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

Extending Inferential Group Analysis in Type 2 Diabetic Patients with Multivariate GLM Implemented in SPM8.

PubMed

Ferreira, Fábio S; Pereira, João M S; Duarte, João V; Castelo-Branco, Miguel

2017-01-01

Although voxel based morphometry studies are still the standard for analyzing brain structure, their dependence on massive univariate inferential methods is a limiting factor. A better understanding of brain pathologies can be achieved by applying inferential multivariate methods, which allow the study of multiple dependent variables, e.g. different imaging modalities of the same subject. Given the widespread use of SPM software in the brain imaging community, the main aim of this work is the implementation of massive multivariate inferential analysis as a toolbox in this software package. applied to the use of T1 and T2 structural data from diabetic patients and controls. This implementation was compared with the traditional ANCOVA in SPM and a similar multivariate GLM toolbox (MRM). We implemented the new toolbox and tested it by investigating brain alterations on a cohort of twenty-eight type 2 diabetes patients and twenty-six matched healthy controls, using information from both T1 and T2 weighted structural MRI scans, both separately - using standard univariate VBM - and simultaneously, with multivariate analyses. Univariate VBM replicated predominantly bilateral changes in basal ganglia and insular regions in type 2 diabetes patients. On the other hand, multivariate analyses replicated key findings of univariate results, while also revealing the thalami as additional foci of pathology. While the presented algorithm must be further optimized, the proposed toolbox is the first implementation of multivariate statistics in SPM8 as a user-friendly toolbox, which shows great potential and is ready to be validated in other clinical cohorts and modalities.

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

PubMed

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

2008-10-01

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

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

PubMed

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

2017-01-01

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

NONPARAMETRIC MANOVA APPROACHES FOR NON-NORMAL MULTIVARIATE OUTCOMES WITH MISSING VALUES

PubMed Central

He, Fanyin; Mazumdar, Sati; Tang, Gong; Bhatia, Triptish; Anderson, Stewart J.; Dew, Mary Amanda; Krafty, Robert; Nimgaonkar, Vishwajit; Deshpande, Smita; Hall, Martica; Reynolds, Charles F.

2017-01-01

Between-group comparisons often entail many correlated response variables. The multivariate linear model, with its assumption of multivariate normality, is the accepted standard tool for these tests. When this assumption is violated, the nonparametric multivariate Kruskal-Wallis (MKW) test is frequently used. However, this test requires complete cases with no missing values in response variables. Deletion of cases with missing values likely leads to inefficient statistical inference. Here we extend the MKW test to retain information from partially-observed cases. Results of simulated studies and analysis of real data show that the proposed method provides adequate coverage and superior power to complete-case analyses. PMID:29416225

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.

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

PubMed

Barrantes, Gilbert; Sandoval, Luis

2009-09-01

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

A guide to statistical analysis in microbial ecology: a community-focused, living review of multivariate data analyses.

PubMed

Buttigieg, Pier Luigi; Ramette, Alban

2014-12-01

The application of multivariate statistical analyses has become a consistent feature in microbial ecology. However, many microbial ecologists are still in the process of developing a deep understanding of these methods and appreciating their limitations. As a consequence, staying abreast of progress and debate in this arena poses an additional challenge to many microbial ecologists. To address these issues, we present the GUide to STatistical Analysis in Microbial Ecology (GUSTA ME): a dynamic, web-based resource providing accessible descriptions of numerous multivariate techniques relevant to microbial ecologists. A combination of interactive elements allows users to discover and navigate between methods relevant to their needs and examine how they have been used by others in the field. We have designed GUSTA ME to become a community-led and -curated service, which we hope will provide a common reference and forum to discuss and disseminate analytical techniques relevant to the microbial ecology community. © 2014 The Authors. FEMS Microbiology Ecology published by John Wiley & Sons Ltd on behalf of Federation of European Microbiological Societies.

Ratio manipulating spectrophotometry versus chemometry as stability indicating methods for cefquinome sulfate determination

NASA Astrophysics Data System (ADS)

Yehia, Ali M.; Arafa, Reham M.; Abbas, Samah S.; Amer, Sawsan M.

2016-01-01

Spectral resolution of cefquinome sulfate (CFQ) in the presence of its degradation products was studied. Three selective, accurate and rapid spectrophotometric methods were performed for the determination of CFQ in the presence of either its hydrolytic, oxidative or photo-degradation products. The proposed ratio difference, derivative ratio and mean centering are ratio manipulating spectrophotometric methods that were satisfactorily applied for selective determination of CFQ within linear range of 5.0-40.0 μg mL- 1. Concentration Residuals Augmented Classical Least Squares was applied and evaluated for the determination of the cited drug in the presence of its all degradation products. Traditional Partial Least Squares regression was also applied and benchmarked against the proposed advanced multivariate calibration. Experimentally designed 25 synthetic mixtures of three factors at five levels were used to calibrate and validate the multivariate models. Advanced chemometrics succeeded in quantitative and qualitative analyses of CFQ along with its hydrolytic, oxidative and photo-degradation products. The proposed methods were applied successfully for different pharmaceutical formulations analyses. These developed methods were simple and cost-effective compared with the manufacturer's RP-HPLC method.

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

PubMed

Adams, Dean C; Collyer, Michael L

2018-01-01

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

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.

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.

A comparison of bivariate, multivariate random-effects, and Poisson correlated gamma-frailty models to meta-analyze individual patient data of ordinal scale diagnostic tests.

PubMed

Simoneau, Gabrielle; Levis, Brooke; Cuijpers, Pim; Ioannidis, John P A; Patten, Scott B; Shrier, Ian; Bombardier, Charles H; de Lima Osório, Flavia; Fann, Jesse R; Gjerdingen, Dwenda; Lamers, Femke; Lotrakul, Manote; Löwe, Bernd; Shaaban, Juwita; Stafford, Lesley; van Weert, Henk C P M; Whooley, Mary A; Wittkampf, Karin A; Yeung, Albert S; Thombs, Brett D; Benedetti, Andrea

2017-11-01

Individual patient data (IPD) meta-analyses are increasingly common in the literature. In the context of estimating the diagnostic accuracy of ordinal or semi-continuous scale tests, sensitivity and specificity are often reported for a given threshold or a small set of thresholds, and a meta-analysis is conducted via a bivariate approach to account for their correlation. When IPD are available, sensitivity and specificity can be pooled for every possible threshold. Our objective was to compare the bivariate approach, which can be applied separately at every threshold, to two multivariate methods: the ordinal multivariate random-effects model and the Poisson correlated gamma-frailty model. Our comparison was empirical, using IPD from 13 studies that evaluated the diagnostic accuracy of the 9-item Patient Health Questionnaire depression screening tool, and included simulations. The empirical comparison showed that the implementation of the two multivariate methods is more laborious in terms of computational time and sensitivity to user-supplied values compared to the bivariate approach. Simulations showed that ignoring the within-study correlation of sensitivity and specificity across thresholds did not worsen inferences with the bivariate approach compared to the Poisson model. The ordinal approach was not suitable for simulations because the model was highly sensitive to user-supplied starting values. We tentatively recommend the bivariate approach rather than more complex multivariate methods for IPD diagnostic accuracy meta-analyses of ordinal scale tests, although the limited type of diagnostic data considered in the simulation study restricts the generalization of our findings. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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

A Methodology for Conducting Integrative Mixed Methods Research and Data Analyses

PubMed Central

Castro, Felipe González; Kellison, Joshua G.; Boyd, Stephen J.; Kopak, Albert

2011-01-01

Mixed methods research has gained visibility within the last few years, although limitations persist regarding the scientific caliber of certain mixed methods research designs and methods. The need exists for rigorous mixed methods designs that integrate various data analytic procedures for a seamless transfer of evidence across qualitative and quantitative modalities. Such designs can offer the strength of confirmatory results drawn from quantitative multivariate analyses, along with “deep structure” explanatory descriptions as drawn from qualitative analyses. This article presents evidence generated from over a decade of pilot research in developing an integrative mixed methods methodology. It presents a conceptual framework and methodological and data analytic procedures for conducting mixed methods research studies, and it also presents illustrative examples from the authors' ongoing integrative mixed methods research studies. PMID:22167325

Development of a non-destructive method for determining protein nitrogen in a yellow fever vaccine by near infrared spectroscopy and multivariate calibration.

PubMed

Dabkiewicz, Vanessa Emídio; de Mello Pereira Abrantes, Shirley; Cassella, Ricardo Jorgensen

2018-08-05

Near infrared spectroscopy (NIR) with diffuse reflectance associated to multivariate calibration has as main advantage the replacement of the physical separation of interferents by the mathematical separation of their signals, rapidly with no need for reagent consumption, chemical waste production or sample manipulation. Seeking to optimize quality control analyses, this spectroscopic analytical method was shown to be a viable alternative to the classical Kjeldahl method for the determination of protein nitrogen in yellow fever vaccine. The most suitable multivariate calibration was achieved by the partial least squares method (PLS) with multiplicative signal correction (MSC) treatment and data mean centering (MC), using a minimum number of latent variables (LV) equal to 1, with the lower value of the square root of the mean squared prediction error (0.00330) associated with the highest percentage value (91%) of samples. Accuracy ranged 95 to 105% recovery in the 4000-5184 cm -1 region. Copyright © 2018 Elsevier B.V. All rights reserved.

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

PubMed

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

2016-03-23

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

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

NASA Astrophysics Data System (ADS)

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

2016-03-01

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

Methods for estimating confidence intervals in interrupted time series analyses of health interventions.

PubMed

Zhang, Fang; Wagner, Anita K; Soumerai, Stephen B; Ross-Degnan, Dennis

2009-02-01

Interrupted time series (ITS) is a strong quasi-experimental research design, which is increasingly applied to estimate the effects of health services and policy interventions. We describe and illustrate two methods for estimating confidence intervals (CIs) around absolute and relative changes in outcomes calculated from segmented regression parameter estimates. We used multivariate delta and bootstrapping methods (BMs) to construct CIs around relative changes in level and trend, and around absolute changes in outcome based on segmented linear regression analyses of time series data corrected for autocorrelated errors. Using previously published time series data, we estimated CIs around the effect of prescription alerts for interacting medications with warfarin on the rate of prescriptions per 10,000 warfarin users per month. Both the multivariate delta method (MDM) and the BM produced similar results. BM is preferred for calculating CIs of relative changes in outcomes of time series studies, because it does not require large sample sizes when parameter estimates are obtained correctly from the model. Caution is needed when sample size is small.

Ratio manipulating spectrophotometry versus chemometry as stability indicating methods for cefquinome sulfate determination.

PubMed

Yehia, Ali M; Arafa, Reham M; Abbas, Samah S; Amer, Sawsan M

2016-01-15

Spectral resolution of cefquinome sulfate (CFQ) in the presence of its degradation products was studied. Three selective, accurate and rapid spectrophotometric methods were performed for the determination of CFQ in the presence of either its hydrolytic, oxidative or photo-degradation products. The proposed ratio difference, derivative ratio and mean centering are ratio manipulating spectrophotometric methods that were satisfactorily applied for selective determination of CFQ within linear range of 5.0-40.0 μg mL(-1). Concentration Residuals Augmented Classical Least Squares was applied and evaluated for the determination of the cited drug in the presence of its all degradation products. Traditional Partial Least Squares regression was also applied and benchmarked against the proposed advanced multivariate calibration. Experimentally designed 25 synthetic mixtures of three factors at five levels were used to calibrate and validate the multivariate models. Advanced chemometrics succeeded in quantitative and qualitative analyses of CFQ along with its hydrolytic, oxidative and photo-degradation products. The proposed methods were applied successfully for different pharmaceutical formulations analyses. These developed methods were simple and cost-effective compared with the manufacturer's RP-HPLC method. Copyright © 2015 Elsevier B.V. All rights reserved.

Power and sample size for multivariate logistic modeling of unmatched case-control studies.

PubMed

Gail, Mitchell H; Haneuse, Sebastien

2017-01-01

Sample size calculations are needed to design and assess the feasibility of case-control studies. Although such calculations are readily available for simple case-control designs and univariate analyses, there is limited theory and software for multivariate unconditional logistic analysis of case-control data. Here we outline the theory needed to detect scalar exposure effects or scalar interactions while controlling for other covariates in logistic regression. Both analytical and simulation methods are presented, together with links to the corresponding software.

Learning investment indicators through data extension

NASA Astrophysics Data System (ADS)

Dvořák, Marek

2017-07-01

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

A multivariate time series approach to modeling and forecasting demand in the emergency department.

PubMed

Jones, Spencer S; Evans, R Scott; Allen, Todd L; Thomas, Alun; Haug, Peter J; Welch, Shari J; Snow, Gregory L

2009-02-01

The goals of this investigation were to study the temporal relationships between the demands for key resources in the emergency department (ED) and the inpatient hospital, and to develop multivariate forecasting models. Hourly data were collected from three diverse hospitals for the year 2006. Descriptive analysis and model fitting were carried out using graphical and multivariate time series methods. Multivariate models were compared to a univariate benchmark model in terms of their ability to provide out-of-sample forecasts of ED census and the demands for diagnostic resources. Descriptive analyses revealed little temporal interaction between the demand for inpatient resources and the demand for ED resources at the facilities considered. Multivariate models provided more accurate forecasts of ED census and of the demands for diagnostic resources. Our results suggest that multivariate time series models can be used to reliably forecast ED patient census; however, forecasts of the demands for diagnostic resources were not sufficiently reliable to be useful in the clinical setting.

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.

The Multivariate Largest Lyapunov Exponent as an Age-Related Metric of Quiet Standing Balance

PubMed Central

Liu, Kun; Wang, Hongrui; Xiao, Jinzhuang

2015-01-01

The largest Lyapunov exponent has been researched as a metric of the balance ability during human quiet standing. However, the sensitivity and accuracy of this measurement method are not good enough for clinical use. The present research proposes a metric of the human body's standing balance ability based on the multivariate largest Lyapunov exponent which can quantify the human standing balance. The dynamic multivariate time series of ankle, knee, and hip were measured by multiple electrical goniometers. Thirty-six normal people of different ages participated in the test. With acquired data, the multivariate largest Lyapunov exponent was calculated. Finally, the results of the proposed approach were analysed and compared with the traditional method, for which the largest Lyapunov exponent and power spectral density from the centre of pressure were also calculated. The following conclusions can be obtained. The multivariate largest Lyapunov exponent has a higher degree of differentiation in differentiating balance in eyes-closed conditions. The MLLE value reflects the overall coordination between multisegment movements. Individuals of different ages can be distinguished by their MLLE values. The standing stability of human is reduced with the increment of age. PMID:26064182

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

PubMed Central

Anderson, Marti J; Santana-Garcon, Julia

2015-01-01

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

Multivariable regression analysis of list experiment data on abortion: results from a large, randomly-selected population based study in Liberia.

PubMed

Moseson, Heidi; Gerdts, Caitlin; Dehlendorf, Christine; Hiatt, Robert A; Vittinghoff, Eric

2017-12-21

The list experiment is a promising measurement tool for eliciting truthful responses to stigmatized or sensitive health behaviors. However, investigators may be hesitant to adopt the method due to previously untestable assumptions and the perceived inability to conduct multivariable analysis. With a recently developed statistical test that can detect the presence of a design effect - the absence of which is a central assumption of the list experiment method - we sought to test the validity of a list experiment conducted on self-reported abortion in Liberia. We also aim to introduce recently developed multivariable regression estimators for the analysis of list experiment data, to explore relationships between respondent characteristics and having had an abortion - an important component of understanding the experiences of women who have abortions. To test the null hypothesis of no design effect in the Liberian list experiment data, we calculated the percentage of each respondent "type," characterized by response to the control items, and compared these percentages across treatment and control groups with a Bonferroni-adjusted alpha criterion. We then implemented two least squares and two maximum likelihood models (four total), each representing different bias-variance trade-offs, to estimate the association between respondent characteristics and abortion. We find no clear evidence of a design effect in list experiment data from Liberia (p = 0.18), affirming the first key assumption of the method. Multivariable analyses suggest a negative association between education and history of abortion. The retrospective nature of measuring lifetime experience of abortion, however, complicates interpretation of results, as the timing and safety of a respondent's abortion may have influenced her ability to pursue an education. Our work demonstrates that multivariable analyses, as well as statistical testing of a key design assumption, are possible with list experiment data, although with important limitations when considering lifetime measures. We outline how to implement this methodology with list experiment data in future research.

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

PubMed Central

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

2015-01-01

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

Victimization and Suicidality among Female College Students

ERIC Educational Resources Information Center

Leone, Janel M.; Carroll, James M.

2016-01-01

Objective: To investigate the predictive role of victimization in suicidality among college women. Participants: Female respondents to the American College Health Association National College Health Assessment II (N = 258). Methods: Multivariate logistic regression analyses examined the relationship between victimization and suicidality. Results:…

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

PubMed

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

2018-03-01

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

Evaluating the role of admixture in cancer therapy via in vitro drug response and multivariate genome-wide associations

PubMed Central

Jack, John; Havener, Tammy M; McLeod, Howard L; Motsinger-Reif, Alison A; Foster, Matthew

2015-01-01

Aim: We investigate the role of ethnicity and admixture in drug response across a broad group of chemotherapeutic drugs. Also, we generate hypotheses on the genetic variants driving differential drug response through multivariate genome-wide association studies. Methods: Immortalized lymphoblastoid cell lines from 589 individuals (Hispanic or non-Hispanic/Caucasian) were used to investigate dose-response for 28 chemotherapeutic compounds. Univariate and multivariate statistical models were used to elucidate associations between genetic variants and differential drug response as well as the role of ethnicity in drug potency and efficacy. Results & Conclusion: For many drugs, the variability in drug response appears to correlate with self-reported race and estimates of genetic ancestry. Additionally, multivariate genome-wide association analyses offered interesting hypotheses governing these differential responses. PMID:26314407

Gene set analysis using variance component tests.

PubMed

Huang, Yen-Tsung; Lin, Xihong

2013-06-28

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

Multivariate pattern analysis of MEG and EEG: A comparison of representational structure in time and space.

PubMed

Cichy, Radoslaw Martin; Pantazis, Dimitrios

2017-09-01

Multivariate pattern analysis of magnetoencephalography (MEG) and electroencephalography (EEG) data can reveal the rapid neural dynamics underlying cognition. However, MEG and EEG have systematic differences in sampling neural activity. This poses the question to which degree such measurement differences consistently bias the results of multivariate analysis applied to MEG and EEG activation patterns. To investigate, we conducted a concurrent MEG/EEG study while participants viewed images of everyday objects. We applied multivariate classification analyses to MEG and EEG data, and compared the resulting time courses to each other, and to fMRI data for an independent evaluation in space. We found that both MEG and EEG revealed the millisecond spatio-temporal dynamics of visual processing with largely equivalent results. Beyond yielding convergent results, we found that MEG and EEG also captured partly unique aspects of visual representations. Those unique components emerged earlier in time for MEG than for EEG. Identifying the sources of those unique components with fMRI, we found the locus for both MEG and EEG in high-level visual cortex, and in addition for MEG in low-level visual cortex. Together, our results show that multivariate analyses of MEG and EEG data offer a convergent and complimentary view on neural processing, and motivate the wider adoption of these methods in both MEG and EEG research. Copyright © 2017 Elsevier Inc. All rights reserved.

Changes in Landscape Greenness and Climatic Factors over ...

EPA Pesticide Factsheets

Monitoring and quantifying changes in vegetation cover over large areas using remote sensing can be achieved using the Normalized Difference Vegetation Index (NDVI), an indicator of greenness. However, distinguishing gradual shifts in NDVI (e.g. climate change) versus direct and rapid changes (e.g., fire, land development) is challenging as changes can be confounded by time-dependent patterns, and variation associated with climatic factors. In the present study we leveraged a method, that we previously developed for a pilot study, to address these confounding factors by evaluating NDVI change using autoregression techniques that compare results from univariate (NDVI vs. time) and multivariate analyses (NDVI vs. time and climatic factors) for ~7,660,636 1-km2 pixels comprising the 48 contiguous states of the USA, over a 25-year period (1989−2013). NDVI changed significantly for 48% of the nation over the 25-year in the univariate analyses where most significant trends (85%) indicated an increase in greenness over time. By including climatic factors in the multivariate analyses of NDVI over time, the detection of significant NDVI trends increased to 53% (an increase of 5%). Comparisons of univariate and multivariate analyses for each pixel showed that less than 4% of the pixels had a significant NDVI trend attributable to gradual climatic changes while the remainder of pixels with a significant NDVI trend indicated that changes were due to direct factors. Whi

A multivariate variational objective analysis-assimilation method. Part 2: Case study results with and without satellite data

NASA Technical Reports Server (NTRS)

Achtemeier, Gary L.; Kidder, Stanley Q.; Scott, Robert W.

1988-01-01

The variational multivariate assimilation method described in a companion paper by Achtemeier and Ochs is applied to conventional and conventional plus satellite data. Ground-based and space-based meteorological data are weighted according to the respective measurement errors and blended into a data set that is a solution of numerical forms of the two nonlinear horizontal momentum equations, the hydrostatic equation, and an integrated continuity equation for a dry atmosphere. The analyses serve first, to evaluate the accuracy of the model, and second to contrast the analyses with and without satellite data. Evaluation criteria measure the extent to which: (1) the assimilated fields satisfy the dynamical constraints, (2) the assimilated fields depart from the observations, and (3) the assimilated fields are judged to be realistic through pattern analysis. The last criterion requires that the signs, magnitudes, and patterns of the hypersensitive vertical velocity and local tendencies of the horizontal velocity components be physically consistent with respect to the larger scale weather systems.

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

PubMed Central

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

2016-01-01

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

Hydrothermal contamination of public supply wells in Napa and Sonoma Valleys, California

USGS Publications Warehouse

Forrest, Matthew J.; Kulongoski, Justin T.; Edwards, Matthew S.; Farrar, Christopher D.; Belitz, Kenneth; Norris, Richard D.

2013-01-01

Groundwater chemistry and isotope data from 44 public supply wells in the Napa and Sonoma Valleys, California were determined to investigate mixing of relatively shallow groundwater with deeper hydrothermal fluids. Multivariate analyses including Cluster Analyses, Multidimensional Scaling (MDS), Principal Components Analyses (PCA), Analysis of Similarities (ANOSIM), and Similarity Percentage Analyses (SIMPER) were used to elucidate constituent distribution patterns, determine which constituents are significantly associated with these hydrothermal systems, and investigate hydrothermal contamination of local groundwater used for drinking water. Multivariate statistical analyses were essential to this study because traditional methods, such as mixing tests involving single species (e.g. Cl or SiO2) were incapable of quantifying component proportions due to mixing of multiple water types. Based on these analyses, water samples collected from the wells were broadly classified as fresh groundwater, saline waters, hydrothermal fluids, or mixed hydrothermal fluids/meteoric water wells. The Multivariate Mixing and Mass-balance (M3) model was applied in order to determine the proportion of hydrothermal fluids, saline water, and fresh groundwater in each sample. Major ions, isotopes, and physical parameters of the waters were used to characterize the hydrothermal fluids as Na–Cl type, with significant enrichment in the trace elements As, B, F and Li. Five of the wells from this study were classified as hydrothermal, 28 as fresh groundwater, two as saline water, and nine as mixed hydrothermal fluids/meteoric water wells. The M3 mixing-model results indicated that the nine mixed wells contained between 14% and 30% hydrothermal fluids. Further, the chemical analyses show that several of these mixed-water wells have concentrations of As, F and B that exceed drinking-water standards or notification levels due to contamination by hydrothermal fluids.

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

TATES: Efficient Multivariate Genotype-Phenotype Analysis for Genome-Wide Association Studies

PubMed Central

van der Sluis, Sophie; Posthuma, Danielle; Dolan, Conor V.

2013-01-01

To date, the genome-wide association study (GWAS) is the primary tool to identify genetic variants that cause phenotypic variation. As GWAS analyses are generally univariate in nature, multivariate phenotypic information is usually reduced to a single composite score. This practice often results in loss of statistical power to detect causal variants. Multivariate genotype–phenotype methods do exist but attain maximal power only in special circumstances. Here, we present a new multivariate method that we refer to as TATES (Trait-based Association Test that uses Extended Simes procedure), inspired by the GATES procedure proposed by Li et al (2011). For each component of a multivariate trait, TATES combines p-values obtained in standard univariate GWAS to acquire one trait-based p-value, while correcting for correlations between components. Extensive simulations, probing a wide variety of genotype–phenotype models, show that TATES's false positive rate is correct, and that TATES's statistical power to detect causal variants explaining 0.5% of the variance can be 2.5–9 times higher than the power of univariate tests based on composite scores and 1.5–2 times higher than the power of the standard MANOVA. Unlike other multivariate methods, TATES detects both genetic variants that are common to multiple phenotypes and genetic variants that are specific to a single phenotype, i.e. TATES provides a more complete view of the genetic architecture of complex traits. As the actual causal genotype–phenotype model is usually unknown and probably phenotypically and genetically complex, TATES, available as an open source program, constitutes a powerful new multivariate strategy that allows researchers to identify novel causal variants, while the complexity of traits is no longer a limiting factor. PMID:23359524

Correlates of Gambling among Eighth-Grade Boys and Girls

ERIC Educational Resources Information Center

Chaumeton, Nigel R.; Ramowski, Sarah K.; Nystrom, Robert J.

2011-01-01

Background: This study examined the correlates of gambling behavior among eighth-grade students. Methods: Children (n = 15,865) enrolled in publicly funded schools in Oregon completed the 2008 Oregon Healthy Teens survey. Multivariate logistic regression analyses assessed the combined and independent associations between risk and protective…

Parental Perceptions of Their Adolescent's Weight Status: The ECHO Study

ERIC Educational Resources Information Center

Hearst, Mary O.; Sherwood, Nancy E.; Klein, Elizabeth G.; Pasch, Keryn E.; Lytle, Leslie A.

2011-01-01

Objectives: To assess the correlates of parental classification of adolescent weight status. Methods: Measured adolescent weight status was compared to parent self-report perception data (n 374 dyads) using multivariate analyses with interactions to identify characteristics associated with inaccurate parent classification of adolescent weight…

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…

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.

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

PubMed

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

2017-04-01

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

Individual, Psychosocial, and Social Correlates of Unprotected Anal Intercourse in a New Generation of Young Men Who Have Sex With Men in New York City

PubMed Central

Kapadia, Farzana; Siconolfi, Daniel E.; Moeller, Robert W.; Figueroa, Rafael Perez; Barton, Staci C.; Blachman-Forshay, Jaclyn

2013-01-01

Objectives. We examined associations of individual, psychosocial, and social factors with unprotected anal intercourse (UAI) among young men who have sex with men in New York City. Methods. Using baseline assessment data from 592 young men who have sex with men participating in an ongoing prospective cohort study, we conducted multivariable logistic regression analyses to examine the associations between covariates and likelihood of recently engaging in UAI with same-sex partners. Results. Nineteen percent reported recent UAI with a same-sex partner. In multivariable models, being in a current relationship with another man (adjusted odds ratio [AOR] = 4.87), an arrest history (AOR = 2.01), greater residential instability (AOR = 1.75), and unstable housing or homelessness (AOR = 3.10) was associated with recent UAI. Although high levels of gay community affinity and low internalized homophobia were associated with engaging in UAI in bivariate analyses, these associations did not persist in multivariable analyses. Conclusions. Associations of psychosocial and socially produced conditions with UAI among a new generation of young men who have sex with men warrant that HIV prevention programs and policies address structural factors that predispose sexual risk behaviors. PMID:23488487

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.

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

The Impact of Primary and Secondary Education on Higher Education Quality

ERIC Educational Resources Information Center

Michaelowa, Katharina

2007-01-01

Purpose: The purpose of this paper is to provide an overview of the relationship among different levels of education. Design/methodology/approach: International cross-country comparisons, bi- and multivariate analyses, with many graphical illustrations. These methods are used to compare educational outcomes at the primary, secondary and tertiary…

The Impact of Bullying and Victimization on Students' Relationships

ERIC Educational Resources Information Center

Demanet, Jannick; Van Houtte, Mieke

2012-01-01

Grschool, in Flemish secondary schools. Methods: We use data from the Flemish Educational Assessment (FlEA), consisting of 11,872 students in 85 schools. Multivariate analyses of variance (ANOVA) were performed. Results: Non-involved students felt most attached to peers, parents, teachers, and school. Bullies matched the level of parental…

Family-Based Rare Variant Association Analysis: A Fast and Efficient Method of Multivariate Phenotype Association Analysis.

PubMed

Wang, Longfei; Lee, Sungyoung; Gim, Jungsoo; Qiao, Dandi; Cho, Michael; Elston, Robert C; Silverman, Edwin K; Won, Sungho

2016-09-01

Family-based designs have been repeatedly shown to be powerful in detecting the significant rare variants associated with human diseases. Furthermore, human diseases are often defined by the outcomes of multiple phenotypes, and thus we expect multivariate family-based analyses may be very efficient in detecting associations with rare variants. However, few statistical methods implementing this strategy have been developed for family-based designs. In this report, we describe one such implementation: the multivariate family-based rare variant association tool (mFARVAT). mFARVAT is a quasi-likelihood-based score test for rare variant association analysis with multiple phenotypes, and tests both homogeneous and heterogeneous effects of each variant on multiple phenotypes. Simulation results show that the proposed method is generally robust and efficient for various disease models, and we identify some promising candidate genes associated with chronic obstructive pulmonary disease. The software of mFARVAT is freely available at http://healthstat.snu.ac.kr/software/mfarvat/, implemented in C++ and supported on Linux and MS Windows. © 2016 WILEY PERIODICALS, INC.

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

PubMed Central

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

2012-01-01

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

Metabolite profiling of Clinacanthus nutans leaves extracts obtained from different drying methods by 1H NMR-based metabolomics

NASA Astrophysics Data System (ADS)

Hashim, Noor Haslinda Noor; Latip, Jalifah; Khatib, Alfi

2016-11-01

The metabolites of Clinacanthus nutans leaves extracts and their dependence on drying process were systematically characterized using 1H nuclear magnetic resonance spectroscopy (NMR) multivariate data analysis. Principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA) were able to distinguish the leaves extracts obtained from different drying methods. The identified metabolites were carbohydrates, amino acid, flavonoids and sulfur glucoside compounds. The major metabolites responsible for the separation in PLS-DA loading plots were lupeol, cycloclinacosides, betulin, cerebrosides and choline. The results showed that the combination of 1H NMR spectroscopy and multivariate data analyses could act as an efficient technique to understand the C. nutans composition and its variation.

Optimizing Functional Network Representation of Multivariate Time Series

NASA Astrophysics Data System (ADS)

Zanin, Massimiliano; Sousa, Pedro; Papo, David; Bajo, Ricardo; García-Prieto, Juan; Pozo, Francisco Del; Menasalvas, Ernestina; Boccaletti, Stefano

2012-09-01

By combining complex network theory and data mining techniques, we provide objective criteria for optimization of the functional network representation of generic multivariate time series. In particular, we propose a method for the principled selection of the threshold value for functional network reconstruction from raw data, and for proper identification of the network's indicators that unveil the most discriminative information on the system for classification purposes. We illustrate our method by analysing networks of functional brain activity of healthy subjects, and patients suffering from Mild Cognitive Impairment, an intermediate stage between the expected cognitive decline of normal aging and the more pronounced decline of dementia. We discuss extensions of the scope of the proposed methodology to network engineering purposes, and to other data mining tasks.

Optimizing Functional Network Representation of Multivariate Time Series

PubMed Central

Zanin, Massimiliano; Sousa, Pedro; Papo, David; Bajo, Ricardo; García-Prieto, Juan; Pozo, Francisco del; Menasalvas, Ernestina; Boccaletti, Stefano

2012-01-01

By combining complex network theory and data mining techniques, we provide objective criteria for optimization of the functional network representation of generic multivariate time series. In particular, we propose a method for the principled selection of the threshold value for functional network reconstruction from raw data, and for proper identification of the network's indicators that unveil the most discriminative information on the system for classification purposes. We illustrate our method by analysing networks of functional brain activity of healthy subjects, and patients suffering from Mild Cognitive Impairment, an intermediate stage between the expected cognitive decline of normal aging and the more pronounced decline of dementia. We discuss extensions of the scope of the proposed methodology to network engineering purposes, and to other data mining tasks. PMID:22953051

Non-proportional odds multivariate logistic regression of ordinal family data.

