adaptive and powerful to environmental changes. In this paper, the first application of IWO, as a very simple and powerful method, to variable selection is reported using different experimental datasets including FTIR and NIR data, so as to undertake classification and multivariate calibration tasks. Accordingly, invasive weed optimization - linear discrimination analysis (IWO-LDA) and invasive weed optimization- partial least squares (IWO-PLS) are introduced for multivariate classification and calibration, respectively. Copyright © 2018 Elsevier B.V. All rights reserved.

A multivariate spatial mixture model for areal data: examining regional differences in standardized test scores

PubMed Central

Neelon, Brian; Gelfand, Alan E.; Miranda, Marie Lynn

2013-01-01

Summary Researchers in the health and social sciences often wish to examine joint spatial patterns for two or more related outcomes. Examples include infant birth weight and gestational length, psychosocial and behavioral indices, and educational test scores from different cognitive domains. We propose a multivariate spatial mixture model for the joint analysis of continuous individual-level outcomes that are referenced to areal units. The responses are modeled as a finite mixture of multivariate normals, which accommodates a wide range of marginal response distributions and allows investigators to examine covariate effects within subpopulations of interest. The model has a hierarchical structure built at the individual level (i.e., individuals are nested within areal units), and thus incorporates both individual- and areal-level predictors as well as spatial random effects for each mixture component. Conditional autoregressive (CAR) priors on the random effects provide spatial smoothing and allow the shape of the multivariate distribution to vary flexibly across geographic regions. We adopt a Bayesian modeling approach and develop an efficient Markov chain Monte Carlo model fitting algorithm that relies primarily on closed-form full conditionals. We use the model to explore geographic patterns in end-of-grade math and reading test scores among school-age children in North Carolina. PMID:26401059

Towards better process understanding: chemometrics and multivariate measurements in manufacturing of solid dosage forms.

PubMed

Matero, Sanni; van Den Berg, Frans; Poutiainen, Sami; Rantanen, Jukka; Pajander, Jari

2013-05-01

The manufacturing of tablets involves many unit operations that possess multivariate and complex characteristics. The interactions between the material characteristics and process related variation are presently not comprehensively analyzed due to univariate detection methods. As a consequence, current best practice to control a typical process is to not allow process-related factors to vary i.e. lock the production parameters. The problem related to the lack of sufficient process understanding is still there: the variation within process and material properties is an intrinsic feature and cannot be compensated for with constant process parameters. Instead, a more comprehensive approach based on the use of multivariate tools for investigating processes should be applied. In the pharmaceutical field these methods are referred to as Process Analytical Technology (PAT) tools that aim to achieve a thorough understanding and control over the production process. PAT includes the frames for measurement as well as data analyzes and controlling for in-depth understanding, leading to more consistent and safer drug products with less batch rejections. In the optimal situation, by applying these techniques, destructive end-product testing could be avoided. In this paper the most prominent multivariate data analysis measuring tools within tablet manufacturing and basic research on operations are reviewed. Copyright © 2013 Wiley Periodicals, Inc.

Risk factors for low receptive vocabulary abilities in the preschool and early school years in the longitudinal study of Australian children.

PubMed

Christensen, Daniel; Zubrick, Stephen R; Lawrence, David; Mitrou, Francis; Taylor, Catherine L

2014-01-01

Receptive vocabulary development is a component of the human language system that emerges in the first year of life and is characterised by onward expansion throughout life. Beginning in infancy, children's receptive vocabulary knowledge builds the foundation for oral language and reading skills. The foundations for success at school are built early, hence the public health policy focus on reducing developmental inequalities before children start formal school. The underlying assumption is that children's development is stable, and therefore predictable, over time. This study investigated this assumption in relation to children's receptive vocabulary ability. We investigated the extent to which low receptive vocabulary ability at 4 years was associated with low receptive vocabulary ability at 8 years, and the predictive utility of a multivariate model that included child, maternal and family risk factors measured at 4 years. The study sample comprised 3,847 children from the first nationally representative Longitudinal Study of Australian Children (LSAC). Multivariate logistic regression was used to investigate risks for low receptive vocabulary ability from 4-8 years and sensitivity-specificity analysis was used to examine the predictive utility of the multivariate model. In the multivariate model, substantial risk factors for receptive vocabulary delay from 4-8 years, in order of descending magnitude, were low receptive vocabulary ability at 4 years, low maternal education, and low school readiness. Moderate risk factors, in order of descending magnitude, were low maternal parenting consistency, socio-economic area disadvantage, low temperamental persistence, and NESB status. The following risk factors were not significant: One or more siblings, low family income, not reading to the child, high maternal work hours, and Aboriginal or Torres Strait Islander ethnicity. The results of the sensitivity-specificity analysis showed that a well-fitted multivariate model featuring risks of substantive magnitude does not do particularly well in predicting low receptive vocabulary ability from 4-8 years.

Risk factors for early failure after peripheral endovascular intervention: application of a reliability engineering approach.

PubMed

Meltzer, Andrew J; Graham, Ashley; Connolly, Peter H; Karwowski, John K; Bush, Harry L; Frazier, Peter I; Schneider, Darren B

2013-01-01

We apply an innovative and novel analytic approach, based on reliability engineering (RE) principles frequently used to characterize the behavior of manufactured products, to examine outcomes after peripheral endovascular intervention. We hypothesized that this would allow for improved prediction of outcome after peripheral endovascular intervention, specifically with regard to identification of risk factors for early failure. Patients undergoing infrainguinal endovascular intervention for chronic lower-extremity ischemia from 2005 to 2010 were identified in a prospectively maintained database. The primary outcome of failure was defined as patency loss detected by duplex ultrasonography, with or without clinical failure. Analysis included univariate and multivariate Cox regression models, as well as RE-based analysis including product life-cycle models and Weibull failure plots. Early failures were distinguished using the RE principle of "basic rating life," and multivariate models identified independent risk factors for early failure. From 2005 to 2010, 434 primary endovascular peripheral interventions were performed for claudication (51.8%), rest pain (16.8%), or tissue loss (31.3%). Fifty-five percent of patients were aged ≥75 years; 57% were men. Failure was noted after 159 (36.6%) interventions during a mean follow-up of 18 months (range, 0-71 months). Using multivariate (Cox) regression analysis, rest pain and tissue loss were independent predictors of patency loss, with hazard ratios of 2.5 (95% confidence interval, 1.6-4.1; P < 0.001) and 3.2 (95% confidence interval, 2.0-5.2, P < 0.001), respectively. The distribution of failure times for both claudication and critical limb ischemia fit distinct Weibull plots, with different characteristics: interventions for claudication demonstrated an increasing failure rate (β = 1.22, θ = 13.46, mean time to failure = 12.603 months, index of fit = 0.99037, R(2) = 0.98084), whereas interventions for critical limb ischemia demonstrated a decreasing failure rate, suggesting the predominance of early failures (β = 0.7395, θ = 6.8, mean time to failure = 8.2, index of fit = 0.99391, R(2) = 0.98786). By 3.1 months, 10% of interventions failed. This point (90% reliability) was identified as the basic rating life. Using multivariate analysis of failure data, independent predictors of early failure (before 3.1 months) included tissue loss, long lesion length, chronic total occlusions, heart failure, and end-stage renal disease. Application of a RE framework to the assessment of clinical outcomes after peripheral interventions is feasible, and potentially more informative than traditional techniques. Conceptualization of interventions as "products" permits application of product life-cycle models that allow for empiric definition of "early failure" may facilitate comparative effectiveness analysis and enable the development of individualized surveillance programs after endovascular interventions. Copyright © 2013 Annals of Vascular Surgery Inc. Published by Elsevier Inc. All rights reserved.

CoSMoMVPA: Multi-Modal Multivariate Pattern Analysis of Neuroimaging Data in Matlab/GNU Octave.

PubMed

Oosterhof, Nikolaas N; Connolly, Andrew C; Haxby, James V

2016-01-01

Recent years have seen an increase in the popularity of multivariate pattern (MVP) analysis of functional magnetic resonance (fMRI) data, and, to a much lesser extent, magneto- and electro-encephalography (M/EEG) data. We present CoSMoMVPA, a lightweight MVPA (MVP analysis) toolbox implemented in the intersection of the Matlab and GNU Octave languages, that treats both fMRI and M/EEG data as first-class citizens. CoSMoMVPA supports all state-of-the-art MVP analysis techniques, including searchlight analyses, classification, correlations, representational similarity analysis, and the time generalization method. These can be used to address both data-driven and hypothesis-driven questions about neural organization and representations, both within and across: space, time, frequency bands, neuroimaging modalities, individuals, and species. It uses a uniform data representation of fMRI data in the volume or on the surface, and of M/EEG data at the sensor and source level. Through various external toolboxes, it directly supports reading and writing a variety of fMRI and M/EEG neuroimaging formats, and, where applicable, can convert between them. As a result, it can be integrated readily in existing pipelines and used with existing preprocessed datasets. CoSMoMVPA overloads the traditional volumetric searchlight concept to support neighborhoods for M/EEG and surface-based fMRI data, which supports localization of multivariate effects of interest across space, time, and frequency dimensions. CoSMoMVPA also provides a generalized approach to multiple comparison correction across these dimensions using Threshold-Free Cluster Enhancement with state-of-the-art clustering and permutation techniques. CoSMoMVPA is highly modular and uses abstractions to provide a uniform interface for a variety of MVP measures. Typical analyses require a few lines of code, making it accessible to beginner users. At the same time, expert programmers can easily extend its functionality. CoSMoMVPA comes with extensive documentation, including a variety of runnable demonstration scripts and analysis exercises (with example data and solutions). It uses best software engineering practices including version control, distributed development, an automated test suite, and continuous integration testing. It can be used with the proprietary Matlab and the free GNU Octave software, and it complies with open source distribution platforms such as NeuroDebian. CoSMoMVPA is Free/Open Source Software under the permissive MIT license. Website: http://cosmomvpa.org Source code: https://github.com/CoSMoMVPA/CoSMoMVPA.

CoSMoMVPA: Multi-Modal Multivariate Pattern Analysis of Neuroimaging Data in Matlab/GNU Octave

PubMed Central

Oosterhof, Nikolaas N.; Connolly, Andrew C.; Haxby, James V.

2016-01-01

Recent years have seen an increase in the popularity of multivariate pattern (MVP) analysis of functional magnetic resonance (fMRI) data, and, to a much lesser extent, magneto- and electro-encephalography (M/EEG) data. We present CoSMoMVPA, a lightweight MVPA (MVP analysis) toolbox implemented in the intersection of the Matlab and GNU Octave languages, that treats both fMRI and M/EEG data as first-class citizens. CoSMoMVPA supports all state-of-the-art MVP analysis techniques, including searchlight analyses, classification, correlations, representational similarity analysis, and the time generalization method. These can be used to address both data-driven and hypothesis-driven questions about neural organization and representations, both within and across: space, time, frequency bands, neuroimaging modalities, individuals, and species. It uses a uniform data representation of fMRI data in the volume or on the surface, and of M/EEG data at the sensor and source level. Through various external toolboxes, it directly supports reading and writing a variety of fMRI and M/EEG neuroimaging formats, and, where applicable, can convert between them. As a result, it can be integrated readily in existing pipelines and used with existing preprocessed datasets. CoSMoMVPA overloads the traditional volumetric searchlight concept to support neighborhoods for M/EEG and surface-based fMRI data, which supports localization of multivariate effects of interest across space, time, and frequency dimensions. CoSMoMVPA also provides a generalized approach to multiple comparison correction across these dimensions using Threshold-Free Cluster Enhancement with state-of-the-art clustering and permutation techniques. CoSMoMVPA is highly modular and uses abstractions to provide a uniform interface for a variety of MVP measures. Typical analyses require a few lines of code, making it accessible to beginner users. At the same time, expert programmers can easily extend its functionality. CoSMoMVPA comes with extensive documentation, including a variety of runnable demonstration scripts and analysis exercises (with example data and solutions). It uses best software engineering practices including version control, distributed development, an automated test suite, and continuous integration testing. It can be used with the proprietary Matlab and the free GNU Octave software, and it complies with open source distribution platforms such as NeuroDebian. CoSMoMVPA is Free/Open Source Software under the permissive MIT license. Website: http://cosmomvpa.org Source code: https://github.com/CoSMoMVPA/CoSMoMVPA PMID:27499741

Assessment: Fad or Fact of Life? Proceedings of the North East Association for Institutional Research Annual Conference (14th, Rochester, New York, October 25-27, 1987).

ERIC Educational Resources Information Center

North East Association for Institutional Research.

Assessment in higher education is addressed in these proceedings of the 1987 conference of the North East Association for Institutional Research. Papers and authors include: "Assessing the Status of Assessment" (Peter T. Ewell); "Has the Middle Class Been Pressured the Most? Multivariate Analysis of Parental Contributions to Higher…

A simple ergonomic measure reduces fluoroscopy time during ERCP: A multivariate analysis.

PubMed

Jowhari, Fahd; Hopman, Wilma M; Hookey, Lawrence

2017-03-01

Background and study aims  Endoscopic retrograde cholangiopancreatgraphy (ERCP) carries a radiation risk to patients undergoing the procedure and the team performing it. Fluoroscopy time (FT) has been shown to have a linear relationship with radiation exposure during ERCP. Recent modifications to our ERCP suite design were felt to impact fluoroscopy time and ergonomics. This multivariate analysis was therefore undertaken to investigate these effects, and to identify and validate various clinical, procedural and ergonomic factors influencing the total fluoroscopy time during ERCP. This would better assist clinicians with predicting prolonged fluoroscopic durations and to undertake relevant precautions accordingly. Patients and methods  A retrospective analysis of 299 ERCPs performed by 4 endoscopists over an 18-month period, at a single tertiary care center was conducted. All inpatients/outpatients (121 males, 178 females) undergoing ERCP for any clinical indication from January 2012 to June 2013 in the chosen ERCP suite were included in the study. Various predetermined clinical, procedural and ergonomic factors were obtained via chart review. Univariate analyses identified factors to be included in the multivariate regression model with FT as the dependent variable. Results  Bringing the endoscopy and fluoroscopy screens next to each other was associated with a significantly lesser FT than when the screens were separated further (-1.4 min, P  = 0.026). Other significant factors associated with a prolonged FT included having a prior ERCP (+ 1.4 min, P  = 0.031), and more difficult procedures (+ 4.2 min for each level of difficulty, P  < 0.001). ERCPs performed by high-volume endoscopists used lesser FT vs. low-volume endoscopists (-1.82, P = 0.015). Conclusions  Our study has identified and validated various factors that affect the total fluoroscopy time during ERCP. This is the first study to show that decreasing the distance between the endoscopy and fluoroscopy screens in the ERCP suite significantly reduces the total fluoroscopy time, and therefore radiation exposure to patients and staff involved in the procedure.

Long-term outcomes and prognostic factors for patients with esophageal cancer following radiotherapy

PubMed Central

Chen, Chuang-Zhen; Chen, Jian-Zhou; Li, De-Rui; Lin, Zhi-Xiong; Zhou, Ming-Zhen; Li, Dong-Sheng; Chen, Zhi-Jian

2013-01-01

AIM: To evaluate long-term outcomes and prognostic factors for esophageal squamous cell carcinoma (SCC) treated with three dimensional conformal radiotherapy (3D-CRT). METHODS: Between January 2005 and December 2006, 153 patients (120 males, 33 females) with pathologically confirmed esophageal SCC and treated with 3D-CRT in Cancer Hospital of Shantou University were included in this retrospective analysis. Median age was 60 years (range: 37-84 years). The proportion of tumor location was as follows: upper thorax (including the cervical region), 73 (48%); middle thorax, 73 (48%); lower thorax, 7 (5%), respectively. The median radiation dose was 64 Gy (range: 50-74 Gy). Fifty four cases (35%) received cisplatin-based concurrent chemotherapy. Univariate and multivariate analysis were performed to determine the association between the correlative factors and prognosis. RESULTS: The five-year overall survival rate was 26.3%, with a median follow-up of 49 mo (range: 3-66 mo) for patients who were still alive. On univariate analysis, lesion location, lesion length by barium esophagogram, computed tomography imaging characteristics including Y diameter (anterior-posterior, AP, extent of tumor), gross tumor volume of primary lesion (GTV-E), volume of positive lymph nodes (GTV-LN), and the total target volume (GTV-T = GTV-E + GTV-LN) were prognostic for overall survival. By multivariate analysis, only the Y diameter [hazard ratio (HR) 2.219, 95%CI 1.141-4.316, P = 0.019] and the GTV-T (HR 1.372, 95%CI 1.044-1.803, P = 0.023) were independent prognostic factors for survival. CONCLUSION: The overall survival of esophageal carcinoma patients undergoing 3D-CRT was promising. The best predictors for survival were GTV-T and Y diameter. PMID:23539205

Long-term outcomes and prognostic factors for patients with esophageal cancer following radiotherapy.