PubMed

Zaloumis, Sophie G; Scurrah, Katrina J; Harrap, Stephen B; Ellis, Justine A; Gurrin, Lyle C

2015-03-01

Methods to examine whether genetic and/or environmental sources can account for the residual variation in ordinal family data usually assume proportional odds. However, standard software to fit the non-proportional odds model to ordinal family data is limited because the correlation structure of family data is more complex than for other types of clustered data. To perform these analyses we propose the non-proportional odds multivariate logistic regression model and take a simulation-based approach to model fitting using Markov chain Monte Carlo methods, such as partially collapsed Gibbs sampling and the Metropolis algorithm. We applied the proposed methodology to male pattern baldness data from the Victorian Family Heart Study. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A system to build distributed multivariate models and manage disparate data sharing policies: implementation in the scalable national network for effectiveness research.

PubMed

Meeker, Daniella; Jiang, Xiaoqian; Matheny, Michael E; Farcas, Claudiu; D'Arcy, Michel; Pearlman, Laura; Nookala, Lavanya; Day, Michele E; Kim, Katherine K; Kim, Hyeoneui; Boxwala, Aziz; El-Kareh, Robert; Kuo, Grace M; Resnic, Frederic S; Kesselman, Carl; Ohno-Machado, Lucila

2015-11-01

Centralized and federated models for sharing data in research networks currently exist. To build multivariate data analysis for centralized networks, transfer of patient-level data to a central computation resource is necessary. The authors implemented distributed multivariate models for federated networks in which patient-level data is kept at each site and data exchange policies are managed in a study-centric manner. The objective was to implement infrastructure that supports the functionality of some existing research networks (e.g., cohort discovery, workflow management, and estimation of multivariate analytic models on centralized data) while adding additional important new features, such as algorithms for distributed iterative multivariate models, a graphical interface for multivariate model specification, synchronous and asynchronous response to network queries, investigator-initiated studies, and study-based control of staff, protocols, and data sharing policies. Based on the requirements gathered from statisticians, administrators, and investigators from multiple institutions, the authors developed infrastructure and tools to support multisite comparative effectiveness studies using web services for multivariate statistical estimation in the SCANNER federated network. The authors implemented massively parallel (map-reduce) computation methods and a new policy management system to enable each study initiated by network participants to define the ways in which data may be processed, managed, queried, and shared. The authors illustrated the use of these systems among institutions with highly different policies and operating under different state laws. Federated research networks need not limit distributed query functionality to count queries, cohort discovery, or independently estimated analytic models. Multivariate analyses can be efficiently and securely conducted without patient-level data transport, allowing institutions with strict local data storage requirements to participate in sophisticated analyses based on federated research networks. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.

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

PubMed

Anderson, Marti J; Santana-Garcon, Julia

2015-01-01

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

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.

A screening method based on UV-Visible spectroscopy and multivariate analysis to assess addition of filler juices and water to pomegranate juices.

PubMed

Boggia, Raffaella; Casolino, Maria Chiara; Hysenaj, Vilma; Oliveri, Paolo; Zunin, Paola

2013-10-15

Consumer demand for pomegranate juice has considerably grown, during the last years, for its potential health benefits. Since it is an expensive functional food, cheaper fruit juices addition (i.e., grape and apple juices) or its simple dilution, or polyphenols subtraction are deceptively used. At present, time-consuming analyses are used to control the quality of this product. Furthermore these analyses are expensive and require well-trained analysts. Thus, the purpose of this study was to propose a high-speed and easy-to-use shortcut. Based on UV-VIS spectroscopy and chemometrics, a screening method is proposed to quickly screening some common fillers of pomegranate juice that could decrease the antiradical scavenging capacity of pure products. The analytical method was applied to laboratory prepared juices, to commercial juices and to representative experimental mixtures at different levels of water and filler juices. The outcomes were evaluated by means of multivariate exploratory analysis. The results indicate that the proposed strategy can be a useful screening tool to assess addition of filler juices and water to pomegranate juices. Copyright © 2012 Elsevier Ltd. All rights reserved.

Groups That Work: Student Achievement in Group Research Projects and Effects on Individual Learning

ERIC Educational Resources Information Center

Monson, Renee

2017-01-01

Group research projects frequently are used to teach undergraduate research methods. This study uses multivariate analyses to examine the characteristics of higher-achieving groups (those that earn higher grades on group research projects) and to estimate the effects of participating in higher-achieving groups on subsequent individual learning…

Colorectal Cancer Screening among Latinos in Three Communities on the Texas-Mexico Border

ERIC Educational Resources Information Center

Fernández, María E.; Savas, Lara S.; Wilson, Katherine M.; Byrd, Theresa L.; Atkinson, John; Torres-Vigil, Isabel; Vernon, Sally W.

2015-01-01

Objective: To assess colorectal cancer screening (CRCS) prevalence and psychosocial correlates of CRCS among Latinos in South Texas. Method: Using multivariable analyses, we examined the association of perceived susceptibility, self-efficacy, pros and cons, subjective norms, knowledge and fatalism on CRCS among 544 Latinos (50 years and older).…

The Relation of Moral Judgment Development and Educational Experience to Recall of Moral Narratives and Expository Texts

ERIC Educational Resources Information Center

Narvaez, Darcia; Gleason, Tracy

2007-01-01

Moral text processing was used as an ecologically valid method for assessing implicit and explicit moral understanding and development. The authors tested undergraduates, seminarians, and graduate students in political science and philosophy for recall of moral narratives and moral expository texts. Multivariate analyses of covariance using…

Parental Youth Assets and Sexual Activity: Differences by Race/Ethnicity

ERIC Educational Resources Information Center

Tolma, Eleni L.; Oman, Roy F.; Vesely, Sara K.; Aspy, Cheryl B.; Beebe, Laura; Fluhr, Janene

2011-01-01

Objectives: To examine how the relationship between parental-related youth assets and youth sexual activity differed by race/ethnicity. Methods: A random sample of 976 youth and their parents living in a Midwestern city participated in the study. Multivariate logistic regression analyses were conducted for 3 major ethnic groups controlling for the…

Use of the Analysis of the Volatile Faecal Metabolome in Screening for Colorectal Cancer

PubMed Central

2015-01-01

Diagnosis of colorectal cancer is an invasive and expensive colonoscopy, which is usually carried out after a positive screening test. Unfortunately, existing screening tests lack specificity and sensitivity, hence many unnecessary colonoscopies are performed. Here we report on a potential new screening test for colorectal cancer based on the analysis of volatile organic compounds (VOCs) in the headspace of faecal samples. Faecal samples were obtained from subjects who had a positive faecal occult blood sample (FOBT). Subjects subsequently had colonoscopies performed to classify them into low risk (non-cancer) and high risk (colorectal cancer) groups. Volatile organic compounds were analysed by selected ion flow tube mass spectrometry (SIFT-MS) and then data were analysed using both univariate and multivariate statistical methods. Ions most likely from hydrogen sulphide, dimethyl sulphide and dimethyl disulphide are statistically significantly higher in samples from high risk rather than low risk subjects. Results using multivariate methods show that the test gives a correct classification of 75% with 78% specificity and 72% sensitivity on FOBT positive samples, offering a potentially effective alternative to FOBT. PMID:26086914

Motivations for genetic testing for lung cancer risk among young smokers.

PubMed

O'Neill, Suzanne C; Lipkus, Isaac M; Sanderson, Saskia C; Shepperd, James; Docherty, Sharron; McBride, Colleen M

2013-11-01

To examine why young people might want to undergo genetic susceptibility testing for lung cancer despite knowing that tested gene variants are associated with small increases in disease risk. The authors used a mixed-method approach to evaluate motives for and against genetic testing and the association between these motivations and testing intentions in 128 college students who smoke. Exploratory factor analysis yielded four reliable factors: Test Scepticism, Test Optimism, Knowledge Enhancement and Smoking Optimism. Test Optimism and Knowledge Enhancement correlated positively with intentions to test in bivariate and multivariate analyses (ps<0.001). Test Scepticism correlated negatively with testing intentions in multivariate analyses (p<0.05). Open-ended questions assessing testing motivations generally replicated themes of the quantitative survey. In addition to learning about health risks, young people may be motivated to seek genetic testing for reasons, such as gaining knowledge about new genetic technologies more broadly.

Radiation Therapy Noncompliance and Clinical Outcomes in an Urban Academic Cancer Center

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

Ohri, Nitin; Rapkin, Bruce D.; Guha, Chandan

Purpose: To examine associations between radiation therapy (RT) noncompliance and clinical outcomes. Methods and Materials: We reviewed all patients who completed courses of external beam RT with curative intent in our department from the years 2007 to 2012 for cancers of the head and neck, breast, lung, cervix, uterus, or rectum. Patients who missed 2 or more scheduled RT appointments (excluding planned treatment breaks) were deemed noncompliant. Univariate, multivariable, and propensity-matched analyses were performed to examine associations between RT noncompliance and clinical outcomes. Results: Of 1227 patients, 266 (21.7%) were noncompliant. With median follow-up of 50.9 months, 108 recurrences (8.8%) and 228more » deaths (18.6%) occurred. In univariate analyses, RT noncompliance was associated with increased recurrence risk (5-year cumulative incidence 16% vs 7%, P<.001), inferior recurrence-free survival (5-year actuarial rate 63% vs 79%, P<.001), and inferior overall survival (5-year actuarial rate 72% vs 83%, P<.001). In multivariable analyses that were adjusted for disease site and stage, comorbidity score, gender, ethnicity, race, and socioeconomic status (SES), RT noncompliance was associated with inferior recurrence, recurrence-free survival, and overall survival rates. Propensity score–matched models yielded results nearly identical to those seen in univariate analyses. Low SES was associated with RT noncompliance and was associated with inferior clinical outcomes in univariate analyses, but SES was not associated with inferior outcomes in multivariable models. Conclusion: For cancer patients being treated with curative intent, RT noncompliance is associated with inferior clinical outcomes. The magnitudes of these effects demonstrate that RT noncompliance can serve as a behavioral biomarker to identify high-risk patients who require additional interventions. Treatment compliance may mediate the associations that have been observed linking SES and clinical outcomes.« less

The classification of secondary colorectal liver cancer in human biopsy samples using angular dispersive x-ray diffraction and multivariate analysis

NASA Astrophysics Data System (ADS)

Theodorakou, Chrysoula; Farquharson, Michael J.

2009-08-01

The motivation behind this study is to assess whether angular dispersive x-ray diffraction (ADXRD) data, processed using multivariate analysis techniques, can be used for classifying secondary colorectal liver cancer tissue and normal surrounding liver tissue in human liver biopsy samples. The ADXRD profiles from a total of 60 samples of normal liver tissue and colorectal liver metastases were measured using a synchrotron radiation source. The data were analysed for 56 samples using nonlinear peak-fitting software. Four peaks were fitted to all of the ADXRD profiles, and the amplitude, area, amplitude and area ratios for three of the four peaks were calculated and used for the statistical and multivariate analysis. The statistical analysis showed that there are significant differences between all the peak-fitting parameters and ratios between the normal and the diseased tissue groups. The technique of soft independent modelling of class analogy (SIMCA) was used to classify normal liver tissue and colorectal liver metastases resulting in 67% of the normal tissue samples and 60% of the secondary colorectal liver tissue samples being classified correctly. This study has shown that the ADXRD data of normal and secondary colorectal liver cancer are statistically different and x-ray diffraction data analysed using multivariate analysis have the potential to be used as a method of tissue classification.

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

PubMed

Achana, Felix A; Cooper, Nicola J; Bujkiewicz, Sylwia; Hubbard, Stephanie J; Kendrick, Denise; Jones, David R; Sutton, Alex J

2014-07-21

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. 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. 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. 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.

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

PubMed

Campos-Filho, N; Franco, E L

1989-02-01

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

Advanced multivariate analysis to assess remediation of hydrocarbons in soils.

PubMed

Lin, Deborah S; Taylor, Peter; Tibbett, Mark

2014-10-01

Accurate monitoring of degradation levels in soils is essential in order to understand and achieve complete degradation of petroleum hydrocarbons in contaminated soils. We aimed to develop the use of multivariate methods for the monitoring of biodegradation of diesel in soils and to determine if diesel contaminated soils could be remediated to a chemical composition similar to that of an uncontaminated soil. An incubation experiment was set up with three contrasting soil types. Each soil was exposed to diesel at varying stages of degradation and then analysed for key hydrocarbons throughout 161 days of incubation. Hydrocarbon distributions were analysed by Principal Coordinate Analysis and similar samples grouped by cluster analysis. Variation and differences between samples were determined using permutational multivariate analysis of variance. It was found that all soils followed trajectories approaching the chemical composition of the unpolluted soil. Some contaminated soils were no longer significantly different to that of uncontaminated soil after 161 days of incubation. The use of cluster analysis allows the assignment of a percentage chemical similarity of a diesel contaminated soil to an uncontaminated soil sample. This will aid in the monitoring of hydrocarbon contaminated sites and the establishment of potential endpoints for successful remediation.

Use of Health Care Claims Data to Study Patients with Ophthalmologic Conditions

PubMed Central

Stein, Joshua D.; Lum, Flora; Lee, Paul P.; Rich, William L.; Coleman, Anne L.

2014-01-01

Objective To describe what information is or is not included in health care claims data, provide an overview of the main advantages and limitations of performing analyses using health care claims data, and offer general guidance on how to report and interpret findings of ophthalmology-related claims data analyses. Design Systematic review. Participants Not applicable. Methods A literature review and synthesis of methods for claims-based data analyses. Main Outcome Measures Not applicable. Results Some advantages of using claims data for analyses include large, diverse sample sizes, longitudinal follow-up, lack of selection bias, and potential for complex, multivariable modeling. The disadvantages include (a) the inherent limitations of claims data, such as incomplete, inaccurate, or missing data, or the lack of specific billing codes for some conditions; and (b) the inability, in some circumstances, to adequately evaluate the appropriateness of care. In general, reports of claims data analyses should include clear descriptions of the following methodological elements: the data source, the inclusion and exclusion criteria, the specific billing codes used, and the potential confounding factors incorporated in the multivariable models. Conclusions The use of claims data for research is expected to increase with the enhanced availability of data from Medicare and other sources. The use of claims data to evaluate resource use and efficiency and to determine the basis for supplementary payment methods for physicians is anticipated. Thus, it will be increasingly important for eye care providers to use accurate and descriptive codes for billing. Adherence to general guidance on the reporting of claims data analyses, as outlined in this article, is important to enhance the credibility and applicability of findings. Guidance on optimal ways to conduct and report ophthalmology-related investigations using claims data will likely continue to evolve as health services researchers refine the metrics to analyze large administrative data sets. PMID:24433971

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

PubMed

Henrard, S; Speybroeck, N; Hermans, C

2015-11-01

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

Multivariate analyses of peripheral blood leukocyte transcripts distinguish Alzheimer's, Parkinson's, control, and those at risk for developing Alzheimer's.

PubMed

Delvaux, Elaine; Mastroeni, Diego; Nolz, Jennifer; Chow, Nienwen; Sabbagh, Marwan; Caselli, Richard J; Reiman, Eric M; Marshall, Frederick J; Coleman, Paul D

2017-10-01

The need for a reliable, simple, and inexpensive blood test for Alzheimer's disease (AD) suitable for use in a primary care setting is widely recognized. This has led to a large number of publications describing blood tests for AD, which have, for the most part, not been replicable. We have chosen to examine transcripts expressed by the cellular, leukocyte compartment of blood. We have used hypothesis-based cDNA arrays and quantitative PCR to quantify the expression of selected sets of genes followed by multivariate analyses in multiple independent samples. Rather than a single study with no replicates, we chose an experimental design in which there were multiple replicates using different platforms and different sample populations. We have divided 177 blood samples and 27 brain samples into multiple replicates to demonstrate the ability to distinguish early clinical AD (Clinical Dementia Rating scale 0.5), Parkinson's disease (PD), and cognitively unimpaired APOE4 homozygotes, as well as to determine persons at risk for future cognitive impairment with significant accuracy. We assess our methods in a training/test set and also show that the variables we use distinguish AD, PD, and control brain. Importantly, we describe the variability of the weights assigned to individual transcripts in multivariate analyses in repeated studies and suggest that the variability we describe may be the cause of inability to repeat many earlier studies. Our data constitute a proof of principle that multivariate analysis of the transcriptome related to cell stress and inflammation of peripheral blood leukocytes has significant potential as a minimally invasive and inexpensive diagnostic tool for diagnosis and early detection of risk for AD. Copyright © 2017 Elsevier Inc. All rights reserved.

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 Network-Based Algorithm for Clustering Multivariate Repeated Measures Data

NASA Technical Reports Server (NTRS)

Koslovsky, Matthew; Arellano, John; Schaefer, Caroline; Feiveson, Alan; Young, Millennia; Lee, Stuart

2017-01-01

The National Aeronautics and Space Administration (NASA) Astronaut Corps is a unique occupational cohort for which vast amounts of measures data have been collected repeatedly in research or operational studies pre-, in-, and post-flight, as well as during multiple clinical care visits. In exploratory analyses aimed at generating hypotheses regarding physiological changes associated with spaceflight exposure, such as impaired vision, it is of interest to identify anomalies and trends across these expansive datasets. Multivariate clustering algorithms for repeated measures data may help parse the data to identify homogeneous groups of astronauts that have higher risks for a particular physiological change. However, available clustering methods may not be able to accommodate the complex data structures found in NASA data, since the methods often rely on strict model assumptions, require equally-spaced and balanced assessment times, cannot accommodate missing data or differing time scales across variables, and cannot process continuous and discrete data simultaneously. To fill this gap, we propose a network-based, multivariate clustering algorithm for repeated measures data that can be tailored to fit various research settings. Using simulated data, we demonstrate how our method can be used to identify patterns in complex data structures found in practice.

Chromatography methods and chemometrics for determination of milk fat adulterants

NASA Astrophysics Data System (ADS)

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

2017-09-01

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

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.

Permutation Tests of Hierarchical Cluster Analyses of Carrion Communities and Their Potential Use in Forensic Entomology.

PubMed

van der Ham, Joris L

2016-05-19

Forensic entomologists can use carrion communities' ecological succession data to estimate the postmortem interval (PMI). Permutation tests of hierarchical cluster analyses of these data provide a conceptual method to estimate part of the PMI, the post-colonization interval (post-CI). This multivariate approach produces a baseline of statistically distinct clusters that reflect changes in the carrion community composition during the decomposition process. Carrion community samples of unknown post-CIs are compared with these baseline clusters to estimate the post-CI. In this short communication, I use data from previously published studies to demonstrate the conceptual feasibility of this multivariate approach. Analyses of these data produce series of significantly distinct clusters, which represent carrion communities during 1- to 20-day periods of the decomposition process. For 33 carrion community samples, collected over an 11-day period, this approach correctly estimated the post-CI within an average range of 3.1 days. © The Authors 2016. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Multivariate data analysis and metabolic profiling of artemisinin and related compounds in high yielding varieties of Artemisia annua field-grown in Madagascar.

PubMed

Suberu, John; Gromski, Piotr S; Nordon, Alison; Lapkin, Alexei

2016-01-05

An improved liquid chromatography-tandem mass spectrometry (LC-MS/MS) protocol for rapid analysis of co-metabolites of A. annua in raw extracts was developed and extensively characterized. The new method was used to analyse metabolic profiles of 13 varieties of A. annua from an in-field growth programme in Madagascar. Several multivariate data analysis techniques consistently show the association of artemisinin with dihydroartemisinic acid. These data support the hypothesis of dihydroartemisinic acid being the late stage precursor to artemisinin in its biosynthetic pathway. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

Combining ANOVA-PCA with POCHEMON to analyse micro-organism development in a polymicrobial environment.

PubMed

Geurts, Brigitte P; Neerincx, Anne H; Bertrand, Samuel; Leemans, Manja A A P; Postma, Geert J; Wolfender, Jean-Luc; Cristescu, Simona M; Buydens, Lutgarde M C; Jansen, Jeroen J

2017-04-22

Revealing the biochemistry associated to micro-organismal interspecies interactions is highly relevant for many purposes. Each pathogen has a characteristic metabolic fingerprint that allows identification based on their unique multivariate biochemistry. When pathogen species come into mutual contact, their co-culture will display a chemistry that may be attributed both to mixing of the characteristic chemistries of the mono-cultures and to competition between the pathogens. Therefore, investigating pathogen development in a polymicrobial environment requires dedicated chemometric methods to untangle and focus upon these sources of variation. The multivariate data analysis method Projected Orthogonalised Chemical Encounter Monitoring (POCHEMON) is dedicated to highlight metabolites characteristic for the interaction of two micro-organisms in co-culture. However, this approach is currently limited to a single time-point, while development of polymicrobial interactions may be highly dynamic. A well-known multivariate implementation of Analysis of Variance (ANOVA) uses Principal Component Analysis (ANOVA-PCA). This allows the overall dynamics to be separated from the pathogen-specific chemistry to analyse the contributions of both aspects separately. For this reason, we propose to integrate ANOVA-PCA with the POCHEMON approach to disentangle the pathogen dynamics and the specific biochemistry in interspecies interactions. Two complementary case studies show great potential for both liquid and gas chromatography - mass spectrometry to reveal novel information on chemistry specific to interspecies interaction during pathogen development. Copyright © 2017 The Author(s). Published by Elsevier B.V. All rights reserved.

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.

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

PubMed

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

2017-02-01

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

No improvement in the reporting of clinical trial subgroup effects in high-impact general medical journals.

PubMed

Gabler, Nicole B; Duan, Naihua; Raneses, Eli; Suttner, Leah; Ciarametaro, Michael; Cooney, Elizabeth; Dubois, Robert W; Halpern, Scott D; Kravitz, Richard L

2016-07-16

When subgroup analyses are not correctly analyzed and reported, incorrect conclusions may be drawn, and inappropriate treatments provided. Despite the increased recognition of the importance of subgroup analysis, little information exists regarding the prevalence, appropriateness, and study characteristics that influence subgroup analysis. The objective of this study is to determine (1) if the use of subgroup analyses and multivariable risk indices has increased, (2) whether statistical methodology has improved over time, and (3) which study characteristics predict subgroup analysis. We randomly selected randomized controlled trials (RCTs) from five high-impact general medical journals during three time periods. Data from these articles were abstracted in duplicate using standard forms and a standard protocol. Subgroup analysis was defined as reporting any subgroup effect. Appropriate methods for subgroup analysis included a formal test for heterogeneity or interaction across treatment-by-covariate groups. We used logistic regression to determine the variables significantly associated with any subgroup analysis or, among RCTs reporting subgroup analyses, using appropriate methodology. The final sample of 416 articles reported 437 RCTs, of which 270 (62 %) reported subgroup analysis. Among these, 185 (69 %) used appropriate methods to conduct such analyses. Subgroup analysis was reported in 62, 55, and 67 % of the articles from 2007, 2010, and 2013, respectively. The percentage using appropriate methods decreased over the three time points from 77 % in 2007 to 63 % in 2013 (p < 0.05). Significant predictors of reporting subgroup analysis included industry funding (OR 1.94 (95 % CI 1.17, 3.21)), sample size (OR 1.98 per quintile (1.64, 2.40), and a significant primary outcome (OR 0.55 (0.33, 0.92)). The use of appropriate methods to conduct subgroup analysis decreased by year (OR 0.88 (0.76, 1.00)) and was less common with industry funding (OR 0.35 (0.18, 0.70)). Only 33 (18 %) of the RCTs examined subgroup effects using a multivariable risk index. While we found no significant increase in the reporting of subgroup analysis over time, our results show a significant decrease in the reporting of subgroup analyses using appropriate methods during recent years. Industry-sponsored trials may more commonly report subgroup analyses, but without utilizing appropriate methods. Suboptimal reporting of subgroup effects may impact optimal physician-patient decision-making.

A Versatile Cell Death Screening Assay Using Dye-Stained Cells and Multivariate Image Analysis.

PubMed

Collins, Tony J; Ylanko, Jarkko; Geng, Fei; Andrews, David W

2015-11-01

A novel dye-based method for measuring cell death in image-based screens is presented. Unlike conventional high- and medium-throughput cell death assays that measure only one form of cell death accurately, using multivariate analysis of micrographs of cells stained with the inexpensive mix, red dye nonyl acridine orange, and a nuclear stain, it was possible to quantify cell death induced by a variety of different agonists even without a positive control. Surprisingly, using a single known cytotoxic agent as a positive control for training a multivariate classifier allowed accurate quantification of cytotoxicity for mechanistically unrelated compounds enabling generation of dose-response curves. Comparison with low throughput biochemical methods suggested that cell death was accurately distinguished from cell stress induced by low concentrations of the bioactive compounds Tunicamycin and Brefeldin A. High-throughput image-based format analyses of more than 300 kinase inhibitors correctly identified 11 as cytotoxic with only 1 false positive. The simplicity and robustness of this dye-based assay makes it particularly suited to live cell screening for toxic compounds.

A Versatile Cell Death Screening Assay Using Dye-Stained Cells and Multivariate Image Analysis

PubMed Central

Collins, Tony J.; Ylanko, Jarkko; Geng, Fei

2015-01-01

Abstract A novel dye-based method for measuring cell death in image-based screens is presented. Unlike conventional high- and medium-throughput cell death assays that measure only one form of cell death accurately, using multivariate analysis of micrographs of cells stained with the inexpensive mix, red dye nonyl acridine orange, and a nuclear stain, it was possible to quantify cell death induced by a variety of different agonists even without a positive control. Surprisingly, using a single known cytotoxic agent as a positive control for training a multivariate classifier allowed accurate quantification of cytotoxicity for mechanistically unrelated compounds enabling generation of dose–response curves. Comparison with low throughput biochemical methods suggested that cell death was accurately distinguished from cell stress induced by low concentrations of the bioactive compounds Tunicamycin and Brefeldin A. High-throughput image-based format analyses of more than 300 kinase inhibitors correctly identified 11 as cytotoxic with only 1 false positive. The simplicity and robustness of this dye-based assay makes it particularly suited to live cell screening for toxic compounds. PMID:26422066

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

EPA Science Inventory

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

Chronic Conditions Among Children Investigated by Child Welfare: A National Sample

PubMed Central

Hurlburt, Michael S.; Heneghan, Amy M.; Zhang, Jinjin; Rolls-Reutz, Jennifer; Silver, Ellen J.; Fisher, Emily; Landsverk, John; Horwitz, Sarah McCue

2013-01-01

OBJECTIVE: To assess the presence of chronic health conditions (CHCs) among a nationally representative sample of children investigated by child welfare agencies. METHODS: The study included 5872 children, aged 0 to 17.5 years, whose families were investigated for maltreatment between February 2008 and April 2009. Using data from the second National Survey of Child and Adolescent Well-Being, we examined the proportion of children who had CHC. We developed 2 categorical and 2 noncategorical measures of CHC from the available data and analyzed them by using bivariate and multivariable analyses. RESULTS: Depending on the measure used, 30.6% to 49.0% of all children investigated were reported by their caregivers to have a CHC. Furthermore, the children identified by using diverse methods were not entirely overlapping. In the multivariable analyses, children with poorer health were more likely to be male, older, and receiving special educational services but not more likely to be in out-of-home placements. CONCLUSIONS: The finding that a much higher proportion of these children have CHC than in the general population underscores the substantial health problems of children investigated by child welfare agencies and the need to monitor their health carefully, regardless of their placement postinvestigation. PMID:23420907

Kidney transplantation from deceased donors with elevated serum creatinine.

PubMed

Gallinat, Anja; Leerhoff, Sabine; Paul, Andreas; Molmenti, Ernesto P; Schulze, Maren; Witzke, Oliver; Sotiropoulos, Georgios C

2016-12-01

Elevated donor serum creatinine has been associated with inferior graft survival in kidney transplantation (KT). The aim of this study was to evaluate the impact of elevated donor serum creatinine on short and long-term outcomes and to determine possible ways to optimize the use of these organs. All kidney transplants from 01-2000 to 12-2012 with donor creatinine ≥ 2 mg/dl were considered. Risk factors for delayed graft function (DGF) were explored with uni- and multivariate regression analyses. Donor and recipient data were analyzed with uni- and multivariate cox proportional hazard analyses. Graft and patient survival were calculated using the Kaplan-Meier method. Seventy-eight patients were considered. Median recipient age and waiting time on dialysis were 53 years and 5.1 years, respectively. After a median follow-up of 6.2 years, 63 patients are alive. 1, 3, and 5-year graft and patient survival rates were 92, 89, and 89 % and 96, 93, and 89 %, respectively. Serum creatinine level at procurement and recipient's dialysis time prior to KT were predictors of DGF in multivariate analysis (p = 0.0164 and p = 0.0101, respectively). Charlson comorbidity score retained statistical significance by multivariate regression analysis for graft survival (p = 0.0321). Recipient age (p = 0.0035) was predictive of patient survival by multivariate analysis. Satisfactory long-term kidney transplant outcomes in the setting of elevated donor serum creatinine ≥2 mg/dl can be achieved when donor creatinine is <3.5 mg/dl, and the recipient has low comorbidities, is under 56 years of age, and remains in dialysis prior to KT for <6.8 years.

Clinical attachment loss: estimation by direct and indirect methods.

PubMed

Barbosa, Viviane Leal; Angst, Patricia D Melchiors; Finger Stadler, Amanda; Oppermann, Rui V; Gomes, Sabrina Carvalho

2016-06-01

This observational study aimed to compare the estimation of clinical attachment loss (CAL) as measured by direct (CALD ) and indirect (CALI ) methods. Periodontitis patients (n = 75; mean age: 50.9 ± 8.02 years; 72.2% women; 50.6% smokers) received a periodontal examination (six sites/tooth) to determine the presence of visible plaque and calculus, the gingival bleeding index (GBI), periodontal probing depth (PPD), bleeding on probing (BOP), CALD and gingival recession (GR). CALI values resulted from the sum of PPD and GR values. Statistical analysis considered only data from sites with visible GR (e.g. the gingival margin apical to the cemento-enamel junction; n = 4,757 sites) and determined the mean difference between CALI and CALD measurements. Based on the mean difference, univariate and multivariate analyses were also performed. Mean CALD and CALI values were 3.96 ± 2.07 mm and 4.47 ± 2.03 mm, respectively. The indirect method overestimated CAL compared with the direct method (mean difference: 0.51 ± 1.23 mm; P < 0.001). On uni- and multivariate analyses, absence of GBI and BOP, PPD and proximal site location had significant influences on the overestimation of CAL by the indirect method (all P ≤ 0.01). The indirect method increased the CAL value by 0.38 mm for each additional 1 mm in PPD. To decrease the number of probing errors in daily practice it is suggested that direct examination is more appropriate than the indirect method for estimating CAL. © 2016 FDI World Dental Federation.

Correlation between smoking habit and surgical outcomes on viral-associated hepatocellular carcinomas

PubMed Central

Kai, Keita; Komukai, Sho; Koga, Hiroki; Yamaji, Koutaro; Ide, Takao; Kawaguchi, Atsushi; Aishima, Shinichi; Noshiro, Hirokazu

2018-01-01

AIM To investigate the association between smoking habits and surgical outcomes in hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) (B-HCC) and hepatitis C virus (HCV)-related HCC (C-HCC) and clarify the clinicopathological features associated with smoking status in B-HCC and C-HCC patients. METHODS We retrospectively examined the cases of the 341 consecutive patients with viral-associated HCC (C-HCC, n = 273; B-HCC, n = 68) who underwent curative surgery for their primary lesion. We categorized smoking status at the time of surgery into never, ex- and current smoker. We analyzed the B-HCC and C-HCC groups’ clinicopathological features and surgical outcomes, i.e., disease-free survival (DFS), overall survival (OS), and disease-specific survival (DSS). Univariate and multivariate analyses were performed using a Cox proportional hazards regression model. We also performed subset analyses in both patient groups comparing the current smokers to the other patients. RESULTS The multivariate analysis in the C-HCC group revealed that current-smoker status was significantly correlated with both OS (P = 0.0039) and DSS (P = 0.0416). In the B-HCC patients, no significant correlation was observed between current-smoker status and DFS, OS, or DSS in the univariate or multivariate analyses. The subset analyses comparing the current smokers to the other patients in both the C-HCC and B-HCC groups revealed that the current smokers developed HCC at significantly younger ages than the other patients irrespective of viral infection status. CONCLUSION A smoking habit is significantly correlated with the overall and disease-specific survivals of patients with C-HCC. In contrast, the B-HCC patients showed a weak association between smoking status and surgical outcomes. PMID:29358882

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

PubMed

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

2009-01-01

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

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

PubMed Central

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

2009-01-01

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

An assessment on the use of bivariate, multivariate and soft computing techniques for collapse susceptibility in GIS environ

NASA Astrophysics Data System (ADS)

Yilmaz, Işik; Marschalko, Marian; Bednarik, Martin

2013-04-01

The paper presented herein compares and discusses the use of bivariate, multivariate and soft computing techniques for collapse susceptibility modelling. Conditional probability (CP), logistic regression (LR) and artificial neural networks (ANN) models representing the bivariate, multivariate and soft computing techniques were used in GIS based collapse susceptibility mapping in an area from Sivas basin (Turkey). Collapse-related factors, directly or indirectly related to the causes of collapse occurrence, such as distance from faults, slope angle and aspect, topographical elevation, distance from drainage, topographic wetness index (TWI), stream power index (SPI), Normalized Difference Vegetation Index (NDVI) by means of vegetation cover, distance from roads and settlements were used in the collapse susceptibility analyses. In the last stage of the analyses, collapse susceptibility maps were produced from the models, and they were then compared by means of their validations. However, Area Under Curve (AUC) values obtained from all three models showed that the map obtained from soft computing (ANN) model looks like more accurate than the other models, accuracies of all three models can be evaluated relatively similar. The results also showed that the conditional probability is an essential method in preparation of collapse susceptibility map and highly compatible with GIS operating features.

Multivariate and repeated measures (MRM): A new toolbox for dependent and multimodal group-level neuroimaging data

PubMed Central

McFarquhar, Martyn; McKie, Shane; Emsley, Richard; Suckling, John; Elliott, Rebecca; Williams, Stephen

2016-01-01

Repeated measurements and multimodal data are common in neuroimaging research. Despite this, conventional approaches to group level analysis ignore these repeated measurements in favour of multiple between-subject models using contrasts of interest. This approach has a number of drawbacks as certain designs and comparisons of interest are either not possible or complex to implement. Unfortunately, even when attempting to analyse group level data within a repeated-measures framework, the methods implemented in popular software packages make potentially unrealistic assumptions about the covariance structure across the brain. In this paper, we describe how this issue can be addressed in a simple and efficient manner using the multivariate form of the familiar general linear model (GLM), as implemented in a new MATLAB toolbox. This multivariate framework is discussed, paying particular attention to methods of inference by permutation. Comparisons with existing approaches and software packages for dependent group-level neuroimaging data are made. We also demonstrate how this method is easily adapted for dependency at the group level when multiple modalities of imaging are collected from the same individuals. Follow-up of these multimodal models using linear discriminant functions (LDA) is also discussed, with applications to future studies wishing to integrate multiple scanning techniques into investigating populations of interest. PMID:26921716

FGWAS: Functional genome wide association analysis.

PubMed

Huang, Chao; Thompson, Paul; Wang, Yalin; Yu, Yang; Zhang, Jingwen; Kong, Dehan; Colen, Rivka R; Knickmeyer, Rebecca C; Zhu, Hongtu

2017-10-01

Functional phenotypes (e.g., subcortical surface representation), which commonly arise in imaging genetic studies, have been used to detect putative genes for complexly inherited neuropsychiatric and neurodegenerative disorders. However, existing statistical methods largely ignore the functional features (e.g., functional smoothness and correlation). The aim of this paper is to develop a functional genome-wide association analysis (FGWAS) framework to efficiently carry out whole-genome analyses of functional phenotypes. FGWAS consists of three components: a multivariate varying coefficient model, a global sure independence screening procedure, and a test procedure. Compared with the standard multivariate regression model, the multivariate varying coefficient model explicitly models the functional features of functional phenotypes through the integration of smooth coefficient functions and functional principal component analysis. Statistically, compared with existing methods for genome-wide association studies (GWAS), FGWAS can substantially boost the detection power for discovering important genetic variants influencing brain structure and function. Simulation studies show that FGWAS outperforms existing GWAS methods for searching sparse signals in an extremely large search space, while controlling for the family-wise error rate. We have successfully applied FGWAS to large-scale analysis of data from the Alzheimer's Disease Neuroimaging Initiative for 708 subjects, 30,000 vertices on the left and right hippocampal surfaces, and 501,584 SNPs. Copyright © 2017 Elsevier Inc. All rights reserved.

Weight-based determination of fluid overload status and mortality in pediatric intensive care unit patients requiring continuous renal replacement therapy

PubMed Central

Selewski, David T.; Cornell, Timothy T.; Lombel, Rebecca M.; Blatt, Neal B.; Han, Yong Y.; Mottes, Theresa; Kommareddi, Mallika; Kershaw, David B.; Shanley, Thomas P.; Heung, Michael

2012-01-01

Purpose In pediatric intensive care unit (PICU) patients, fluid overload (FO) at initiation of continuous renal replacement therapy (CRRT) has been reported to be an independent risk factor for mortality. Previous studies have calculated FO based on daily fluid balance during ICU admission, which is labor intensive and error prone. We hypothesized that a weight-based definition of FO at CRRT initiation would correlate with the fluid balance method and prove predictive of outcome. Methods This is a retrospective single-center review of PICU patients requiring CRRT from July 2006 through February 2010 (n = 113). We compared the degree of FO at CRRT initiation using the standard fluid balance method versus methods based on patient weight changes assessed by both univariate and multivariate analyses. Results The degree of fluid overload at CRRT initiation was significantly greater in nonsurvivors, irrespective of which method was used. The univariate odds ratio for PICU mortality per 1% increase in FO was 1.056 [95% confidence interval (CI) 1.025, 1.087] by the fluid balance method, 1.044 (95% CI 1.019, 1.069) by the weight-based method using PICU admission weight, and 1.045 (95% CI 1.022, 1.07) by the weight-based method using hospital admission weight. On multivariate analyses, all three methods approached significance in predicting PICU survival. Conclusions Our findings suggest that weight-based definitions of FO are useful in defining FO at CRRT initiation and are associated with increased mortality in a broad PICU patient population. This study provides evidence for a more practical weight-based definition of FO that can be used at the bedside. PMID:21533569

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

PubMed Central

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

2009-01-01

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

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.

Rare Variant Association Test with Multiple Phenotypes

PubMed Central

Lee, Selyeong; Won, Sungho; Kim, Young Jin; Kim, Yongkang; Kim, Bong-Jo; Park, Taesung

2016-01-01

Although genome-wide association studies (GWAS) have now discovered thousands of genetic variants associated with common traits, such variants cannot explain the large degree of “missing heritability,” likely due to rare variants. The advent of next generation sequencing technology has allowed rare variant detection and association with common traits, often by investigating specific genomic regions for rare variant effects on a trait. Although multiply correlated phenotypes are often concurrently observed in GWAS, most studies analyze only single phenotypes, which may lessen statistical power. To increase power, multivariate analyses, which consider correlations between multiple phenotypes, can be used. However, few existing multi-variant analyses can identify rare variants for assessing multiple phenotypes. Here, we propose Multivariate Association Analysis using Score Statistics (MAAUSS), to identify rare variants associated with multiple phenotypes, based on the widely used Sequence Kernel Association Test (SKAT) for a single phenotype. We applied MAAUSS to Whole Exome Sequencing (WES) data from a Korean population of 1,058 subjects, to discover genes associated with multiple traits of liver function. We then assessed validation of those genes by a replication study, using an independent dataset of 3,445 individuals. Notably, we detected the gene ZNF620 among five significant genes. We then performed a simulation study to compare MAAUSS's performance with existing methods. Overall, MAAUSS successfully conserved type 1 error rates and in many cases, had a higher power than the existing methods. This study illustrates a feasible and straightforward approach for identifying rare variants correlated with multiple phenotypes, with likely relevance to missing heritability. PMID:28039885

Assessment of self-organizing maps to analyze sole-carbon source utilization profiles.

PubMed

Leflaive, Joséphine; Céréghino, Régis; Danger, Michaël; Lacroix, Gérard; Ten-Hage, Loïc

2005-07-01

The use of community-level physiological profiles obtained with Biolog microplates is widely employed to consider the functional diversity of bacterial communities. Biolog produces a great amount of data which analysis has been the subject of many studies. In most cases, after some transformations, these data were investigated with classical multivariate analyses. Here we provided an alternative to this method, that is the use of an artificial intelligence technique, the Self-Organizing Maps (SOM, unsupervised neural network). We used data from a microcosm study of algae-associated bacterial communities placed in various nutritive conditions. Analyses were carried out on the net absorbances at two incubation times for each substrates and on the chemical guild categorization of the total bacterial activity. Compared to Principal Components Analysis and cluster analysis, SOM appeared as a valuable tool for community classification, and to establish clear relationships between clusters of bacterial communities and sole-carbon sources utilization. Specifically, SOM offered a clear bidimensional projection of a relatively large volume of data and were easier to interpret than plots commonly obtained with multivariate analyses. They would be recommended to pattern the temporal evolution of communities' functional diversity.

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

Iodine-131: An Effective Method for Treating Lymph Node Metastases of Differentiated Thyroid Cancer.

PubMed

He, Ying; Pan, Ming-Zhi; Huang, Jian-Min; Xie, Peng; Zhang, Fang; Wei, Ling-Ge

2016-12-15

BACKGROUND The aim of this study was to assess the efficacy of radioactive iodine-131 (¹³¹I) therapy for lymph node metastasis of differentiated thyroid cancer (DTC) and to identify influential factors using univariate and multivariate analyses to determine if identified factors influence the efficacy of treatment. MATERIAL AND METHODS This study included a retrospective review of 218 patients with histologically proven DTC in the post-operation stage. After thyroid tissue remnants were eliminated with ¹³¹I therapy, patients' lymph node status was confirmed by ultrasound and by ¹³¹I whole body scan regarding lymph node metastasis, and then patients were treated with ¹³¹I as appropriate. The treatment efficacy was assessed and possible influencing factors were identified using univariate and multivariate analyses. RESULTS The total effective rate of ¹³¹I therapy was 88.07% (including a cure rate of 20.64% and an improvement rate of 67.43%). The non-effective rate was 11.93%. Of the total 406 lymph nodes of 218 patients, 319 lymph nodes (78.57%) were judged to be effectively cured, including 133 (32.75%) lymph nodes that were totally eliminated and 186 (45.82%) lymph nodes that shrank. Eighty-seven (21.43%) of the 406 lymph nodes had no obvious change. No lymph nodes were found to be in a continuously enlarging state. Distant metastasis, size of lymph node, human serum thyroglobulin (HTG) level, and condition of thyroid remnants ablation were identified as the independent factors influencing the efficacy of treatment using univariate and multivariate analyses. CONCLUSIONS The use of ¹³¹I is a promising treatment for lymph node metastasis of DCT. Distant metastasis, size of lymph nodes, HTG level, and condition of thyroid remnant ablation were independent factors influencing the treatment efficacy.

Advanced spectrophotometric chemometric methods for resolving the binary mixture of doxylamine succinate and pyridoxine hydrochloride.

PubMed

Katsarov, Plamen; Gergov, Georgi; Alin, Aylin; Pilicheva, Bissera; Al-Degs, Yahya; Simeonov, Vasil; Kassarova, Margarita

2018-03-01

The prediction power of partial least squares (PLS) and multivariate curve resolution-alternating least squares (MCR-ALS) methods have been studied for simultaneous quantitative analysis of the binary drug combination - doxylamine succinate and pyridoxine hydrochloride. Analysis of first-order UV overlapped spectra was performed using different PLS models - classical PLS1 and PLS2 as well as partial robust M-regression (PRM). These linear models were compared to MCR-ALS with equality and correlation constraints (MCR-ALS-CC). All techniques operated within the full spectral region and extracted maximum information for the drugs analysed. The developed chemometric methods were validated on external sample sets and were applied to the analyses of pharmaceutical formulations. The obtained statistical parameters were satisfactory for calibration and validation sets. All developed methods can be successfully applied for simultaneous spectrophotometric determination of doxylamine and pyridoxine both in laboratory-prepared mixtures and commercial dosage forms.

A model-based approach to wildland fire reconstruction using sediment charcoal records

USGS Publications Warehouse

Itter, Malcolm S.; Finley, Andrew O.; Hooten, Mevin B.; Higuera, Philip E.; Marlon, Jennifer R.; Kelly, Ryan; McLachlan, Jason S.

2017-01-01

Lake sediment charcoal records are used in paleoecological analyses to reconstruct fire history, including the identification of past wildland fires. One challenge of applying sediment charcoal records to infer fire history is the separation of charcoal associated with local fire occurrence and charcoal originating from regional fire activity. Despite a variety of methods to identify local fires from sediment charcoal records, an integrated statistical framework for fire reconstruction is lacking. We develop a Bayesian point process model to estimate the probability of fire associated with charcoal counts from individual-lake sediments and estimate mean fire return intervals. A multivariate extension of the model combines records from multiple lakes to reduce uncertainty in local fire identification and estimate a regional mean fire return interval. The univariate and multivariate models are applied to 13 lakes in the Yukon Flats region of Alaska. Both models resulted in similar mean fire return intervals (100–350 years) with reduced uncertainty under the multivariate model due to improved estimation of regional charcoal deposition. The point process model offers an integrated statistical framework for paleofire reconstruction and extends existing methods to infer regional fire history from multiple lake records with uncertainty following directly from posterior distributions.

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

Evoked prior learning experience and approach to learning as predictors of academic achievement.

PubMed

Trigwell, Keith; Ashwin, Paul; Millan, Elena S

2013-09-01

In separate studies and research from different perspectives, five factors are found to be among those related to higher quality outcomes of student learning (academic achievement). Those factors are higher self-efficacy, deeper approaches to learning, higher quality teaching, students' perceptions that their workload is appropriate, and greater learning motivation. University learning improvement strategies have been built on these research results. To investigate how students' evoked prior experience, perceptions of their learning environment, and their approaches to learning collectively contribute to academic achievement. This is the first study to investigate motivation and self-efficacy in the same educational context as conceptions of learning, approaches to learning and perceptions of the learning environment. Undergraduate students (773) from the full range of disciplines were part of a group of over 2,300 students who volunteered to complete a survey of their learning experience. On completing their degrees 6 and 18 months later, their academic achievement was matched with their learning experience survey data. A 77-item questionnaire was used to gather students' self-report of their evoked prior experience (self-efficacy, learning motivation, and conceptions of learning), perceptions of learning context (teaching quality and appropriate workload), and approaches to learning (deep and surface). Academic achievement was measured using the English honours degree classification system. Analyses were conducted using correlational and multi-variable (structural equation modelling) methods. The results from the correlation methods confirmed those found in numerous earlier studies. The results from the multi-variable analyses indicated that surface approach to learning was the strongest predictor of academic achievement, with self-efficacy and motivation also found to be directly related. In contrast to the correlation results, a deep approach to learning was not related to academic achievement, and teaching quality and conceptions of learning were only indirectly related to achievement. Research aimed at understanding how students experience their learning environment and how that experience relates to the quality of their learning needs to be conducted using a wider range of variables and more sophisticated analytical methods. In this study of one context, some of the relations found in earlier bivariate studies, and on which learning intervention strategies have been built, are not confirmed when more holistic teaching-learning contexts are analysed using multi-variable methods. © 2012 The British Psychological Society.

Sample size calculations for case-control studies

Cancer.gov

This R package can be used to calculate the required samples size for unconditional multivariate analyses of unmatched case-control studies. The sample sizes are for a scalar exposure effect, such as binary, ordinal or continuous exposures. The sample sizes can also be computed for scalar interaction effects. The analyses account for the effects of potential confounder variables that are also included in the multivariate logistic model.

[Mortality in early-stage, surgically resected non-small cell lung cancer less than 3 cm of size: Competing risk analysis].

PubMed

Jordá Aragón, Carlos; Peñalver Cuesta, Juan Carlos; Mancheño Franch, Nuria; de Aguiar Quevedo, Karol; Vera Sempere, Francisco; Padilla Alarcón, José

2015-09-07

Survival studies of non-small cell lung cancer (NSCLC) are usually based on the Kaplan-Meier method. However, other factors not covered by this method may modify the observation of the event of interest. There are models of cumulative incidence (CI), that take into account these competing risks, enabling more accurate survival estimates and evaluation of the risk of death from other causes. We aimed to evaluate these models in resected early-stage NSCLC patients. This study included 263 patients with resected NSCLC whose diameter was ≤ 3 cm without node involvement (N0). Demographic, clinical, morphopathological and surgical variables, TNM classification and long-term evolution were analysed. To analyse CI, death by another cause was considered to be competitive event. For the univariate analysis, Gray's method was used, while Fine and Gray's method was employed for the multivariate analysis. Mortality by NSCLC was 19.4% at 5 years and 14.3% by another cause. Both curves crossed at 6.3 years, and probability of death by another cause became greater from this point. In multivariate analysis, cancer mortality was conditioned by visceral pleural invasion (VPI) (P=.001) and vascular invasion (P=.020), with age>50 years (P=.034), smoking (P=.009) and the Charlson index ≥ 2 (P=.000) being by no cancer. By the method of CI, VPI and vascular invasion conditioned cancer death in NSCLC >3 cm, while non-tumor causes of long-term death were determined. Copyright © 2014 Elsevier España, S.L.U. All rights reserved.

Multidisciplinary optimization of a controlled space structure using 150 design variables

NASA Technical Reports Server (NTRS)

James, Benjamin B.

1992-01-01

A general optimization-based method for the design of large space platforms through integration of the disciplines of structural dynamics and control is presented. The method uses the global sensitivity equations approach and is especially appropriate for preliminary design problems in which the structural and control analyses are tightly coupled. The method is capable of coordinating general purpose structural analysis, multivariable control, and optimization codes, and thus, can be adapted to a variety of controls-structures integrated design projects. The method is used to minimize the total weight of a space platform while maintaining a specified vibration decay rate after slewing maneuvers.

Multivariate methods to visualise colour-space and colour discrimination data.

PubMed

Hastings, Gareth D; Rubin, Alan

2015-01-01

Despite most modern colour spaces treating colour as three-dimensional (3-D), colour data is usually not visualised in 3-D (and two-dimensional (2-D) projection-plane segments and multiple 2-D perspective views are used instead). The objectives of this article are firstly, to introduce a truly 3-D percept of colour space using stereo-pairs, secondly to view colour discrimination data using that platform, and thirdly to apply formal statistics and multivariate methods to analyse the data in 3-D. This is the first demonstration of the software that generated stereo-pairs of RGB colour space, as well as of a new computerised procedure that investigated colour discrimination by measuring colour just noticeable differences (JND). An initial pilot study and thorough investigation of instrument repeatability were performed. Thereafter, to demonstrate the capabilities of the software, five colour-normal and one colour-deficient subject were examined using the JND procedure and multivariate methods of data analysis. Scatter plots of responses were meaningfully examined in 3-D and were useful in evaluating multivariate normality as well as identifying outliers. The extent and direction of the difference between each JND response and the stimulus colour point was calculated and appreciated in 3-D. Ellipsoidal surfaces of constant probability density (distribution ellipsoids) were fitted to response data; the volumes of these ellipsoids appeared useful in differentiating the colour-deficient subject from the colour-normals. Hypothesis tests of variances and covariances showed many statistically significant differences between the results of the colour-deficient subject and those of the colour-normals, while far fewer differences were found when comparing within colour-normals. The 3-D visualisation of colour data using stereo-pairs, as well as the statistics and multivariate methods of analysis employed, were found to be unique and useful tools in the representation and study of colour. Many additional studies using these methods along with the JND and other procedures have been identified and will be reported in future publications. © 2014 The Authors Ophthalmic & Physiological Optics © 2014 The College of Optometrists.

Why Multivariate Methods Are Usually Vital in Research: Some Basic Concepts.

ERIC Educational Resources Information Center

Thompson, Bruce

The present paper suggests that multivariate methods ought to be used more frequently in behavioral research and explores the potential consequences of failing to use multivariate methods when these methods are appropriate. The paper explores in detail two reasons why multivariate methods are usually vital. The first is that they limit the…

How to engage occupational physicians in recruitment of research participants: a mixed-methods study of challenges and opportunities.

PubMed

Arends, Iris; Bültmann, Ute; Shaw, William S; van Rhenen, Willem; Roelen, Corné; Nielsen, Karina; van der Klink, Jac J L

2014-03-01

To investigate barriers and facilitators for research participant recruitment by occupational physicians (OPs). A mixed-methods approach was used. Focus groups and interviews were conducted with OPs to explore perceived barriers and facilitators for recruitment. Based on data of a cluster-randomised controlled trial (cluster-RCT), univariate and multivariate analyses were conducted to investigate associations between OPs' personal and work characteristics and the number of recruited participants for the cluster-RCT per OP. Perceived barriers and facilitators for recruitment were categorised into: study characteristics (e.g. concise inclusion criteria); study population characteristics; OP's attention; OP's workload; context (e.g. working at different locations); and OP's characteristics (e.g. motivated to help). Important facilitators were encouragement by colleagues and reminders by information technology tools. Multivariate analyses showed that the number of OPs within the clinical unit who recruited participants was positively associated with the number of recruited participants per OP [rate ratio of 1.43, 95 % confidence interval 1.24-1.64]. When mobilising OPs for participant recruitment, researchers need to engage entire clinical units rather than approach OPs on an individual basis. OPs consider regular communication, especially face-to-face contact and information technology tools serving as reminders, as helpful.

Genetic Structure of Bluefin Tuna in the Mediterranean Sea Correlates with Environmental Variables

PubMed Central

Riccioni, Giulia; Stagioni, Marco; Landi, Monica; Ferrara, Giorgia; Barbujani, Guido; Tinti, Fausto

2013-01-01

Background Atlantic Bluefin Tuna (ABFT) shows complex demography and ecological variation in the Mediterranean Sea. Genetic surveys have detected significant, although weak, signals of population structuring; catch series analyses and tagging programs identified complex ABFT spatial dynamics and migration patterns. Here, we tested the hypothesis that the genetic structure of the ABFT in the Mediterranean is correlated with mean surface temperature and salinity. Methodology We used six samples collected from Western and Central Mediterranean integrated with a new sample collected from the recently identified easternmost reproductive area of Levantine Sea. To assess population structure in the Mediterranean we used a multidisciplinary framework combining classical population genetics, spatial and Bayesian clustering methods and a multivariate approach based on factor analysis. Conclusions FST analysis and Bayesian clustering methods detected several subpopulations in the Mediterranean, a result also supported by multivariate analyses. In addition, we identified significant correlations of genetic diversity with mean salinity and surface temperature values revealing that ABFT is genetically structured along two environmental gradients. These results suggest that a preference for some spawning habitat conditions could contribute to shape ABFT genetic structuring in the Mediterranean. However, further studies should be performed to assess to what extent ABFT spawning behaviour in the Mediterranean Sea can be affected by environmental variation. PMID:24260341

Gender differences in health-related quality of life of adolescents with cystic fibrosis

PubMed Central

Arrington-Sanders, Renata; Yi, Michael S; Tsevat, Joel; Wilmott, Robert W; Mrus, Joseph M; Britto, Maria T

2006-01-01

Background Female patients with cystic fibrosis (CF) have consistently poorer survival rates than males across all ages. To determine if gender differences exist in health-related quality of life (HRQOL) of adolescent patients with CF, we performed a cross-section analysis of CF patients recruited from 2 medical centers in 2 cities during 1997–2001. Methods We used the 87-item child self-report form of the Child Health Questionnaire to measure 12 health domains. Data was also collected on age and forced expiratory volume in 1 second (FEV1). We analyzed data from 98 subjects and performed univariate analyses and linear regression or ordinal logistic regression for multivariable analyses. Results The mean (SD) age was 14.6 (2.5) years; 50 (51.0%) were female; and mean FEV1 was 71.6% (25.6%) of predicted. There were no statistically significant gender differences in age or FEV1. In univariate analyses, females reported significantly poorer HRQOL in 5 of the 12 domains. In multivariable analyses controlling for FEV1 and age, we found that female gender was associated with significantly lower global health (p < 0.05), mental health (p < 0.01), and general health perceptions (p < 0.05) scores. Conclusion Further research will need to focus on the causes of these differences in HRQOL and on potential interventions to improve HRQOL of adolescent patients with CF. PMID:16433917

Evaluating online data of water quality changes in a pilot drinking water distribution system with multivariate data exploration methods.

PubMed

Mustonen, Satu M; Tissari, Soile; Huikko, Laura; Kolehmainen, Mikko; Lehtola, Markku J; Hirvonen, Arja

2008-05-01

The distribution of drinking water generates soft deposits and biofilms in the pipelines of distribution systems. Disturbances in water distribution can detach these deposits and biofilms and thus deteriorate the water quality. We studied the effects of simulated pressure shocks on the water quality with online analysers. The study was conducted with copper and composite plastic pipelines in a pilot distribution system. The online data gathered during the study was evaluated with Self-Organising Map (SOM) and Sammon's mapping, which are useful methods in exploring large amounts of multivariate data. The objective was to test the usefulness of these methods in pinpointing the abnormal water quality changes in the online data. The pressure shocks increased temporarily the number of particles, turbidity and electrical conductivity. SOM and Sammon's mapping were able to separate these situations from the normal data and thus make those visible. Therefore these methods make it possible to detect abrupt changes in water quality and thus to react rapidly to any disturbances in the system. These methods are useful in developing alert systems and predictive applications connected to online monitoring.

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

PubMed Central

2011-01-01

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

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

PubMed

Keithley, Richard B; Wightman, R Mark

2011-06-07

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

Multivariate and repeated measures (MRM): A new toolbox for dependent and multimodal group-level neuroimaging data.

PubMed

McFarquhar, Martyn; McKie, Shane; Emsley, Richard; Suckling, John; Elliott, Rebecca; Williams, Stephen

2016-05-15

Repeated measurements and multimodal data are common in neuroimaging research. Despite this, conventional approaches to group level analysis ignore these repeated measurements in favour of multiple between-subject models using contrasts of interest. This approach has a number of drawbacks as certain designs and comparisons of interest are either not possible or complex to implement. Unfortunately, even when attempting to analyse group level data within a repeated-measures framework, the methods implemented in popular software packages make potentially unrealistic assumptions about the covariance structure across the brain. In this paper, we describe how this issue can be addressed in a simple and efficient manner using the multivariate form of the familiar general linear model (GLM), as implemented in a new MATLAB toolbox. This multivariate framework is discussed, paying particular attention to methods of inference by permutation. Comparisons with existing approaches and software packages for dependent group-level neuroimaging data are made. We also demonstrate how this method is easily adapted for dependency at the group level when multiple modalities of imaging are collected from the same individuals. Follow-up of these multimodal models using linear discriminant functions (LDA) is also discussed, with applications to future studies wishing to integrate multiple scanning techniques into investigating populations of interest. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

NIH disease funding levels and burden of disease.

PubMed

Gillum, Leslie A; Gouveia, Christopher; Dorsey, E Ray; Pletcher, Mark; Mathers, Colin D; McCulloch, Charles E; Johnston, S Claiborne

2011-02-24

An analysis of NIH funding in 1996 found that the strongest predictor of funding, disability-adjusted life-years (DALYs), explained only 39% of the variance in funding. In 1998, Congress requested that the Institute of Medicine (IOM) evaluate priority-setting criteria for NIH funding; the IOM recommended greater consideration of disease burden. We examined whether the association between current burden and funding has changed since that time. We analyzed public data on 2006 NIH funding for 29 common conditions. Measures of US disease burden in 2004 were obtained from the World Health Organization's Global Burden of Disease study and national databases. We assessed the relationship between disease burden and NIH funding dollars in univariate and multivariable log-linear models that evaluated all measures of disease burden. Sensitivity analyses examined associations with future US burden, current and future measures of world disease burden, and a newly standardized NIH accounting method. In univariate and multivariable analyses, disease-specific NIH funding levels increased with burden of disease measured in DALYs (p = 0.001), which accounted for 33% of funding level variation. No other factor predicted funding in multivariable models. Conditions receiving the most funding greater than expected based on disease burden were AIDS ($2474 M), diabetes mellitus ($390 M), and perinatal conditions ($297 M). Depression ($719 M), injuries ($691 M), and chronic obstructive pulmonary disease ($613 M) were the most underfunded. Results were similar using estimates of future US burden, current and future world disease burden, and alternate NIH accounting methods. Current levels of NIH disease-specific research funding correlate modestly with US disease burden, and correlation has not improved in the last decade.

Nontargeted, Rapid Screening of Extra Virgin Olive Oil Products for Authenticity Using Near-Infrared Spectroscopy in Combination with Conformity Index and Multivariate Statistical Analyses.

PubMed

Karunathilaka, Sanjeewa R; Kia, Ali-Reza Fardin; Srigley, Cynthia; Chung, Jin Kyu; Mossoba, Magdi M

2016-10-01

A rapid tool for evaluating authenticity was developed and applied to the screening of extra virgin olive oil (EVOO) retail products by using Fourier-transform near infrared (FT-NIR) spectroscopy in combination with univariate and multivariate data analysis methods. Using disposable glass tubes, spectra for 62 reference EVOO, 10 edible oil adulterants, 20 blends consisting of EVOO spiked with adulterants, 88 retail EVOO products and other test samples were rapidly measured in the transmission mode without any sample preparation. The univariate conformity index (CI) and the multivariate supervised soft independent modeling of class analogy (SIMCA) classification tool were used to analyze the various olive oil products which were tested for authenticity against a library of reference EVOO. Better discrimination between the authentic EVOO and some commercial EVOO products was observed with SIMCA than with CI analysis. Approximately 61% of all EVOO commercial products were flagged by SIMCA analysis, suggesting that further analysis be performed to identify quality issues and/or potential adulterants. Due to its simplicity and speed, FT-NIR spectroscopy in combination with multivariate data analysis can be used as a complementary tool to conventional official methods of analysis to rapidly flag EVOO products that may not belong to the class of authentic EVOO. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.

Histological changes associated with neoadjuvant chemotherapy are predictive of nodal metastases in high-risk prostate cancer patients

PubMed Central

O’Brien, Catherine; True, Lawrence D.; Higano, Celestia S.; Rademacher, Brooks L. S.; Garzotto, Mark; Beer, Tomasz M.

2011-01-01

Clinical trials are evaluating the effect of neoadjuvant chemotherapy on men with high risk prostate cancer. Little is known about the clinical significance of post-chemotherapy tumor histopathology. We assessed the prognostic and predictive value of histological features (intraductal carcinoma, vacuolated cell morphology, inconspicuous glands, cribriform architecture, and inconspicuous cancer cells) observed in 50 high-risk prostate cancers treated with pre-prostatectomy docetaxel and mitoxantrone. At a median follow-up of 65 months, the overall relapse-free survival (RFS) at 2 and 5 years was 65% and 49%, respectively. In univariate analyses (using Kaplan-Meier method and log-rank tests) intraductal (p=0.001) and cribriform (p=0.014) histologies were associated with shorter RFS. In multivariate analyses, using Cox’s proportional hazards regression, baseline PSA (p=0.004), lymph node metastases (p<0.001), and cribriform histology (p=0.007) were associated with shorter RFS. In multivariable logistic regression analysis, only intraductal pattern (p=0.007) predicted lymph node metastases. Intraductal and cribriform histologies apparently predict post-chemotherapy outcome. PMID:20231619

Predictors of Low Back Pain Onset in a Prospective British Study

PubMed Central

Power, Chris; Frank, John; Hertzman, Clyde; Schierhout, Gill; Li, Leah

2001-01-01

Objectives. This study examined predictors of low back pain onset in a British birth cohort. Methods. Univariate and multivariate analyses focused on individuals who experienced onset of low back pain at 32 to 33 years of age (n= 571) and individuals who were pain free (n = 5210). Participants were members of the 1958 British birth cohort. Results. Incident pain was elevated among those with psychological distress at 23 years of age (adjusted odds ratio [OR] = 2.52, 95% confidence interval [CI] = 1.65, 3.86) and among persistent moderate or heavy smokers (adjusted OR = 1.63, 95% CI = 1.23, 2.17). Significant univariate associations involving other factors (e.g., social class, childhood emotional status, body mass index, job satisfaction) did not persist in multivariate analyses. Conclusions. This prospectively studied cohort provides evidence that psychological distress more than doubles later risk of low back pain, with smoking having a modest independent effect. Other prospective studies are needed to confirm these findings before implications for low back pain prevention can be assessed. PMID:11574334

Alignment-Independent Comparisons of Human Gastrointestinal Tract Microbial Communities in a Multidimensional 16S rRNA Gene Evolutionary Space▿

PubMed Central

Rudi, Knut; Zimonja, Monika; Kvenshagen, Bente; Rugtveit, Jarle; Midtvedt, Tore; Eggesbø, Merete

2007-01-01

We present a novel approach for comparing 16S rRNA gene clone libraries that is independent of both DNA sequence alignment and definition of bacterial phylogroups. These steps are the major bottlenecks in current microbial comparative analyses. We used direct comparisons of taxon density distributions in an absolute evolutionary coordinate space. The coordinate space was generated by using alignment-independent bilinear multivariate modeling. Statistical analyses for clone library comparisons were based on multivariate analysis of variance, partial least-squares regression, and permutations. Clone libraries from both adult and infant gastrointestinal tract microbial communities were used as biological models. We reanalyzed a library consisting of 11,831 clones covering complete colons from three healthy adults in addition to a smaller 390-clone library from infant feces. We show that it is possible to extract detailed information about microbial community structures using our alignment-independent method. Our density distribution analysis is also very efficient with respect to computer operation time, meeting the future requirements of large-scale screenings to understand the diversity and dynamics of microbial communities. PMID:17337554

A Shortened Version of the Suicide Cognitions Scale for Identifying Chronic Pain Patients at Risk for Suicide.