PubMed

Chen, Chuang-Zhen; Chen, Jian-Zhou; Li, De-Rui; Lin, Zhi-Xiong; Zhou, Ming-Zhen; Li, Dong-Sheng; Chen, Zhi-Jian

2013-03-14

To evaluate long-term outcomes and prognostic factors for esophageal squamous cell carcinoma (SCC) treated with three dimensional conformal radiotherapy (3D-CRT). Between January 2005 and December 2006, 153 patients (120 males, 33 females) with pathologically confirmed esophageal SCC and treated with 3D-CRT in Cancer Hospital of Shantou University were included in this retrospective analysis. Median age was 60 years (range: 37-84 years). The proportion of tumor location was as follows: upper thorax (including the cervical region), 73 (48%); middle thorax, 73 (48%); lower thorax, 7 (5%), respectively. The median radiation dose was 64 Gy (range: 50-74 Gy). Fifty four cases (35%) received cisplatin-based concurrent chemotherapy. Univariate and multivariate analysis were performed to determine the association between the correlative factors and prognosis. The five-year overall survival rate was 26.3%, with a median follow-up of 49 mo (range: 3-66 mo) for patients who were still alive. On univariate analysis, lesion location, lesion length by barium esophagogram, computed tomography imaging characteristics including Y diameter (anterior-posterior, AP, extent of tumor), gross tumor volume of primary lesion (GTV-E), volume of positive lymph nodes (GTV-LN), and the total target volume (GTV-T = GTV-E + GTV-LN) were prognostic for overall survival. By multivariate analysis, only the Y diameter [hazard ratio (HR) 2.219, 95%CI 1.141-4.316, P = 0.019] and the GTV-T (HR 1.372, 95%CI 1.044-1.803, P = 0.023) were independent prognostic factors for survival. The overall survival of esophageal carcinoma patients undergoing 3D-CRT was promising. The best predictors for survival were GTV-T and Y diameter.

Visual Impairment Is Associated With Depressive Symptoms-Results From the Nationwide German DEGS1 Study.

PubMed

Schuster, Alexander K; Tesarz, Jonas; Rezapour, Jasmin; Beutel, Manfred E; Bertram, Bernd; Pfeiffer, Norbert

2018-01-01

Visual impairment (VI) is associated with a variety of comorbidities including physical and mental health in industrial countries. Our aim is to examine associations between self-reported impairment and depressive symptoms in the German population. The point prevalence of self-reported VI in Germany was computed using data from the German Health Interview and Examination Survey for adults from 2008 to 2011 ( N  = 7.783, 50.5% female, age range 18-79 years). VI was surveyed by two questions, one for seeing faces at a distance of 4 m and one for reading newspapers. Depressive symptoms were evaluated with the Patient Health Questionnaire-9 questionnaire and 2-week prevalence was computed with weighted data. Depressive symptoms were defined by a value of ≥10. Logistic regression analysis was performed to analyze an association between self-reported VI and depressive symptoms. Multivariable analysis including adjustment for age, gender, socioeconomic status, and chronic diseases were carried out with weighted data. The 2-week prevalence of depressive symptoms was 20.8% (95% CI: 16.6-25.7%) for some difficulties in distance vision and 14.4% (95% CI: 7.5-25.9%) for severe difficulties in distance vision, while 17.0% (95% CI: 13.3-21.4%), respectively, 16.7% (95% CI: 10.7-25.1%) for near vision. Analysis revealed that depressive symptoms were associated with self-reported VI for reading, respectively, with low VI for distance vision. Multivariable regression analysis including potential confounders confirmed these findings. Depressive symptoms are a frequent finding in subjects with difficulties in distance and near vision with a prevalence of up to 24%. Depressive comorbidity should therefore be evaluated in subjects reporting VI.

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

Comparison of family-planning service quality reported by adolescents and young adult women in Mexico.

PubMed

Darney, Blair G; Saavedra-Avendano, Biani; Sosa-Rubi, Sandra G; Lozano, Rafael; Rodriguez, Maria I

2016-07-01

Associations between age and patient-reported quality of family planning services were examined among young women in Mexico. A repeated cross-sectional analysis of survey data collected in 2006, 2009, and 2014 was performed. Data from women aged 15-29years who had not undergone sterilization and were currently using a modern contraceptive method were included. The primary outcome was high-quality care, defined as positive responses to all five quality items regarding contraceptive services included in the survey. Multivariable logistic regression and marginal probabilities were used to compare adolescents and women aged 20-29years. The responses of respondents using different contraceptive methods were compared. Data were included from 15 835 individuals. The multivariable analysis demonstrated lower odds of reporting high-quality care among women aged 15-19years (odds ratio 0.73; 95% confidence interval 0.60-0.88) and 20-24years (odds ratio 0.85; 95% confidence interval 0.75-0.96) compared with women aged 25-29years. Adolescents using hormonal and long-acting reversible contraception had significantly lower odds of reporting high-quality care compared with women aged 25-29. Adolescents in Mexico reported a lower quality of family planning services compared with young adult women. Continued research and policies are needed to improve the quality of contraceptive services. Copyright © 2016 International Federation of Gynecology and Obstetrics. Published by Elsevier Ireland Ltd. All rights reserved.

Risk factors for early post-operative neurological deterioration in dogs undergoing a cervical dorsal laminectomy or hemilaminectomy: 100 cases (2002-2014).

PubMed

Taylor-Brown, F E; Cardy, T J A; Liebel, F X; Garosi, L; Kenny, P J; Volk, H A; De Decker, S

2015-12-01

Early post-operative neurological deterioration is a well-known complication following dorsal cervical laminectomies and hemilaminectomies in dogs. This study aimed to evaluate potential risk factors for early post-operative neurological deterioration following these surgical procedures. Medical records of 100 dogs that had undergone a cervical dorsal laminectomy or hemilaminectomy between 2002 and 2014 were assessed retrospectively. Assessed variables included signalment, bodyweight, duration of clinical signs, neurological status before surgery, diagnosis, surgical site, type and extent of surgery and duration of procedure. Outcome measures were neurological status immediately following surgery and duration of hospitalisation. Univariate statistical analysis was performed to identify variables to be included in a multivariate model. Diagnoses included osseous associated cervical spondylomyelopathy (OACSM; n = 41), acute intervertebral disk extrusion (IVDE; 31), meningioma (11), spinal arachnoid diverticulum (10) and vertebral arch anomalies (7). Overall 54% (95% CI 45.25-64.75) of dogs were neurologically worse 48 h post-operatively. Multivariate statistical analysis identified four factors significantly related to early post-operative neurological outcome. Diagnoses of OACSM or meningioma were considered the strongest variables to predict early post-operative neurological deterioration, followed by higher (more severely affected) neurological grade before surgery and longer surgery time. This information can aid in the management of expectations of clinical staff and owners with dogs undergoing these surgical procedures. Copyright © 2015 Elsevier Ltd. All rights reserved.

Rapid discrimination of sea buckthorn berries from different H. rhamnoides subspecies by multi-step IR spectroscopy coupled with multivariate data analysis

NASA Astrophysics Data System (ADS)

Liu, Yue; Zhang, Ying; Zhang, Jing; Fan, Gang; Tu, Ya; Sun, Suqin; Shen, Xudong; Li, Qingzhu; Zhang, Yi

2018-03-01

As an important ethnic medicine, sea buckthorn was widely used to prevent and treat various diseases due to its nutritional and medicinal properties. According to the Chinese Pharmacopoeia, sea buckthorn was originated from H. rhamnoides, which includes five subspecies distributed in China. Confusion and misidentification usually occurred due to their similar morphology, especially in dried and powdered forms. Additionally, these five subspecies have vital differences in quality and physiological efficacy. This paper focused on the quick classification and identification method of sea buckthorn berry powders from five H. rhamnoides subspecies using multi-step IR spectroscopy coupled with multivariate data analysis. The holistic chemical compositions revealed by the FT-IR spectra demonstrated that flavonoids, fatty acids and sugars were the main chemical components. Further, the differences in FT-IR spectra regarding their peaks, positions and intensities were used to identify H. rhamnoides subspecies samples. The discrimination was achieved using principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA). The results showed that the combination of multi-step IR spectroscopy and chemometric analysis offered a simple, fast and reliable method for the classification and identification of the sea buckthorn berry powders from different H. rhamnoides subspecies.

[Application of chemometrics in composition-activity relationship research of traditional Chinese medicine].

PubMed

Han, Sheng-Nan

2014-07-01

Chemometrics is a new branch of chemistry which is widely applied to various fields of analytical chemistry. Chemometrics can use theories and methods of mathematics, statistics, computer science and other related disciplines to optimize the chemical measurement process and maximize access to acquire chemical information and other information on material systems by analyzing chemical measurement data. In recent years, traditional Chinese medicine has attracted widespread attention. In the research of traditional Chinese medicine, it has been a key problem that how to interpret the relationship between various chemical components and its efficacy, which seriously restricts the modernization of Chinese medicine. As chemometrics brings the multivariate analysis methods into the chemical research, it has been applied as an effective research tool in the composition-activity relationship research of Chinese medicine. This article reviews the applications of chemometrics methods in the composition-activity relationship research in recent years. The applications of multivariate statistical analysis methods (such as regression analysis, correlation analysis, principal component analysis, etc. ) and artificial neural network (such as back propagation artificial neural network, radical basis function neural network, support vector machine, etc. ) are summarized, including the brief fundamental principles, the research contents and the advantages and disadvantages. Finally, the existing main problems and prospects of its future researches are proposed.

Characterization and discrimination of raw and vinegar-baked Bupleuri radix based on UHPLC-Q-TOF-MS coupled with multivariate statistical analysis.

PubMed

Lei, Tianli; Chen, Shifeng; Wang, Kai; Zhang, Dandan; Dong, Lin; Lv, Chongning; Wang, Jing; Lu, Jincai

2018-02-01

Bupleuri Radix is a commonly used herb in clinic, and raw and vinegar-baked Bupleuri Radix are both documented in the Pharmacopoeia of People's Republic of China. According to the theories of traditional Chinese medicine, Bupleuri Radix possesses different therapeutic effects before and after processing. However, the chemical mechanism of this processing is still unknown. In this study, ultra-high-performance liquid chromatography with quadruple time-of-flight mass spectrometry coupled with multivariate statistical analysis including principal component analysis and orthogonal partial least square-discriminant analysis was developed to holistically compare the difference between raw and vinegar-baked Bupleuri Radix for the first time. As a result, 50 peaks in raw and processed Bupleuri Radix were detected, respectively, and a total of 49 peak chemical compounds were identified. Saikosaponin a, saikosaponin d, saikosaponin b 3 , saikosaponin e, saikosaponin c, saikosaponin b 2 , saikosaponin b 1 , 4''-O-acetyl-saikosaponin d, hyperoside and 3',4'-dimethoxy quercetin were explored as potential markers of raw and vinegar-baked Bupleuri Radix. This study has been successfully applied for global analysis of raw and vinegar-processed samples. Furthermore, the underlying hepatoprotective mechanism of Bupleuri Radix was predicted, which was related to the changes of chemical profiling. Copyright © 2017 John Wiley & Sons, Ltd.

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…

Transition from LDR to HDR brachytherapy for cervical cancer: Evaluation of tumor control, survival, and toxicity.

PubMed

Romano, K D; Pugh, K J; Trifiletti, D M; Libby, B; Showalter, T N

In 2012, our institution transitioned from low-dose-rate (LDR) brachytherapy to high dose-rate (HDR) brachytherapy. We report clinical outcomes after brachytherapy for cervical cancer at our institution over a continuous 10-year period. From 2004 to 2014, 258 women (184 LDR and 74 HDR) were treated with tandem and ovoid brachytherapy in the multidisciplinary management of International Federation of Gynecology and Obstetrics Stages IA-IVB cervical cancer. Clinical and treatment-related prognostic factors including age, stage, smoking status, relevant doses, and toxicity data were recorded. Median followup for the LDR and HDR groups was 46 months and 12 months, respectively. The majority of patients (92%) received external beam radiotherapy as well as concurrent chemotherapy (83%) before the start of brachytherapy. For all stages, the 1-year local control and overall survival (OS) rates were comparable between the LDR and HDR groups (87% vs. 81%, p = 0.12; and 75% vs. 85%, p = 0.16), respectively. Factors associated with OS on multivariate analysis include age, stage, and nodal involvement. On multivariate analysis, severe toxicity (acute or chronic) was higher with HDR than LDR (24% vs. 10%, p = 0.04). Additional prognostic factors associated with increased severe toxicity include former/current smokers and total dose to lymph nodes. This comparative retrospective analysis of a large cohort of women treated with brachytherapy demonstrates no significant difference in OS or local control between the LDR and HDR. Acute and chronic toxicity increased shortly after the implementation of HDR, highlighting the importance of continued refinement of HDR methods, including integrating advanced imaging. Copyright © 2016 American Brachytherapy Society. Published by Elsevier Inc. All rights reserved.

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

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.

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.

A multivariate analysis of youth violence and aggression: the influence of family, peers, depression, and media violence.

PubMed

Ferguson, Christopher J; San Miguel, Claudia; Hartley, Richard D

2009-12-01

To examine the multivariate nature of risk factors for youth violence including delinquent peer associations, exposure to domestic violence in the home, family conflict, neighborhood stress, antisocial personality traits, depression level, and exposure to television and video game violence. A population of 603 predominantly Hispanic children (ages 10-14 years) and their parents or guardians responded to multiple behavioral measures. Outcomes included aggression and rule-breaking behavior on the Child Behavior Checklist (CBCL), as well as violent and nonviolent criminal activity and bullying behavior. Delinquent peer influences, antisocial personality traits, depression, and parents/guardians who use psychological abuse in intimate relationships were consistent risk factors for youth violence and aggression. Neighborhood quality, parental use of domestic violence in intimate relationships, and exposure to violent television or video games were not predictive of youth violence and aggression. Childhood depression, delinquent peer association, and parental use of psychological abuse may be particularly fruitful avenues for future prevention or intervention efforts.

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.

The analysis of morphometric data on rocky mountain wolves and artic wolves using statistical method

NASA Astrophysics Data System (ADS)

Ammar Shafi, Muhammad; Saifullah Rusiman, Mohd; Hamzah, Nor Shamsidah Amir; Nor, Maria Elena; Ahmad, Noor’ani; Azia Hazida Mohamad Azmi, Nur; Latip, Muhammad Faez Ab; Hilmi Azman, Ahmad

2018-04-01

Morphometrics is a quantitative analysis depending on the shape and size of several specimens. Morphometric quantitative analyses are commonly used to analyse fossil record, shape and size of specimens and others. The aim of the study is to find the differences between rocky mountain wolves and arctic wolves based on gender. The sample utilised secondary data which included seven variables as independent variables and two dependent variables. Statistical modelling was used in the analysis such was the analysis of variance (ANOVA) and multivariate analysis of variance (MANOVA). The results showed there exist differentiating results between arctic wolves and rocky mountain wolves based on independent factors and gender.

Anal sphincter injury in vaginal deliveries complicated by shoulder dystocia.

PubMed

Hehir, Mark P; Rubeo, Zachary; Flood, Karen; Mardy, Anne H; O'Herlihy, Colm; Boylan, Peter C; D'Alton, Mary E

2018-03-01

Shoulder dystocia is an obstetric emergency that occurs in 0.2-3% of all cephalic vaginal deliveries. We hypothesized that because of the difficult nature of deliveries complicated by shoulder dystocia, the condition may be associated with anal sphincter injury. We sought to identify risk factors for obstetric anal sphincter injury in women with shoulder dystocia. This retrospective analysis included all cases of shoulder dystocia from 2007 to 2011 at two large tertiary referral centers, in the USA and Ireland. Details of maternal demographics, intrapartum characteristics, and delivery outcomes in cases of shoulder dystocia were analyzed. Univariate and multivariate analyses were used to describe the association between shoulder dystocia and anal sphincter injury. There were 685 cases of shoulder dystocia, and the rate of shoulder dystocia was similar at both institutions. The incidence of anal sphincter injury was 8.8% (60 out of 685). The rate was 14% (45 out of 324) in nulliparas and 4.2% (15 out of 361) in multiparas. Women with sphincter injury were more likely to be nulliparous (75% [45 out of 60] vs 45% [279 out of 625]; p < 0.0001), have had an operative vaginal delivery (50% [30 out of 60] vs 36% [226 out of 625]; p = 0.03) and require internal maneuvers (50% [30 out of 60] vs 32% [198 out of 625], p = 0.004) than those with an intact sphincter. On multivariate regression analysis, these predictors of sphincter injury remained significant when adjusted for other risk factors. Episiotomy was negatively associated with sphincter injury on multivariate regression analysis. In a retrospective cohort of 685 women with shoulder dystocia, the risk of anal sphincter injury is 9%. Risk factors include nulliparity, operative vaginal delivery, and use of internal maneuvers, whereas episiotomy was found to have a protective effect against anal sphincter injury during cases of shoulder dystocia.

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

Morganti, Alessio G.; Unit of Radiotherapy, Unit of General Oncology, Fondazione Giovanni Paolo II, Campobasso; Falconi, Massimo

Purpose: To determine the impact of chemoradiation therapy (CRT) on overall survival (OS) after resection of pancreatic adenocarcinoma. Methods and Materials: A multicenter retrospective review of 955 consecutive patients who underwent complete resection with macroscopically negative margins (R0-1) for invasive carcinoma (T1-4; N0-1; M0) of the pancreas was performed. Exclusion criteria included metastatic or unresectable disease at surgery, macroscopic residual disease (R2), treatment with intraoperative radiation therapy (IORT), and a histological diagnosis of no ductal carcinoma, or postoperative death (within 60 days of surgery). In all, 623 patients received postoperative radiation therapy (RT), 575 patients received concurrent chemotherapy (CT), and 462more » patients received adjuvant CT. Results: Median follow-up was 21.0 months. Median OS after adjuvant CRT was 39.9 versus 24.8 months after no adjuvant CRT (P<.001) and 27.8 months after CT alone (P<.001). Five-year OS was 41.2% versus 24.8% with and without postoperative CRT, respectively. The positive impact of CRT was confirmed by multivariate analysis (hazard ratio [HR] = 0.72; confidence interval [CI], 0.60-0.87; P=.001). Adverse prognostic factors identified by multivariate analysis included the following: R1 resection (HR = 1.17; CI = 1.07-1.28; P<.001), higher pT stage (HR = 1.23; CI = 1.11-1.37; P<.001), positive lymph nodes (HR = 1.27; CI = 1.15-1.41; P<.001), and tumor diameter >20 mm (HR = 1.14; CI = 1.05-1.23; P=.002). Multivariate analysis also showed a better prognosis in patients treated in centers with >10 pancreatic resections per year (HR = 0.87; CI = 0.78-0.97; P=.014) Conclusion: This study represents the largest comparative study on adjuvant therapy in patients after resection of carcinoma of the pancreas. Overall survival was better in patients who received adjuvant CRT.« less

Is the HAS-BLED score useful in predicting post-extraction bleeding in patients taking warfarin? A retrospective cohort study

PubMed Central

Kataoka, Toshiyuki; Hoshi, Keika; Ando, Tomohiro

2016-01-01

Objective Unexpected post-extraction bleeding is often experienced in clinical practice. Therefore, determining the risk of post-extraction bleeding in patients receiving anticoagulant therapy prior to surgery is beneficial. This study aimed to verify whether the HAS-BLED score was useful in predicting post-extraction bleeding in patients taking warfarin. Design Retrospective cohort study. Setting Department of Oral and Maxillofacial Surgery, Tokyo Women's Medical University. Participants Participants included 258 sequential cases (462 teeth) who had undergone tooth extraction between 1 January 2010 and 31 December 2012 while continuing warfarin therapy. Main outcome measure Post-extraction risk factors for bleeding. The following data were collected as the predicting variables for multivariate logistic analysis: the HAS-BLED score, extraction site, tooth type, stability of teeth, extraction procedure, prothrombin time-international normalised ratio value, platelet count and the use of concomitant antiplatelet agents. Results Post-extraction bleeding was noted in 21 (8.1%) of the 258 cases. Haemostasis was achieved with localised haemostatic procedures in all the cases of post-extraction bleeding. The HAS-BLED score was found to be insufficient in predicting post-extraction bleeding (area under the curve=0.548, p=0.867, multivariate analysis). The risk of post-extraction bleeding was approximately three times greater in patients taking concomitant oral antiplatelet agents (risk ratio=2.881, p=0.035, multivariate analysis). Conclusions The HAS-BLED score alone could not predict post-extraction bleeding. The concomitant use of oral antiplatelet agents was a risk factor for post-extraction bleeding. No episodes of post-extraction bleeding required more than local measures for haemostasis. However, because this was a retrospective study conducted at a single institution, large-scale prospective cohort studies, which include cases of outpatient tooth extraction, will be necessary in the future. PMID:26936909

Discrimination of honeys using colorimetric sensor arrays, sensory analysis and gas chromatography techniques.

PubMed

Tahir, Haroon Elrasheid; Xiaobo, Zou; Xiaowei, Huang; Jiyong, Shi; Mariod, Abdalbasit Adam

2016-09-01

Aroma profiles of six honey varieties of different botanical origins were investigated using colorimetric sensor array, gas chromatography-mass spectrometry (GC-MS) and descriptive sensory analysis. Fifty-eight aroma compounds were identified, including 2 norisoprenoids, 5 hydrocarbons, 4 terpenes, 6 phenols, 7 ketones, 9 acids, 12 aldehydes and 13 alcohols. Twenty abundant or active compounds were chosen as key compounds to characterize honey aroma. Discrimination of the honeys was subsequently implemented using multivariate analysis, including hierarchical clustering analysis (HCA) and principal component analysis (PCA). Honeys of the same botanical origin were grouped together in the PCA score plot and HCA dendrogram. SPME-GC/MS and colorimetric sensor array were able to discriminate the honeys effectively with the advantages of being rapid, simple and low-cost. Moreover, partial least squares regression (PLSR) was applied to indicate the relationship between sensory descriptors and aroma compounds. Copyright © 2016 Elsevier Ltd. All rights reserved.

Determining sources of elevated salinity in pre-hydraulic fracturing water quality data using a multivariate discriminant analysis model

NASA Astrophysics Data System (ADS)

Lautz, L. K.; Hoke, G. D.; Lu, Z.; Siegel, D. I.

2013-12-01

Hydraulic fracturing has the potential to introduce saline water into the environment due to migration of deep formation water to shallow aquifers and/or discharge of flowback water to the environment during transport and disposal. It is challenging to definitively identify whether elevated salinity is associated with hydraulic fracturing, in part, due to the real possibility of other anthropogenic sources of salinity in the human-impacted watersheds in which drilling is taking place and some formation water present naturally in shallow groundwater aquifers. We combined new and published chemistry data for private drinking water wells sampled across five southern New York (NY) counties overlying the Marcellus Shale (Broome, Chemung, Chenango, Steuben, and Tioga). Measurements include Cl, Na, Br, I, Ca, Mg, Ba, SO4, and Sr. We compared this baseline groundwater quality data in NY, now under a moratorium on hydraulic fracturing, with published chemistry data for 6 different potential sources of elevated salinity in shallow groundwater, including Appalachian Basin formation water, road salt runoff, septic effluent, landfill leachate, animal waste, and water softeners. A multivariate random number generator was used to create a synthetic, low salinity (< 20 mg/L Cl) groundwater data set (n=1000) based on the statistical properties of the observed low salinity groundwater. The synthetic, low salinity groundwater was then artificially mixed with variable proportions of different potential sources of salinity to explore chemical differences between groundwater impacted by formation water, road salt runoff, septic effluent, landfill leachate, animal waste, and water softeners. We then trained a multivariate, discriminant analysis model on the resulting data set to classify observed high salinity groundwater (> 20 mg/L Cl) as being affected by formation water, road salt, septic effluent, landfill leachate, animal waste, or water softeners. Single elements or pairs of elements (e.g. Cl and Br) were not effective at discriminating between sources of salinity, indicating multivariate methods are needed. The discriminant analysis model classified most accurately samples affected by formation water and landfill leachate, whereas those contaminated by road salt, animal waste, and water softeners were more likely to be discriminated as contaminated by a different source. Using this approach, no shallow groundwater samples from NY appear to be affected by formation water, suggesting the source of salinity pre-hydraulic fracturing is primarily a combination of road salt, septic effluent, landfill leachate, and animal waste.

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.

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.

Modern CACSD using the Robust-Control Toolbox

NASA Technical Reports Server (NTRS)

Chiang, Richard Y.; Safonov, Michael G.

1989-01-01

The Robust-Control Toolbox is a collection of 40 M-files which extend the capability of PC/PRO-MATLAB to do modern multivariable robust control system design. Included are robust analysis tools like singular values and structured singular values, robust synthesis tools like continuous/discrete H(exp 2)/H infinity synthesis and Linear Quadratic Gaussian Loop Transfer Recovery methods and a variety of robust model reduction tools such as Hankel approximation, balanced truncation and balanced stochastic truncation, etc. The capabilities of the toolbox are described and illustated with examples to show how easily they can be used in practice. Examples include structured singular value analysis, H infinity loop-shaping and large space structure model reduction.

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.

Comparing risk factors of HIV among hijra sex workers in Larkana and other cities of Pakistan: an analytical cross sectional study.

PubMed

Altaf, Arshad; Zahidie, Aysha; Agha, Ajmal

2012-04-10

In 2005, Pakistan was first labeled as a country with concentrated epidemic of Human Immunodeficiency Virus (HIV). This was revealed through second generation surveillance conducted by HIV/AIDS Surveillance Project (HASP). While injection drug users (IDUs) were driving the epidemic, subsequent surveys showed that Hijra (transgender) sex workers (HSWs) were emerging as the second most vulnerable group with an average national prevalence of 6.4%. An exceptionally high prevalence (27.6%) was found in Larkana, which is a small town on the right bank of river Indus near the ruins of Mohenjo-Daro in the province of Sindh. This paper presents the risk factors associated with high prevalence of HIV among HSWs in Larkana as compared to other cities of the country. Data were extracted for secondary analysis from 2008 Integrated behavioral and biological survey (IBBS) to compare HSWs living in Larkana with those living in other cities including Karachi and Hyderabad in Sindh; Lahore and Faisalabad in Punjab; and Peshawar in Khyber Pakhtunkhwa provinces. After descriptive analysis, univariate and multivariate analyses were performed to identify risk factors. P value of 0.25 or less was used to include factors in multivariate analysis. We compared 199 HSWs from Larkana with 420 HSWs from other cities. The average age of HSWs in Larkana was 26.42 (±5.4) years. Majority were Sindhi speaking (80%), uneducated (68%) and unmarried (97%). In univariate analysis, factors associated with higher prevalence of HIV in Larkana included younger age i.e. 20-24 years (OR: 5.8, CI: 2.809-12.15), being unmarried (OR: 2.4, CI: 1.0-5.7), sex work as the only mode of income (OR: 5.5, CI: 3.70-8.2) and longer duration of being involved in sex work 5-10 years (OR: 3.3, CI: 1.7-6.12). In multivariate logistic regression the HSWs from Larkana were more likely to lack knowledge regarding preventive measures against HIV (OR 11.9, CI: 3.4-41.08) and were more prone to use of alcohol during anal intercourse (OR: 6.3, CI: 2.77-17.797). Outreach programs focusing on safer sexual practices and VCT are urgently needed to address the upsurge of HIV among HSWs in Larkana.

Trochanteric entry femoral nails yield better femoral version and lower revision rates-A large cohort multivariate regression analysis.

PubMed

Yoon, Richard S; Gage, Mark J; Galos, David K; Donegan, Derek J; Liporace, Frank A

2017-06-01

Intramedullary nailing (IMN) has become the standard of care for the treatment of most femoral shaft fractures. Different IMN options include trochanteric and piriformis entry as well as retrograde nails, which may result in varying degrees of femoral rotation. The objective of this study was to analyze postoperative femoral version between three types of nails and to delineate any significant differences in femoral version (DFV) and revision rates. Over a 10-year period, 417 patients underwent IMN of a diaphyseal femur fracture (AO/OTA 32A-C). Of these patients, 316 met inclusion criteria and obtained postoperative computed tomography (CT) scanograms to calculate femoral version and were thus included in the study. In this study, our main outcome measure was the difference in femoral version (DFV) between the uninjured limb and the injured limb. The effect of the following variables on DFV and revision rates were determined via univariate, multivariate, and ordinal regression analyses: gender, age, BMI, ethnicity, mechanism of injury, operative side, open fracture, and table type/position. Statistical significance was set at p<0.05. A total of 316 patients were included. Piriformis entry nails made up the majority (n=141), followed by retrograde (n=108), then trochanteric entry nails (n=67). Univariate regression analysis revealed that a lower BMI was significantly associated with a lower DFV (p=0.006). Controlling for possible covariables, multivariate analysis yielded a significantly lower DFV for trochanteric entry nails than piriformis or retrograde nails (7.9±6.10 vs. 9.5±7.4 vs. 9.4±7.8°, p<0.05). Using revision as an endpoint, trochanteric entry nails also had a significantly lower revision rate, even when controlling for all other variables (p<0.05). Comparative, objective comparisons between DFV between different nails based on entry point revealed that trochanteric nails had a significantly lower DFV and a lower revision rate, even after regression analysis. However, this is not to state that the other nail types exhibited abnormal DFV. Translation to the clinical impact of a few degrees of DFV is also unknown. Future studies to more in-depth study the intricacies of femoral version may lead to improved technology in addition to potentially improved clinical outcomes. Copyright © 2017 Elsevier Ltd. All rights reserved.

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.

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.

Antimicrobial resistance: the major contribution of poor governance and corruption to this growing problem.

PubMed

Collignon, Peter; Athukorala, Prema-Chandra; Senanayake, Sanjaya; Khan, Fahad

2015-01-01

To determine how important governmental, social, and economic factors are in driving antibiotic resistance compared to the factors usually considered the main driving factors-antibiotic usage and levels of economic development. A retrospective multivariate analysis of the variation of antibiotic resistance in Europe in terms of human antibiotic usage, private health care expenditure, tertiary education, the level of economic advancement (per capita GDP), and quality of governance (corruption). The model was estimated using a panel data set involving 7 common human bloodstream isolates and covering 28 European countries for the period 1998-2010. Only 28% of the total variation in antibiotic resistance among countries is attributable to variation in antibiotic usage. If time effects are included the explanatory power increases to 33%. However when the control of corruption indicator is included as an additional variable, 63% of the total variation in antibiotic resistance is now explained by the regression. The complete multivariate regression only accomplishes an additional 7% in terms of goodness of fit, indicating that corruption is the main socioeconomic factor that explains antibiotic resistance. The income level of a country appeared to have no effect on resistance rates in the multivariate analysis. The estimated impact of corruption was statistically significant (p< 0.01). The coefficient indicates that an improvement of one unit in the corruption indicator is associated with a reduction in antibiotic resistance by approximately 0.7 units. The estimated coefficient of private health expenditure showed that one unit reduction is associated with a 0.2 unit decrease in antibiotic resistance. These findings support the hypothesis that poor governance and corruption contributes to levels of antibiotic resistance and correlate better than antibiotic usage volumes with resistance rates. We conclude that addressing corruption and improving governance will lead to a reduction in antibiotic resistance.

Minia, Egypt: Principal Component Analysis

PubMed

Abdelrehim, Marwa G; Mahfouz, Eman M; Ewis, Ashraf A; Seedhom, Amany E; Afifi, Hassan M; Shebl, Fatma M

2018-02-26

Background: Pancreatic cancer (PC) is a serious and rapidly progressing malignancy. Identifying risk factors including dietary elements is important to develop preventive strategies. This study focused on possible links between diet and PC. Methods: We conducted a case-control study including all PC patients diagnosed at Minia Cancer Center and controls from general population from June 2014 to December 2015. Dietary data were collected directly through personal interviews. Principal component analysis (PCA) was performed to identify dietary groups. The data were analyzed using crude odds ratios (ORs) and multivariable logistic regression with adjusted ORs and 95% confidence intervals (CIs). Results: A total of 75 cases and 149 controls were included in the study. PCA identified six dietary groups, labeled as cereals and grains, vegetables, proteins, dairy products, fruits, and sugars. Bivariate analysis showed that consumption of vegetables, fruits, sugars, and total energy intake were associated with change in PC risk. In multivariable-adjusted models comparing highest versus lowest levels of intake, we observed significant lower odds of PC in association with vegetable intake (OR 0.24; 95% CI, 0.07-0.85, P=0.012) and a higher likelihood with the total energy intake (OR 9.88; 95% CI, 2.56-38.09, P<0.0001). There was also a suggested link between high fruit consumption and reduced odds of PC. Conclusions: The study supports the association between dietary factors and the odds of PC development in Egypt. It was found that higher energy intake is associated with an increase in likelihood of PC, while increased vegetable consumption is associated with a lower odds ratio. Creative Commons Attribution License

Primary Surgery vs Radiotherapy for Early Stage Oral Cavity Cancer.