PubMed

Bryan, Craig J; Kanzler, Kathryn E; Grieser, Emily; Martinez, Annette; Allison, Sybil; McGeary, Donald

2017-03-01

Research in psychiatric outpatient and inpatient populations supports the utility of the Suicide Cognitions Scale (SCS) as an indicator of current and future risk for suicidal thoughts and behaviors. Designed to assess suicide-specific thoughts and beliefs, the SCS has yet to be evaluated among chronic pain patients, a group with elevated risk for suicide. The purpose of the present study was to develop and test a shortened version of the SCS (the SCS-S). A total of 228 chronic pain patients completed a battery of self-report surveys before or after a scheduled appointment. Three outpatient medical clinics (pain medicine, orofacial pain, and clinical health psychology). Confirmatory factor analysis, multivariate regression, and graded item response theory model analyses. Results of the CFAs suggested that a 3-factor solution was optimal. A shortened 9-item scale was identified based on the results of graded item response theory model analyses. Correlation and multivariate analyses supported the construct and incremental validity of the SCS-S. Results support the reliability and validity of the SCS-S among chronic pain patients, and suggest the scale may be a useful method for identifying high-risk patients in medical settings. © 2016 World Institute of Pain.

A comparison of two follow-up analyses after multiple analysis of variance, analysis of variance, and descriptive discriminant analysis: A case study of the program effects on education-abroad programs

Treesearch

Alvin H. Yu; Garry Chick

2010-01-01

This study compared the utility of two different post-hoc tests after detecting significant differences within factors on multiple dependent variables using multivariate analysis of variance (MANOVA). We compared the univariate F test (the Scheffé method) to descriptive discriminant analysis (DDA) using an educational-tour survey of university study-...

A proposed case-control framework to probabilistically classify individual deaths as expected or excess during extreme hot weather events.

PubMed

Henderson, Sarah B; Gauld, Jillian S; Rauch, Stephen A; McLean, Kathleen E; Krstic, Nikolas; Hondula, David M; Kosatsky, Tom

2016-11-15

Most excess deaths that occur during extreme hot weather events do not have natural heat recorded as an underlying or contributing cause. This study aims to identify the specific individuals who died because of hot weather using only secondary data. A novel approach was developed in which the expected number of deaths was repeatedly sampled from all deaths that occurred during a hot weather event, and compared with deaths during a control period. The deaths were compared with respect to five factors known to be associated with hot weather mortality. Individuals were ranked by their presence in significant models over 100 trials of 10,000 repetitions. Those with the highest rankings were identified as probable excess deaths. Sensitivity analyses were performed on a range of model combinations. These methods were applied to a 2009 hot weather event in greater Vancouver, Canada. The excess deaths identified were sensitive to differences in model combinations, particularly between univariate and multivariate approaches. One multivariate and one univariate combination were chosen as the best models for further analyses. The individuals identified by multiple combinations suggest that marginalized populations in greater Vancouver are at higher risk of death during hot weather. This study proposes novel methods for classifying specific deaths as expected or excess during a hot weather event. Further work is needed to evaluate performance of the methods in simulation studies and against clinically identified cases. If confirmed, these methods could be applied to a wide range of populations and events of interest.

EEG artifacts reduction by multivariate empirical mode decomposition and multiscale entropy for monitoring depth of anaesthesia during surgery.

PubMed

Liu, Quan; Chen, Yi-Feng; Fan, Shou-Zen; Abbod, Maysam F; Shieh, Jiann-Shing

2017-08-01

Electroencephalography (EEG) has been widely utilized to measure the depth of anaesthesia (DOA) during operation. However, the EEG signals are usually contaminated by artifacts which have a consequence on the measured DOA accuracy. In this study, an effective and useful filtering algorithm based on multivariate empirical mode decomposition and multiscale entropy (MSE) is proposed to measure DOA. Mean entropy of MSE is used as an index to find artifacts-free intrinsic mode functions. The effect of different levels of artifacts on the performances of the proposed filtering is analysed using simulated data. Furthermore, 21 patients' EEG signals are collected and analysed using sample entropy to calculate the complexity for monitoring DOA. The correlation coefficients of entropy and bispectral index (BIS) results show 0.14 ± 0.30 and 0.63 ± 0.09 before and after filtering, respectively. Artificial neural network (ANN) model is used for range mapping in order to correlate the measurements with BIS. The ANN method results show strong correlation coefficient (0.75 ± 0.08). The results in this paper verify that entropy values and BIS have a strong correlation for the purpose of DOA monitoring and the proposed filtering method can effectively filter artifacts from EEG signals. The proposed method performs better than the commonly used wavelet denoising method. This study provides a fully adaptive and automated filter for EEG to measure DOA more accuracy and thus reduce risk related to maintenance of anaesthetic agents.

Predictive analysis of beer quality by correlating sensory evaluation with higher alcohol and ester production using multivariate statistics methods.

PubMed

Dong, Jian-Jun; Li, Qing-Liang; Yin, Hua; Zhong, Cheng; Hao, Jun-Guang; Yang, Pan-Fei; Tian, Yu-Hong; Jia, Shi-Ru

2014-10-15

Sensory evaluation is regarded as a necessary procedure to ensure a reproducible quality of beer. Meanwhile, high-throughput analytical methods provide a powerful tool to analyse various flavour compounds, such as higher alcohol and ester. In this study, the relationship between flavour compounds and sensory evaluation was established by non-linear models such as partial least squares (PLS), genetic algorithm back-propagation neural network (GA-BP), support vector machine (SVM). It was shown that SVM with a Radial Basis Function (RBF) had a better performance of prediction accuracy for both calibration set (94.3%) and validation set (96.2%) than other models. Relatively lower prediction abilities were observed for GA-BP (52.1%) and PLS (31.7%). In addition, the kernel function of SVM played an essential role of model training when the prediction accuracy of SVM with polynomial kernel function was 32.9%. As a powerful multivariate statistics method, SVM holds great potential to assess beer quality. Copyright © 2014 Elsevier Ltd. All rights reserved.

Non-destructive analysis of the conformational differences among feedstock sources and their corresponding co-products from bioethanol production with molecular spectroscopy.

PubMed

Gamage, I H; Jonker, A; Zhang, X; Yu, P

2014-01-24

The objective of this study was to determine the possibility of using molecular spectroscopy with multivariate technique as a fast method to detect the source effects among original feedstock sources of wheat and their corresponding co-products, wheat DDGS, from bioethanol production. Different sources of the bioethanol feedstock and their corresponding bioethanol co-products, three samples per source, were collected from the same newly-built bioethanol plant with current bioethanol processing technology. Multivariate molecular spectral analyses were carried out using agglomerative hierarchical cluster analysis (AHCA) and principal component analysis (PCA). The molecular spectral data of different feedstock sources and their corresponding co-products were compared at four different regions of ca. 1800-1725 cm(-1) (carbonyl CO ester, mainly related to lipid structure conformation), ca. 1725-1482 cm(-1) (amide I and amide II region mainly related to protein structure conformation), ca. 1482-1180 cm(-1) (mainly associated with structural carbohydrate) and ca. 1180-800 cm(-1) (mainly related to carbohydrates) in complex plant-based system. The results showed that the molecular spectroscopy with multivariate technique could reveal the structural differences among the bioethanol feedstock sources and among their corresponding co-products. The AHCA and PCA analyses were able to distinguish the molecular structure differences associated with chemical functional groups among the different sources of the feedstock and their corresponding co-products. The molecular spectral differences indicated the differences in functional, biomolecular and biopolymer groups which were confirmed by wet chemical analysis. These biomolecular and biopolymer structural differences were associated with chemical and nutrient profiles and nutrient utilization and availability. Molecular spectral analyses had the potential to identify molecular structure difference among bioethanol feedstock sources and their corresponding co-products. Copyright © 2013 Elsevier B.V. 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

Non-destructive analysis of the conformational differences among feedstock sources and their corresponding co-products from bioethanol production with molecular spectroscopy

NASA Astrophysics Data System (ADS)

Gamage, I. H.; Jonker, A.; Zhang, X.; Yu, P.

2014-01-01

The objective of this study was to determine the possibility of using molecular spectroscopy with multivariate technique as a fast method to detect the source effects among original feedstock sources of wheat and their corresponding co-products, wheat DDGS, from bioethanol production. Different sources of the bioethanol feedstock and their corresponding bioethanol co-products, three samples per source, were collected from the same newly-built bioethanol plant with current bioethanol processing technology. Multivariate molecular spectral analyses were carried out using agglomerative hierarchical cluster analysis (AHCA) and principal component analysis (PCA). The molecular spectral data of different feedstock sources and their corresponding co-products were compared at four different regions of ca. 1800-1725 cm-1 (carbonyl Cdbnd O ester, mainly related to lipid structure conformation), ca. 1725-1482 cm-1 (amide I and amide II region mainly related to protein structure conformation), ca. 1482-1180 cm-1 (mainly associated with structural carbohydrate) and ca. 1180-800 cm-1 (mainly related to carbohydrates) in complex plant-based system. The results showed that the molecular spectroscopy with multivariate technique could reveal the structural differences among the bioethanol feedstock sources and among their corresponding co-products. The AHCA and PCA analyses were able to distinguish the molecular structure differences associated with chemical functional groups among the different sources of the feedstock and their corresponding co-products. The molecular spectral differences indicated the differences in functional, biomolecular and biopolymer groups which were confirmed by wet chemical analysis. These biomolecular and biopolymer structural differences were associated with chemical and nutrient profiles and nutrient utilization and availability. Molecular spectral analyses had the potential to identify molecular structure difference among bioethanol feedstock sources and their corresponding co-products.

BRAF and RAS mutations as prognostic factors in metastatic colorectal cancer patients undergoing liver resection

PubMed Central

Schirripa, M; Bergamo, F; Cremolini, C; Casagrande, M; Lonardi, S; Aprile, G; Yang, D; Marmorino, F; Pasquini, G; Sensi, E; Lupi, C; De Maglio, G; Borrelli, N; Pizzolitto, S; Fasola, G; Bertorelle, R; Rugge, M; Fontanini, G; Zagonel, V; Loupakis, F; Falcone, A

2015-01-01

Background: Despite major advances in the management of metastatic colorectal cancer (mCRC) with liver-only involvement, relapse rates are high and reliable prognostic markers are needed. Methods: To assess the prognostic impact of BRAF and RAS mutations in a large series of liver-resected patients, medical records of 3024 mCRC patients were reviewed. Eligible cases undergoing potentially curative liver resection were selected. BRAF and RAS mutational status was tested on primary and/or metastases by means of pyrosequencing and mass spectrometry genotyping assay. Primary endpoint was relapse-free survival (RFS). Results: In the final study population (N=309) BRAF mutant, RAS mutant and all wild-type (wt) patients were 12(4%), 160(52%) and 137(44%), respectively. Median RFS was 5.7, 11.0 and 14.4 months respectively and differed significantly (Log-rank, P=0.043). At multivariate analyses, BRAF mutant had a higher risk of relapse in comparison to all wt (multivariate hazard ratio (HR)=2.31; 95% CI, 1.09–4.87; P=0.029) and to RAS mutant (multivariate HR=2.06; 95% CI, 1.02–4.14; P=0.044). Similar results were obtained in terms of overall survival. Compared with all wt patients, RAS mutant showed a higher risk of death (HR=1.47; 95% CI, 1.05–2.07; P=0.025), but such effect was lost at multivariate analyses. Conclusions: BRAF mutation is associated with an extremely poor median RFS after liver resection and with higher probability of relapse and death. Knowledge of BRAF mutational status may optimise clinical decision making in mCRC patients potentially candidate to hepatic surgery. RAS status as useful marker in this setting might require further studies. PMID:25942399

Racial Differences in Circulating Natriuretic Peptide Levels: The Atherosclerosis Risk in Communities Study

PubMed Central

Gupta, Deepak K; Claggett, Brian; Wells, Quinn; Cheng, Susan; Li, Man; Maruthur, Nisa; Selvin, Elizabeth; Coresh, Josef; Konety, Suma; Butler, Kenneth R; Mosley, Thomas; Boerwinkle, Eric; Hoogeveen, Ron; Ballantyne, Christie M; Solomon, Scott D

2015-01-01

Background Natriuretic peptides promote natriuresis, diuresis, and vasodilation. Experimental deficiency of natriuretic peptides leads to hypertension (HTN) and cardiac hypertrophy, conditions more common among African Americans. Hospital-based studies suggest that African Americans may have reduced circulating natriuretic peptides, as compared to Caucasians, but definitive data from community-based cohorts are lacking. Methods and Results We examined plasma N-terminal pro B-type natriuretic peptide (NTproBNP) levels according to race in 9137 Atherosclerosis Risk in Communities (ARIC) Study participants (22% African American) without prevalent cardiovascular disease at visit 4 (1996–1998). Multivariable linear and logistic regression analyses were performed adjusting for clinical covariates. Among African Americans, percent European ancestry was determined from genetic ancestry informative markers and then examined in relation to NTproBNP levels in multivariable linear regression analysis. NTproBNP levels were significantly lower in African Americans (median, 43 pg/mL; interquartile range [IQR], 18, 88) than Caucasians (median, 68 pg/mL; IQR, 36, 124; P<0.0001). In multivariable models, adjusted log NTproBNP levels were 40% lower (95% confidence interval [CI], −43, −36) in African Americans, compared to Caucasians, which was consistent across subgroups of age, gender, HTN, diabetes, insulin resistance, and obesity. African-American race was also significantly associated with having nondetectable NTproBNP (adjusted OR, 5.74; 95% CI, 4.22, 7.80). In multivariable analyses in African Americans, a 10% increase in genetic European ancestry was associated with a 7% (95% CI, 1, 13) increase in adjusted log NTproBNP. Conclusions African Americans have lower levels of plasma NTproBNP than Caucasians, which may be partially owing to genetic variation. Low natriuretic peptide levels in African Americans may contribute to the greater risk for HTN and its sequalae in this population. PMID:25999400

Prognostic Factors Affecting Locally Recurrent Rectal Cancer and Clinical Significance of Hemoglobin

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

Rades, Dirk; Kuhn, Hildegard; Schultze, Juergen

2008-03-15

Purpose: To investigate potential prognostic factors, including hemoglobin levels before and during radiotherapy, for associations with survival and local control in patients with unirradiated locally recurrent rectal cancer. Patients and Methods: Ten potential prognostic factors were investigated in 94 patients receiving radiotherapy for recurrent rectal cancer: age ({<=}68 vs. {>=}69 years), gender, Eastern Cooperative Oncology Group performance status (0-1 vs. 2-3), American Joint Committee on Cancer (AJCC) stage ({<=}II vs. III vs. IV), grading (G1-2 vs. G3), surgery, administration of chemotherapy, radiation dose (equivalent dose in 2-Gy fractions: {<=}50 vs. >50 Gy), and hemoglobin levels before (<12 vs. {>=}12 g/dL)more » and during (majority of levels: <12 vs. {>=}12 g/dL) radiotherapy. Multivariate analyses were performed, including hemoglobin levels, either before or during radiotherapy (not both) because these are confounding variables. Results: Improved survival was associated with better performance status (p < 0.001), lower AJCC stage (p = 0.023), surgery (p = 0.011), chemotherapy (p = 0.003), and hemoglobin levels {>=}12 g/dL both before (p = 0.031) and during (p < 0.001) radiotherapy. On multivariate analyses, performance status, AJCC stage, and hemoglobin levels during radiotherapy maintained significance. Improved local control was associated with better performance status (p = 0.040), lower AJCC stage (p = 0.010), lower grading (p = 0.012), surgery (p < 0.001), chemotherapy (p < 0.001), and hemoglobin levels {>=}12 g/dL before (p < 0.001) and during (p < 0.001) radiotherapy. On multivariate analyses, chemotherapy, grading, and hemoglobin levels before and during radiotherapy remained significant. Subgroup analyses of the patients having surgery demonstrated the extent of resection to be significantly associated with local control (p = 0.011) but not with survival (p = 0.45). Conclusion: Predictors for outcome in patients who received radiotherapy for locally recurrent rectal cancer were performance status, AJCC stage, chemotherapy, surgery, extent of resection, histologic grading, and hemoglobin levels both before and during radiotherapy.« less

Does speed matter? The impact of operative time on outcome in laparoscopic surgery

PubMed Central

Jackson, Timothy D.; Wannares, Jeffrey J.; Lancaster, R. Todd; Rattner, David W.

2012-01-01

Introduction Controversy exists concerning the importance of operative time on patient outcomes. It is unclear whether faster is better or haste makes waste or similarly whether slower procedures represent a safe, meticulous approach or inexperienced dawdling. The objective of the present study was to determine the effect of operative time on 30-day outcomes in laparoscopic surgery. Methods Patients who underwent laparoscopic general surgery procedures (colectomy, cholecystectomy, Nissen fundoplication, inguinal hernia, and gastric bypass) from the ACS-NSQIP 2005–2008 participant use file were identified. Exclusion criteria were defined a priori to identify same-day admission, elective procedures. Operative time was divided into deciles and summary statistics were analyzed. Univariate analyses using a Cochran-Armitage test for trend were completed. The effect of operative time on 30-day morbidity was further analyzed for each procedure type using multivariate regression controlling for case complexity and additional patient factors. Patients within the highest deciles were excluded to reduce outlier effect. Results A total of 76,748 elective general surgical patients who underwent laparoscopic procedures were analyzed. Univariate analyses of deciles of operative time demonstrated a statistically significant trend (p \\ 0.0001) toward increasing odds of complications with increasing operative time for laparoscopic colectomy (n = 10,135), cholecystectomy (n = 37,407), Nissen fundoplication (n = 4,934), and gastric bypass (n = 17,842). The trend was not found to be significant for laparoscopic inguinal hernia repair (n = 6,430; p = 0.14). Multivariate modeling revealed the effect of operative time to remain significant after controlling for additional patient factors. Conclusion Increasing operative time was associated with increased odds of complications and, therefore, it appears that speed may matter in laparoscopic surgery. These analyses are limited in their inability to adjust for all patient factors, potential confounders, and case complexities. Additional hierarchical multivariate analyses at the surgeon level would be important to examine this relationship further. PMID:21298533

An analysis of the influence of production conditions on the development of the microporous structure of the activated carbon fibres using the LBET method

NASA Astrophysics Data System (ADS)

Kwiatkowski, Mirosław

2017-12-01

The paper presents the results of the research on the application of the new analytical models of multilayer adsorption on heterogeneous surfaces with the unique fast multivariant identification procedure, together called LBET method, as a tool for analysing the microporous structure of the activated carbon fibres obtained from polyacrylonitrile by chemical activation using potassium and sodium hydroxides. The novel LBET method was employed particularly to evaluate the impact of the used activator and the hydroxide to polyacrylonitrile ratio on the obtained microporous structure of the activated carbon fibres.

Handwriting Examination: Moving from Art to Science

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

Jarman, K.H.; Hanlen, R.C.; Manzolillo, P.A.

In this document, we present a method for validating the premises and methodology of forensic handwriting examination. This method is intuitively appealing because it relies on quantitative measurements currently used qualitatively by FDE's in making comparisons, and it is scientifically rigorous because it exploits the power of multivariate statistical analysis. This approach uses measures of both central tendency and variation to construct a profile for a given individual. (Central tendency and variation are important for characterizing an individual's writing and both are currently used by FDE's in comparative analyses). Once constructed, different profiles are then compared for individuality using clustermore » analysis; they are grouped so that profiles within a group cannot be differentiated from one another based on the measured characteristics, whereas profiles between groups can. The cluster analysis procedure used here exploits the power of multivariate hypothesis testing. The result is not only a profile grouping but also an indication of statistical significance of the groups generated.« less

Controlled pattern imputation for sensitivity analysis of longitudinal binary and ordinal outcomes with nonignorable dropout.

PubMed

Tang, Yongqiang

2018-04-30

The controlled imputation method refers to a class of pattern mixture models that have been commonly used as sensitivity analyses of longitudinal clinical trials with nonignorable dropout in recent years. These pattern mixture models assume that participants in the experimental arm after dropout have similar response profiles to the control participants or have worse outcomes than otherwise similar participants who remain on the experimental treatment. In spite of its popularity, the controlled imputation has not been formally developed for longitudinal binary and ordinal outcomes partially due to the lack of a natural multivariate distribution for such endpoints. In this paper, we propose 2 approaches for implementing the controlled imputation for binary and ordinal data based respectively on the sequential logistic regression and the multivariate probit model. Efficient Markov chain Monte Carlo algorithms are developed for missing data imputation by using the monotone data augmentation technique for the sequential logistic regression and a parameter-expanded monotone data augmentation scheme for the multivariate probit model. We assess the performance of the proposed procedures by simulation and the analysis of a schizophrenia clinical trial and compare them with the fully conditional specification, last observation carried forward, and baseline observation carried forward imputation methods. Copyright © 2018 John Wiley & Sons, Ltd.

Chemometric and biological validation of a capillary electrophoresis metabolomic experiment of Schistosoma mansoni infection in mice.

PubMed

Garcia-Perez, Isabel; Angulo, Santiago; Utzinger, Jürg; Holmes, Elaine; Legido-Quigley, Cristina; Barbas, Coral

2010-07-01

Metabonomic and metabolomic studies are increasingly utilized for biomarker identification in different fields, including biology of infection. The confluence of improved analytical platforms and the availability of powerful multivariate analysis software have rendered the multiparameter profiles generated by these omics platforms a user-friendly alternative to the established analysis methods where the quality and practice of a procedure is well defined. However, unlike traditional assays, validation methods for these new multivariate profiling tools have yet to be established. We propose a validation for models obtained by CE fingerprinting of urine from mice infected with the blood fluke Schistosoma mansoni. We have analysed urine samples from two sets of mice infected in an inter-laboratory experiment where different infection methods and animal husbandry procedures were employed in order to establish the core biological response to a S. mansoni infection. CE data were analysed using principal component analysis. Validation of the scores consisted of permutation scrambling (100 repetitions) and a manual validation method, using a third of the samples (not included in the model) as a test or prediction set. The validation yielded 100% specificity and 100% sensitivity, demonstrating the robustness of these models with respect to deciphering metabolic perturbations in the mouse due to a S. mansoni infection. A total of 20 metabolites across the two experiments were identified that significantly discriminated between S. mansoni-infected and noninfected control samples. Only one of these metabolites, allantoin, was identified as manifesting different behaviour in the two experiments. This study shows the reproducibility of CE-based metabolic profiling methods for disease characterization and screening and highlights the importance of much needed validation strategies in the emerging field of metabolomics.

GSimp: A Gibbs sampler based left-censored missing value imputation approach for metabolomics studies

PubMed Central

Jia, Erik; Chen, Tianlu

2018-01-01

Left-censored missing values commonly exist in targeted metabolomics datasets and can be considered as missing not at random (MNAR). Improper data processing procedures for missing values will cause adverse impacts on subsequent statistical analyses. However, few imputation methods have been developed and applied to the situation of MNAR in the field of metabolomics. Thus, a practical left-censored missing value imputation method is urgently needed. We developed an iterative Gibbs sampler based left-censored missing value imputation approach (GSimp). We compared GSimp with other three imputation methods on two real-world targeted metabolomics datasets and one simulation dataset using our imputation evaluation pipeline. The results show that GSimp outperforms other imputation methods in terms of imputation accuracy, observation distribution, univariate and multivariate analyses, and statistical sensitivity. Additionally, a parallel version of GSimp was developed for dealing with large scale metabolomics datasets. The R code for GSimp, evaluation pipeline, tutorial, real-world and simulated targeted metabolomics datasets are available at: https://github.com/WandeRum/GSimp. PMID:29385130

Time-Domain Nuclear Magnetic Resonance (TD-NMR) and Chemometrics for Determination of Fat Content in Commercial Products of Milk Powder.

PubMed

Nascimento, Paloma Andrade Martins; Barsanelli, Paulo Lopes; Rebellato, Ana Paula; Pallone, Juliana Azevedo Lima; Colnago, Luiz Alberto; Pereira, Fabíola Manhas Verbi

2017-03-01

This study shows the use of time-domain (TD)-NMR transverse relaxation (T2) data and chemometrics in the nondestructive determination of fat content for powdered food samples such as commercial dried milk products. Most proposed NMR spectroscopy methods for measuring fat content correlate free induction decay or echo intensities with the sample's mass. The need for the sample's mass limits the analytical frequency of NMR determination, because weighing the samples is an additional step in this procedure. Therefore, the method proposed here is based on a multivariate model of T2 decay, measured with Carr-Purcell-Meiboom-Gill pulse sequence and reference values of fat content. The TD-NMR spectroscopy method shows high correlation (r = 0.95) with the lipid content, determined by the standard extraction method of Bligh and Dyer. For comparison, fat content determination was also performed using a multivariate model with near-IR (NIR) spectroscopy, which is also a nondestructive method. The advantages of the proposed TD-NMR method are that it (1) minimizes toxic residue generation, (2) performs measurements with high analytical frequency (a few seconds per analysis), and (3) does not require sample preparation (such as pelleting, needed for NIR spectroscopy analyses) or weighing the samples.

Collagen Triple Helix Repeat Containing-1 (CTHRC1) Expression in Oral Squamous Cell Carcinoma (OSCC): Prognostic Value and Clinico-Pathological Implications

PubMed Central

Lee, Chia Ee; Vincent-Chong, Vui King; Ramanathan, Anand; Kallarakkal, Thomas George; Karen-Ng, Lee Peng; Ghani, Wan Maria Nabillah; Rahman, Zainal Ariff Abdul; Ismail, Siti Mazlipah; Abraham, Mannil Thomas; Tay, Keng Kiong; Mustafa, Wan Mahadzir Wan; Cheong, Sok Ching; Zain, Rosnah Binti

2015-01-01

BACKGROUND: Collagen Triple Helix Repeat Containing 1 (CTHRC1) is a protein often found to be over-expressed in various types of human cancers. However, correlation between CTHRC1 expression level with clinico-pathological characteristics and prognosis in oral cancer remains unclear. Therefore, this study aimed to determine mRNA and protein expression of CTHRC1 in oral squamous cell carcinoma (OSCC) and to evaluate the clinical and prognostic impact of CTHRC1 in OSCC. METHODS: In this study, mRNA and protein expression of CTHRC1 in OSCCs were determined by quantitative PCR and immunohistochemistry, respectively. The association between CTHRC1 and clinico-pathological parameters were evaluated by univariate and multivariate binary logistic regression analyses. Correlation between CTHRC1 protein expressions with survival were analysed using Kaplan-Meier and Cox regression models. RESULTS: Current study demonstrated CTHRC1 was significantly overexpressed at the mRNA level in OSCC. Univariate analyses indicated a high-expression of CTHRC1 that was significantly associated with advanced stage pTNM staging, tumour size ≥ 4 cm and positive lymph node metastasis (LNM). However, only positive LNM remained significant after adjusting with other confounder factors in multivariate logistic regression analyses. Kaplan-Meier survival analyses and Cox model demonstrated that patients with high-expression of CTHRC1 protein were associated with poor prognosis and is an independent prognostic factor in OSCC. CONCLUSION: This study indicated that over-expression of CTHRC1 potentially as an independent predictor for positive LNM and poor prognosis in OSCC. PMID:26664254

Causal diagrams and multivariate analysis II: precision work.

PubMed

Jupiter, Daniel C

2014-01-01

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

On the potential for the Partial Triadic Analysis to grasp the spatio-temporal variability of groundwater hydrochemistry

NASA Astrophysics Data System (ADS)

Gourdol, L.; Hissler, C.; Pfister, L.

2012-04-01

The Luxembourg sandstone aquifer is of major relevance for the national supply of drinking water in Luxembourg. The city of Luxembourg (20% of the country's population) gets almost 2/3 of its drinking water from this aquifer. As a consequence, the study of both the groundwater hydrochemistry, as well as its spatial and temporal variations, are considered as of highest priority. Since 2005, a monitoring network has been implemented by the Water Department of Luxembourg City, with a view to a more sustainable management of this strategic water resource. The data collected to date forms a large and complex dataset, describing spatial and temporal variations of many hydrochemical parameters. The data treatment issue is tightly connected to this kind of water monitoring programs and complex databases. Standard multivariate statistical techniques, such as principal components analysis and hierarchical cluster analysis, have been widely used as unbiased methods for extracting meaningful information from groundwater quality data and are now classically used in many hydrogeological studies, in particular to characterize temporal or spatial hydrochemical variations induced by natural and anthropogenic factors. But these classical multivariate methods deal with two-way matrices, usually parameters/sites or parameters/time, while often the dataset resulting from qualitative water monitoring programs should be seen as a datacube parameters/sites/time. Three-way matrices, such as the one we propose here, are difficult to handle and to analyse by classical multivariate statistical tools and thus should be treated with approaches dealing with three-way data structures. One possible analysis approach consists in the use of partial triadic analysis (PTA). The PTA was previously used with success in many ecological studies but never to date in the domain of hydrogeology. Applied to the dataset of the Luxembourg Sandstone aquifer, the PTA appears as a new promising statistical instrument for hydrogeologists, in particular to characterize temporal and spatial hydrochemical variations induced by natural and anthropogenic factors. This new approach for groundwater management offers potential for 1) identifying a common multivariate spatial structure, 2) untapping the different hydrochemical patterns and explaining their controlling factors and 3) analysing the temporal variability of this structure and grasping hydrochemical changes.

Performance Assessment of Kernel Density Clustering for Gene Expression Profile Data

PubMed Central

Zeng, Beiyan; Chen, Yiping P.; Smith, Oscar H.

2003-01-01

Kernel density smoothing techniques have been used in classification or supervised learning of gene expression profile (GEP) data, but their applications to clustering or unsupervised learning of those data have not been explored and assessed. Here we report a kernel density clustering method for analysing GEP data and compare its performance with the three most widely-used clustering methods: hierarchical clustering, K-means clustering, and multivariate mixture model-based clustering. Using several methods to measure agreement, between-cluster isolation, and withincluster coherence, such as the Adjusted Rand Index, the Pseudo F test, the r2 test, and the profile plot, we have assessed the effectiveness of kernel density clustering for recovering clusters, and its robustness against noise on clustering both simulated and real GEP data. Our results show that the kernel density clustering method has excellent performance in recovering clusters from simulated data and in grouping large real expression profile data sets into compact and well-isolated clusters, and that it is the most robust clustering method for analysing noisy expression profile data compared to the other three methods assessed. PMID:18629292

Monitoring the quality consistency of Weibizhi tablets by micellar electrokinetic chromatography fingerprints combined with multivariate statistical analyses, the simple quantified ratio fingerprint method, and the fingerprint-efficacy relationship.