PubMed

Ellis, Mark A; Graboyes, Evan M; Wahlquist, Amy E; Neskey, David M; Kaczmar, John M; Schopper, Heather K; Sharma, Anand K; Morgan, Patrick F; Nguyen, Shaun A; Day, Terry A

2018-04-01

Objective The goal of this study is to determine the effect of primary surgery vs radiotherapy (RT) on overall survival (OS) in patients with early stage oral cavity squamous cell carcinoma (OCSCC). In addition, this study attempts to identify factors associated with receiving primary RT. Study Design Retrospective cohort study. Setting National Cancer Database (NCDB, 2004-2013). Subjects and Methods Reviewing the NCDB from 2004 to 2013, patients with early stage I to II OCSCC were identified. Kaplan-Meier estimates of survival, Cox regression analysis, and propensity score matching were used to examine differences in OS between primary surgery and primary RT. Multivariable logistic regression analysis was performed to identify factors associated with primary RT. Results Of the 20,779 patients included in the study, 95.4% (19,823 patients) underwent primary surgery and 4.6% (956 patients) underwent primary RT. After adjusting for covariates, primary RT was associated with an increased risk of mortality (adjusted hazard ratio [aHR], 1.97; 99% confidence interval [CI], 1.74-2.22). On multivariable analysis, factors associated with primary RT included age ≥70 years, black race, Medicaid or Medicare insurance, no insurance, oral cavity subsite other than tongue, clinical stage II disease, low-volume treatment facilities, and earlier treatment year. Conclusion Primary RT for early stage OCSCC is associated with increased mortality. Approximately 5% of patients receive primary RT; however, this percentage is decreasing. Patients at highest risk for receiving primary RT include those who are elderly, black, with public insurance, and treated at low-volume facilities.

Type of mask may impact on continuous positive airway pressure adherence in apneic patients.

PubMed

Borel, Jean Christian; Tamisier, Renaud; Dias-Domingos, Sonia; Sapene, Marc; Martin, Francis; Stach, Bruno; Grillet, Yves; Muir, Jean François; Levy, Patrick; Series, Frederic; Pepin, Jean-Louis

2013-01-01

In obstructive sleep apnea patients (OSA), continuous positive airway pressure (CPAP) adherence is crucial to improve symptoms and cardiometabolic outcomes. The choice of mask may influence CPAP adherence but this issue has never been addressed properly. To evaluate the impact of nasal pillows, nasal and oronasal masks on CPAP adherence in a cohort of OSA. Newly CPAP treated OSA participating in "Observatoire Sommeil de la Fédération de Pneumologie", a French national prospective cohort, were included between March 2009 and December 2011. Anthropometric data, medical history, OSA severity, sleepiness, depressive status, treatment modalities (auto-CPAP versus fixed pressure, pressure level, interface type, use of humidifiers) and CPAP-related side effects were included in multivariate analysis to determine independent variables associated with CPAP adherence. 2311 OSA (age = 57(12) years, apnea+hypopnea index = 41(21)/h, 29% female) were included. Nasal masks, oronasal masks and nasal pillows were used by 62.4, 26.2 and 11.4% of the patients, respectively. In univariate analysis, oronasal masks and nasal pillows were associated with higher risk of CPAP non-adherence. CPAP non-adherence was also associated with younger age, female gender, mild OSA, gastroesophageal reflux, depression status, low effective pressure and CPAP-related side effects. In multivariate analysis, CPAP non-adherence was associated with the use of oronasal masks (OR = 2.0; 95%CI = 1.6; 2.5), depression, low effective pressure, and side effects. As oronasal masks negatively impact on CPAP adherence, a nasal mask should be preferred as the first option. Patients on oronasal masks should be carefully followed.

Post-traumatic stress disorder status in a rescue group after the Wenchuan earthquake relief

PubMed Central

Huang, Junhua; Liu, Qunying; Li, Jinliang; Li, Xuejiang; You, Jin; Zhang, Liang; Tian, Changfu; Luan, Rongsheng

2013-01-01

Previous studies have suggested that the incidence of post-traumatic stress disorder in earthquake rescue workers is relatively high. Risk factors for this disorder include demographic characteristics, earthquake-related high-risk factors, risk factors in the rescue process, personality, social support and coping style. This study examined the current status of a unit of 1 040 rescue workers who participated in earthquake relief for the Wenchuan earthquake that occurred on May 12th, 2008. Post-traumatic stress disorder was diagnosed primarily using the Clinician-Administered Post-traumatic Stress Disorder Scale during structured interviews. Univariate and multivariate statistical analyses were used to examine major risk factors that contributed to the incidence of post-traumatic stress disorder. Results revealed that the incidence of this disorder in the rescue group was 5.96%. The impact factors in univariate analysis included death of family members, contact with corpses or witnessing of the deceased or seriously injured, near-death experience, severe injury or mental trauma in the rescue process and working at the epicenter of the earthquake. Correlation analysis suggested that post-traumatic stress disorder was positively correlated with psychotic and neurotic personalities, negative coping and low social support. Impact factors in multivariate logistic regression analysis included near-death experience, severe injury or mental trauma, working in the epicenter of the rescue, neurotic personality, negative coping and low social support, among which low social support had the largest odds ratio of 20.42. Findings showed that the occurrence of post-traumatic stress disorder was the result of the interaction of multiple factors. PMID:25206499

Factors predicting survival following complete surgical remission of pulmonary metastasis in osteosarcoma

PubMed Central

SALAH, SAMER; TOUBASI, SAMAR

2015-01-01

Pulmonary metastasectomy (PM) is associated with improved survival of patients with metastatic osteosarcoma; however, the factors affecting survival following achievement of complete surgical remission remain controversial. The main objective of this study was to report the outcomes and prognostic factors of osteosarcoma patients who achieved complete remission (CR) following PM. We analyzed the effect of demographic and disease-related characteristics on the overall survival (OS) of consecutive patients with metastatic osteosarcoma who were treated at a single institution and achieved CR following PM, through univariate and multivariate analyses. Between January, 2000 and August, 2013, 62 patients with metastatic osteosarcoma were treated and followed up at our institution. A total of 25 patients achieved CR following PM and were included in this analysis. The 5-year OS and disease-free survival following PM were 30 and 21%, respectively. The factors correlated with inferior OS in the univariate analysis included chondroblastic subtype, post-chemotherapy necrosis <90% in the primary tumor, metastasis detected during neoadjuvant or adjuvant chemotherapy and pathological identification of tumor cells reaching the visceral pleural surface of any of the resected nodules. In the multivariate analysis, the chondroblastic subtype was the sole independent adverse prognostic factor (HR=4.6, 95% CI: 1.0–21.3, P=0.044). Therefore, factors associated with tumor biology, including poor tumor necrosis in the primary tumor and detection of metastasis during primary chemotherapy, are associated with poor post-metastasectomy survival. In addition, chondroblastic subtype and visceral pleural involvement predicted poor prognosis in our series. PMID:25469287

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

PubMed

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

2015-10-01

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

SandiaMRCR

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

2012-01-05

SandiaMCR was developed to identify pure components and their concentrations from spectral data. This software efficiently implements the multivariate calibration regression alternating least squares (MCR-ALS), principal component analysis (PCA), and singular value decomposition (SVD). Version 3.37 also includes the PARAFAC-ALS Tucker-1 (for trilinear analysis) algorithms. The alternating least squares methods can be used to determine the composition without or with incomplete prior information on the constituents and their concentrations. It allows the specification of numerous preprocessing, initialization and data selection and compression options for the efficient processing of large data sets. The software includes numerous options including the definition ofmore » equality and non-negativety constraints to realistically restrict the solution set, various normalization or weighting options based on the statistics of the data, several initialization choices and data compression. The software has been designed to provide a practicing spectroscopist the tools required to routinely analysis data in a reasonable time and without requiring expert intervention.« less

Usual Dietary Intakes: SAS Macros for Fitting Multivariate Measurement Error Models & Estimating Multivariate Usual Intake Distributions

Cancer.gov

The following SAS macros can be used to create a multivariate usual intake distribution for multiple dietary components that are consumed nearly every day or episodically. A SAS macro for performing balanced repeated replication (BRR) variance estimation is also included.

A Robust Bayesian Approach for Structural Equation Models with Missing Data

ERIC Educational Resources Information Center

Lee, Sik-Yum; Xia, Ye-Mao

2008-01-01

In this paper, normal/independent distributions, including but not limited to the multivariate t distribution, the multivariate contaminated distribution, and the multivariate slash distribution, are used to develop a robust Bayesian approach for analyzing structural equation models with complete or missing data. In the context of a nonlinear…

Are prostatic calculi independent predictive factors of lower urinary tract symptoms?

PubMed Central

Park, Sung-Woo; Nam, Jong-Kil; Lee, Sang-Don; Chung, Moon-Kee

2010-01-01

We determined the correlation between prostatic calculi and lower urinary tract symptoms (LUTS), as well as the predisposing factors of prostatic calculi. Of the 1 527 patients who presented at our clinic for LUTS, 802 underwent complete evaluations, including transrectal ultrasonography, voided bladder-3 specimen and international prostatic symptoms score (IPSS). A total of 335 patients with prostatic calculi and 467 patients without prostatic calculi were divided into calculi and no calculi groups, respectively. Predictive factors of severe LUTS and prostatic calculi were determined using uni/multivariate analysis. The overall IPSS score was 15.7 ± 9.2 and 14.1 ± 9.2 in the calculi and no calculi group, respectively (P = 0.013). The maximum flow rate was 12.1 ± 6.9 and 14.2 ± 8.2 mL s−1 in the calculi and no calculi group, respectively (P = 0.003). On univariate analysis for predicting factors of severe LUTS, differences on age (P = 0.042), prostatic calculi (P = 0.048) and prostatitis (P = 0.018) were statistically significant. However, on multivariate analysis, no factor was significant. On multivariate analysis for predisposing factors of prostatic calculi, differences on age (P < 0.001) and prostate volume (P = 0.001) were significant. To our knowledge, patients who have prostatic calculi complain of more severe LUTS. However, prostatic calculi are not an independent predictive factor of severe LUTS. Therefore, men with prostatic calculi have more severe LUTS not only because of prostatic calculi but also because of age and other factors. In addition, old age and large prostate volume are independent predisposing factors for prostatic calculi. PMID:19966831

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.

Factors affecting survival outcomes of patients with non-metastatic Ewing's sarcoma family tumors in the spine: a retrospective analysis of 63 patients in a single center.

PubMed

Wan, Wei; Lou, Yan; Hu, Zhiqi; Wang, Ting; Li, Jinsong; Tang, Yu; Wu, Zhipeng; Xu, Leqin; Yang, Xinghai; Song, Dianwen; Xiao, Jianru

2017-01-01

Little information has been published in the literature regarding survival outcomes of patients with Ewing's sarcoma family tumors (ESFTs) of the spine. The purpose of this study is to explore factors that may affect the prognosis of patients with non-metastatic spinal ESFTs. A retrospective analysis of survival outcomes was performed in patients with non-metastatic spinal ESFTs. Univariate and multivariate analyses were employed to identify prognostic factors for recurrence and survival. Recurrence-free survival (RFS) and overall survival (OS) were defined as the date of surgery to the date of local relapse and death. Kaplan-Meier methods were applied to estimate RFS and OS. Log-rank test was used to analyze single factors for RFS and OS. Factors with p values ≤0.1 were subjected to multivariate analysis. A total of 63 patients with non-metastatic spinal ESFTs were included in this study. The mean follow-up period was 35.1 months (range 1-155). Postoperative recurrence was detected in 25 patients, and distant metastasis and death occurred in 22 and 36 patients respectively. The result of multivariate analysis suggested that age older than 25 years and neoadjuvant chemotherapy were favorable independent prognostic factors for RFS and OS. In addition, total en-bloc resection, postoperative chemotherapy, radiotherapy and non-distant metastasis were favorable independent prognostic factors for OS. Age older than 25 years and neoadjuvant chemotherapy are favorable prognostic factors for both RFS and OS. In addition, total en-bloc resection, postoperative chemotherapy, radiotherapy and non-distant metastasis are closely associated with favorable survival.

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.

The Cancer of the Prostate Risk Assessment (CAPRA) score predicts biochemical recurrence in intermediate-risk prostate cancer treated with external beam radiotherapy (EBRT) dose escalation or low-dose rate (LDR) brachytherapy.

PubMed

Krishnan, Vimal; Delouya, Guila; Bahary, Jean-Paul; Larrivée, Sandra; Taussky, Daniel

2014-12-01

To study the prognostic value of the University of California, San Francisco Cancer of the Prostate Risk Assessment (CAPRA) score to predict biochemical failure (bF) after various doses of external beam radiotherapy (EBRT) and/or permanent seed low-dose rate (LDR) prostate brachytherapy (PB). We retrospectively analysed 345 patients with intermediate-risk prostate cancer, with PSA levels of 10-20 ng/mL and/or Gleason 7 including 244 EBRT patients (70.2-79.2 Gy) and 101 patients treated with LDR PB. The minimum follow-up was 3 years. No patient received primary androgen-deprivation therapy. bF was defined according to the Phoenix definition. Cox regression analysis was used to estimate the differences between CAPRA groups. The overall bF rate was 13% (45/345). The CAPRA score, as a continuous variable, was statistically significant in multivariate analysis for predicting bF (hazard ratio [HR] 1.37, 95% confidence interval [CI] 1.10-1.72, P = 0.006). There was a trend for a lower bF rate in patients treated with LDR PB when compared with those treated by EBRT ≤ 74 Gy (HR 0.234, 95% CI 0.05-1.03, P = 0.055) in multivariate analysis. In the subgroup of patients with a CAPRA score of 3-5, CAPRA remained predictive of bF as a continuous variable (HR 1.51, 95% CI 1.01-2.27, P = 0.047) in multivariate analysis. The CAPRA score is useful for predicting biochemical recurrence in patients treated for intermediate-risk prostate cancer with EBRT or LDR PB. It could help in treatment decisions. © 2013 The Authors. BJU International © 2013 BJU International.

The association between tranexamic acid and convulsive seizures after cardiac surgery: a multivariate analysis in 11 529 patients.

PubMed

Sharma, V; Katznelson, R; Jerath, A; Garrido-Olivares, L; Carroll, J; Rao, V; Wasowicz, M; Djaiani, G

2014-02-01

Because of a lack of contemporary data regarding seizures after cardiac surgery, we undertook a retrospective analysis of prospectively collected data from 11 529 patients in whom cardiopulmonary bypass was used from January 2004 to December 2010. A convulsive seizure was defined as a transient episode of disturbed brain function characterised by abnormal involuntary motor movements. Multivariate regression analysis was performed to identify independent predictors of postoperative seizures. A total of 100 (0.9%) patients developed postoperative convulsive seizures. Generalised and focal seizures were identified in 68 and 32 patients, respectively. The median (IQR [range]) time after surgery when the seizure occurred was 7 (6-12 [1-216]) h and 8 (6-11 [4-18]) h, respectively. Epileptiform findings on electroencephalography were seen in 19 patients. Independent predictors of postoperative seizures included age, female sex, redo cardiac surgery, calcification of ascending aorta, congestive heart failure, deep hypothermic circulatory arrest, duration of aortic cross-clamp and tranexamic acid. When tested in a multivariate regression analysis, tranexamic acid was a strong independent predictor of seizures (OR 14.3, 95% CI 5.5-36.7; p < 0.001). Patients with convulsive seizures had 2.5 times higher in-hospital mortality rates and twice the length of hospital stay compared with patients without convulsive seizures. Mean (IQR [range]) length of stay in the intensive care unit was 115 (49-228 [32-481]) h in patients with convulsive seizures compared with 26 (22-69 [14-1080]) h in patients without seizures (p < 0.001). Convulsive seizures are a serious postoperative complication after cardiac surgery. As tranexamic acid is the only modifiable factor, its administration, particularly in doses exceeding 80 mg.kg(-1), should be weighed against the risk of postoperative seizures.

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.

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.

[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.

Preliminary Cost Model for Space Telescopes

NASA Technical Reports Server (NTRS)

Stahl, H. Philip; Prince, F. Andrew; Smart, Christian; Stephens, Kyle; Henrichs, Todd

2009-01-01

Parametric cost models are routinely used to plan missions, compare concepts and justify technology investments. However, great care is required. Some space telescope cost models, such as those based only on mass, lack sufficient detail to support such analysis and may lead to inaccurate conclusions. Similarly, using ground based telescope models which include the dome cost will also lead to inaccurate conclusions. This paper reviews current and historical models. Then, based on data from 22 different NASA space telescopes, this paper tests those models and presents preliminary analysis of single and multi-variable space telescope cost models.

Visual classification of very fine-grained sediments: Evaluation through univariate and multivariate statistics

USGS Publications Warehouse

Hohn, M. Ed; Nuhfer, E.B.; Vinopal, R.J.; Klanderman, D.S.

1980-01-01

Classifying very fine-grained rocks through fabric elements provides information about depositional environments, but is subject to the biases of visual taxonomy. To evaluate the statistical significance of an empirical classification of very fine-grained rocks, samples from Devonian shales in four cored wells in West Virginia and Virginia were measured for 15 variables: quartz, illite, pyrite and expandable clays determined by X-ray diffraction; total sulfur, organic content, inorganic carbon, matrix density, bulk density, porosity, silt, as well as density, sonic travel time, resistivity, and ??-ray response measured from well logs. The four lithologic types comprised: (1) sharply banded shale, (2) thinly laminated shale, (3) lenticularly laminated shale, and (4) nonbanded shale. Univariate and multivariate analyses of variance showed that the lithologic classification reflects significant differences for the variables measured, difference that can be detected independently of stratigraphic effects. Little-known statistical methods found useful in this work included: the multivariate analysis of variance with more than one effect, simultaneous plotting of samples and variables on canonical variates, and the use of parametric ANOVA and MANOVA on ranked data. ?? 1980 Plenum Publishing Corporation.

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

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

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.

Predicting Outcomes After Chemo-Embolization in Patients with Advanced-Stage Hepatocellular Carcinoma: An Evaluation of Different Radiologic Response Criteria

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

Gunn, Andrew J., E-mail: agunn@uabmc.edu; Sheth, Rahul A.; Luber, Brandon

2017-01-15

PurposeThe purpse of this study was to evaluate the ability of various radiologic response criteria to predict patient outcomes after trans-arterial chemo-embolization with drug-eluting beads (DEB-TACE) in patients with advanced-stage (BCLC C) hepatocellular carcinoma (HCC).Materials and methodsHospital records from 2005 to 2011 were retrospectively reviewed. Non-infiltrative lesions were measured at baseline and on follow-up scans after DEB-TACE according to various common radiologic response criteria, including guidelines of the World Health Organization (WHO), Response Evaluation Criteria in Solid Tumors (RECIST), the European Association for the Study of the Liver (EASL), and modified RECIST (mRECIST). Statistical analysis was performed to see which,more » if any, of the response criteria could be used as a predictor of overall survival (OS) or time-to-progression (TTP).Results75 patients met inclusion criteria. Median OS and TTP were 22.6 months (95 % CI 11.6–24.8) and 9.8 months (95 % CI 7.1–21.6), respectively. Univariate and multivariate Cox analyses revealed that none of the evaluated criteria had the ability to be used as a predictor for OS or TTP. Analysis of the C index in both univariate and multivariate models showed that the evaluated criteria were not accurate predictors of either OS (C-statistic range: 0.51–0.58 in the univariate model; range: 0.54–0.58 in the multivariate model) or TTP (C-statistic range: 0.55–0.59 in the univariate model; range: 0.57–0.61 in the multivariate model).ConclusionCurrent response criteria are not accurate predictors of OS or TTP in patients with advanced-stage HCC after DEB-TACE.« less

Prenatal Sonographic Predictors of Neonatal Coarctation of the Aorta.

PubMed

Anuwutnavin, Sanitra; Satou, Gary; Chang, Ruey-Kang; DeVore, Greggory R; Abuel, Ashley; Sklansky, Mark

2016-11-01

To identify practical prenatal sonographic markers for the postnatal diagnosis of coarctation of the aorta. We reviewed the fetal echocardiograms and postnatal outcomes of fetal cases of suspected coarctation of the aorta seen at a single institution between 2010 and 2014. True- and false-positive cases were compared. Logistic regression analysis was used to determine echocardiographic predictors of coarctation of the aorta. Optimal cutoffs for these markers and a multivariable threshold scoring system were derived to discriminate fetuses with coarctation of the aorta from those without coarctation of the aorta. Among 35 patients with prenatal suspicion of coarctation of the aorta, the diagnosis was confirmed postnatally in 9 neonates (25.7% true-positive rate). Significant predictors identified from multivariate analysis were as follows: Z score for the ascending aorta diameter of -2 or less (P = < .001), Z score for the mitral valve annulus of -2 or less (P= .033), Zscore for the transverse aortic arch diameter of -2 or less (P= .028), and abnormal aortic valve morphologic features (P= .026). Among all variables studied, the ascending aortic Z score had the highest sensitivity (78%) and specificity (92%) for detection of coarctation of the aorta. A multivariable threshold scoring system identified fetuses with coarctation of the aorta with still greater sensitivity (89%) and only mildly decreased specificity (88%). The finding of a diminutive ascending aorta represents a powerful and practical prenatal predictor of neonatal coarctation of the aorta. A multivariable scoring system, including dimensions of the ascending and transverse aortas, mitral valve annulus, and morphologic features of the aortic valve, provides excellent sensitivity and specificity. The use of these practical sonographic markers may improve prenatal detection of coarctation of the aorta. © 2016 by the American Institute of Ultrasound in Medicine.

Predicting Outcomes After Chemo-Embolization in Patients with Advanced-Stage Hepatocellular Carcinoma: An Evaluation of Different Radiologic Response Criteria.

PubMed

Gunn, Andrew J; Sheth, Rahul A; Luber, Brandon; Huynh, Minh-Huy; Rachamreddy, Niranjan R; Kalva, Sanjeeva P

2017-01-01

The purpse of this study was to evaluate the ability of various radiologic response criteria to predict patient outcomes after trans-arterial chemo-embolization with drug-eluting beads (DEB-TACE) in patients with advanced-stage (BCLC C) hepatocellular carcinoma (HCC). Hospital records from 2005 to 2011 were retrospectively reviewed. Non-infiltrative lesions were measured at baseline and on follow-up scans after DEB-TACE according to various common radiologic response criteria, including guidelines of the World Health Organization (WHO), Response Evaluation Criteria in Solid Tumors (RECIST), the European Association for the Study of the Liver (EASL), and modified RECIST (mRECIST). Statistical analysis was performed to see which, if any, of the response criteria could be used as a predictor of overall survival (OS) or time-to-progression (TTP). 75 patients met inclusion criteria. Median OS and TTP were 22.6 months (95 % CI 11.6-24.8) and 9.8 months (95 % CI 7.1-21.6), respectively. Univariate and multivariate Cox analyses revealed that none of the evaluated criteria had the ability to be used as a predictor for OS or TTP. Analysis of the C index in both univariate and multivariate models showed that the evaluated criteria were not accurate predictors of either OS (C-statistic range: 0.51-0.58 in the univariate model; range: 0.54-0.58 in the multivariate model) or TTP (C-statistic range: 0.55-0.59 in the univariate model; range: 0.57-0.61 in the multivariate model). Current response criteria are not accurate predictors of OS or TTP in patients with advanced-stage HCC after DEB-TACE.

THE AFRICAN DESCENT AND GLAUCOMA EVALUATION STUDY (ADAGES): PREDICTORS OF VISUAL FIELD DAMAGE IN GLAUCOMA SUSPECTS

PubMed Central

Khachatryan, Naira; Medeiros, Felipe A.; Sharpsten, Lucie; Bowd, Christopher; Sample, Pamela A.; Liebmann, Jeffrey M.; Girkin, Christopher A.; Weinreb, Robert N.; Miki, Atsuya; Hammel, Na’ama; Zangwill, Linda M.

2015-01-01

Purpose To evaluate racial differences in the development of visual field (VF) damage in glaucoma suspects. Design Prospective, observational cohort study. Methods Six hundred thirty six eyes from 357 glaucoma suspects with normal VF at baseline were included from the multicenter African Descent and Glaucoma Evaluation Study (ADAGES). Racial differences in the development of VF damage were examined using multivariable Cox Proportional Hazard models. Results Thirty one (25.4%) of 122 African descent participants and 47 (20.0%) of 235 European descent participants developed VF damage (p=0.078). In multivariable analysis, worse baseline VF mean deviation, higher mean arterial pressure during follow up, and a race *mean intraocular pressure (IOP) interaction term were significantly associated with the development of VF damage suggesting that racial differences in the risk of VF damage varied by IOP. At higher mean IOP levels, race was predictive of the development of VF damage even after adjusting for potentially confounding factors. At mean IOPs during follow-up of 22, 24 and 26 mmHg, multivariable hazard ratios (95%CI) for the development of VF damage in African descent compared to European descent subjects were 2.03 (1.15–3.57), 2.71 (1.39–5.29), and 3.61 (1.61–8.08), respectively. However, at lower mean IOP levels (below 22 mmHg) during follow-up, African descent was not predictive of the development of VF damage. Conclusion In this cohort of glaucoma suspects with similar access to treatment, multivariate analysis revealed that at higher mean IOP during follow-up, individuals of African descent were more likely to develop VF damage than individuals of European descent. PMID:25597839

Apolipoprotein E Polymorphism and Left Ventricular Failure in Beta-Thalassemia: A Multivariate Meta-Analysis.

PubMed

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

2017-09-01

Apolipoprotein E (ApoE) is potentially a genetic risk factor for the development of left ventricular failure (LVF), the main cause of death in beta-thalassemia homozygotes. In the present study, we synthesize the results of independent studies examining the effect of ApoE on LVF development in thalassemic patients through a meta-analytic approach. However, all studies report more than one outcome, as patients are classified into three groups according to the severity of the symptoms and the genetic polymorphism. Thus, a multivariate meta-analytic method that addresses simultaneously multiple exposures and multiple comparison groups was developed. Four individual studies were included in the meta-analysis involving 613 beta-thalassemic patients and 664 controls. The proposed method that takes into account the correlation of log odds ratios (log(ORs)), revealed a statistically significant overall association (P-value  =  0.009), mainly attributed to the contrast of E4 versus E3 allele for patients with evidence (OR: 2.32, 95% CI: 1.19, 4.53) or patients with clinical and echocardiographic findings (OR: 3.34, 95% CI: 1.78, 6.26) of LVF. This study suggests that E4 is a genetic risk factor for LVF in beta-thalassemia major. The presented multivariate approach can be applied in several fields of research. © 2017 John Wiley & Sons Ltd/University College London.

Comparison of DNA aneuploidy, chromosome 1 abnormalities, MYCN amplification and CD44 expression as prognostic factors in neuroblastoma.

PubMed

Christiansen, H; Sahin, K; Berthold, F; Hero, B; Terpe, H J; Lampert, F

1995-01-01

A comparison of the prognostic impact of five molecular variables in a large series was made, including tests of their nonrandom association and multivariate analysis. Molecular data were available for 377 patients and MYCN amplification, cytogenetic chromosome 1p deletion, loss of chromosome 1p heterozygosity, DNA ploidy and CD44 expression were investigated. Their interdependence and influence on event-free survival was tested uni- and multivariately using Pearson's chi 2-test, Kaplan-Meier estimates, log rank tests and the Cox's regression model. MYCN amplification was present in 18% (58/322) of cases and predicted poorer prognosis in localised (P < 0.001), metastatic (P = 0.002) and even 4S (P = 0.040) disease. CD44 expression was found in 86% (127/148) of cases, and was a marker for favourable outcome in patients with neuroblastoma stages 1-3 (P = 0.003) and 4 (P = 0.017). Chromosome 1p deletion was cytogenetically detected in 51% (28/55), and indicated reduced event-free survival in localised neuroblastoma (P = 0.020). DNA ploidy and loss of heterozygosity on chromosome 1p were of less prognostic value. Most factors of prognostic significance were associated with each other. By multivariate analysis, MYCN was selected as the only relevant factor. Risk estimation of high discriminating power is, therefore, possible for patients with localised and metastatic neuroblastoma using stage and MYCN.

Bayesian bivariate meta-analysis of correlated effects: Impact of the prior distributions on the between-study correlation, borrowing of strength, and joint inferences

PubMed Central

Bujkiewicz, Sylwia; Riley, Richard D

2016-01-01

Multivariate random-effects meta-analysis allows the joint synthesis of correlated results from multiple studies, for example, for multiple outcomes or multiple treatment groups. In a Bayesian univariate meta-analysis of one endpoint, the importance of specifying a sensible prior distribution for the between-study variance is well understood. However, in multivariate meta-analysis, there is little guidance about the choice of prior distributions for the variances or, crucially, the between-study correlation, ρB; for the latter, researchers often use a Uniform(−1,1) distribution assuming it is vague. In this paper, an extensive simulation study and a real illustrative example is used to examine the impact of various (realistically) vague prior distributions for ρB and the between-study variances within a Bayesian bivariate random-effects meta-analysis of two correlated treatment effects. A range of diverse scenarios are considered, including complete and missing data, to examine the impact of the prior distributions on posterior results (for treatment effect and between-study correlation), amount of borrowing of strength, and joint predictive distributions of treatment effectiveness in new studies. Two key recommendations are identified to improve the robustness of multivariate meta-analysis results. First, the routine use of a Uniform(−1,1) prior distribution for ρB should be avoided, if possible, as it is not necessarily vague. Instead, researchers should identify a sensible prior distribution, for example, by restricting values to be positive or negative as indicated by prior knowledge. Second, it remains critical to use sensible (e.g. empirically based) prior distributions for the between-study variances, as an inappropriate choice can adversely impact the posterior distribution for ρB, which may then adversely affect inferences such as joint predictive probabilities. These recommendations are especially important with a small number of studies and missing data. PMID:26988929

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...

BANYAN. XI. The BANYAN Σ Multivariate Bayesian Algorithm to Identify Members of Young Associations with 150 pc

NASA Astrophysics Data System (ADS)

Gagné, Jonathan; Mamajek, Eric E.; Malo, Lison; Riedel, Adric; Rodriguez, David; Lafrenière, David; Faherty, Jacqueline K.; Roy-Loubier, Olivier; Pueyo, Laurent; Robin, Annie C.; Doyon, René

2018-03-01

BANYAN Σ is a new Bayesian algorithm to identify members of young stellar associations within 150 pc of the Sun. It includes 27 young associations with ages in the range ∼1–800 Myr, modeled with multivariate Gaussians in six-dimensional (6D) XYZUVW space. It is the first such multi-association classification tool to include the nearest sub-groups of the Sco-Cen OB star-forming region, the IC 2602, IC 2391, Pleiades and Platais 8 clusters, and the ρ Ophiuchi, Corona Australis, and Taurus star formation regions. A model of field stars is built from a mixture of multivariate Gaussians based on the Besançon Galactic model. The algorithm can derive membership probabilities for objects with only sky coordinates and proper motion, but can also include parallax and radial velocity measurements, as well as spectrophotometric distance constraints from sequences in color–magnitude or spectral type–magnitude diagrams. BANYAN Σ benefits from an analytical solution to the Bayesian marginalization integrals over unknown radial velocities and distances that makes it more accurate and significantly faster than its predecessor BANYAN II. A contamination versus hit rate analysis is presented and demonstrates that BANYAN Σ achieves a better classification performance than other moving group tools available in the literature, especially in terms of cross-contamination between young associations. An updated list of bona fide members in the 27 young associations, augmented by the Gaia-DR1 release, as well as all parameters for the 6D multivariate Gaussian models for each association and the Galactic field neighborhood within 300 pc are presented. This new tool will make it possible to analyze large data sets such as the upcoming Gaia-DR2 to identify new young stars. IDL and Python versions of BANYAN Σ are made available with this publication, and a more limited online web tool is available at http://www.exoplanetes.umontreal.ca/banyan/banyansigma.php.

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

USGS Publications Warehouse

Law, George S.; Tasker, Gary D.

2003-01-01

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

Multivariate 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.

Revision of the Malagasy Camponotus edmondi species group (Hymenoptera, Formicidae, Formicinae): integrating qualitative morphology and multivariate morphometric analysis.