PubMed

Liu, Yingchun; Sun, Guoxiang; Wang, Yan; Yang, Lanping; Yang, Fangliang

2015-06-01

Micellar electrokinetic chromatography fingerprinting combined with quantification was successfully developed and applied to monitor the quality consistency of Weibizhi tablets, which is a classical compound preparation used to treat gastric ulcers. A background electrolyte composed of 57 mmol/L sodium borate, 21 mmol/L sodium dodecylsulfate and 100 mmol/L sodium hydroxide was used to separate compounds. To optimize capillary electrophoresis conditions, multivariate statistical analyses were applied. First, the most important factors influencing sample electrophoretic behavior were identified as background electrolyte concentrations. Then, a Box-Benhnken design response surface strategy using resolution index RF as an integrated response was set up to correlate factors with response. RF reflects the effective signal amount, resolution, and signal homogenization in an electropherogram, thus, it was regarded as an excellent indicator. In fingerprint assessments, simple quantified ratio fingerprint method was established for comprehensive quality discrimination of traditional Chinese medicines/herbal medicines from qualitative and quantitative perspectives, by which the quality of 27 samples from the same manufacturer were well differentiated. In addition, the fingerprint-efficacy relationship between fingerprints and antioxidant activities was established using partial least squares regression, which provided important medicinal efficacy information for quality control. The present study offered an efficient means for monitoring Weibizhi tablet quality consistency. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula

PubMed Central

Giordano, Bruno L.; Kayser, Christoph; Rousselet, Guillaume A.; Gross, Joachim; Schyns, Philippe G.

2016-01-01

Abstract We begin by reviewing the statistical framework of information theory as applicable to neuroimaging data analysis. A major factor hindering wider adoption of this framework in neuroimaging is the difficulty of estimating information theoretic quantities in practice. We present a novel estimation technique that combines the statistical theory of copulas with the closed form solution for the entropy of Gaussian variables. This results in a general, computationally efficient, flexible, and robust multivariate statistical framework that provides effect sizes on a common meaningful scale, allows for unified treatment of discrete, continuous, unidimensional and multidimensional variables, and enables direct comparisons of representations from behavioral and brain responses across any recording modality. We validate the use of this estimate as a statistical test within a neuroimaging context, considering both discrete stimulus classes and continuous stimulus features. We also present examples of analyses facilitated by these developments, including application of multivariate analyses to MEG planar magnetic field gradients, and pairwise temporal interactions in evoked EEG responses. We show the benefit of considering the instantaneous temporal derivative together with the raw values of M/EEG signals as a multivariate response, how we can separately quantify modulations of amplitude and direction for vector quantities, and how we can measure the emergence of novel information over time in evoked responses. Open‐source Matlab and Python code implementing the new methods accompanies this article. Hum Brain Mapp 38:1541–1573, 2017. © 2016 Wiley Periodicals, Inc. PMID:27860095

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

PubMed Central

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

2009-01-01

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

A Comparison of Anthropometric and Training Characteristics between Female and Male Half-Marathoners and the Relationship to Race Time

PubMed Central

Friedrich, Miriam; Rüst, Christoph A.; Rosemann, Thomas; Knechtle, Patrizia; Barandun, Ursula; Lepers, Romuald; Knechtle, Beat

2013-01-01

Purpose Lower limb skin-fold thicknesses have been differentially associated with sex in elite runners. Front thigh and medial calf skin-fold appear to be related to 1,500m and 10,000m time in men but 400m time in women. The aim of the present study was to compare anthropometric and training characteristics in recreational female and male half-marathoners. Methods The association between both anthropometry and training characteristics and race time was investigated in 83 female and 147 male recreational half marathoners using bi- and multi-variate analyses. Results In men, body fat percentage (β=0.6), running speed during training (β=-3.7), and body mass index (β=1.9) were related to half-marathon race time after multi-variate analysis. After exclusion of body mass index, r2 decreased from 0.51 to 0.49, but body fat percentage (β=0.8) and running speed during training (β=-4.1) remained predictive. In women, body fat percentage (β=0.75) and speed during training (β=-6.5) were related to race time (r2=0.73). For women, the exclusion of body mass index had no consequence on the predictive variables for half-marathon race time. Conclusion To summarize, in both female and male recreational half-marathoners, both body fat percentage and running speed during training sessions were related to half-marathon race times when corrected with co-variates after multi-variate regression analyses. PMID:24868427

Multivariate statistical approach to estimate mixing proportions for unknown end members

USGS Publications Warehouse

Valder, Joshua F.; Long, Andrew J.; Davis, Arden D.; Kenner, Scott J.

2012-01-01

A multivariate statistical method is presented, which includes principal components analysis (PCA) and an end-member mixing model to estimate unknown end-member hydrochemical compositions and the relative mixing proportions of those end members in mixed waters. PCA, together with the Hotelling T2 statistic and a conceptual model of groundwater flow and mixing, was used in selecting samples that best approximate end members, which then were used as initial values in optimization of the end-member mixing model. This method was tested on controlled datasets (i.e., true values of estimates were known a priori) and found effective in estimating these end members and mixing proportions. The controlled datasets included synthetically generated hydrochemical data, synthetically generated mixing proportions, and laboratory analyses of sample mixtures, which were used in an evaluation of the effectiveness of this method for potential use in actual hydrological settings. For three different scenarios tested, correlation coefficients (R2) for linear regression between the estimated and known values ranged from 0.968 to 0.993 for mixing proportions and from 0.839 to 0.998 for end-member compositions. The method also was applied to field data from a study of end-member mixing in groundwater as a field example and partial method validation.

In Quest of the Alanine R3 Radical: Multivariate EPR Spectral Analyses of X-Irradiated Alanine in the Solid State.

PubMed

Jåstad, Eirik O; Torheim, Turid; Villeneuve, Kathleen M; Kvaal, Knut; Hole, Eli O; Sagstuen, Einar; Malinen, Eirik; Futsaether, Cecilia M

2017-09-28

The amino acid l-α-alanine is the most commonly used material for solid-state electron paramagnetic resonance (EPR) dosimetry, due to the formation of highly stable radicals upon irradiation, with yields proportional to the radiation dose. Two major alanine radical components designated R1 and R2 have previously been uniquely characterized from EPR and electron-nuclear double resonance (ENDOR) studies as well as from quantum chemical calculations. There is also convincing experimental evidence of a third minor radical component R3, and a tentative radical structure has been suggested, even though no well-defined spectral signature has been observed experimentally. In the present study, temperature dependent EPR spectra of X-ray irradiated polycrystalline alanine were analyzed using five multivariate methods in further attempts to understand the composite nature of the alanine dosimeter EPR spectrum. Principal component analysis (PCA), maximum likelihood common factor analysis (MLCFA), independent component analysis (ICA), self-modeling mixture analysis (SMA), and multivariate curve resolution (MCR) were used to extract pure radical spectra and their fractional contributions from the experimental EPR spectra. All methods yielded spectral estimates resembling the established R1 spectrum. Furthermore, SMA and MCR consistently predicted both the established R2 spectrum and the shape of the R3 spectrum. The predicted shape of the R3 spectrum corresponded well with the proposed tentative spectrum derived from spectrum simulations. Thus, results from two independent multivariate data analysis techniques strongly support the previous evidence that three radicals are indeed present in irradiated alanine samples.

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.

MANOVA vs nonlinear mixed effects modeling: The comparison of growth patterns of female and male quail

NASA Astrophysics Data System (ADS)

Gürcan, Eser Kemal

2017-04-01

The most commonly used methods for analyzing time-dependent data are multivariate analysis of variance (MANOVA) and nonlinear regression models. The aim of this study was to compare some MANOVA techniques and nonlinear mixed modeling approach for investigation of growth differentiation in female and male Japanese quail. Weekly individual body weight data of 352 male and 335 female quail from hatch to 8 weeks of age were used to perform analyses. It is possible to say that when all the analyses are evaluated, the nonlinear mixed modeling is superior to the other techniques because it also reveals the individual variation. In addition, the profile analysis also provides important information.

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.

Sex Hormones and Sleep in Men and Women From the General Population: A Cross-Sectional Observational Study.

PubMed

Kische, Hanna; Ewert, Ralf; Fietze, Ingo; Gross, Stefan; Wallaschofski, Henri; Völzke, Henry; Dörr, Marcus; Nauck, Matthias; Obst, Anne; Stubbe, Beate; Penzel, Thomas; Haring, Robin

2016-11-01

Associations between sex hormones and sleep habits originate mainly from small and selected patient-based samples. We examined data from a population-based sample with various sleep characteristics and the major part of sex hormones measured by mass spectrometry. We used data from 204 men and 213 women of the cross-sectional Study of Health in Pomerania-TREND. Associations of total T (TT) and free T, androstenedione (ASD), estrone, estradiol (E2), dehydroepiandrosterone-sulphate, SHBG, and E2 to TT ratio with sleep measures (including total sleep time, sleep efficiency, wake after sleep onset, apnea-hypopnea index [AHI], Insomnia Severity Index, Epworth Sleepiness Scale, and Pittsburgh Sleep Quality Index) were assessed by sex-specific multivariable regression models. In men, age-adjusted associations of TT (odds ratio 0.62; 95% confidence interval (CI) 0.46-0.83), free T, and SHBG with AHI were rendered nonsignificant after multivariable adjustment. In multivariable analyses, ASD was associated with Epworth Sleepiness Scale (β-coefficient per SD increase in ASD: -0.71; 95% CI: -1.18 to -0.25). In women, multivariable analyses showed positive associations of dehydroepiandrosterone-sulphate with wake after sleep onset (β-coefficient: .16; 95% CI 0.03-0.28) and of E2 and E2 to TT ratio with Epworth Sleepiness Scale. Additionally, free T and SHBG were associated with AHI in multivariable models among premenopausal women. The present cross-sectional, population-based study observed sex-specific associations of androgens, E2, and SHBG with sleep apnea and daytime sleepiness. However, multivariable-adjusted analyses confirmed the impact of body composition and health-related lifestyle on the association between sex hormones and sleep.

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

PubMed Central

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

2013-01-01

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

Rhythmic entrainment source separation: Optimizing analyses of neural responses to rhythmic sensory stimulation.

PubMed

Cohen, Michael X; Gulbinaite, Rasa

2017-02-15

Steady-state evoked potentials (SSEPs) are rhythmic brain responses to rhythmic sensory stimulation, and are often used to study perceptual and attentional processes. We present a data analysis method for maximizing the signal-to-noise ratio of the narrow-band steady-state response in the frequency and time-frequency domains. The method, termed rhythmic entrainment source separation (RESS), is based on denoising source separation approaches that take advantage of the simultaneous but differential projection of neural activity to multiple electrodes or sensors. Our approach is a combination and extension of existing multivariate source separation methods. We demonstrate that RESS performs well on both simulated and empirical data, and outperforms conventional SSEP analysis methods based on selecting electrodes with the strongest SSEP response, as well as several other linear spatial filters. We also discuss the potential confound of overfitting, whereby the filter captures noise in absence of a signal. Matlab scripts are available to replicate and extend our simulations and methods. We conclude with some practical advice for optimizing SSEP data analyses and interpreting the results. Copyright © 2016 Elsevier Inc. All rights reserved.

[The impact of stress and personality on resilience of patients with ulcerative colitis].

PubMed

Liu, W; Wang, J; Wang, H; Chen, X Y; Li, J S

2018-02-01

Objective: To study relevant factors that influence psychological resilience in patients with ulcerative colitis(UC), especially the role of perceived stress and personality. Methods: Patients with UC were recruited from January 2015 to December 2016 in the First Hospital of Zhengzhou University. Education levels, income, duration of disease, Mayo score and disease phenotype according to Montreal classification were collected. Resilience was measured using Connor-Davidson resilience scale (CD-RISC). Perceived stress was measured by perceived stress scale (PSS). Personality was evaluated using Eysenck personality questionnaire (EPQ). Univariate analyses were conducted to determine the correlation of variables with resilience and thereafter those statistically significant were reanalyzed via a multivariate regression model. Results: A total of 188 patients with UC were finally recruited. Univariate analyses demonstrated resilience was inversely associated with perceived stress, Mayo score and neuroticism. Extraversion, income, college education were positively related to resilience. However, multivariate analyses revealed that perceived stress( OR= 0.901, 95% CI 0.833-0.975), extraversion ( OR= 1.257, 95% CI 1.087-1.454), neuroticism ( OR= 0.818, 95% CI 0.679-0.985), Mayo score ( OR= 0.856, 95% CI 0.742-0.988) and income ( OR= 6.411, 95% CI 2.136-9.244) were significantly related to resilience. Conclusions: Resilience of UC patients is not only associated with disease activity, but also with personality, perceived stress and income.

Do state characteristics matter? State level factors related to tobacco cessation quitlines

PubMed Central

Keller, Paula A; Koss, Kalsea J; Baker, Timothy B; Bailey, Linda A; Fiore, Michael C

2007-01-01

Background Quitline services are an effective population‐wide tobacco cessation strategy adopted widely in the United States as part of state comprehensive tobacco control efforts. Despite widespread evidence supporting quitlines' effectiveness, many states lack sufficient financial resources to adequately fund and promote this service. Efforts to augment state tobacco control efforts might be fostered by greater knowledge of state level factors associated with the funding and implementation of those efforts. Methods We analysed data from the 2004 North American Quitline Consortium survey and from publicly available sources to identify state level factors related to quitline implementation and funding. Factors included in the analyses were state demographic characteristics, tobacco use variables, state tobacco control spending, and economic and political climate variables. Univariate and multivariate regression analyses were conducted. Results The best fitting multivariate model that significantly predicted the presence or absence of a state quitline included only cigarette excise tax rate (p = 0.020). In terms of funding levels, states with high rates of cigarette consumption (p = 0.047) and with higher per capita expenditures for tobacco control programmes (p = 0 .0.004) were most likely to spend more on per capita operations budget for quitlines. Conclusion State level factors appear to play a part in whether states had established quitlines by mid‐2004 and the amount of per capita quitline funding. PMID:18048637

Cluster and Multiple Correspondence Analyses in Rheumatology: Paths to Uncovering Relationships in a Sea of Data.

PubMed

Han, Lu; Benseler, Susanne M; Tyrrell, Pascal N

2018-05-01

Rheumatic diseases encompass a wide range of conditions caused by inflammation and dysregulation of the immune system resulting in organ damage. Research in these heterogeneous diseases benefits from multivariate methods. The aim of this review was to describe and evaluate current literature in rheumatology regarding cluster analysis and correspondence analysis. A systematic review showed an increase in studies making use of these 2 methods. However, standardization in how these methods are applied and reported is needed. Researcher expertise was determined to be the main barrier to considering these approaches, whereas education and collaborating with a biostatistician were suggested ways forward. Copyright © 2018 Elsevier Inc. All rights reserved.

mvMapper: statistical and geographical data exploration and visualization of multivariate analysis of population structure

USDA-ARS?s Scientific Manuscript database

Characterizing population genetic structure across geographic space is a fundamental challenge in population genetics. Multivariate statistical analyses are powerful tools for summarizing genetic variability, but geographic information and accompanying metadata is not always easily integrated into t...

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.

Relationship between the prognostic value of ventilatory efficiency and age in patients with heart failure.

PubMed

Kato, Yuko; Suzuki, Shinya; Uejima, Tokuhisa; Semba, Hiroaki; Nagayama, Osamu; Hayama, Etsuko; Arita, Takuto; Yagi, Naoharu; Kano, Hiroto; Matsuno, Shunsuke; Otsuka, Takayuki; Oikawa, Yuji; Kunihara, Takashi; Yajima, Junji; Yamashita, Takeshi

2018-05-01

Background Ventilatory efficiency decreases with age. This study aimed to investigate the prognostic significance and cut-off value of the minute ventilation/carbon dioxide production (VE/VCO 2 ) slope according to age in patients with heart failure. Methods and results We analysed 1501 patients with heart failure from our observational cohort who performed maximal symptom-limited cardiopulmonary exercise testing and separated them into three age groups (≤55 years, 56-70 years and ≥71 years) in total and according to the three ejection fraction categories defined by European Society of Cardiology guidelines. The endpoint was set as heart failure events, hospitalisation for heart failure or death from heart failure. The VE/VCO 2 slope increased with age. During the median follow-up period of 4 years, 141 heart failure (9%) events occurred. In total, univariate Cox analyses showed that the VE/VCO 2 slope (cont.) was significantly related to heart failure events, while on multivariate analysis, the prognostic significance of the VE/VCO 2 slope (cont.) was poor, accompanied by a significant interaction with age ( P < 0.0001). The cut-off value of the VE/VCO 2 slope increased with the increase in age in not only the total but also the sub-ejection fraction categories. Multivariate analyses with a stepwise method adjusted for estimated glomerular filtration rate, peak oxygen consumption, atrial fibrillation and brain natriuretic peptide, showed that the predictive value of the binary VE/VCO 2 slope separated by the cut-off value varied according to age. There was a tendency for the prognostic significance to increase with age irrespective of ejection fraction. Conclusion The prognostic significance and cut-off value of the VE/VCO 2 slope may increase with advancing age.

Linkage Analysis of a Model Quantitative Trait in Humans: Finger Ridge Count Shows Significant Multivariate Linkage to 5q14.1

PubMed Central

Medland, Sarah E; Loesch, Danuta Z; Mdzewski, Bogdan; Zhu, Gu; Montgomery, Grant W; Martin, Nicholas G

2007-01-01

The finger ridge count (a measure of pattern size) is one of the most heritable complex traits studied in humans and has been considered a model human polygenic trait in quantitative genetic analysis. Here, we report the results of the first genome-wide linkage scan for finger ridge count in a sample of 2,114 offspring from 922 nuclear families. Both univariate linkage to the absolute ridge count (a sum of all the ridge counts on all ten fingers), and multivariate linkage analyses of the counts on individual fingers, were conducted. The multivariate analyses yielded significant linkage to 5q14.1 (Logarithm of odds [LOD] = 3.34, pointwise-empirical p-value = 0.00025) that was predominantly driven by linkage to the ring, index, and middle fingers. The strongest univariate linkage was to 1q42.2 (LOD = 2.04, point-wise p-value = 0.002, genome-wide p-value = 0.29). In summary, the combination of univariate and multivariate results was more informative than simple univariate analyses alone. Patterns of quantitative trait loci factor loadings consistent with developmental fields were observed, and the simple pleiotropic model underlying the absolute ridge count was not sufficient to characterize the interrelationships between the ridge counts of individual fingers. PMID:17907812

Risk Factors for Anthroponotic Cutaneous Leishmaniasis at the Household Level in Kabul, Afghanistan

PubMed Central

Reithinger, Richard; Mohsen, Mohammad; Leslie, Toby

2010-01-01

Background Kabul, Afghanistan, is the largest focus of anthroponotic cutaneous leishmaniasis (ACL) in the world. ACL is a protozoan disease transmitted to humans by the bite of phlebotomine sand flies. Although not fatal, ACL can lead to considerable stigmatization of affected populations. Methods Using data from a standardized survey of 872 households in 4 wards of Kabul, Afghanistan, univariate and multivariate logistic regression analyses tested associations between presence of active ACL and ACL scars with 15 household-level variables. Findings Univariate analyses showed that active ACL was positively associated with household member's age, ACL prevalence, and brick wall type, but negatively associated with household number of rooms, bednet use, and proportion of windows with screens. Multivariate analysis showed a positive association between active ACL and household member's age, ACL prevalence, and brick wall type, and a negative association with household proportion of windows with screens. Conclusion Household-level charateristics were shown to be risk factors for ACL. Monitoring a selected number of household characteristics could assist in rapid assessments of household-level variation in risk of ACL. ACL prevention and control programs should consider improving house construction, including smoothing of walls and screening of windows. PMID:20351787

Morphological and Hemodynamic Discriminators for Rupture Status in Posterior Communicating Artery Aneurysms

PubMed Central

Karmonik, Christof; Fang, Yibin; Xu, Jinyu; Yu, Ying; Cao, Wei; Liu, Jianmin; Huang, Qinghai

2016-01-01

Background and Purpose The conflicting findings of previous morphological and hemodynamic studies on intracranial aneurysm rupture may be caused by the relatively small sample sizes and the variation in location of the patient-specific aneurysm models. We aimed to determine the discriminators for aneurysm rupture status by focusing on only posterior communicating artery (PCoA) aneurysms. Materials and Methods In 129 PCoA aneurysms (85 ruptured, 44 unruptured), clinical, morphological and hemodynamic characteristics were compared between the ruptured and unruptured cases. Multivariate logistic regression analysis was performed to determine the discriminators for rupture status of PCoA aneurysms. Results While univariate analyses showed that the size of aneurysm dome, aspect ratio (AR), size ratio (SR), dome-to-neck ratio (DN), inflow angle (IA), normalized wall shear stress (NWSS) and percentage of low wall shear stress area (LSA) were significantly associated with PCoA aneurysm rupture status. With multivariate analyses, significance was only retained for higher IA (OR = 1.539, p < 0.001) and LSA (OR = 1.393, p = 0.041). Conclusions Hemodynamics and morphology were related to rupture status of intracranial aneurysms. Higher IA and LSA were identified as discriminators for rupture status of PCoA aneurysms. PMID:26910518

Novel adipokines WISP1 and betatrophin in PCOS: relationship to AMH levels, atherogenic and metabolic profile.

PubMed

Sahin Ersoy, Gulcin; Altun Ensari, Tugba; Vatansever, Dogan; Emirdar, Volkan; Cevik, Ozge

2017-02-01

To determine the levels of WISP1 and betatrophin in normal weight and obese women with polycystic ovary syndrome (PCOS) and to assess their relationship with anti-Müllerian hormone (AMH) levels, atherogenic profile and metabolic parameters Methods: In this prospective cross-sectional study, the study group was composed of 49 normal weighed and 34 obese women with PCOS diagnosed based on the Rotterdam criteria; 36 normal weight and 26 obese age matched non-hyperandrogenemic women with regular menstrual cycle. Serum WISP1, betatrophin, homeostasis model assessment of insulin resistance (HOMA-IR) and AMH levels were evaluated. Univariate and multivariate analyses were performed between betatrophin, WISP1 levels and AMH levels, metabolic and atherogenic parameters. Serum WISP1 and betatrophin values were elevated in the PCOS group than in the control group. Moreover, serum WISP1 and betatrophin levels were higher in the obese PCOS subgroup than in normal weight and obese control subgroups. Multivariate analyses revealed that Body mass index, HOMA-IR, AMH independently and positively predicted WISP1 levels. Serum betatrophin level variability was explained by homocysteine, HOMA-IR and androstenedione levels. WISP1 and betatrophin may play a key role on the pathogenesis of PCOS.

Effects of coffee, smoking, and alcohol on liver function tests: a comprehensive cross-sectional study

PubMed Central

2012-01-01

Background Liver function tests (LFTs) can be affected by many factors and the proposed effects of coffee on LFT require a comprehensive evaluation. The aim of this study was to elucidate whether drinking coffee, smoking, or drinking alcohol have independent effects on LFTs in Korean health-check examinees. Methods We used the responses of 500 health-check examinees, who had participated in a self-administered questionnaire survey about coffee, alcohol drinking, and smoking habits. Results Coffee consumption was closely related to male gender, high body mass index (BMI), alcohol drinking, and smoking. On univariable and multivariable analyses, drinking coffee lowered serum levels of total protein, albumin, and aspartate aminotransferases (AST). On multivariable analyses, smoking raised serum γ-glutamyl transferase (GGT) level and decreased serum protein and albumin levels, while alcohol drinking raised GGT level after adjustment for age, gender, regular medication, BMI, coffee and alcohol drinking amounts, and smoking. Conclusions Coffee consumption, smoking, and alcohol drinking affect the individual components of LFT in different ways, and the above 3 habits each have an impact on LFTs. Therefore, their effects on LFTs should be carefully interpreted, and further study on the mechanism of the effects is warranted. PMID:23075166

The High Prevalence of Incarceration History Among Black Men Who Have Sex With Men in the United States: Associations and Implications

PubMed Central

Magnus, Manya; Kuo, Irene; Wang, Lei; Liu, Ting-Yuan; Mayer, Kenneth H.

2014-01-01

Objectives. We examined lifetime incarceration history and its association with key characteristics among 1553 Black men who have sex with men (BMSM) recruited in 6 US cities. Methods. We conducted bivariate analyses of data collected from the HIV Prevention Trials Network 061 study from July 2009 through December 2011 to examine the relationship between incarceration history and demographic and psychosocial variables predating incarceration and multivariate logistic regression analyses to explore the associations between incarceration history and demographic and psychosocial variables found to be significant. We then used multivariate logistic regression models to explore the independent association between incarceration history and 6 outcome variables. Results. After adjusting for confounders, we found that increasing age, transgender identity, heterosexual or straight identity, history of childhood violence, and childhood sexual experience were significantly associated with incarceration history. A history of incarceration was also independently associated with any alcohol and drug use in the past 6 months. Conclusions. The findings highlight an elevated lifetime incarceration history among a geographically diverse sample of BMSM and the need to adequately assess the impact of incarceration among BMSM in the United States. PMID:24432948

[Modulating variables of work disability in depressive disorders].

PubMed

Catalina Romero, C; Cabrera Sierra, M; Sainz Gutiérrez, J C; Barrenechea Albarrán, J L; Madrid Conesa, A; Calvo Bonacho, E

2011-01-01

To describe the duration of sickness absence in unipolar depression and to determine the relationship of demographic, job-related and clinical variables with length of temporary work disability in depressive disorders. Prospective observational study. A total of 1,292 subjects with depressive disorder diagnosis (ICD-9-CM) were selected claiming sick leave in an Occupational Diseases and Accident sat Work Insurance Scheme (sampling on successive occasions). Descriptive analyses of sickness absence duration, and bivariate (median test) and multivariate analysis (logistic regression) were performed to find relationships between demographic, job-related and clinical variables. Mean duration of sickness absence episodes due to a depressive disorder was 120 days. After multivariate analyses, female sex (p < 0.01), higher age (p < 0.01), lower educational level (p < 0.01), method of payment according to whether self-employed or unemployed workers (p < 0.01) and being referred to both psychiatrist and psychologist (p < 0.01) remained significantly associated with sick leave length. These findings confirm a strong association of depression with long periods of work disability and high absenteeism, and also suggest the need for improvements in functional ability assessment and promotion, treatment and referral of depressed patients. Copyright © 2010 SECA. Published by Elsevier Espana. All rights reserved.

Optimization of large animal MI models; a systematic analysis of control groups from preclinical studies.

PubMed

Zwetsloot, P P; Kouwenberg, L H J A; Sena, E S; Eding, J E; den Ruijter, H M; Sluijter, J P G; Pasterkamp, G; Doevendans, P A; Hoefer, I E; Chamuleau, S A J; van Hout, G P J; Jansen Of Lorkeers, S J

2017-10-27

Large animal models are essential for the development of novel therapeutics for myocardial infarction. To optimize translation, we need to assess the effect of experimental design on disease outcome and model experimental design to resemble the clinical course of MI. The aim of this study is therefore to systematically investigate how experimental decisions affect outcome measurements in large animal MI models. We used control animal-data from two independent meta-analyses of large animal MI models. All variables of interest were pre-defined. We performed univariable and multivariable meta-regression to analyze whether these variables influenced infarct size and ejection fraction. Our analyses incorporated 246 relevant studies. Multivariable meta-regression revealed that infarct size and cardiac function were influenced independently by choice of species, sex, co-medication, occlusion type, occluded vessel, quantification method, ischemia duration and follow-up duration. We provide strong systematic evidence that commonly used endpoints significantly depend on study design and biological variation. This makes direct comparison of different study-results difficult and calls for standardized models. Researchers should take this into account when designing large animal studies to most closely mimic the clinical course of MI and enable translational success.

Associations of Adolescent Hopelessness and Self-Worth With Pregnancy Attempts and Pregnancy Desire

PubMed Central

Fedorowicz, Anna R.; Schreiner, Pamela J.; Bolland, John M.

2014-01-01

Objectives. We examined the associations of pregnancy desire (ambivalence or happiness about a pregnancy in the next year) and recent pregnancy attempts with hopelessness and self-worth among low-income adolescents. Methods. To evaluate independent associations among the study variables, we conducted gender-stratified multivariable logistic regression analyses with data derived from 2285 sexually experienced 9- to 18-year-old participants in the Mobile Youth Survey between 2006 and 2009. Results. Fifty-seven percent of youths reported a desire for pregnancy and 9% reported pregnancy attempts. In multivariable analyses, hopelessness was positively associated and self-worth was negatively associated with pregnancy attempts among both female and male youths. Hopelessness was weakly associated (P = .05) with pregnancy desire among female youths. Conclusions. The negative association of self-worth and the positive association of hopelessness with pregnancy attempts among young men as well as young women and the association of hopelessness with pregnancy desire among young women raise questions about why pregnancy is apparently valued by youths who rate their social and cognitive competence as low and who live in an environment with few options for material success. PMID:24922147

The impact of ethnicity, family income, and parental education on children's health and use of health services.

PubMed Central

Flores, G; Bauchner, H; Feinstein, A R; Nguyen, U S

1999-01-01

OBJECTIVES: This study characterized ethnic disparities for children in demographics, health status, and use of services; explored whether ethnic subgroups (Puerto Rican, Cuban, and Mexican) have additional distinctive differences; and determined whether disparities are explained by differences in family income and parental education. METHODS: Bivariate and multivariate analyses of data on 99,268 children from the 1989-91 National Health Interview Surveys were conducted. RESULTS: Native American, Black, and Hispanic children are poorest (35%, 41% below poverty level vs 10% of Whites), least healthy (66%-74% in excellent or very good health vs 85% of Whites), and have the least well educated parents. Compared with Whites, non-White children average fewer doctor visits and are more likely to have excessive intervals between visits. Hispanic subgroup differences in demographics, health, and use of services equal or surpass differences among major ethnic groups. In multivariate analyses, almost all ethnic group disparities persisted after adjustment for family income, parental education, and other relevant covariates. CONCLUSIONS: Major ethnic groups and subgroups of children differ strikingly in demographics, health, and use of services; subgroup differences are easily overlooked; and most disparities persist even after adjustment for family income and parental education. PMID:10394317

Application of Rapid Visco Analyser (RVA) viscograms and chemometrics for maize hardness characterisation.

PubMed

Guelpa, Anina; Bevilacqua, Marta; Marini, Federico; O'Kennedy, Kim; Geladi, Paul; Manley, Marena

2015-04-15

It has been established in this study that the Rapid Visco Analyser (RVA) can describe maize hardness, irrespective of the RVA profile, when used in association with appropriate multivariate data analysis techniques. Therefore, the RVA can complement or replace current and/or conventional methods as a hardness descriptor. Hardness modelling based on RVA viscograms was carried out using seven conventional hardness methods (hectoliter mass (HLM), hundred kernel mass (HKM), particle size index (PSI), percentage vitreous endosperm (%VE), protein content, percentage chop (%chop) and near infrared (NIR) spectroscopy) as references and three different RVA profiles (hard, soft and standard) as predictors. An approach using locally weighted partial least squares (LW-PLS) was followed to build the regression models. The resulted prediction errors (root mean square error of cross-validation (RMSECV) and root mean square error of prediction (RMSEP)) for the quantification of hardness values were always lower or in the same order of the laboratory error of the reference method. Copyright © 2014 Elsevier Ltd. All rights reserved.

Sustained modelling ability of artificial neural networks in the analysis of two pharmaceuticals (dextropropoxyphene and dipyrone) present in unequal concentrations.

PubMed

Cámara, María S; Ferroni, Félix M; De Zan, Mercedes; Goicoechea, Héctor C

2003-07-01

An improvement is presented on the simultaneous determination of two active ingredients present in unequal concentrations in injections. The analysis was carried out with spectrophotometric data and non-linear multivariate calibration methods, in particular artificial neural networks (ANNs). The presence of non-linearities caused by the major analyte concentrations which deviate from Beer's law was confirmed by plotting actual vs. predicted concentrations, and observing curvatures in the residuals for the estimated concentrations with linear methods. Mixtures of dextropropoxyphene and dipyrone have been analysed by using linear and non-linear partial least-squares (PLS and NPLSs) and ANNs. Notwithstanding the high degree of spectral overlap and the occurrence of non-linearities, rapid and simultaneous analysis has been achieved, with reasonably good accuracy and precision. A commercial sample was analysed by using the present methodology, and the obtained results show reasonably good agreement with those obtained by using high-performance liquid chromatography (HPLC) and a UV-spectrophotometric comparative methods.