PubMed

Rakotonirina, Jean Claude; Csősz, Sándor; Fisher, Brian L

2016-01-01

The Malagasy Camponotus edmondi species group is revised based on both qualitative morphological traits and multivariate analysis of continuous morphometric data. To minimize the effect of the scaling properties of diverse traits due to worker caste polymorphism, and to achieve the desired near-linearity of data, morphometric analyses were done only on minor workers. The majority of traits exhibit broken scaling on head size, dividing Camponotus workers into two discrete subcastes, minors and majors. This broken scaling prevents the application of algorithms that uses linear combination of data to the entire dataset, hence only minor workers were analyzed statistically. The elimination of major workers resulted in linearity and the data meet required assumptions. However, morphometric ratios for the subsets of minor and major workers were used in species descriptions and redefinitions. Prior species hypotheses and the goodness of clusters were tested on raw data by confirmatory linear discriminant analysis. Due to the small sample size available for some species, a factor known to reduce statistical reliability, hypotheses generated by exploratory analyses were tested with extreme care and species delimitations were inferred via the combined evidence of both qualitative (morphology and biology) and quantitative data. Altogether, fifteen species are recognized, of which 11 are new to science: Camponotus alamaina sp. n. , Camponotus androy sp. n. , Camponotus bevohitra sp. n. , Camponotus galoko sp. n. , Camponotus matsilo sp. n. , Camponotus mifaka sp. n. , Camponotus orombe sp. n. , Camponotus tafo sp. n. , Camponotus tratra sp. n. , Camponotus varatra sp. n. , and Camponotus zavo sp. n. Four species are redescribed: Camponotus echinoploides Forel, Camponotus edmondi André, Camponotus ethicus Forel, and Camponotus robustus Roger. Camponotus edmondi ernesti Forel, syn. n. is synonymized under Camponotus edmondi . This revision also includes an identification key to species for both minor and major castes, information on geographic distribution and biology, taxonomic discussions, and descriptions of intraspecific variation. Traditional taxonomy and multivariate morphometric analysis are independent sources of information which, in combination, allow more precise species delimitation. Moreover, quantitative characters included in identification keys improve accuracy of determination in difficult cases.

Revision of the Malagasy Camponotus edmondi species group (Hymenoptera, Formicidae, Formicinae): integrating qualitative morphology and multivariate morphometric analysis

PubMed Central

Rakotonirina, Jean Claude; Csősz, Sándor; Fisher, Brian L.

2016-01-01

Abstract The Malagasy Camponotus edmondi species group is revised based on both qualitative morphological traits and multivariate analysis of continuous morphometric data. To minimize the effect of the scaling properties of diverse traits due to worker caste polymorphism, and to achieve the desired near-linearity of data, morphometric analyses were done only on minor workers. The majority of traits exhibit broken scaling on head size, dividing Camponotus workers into two discrete subcastes, minors and majors. This broken scaling prevents the application of algorithms that uses linear combination of data to the entire dataset, hence only minor workers were analyzed statistically. The elimination of major workers resulted in linearity and the data meet required assumptions. However, morphometric ratios for the subsets of minor and major workers were used in species descriptions and redefinitions. Prior species hypotheses and the goodness of clusters were tested on raw data by confirmatory linear discriminant analysis. Due to the small sample size available for some species, a factor known to reduce statistical reliability, hypotheses generated by exploratory analyses were tested with extreme care and species delimitations were inferred via the combined evidence of both qualitative (morphology and biology) and quantitative data. Altogether, fifteen species are recognized, of which 11 are new to science: Camponotus alamaina sp. n., Camponotus androy sp. n., Camponotus bevohitra sp. n., Camponotus galoko sp. n., Camponotus matsilo sp. n., Camponotus mifaka sp. n., Camponotus orombe sp. n., Camponotus tafo sp. n., Camponotus tratra sp. n., Camponotus varatra sp. n., and Camponotus zavo sp. n. Four species are redescribed: Camponotus echinoploides Forel, Camponotus edmondi André, Camponotus ethicus Forel, and Camponotus robustus Roger. Camponotus edmondi ernesti Forel, syn. n. is synonymized under Camponotus edmondi. This revision also includes an identification key to species for both minor and major castes, information on geographic distribution and biology, taxonomic discussions, and descriptions of intraspecific variation. Traditional taxonomy and multivariate morphometric analysis are independent sources of information which, in combination, allow more precise species delimitation. Moreover, quantitative characters included in identification keys improve accuracy of determination in difficult cases. PMID:28050160

Combinations of NIR, Raman spectroscopy and physicochemical measurements for improved monitoring of solvent extraction processes using hierarchical multivariate analysis models

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

Nee, K.; Bryan, S.; Levitskaia, T.

The reliability of chemical processes can be greatly improved by implementing inline monitoring systems. Combining multivariate analysis with non-destructive sensors can enhance the process without interfering with the operation. Here, we present here hierarchical models using both principal component analysis and partial least square analysis developed for different chemical components representative of solvent extraction process streams. A training set of 380 samples and an external validation set of 95 samples were prepared and Near infrared and Raman spectral data as well as conductivity under variable temperature conditions were collected. The results from the models indicate that careful selection of themore » spectral range is important. By compressing the data through Principal Component Analysis (PCA), we lower the rank of the data set to its most dominant features while maintaining the key principal components to be used in the regression analysis. Within the studied data set, concentration of five chemical components were modeled; total nitrate (NO 3 -), total acid (H +), neodymium (Nd 3+), sodium (Na +), and ionic strength (I.S.). The best overall model prediction for each of the species studied used a combined data set comprised of complementary techniques including NIR, Raman, and conductivity. Finally, our study shows that chemometric models are powerful but requires significant amount of carefully analyzed data to capture variations in the chemistry.« less

Combinations of NIR, Raman spectroscopy and physicochemical measurements for improved monitoring of solvent extraction processes using hierarchical multivariate analysis models

DOE PAGES

Nee, K.; Bryan, S.; Levitskaia, T.; ...

2017-12-28

The reliability of chemical processes can be greatly improved by implementing inline monitoring systems. Combining multivariate analysis with non-destructive sensors can enhance the process without interfering with the operation. Here, we present here hierarchical models using both principal component analysis and partial least square analysis developed for different chemical components representative of solvent extraction process streams. A training set of 380 samples and an external validation set of 95 samples were prepared and Near infrared and Raman spectral data as well as conductivity under variable temperature conditions were collected. The results from the models indicate that careful selection of themore » spectral range is important. By compressing the data through Principal Component Analysis (PCA), we lower the rank of the data set to its most dominant features while maintaining the key principal components to be used in the regression analysis. Within the studied data set, concentration of five chemical components were modeled; total nitrate (NO 3 -), total acid (H +), neodymium (Nd 3+), sodium (Na +), and ionic strength (I.S.). The best overall model prediction for each of the species studied used a combined data set comprised of complementary techniques including NIR, Raman, and conductivity. Finally, our study shows that chemometric models are powerful but requires significant amount of carefully analyzed data to capture variations in the chemistry.« less

Identification by random forest method of HLA class I amino acid substitutions associated with lower survival at day 100 in unrelated donor hematopoietic cell transplantation.

PubMed

Marino, S R; Lin, S; Maiers, M; Haagenson, M; Spellman, S; Klein, J P; Binkowski, T A; Lee, S J; van Besien, K

2012-02-01

The identification of important amino acid substitutions associated with low survival in hematopoietic cell transplantation (HCT) is hampered by the large number of observed substitutions compared with the small number of patients available for analysis. Random forest analysis is designed to address these limitations. We studied 2107 HCT recipients with good or intermediate risk hematological malignancies to identify HLA class I amino acid substitutions associated with reduced survival at day 100 post transplant. Random forest analysis and traditional univariate and multivariate analyses were used. Random forest analysis identified amino acid substitutions in 33 positions that were associated with reduced 100 day survival, including HLA-A 9, 43, 62, 63, 76, 77, 95, 97, 114, 116, 152, 156, 166 and 167; HLA-B 97, 109, 116 and 156; and HLA-C 6, 9, 11, 14, 21, 66, 77, 80, 95, 97, 99, 116, 156, 163 and 173. In all 13 had been previously reported by other investigators using classical biostatistical approaches. Using the same data set, traditional multivariate logistic regression identified only five amino acid substitutions associated with lower day 100 survival. Random forest analysis is a novel statistical methodology for analysis of HLA mismatching and outcome studies, capable of identifying important amino acid substitutions missed by other methods.

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.

Prognostic value of stromal decorin expression in patients with breast cancer: a meta-analysis.

PubMed

Li, Shuang-Jiang; Chen, Da-Li; Zhang, Wen-Biao; Shen, Cheng; Che, Guo-Wei

2015-11-01

Numbers of studies have investigated the biological functions of decorin (DCN) in oncogenesis, tumor progression, angiogenesis and metastasis. Although many of them aim to highlight the prognostic value of stromal DCN expression in breast cancer, some controversial results still exist and a consensus has not been reached until now. Therefore, our meta-analysis aims to determine the prognostic significance of stromal DCN expression in breast cancer patients. PubMed, EMBASE, the Web of Science and China National Knowledge Infrastructure (CNKI) databases were searched for full-text literatures met out inclusion criteria. We applied the hazard ratio (HR) with 95% confidence interval (CI) as the appropriate summarized statistics. Q-test and I(2) statistic were employed to estimate the level of heterogeneity across the included studies. Sensitivity analysis was conducted to further identify the possible origins of heterogeneity. The publication bias was detected by Begg's test and Egger's test. There were three English literatures (involving 6 studies) included into our meta-analysis. On the one hand, both the summarized outcomes based on univariate analysis (HR: 0.513; 95% CI: 0.406-0.648; P<0.001) and multivariate analysis (HR: 0.544; 95% CI: 0.388-0.763; P<0.001) indicated that stromal DCN expression could promise the high cancer-specific survival (CSS) of breast cancer patients. On the other hand, both the summarized outcomes based on univariate analysis (HR: 0.504; 95% CI: 0.389-0.651; P<0.001) and multivariate analysis (HR: 0.568; 95% CI: 0.400-0.806; P=0.002) also indicated that stromal DCN expression was positively associated with high disease-free survival (DFS) of breast cancer patients. No significant heterogeneity or publication bias was observed within this meta-analysis. The present evidences indicate that high stromal DCN expression can significantly predict the good prognosis in patients with breast cancer. The discoveries from our meta-analysis have better be confirmed in the updated review pooling more relevant investigations in the future.

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

Abandonment of antiretroviral therapy among HIV-positive patients attended at the reference center for HIV/AIDS in Vitória, Brazil.

PubMed

Zago, Adriana Marchon; Morelato, Paola; Endringer, Emmanuele de Angeli; Dan, Germano de Freitas; Ribeiro, Evanira Mendes; Miranda, Angelica Espinosa

2012-01-01

This study evaluates the risk factors for the abandonment of antiretroviral therapy (ART) among patients receiving care in an AIDS clinic in Vitória, Brazil. We conducted a case-control study of patients with AIDS attending a reference center for sexually transmitted disease (STD)/AIDS. A total of 62 patients, who abandoned therapy in 2008, and 188 HIV-infected patients answered an interview including demographic, social, and clinical characteristics. Risk factors associated with abandon in univariate analysis were entered into logistic regression models. A total of 250 patients were included in the study. Groups were similar regarding age, gender, and monthly income. In the final multivariate model, illicit drug use (adjusted odds ratio [AOR], 2.3; 95% confidence interval [CI], 1.03-5.07), previous abandon of medication (AOR 38.6; 95% CI 10.49-142.25), last CD4 count <200 cells/mm(3) (AOR 1.5; 95% CI 1.03-2.10), and viral load higher than 1000 copies/mL (AOR 2.0 (95% CI 1.34-3.09) were independent predictors of abandonment of ART. In addition to the clinical indicators, behavioral factors remained important throughout the multivariate analysis in our study.

Noninvasive assessment of the risk of tobacco abuse in oral mucosa using fluorescence spectroscopy: a clinical approach

NASA Astrophysics Data System (ADS)

Nazeer, Shaiju S.; Asish, Rajashekharan; Venugopal, Chandrashekharan; Anita, Balan; Gupta, Arun Kumar; Jayasree, Ramapurath S.

2014-05-01

Tobacco abuse and alcoholism cause cancer, emphysema, and heart disease, which contribute to high death rates, globally. Society pays a significant cost for these habits whose first demonstration in many cases is in the oral cavity. Oral cavity disorders are highly curable if a screening procedure is available to diagnose them in the earliest stages. The aim of the study is to identify the severity of tobacco abuse, in oral cavity, as reflected by the emission from endogenous fluorophores and the chromophore hemoglobin. A group who had no tobacco habits and another with a history of tobacco abuse were included in this study. To compare the results with a pathological condition, a group of leukoplakia patients were also included. Emission from porphyrin and the spectral filtering modulation effect of hemoglobin were collected from different sites. Multivariate analysis strengthened the spectral features with a sensitivity of 60% to 100% and a specificity of 76% to 100% for the discrimination. Total hemoglobin and porphyrin levels of habitués and leukoplakia groups were comparable, indicating the alarming situation about the risk of tobacco abuse. Results prove that fluorescence spectroscopy along with multivariate analysis is an effective noninvasive tool for the early diagnosis of pathological changes due to tobacco abuse.

Clinical and dermoscopic clues to differentiate pigmented nail bands: an International Dermoscopy Society study.

PubMed

Benati, E; Ribero, S; Longo, C; Piana, S; Puig, S; Carrera, C; Cicero, F; Kittler, H; Deinlein, T; Zalaudek, I; Stolz, W; Scope, A; Pellacani, G; Moscarella, E; Piraccini, B M; Starace, M; Argenziano, G

2017-04-01

Longitudinal melanonychia might be difficult to differentiate and the use of dermoscopy can be useful for the preoperative evaluation and management decision. The aim of our study was to investigate clinical and dermoscopic criteria of acquired longitudinal melanonychia in adults to identify the best predictors of melanoma using a multivariate analysis and to explore eventual new dermoscopic criteria for nail melanoma diagnosis. In this retrospective observational study, 82 histopathologically diagnosed, acquired nail pigmented bands were collected and examined. All variables were included in the analysis and examined as possible predictors of nail melanoma. Both univariate and multivariable analyses have been performed. Among 82 cases, 25 were diagnosed as nail melanoma and 57 as benign lesions (including 32 melanocytic nevi and 25 benign melanocytic hyperplasia). Melanoma cases were significantly associated with a width of the pigmented band higher than 2/3 of the nail plate, grey and black colours, irregularly pigmented lines, Hutchinson and micro-Hutchinson signs, and nail dystrophy. Granular pigmentation, a newly defined dermoscopic criterion, was found in 40% of melanomas and only in 3.51% of benign lesions. Dermoscopic examination of longitudinal melanonychia provides useful information that could help clinicians to improve melanoma recognition. © 2016 European Academy of Dermatology and Venereology.

Safety and effectiveness of olanzapine in monotherapy: a multivariate analysis of a naturalistic study.

PubMed

Ciudad, Antonio; Gutiérrez, Miguel; Cañas, Fernando; Gibert, Juan; Gascón, Josep; Carrasco, José-Luis; Bobes, Julio; Gómez, Juan-Carlos; Alvarez, Enrique

2005-07-01

This study investigated safety and effectiveness of olanzapine in monotherapy compared with conventional antipsychotics in treatment of acute inpatients with schizophrenia. This was a prospective, comparative, nonrandomized, open-label, multisite, observational study of Spanish inpatients with an acute episode of schizophrenia. Data included safety assessments with an extrapyramidal symptoms (EPS) questionnaire and the report of spontaneous adverse events, plus clinical assessments with the Brief Psychiatric Rating Scale (BPRS) and the Clinical Global Impressions-Severity of Illness (CGI-S). A multivariate methodology was used to more adequately determine which factors can influence safety and effectiveness of olanzapine in monotherapy. 339 patients treated with olanzapine in monotherapy (OGm) and 385 patients treated with conventional antipsychotics (CG) were included in the analysis. Treatment-emergent EPS were significantly higher in the CG (p<0.0001). Response rate was significantly higher in the OGm (p=0.005). Logistic regression analyses revealed that the only variable significantly correlated with treatment-emergent EPS and clinical response was treatment strategy, with patients in OGm having 1.5 times the probability of obtaining a clinical response and patients in CG having 5 times the risk of developing EPS. In this naturalistic study olanzapine in monotherapy was better-tolerated and at least as effective as conventional antipsychotics.