Parasites as valuable stock markers for fisheries in Australasia, East Asia and the Pacific Islands.

PubMed

Lester, R J G; Moore, B R

2015-01-01

Over 30 studies in Australasia, East Asia and the Pacific Islands region have collected and analysed parasite data to determine the ranges of individual fish, many leading to conclusions about stock delineation. Parasites used as biological tags have included both those known to have long residence times in the fish and those thought to be relatively transient. In many cases the parasitological conclusions have been supported by other methods especially analysis of the chemical constituents of otoliths, and to a lesser extent, genetic data. In analysing parasite data, authors have applied multiple different statistical methodologies, including summary statistics, and univariate and multivariate approaches. Recently, a growing number of researchers have found non-parametric methods, such as analysis of similarities and cluster analysis, to be valuable. Future studies into the residence times, life cycles and geographical distributions of parasites together with more robust analytical methods will yield much important information to clarify stock structures in the area.

A multivariate model and statistical method for validating tree grade lumber yield equations

Treesearch

Donald W. Seegrist

1975-01-01

Lumber yields within lumber grades can be described by a multivariate linear model. A method for validating lumber yield prediction equations when there are several tree grades is presented. The method is based on multivariate simultaneous test procedures.

Decoding of visual activity patterns from fMRI responses using multivariate pattern analyses and convolutional neural network.

PubMed

Zafar, Raheel; Kamel, Nidal; Naufal, Mohamad; Malik, Aamir Saeed; Dass, Sarat C; Ahmad, Rana Fayyaz; Abdullah, Jafri M; Reza, Faruque

2017-01-01

Decoding of human brain activity has always been a primary goal in neuroscience especially with functional magnetic resonance imaging (fMRI) data. In recent years, Convolutional neural network (CNN) has become a popular method for the extraction of features due to its higher accuracy, however it needs a lot of computation and training data. In this study, an algorithm is developed using Multivariate pattern analysis (MVPA) and modified CNN to decode the behavior of brain for different images with limited data set. Selection of significant features is an important part of fMRI data analysis, since it reduces the computational burden and improves the prediction performance; significant features are selected using t-test. MVPA uses machine learning algorithms to classify different brain states and helps in prediction during the task. General linear model (GLM) is used to find the unknown parameters of every individual voxel and the classification is done using multi-class support vector machine (SVM). MVPA-CNN based proposed algorithm is compared with region of interest (ROI) based method and MVPA based estimated values. The proposed method showed better overall accuracy (68.6%) compared to ROI (61.88%) and estimation values (64.17%).

On the interpretation of weight vectors of linear models in multivariate neuroimaging.

PubMed

Haufe, Stefan; Meinecke, Frank; Görgen, Kai; Dähne, Sven; Haynes, John-Dylan; Blankertz, Benjamin; Bießmann, Felix

2014-02-15

The increase in spatiotemporal resolution of neuroimaging devices is accompanied by a trend towards more powerful multivariate analysis methods. Often it is desired to interpret the outcome of these methods with respect to the cognitive processes under study. Here we discuss which methods allow for such interpretations, and provide guidelines for choosing an appropriate analysis for a given experimental goal: For a surgeon who needs to decide where to remove brain tissue it is most important to determine the origin of cognitive functions and associated neural processes. In contrast, when communicating with paralyzed or comatose patients via brain-computer interfaces, it is most important to accurately extract the neural processes specific to a certain mental state. These equally important but complementary objectives require different analysis methods. Determining the origin of neural processes in time or space from the parameters of a data-driven model requires what we call a forward model of the data; such a model explains how the measured data was generated from the neural sources. Examples are general linear models (GLMs). Methods for the extraction of neural information from data can be considered as backward models, as they attempt to reverse the data generating process. Examples are multivariate classifiers. Here we demonstrate that the parameters of forward models are neurophysiologically interpretable in the sense that significant nonzero weights are only observed at channels the activity of which is related to the brain process under study. In contrast, the interpretation of backward model parameters can lead to wrong conclusions regarding the spatial or temporal origin of the neural signals of interest, since significant nonzero weights may also be observed at channels the activity of which is statistically independent of the brain process under study. As a remedy for the linear case, we propose a procedure for transforming backward models into forward models. This procedure enables the neurophysiological interpretation of the parameters of linear backward models. We hope that this work raises awareness for an often encountered problem and provides a theoretical basis for conducting better interpretable multivariate neuroimaging analyses. Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.

Multivariate Methods for Meta-Analysis of Genetic Association Studies.

PubMed

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

2018-01-01

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

Multivariate stochastic analysis for Monthly hydrological time series at Cuyahoga River Basin

NASA Astrophysics Data System (ADS)

zhang, L.

2011-12-01

Copula has become a very powerful statistic and stochastic methodology in case of the multivariate analysis in Environmental and Water resources Engineering. In recent years, the popular one-parameter Archimedean copulas, e.g. Gumbel-Houggard copula, Cook-Johnson copula, Frank copula, the meta-elliptical copula, e.g. Gaussian Copula, Student-T copula, etc. have been applied in multivariate hydrological analyses, e.g. multivariate rainfall (rainfall intensity, duration and depth), flood (peak discharge, duration and volume), and drought analyses (drought length, mean and minimum SPI values, and drought mean areal extent). Copula has also been applied in the flood frequency analysis at the confluences of river systems by taking into account the dependence among upstream gauge stations rather than by using the hydrological routing technique. In most of the studies above, the annual time series have been considered as stationary signal which the time series have been assumed as independent identically distributed (i.i.d.) random variables. But in reality, hydrological time series, especially the daily and monthly hydrological time series, cannot be considered as i.i.d. random variables due to the periodicity existed in the data structure. Also, the stationary assumption is also under question due to the Climate Change and Land Use and Land Cover (LULC) change in the fast years. To this end, it is necessary to revaluate the classic approach for the study of hydrological time series by relaxing the stationary assumption by the use of nonstationary approach. Also as to the study of the dependence structure for the hydrological time series, the assumption of same type of univariate distribution also needs to be relaxed by adopting the copula theory. In this paper, the univariate monthly hydrological time series will be studied through the nonstationary time series analysis approach. The dependence structure of the multivariate monthly hydrological time series will be studied through the copula theory. As to the parameter estimation, the maximum likelihood estimation (MLE) will be applied. To illustrate the method, the univariate time series model and the dependence structure will be determined and tested using the monthly discharge time series of Cuyahoga River Basin.

Toward DSM-V: mapping the alcohol use disorder continuum in college students.

PubMed

Hagman, Brett T; Cohn, Amy M

2011-11-01

The present study examined the dimensionality of DSM-IV Alcohol Use Disorder (AUD) criteria using Item Response Theory (IRT) methods and tested the validity of the proposed DSM-V AUD guidelines in a sample of college students. Participants were 396 college students who reported any alcohol use in the past 90 days and were aged 18 years or older. We conducted factor analyses to determine whether a one- or two-factor model provided a better fit to the AUD criteria. IRT analyses estimated item severity and discrimination parameters for each criterion. Multivariate analyses examined differences among the DSM-V diagnostic cut-off (AUD vs. No AUD) and severity qualifiers (no diagnosis, moderate, severe) across several validating measures of alcohol use. A dominant single-factor model provided the best fit to the AUD criteria. IRT analyses indicated that abuse and dependence criteria were intermixed along the latent continuum. The "legal problems" criterion had the highest severity parameter and the tolerance criterion had the lowest severity parameter. The abuse criterion "social/interpersonal problems" and dependence criterion "activities to obtain alcohol" had the highest discrimination parameter estimates. Multivariate analysis indicated that the DSM-V cut-off point, and severity qualifier groups were distinguishable on several measures of alcohol consumption, drinking consequences, and drinking restraint. Findings suggest that the AUD criteria reflect a latent variable that represents a primary disorder and provide support for the proposed DSM-V AUD criteria in a sample of college students. Continued research in other high-risk samples of college students is needed. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Deconstructing multivariate decoding for the study of brain function.

PubMed

Hebart, Martin N; Baker, Chris I

2017-08-04

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

Phospholipid and Respiratory Quinone Analyses From Extreme Environments

NASA Astrophysics Data System (ADS)

Pfiffner, S. M.

2008-12-01

Extreme environments on Earth have been chosen as surrogate sites to test methods and strategies for the deployment of space craft in the search for extraterrestrial life. Surrogate sites for many of the NASA astrobiology institutes include the South African gold mines, Canadian subpermafrost, Atacama Desert, and acid rock drainage. Soils, sediments, rock cores, fracture waters, biofilms, and service and drill waters represent the types of samples collected from these sites. These samples were analyzed by gas chromatography mass spectrometry for phospholipid fatty acid methyl esters and by high performance liquid chromatography atmospheric pressure chemical ionization tandem mass spectrometry for respiratory quinones. Phospholipid analyses provided estimates of biomass, community composition, and compositional changes related to nutritional limitations or exposure to toxic conditions. Similar to phospholipid analyses, respiratory quinone analyses afforded identification of certain types of microorganisms in the community based on respiration and offered clues to in situ redox conditions. Depending on the number of samples analyzed, selected multivariate statistical methods were applied to relate membrane lipid results with site biogeochemical parameters. Successful detection of life signatures and refinement of methodologies at surrogate sites on Earth will be critical for the recognition of extraterrestrial life. At this time, membrane lipid analyses provide useful information not easily obtained by other molecular techniques.

Selection Indices and Multivariate Analysis Show Similar Results in the Evaluation of Growth and Carcass Traits in Beef Cattle

PubMed Central

Brito Lopes, Fernando; da Silva, Marcelo Corrêa; Magnabosco, Cláudio Ulhôa; Goncalves Narciso, Marcelo; Sainz, Roberto Daniel

2016-01-01

This research evaluated a multivariate approach as an alternative tool for the purpose of selection regarding expected progeny differences (EPDs). Data were fitted using a multi-trait model and consisted of growth traits (birth weight and weights at 120, 210, 365 and 450 days of age) and carcass traits (longissimus muscle area (LMA), back-fat thickness (BF), and rump fat thickness (RF)), registered over 21 years in extensive breeding systems of Polled Nellore cattle in Brazil. Multivariate analyses were performed using standardized (zero mean and unit variance) EPDs. The k mean method revealed that the best fit of data occurred using three clusters (k = 3) (P < 0.001). Estimates of genetic correlation among growth and carcass traits and the estimates of heritability were moderate to high, suggesting that a correlated response approach is suitable for practical decision making. Estimates of correlation between selection indices and the multivariate index (LD1) were moderate to high, ranging from 0.48 to 0.97. This reveals that both types of indices give similar results and that the multivariate approach is reliable for the purpose of selection. The alternative tool seems very handy when economic weights are not available or in cases where more rapid identification of the best animals is desired. Interestingly, multivariate analysis allowed forecasting information based on the relationships among breeding values (EPDs). Also, it enabled fine discrimination, rapid data summarization after genetic evaluation, and permitted accounting for maternal ability and the genetic direct potential of the animals. In addition, we recommend the use of longissimus muscle area and subcutaneous fat thickness as selection criteria, to allow estimation of breeding values before the first mating season in order to accelerate the response to individual selection. PMID:26789008

Selection Indices and Multivariate Analysis Show Similar Results in the Evaluation of Growth and Carcass Traits in Beef Cattle.

PubMed

Brito Lopes, Fernando; da Silva, Marcelo Corrêa; Magnabosco, Cláudio Ulhôa; Goncalves Narciso, Marcelo; Sainz, Roberto Daniel

2016-01-01

This research evaluated a multivariate approach as an alternative tool for the purpose of selection regarding expected progeny differences (EPDs). Data were fitted using a multi-trait model and consisted of growth traits (birth weight and weights at 120, 210, 365 and 450 days of age) and carcass traits (longissimus muscle area (LMA), back-fat thickness (BF), and rump fat thickness (RF)), registered over 21 years in extensive breeding systems of Polled Nellore cattle in Brazil. Multivariate analyses were performed using standardized (zero mean and unit variance) EPDs. The k mean method revealed that the best fit of data occurred using three clusters (k = 3) (P < 0.001). Estimates of genetic correlation among growth and carcass traits and the estimates of heritability were moderate to high, suggesting that a correlated response approach is suitable for practical decision making. Estimates of correlation between selection indices and the multivariate index (LD1) were moderate to high, ranging from 0.48 to 0.97. This reveals that both types of indices give similar results and that the multivariate approach is reliable for the purpose of selection. The alternative tool seems very handy when economic weights are not available or in cases where more rapid identification of the best animals is desired. Interestingly, multivariate analysis allowed forecasting information based on the relationships among breeding values (EPDs). Also, it enabled fine discrimination, rapid data summarization after genetic evaluation, and permitted accounting for maternal ability and the genetic direct potential of the animals. In addition, we recommend the use of longissimus muscle area and subcutaneous fat thickness as selection criteria, to allow estimation of breeding values before the first mating season in order to accelerate the response to individual selection.

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.

Reproductive Health Assessment of Female Elephants in North American Zoos and Association of Husbandry Practices with Reproductive Dysfunction in African Elephants (Loxodonta africana)

PubMed Central

Meehan, Cheryl L.; Hogan, Jennifer N.; Morfeld, Kari A.; Carlstead, Kathy

2016-01-01

As part of a multi-institutional study of zoo elephant welfare, we evaluated female elephants managed by zoos accredited by the Association of Zoos and Aquariums and applied epidemiological methods to determine what factors in the zoo environment are associated with reproductive problems, including ovarian acyclicity and hyperprolactinemia. Bi-weekly blood samples were collected from 95 African (Loxodonta africana) and 75 Asian (Elephas maximus) (8–55 years of age) elephants over a 12-month period for analysis of serum progestogens and prolactin. Females were categorized as normal cycling (regular 13- to 17-week cycles), irregular cycling (cycles longer or shorter than normal) or acyclic (baseline progestogens, <0.1 ng/ml throughout), and having Low/Normal (<14 or 18 ng/ml) or High (≥14 or 18 ng/ml) prolactin for Asian and African elephants, respectively. Rates of normal cycling, acyclicity and irregular cycling were 73.2, 22.5 and 4.2% for Asian, and 48.4, 37.9 and 13.7% for African elephants, respectively, all of which differed between species (P < 0.05). For African elephants, univariate assessment found that social isolation decreased and higher enrichment diversity increased the chance a female would cycle normally. The strongest multi-variable models included Age (positive) and Enrichment Diversity (negative) as important factors of acyclicity among African elephants. The Asian elephant data set was not robust enough to support multi-variable analyses of cyclicity status. Additionally, only 3% of Asian elephants were found to be hyperprolactinemic as compared to 28% of Africans, so predictive analyses of prolactin status were conducted on African elephants only. The strongest multi-variable model included Age (positive), Enrichment Diversity (negative), Alternate Feeding Methods (negative) and Social Group Contact (positive) as predictors of hyperprolactinemia. In summary, the incidence of ovarian cycle problems and hyperprolactinemia predominantly affects African elephants, and increases in social stability and feeding and enrichment diversity may have positive influences on hormone status. PMID:27416141

Reproductive Health Assessment of Female Elephants in North American Zoos and Association of Husbandry Practices with Reproductive Dysfunction in African Elephants (Loxodonta africana).

PubMed

Brown, Janine L; Paris, Stephen; Prado-Oviedo, Natalia A; Meehan, Cheryl L; Hogan, Jennifer N; Morfeld, Kari A; Carlstead, Kathy

2016-01-01

As part of a multi-institutional study of zoo elephant welfare, we evaluated female elephants managed by zoos accredited by the Association of Zoos and Aquariums and applied epidemiological methods to determine what factors in the zoo environment are associated with reproductive problems, including ovarian acyclicity and hyperprolactinemia. Bi-weekly blood samples were collected from 95 African (Loxodonta africana) and 75 Asian (Elephas maximus) (8-55 years of age) elephants over a 12-month period for analysis of serum progestogens and prolactin. Females were categorized as normal cycling (regular 13- to 17-week cycles), irregular cycling (cycles longer or shorter than normal) or acyclic (baseline progestogens, <0.1 ng/ml throughout), and having Low/Normal (<14 or 18 ng/ml) or High (≥14 or 18 ng/ml) prolactin for Asian and African elephants, respectively. Rates of normal cycling, acyclicity and irregular cycling were 73.2, 22.5 and 4.2% for Asian, and 48.4, 37.9 and 13.7% for African elephants, respectively, all of which differed between species (P < 0.05). For African elephants, univariate assessment found that social isolation decreased and higher enrichment diversity increased the chance a female would cycle normally. The strongest multi-variable models included Age (positive) and Enrichment Diversity (negative) as important factors of acyclicity among African elephants. The Asian elephant data set was not robust enough to support multi-variable analyses of cyclicity status. Additionally, only 3% of Asian elephants were found to be hyperprolactinemic as compared to 28% of Africans, so predictive analyses of prolactin status were conducted on African elephants only. The strongest multi-variable model included Age (positive), Enrichment Diversity (negative), Alternate Feeding Methods (negative) and Social Group Contact (positive) as predictors of hyperprolactinemia. In summary, the incidence of ovarian cycle problems and hyperprolactinemia predominantly affects African elephants, and increases in social stability and feeding and enrichment diversity may have positive influences on hormone status.

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.

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.

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

Detecting spatial regimes in ecosystems

USGS Publications Warehouse

Sundstrom, Shana M.; Eason, Tarsha; Nelson, R. John; Angeler, David G.; Barichievy, Chris; Garmestani, Ahjond S.; Graham, Nicholas A.J.; Granholm, Dean; Gunderson, Lance; Knutson, Melinda; Nash, Kirsty L.; Spanbauer, Trisha; Stow, Craig A.; Allen, Craig R.

2017-01-01

Research on early warning indicators has generally focused on assessing temporal transitions with limited application of these methods to detecting spatial regimes. Traditional spatial boundary detection procedures that result in ecoregion maps are typically based on ecological potential (i.e. potential vegetation), and often fail to account for ongoing changes due to stressors such as land use change and climate change and their effects on plant and animal communities. We use Fisher information, an information theory-based method, on both terrestrial and aquatic animal data (U.S. Breeding Bird Survey and marine zooplankton) to identify ecological boundaries, and compare our results to traditional early warning indicators, conventional ecoregion maps and multivariate analyses such as nMDS and cluster analysis. We successfully detected spatial regimes and transitions in both terrestrial and aquatic systems using Fisher information. Furthermore, Fisher information provided explicit spatial information about community change that is absent from other multivariate approaches. Our results suggest that defining spatial regimes based on animal communities may better reflect ecological reality than do traditional ecoregion maps, especially in our current era of rapid and unpredictable ecological change.

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.

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

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.

Written violence policies and risk of physical assault against Minnesota educators.

PubMed

Feda, Denise M; Gerberich, Susan G; Ryan, Andrew D; Nachreiner, Nancy M; McGovern, Patricia M

2010-12-01

Few research studies on school violence policies use quantitative methods to evaluate the impact of policies on workplace violence. This study analyzed nine different written violence policies and their impact on work-related physical assault in educational settings. Data were from the Minnesota Educators' Study. This large, nested case control study included cases (n=372) who reported physical assaults within the last year, and controls (n=1116) who did not. Multivariate logistic regression analyses, using directed acyclic graphs, estimated risk of assault. Results of the adjusted multivariate model suggested decreased risks of physical assault were associated with the presence of policies regarding how to report sexual harassment, verbal abuse, and threat (OR 0.53; 95 per cent CI: 0.30-0.95); assurance of confidential reporting of events (OR 0.67; 95 per cent CI: 0.44-1.04); and zero tolerance for violence (OR 0.70; 95 per cent CI: 0.47-1.04).

Geographic disparities in Healthy Eating Index scores (HEI-2005 and 2010) by residential property values: Findings from Seattle Obesity Study (SOS)

PubMed Central

Drewnowski, Adam; Aggarwal, Anju; Cook, Andrea; Stewart, Orion; Vernez Moudon, Anne

2016-01-01

Background Higher socioeconomic status (SES) has been linked with higher-quality diets. New GIS methods allow for geographic mapping of diet quality at a very granular level. Objective To examine the geographic distribution of two measures of diet quality: Healthy Eating Index (HEI 2005 and HEI 2010) in relation to residential property values in Seattle-King County. Methods The Seattle Obesity Study (SOS) collected data from a population-based sample of King County adults in 2008–09. Socio-demographic data were obtained by 20-min telephone survey. Dietary data were obtained from food frequency questionnaires (FFQs). Home addresses were geocoded to the tax parcel and residential property values were obtained from the King County tax assessor. Multivariable regression analyses using 1,116 adults tested associations between SES variables and diet quality measured (HEI scores). Results Residential property values, education, and incomes were associated with higher HEI scores in bivariate analyses. Property values were not collinear with either education or income. In adjusted multivariable models, education and residential property were better associated with HEI, compared to than income. Mapping of HEI-2005 and HEI-2010 at the census block level illustrated the geographic distribution of diet quality across Seattle-King County. Conclusion The use of residential property values, an objective measure of SES, allowed for the first visual exploration of diet quality at high spatial resolution: the census block level. PMID:26657348

An integrated phenomic approach to multivariate allelic association

PubMed Central

Medland, Sarah Elizabeth; Neale, Michael Churton

2010-01-01

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

Acoustic neuroma: potential risk factors and audiometric surveillance in the aluminium industry

PubMed Central

Taiwo, Oyebode; Galusha, Deron; Tessier-Sherman, Baylah; Kirsche, Sharon; Cantley, Linda; Slade, Martin D; Cullen, Mark R; Donoghue, A Michael

2014-01-01

Objectives To look for an association between acoustic neuroma (AN) and participation in a hearing conservation programme (HCP) and also for an association between AN and possible occupational risk factors in the aluminium industry. Methods We conducted a case–control analysis of a population of US aluminium production workers in 8 smelters and 43 other plants. Using insurance claims data, 97 cases of AN were identified between 1996 and 2009. Each was matched with four controls. Covariates included participation in a HCP, working in an aluminium smelter, working in an electrical job and hearing loss. Results In the bivariate analyses, covariates associated with AN were participation in the HCP (OR=1.72; 95% CI 1.09 to 2.69) and smelter work (OR=1.88; 95% CI 1.06 to 3.36). Electrical work was not significant (OR=1.60; 95% CI 0.65 to 3.94). Owing to high participation in the HCP in smelters, multivariate subanalyses were required. In the multivariate analyses, participation in the HCP was the only statistically significant risk factor for AN. In the multivariate analysis restricted to employees not working in a smelter, the OR was 1.81 (95% CI 1.04 to 3.17). Hearing loss, an indirect measure of in-ear noise dose, was not predictive of AN. Conclusions Our results suggest the incidental detection of previously undiagnosed tumours in workers who participated in the company-sponsored HCP. The increased medical surveillance among this population of workers most likely introduced detection bias, leading to the identification of AN cases that would have otherwise remained undetected. PMID:25015928

Assessment of water quality parameters using multivariate analysis for Klang River basin, Malaysia.

PubMed

Mohamed, Ibrahim; Othman, Faridah; Ibrahim, Adriana I N; Alaa-Eldin, M E; Yunus, Rossita M

2015-01-01

This case study uses several univariate and multivariate statistical techniques to evaluate and interpret a water quality data set obtained from the Klang River basin located within the state of Selangor and the Federal Territory of Kuala Lumpur, Malaysia. The river drains an area of 1,288 km(2), from the steep mountain rainforests of the main Central Range along Peninsular Malaysia to the river mouth in Port Klang, into the Straits of Malacca. Water quality was monitored at 20 stations, nine of which are situated along the main river and 11 along six tributaries. Data was collected from 1997 to 2007 for seven parameters used to evaluate the status of the water quality, namely dissolved oxygen, biochemical oxygen demand, chemical oxygen demand, suspended solids, ammoniacal nitrogen, pH, and temperature. The data were first investigated using descriptive statistical tools, followed by two practical multivariate analyses that reduced the data dimensions for better interpretation. The analyses employed were factor analysis and principal component analysis, which explain 60 and 81.6% of the total variation in the data, respectively. We found that the resulting latent variables from the factor analysis are interpretable and beneficial for describing the water quality in the Klang River. This study presents the usefulness of several statistical methods in evaluating and interpreting water quality data for the purpose of monitoring the effectiveness of water resource management. The results should provide more straightforward data interpretation as well as valuable insight for managers to conceive optimum action plans for controlling pollution in river water.

[Relationship between highly sensitive cardiac troponin T and sepsis and outcome in critically ill patients].

PubMed

Wang, T T; Jiang, L

2017-10-01

Objective: To investigate the prognostic value of highly sensitive cardiac Troponin T (hs-cTn T) for sepsis in critically ill patients. Methods: Patients estimated to stay in the ICU of Fuxing Hospital for more than 24h were enrolled at from March 2014 to December 2014. Serum hs-cTn T was tested within two hours. Univariate and multivariate linear regression analyses were used to determine the association of variables with the hs-cTn T. Multivariable logistic regression analysis was used to evaluate the risk factors of 28-day mortality. Results: A total of 125 patients were finally enrolled including 68 patients with sepsis and 57 without. The levels of hs-cTn T in sepsis and non-sepsis groups were significantly different[52.0(32.5, 87.5) ng/L vs 14.0(6.5, 29.0) ng/L respectively, P <0.001]. In sepsis group, hs-cTn T among common sepsis, severe sepsis and septic shock were similar. Hs-cTn T was significantly higher in non-survivors than survivors [27(13, 52)ng/L vs 44.5(28.8, 83.5)ng/L, P <0.001]. Age, sepsis, serum creatinine were independent risk factors affecting hs-cTn T by multivariate linear regression analyses. But hs-cTn T was not a risk factor for death. Conclusion: Patients with sepsis had higher serum hs-cTn T than those without sepsis. but it was not found to be associated with the severity of sepsis.

A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula.

PubMed

Ince, Robin A A; Giordano, Bruno L; Kayser, Christoph; Rousselet, Guillaume A; Gross, Joachim; Schyns, Philippe G

2017-03-01

We begin by reviewing the statistical framework of information theory as applicable to neuroimaging data analysis. A major factor hindering wider adoption of this framework in neuroimaging is the difficulty of estimating information theoretic quantities in practice. We present a novel estimation technique that combines the statistical theory of copulas with the closed form solution for the entropy of Gaussian variables. This results in a general, computationally efficient, flexible, and robust multivariate statistical framework that provides effect sizes on a common meaningful scale, allows for unified treatment of discrete, continuous, unidimensional and multidimensional variables, and enables direct comparisons of representations from behavioral and brain responses across any recording modality. We validate the use of this estimate as a statistical test within a neuroimaging context, considering both discrete stimulus classes and continuous stimulus features. We also present examples of analyses facilitated by these developments, including application of multivariate analyses to MEG planar magnetic field gradients, and pairwise temporal interactions in evoked EEG responses. We show the benefit of considering the instantaneous temporal derivative together with the raw values of M/EEG signals as a multivariate response, how we can separately quantify modulations of amplitude and direction for vector quantities, and how we can measure the emergence of novel information over time in evoked responses. Open-source Matlab and Python code implementing the new methods accompanies this article. Hum Brain Mapp 38:1541-1573, 2017. © 2016 Wiley Periodicals, Inc. 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

Origin Discrimination of Osmanthus fragrans var. thunbergii Flowers using GC-MS and UPLC-PDA Combined with Multivariable Analysis Methods.

PubMed

Zhou, Fei; Zhao, Yajing; Peng, Jiyu; Jiang, Yirong; Li, Maiquan; Jiang, Yuan; Lu, Baiyi

2017-07-01

Osmanthus fragrans flowers are used as folk medicine and additives for teas, beverages and foods. The metabolites of O. fragrans flowers from different geographical origins were inconsistent in some extent. Chromatography and mass spectrometry combined with multivariable analysis methods provides an approach for discriminating the origin of O. fragrans flowers. To discriminate the Osmanthus fragrans var. thunbergii flowers from different origins with the identified metabolites. GC-MS and UPLC-PDA were conducted to analyse the metabolites in O. fragrans var. thunbergii flowers (in total 150 samples). Principal component analysis (PCA), soft independent modelling of class analogy analysis (SIMCA) and random forest (RF) analysis were applied to group the GC-MS and UPLC-PDA data. GC-MS identified 32 compounds common to all samples while UPLC-PDA/QTOF-MS identified 16 common compounds. PCA of the UPLC-PDA data generated a better clustering than PCA of the GC-MS data. Ten metabolites (six from GC-MS and four from UPLC-PDA) were selected as effective compounds for discrimination by PCA loadings. SIMCA and RF analysis were used to build classification models, and the RF model, based on the four effective compounds (caffeic acid derivative, acteoside, ligustroside and compound 15), yielded better results with the classification rate of 100% in the calibration set and 97.8% in the prediction set. GC-MS and UPLC-PDA combined with multivariable analysis methods can discriminate the origin of Osmanthus fragrans var. thunbergii flowers. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Mouse Allergen, Lung Function, and Atopy in Puerto Rican Children

PubMed Central

Forno, Erick; Cloutier, Michelle M.; Datta, Soma; Paul, Kathryn; Sylvia, Jody; Calvert, Deanna; Thornton-Thompson, Sherell; Wakefield, Dorothy B.; Brehm, John; Hamilton, Robert G.; Alvarez, María; Colón-Semidey, Angel; Acosta-Pérez, Edna; Canino, Glorisa; Celedón, Juan C.

2012-01-01

Objective To examine the relation between mouse allergen exposure and asthma in Puerto Rican children. Methods Mus m 1, Der p 1, Bla g 2, and Fel d 1 allergens were measured in dust samples from homes of Puerto Rican children with (cases) and without (controls) asthma in Hartford, CT (n = 449) and San Juan (SJ), Puerto Rico (n = 678). Linear or logistic regression was used for the multivariate analysis of mouse allergen (Mus m 1) and lung function (FEV1 and FEV1/FVC) and allergy (total IgE and skin test reactivity (STR) to ≥1 allergen) measures. Results Homes in SJ had lower mouse allergen levels than those in Hartford. In multivariate analyses, mouse allergen was associated with higher FEV1 in cases in Hartford (+70.6 ml, 95% confidence interval (CI) = 8.6–132.7 ml, P = 0.03) and SJ (+45.1 ml, 95% CI =  −0.5 to 90.6 ml, P = 0.05). In multivariate analyses of controls, mouse allergen was inversely associated with STR to ≥1 allergen in non-sensitized children (odds ratio [OR] for each log-unit increment in Mus m 1 = 0.7, 95% CI = 0.5–0.9, P<0.01). In a multivariate analysis including all children at both study sites, each log-increment in mouse allergen was positively associated with FEV1 (+28.3 ml, 95% CI = 1.4–55.2 ml, P = 0.04) and inversely associated with STR to ≥1 allergen (OR for each log-unit increment in Mus m 1 = 0.8, 95% CI = 0.6–0.9, P<0.01). Conclusions Mouse allergen is associated with a higher FEV1 and lower odds of STR to ≥1 allergen in Puerto Rican children. This may be explained by the allergen itself or correlated microbial exposures. PMID:22815744

Increasing Awareness about HIV Prevention among Young People Who Initiated Injection Drug Use in a Canadian Setting, 1988–2014

PubMed Central

Bahji, Anees; Wood, Evan; Ahamad, Keith; Dong, Huiru; DeBeck, Kora; Milloy, M-J; Kerr, Thomas; Hayashi, Kanna

2015-01-01

Background Globally, harm reduction interventions, including needle and syringe programs (NSPs), have been shown to reduce HIV risks among people who inject drugs (PWID). However, little is known about the impact of these efforts on the circumstances of first injection. Therefore, we sought to identify changes in the awareness about HIV prevention and syringe borrowing at the time of first injection drug use in Vancouver, Canada, during a period of NSP expansion. Methods Data were drawn from prospective cohorts of PWID in Vancouver, who initiated injecting between 1988 and 2014. Multivariable regression was used to assess changes in the awareness about HIV and NSPs and syringe borrowing behaviour at first injection against calendar year of first injection. Results Among 1,044 participants (36.9% female), at the time of first injection 73.9% reported having known syringe sharing was an HIV risk, 54.1% reported having heard of NSPs, and 7.8% reported having borrowed a syringe used by others. In multivariable analyses, calendar year of first injection was independently and positively associated with awareness about HIV (adjusted prevalence ratio [APR]: 1.09; 95% confidence interval [CI]: 1.06, 1.11) and awareness about NSPs (APR: 1.18; 95% CI: 1.13, 1.24). While calendar year of first injection was significantly and negatively associated with syringe borrowing at first injection in bivariable analyses, the association did not remain significant in multivariable analyses (adjusted odds ratio: 0.90; 95% CI: 0.72, 1.14). Conclusions We found that awareness about HIV and NSPs at first injection have increased over time amongst PWID in this setting. However, declining trends in syringe borrowing at first injection were not determined after adjustment for socio-demographic characteristics. This suggests that HIV prevention efforts may have contributed to increased awareness about HIV prevention, but further research is needed to identify sub-populations at heightened risk of HIV at first injection. PMID:26514080

Fat metaplasia and backfill are key intermediaries in the development of sacroiliac joint ankylosis in patients with ankylosing spondylitis.

PubMed

Maksymowych, Walter P; Wichuk, Stephanie; Chiowchanwisawakit, Praveena; Lambert, Robert G; Pedersen, Susanne J

2014-11-01

Fat metaplasia in bone marrow on T1-weighted magnetic resonance imaging (MRI) scans may develop after resolution of inflammation in patients with ankylosing spondylitis (AS) and may predict new bone formation in the spine. Similar tissue, termed backfill, may also fill areas of excavated bone in the sacroiliac (SI) joints and may reflect resolution of inflammation and tissue repair at sites of erosions. The purpose of this study was to test our hypothesis that SI joint ankylosis develops following repair of erosions and that tissue characterized by fat metaplasia is a key intermediary step in this pathway. We used the Spondyloarthritis Research Consortium of Canada (SPARCC) SI structural lesion score (SSS) method to assess fat metaplasia, erosions, backfill, and ankylosis on MRIs of the SI joints in 147 patients with AS monitored for 2 years. Univariate and multivariate regression analyses focused first on identifying significant MRI predictors of new backfill and fat metaplasia. We then assessed the role of backfill and fat metaplasia in the development of new ankylosis. All analyses were adjusted for demographic features, treatment, and baseline and 2-year change in SSS values for parameters of inflammation and MRI structural lesions. Resolution of inflammation and reduction of erosions were each independently associated with the development of new backfill and fat metaplasia at 2 years on multivariate analyses. Multivariate regression analysis that included demographic features, baseline and 2-year change in parameters of inflammation and MRI structural lesion showed that reduction in erosions (P = 0.0005) and increase in fat metaplasia (P = 0.002) at 2 years was each independently associated with the development of new ankylosis. Our data support a disease model whereby ankylosis develops following repair of erosions, and fat metaplasia and backfill are key intermediary steps in this pathway. Copyright © 2014 by the American College of Rheumatology.

TU-FG-201-05: Varian MPC as a Statistical Process Control Tool

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

Carver, A; Rowbottom, C

Purpose: Quality assurance in radiotherapy requires the measurement of various machine parameters to ensure they remain within permitted values over time. In Truebeam release 2.0 the Machine Performance Check (MPC) was released allowing beam output and machine axis movements to be assessed in a single test. We aim to evaluate the Varian Machine Performance Check (MPC) as a tool for Statistical Process Control (SPC). Methods: Varian’s MPC tool was used on three Truebeam and one EDGE linac for a period of approximately one year. MPC was commissioned against independent systems. After this period the data were reviewed to determine whethermore » or not the MPC was useful as a process control tool. Analyses on individual tests were analysed using Shewhart control plots, using Matlab for analysis. Principal component analysis was used to determine if a multivariate model was of any benefit in analysing the data. Results: Control charts were found to be useful to detect beam output changes, worn T-nuts and jaw calibration issues. Upper and lower control limits were defined at the 95% level. Multivariate SPC was performed using Principal Component Analysis. We found little evidence of clustering beyond that which might be naively expected such as beam uniformity and beam output. Whilst this makes multivariate analysis of little use it suggests that each test is giving independent information. Conclusion: The variety of independent parameters tested in MPC makes it a sensitive tool for routine machine QA. We have determined that using control charts in our QA programme would rapidly detect changes in machine performance. The use of control charts allows large quantities of tests to be performed on all linacs without visual inspection of all results. The use of control limits alerts users when data are inconsistent with previous measurements before they become out of specification. A. Carver has received a speaker’s honorarium from Varian.« less

Prediabetes Is Associated with an Increased Risk of Testosterone Deficiency, Independent of Obesity and Metabolic Syndrome

PubMed Central

Ho, Chen-Hsun; Yu, Hong-Jeng; Wang, Chih-Yuan; Jaw, Fu-Shan; Hsieh, Ju-Ton; Liao, Wan-Chung; Pu, Yeong-Shiau; Liu, Shih-Ping

2013-01-01

Objective The association between type 2 diabetes and low testosterone has been well recognized. However, testosterone levels in men with prediabetes have been rarely reported. We aimed to investigate whether prediabetes was associated with an increased risk of testosterone deficiency. Methods This study included 1,306 men whose sex hormones was measured during a medical examination. Serum total testosterone and sex hormone-binding globulin were measured; free and bioavailable testosterone concentrations were calculated by Vermeulen’s formula. Prediabetes was defined by impaired fasting glucose (IFG), impaired postprandial glucose (IPG), or glycated hemoglobin (HbA1c) 5.7%-6.4%. Logistic regression was performed to obtain the odds ratios (OR) for subnormal total testosterone (<300 ng/dL) or free testosterone (<6 ng/dL) in prediabetic and diabetic men compared with normoglycemic individuals, while adjusting for age, BMI, waist circumference, and metabolic syndrome (MetS). Results Normoglycemia, prediabetes, and diabetes were diagnosed in 577 (44.2%), 543 (41.6%), and 186 (14.2%) men, respectively. Prediabetes was associated with an increased risk of subnormal total testosterone compared to normoglycemic individuals (age-adjusted OR=1.87; 95%CI=1.38-2.54). The risk remained significant in all multivariate analyses. After adjusting for MetS, the OR in prediabetic men equals that of diabetic patients (1.49 versus 1.50). IFG, IPG, and HbA1c 5.7%-6.4% were all associated with an increased risk of testosterone deficiency, with different levels of significance in multivariate analyses. However, neither prediabetes nor diabetes was associated with subnormal free testosterone in multivariate analyses. Conclusions Prediabetes is associated with an increased risk of testosterone deficiency, independent of obesity and MetS. After adjusting for MetS, the risk equals that of diabetes. Our data suggest that testosterone should be measured routinely in men with prediabetes. PMID:24069277

Incorporating Single-nucleotide Polymorphisms Into the Lyman Model to Improve Prediction of Radiation Pneumonitis

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

Tucker, Susan L., E-mail: sltucker@mdanderson.org; Li Minghuan; Xu Ting

2013-01-01

Purpose: To determine whether single-nucleotide polymorphisms (SNPs) in genes associated with DNA repair, cell cycle, transforming growth factor-{beta}, tumor necrosis factor and receptor, folic acid metabolism, and angiogenesis can significantly improve the fit of the Lyman-Kutcher-Burman (LKB) normal-tissue complication probability (NTCP) model of radiation pneumonitis (RP) risk among patients with non-small cell lung cancer (NSCLC). Methods and Materials: Sixteen SNPs from 10 different genes (XRCC1, XRCC3, APEX1, MDM2, TGF{beta}, TNF{alpha}, TNFR, MTHFR, MTRR, and VEGF) were genotyped in 141 NSCLC patients treated with definitive radiation therapy, with or without chemotherapy. The LKB model was used to estimate the risk ofmore » severe (grade {>=}3) RP as a function of mean lung dose (MLD), with SNPs and patient smoking status incorporated into the model as dose-modifying factors. Multivariate analyses were performed by adding significant factors to the MLD model in a forward stepwise procedure, with significance assessed using the likelihood-ratio test. Bootstrap analyses were used to assess the reproducibility of results under variations in the data. Results: Five SNPs were selected for inclusion in the multivariate NTCP model based on MLD alone. SNPs associated with an increased risk of severe RP were in genes for TGF{beta}, VEGF, TNF{alpha}, XRCC1 and APEX1. With smoking status included in the multivariate model, the SNPs significantly associated with increased risk of RP were in genes for TGF{beta}, VEGF, and XRCC3. Bootstrap analyses selected a median of 4 SNPs per model fit, with the 6 genes listed above selected most often. Conclusions: This study provides evidence that SNPs can significantly improve the predictive ability of the Lyman MLD model. With a small number of SNPs, it was possible to distinguish cohorts with >50% risk vs <10% risk of RP when they were exposed to high MLDs.« less

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®

Multivariate approach in popcorn genotypes using the Ward-MLM strategy: morpho-agronomic analysis and incidence of Fusarium spp.

PubMed

Kurosawa, R N F; do Amaral Junior, A T; Silva, F H L; Dos Santos, A; Vivas, M; Kamphorst, S H; Pena, G F

2017-02-08

The multivariate analyses are useful tools to estimate the genetic variability between accessions. In the breeding programs, the Ward-Modified Location Model (MLM) multivariate method has been a powerful strategy to quantify variability using quantitative and qualitative variables simultaneously. The present study was proposed in view of the dearth of information about popcorn breeding programs under a multivariate approach using the Ward-MLM methodology. The objective of this study was thus to estimate the genetic diversity among 37 genotypes of popcorn aiming to identify divergent groups associated with morpho-agronomic traits and traits related to resistance to Fusarium spp. To this end, 7 qualitative and 17 quantitative variables were analyzed. The experiment was conducted in 2014, at Universidade Estadual do Norte Fluminense, located in Campos dos Goytacazes, RJ, Brazil. The Ward-MLM strategy allowed the identification of four groups as follows: Group I with 10 genotypes, Group II with 11 genotypes, Group III with 9 genotypes, and Group IV with 7 genotypes. Group IV was distant in relation to the other groups, while groups I, II, and III were near. The crosses between genotypes from the other groups with those of group IV allow an exploitation of heterosis. The Ward-MLM strategy provided an appropriate grouping of genotypes; ear weight, ear diameter, and grain yield were the traits that most contributed to the analysis of genetic diversity.

Usefulness of amino acid composition to discriminate between honeydew and floral honeys. Application to honeys from a small geographic area.

PubMed

Iglesias, María Teresa; De Lorenzo, Cristina; Del Carmen Polo, María; Martín-Alvarez, Pedro Jésus; Pueyo, Encarnacíon

2004-01-14

With the aim of finding methods that could constitute a solid alternative to melissopalynological and physicochemical analyses to determine the botanical origin (floral or honeydew) of honeys, the free amino acid content of 46 honey samples has been determined. The honeys were collected in a small geographic area of approximately 2000 km(2) in central Spain. Twenty-seven honey samples were classified as floral and 19 as honeydew according to their palynological and physicochemical analyses. The resulting data have been subjected to different multivariant analysis techniques. One hundred percent of honey samples have been correctly classified into either the floral or the honeydew groups, according to their content in glutamic acid and tryptophan. It is concluded that free amino acids are good indicators of the botanical origin of honeys, saving time compared with more tedious analyses.

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...

Costing Hospital Surgery Services: The Method Matters

PubMed Central

Mercier, Gregoire; Naro, Gerald

2014-01-01

Background Accurate hospital costs are required for policy-makers, hospital managers and clinicians to improve efficiency and transparency. However, different methods are used to allocate direct costs, and their agreement is poorly understood. The aim of this study was to assess the agreement between bottom-up and top-down unit costs of a large sample of surgical operations in a French tertiary centre. Methods Two thousand one hundred and thirty consecutive procedures performed between January and October 2010 were analysed. Top-down costs were based on pre-determined weights, while bottom-up costs were calculated through an activity-based costing (ABC) model. The agreement was assessed using correlation coefficients and the Bland and Altman method. Variables associated with the difference between methods were identified with bivariate and multivariate linear regressions. Results The correlation coefficient amounted to 0.73 (95%CI: 0.72; 0.76). The overall agreement between methods was poor. In a multivariate analysis, the cost difference was independently associated with age (Beta = −2.4; p = 0.02), ASA score (Beta = 76.3; p<0.001), RCI (Beta = 5.5; p<0.001), staffing level (Beta = 437.0; p<0.001) and intervention duration (Beta = −10.5; p<0.001). Conclusions The ability of the current method to provide relevant information to managers, clinicians and payers is questionable. As in other European countries, a shift towards time-driven activity-based costing should be advocated. PMID:24817167

Multivariate Meta-Analysis of Genetic Association Studies: A Simulation Study

PubMed Central

Neupane, Binod; Beyene, Joseph

2015-01-01

In a meta-analysis with multiple end points of interests that are correlated between or within studies, multivariate approach to meta-analysis has a potential to produce more precise estimates of effects by exploiting the correlation structure between end points. However, under random-effects assumption the multivariate estimation is more complex (as it involves estimation of more parameters simultaneously) than univariate estimation, and sometimes can produce unrealistic parameter estimates. Usefulness of multivariate approach to meta-analysis of the effects of a genetic variant on two or more correlated traits is not well understood in the area of genetic association studies. In such studies, genetic variants are expected to roughly maintain Hardy-Weinberg equilibrium within studies, and also their effects on complex traits are generally very small to modest and could be heterogeneous across studies for genuine reasons. We carried out extensive simulation to explore the comparative performance of multivariate approach with most commonly used univariate inverse-variance weighted approach under random-effects assumption in various realistic meta-analytic scenarios of genetic association studies of correlated end points. We evaluated the performance with respect to relative mean bias percentage, and root mean square error (RMSE) of the estimate and coverage probability of corresponding 95% confidence interval of the effect for each end point. Our simulation results suggest that multivariate approach performs similarly or better than univariate method when correlations between end points within or between studies are at least moderate and between-study variation is similar or larger than average within-study variation for meta-analyses of 10 or more genetic studies. Multivariate approach produces estimates with smaller bias and RMSE especially for the end point that has randomly or informatively missing summary data in some individual studies, when the missing data in the endpoint are imputed with null effects and quite large variance. PMID:26196398

Multivariate Meta-Analysis of Genetic Association Studies: A Simulation Study.

PubMed

Neupane, Binod; Beyene, Joseph

2015-01-01

In a meta-analysis with multiple end points of interests that are correlated between or within studies, multivariate approach to meta-analysis has a potential to produce more precise estimates of effects by exploiting the correlation structure between end points. However, under random-effects assumption the multivariate estimation is more complex (as it involves estimation of more parameters simultaneously) than univariate estimation, and sometimes can produce unrealistic parameter estimates. Usefulness of multivariate approach to meta-analysis of the effects of a genetic variant on two or more correlated traits is not well understood in the area of genetic association studies. In such studies, genetic variants are expected to roughly maintain Hardy-Weinberg equilibrium within studies, and also their effects on complex traits are generally very small to modest and could be heterogeneous across studies for genuine reasons. We carried out extensive simulation to explore the comparative performance of multivariate approach with most commonly used univariate inverse-variance weighted approach under random-effects assumption in various realistic meta-analytic scenarios of genetic association studies of correlated end points. We evaluated the performance with respect to relative mean bias percentage, and root mean square error (RMSE) of the estimate and coverage probability of corresponding 95% confidence interval of the effect for each end point. Our simulation results suggest that multivariate approach performs similarly or better than univariate method when correlations between end points within or between studies are at least moderate and between-study variation is similar or larger than average within-study variation for meta-analyses of 10 or more genetic studies. Multivariate approach produces estimates with smaller bias and RMSE especially for the end point that has randomly or informatively missing summary data in some individual studies, when the missing data in the endpoint are imputed with null effects and quite large variance.

Multivariate model of female black bear habitat use for a Geographic Information System

USGS Publications Warehouse

Clark, Joseph D.; Dunn, James E.; Smith, Kimberly G.

1993-01-01

Simple univariate statistical techniques may not adequately assess the multidimensional nature of habitats used by wildlife. Thus, we developed a multivariate method to model habitat-use potential using a set of female black bear (Ursus americanus) radio locations and habitat data consisting of forest cover type, elevation, slope, aspect, distance to roads, distance to streams, and forest cover type diversity score in the Ozark Mountains of Arkansas. The model is based on the Mahalanobis distance statistic coupled with Geographic Information System (GIS) technology. That statistic is a measure of dissimilarity and represents a standardized squared distance between a set of sample variates and an ideal based on the mean of variates associated with animal observations. Calculations were made with the GIS to produce a map containing Mahalanobis distance values within each cell on a 60- × 60-m grid. The model identified areas of high habitat use potential that could not otherwise be identified by independent perusal of any single map layer. This technique avoids many pitfalls that commonly affect typical multivariate analyses of habitat use and is a useful tool for habitat manipulation or mitigation to favor terrestrial vertebrates that use habitats on a landscape scale.

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

PubMed

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

2017-01-01

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

Socioeconomic Correlates of Contraceptive Use among the Ethnic Tribal Women of Bangladesh: Does Sex Preference Matter?

PubMed Central

Hassan, Che Hashim

2013-01-01

Objective To examine the relationship between socioeconomic factors affecting contraceptive use among tribal women of Bangladesh with focusing on son preference over daughter. Materials and methods The study used data gathered through a cross sectional survey on four tribal communities resided in the Rangamati Hill District of the Chittagong Hill Tracts, Bangladesh. A multistage random sampling procedure was applied to collect data from 865 currently married women of whom 806 women were currently married, non-pregnant and had at least one living child, which are the basis of this study. The information was recorded in a pre-structured questionnaire. Simple cross tabulation, chi-square tests and logistic regression analyses were performed to analyzing data. Results The contraceptive prevalence rate among the study tribal women was 73%. The multivariate analyses yielded quantitatively important and reliable estimates of likelihood of contraceptive use. Findings revealed that after controlling for other variables, the likelihood of contraceptive use was found not to be significant among women with at least one son than those who had only daughters, indicating no preference of son over daughter. Multivariate logistic regression analysis suggests that home visitations by family planning workers, tribal identity, place of residence, husband's education, and type of family, television ownership, electricity connection in the household and number of times married are important determinants of any contraceptive method use among the tribal women. Conclusion The contraceptive use rate among the disadvantaged tribal women was more than that of the national level. Door-step delivery services of modern methods should be reached and available targeting the poor and remote zones. PMID:24971107

Religion and HPV vaccine-related awareness, knowledge, and receipt among insured women aged 18-26 in Utah

PubMed Central

2017-01-01

Introduction We investigate the associations between religious practice and human papillomavirus (HPV) vaccine-related awareness, knowledge, and receipt among young women in Utah. Methods We surveyed 326 insured women aged 18–26 by mail. Fisher's Exact Tests and multivariable logistic regression models were used to evaluate the relations between religious practice and HPV vaccine-related outcomes. Data collection occurred January-December 2013; analyses were conducted June-September 2015. Results Multivariable analyses reveal that when controlling for age, educational attainment, and marital status, participants who practiced an organized religion were significantly less likely to have heard of HPV (aOR = 0.25, p = 0.0123), to have heard of the HPV vaccine (aOR = 0.41, p = 0.0368), to know how HPV is spread (aOR = 0.45, p = 0.0074), to have received a provider recommendation for the HPV vaccine (aOR = 0.36, p = 0.0332), and to have received at least one (aOR = 0.50, p = 0.0073) or all three (aOR = 0.47, p = 0.0026) doses of the HPV vaccine. Bivariate analyses produce parallel results. Conclusions Results indicate that religious young women in Utah are not only under-vaccinated, but are also under-informed about HPV and the HPV vaccine. These results suggest that suboptimal vaccine coverage among religious young women may present a serious health risk for the community. Strategies for educational interventions targeted to this population are discussed. PMID:28841681

Lifestyle Risk Factors Predict Disability and Death in Healthy Aging Adults

PubMed Central

Chakravarty, Eliza F.; Hubert, Helen B.; Krishnan, Eswar; Bruce, Bonnie B.; Lingala, Vijaya B.; Fries, James F.

2011-01-01

Background Associations between modifiable health risk factors during middle age with disability and mortality in later life are critical to maximizing longevity while preserving function. Positive health effects of maintaining normal weight, routine exercise, and non-smoking are known for the short and intermediate term. We studied the effects of these risk factors into advanced age. Methods A cohort of 2,327 college alumnae ≥60 years was followed annually (1986–2005) by questionnaires addressing health risk factors, history, and Health Assessment Questionnaire disability (HAQ-DI). Mortality data were ascertained from the National Death Index. Low, medium, and high risk groups were created based upon the number (0, 1, ≥2) of health risk factors (overweight, smoking, inactivity) at baseline. Disability and mortality for each group were estimated from unadjusted data and regression analyses. Multivariable survival analyses estimated time to disability or death. Results Medium and high-risk groups had higher disability than the low risk group throughout the study (p<0.001). Low-risk subjects had onset of moderate disability delayed 8.3 years compared with high-risk. Mortality rates were higher in the high risk group (384 versus 247 per 10,000 person-years). Multivariable survival analyses showed the number of risk factors to be associated with cumulative disability and increased mortality. Conclusions Seniors with fewer behavioral risk factors during middle age have lower disability and improved survival. These data document that the associations of lifestyle risk factors upon health continue into the ninth decade. PMID:22269623

Application of multivariate statistical analysis in the pollution and health risk of traffic-related heavy metals.

PubMed

Ebqa'ai, Mohammad; Ibrahim, Bashar

2017-12-01

This study aims to analyse the heavy metal pollutants in Jeddah, the second largest city in the Gulf Cooperation Council with a population exceeding 3.5 million, and many vehicles. Ninety-eight street dust samples were collected seasonally from the six major roads as well as the Jeddah Beach, and subsequently digested using modified Leeds Public Analyst method. The heavy metals (Fe, Zn, Mn, Cu, Cd, and Pb) were extracted from the ash using methyl isobutyl ketone as solvent extraction and eventually analysed by atomic absorption spectroscopy. Multivariate statistical techniques, principal component analysis (PCA), and hierarchical cluster analysis were applied to these data. Heavy metal concentrations were ranked according to the following descending order: Fe > Zn > Mn > Cu > Pb > Cd. In order to study the pollution and health risk from these heavy metals as well as estimating their effect on the environment, pollution indices, integrated pollution index, enrichment factor, daily dose average, hazard quotient, and hazard index were all analysed. The PCA showed high levels of Zn, Fe, and Cd in Al Kurnish road, while these elements were consistently detected on King Abdulaziz and Al Madina roads. The study indicates that high levels of Zn and Pb pollution were recorded for major roads in Jeddah. Six out of seven roads had high pollution indices. This study is the first step towards further investigations into current health problems in Jeddah, such as anaemia and asthma.

[The significance of lymph node status in papillary and follicular thyroid gland carcinoma for the nuclear medicine physician].

PubMed

Farahati, J; Mörtl, M; Reiners, C

2000-01-01

The impact of lymph node metastases on prognosis of differentiated thyroid cancer is discussed controversially. Therefore the data of 596 patients with papillary or follicular thyroid cancer are analysed retrospectively, which have been treated between 1980 and 1995 at the Clinic and Policlinic for Nuclear Medicine of the University of Würzburg. The influence of lymph node metastases on prognosis with respect to survival is analysed with the univariate Kaplan-Meier-method and with the multivariate discriminant analysis. In addition, the influence of the prognostic factor "lymph node involvement" on distant metastases is analysed by a stratified comparison and an univariate test. In papillary thyroid cancer, the 15 year-survival-rate for stage pN1 is significantly lower (p < 0.001) with 88.7% as compared to stage pN0 (99.4%). In patients with follicular thyroid cancer this difference is even more pronounced (64.7% versus 97.2%, p < 0.001). However, the multivariate discriminant analysis shows that the only prognostic factors are tumour stage and distant metastases, and--in papillary thyroid cancer--patient's age. So lymph node metastases are not an independent prognostic factor concerning survival. However, lymph node metastases have a prognostic unfavourable influence with respect to distant metastases especially in papillary thyroid cancer stage pT4 (distant metastases in patients with negative lymph nodes 0% and in patients with positive lymph nodes 35.3% [p < 0.001]).

Dosimetric Predictors of Hypothyroidism After Radical Intensity-modulated Radiation Therapy for Non-metastatic Nasopharyngeal Carcinoma.

PubMed

Lee, V; Chan, Sum-Yin; Choi, Cheuk-Wai; Kwong, D; Lam, Ka-On; Tong, Chi-Chung; Sze, Chun-Kin; Ng, S; Leung, To-Wai; Lee, A

2016-08-01

To investigate dosimetric predictors of hypothyroidism after radical intensity-modulated radiation therapy (IMRT) for non-metastatic nasopharyngeal carcinoma (NPC). Patients with non-metastatic NPC treated with radical IMRT from 2008 to 2013 were reviewed. Serum thyroid function tests before and after IMRT were regularly monitored. Univariable and multivariable analyses were carried out for predictors of biochemical and clinical hypothyroidism. In total, 149 patients were recruited. After a median follow-up duration of 3.1 years, 33 (22.1%) and 21 (14.1%) patients developed biochemical and clinical hypothyroidism, respectively. Eight (24.2%) patients who had biochemical hypothyroidism developed clinical hypothyroidism later. Univariable and multivariable analyses revealed that the volume of the thyroid (P=0.002, multivariable), VS60 (the absolute thyroid volume spared from 60 Gy or less) (P<0.001, multivariable) and VS45 (P<0.001, multivariable) of the thyroid were significant predictors of biochemical hypothyroidism. The freedom from biochemical hypothyroidism was longer for those whose VS60 ≥ 10 cm(3) (mean 90.9 versus 62.6 months; P<0.001) and VS45 ≥ 5 cm(3) (mean 91.9 versus 65.2 months; P=0.001). Similarly multivariable analyses revealed that VS60 (P=0.001) and VS45 (P=0.003) were significant predictors of clinical hypothyroidism. The freedom from clinical hypothyroidism was longer for those whose VS60 ≥ 10 cm(3) (91.5 versus 73.3 months; P=0.002) and VS45 ≥ 5 cm(3) (91.5 versus 75.9 months; P=0.007). VS60 and VS45 of the thyroid should be considered important dose constraints against hypothyroidism without compromising target coverage during IMRT optimisation for NPC. Copyright © 2016 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

Socioeconomic factors associated with contraceptive use and method choice in urban slums of Bangladesh.

PubMed

Kamal, S M Mostafa

2015-03-01

This article explores the socioeconomic factors affecting contraceptive use and method choice among women of urban slums using the nationally representative 2006 Bangladesh Urban Health Survey. Both bivariate and multivariate statistical analyses were applied to examine the relationship between a set of sociodemographic factors and the dependent variables. Overall, the contraceptive prevalence rate was 58.1%, of which 53.2% were modern methods. Women's age, access to TV, number of unions, nongovernmental organization membership, working status of women, number of living children, child mortality, and wealth index were important determinants of contraceptive use and method preference. Sex composition of surviving children and women's education were the most important determinants of contraceptive use and method choice. Programs should be strengthened to provide nonclinical modern methods free of cost among the slum dwellers. Doorstep delivery services of modern contraceptive methods may raise the contraceptive prevalence rate among the slum dwellers in Bangladesh. © 2011 APJPH.

Multivariate assessment of event-related potentials with the t-CWT method.

PubMed

Bostanov, Vladimir

2015-11-05

Event-related brain potentials (ERPs) are usually assessed with univariate statistical tests although they are essentially multivariate objects. Brain-computer interface applications are a notable exception to this practice, because they are based on multivariate classification of single-trial ERPs. Multivariate ERP assessment can be facilitated by feature extraction methods. One such method is t-CWT, a mathematical-statistical algorithm based on the continuous wavelet transform (CWT) and Student's t-test. This article begins with a geometric primer on some basic concepts of multivariate statistics as applied to ERP assessment in general and to the t-CWT method in particular. Further, it presents for the first time a detailed, step-by-step, formal mathematical description of the t-CWT algorithm. A new multivariate outlier rejection procedure based on principal component analysis in the frequency domain is presented as an important pre-processing step. The MATLAB and GNU Octave implementation of t-CWT is also made publicly available for the first time as free and open source code. The method is demonstrated on some example ERP data obtained in a passive oddball paradigm. Finally, some conceptually novel applications of the multivariate approach in general and of the t-CWT method in particular are suggested and discussed. Hopefully, the publication of both the t-CWT source code and its underlying mathematical algorithm along with a didactic geometric introduction to some basic concepts of multivariate statistics would make t-CWT more accessible to both users and developers in the field of neuroscience research.

Multimodal Feature Integration in the Angular Gyrus during Episodic and Semantic Retrieval

PubMed Central

Bonnici, Heidi M.; Richter, Franziska R.; Yazar, Yasemin

2016-01-01

Much evidence from distinct lines of investigation indicates the involvement of angular gyrus (AnG) in the retrieval of both episodic and semantic information, but the region's precise function and whether that function differs across episodic and semantic retrieval have yet to be determined. We used univariate and multivariate fMRI analysis methods to examine the role of AnG in multimodal feature integration during episodic and semantic retrieval. Human participants completed episodic and semantic memory tasks involving unimodal (auditory or visual) and multimodal (audio-visual) stimuli. Univariate analyses revealed the recruitment of functionally distinct AnG subregions during the retrieval of episodic and semantic information. Consistent with a role in multimodal feature integration during episodic retrieval, significantly greater AnG activity was observed during retrieval of integrated multimodal episodic memories compared with unimodal episodic memories. Multivariate classification analyses revealed that individual multimodal episodic memories could be differentiated in AnG, with classification accuracy tracking the vividness of participants' reported recollections, whereas distinct unimodal memories were represented in sensory association areas only. In contrast to episodic retrieval, AnG was engaged to a statistically equivalent degree during retrieval of unimodal and multimodal semantic memories, suggesting a distinct role for AnG during semantic retrieval. Modality-specific sensory association areas exhibited corresponding activity during both episodic and semantic retrieval, which mirrored the functional specialization of these regions during perception. The results offer new insights into the integrative processes subserved by AnG and its contribution to our subjective experience of remembering. SIGNIFICANCE STATEMENT Using univariate and multivariate fMRI analyses, we provide evidence that functionally distinct subregions of angular gyrus (AnG) contribute to the retrieval of episodic and semantic memories. Our multivariate pattern classifier could distinguish episodic memory representations in AnG according to whether they were multimodal (audio-visual) or unimodal (auditory or visual) in nature, whereas statistically equivalent AnG activity was observed during retrieval of unimodal and multimodal semantic memories. Classification accuracy during episodic retrieval scaled with the trial-by-trial vividness with which participants experienced their recollections. Therefore, the findings offer new insights into the integrative processes subserved by AnG and how its function may contribute to our subjective experience of remembering. PMID:27194327

Multimodal Feature Integration in the Angular Gyrus during Episodic and Semantic Retrieval.

PubMed

Bonnici, Heidi M; Richter, Franziska R; Yazar, Yasemin; Simons, Jon S

2016-05-18

Much evidence from distinct lines of investigation indicates the involvement of angular gyrus (AnG) in the retrieval of both episodic and semantic information, but the region's precise function and whether that function differs across episodic and semantic retrieval have yet to be determined. We used univariate and multivariate fMRI analysis methods to examine the role of AnG in multimodal feature integration during episodic and semantic retrieval. Human participants completed episodic and semantic memory tasks involving unimodal (auditory or visual) and multimodal (audio-visual) stimuli. Univariate analyses revealed the recruitment of functionally distinct AnG subregions during the retrieval of episodic and semantic information. Consistent with a role in multimodal feature integration during episodic retrieval, significantly greater AnG activity was observed during retrieval of integrated multimodal episodic memories compared with unimodal episodic memories. Multivariate classification analyses revealed that individual multimodal episodic memories could be differentiated in AnG, with classification accuracy tracking the vividness of participants' reported recollections, whereas distinct unimodal memories were represented in sensory association areas only. In contrast to episodic retrieval, AnG was engaged to a statistically equivalent degree during retrieval of unimodal and multimodal semantic memories, suggesting a distinct role for AnG during semantic retrieval. Modality-specific sensory association areas exhibited corresponding activity during both episodic and semantic retrieval, which mirrored the functional specialization of these regions during perception. The results offer new insights into the integrative processes subserved by AnG and its contribution to our subjective experience of remembering. Using univariate and multivariate fMRI analyses, we provide evidence that functionally distinct subregions of angular gyrus (AnG) contribute to the retrieval of episodic and semantic memories. Our multivariate pattern classifier could distinguish episodic memory representations in AnG according to whether they were multimodal (audio-visual) or unimodal (auditory or visual) in nature, whereas statistically equivalent AnG activity was observed during retrieval of unimodal and multimodal semantic memories. Classification accuracy during episodic retrieval scaled with the trial-by-trial vividness with which participants experienced their recollections. Therefore, the findings offer new insights into the integrative processes subserved by AnG and how its function may contribute to our subjective experience of remembering. Copyright © 2016 Bonnici, Richter, et al.