Reporting of research quality characteristics of studies published in 6 major clinical dental specialty journals.

PubMed

Pandis, Nikolaos; Polychronopoulou, Argy; Madianos, Phoebus; Makou, Margarita; Eliades, Theodore

2011-06-01

The objective of this article was to record reporting characteristics related to study quality of research published in major specialty dental journals with the highest impact factor (Journal of Endodontics, Journal of Oral and Maxillofacial Surgery, American Journal of Orthodontics and Dentofacial Orthopedics; Pediatric Dentistry, Journal of Clinical Periodontology, and International Journal of Prosthetic Dentistry). The included articles were classified into the following 3 broad subject categories: (1) cross-sectional (snap-shot), (2) observational, and (3) interventional. Multinomial logistic regression was conducted for effect estimation using the journal as the response and randomization, sample calculation, confounding discussed, multivariate analysis, effect measurement, and confidence intervals as the explanatory variables. The results showed that cross-sectional studies were the dominant design (55%), whereas observational investigations accounted for 13%, and interventions/clinical trials for 32%. Reporting on quality characteristics was low for all variables: random allocation (15%), sample size calculation (7%), confounding issues/possible confounders (38%), effect measurements (16%), and multivariate analysis (21%). Eighty-four percent of the published articles reported a statistically significant main finding and only 13% presented confidence intervals. The Journal of Clinical Periodontology showed the highest probability of including quality characteristics in reporting results among all dental journals. Copyright © 2011 Elsevier Inc. All rights reserved.

Predicting trauma patient mortality: ICD [or ICD-10-AM] versus AIS based approaches.

PubMed

Willis, Cameron D; Gabbe, Belinda J; Jolley, Damien; Harrison, James E; Cameron, Peter A

2010-11-01

The International Classification of Diseases Injury Severity Score (ICISS) has been proposed as an International Classification of Diseases (ICD)-10-based alternative to mortality prediction tools that use Abbreviated Injury Scale (AIS) data, including the Trauma and Injury Severity Score (TRISS). To date, studies have not examined the performance of ICISS using Australian trauma registry data. This study aimed to compare the performance of ICISS with other mortality prediction tools in an Australian trauma registry. This was a retrospective review of prospectively collected data from the Victorian State Trauma Registry. A training dataset was created for model development and a validation dataset for evaluation. The multiplicative ICISS model was compared with a worst injury ICISS approach, Victorian TRISS (V-TRISS, using local coefficients), maximum AIS severity and a multivariable model including ICD-10-AM codes as predictors. Models were investigated for discrimination (C-statistic) and calibration (Hosmer-Lemeshow statistic). The multivariable approach had the highest level of discrimination (C-statistic 0.90) and calibration (H-L 7.65, P= 0.468). Worst injury ICISS, V-TRISS and maximum AIS had similar performance. The multiplicative ICISS produced the lowest level of discrimination (C-statistic 0.80) and poorest calibration (H-L 50.23, P < 0.001). The performance of ICISS may be affected by the data used to develop estimates, the ICD version employed, the methods for deriving estimates and the inclusion of covariates. In this analysis, a multivariable approach using ICD-10-AM codes was the best-performing method. A multivariable ICISS approach may therefore be a useful alternative to AIS-based methods and may have comparable predictive performance to locally derived TRISS models. © 2010 The Authors. ANZ Journal of Surgery © 2010 Royal Australasian College of Surgeons.

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.

Systematic wavelength selection for improved multivariate spectral analysis

DOEpatents

Thomas, Edward V.; Robinson, Mark R.; Haaland, David M.

1995-01-01

Methods and apparatus for determining in a biological material one or more unknown values of at least one known characteristic (e.g. the concentration of an analyte such as glucose in blood or the concentration of one or more blood gas parameters) with a model based on a set of samples with known values of the known characteristics and a multivariate algorithm using several wavelength subsets. The method includes selecting multiple wavelength subsets, from the electromagnetic spectral region appropriate for determining the known characteristic, for use by an algorithm wherein the selection of wavelength subsets improves the model's fitness of the determination for the unknown values of the known characteristic. The selection process utilizes multivariate search methods that select both predictive and synergistic wavelengths within the range of wavelengths utilized. The fitness of the wavelength subsets is determined by the fitness function F=.function.(cost, performance). The method includes the steps of: (1) using one or more applications of a genetic algorithm to produce one or more count spectra, with multiple count spectra then combined to produce a combined count spectrum; (2) smoothing the count spectrum; (3) selecting a threshold count from a count spectrum to select these wavelength subsets which optimize the fitness function; and (4) eliminating a portion of the selected wavelength subsets. The determination of the unknown values can be made: (1) noninvasively and in vivo; (2) invasively and in vivo; or (3) in vitro.

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

PubMed

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

2017-12-01

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

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.

Oasis: online analysis of small RNA deep sequencing data.

PubMed

Capece, Vincenzo; Garcia Vizcaino, Julio C; Vidal, Ramon; Rahman, Raza-Ur; Pena Centeno, Tonatiuh; Shomroni, Orr; Suberviola, Irantzu; Fischer, Andre; Bonn, Stefan

2015-07-01

Oasis is a web application that allows for the fast and flexible online analysis of small-RNA-seq (sRNA-seq) data. It was designed for the end user in the lab, providing an easy-to-use web frontend including video tutorials, demo data and best practice step-by-step guidelines on how to analyze sRNA-seq data. Oasis' exclusive selling points are a differential expression module that allows for the multivariate analysis of samples, a classification module for robust biomarker detection and an advanced programming interface that supports the batch submission of jobs. Both modules include the analysis of novel miRNAs, miRNA targets and functional analyses including GO and pathway enrichment. Oasis generates downloadable interactive web reports for easy visualization, exploration and analysis of data on a local system. Finally, Oasis' modular workflow enables for the rapid (re-) analysis of data. Oasis is implemented in Python, R, Java, PHP, C++ and JavaScript. It is freely available at http://oasis.dzne.de. stefan.bonn@dzne.de Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press.

A Multivariate Model of Parent-Adolescent Relationship Variables in Early Adolescence

ERIC Educational Resources Information Center

McKinney, Cliff; Renk, Kimberly

2011-01-01

Given the importance of predicting outcomes for early adolescents, this study examines a multivariate model of parent-adolescent relationship variables, including parenting, family environment, and conflict. Participants, who completed measures assessing these variables, included 710 culturally diverse 11-14-year-olds who were attending a middle…

Development of a multivariate model to predict the likelihood of carcinoma in patients with indeterminate peripheral lung nodules after a nondiagnostic bronchoscopic evaluation.

PubMed

Voss, Jesse S; Iqbal, Seher; Jenkins, Sarah M; Henry, Michael R; Clayton, Amy C; Jett, James R; Kipp, Benjamin R; Halling, Kevin C; Maldonado, Fabien

2014-01-01

Studies have shown that fluorescence in situ hybridization (FISH) testing increases lung cancer detection on cytology specimens in peripheral nodules. The goal of this study was to determine whether a predictive model using clinical features and routine cytology with FISH results could predict lung malignancy after a nondiagnostic bronchoscopic evaluation. Patients with an indeterminate peripheral lung nodule that had a nondiagnostic bronchoscopic evaluation were included in this study (N = 220). FISH was performed on residual bronchial brushing cytology specimens diagnosed as negative (n = 195), atypical (n = 16), or suspicious (n = 9). FISH results included hypertetrasomy (n = 30) and negative (n = 190). Primary study end points included lung cancer status along with time to diagnosis of lung cancer or date of last clinical follow-up. Hazard ratios (HRs) were calculated using Cox proportional hazards regression model analyses, and P values < .05 were considered statistically significant. The mean age of the 220 patients was 66.7 years (range, 35-91), and most (58%) were men. Most patients (79%) were current or former smokers with a mean pack year history of 43.2 years (median, 40; range, 1-200). After multivariate analysis, hypertetrasomy FISH (HR = 2.96, P < .001), pack years (HR = 1.03 per pack year up to 50, P = .001), age (HR = 1.04 per year, P = .02), atypical or suspicious cytology (HR = 2.02, P = .04), and nodule spiculation (HR = 2.36, P = .003) were independent predictors of malignancy over time and were used to create a prediction model (C-statistic = 0.78). These results suggest that this multivariate model including test results and clinical features may be useful following a nondiagnostic bronchoscopic examination. © 2013.

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)

Gauging Skills of Hospital Security Personnel: a Statistically-driven, Questionnaire-based Approach.

PubMed

Rinkoo, Arvind Vashishta; Mishra, Shubhra; Rahesuddin; Nabi, Tauqeer; Chandra, Vidha; Chandra, Hem

2013-01-01

This study aims to gauge the technical and soft skills of the hospital security personnel so as to enable prioritization of their training needs. A cross sectional questionnaire based study was conducted in December 2011. Two separate predesigned and pretested questionnaires were used for gauging soft skills and technical skills of the security personnel. Extensive statistical analysis, including Multivariate Analysis (Pillai-Bartlett trace along with Multi-factorial ANOVA) and Post-hoc Tests (Bonferroni Test) was applied. The 143 participants performed better on the soft skills front with an average score of 6.43 and standard deviation of 1.40. The average technical skills score was 5.09 with a standard deviation of 1.44. The study avowed a need for formal hands on training with greater emphasis on technical skills. Multivariate analysis of the available data further helped in identifying 20 security personnel who should be prioritized for soft skills training and a group of 36 security personnel who should receive maximum attention during technical skills training. This statistically driven approach can be used as a prototype by healthcare delivery institutions worldwide, after situation specific customizations, to identify the training needs of any category of healthcare staff.

The Prognostic Role of Cancer Stem Cell Markers for Long-term Outcome After Resection of Colonic Liver Metastases.

PubMed

Spelt, Lidewij; Sasor, Agata; Ansari, Daniel; Hilmersson, Katarzyna Said; Andersson, Roland

2018-01-01

To assess the expression of cancer stem cell (CSC) markers CD44, CD133 and CD24 in colon cancer liver metastases and analyse their predictive value for overall survival (OS) and disease-free survival (DFS) after liver resection. Patients operated on for colon cancer liver metastases were included. CSC marker expression was determined through immunohistochemistry analysis. OS and DFS were compared between marker-positive and marker-negative patients. Multivariate analysis was performed to select predictive variables for OS and DFS. CD133-positive patients had a worse DFS than CD133-negative patients, with a median DFS of 12 and 25 months (p=0.051). Multivariate analysis selected CD133 expression as a significant predictor for DFS. CD44 and CD24 were not found to predict OS or DFS. CD133 expression in colonic liver metastases is a negative prognostic factor for DFS after liver resection. In the future, CD133 could be used as a biomarker for risk stratification, and possibly for developing novel targeted therapy. Copyright© 2018, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

Gauging Skills of Hospital Security Personnel: a Statistically-driven, Questionnaire-based Approach

PubMed Central

Rinkoo, Arvind Vashishta; Mishra, Shubhra; Rahesuddin; Nabi, Tauqeer; Chandra, Vidha; Chandra, Hem

2013-01-01

Objectives This study aims to gauge the technical and soft skills of the hospital security personnel so as to enable prioritization of their training needs. Methodology A cross sectional questionnaire based study was conducted in December 2011. Two separate predesigned and pretested questionnaires were used for gauging soft skills and technical skills of the security personnel. Extensive statistical analysis, including Multivariate Analysis (Pillai-Bartlett trace along with Multi-factorial ANOVA) and Post-hoc Tests (Bonferroni Test) was applied. Results The 143 participants performed better on the soft skills front with an average score of 6.43 and standard deviation of 1.40. The average technical skills score was 5.09 with a standard deviation of 1.44. The study avowed a need for formal hands on training with greater emphasis on technical skills. Multivariate analysis of the available data further helped in identifying 20 security personnel who should be prioritized for soft skills training and a group of 36 security personnel who should receive maximum attention during technical skills training. Conclusion This statistically driven approach can be used as a prototype by healthcare delivery institutions worldwide, after situation specific customizations, to identify the training needs of any category of healthcare staff. PMID:23559904

A review of multivariate methods in brain imaging data fusion

NASA Astrophysics Data System (ADS)

Sui, Jing; Adali, Tülay; Li, Yi-Ou; Yang, Honghui; Calhoun, Vince D.

2010-03-01

On joint analysis of multi-task brain imaging data sets, a variety of multivariate methods have shown their strengths and been applied to achieve different purposes based on their respective assumptions. In this paper, we provide a comprehensive review on optimization assumptions of six data fusion models, including 1) four blind methods: joint independent component analysis (jICA), multimodal canonical correlation analysis (mCCA), CCA on blind source separation (sCCA) and partial least squares (PLS); 2) two semi-blind methods: parallel ICA and coefficient-constrained ICA (CC-ICA). We also propose a novel model for joint blind source separation (BSS) of two datasets using a combination of sCCA and jICA, i.e., 'CCA+ICA', which, compared with other joint BSS methods, can achieve higher decomposition accuracy as well as the correct automatic source link. Applications of the proposed model to real multitask fMRI data are compared to joint ICA and mCCA; CCA+ICA further shows its advantages in capturing both shared and distinct information, differentiating groups, and interpreting duration of illness in schizophrenia patients, hence promising applicability to a wide variety of medical imaging problems.

Complex codon usage pattern and compositional features of retroviruses.

PubMed

RoyChoudhury, Sourav; Mukherjee, Debaprasad

2013-01-01

Retroviruses infect a wide range of organisms including humans. Among them, HIV-1, which causes AIDS, has now become a major threat for world health. Some of these viruses are also potential gene transfer vectors. In this study, the patterns of synonymous codon usage in retroviruses have been studied through multivariate statistical methods on ORFs sequences from the available 56 retroviruses. The principal determinant for evolution of the codon usage pattern in retroviruses seemed to be the compositional constraints, while selection for translation of the viral genes plays a secondary role. This was further supported by multivariate analysis on relative synonymous codon usage. Thus, it seems that mutational bias might have dominated role over translational selection in shaping the codon usage of retroviruses. Codon adaptation index was used to identify translationally optimal codons among genes from retroviruses. The comparative analysis of the preferred and optimal codons among different retroviral groups revealed that four codons GAA, AAA, AGA, and GGA were significantly more frequent in most of the retroviral genes inspite of some differences. Cluster analysis also revealed that phylogenetically related groups of retroviruses have probably evolved their codon usage in a concerted manner under the influence of their nucleotide composition.

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

PubMed

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

2015-04-01

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

Effects of Lacunar Infarctions on Cognitive Impairment in Patients with Cerebral Autosomal-Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy

PubMed Central

Choi, Jay Chol; Kang, Sa-Yoon; Kang, Ji-Hoon; Na, Hae Ri; Park, Ji-Kang

2011-01-01

Background and Purpose Cerebral autosomal-dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is an inherited microangiopathy caused by mutations in the Notch3 gene. Although previous studies have shown an association between lacunar infarction and cognitive impairment, the relationship between MRI parameters and cognition remains unclear. In this study we investigated the influence of MRI parameters on cognitive impairment in CADASIL. Methods We applied a prospective protocol to 40 patients. MRI analysis included the normalized volume of white-matter hyperintensities (nWMHs), number of lacunes, and number of cerebral microbleeds. Cognition was assessed with the aid of psychometric tests [Mini-Mental State Examination (MMSE), Alzheimer's Disease Assessment Scale-cognition (ADAS-cog), Trail-Making Test, and Stroop interference (Stroop IF)]. Results A multivariate regression analysis revealed that the total number of lacunes influenced the performance in the MMSE, ADAS-cog, and Stroop IF, while nWMHs had a strong univariate association with ADAS-cog and Stroop IF scores. However, this association disappeared in the multivariate analysis. Conclusions These findings demonstrate that the number of lacunes is the main predictive factor of cognitive impairment in CADASIL. PMID:22259617

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.

Statistical analysis of Thematic Mapper Simulator data for the geobotanical discrimination of rock types in southwest Oregon

NASA Technical Reports Server (NTRS)

Morrissey, L. A.; Weinstock, K. J.; Mouat, D. A.; Card, D. H.

1984-01-01

An evaluation of Thematic Mapper Simulator (TMS) data for the geobotanical discrimination of rock types based on vegetative cover characteristics is addressed in this research. A methodology for accomplishing this evaluation utilizing univariate and multivariate techniques is presented. TMS data acquired with a Daedalus DEI-1260 multispectral scanner were integrated with vegetation and geologic information for subsequent statistical analyses, which included a chi-square test, an analysis of variance, stepwise discriminant analysis, and Duncan's multiple range test. Results indicate that ultramafic rock types are spectrally separable from nonultramafics based on vegetative cover through the use of statistical analyses.

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

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.

Metabolomic profiling of the phytomedicinal constituents of Carica papaya L. leaves and seeds by 1H NMR spectroscopy and multivariate statistical analysis.

PubMed

Gogna, Navdeep; Hamid, Neda; Dorai, Kavita

2015-11-10

Extracts from the Carica papaya L. plant are widely reported to contain metabolites with antibacterial, antioxidant and anticancer activity. This study aims to analyze the metabolic profiles of papaya leaves and seeds in order to gain insights into their phytomedicinal constituents. We performed metabolite fingerprinting using 1D and 2D 1H NMR experiments and used multivariate statistical analysis to identify those plant parts that contain the most concentrations of metabolites of phytomedicinal value. Secondary metabolites such as phenyl propanoids, including flavonoids, were found in greater concentrations in the leaves as compared to the seeds. UPLC-ESI-MS verified the presence of significant metabolites in the papaya extracts suggested by the NMR analysis. Interestingly, the concentration of eleven secondary metabolites namely caffeic, cinnamic, chlorogenic, quinic, coumaric, vanillic, and protocatechuic acids, naringenin, hesperidin, rutin, and kaempferol, were higher in young as compared to old papaya leaves. The results of the NMR analysis were corroborated by estimating the total phenolic and flavonoid content of the extracts. Estimation of antioxidant activity in leaves and seed extracts by DPPH and ABTS in-vitro assays and antioxidant capacity in C2C12 cell line also showed that papaya extracts exhibit high antioxidant activity. Copyright © 2015 Elsevier B.V. All rights reserved.

Role of Surgical Services in Profitability of Hospitals in California: An Analysis of Office of Statewide Health Planning and Development Annual Financial Data.

PubMed

Moazzez, Ashkan; de Virgilio, Christian

2016-10-01

With constant changes in health-care laws and payment methods, profitability, and financial sustainability of hospitals are of utmost importance. The purpose of this study is to determine the relationship between surgical services and hospital profitability. The Office of Statewide Health Planning and Development annual financial databases for the years 2009 to 2011 were used for this study. The hospitals' characteristics and income statement elements were extracted for statistical analysis using bivariate and multivariate linear regression. A total of 989 financial records of 339 hospitals were included. On bivariate analysis, the number of inpatient and ambulatory operating rooms (ORs), the number of cases done both as inpatient and outpatient in each OR, and the average minutes used in inpatient ORs were significantly related with the net income of the hospital. On multivariate regression analysis, when controlling for hospitals' payer mix and the study year, only the number of inpatient cases done in the inpatient ORs (β = 832, P = 0.037), and the number of ambulatory ORs (β = 1,485, 466, P = 0.001) were significantly related with the net income of the hospital. These findings suggest that hospitals can maximize their profitability by diverting and allocating outpatient surgeries to ambulatory ORs, to allow for more inpatient surgeries.

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.

Multivariate Analysis as a Method for Evaluating the Conceptual Perceptions of Korean Medicine Students regarding Phlegm Pattern

PubMed Central

Kim, Hyungsuk; Park, Young-Jae; Park, Young-Bae

2013-01-01

Individuals may perceive the concepts in Korean medicine pattern classification differently because it is performed according to the integration of a variety of information. Therefore, analysis about individual perspective is very important for examining the cross-sectional perspective state of Korean medicine concepts and developing both the clinical guideline including diagnosis and the curriculum of Korean medicine colleges. Moreover, because this conceptual difference is thought to begin with college education, it is worthwhile to observe students' viewpoints. So, we suggested multivariate analysis to explore the dimensional structure of Korean medicine students' conceptual perceptions regarding phlegm pattern. We surveyed 326 students divided into 5 groups based on their year of study. Data were analyzed using multidimensional scaling and factor analysis. Within-group difference was the smallest for third-year students, who have received Korean medicine education in full for the first time. With the exception of first-year students, the conceptual map revealed that each group's mean perceptions of phlegm pattern were distributed in almost linear fashion. To determine the effect of education, we investigated the preference rankings and scores of each symptom. We also extracted factors to identify latent variables and to compare the between-group conceptual characteristics regarding phlegm pattern. PMID:24062789

Prognostic molecular markers with no impact on decision-making: the paradox of gliomas based on a prospective study.

PubMed

Wager, M; Menei, P; Guilhot, J; Levillain, P; Michalak, S; Bataille, B; Blanc, J-L; Lapierre, F; Rigoard, P; Milin, S; Duthe, F; Bonneau, D; Larsen, C-J; Karayan-Tapon, L

2008-06-03

This study assessed the prognostic value of several markers involved in gliomagenesis, and compared it with that of other clinical and imaging markers already used. Four-hundred and sixteen adult patients with newly diagnosed glioma were included over a 3-year period and tumour suppressor genes, oncogenes, MGMT and hTERT expressions, losses of heterozygosity, as well as relevant clinical and imaging information were recorded. This prospective study was based on all adult gliomas. Analyses were performed on patient groups selected according to World Health Organization histoprognostic criteria and on the entire cohort. The endpoint was overall survival, estimated by the Kaplan-Meier method. Univariate analysis was followed by multivariate analysis according to a Cox model. p14(ARF), p16(INK4A) and PTEN expressions, and 10p 10q23, 10q26 and 13q LOH for the entire cohort, hTERT expression for high-grade tumours, EGFR for glioblastomas, 10q26 LOH for grade III tumours and anaplastic oligodendrogliomas were found to be correlated with overall survival on univariate analysis and age and grade on multivariate analysis only. This study confirms the prognostic value of several markers. However, the scattering of the values explained by tumour heterogeneity prevents their use in individual decision-making.

Study of Refractory Status Epilepticus from a Tertiary Care Center.

PubMed

Kohli, Sahil; Pasangulapati, Suresh Babu; Yoganathan, Sangeetha; Rynjah, Gideon Lyngsyun; Prabhakar, A T; Aaron, Sanjith; Alexander, Mathew; Mathew, Vivek

2017-01-01

To determine the proportion of refractory status epilepticus (RSE) and super-RSE (SRSE) among patients with status epilepticus (SE) and to analyze RSE and non-RSE (NRSE) in terms of etiology and predictors for RSE. Patients were identified from discharge summaries database with keywords of SE and records of the portable electroencephalogram (EEG) machine from January 2011 to March 2016. Two hundred and eighteen events were included in the study with 114 (52.3%) males, bimodal age preponderance age <5 years 30%, and second peak in age 15-65 years 52.8%, preexisting seizures were present in 34.4% ( n = 75). Nearly 77.1% had NRSE ( n = 168) and 22.9% had RSE ( n = 50). This included 17 patients with SRSE ( n = 17, 7.8% of all SE). Central nervous system (CNS) infection was a single largest etiological group in SE (69/218, 31.7%). In RSE, autoimmune encephalitis (17/50) and CNS infection (13/50) were the largest groups. De novo seizures ( P = 0.007), low sensorium at admission ( P = 0.001), low albumin at admission ( P = 0.002), and first EEG being abnormal ( P = 0.001) were risk factors on bivariate analysis. An unfavorable status epilepticus severity score (STESS) was predictive for RSE ( P = 0.001). On multivariate analysis, de novo seizures ( P = 0.009) and abnormal EEG at admission ( P = 0.03) were predictive for RSE. Fifty patients had RSE (22.9%), of which 17 went on to become SRSE (7.8%). Unfavorable STESS score was predictive for RSE on bivariate analysis. On multivariate analysis, de novo seizures and abnormal initial EEG were predictors of RSE.

The Effect of Coffee and Quantity of Consumption on Specific Cardiovascular and All-Cause Mortality: Coffee Consumption Does Not Affect Mortality.

PubMed

Loomba, Rohit S; Aggarwal, Saurabh; Arora, Rohit R

2016-01-01

Previous studies have examined whether or not an association exists between the consumption of caffeinated coffee to all-cause and cardiovascular mortality. This study aimed to delineate this association using population representative data from the National Health and Nutrition Examination Survey III. Patients were included in the study if all the following criteria were met: (1) follow-up mortality data were available, (2) age of at least 45 years, and (3) reported amount of average coffee consumption. A total of 8608 patients were included, with patients stratified into the following groups of average daily coffee consumption: (1) no coffee consumption, (2) less than 1 cup, (3) 1 cup a day, (4) 2-3 cups, (5) 4-5 cups, (6) more than 6 cups a day. Odds ratios, 95% confidence intervals, and P values were calculated for univariate analysis to compare the prevalence of all-cause mortality, ischemia-related mortality, congestive heart failure-related mortality, and stroke-related mortality, using the no coffee consumption group as reference. These were then adjusted for confounding factors for a multivariate analysis. P < 0.05 were considered statistically significant. Univariate analysis demonstrated an association between coffee consumption and mortality, although this became insignificant on multivariate analysis. Coffee consumption, thus, does not seem to impact all-cause mortality or specific cardiovascular mortality. These findings do differ from those of recently published studies. Coffee consumption of any quantity seems to be safe without any increased mortality risk. There may be some protective effects but additional data are needed to further delineate this.

Additive genetic variation and evolvability of a multivariate trait can be increased by epistatic gene action.

PubMed

Griswold, Cortland K

2015-12-21

Epistatic gene action occurs when mutations or alleles interact to produce a phenotype. Theoretically and empirically it is of interest to know whether gene interactions can facilitate the evolution of diversity. In this paper, we explore how epistatic gene action affects the additive genetic component or heritable component of multivariate trait variation, as well as how epistatic gene action affects the evolvability of multivariate traits. The analysis involves a sexually reproducing and recombining population. Our results indicate that under stabilizing selection conditions a population with a mixed additive and epistatic genetic architecture can have greater multivariate additive genetic variation and evolvability than a population with a purely additive genetic architecture. That greater multivariate additive genetic variation can occur with epistasis is in contrast to previous theory that indicated univariate additive genetic variation is decreased with epistasis under stabilizing selection conditions. In a multivariate setting, epistasis leads to less relative covariance among individuals in their genotypic, as well as their breeding values, which facilitates the maintenance of additive genetic variation and increases a population׳s evolvability. Our analysis involves linking the combinatorial nature of epistatic genetic effects to the ancestral graph structure of a population to provide insight into the consequences of epistasis on multivariate trait variation and evolution. Copyright © 2015 Elsevier Ltd. All rights reserved.

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.

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.

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

PubMed

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

2017-02-01

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

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.

Genes@Work: an efficient algorithm for pattern discovery and multivariate feature selection in gene expression data.

PubMed

Lepre, Jorge; Rice, J Jeremy; Tu, Yuhai; Stolovitzky, Gustavo

2004-05-01

Despite the growing literature devoted to finding differentially expressed genes in assays probing different tissues types, little attention has been paid to the combinatorial nature of feature selection inherent to large, high-dimensional gene expression datasets. New flexible data analysis approaches capable of searching relevant subgroups of genes and experiments are needed to understand multivariate associations of gene expression patterns with observed phenotypes. We present in detail a deterministic algorithm to discover patterns of multivariate gene associations in gene expression data. The patterns discovered are differential with respect to a control dataset. The algorithm is exhaustive and efficient, reporting all existent patterns that fit a given input parameter set while avoiding enumeration of the entire pattern space. The value of the pattern discovery approach is demonstrated by finding a set of genes that differentiate between two types of lymphoma. Moreover, these genes are found to behave consistently in an independent dataset produced in a different laboratory using different arrays, thus validating the genes selected using our algorithm. We show that the genes deemed significant in terms of their multivariate statistics will be missed using other methods. Our set of pattern discovery algorithms including a user interface is distributed as a package called Genes@Work. This package is freely available to non-commercial users and can be downloaded from our website (http://www.research.ibm.com/FunGen).

Rank estimation and the multivariate analysis of in vivo fast-scan cyclic voltammetric data

PubMed Central

Keithley, Richard B.; Carelli, Regina M.; Wightman, R. Mark

2010-01-01

Principal component regression has been used in the past to separate current contributions from different neuromodulators measured with in vivo fast-scan cyclic voltammetry. Traditionally, a percent cumulative variance approach has been used to determine the rank of the training set voltammetric matrix during model development, however this approach suffers from several disadvantages including the use of arbitrary percentages and the requirement of extreme precision of training sets. Here we propose that Malinowski’s F-test, a method based on a statistical analysis of the variance contained within the training set, can be used to improve factor selection for the analysis of in vivo fast-scan cyclic voltammetric data. These two methods of rank estimation were compared at all steps in the calibration protocol including the number of principal components retained, overall noise levels, model validation as determined using a residual analysis procedure, and predicted concentration information. By analyzing 119 training sets from two different laboratories amassed over several years, we were able to gain insight into the heterogeneity of in vivo fast-scan cyclic voltammetric data and study how differences in factor selection propagate throughout the entire principal component regression analysis procedure. Visualizing cyclic voltammetric representations of the data contained in the retained and discarded principal components showed that using Malinowski’s F-test for rank estimation of in vivo training sets allowed for noise to be more accurately removed. Malinowski’s F-test also improved the robustness of our criterion for judging multivariate model validity, even though signal-to-noise ratios of the data varied. In addition, pH change was the majority noise carrier of in vivo training sets while dopamine prediction was more sensitive to noise. PMID:20527815

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

Hypothyroidism and Glaucoma in The United States

PubMed Central

Kakigi, Caitlin; Kasuga, Toshimitsu; Wang, Sophia Y.; Singh, Kuldev; Hiratsuka, Yoshimune; Murakami, Akira; Lin, Shan C.

2015-01-01

Purpose To investigate the association between hypothyroidism and glaucomatous disease. Methods This cross-sectional study included all subjects above the age of 40 years from two nationwide surveys: the 2008 National Health Interview Survey (NHIS) as well as the 2007 and 2008 National Health and Nutrition Examination Survey (NHANES). The presence or absence of glaucoma, thyroid disease and other demographic and health-related information including comorbidities was ascertained via interview. Blood samples were collected from NHANES subjects and analyzed for thyrotropin (TSH). Results A total of 13,599 and 3,839 NHIS and NHANES participants respectively were analyzed to assess for a possible relationship between self-reported glaucoma, and self-reported hypothyroidism as well as self-reported thyroid disease. The unadjusted odds ratio (OR) for NHIS showed a significant association between self-reported glaucoma and self-reported hypothyroidism (OR 1.46, 95% confidence interval [CI] 1.07-1.99). Multivariate logistic regression analysis adjusted for age, gender, race, comorbidities, and health-related behavior, however, showed no association between self-reported glaucoma and hypothyroidism or thyroid disease in both surveys (OR 1.60, 95%CI 0.87-2.95 for NHIS; OR 1.05, 95%CI 0.59-1.88 for NHANES). Conclusion A previously reported association between hypothyroidism and glaucomatous disease was not confirmed in two large U.S. health survey populations. While such an association was noted in the univariate analysis for the NHIS survey, such a relationship was not found in the multivariate analysis after adjustment for potential confounding variables. PMID:26230664

Analysis of pulmonary pure ground-glass nodule in enhanced dual energy CT imaging for predicting invasive adenocarcinoma: comparing with conventional thin-section CT imaging

PubMed Central

Zhang, Ying; Tang, Jian; Xu, Jianrong

2017-01-01

Background To investigate the value of dual energy computed tomography (DECT) parameters (including iodine concentration and monochromatic CT numbers) for predicting pure ground-glass nodules (pGGNs) of invasive adenocarcinoma (IA). Methods A total of 55 resected pGGNs evaluated with both unenhanced thin-section CT (TSCT) and enhanced DECT scans were included. Correlations between histopathology [adenocarcinoma in situ (AIS), minimally IA (MIA), and IA] and CT scan characteristics were examined. CT scan and clinicodemographic data were investigated by univariate and multivariate analysis to identify features that helped distinguish IA from AIS or MIA. Results Both normalized iodine concentration (NIC) of IA and slope of spectral curve [slope(k)] were not significantly different between IA and AIS or MIA. Size, performance of pleural retraction and enhanced monochromatic CT attenuation values of 120–140 keV were significantly higher for IA. In multivariate regression analysis, size and enhanced monochromatic CT number of 140 keV were independent predictors for IA. Using the two parameters together, the diagnostic capacity of IA could be improved from 0.697 or 0.635 to 0.713. Conclusions DECT could help demonstrate blood supply and indicate invasion extent of pGGNs, and monochromatic CT number of higher energy (especially 140 keV) would be better for diagnosing IA than lower energies. Together with size of pGGNs, the diagnostic capacity of IA could be better. PMID:29312701