Multivariate Boosting for Integrative Analysis of High-Dimensional Cancer Genomic Data

PubMed Central

Xiong, Lie; Kuan, Pei-Fen; Tian, Jianan; Keles, Sunduz; Wang, Sijian

2015-01-01

In this paper, we propose a novel multivariate component-wise boosting method for fitting multivariate response regression models under the high-dimension, low sample size setting. Our method is motivated by modeling the association among different biological molecules based on multiple types of high-dimensional genomic data. Particularly, we are interested in two applications: studying the influence of DNA copy number alterations on RNA transcript levels and investigating the association between DNA methylation and gene expression. For this purpose, we model the dependence of the RNA expression levels on DNA copy number alterations and the dependence of gene expression on DNA methylation through multivariate regression models and utilize boosting-type method to handle the high dimensionality as well as model the possible nonlinear associations. The performance of the proposed method is demonstrated through simulation studies. Finally, our multivariate boosting method is applied to two breast cancer studies. PMID:26609213

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

PubMed

Makambi, Kepher H; Seung, Hyunuk

2015-01-01

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

Linking multimetric and multivariate approaches to assess the ecological condition of streams.

PubMed

Collier, Kevin J

2009-10-01

Few attempts have been made to combine multimetric and multivariate analyses for bioassessment despite recognition that an integrated method could yield powerful tools for bioassessment. An approach is described that integrates eight macroinvertebrate community metrics into a Principal Components Analysis to develop a Multivariate Condition Score (MCS) from a calibration dataset of 511 samples. The MCS is compared to an Index of Biotic Integrity (IBI) derived using the same metrics based on the ratio to the reference site mean. Both approaches were highly correlated although the MCS appeared to offer greater potential for discriminating a wider range of impaired conditions. Both the MCS and IBI displayed low temporal variability within reference sites, and were able to distinguish between reference conditions and low levels of catchment modification and local habitat degradation, although neither discriminated among three levels of low impact. Pseudosamples developed to test the response of the metric aggregation approaches to organic enrichment, urban, mining, pastoral and logging stressor scenarios ranked pressures in the same order, but the MCS provided a lower score for the urban scenario and a higher score for the pastoral scenario. The MCS was calculated for an independent test dataset of urban and reference sites, and yielded similar results to the IBI. Although both methods performed comparably, the MCS approach may have some advantages because it removes the subjectivity of assigning thresholds for scoring biological condition, and it appears to discriminate a wider range of degraded conditions.

Son Preference and Family Limitation in Pakistan: A Parity- and Contraceptive Method-Specific Analysis.

PubMed

Channon, Melanie Dawn

2017-09-01

Son preference exerts a strong influence over contraceptive and fertility decisions in many South Asian countries. In Pakistan, where fertility remains high and contraceptive use low, research on son preference has been limited. Data from Pakistan Demographic and Health Surveys conducted in 1990-1991, 2006-2007 and 2012-2013 were used to examine potential indicators and outcomes of son preference. Descriptive analyses looked at sex composition preferences of men and women, as well as the sex ratio at last birth. Multivariate logistic regression analyses examined parity progression by birth order, while multinomial logistic regression was used to identify associations between sex composition and use of permanent, temporary and traditional contraceptive methods. Parity progression and choice of contraceptive method are increasingly associated with the sex composition of children. Many respondents wanted at least two sons, though most also wanted at least one daughter. Analyses suggest that the prevalence of modern contraceptive use among parous women would have been 19% higher in 2012-2013 in the absence of son preference. Permanent method use was extremely low among women with no sons and increased significantly with number of sons. The association between number of sons and use of temporary methods was weaker, while son preference had little relationship with traditional method use. The association of son preference with parity progression and modern contraceptive use has become stronger in Pakistan. Continuation of the fertility transition may be difficult unless the degrees of differential stopping behavior and differential contraceptive use decline.

A Multivariate Genetic Analysis of Specific Phobia, Separation Anxiety and Social Phobia in Early Childhood

ERIC Educational Resources Information Center

Eley, Thalia C.; Rijsdijk, Fruhling V.; Perrin, Sean; O'Connor, Thomas G.; Bolton, Derek

2008-01-01

Background: Comorbidity amongst anxiety disorders is very common in children as in adults and leads to considerable distress and impairment, yet is poorly understood. Multivariate genetic analyses can shed light on the origins of this comorbidity by revealing whether genetic or environmental risks for one disorder also influence another. We…

Structural Equation Modeling: Applications in ecological and evolutionary biology research

USGS Publications Warehouse

Pugesek, Bruce H.; von Eye, Alexander; Tomer, Adrian

2003-01-01

This book presents an introduction to the methodology of structural equation modeling, illustrates its use, and goes on to argue that it has revolutionary implications for the study of natural systems. A major theme of this book is that we have, up to this point, attempted to study systems primarily using methods (such as the univariate model) that were designed only for considering individual processes. Understanding systems requires the capacity to examine simultaneous influences and responses. Structural equation modeling (SEM) has such capabilities. It also possesses many other traits that add strength to its utility as a means of making scientific progress. In light of the capabilities of SEM, it can be argued that much of ecological theory is currently locked in an immature state that impairs its relevance. It is further argued that the principles of SEM are capable of leading to the development and evaluation of multivariate theories of the sort vitally needed for the conservation of natural systems. Supplementary information can be found at the authors website, http://www.jamesbgrace.com/. • Details why multivariate analyses should be used to study ecological systems • Exposes unappreciated weakness in many current popular analyses • Emphasizes the future methodological developments needed to advance our understanding of ecological systems.

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

PubMed Central

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

2015-01-01

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

Impact of Primary Gleason Grade on Risk Stratification for Gleason Score 7 Prostate Cancers

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

Koontz, Bridget F., E-mail: bridget.koontz@duke.edu; Tsivian, Matvey; Mouraviev, Vladimir

Purpose: To evaluate the primary Gleason grade (GG) in Gleason score (GS) 7 prostate cancers for risk of non-organ-confined disease with the goal of optimizing radiotherapy treatment option counseling. Methods: One thousand three hundred thirty-three patients with pathologic GS7 were identified in the Duke Prostate Center research database. Clinical factors including age, race, clinical stage, prostate-specific antigen at diagnosis, and pathologic stage were obtained. Data were stratified by prostate-specific antigen and clinical stage at diagnosis into adapted D'Amico risk groups. Univariate and multivariate analyses were performed evaluating for association of primary GG with pathologic outcome. Results: Nine hundred seventy-nine patientsmore » had primary GG3 and 354 had GG4. On univariate analyses, GG4 was associated with an increased risk of non-organ-confined disease. On multivariate analysis, GG4 was independently associated with seminal vesicle invasion (SVI) but not extracapsular extension. Patients with otherwise low-risk disease and primary GG3 had a very low risk of SVI (4%). Conclusions: Primary GG4 in GS7 cancers is associated with increased risk of SVI compared with primary GG3. Otherwise low-risk patients with GS 3+4 have a very low risk of SVI and may be candidates for prostate-only radiotherapy modalities.« less

Are social relationships a healthy influence on obesogenic behaviors among racially/ethnically diverse and socio-economically disadvantaged residents?

PubMed Central

Tamers, Sara L.; Okechukwu, Cassandra; Allen, Jennifer; Yang, May; Stoddard, Anne; Tucker-Seeley, Reginald; Sorensen, Glorian

2012-01-01

Objective To examine associations between social support and ties (family, friend, neighbors) individually and jointly with diet and physical activity among an ethnically-diverse, low-income population. Methods The Health in Common study (2005–2009) was designed to examine risk factors among individuals residing in low-income housing in the Boston, MA area. Cross-sectional surveys (n = 828) were administered in residents’ homes. Linear/logistic multivariable analyses were employed with clustering of individuals within housing sites controlled as a random effect. Results In multivariable analyses, total social support was significantly associated with higher red meat consumption per day (p = 0.029). Having more friends was significantly associated with more daily fruit and vegetable intake (p = 0.007) and higher levels of daily vigorous physical activity (p = 0.011). Those who reported having a greater number of family ties also reported higher daily consumption of sugary drinks (p = 0.013) and fast food (p = 0.011). More neighbor social ties was associated with more fast food per day (p = 0.024). Conclusions Social relationships can have both positive and negative associations with health behaviors. Understanding these relationships could help to inform the design of interventions that promote healthy behavior change among vulnerable populations. PMID:23200880

High serum uric acid concentration predicts poor survival in patients with breast cancer.

PubMed

Yue, Cai-Feng; Feng, Pin-Ning; Yao, Zhen-Rong; Yu, Xue-Gao; Lin, Wen-Bin; Qian, Yuan-Min; Guo, Yun-Miao; Li, Lai-Sheng; Liu, Min

2017-10-01

Uric acid is a product of purine metabolism. Recently, uric acid has gained much attraction in cancer. In this study, we aim to investigate the clinicopathological and prognostic significance of serum uric acid concentration in breast cancer patients. A total of 443 female patients with histopathologically diagnosed breast cancer were included. After a mean follow-up time of 56months, survival was analysed using the Kaplan-Meier method. To further evaluate the prognostic significance of uric acid concentrations, univariate and multivariate Cox regression analyses were applied. Of the clinicopathological parameters, uric acid concentration was associated with age, body mass index, ER status and PR status. Univariate analysis identified that patients with increased uric acid concentration had a significantly inferior overall survival (HR 2.13, 95% CI 1.15-3.94, p=0.016). In multivariate analysis, we found that high uric acid concentration is an independent prognostic factor predicting death, but insufficient to predict local relapse or distant metastasis. Kaplan-Meier analysis indicated that high uric acid concentration is related to the poor overall survival (p=0.013). High uric acid concentration predicts poor survival in patients with breast cancer, and might serve as a potential marker for appropriate management of breast cancer patients. Copyright © 2017 Elsevier B.V. All rights reserved.

A stress ecology framework for comprehensive risk assessment of diffuse pollution.

PubMed

van Straalen, Nico M; van Gestel, Cornelis A M

2008-12-01

Environmental pollution is traditionally classified as either localized or diffuse. Local pollution comes from a point source that emits a well-defined cocktail of chemicals, distributed in the environment in the form of a gradient around the source. Diffuse pollution comes from many sources, small and large, that cause an erratic distribution of chemicals, interacting with those from other sources into a complex mixture of low to moderate concentrations over a large area. There is no good method for ecological risk assessment of such types of pollution. We argue that effects of diffuse contamination in the field must be analysed in the wider framework of stress ecology. A multivariate approach can be applied to filter effects of contaminants from the many interacting factors at the ecosystem level. Four case studies are discussed (1) functional and structural properties of terrestrial model ecosystems, (2) physiological profiles of microbial communities, (3) detritivores in reedfield litter, and (4) benthic invertebrates in canal sediment. In each of these cases the data were analysed by multivariate statistics and associations between ecological variables and the levels of contamination were established. We argue that the stress ecology framework is an appropriate assessment instrument for discriminating effects of pollution from other anthropogenic disturbances and naturally varying factors.

Exploratory multivariate modeling and prediction of the physico-chemical properties of surface water and groundwater

NASA Astrophysics Data System (ADS)

Ayoko, Godwin A.; Singh, Kirpal; Balerea, Steven; Kokot, Serge

2007-03-01

SummaryPhysico-chemical properties of surface water and groundwater samples from some developing countries have been subjected to multivariate analyses by the non-parametric multi-criteria decision-making methods, PROMETHEE and GAIA. Complete ranking information necessary to select one source of water in preference to all others was obtained, and this enabled relationships between the physico-chemical properties and water quality to be assessed. Thus, the ranking of the quality of the water bodies was found to be strongly dependent on the total dissolved solid, phosphate, sulfate, ammonia-nitrogen, calcium, iron, chloride, magnesium, zinc, nitrate and fluoride contents of the waters. However, potassium, manganese and zinc composition showed the least influence in differentiating the water bodies. To model and predict the water quality influencing parameters, partial least squares analyses were carried out on a matrix made up of the results of water quality assessment studies carried out in Nigeria, Papua New Guinea, Egypt, Thailand and India/Pakistan. The results showed that the total dissolved solid, calcium, sulfate, sodium and chloride contents can be used to predict a wide range of physico-chemical characteristics of water. The potential implications of these observations on the financial and opportunity costs associated with elaborate water quality monitoring are discussed.

Discrimination between glycosylation patterns of therapeutic antibodies using a microfluidic platform, MALDI-MS and multivariate statistics.

PubMed

Thuy, Tran Thi; Tengstrand, Erik; Aberg, Magnus; Thorsén, Gunnar

2012-11-01

Optimal glycosylation with respect to the efficacy, serum half-life time, and immunogenic properties is essential in the generation of therapeutic antibodies. The glycosylation pattern can be affected by several different parameters during the manufacture of antibodies and may change significantly over cultivation time. Fast and robust methods for determination of the glycosylation patterns of therapeutic antibodies are therefore needed. We have recently presented an efficient method for the determination of glycans on therapeutic antibodies using a microfluidic CD platform for sample preparation prior to matrix-assisted laser-desorption mass spectrometry analysis. In the present work, this method is applied to analyse the glycosylation patterns of three commercially available therapeutic antibodies and one intended for therapeutic use. Two of the antibodies produced in mouse myeloma cell line (SP2/0) and one produced in Chinese hamster ovary (CHO) cells exhibited similar glycosylation patterns but could still be readily differentiated from each other using multivariate statistical methods. The two antibodies with most similar glycosylation patterns were also studied in an assessment of the method's applicability for quality control of therapeutic antibodies. The method presented in this paper is highly automated and rapid. It can therefore efficiently generate data that helps to keep a production process within the desired design space or assess that an identical product is being produced after changes to the process. Copyright © 2012 Elsevier B.V. All rights reserved.

Susceptible genes of restless legs syndrome in migraine.

PubMed

Fuh, Jong-Ling; Chung, Ming-Yi; Yao, Shu-Chih; Chen, Ping-Kun; Liao, Yi-Chu; Hsu, Chia-Lin; Wang, Po-Jen; Wang, Yen-Feng; Chen, Shih-Pin; Fann, Cathy S-J; Kao, Lung-Sen; Wang, Shuu-Jiun

2016-10-01

Objective Several genetic variants have been found to increase the risk of restless legs syndrome (RLS). The aim of the present study was to determine if these genetic variants were also associated with the comorbidity of RLS and migraine in patients. Methods Thirteen single-nucleotide polymorphisms (SNPs) at six RLS risk loci ( MEIS1, BTBD9, MAP2K5, PTPRD, TOX3, and an intergenic region on chromosome 2p14) were genotyped in 211 migraine patients with RLS and 781 migraine patients without RLS. Association analyses were performed for the overall cohort, as well as for the subgroups of patients who experienced migraines with and without aura and episodic migraines (EMs) vs. chronic migraines (CMs). In order to verify which genetic markers were potentially related to the incidence of RLS in migraine patients, multivariate regression analyses were also performed. Results Among the six tested loci, only MEIS1 was significantly associated with RLS. The most significant SNP of MEIS1, rs2300478, increased the risk of RLS by 1.42-fold in the overall cohort ( p = 0.0047). In the subgroup analyses, MEIS1 augmented the risk of RLS only in the patients who experienced EMs (odds ratio (OR) = 1.99, p = 0.0004) and not those experiencing CMs. Multivariate regression analyses further showed that rs2300478 in MEIS1 (OR = 1.39, p = 0.018), a CM diagnosis (OR = 1.52, p = 0.022), and depression (OR = 1.86, p = 0.005) were independent predictors of RLS in migraine. Conclusions MEIS1 variants were associated with an increased risk of RLS in migraine patients. It is possible that an imbalance in iron homeostasis and the dopaminergic system may represent a link between RLS incidence and migraines.

HPV infection among a population-based sample of sexual minority women from USA

PubMed Central

Reiter, Paul L; McRee, Annie-Laurie

2017-01-01

Objectives Sexual minority women are at risk for infection with human papillomavirus (HPV); yet, relatively little is known about the prevalence of HPV infection among this population. Methods We analysed data from the 2003–2012 National Health and Nutrition Examination Survey among women aged 20–59 (n=7132). We examined two dimensions of sexual orientation (sexual identity and sexual behaviour) and used weighted logistic regression to determine how HPV infection outcomes (any HPV type, high-risk HPV type and vaccine HPV type) vary by dimension. Results Similar patterns emerged for sexual identity and sexual behaviour. In bivariate analyses, HPV infection outcomes were more common among non-heterosexual women compared with heterosexual women (any type: 49.7% vs 41.1%; high-risk type: 37.0% vs 27.9%), as well as among women who reported any same-sex partners compared with women who reported only opposite-sex partners (any type: 55.9% vs 41.0%; high-risk type: 37.7% vs 28.2%; vaccine type: 19.1% vs 14.0%) (p<0.05). When we disaggregated measures of sexual orientation into subgroups, bisexual women and women who reported partners of both sexes had greater odds of HPV infection outcomes (p<0.05 in bivariate analyses). Multivariate models attenuated several of these differences, though lesbian women and women who reported only same-sex partners had lower odds of most HPV infection outcomes in multivariate analyses (p<0.05). Conclusions HPV infection is common among sexual minority women, though estimates vary depending on how sexual orientation is operationalised. Results can help inform targeted HPV and cervical cancer prevention efforts for sexual minority women. PMID:27165699

Hydronephrosis in patients with cervical cancer: an assessment of morbidity and survival

PubMed Central

Patel, Krishna; Foster, Nathan R.; Kumar, Amanika; Grudem, Megan; Longenbach, Sherri; Bakkum-Gamez, Jamie; Haddock, Michael; Dowdy, Sean; Jatoi, Aminah

2015-01-01

Purpose Hydronephrosis is a frequently observed but understudied complication in patients with cervical cancer. To better characterize hydronephrosis in cervical cancer patients, the current study sought (1) to describe hydronephrosis-associated morbidity and (2) to analyze the prognostic effect of hydronephrosis in patients with a broad range of cancer stages over time. Methods The Mayo Clinic Tumor Registry was interrogated for all invasive cervical cancer patients seen at the Mayo Clinic from 2008 through 2013 in Rochester, Minnesota; these patients’ medical records were then reviewed in detail. Results Two hundred seventy-nine cervical cancer patients with a median age of 49 years and a range of cancer stages were included. Sixty-five patients (23 %) were diagnosed with hydronephrosis at some point during their disease course. In univariate analyses, hydronephrosis was associated with advanced cancer stage (p<0.0001), squamous histology (p=0.0079), and nonsurgical cancer treatment (p=0.0039). In multivariate analyses, stage and tumor histology were associated with hydronephrosis. All but one patient underwent stent placement or urinary diversion; hydronephrosis-related morbidity included pain, urinary tract infections, nausea and vomiting, renal failure, and urinary tract bleeding. In landmark univariate survival analyses, hydronephrosis was associated with worse survival at all time points. In landmark multivariate analyses (adjusted for patient age, stage, cancer treatment, and tumor histology), hydronephrosis was associated with a trend toward worse survival over time (hazard ratios ranged from 1.47 to 4.69). Conclusion Hydronephrosis in cervical cancer patients is associated with notable morbidity. It is also associated with trends toward worse survival—even if it occurs after the original cancer diagnosis. PMID:25339620

Epidemiology of antibiotic-resistant wound infections from six countries in Africa

PubMed Central

Bebell, Lisa M; Meney, Carron; Valeri, Linda

2017-01-01

Introduction Little is known about the antimicrobial susceptibility of common bacteria responsible for wound infections from many countries in sub-Saharan Africa. Methods We performed a retrospective review of microbial isolates collected based on clinical suspicion of wound infection between 2004 and 2016 from Mercy Ships, a non-governmental organisation operating a single mobile surgical unit in Benin, Congo, Liberia, Madagascar, Sierra Leone and Togo. Antimicrobial resistant organisms of interest were defined as methicillin-resistant Staphylococcus aureus (MRSA) or Enterobacteriaceae resistant to third-generation cephalosporins. Generalised mixed-effects models accounting for repeated isolates in a patient, potential clustering by case mix for each field service, age, gender and country were used to test the hypothesis that rates of antimicrobial resistance differed between countries. Results 3145 isolates from repeated field services in six countries were reviewed. In univariate analyses, the highest proportion of MRSA was found in Benin (34.6%) and Congo (31.9%), while the lowest proportion was found in Togo (14.3%) and Madagascar (14.5%); country remained a significant predictor in multivariate analyses (P=0.002). In univariate analyses, the highest proportion of third-generation cephalosporin-resistant Enterobacteriaceae was found in Benin (35.8%) and lowest in Togo (14.3%) and Madagascar (16.3%). Country remained a significant predictor for antimicrobial-resistant isolates in multivariate analyses (P=0.009). Conclusion A significant proportion of isolates from wound cultures were resistant to first-line antimicrobials in each country. Though antimicrobial resistance isolates were not verified in a reference laboratory and these data may not be representative of all regions of the countries studied, differences in the proportion of antimicrobial-resistant isolates and resistance profiles between countries suggest site-specific surveillance should be a priority and local antimicrobial resistance profiles should be used to guide empiric antibiotic selection. PMID:29588863

Effects of unplanned treatment interruptions on HIV treatment failure– results from TAHOD

PubMed Central

Jiamsakul, Awachana; Kerr, Stephen J.; Ng, Oon Tek; Lee, Man Po; Chaiwarith, Romanee; Yunihastuti, Evy; Van Nguyen, Kinh; Pham, Thuy Thanh; Kiertiburanakul, Sasisopin; Ditangco, Rossana; Saphonn, Vonthanak; Sim, Benedict L. H.; Merati, Tuti Parwati; Wong, Wingwai; Kantipong, Pacharee; Zhang, Fujie; Choi, Jun Yong; Pujari, Sanjay; Kamarulzaman, Adeeba; Oka, Shinichi; Mustafa, Mahiran; Ratanasuwan, Winai; Petersen, Boondarika; Law, Matthew; Kumarasamy, Nagalingeswaran

2016-01-01

Objectives Treatment interruptions (TI) of combination antiretroviral therapy (cART) are known to lead to unfavourable treatment outcomes but do still occur in resource-limited settings. We investigated the effects of TI associated with adverse events (AEs) and non-AE-related reasons, including their durations, on treatment failure after cART resumption in HIV-infected individuals in Asia. Methods Patients initiating cART between 2006-2013 were included. TI was defined as stopping cART for >1 day. Treatment failure was defined as confirmed virological, immunological or clinical failure. Time to treatment failure during cART was analysed using Cox regression, not including periods off treatment. Co-variables with p<0.10 in univariable analyses were included in multivariable analyses, where p<0.05 was considered statistically significant. Results Of 4549 patients from 13 countries in Asia, 3176 (69.8%) were male and the median age was 34 years. A total of 111 (2.4%) had TIs due to AEs and 135 (3.0%) had TIs for other reasons. Median interruption times were 22 days for AE and 148 days for non-AE TIs. In multivariable analyses, interruptions >30 days were associated with failure (31-180 days HR=2.66, 95%CI (1.70-4.16); 181-365 days HR=6.22, 95%CI (3.26-11.86); and >365 days HR=9.10, 95% CI (4.27-19.38), all p<0.001, compared to 0-14 days). Reasons for previous TI were not statistically significant (p=0.158). Conclusions Duration of interruptions of more than 30 days was the key factor associated with large increases in subsequent risk of treatment failure. If TI is unavoidable, its duration should be minimised to reduce the risk of failure after treatment resumption. PMID:26950901

Measurement issues in research on social support and health.

PubMed Central

Dean, K; Holst, E; Kreiner, S; Schoenborn, C; Wilson, R

1994-01-01

STUDY OBJECTIVE--The aims were: (1) to identify methodological problems that may explain the inconsistencies and contradictions in the research evidence on social support and health, and (2) to validate a frequently used measure of social support in order to determine whether or not it could be used in multivariate analyses of population data in research on social support and health. DESIGN AND METHODS--Secondary analysis of data collected in a cross sectional survey of a multistage cluster sample of the population of the United States, designed to study relationships in behavioural, social support and health variables. Statistical models based on item response theory and graph theory were used to validate the measure of social support to be used in subsequent analyses. PARTICIPANTS--Data on 1755 men and women aged 20 to 64 years were available for the scale validation. RESULTS--Massive evidence of item bias was found for all items of a group membership subscale. The most serious problems were found in relationship to an item measuring membership in work related groups. Using that item in the social network scale in multivariate analyses would distort findings on the statistical effects of education, employment status, and household income. Evidence of item bias was also found for a sociability subscale. When marital status was included to create what is called an intimate contacts subscale, the confounding grew worse. CONCLUSIONS--The composite measure of social network is not valid and would seriously distort the findings of analyses attempting to study relationships between the index and other variables. The findings show that valid measurement is a methodological issue that must be addressed in scientific research on population health. PMID:8189179

Traditional food consumption and nutritional status of Dalit mothers in rural Andhra Pradesh, South India.

PubMed

Schmid, M A; Egeland, G M; Salomeyesudas, B; Satheesh, P V; Kuhnlein, H V

2006-11-01

To describe prevalence of malnutrition and their correlates of nutrient and traditional food consumption in rural Dalit mothers. In a cross-sectional study, we used socio-cultural questionnaires, anthropometric measurements and clinical eye examinations during the rainy season in 2003. Food frequency questionnaires and 24-h recalls were conducted during both summer and rainy seasons. Dalit mothers with young children were recruited from 37 villages in the Medak District of rural Andhra Pradesh, India. Dalit mothers (n = 220) participated. The prevalence of chronic energy-deficient (CED) mothers (body mass index <18.5 kg/m2) was 58%. Illiterate women and active women were more likely to have CED than those literate and non-active (relative risks (RR) = 1.6 and 1.4, respectively, P < or = 0.05), but literacy and activity level were not significant in multivariable analyses including sanitation and number of children < or =5 years of age. Increasing levels of fat intake, as a percent of total energy, was significantly associated with lower risk of CED (RR of the lowest 25th percentile compared to those in the 75th percentile or above was 1.6, P < or = 0.05), findings that remained significant in multivariable analyses. Consumption of pulses (g/day) was also inversely related to CED in univariate and multivariable analyses. Carbohydrate intake, as a percent of total energy, was inversely related to percent energy from fat (r = -0.96, P < or = 0.01), and, although positively related to CED in univariate analyses, carbohydrate consumption was not significant in multivariable analyses. Mothers' age in years and income was positively related to vitamin A deficiency. These results confirm that CED and vitamin A malnutrition among Dalit women are predominant problems in this area. Increased consumption of local traditional Dalit food (particularly sorghum, pulses, vegetables and animal source food) should be incorporated as an important component of intervention strategies to improve nutritional status.

Multivariate Time Series Decomposition into Oscillation Components.

PubMed

Matsuda, Takeru; Komaki, Fumiyasu

2017-08-01

Many time series are considered to be a superposition of several oscillation components. We have proposed a method for decomposing univariate time series into oscillation components and estimating their phases (Matsuda & Komaki, 2017 ). In this study, we extend that method to multivariate time series. We assume that several oscillators underlie the given multivariate time series and that each variable corresponds to a superposition of the projections of the oscillators. Thus, the oscillators superpose on each variable with amplitude and phase modulation. Based on this idea, we develop gaussian linear state-space models and use them to decompose the given multivariate time series. The model parameters are estimated from data using the empirical Bayes method, and the number of oscillators is determined using the Akaike information criterion. Therefore, the proposed method extracts underlying oscillators in a data-driven manner and enables investigation of phase dynamics in a given multivariate time series. Numerical results show the effectiveness of the proposed method. From monthly mean north-south sunspot number data, the proposed method reveals an interesting phase relationship.

Upsampling to 400-ms Resolution for Assessing Effective Connectivity in Functional Magnetic Resonance Imaging Data with Granger Causality

PubMed Central

Kerr, Deborah L.; Nitschke, Jack B.

2013-01-01

Abstract Granger causality analysis of functional magnetic resonance imaging (fMRI) blood-oxygen-level-dependent signal data allows one to infer the direction and magnitude of influence that brain regions exert on one another. We employed a method for upsampling the time resolution of fMRI data that does not require additional interpolation beyond the interpolation that is regularly used for slice-timing correction. The mathematics for this new method are provided, and simulations demonstrate its viability. Using fMRI, 17 snake phobics and 19 healthy controls viewed snake, disgust, and neutral fish video clips preceded by anticipatory cues. Multivariate Granger causality models at the native 2-sec resolution and at the upsampled 400-ms resolution assessed directional associations of fMRI data among 13 anatomical regions of interest identified in prior research on anxiety and emotion. Superior sensitivity was observed for the 400-ms model, both for connectivity within each group and for group differences in connectivity. Context-dependent analyses for the 400-ms multivariate Granger causality model revealed the specific trial types showing group differences in connectivity. This is the first demonstration of effective connectivity of fMRI data using a method for achieving 400-ms resolution without sacrificing accuracy available at 2-sec resolution. PMID:23134194

Neuropsychological tests for predicting cognitive decline in older adults

PubMed Central

Baerresen, Kimberly M; Miller, Karen J; Hanson, Eric R; Miller, Justin S; Dye, Richelin V; Hartman, Richard E; Vermeersch, David; Small, Gary W

2015-01-01

Summary Aim To determine neuropsychological tests likely to predict cognitive decline. Methods A sample of nonconverters (n = 106) was compared with those who declined in cognitive status (n = 24). Significant univariate logistic regression prediction models were used to create multivariate logistic regression models to predict decline based on initial neuropsychological testing. Results Rey–Osterrieth Complex Figure Test (RCFT) Retention predicted conversion to mild cognitive impairment (MCI) while baseline Buschke Delay predicted conversion to Alzheimer’s disease (AD). Due to group sample size differences, additional analyses were conducted using a subsample of demographically matched nonconverters. Analyses indicated RCFT Retention predicted conversion to MCI and AD, and Buschke Delay predicted conversion to AD. Conclusion Results suggest RCFT Retention and Buschke Delay may be useful in predicting cognitive decline. PMID:26107318

Performance of the disease risk score in a cohort study with policy-induced selection bias.

PubMed

Tadrous, Mina; Mamdani, Muhammad M; Juurlink, David N; Krahn, Murray D; Lévesque, Linda E; Cadarette, Suzanne M

2015-11-01

To examine the performance of the disease risk score (DRS) in a cohort study with evidence of policy-induced selection bias. We examined two cohorts of new users of bisphosphonates. Estimates for 1-year hip fracture rates between agents using DRS, exposure propensity scores and traditional multivariable analysis were compared. The results for the cohort with no evidence of policy-induced selection bias showed little variation across analyses (-4.1-2.0%). Analysis of the cohort with evidence of policy-induced selection bias showed greater variation (-13.5-8.1%), with the greatest difference seen with DRS analyses. Our findings suggest that caution may be warranted when using DRS methods in cohort studies with policy-induced selection bias, further research is needed.

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

Improved neutron-gamma discrimination for a 3He neutron detector using subspace learning methods

DOE PAGES

Wang, C. L.; Funk, L. L.; Riedel, R. A.; ...

2017-02-10

3He gas based neutron linear-position-sensitive detectors (LPSDs) have been applied for many neutron scattering instruments. Traditional Pulse-Height Analysis (PHA) for Neutron-Gamma Discrimination (NGD) resulted in the neutron-gamma efficiency ratio on the orders of 10 5-10 6. The NGD ratios of 3He detectors need to be improved for even better scientific results from neutron scattering. Digital Signal Processing (DSP) analyses of waveforms were proposed for obtaining better NGD ratios, based on features extracted from rise-time, pulse amplitude, charge integration, a simplified Wiener filter, and the cross-correlation between individual and template waveforms of neutron and gamma events. Fisher linear discriminant analysis (FLDA)more » and three multivariate analyses (MVAs) of the features were performed. The NGD ratios are improved by about 10 2-10 3 times compared with the traditional PHA method. Finally, our results indicate the NGD capabilities of 3He tube detectors can be significantly improved with subspace-learning based methods, which may result in a reduced data-collection time and better data quality for further data reduction.« less

Global approach for the validation of an in-line Raman spectroscopic method to determine the API content in real-time during a hot-melt extrusion process.

PubMed

Netchacovitch, L; Thiry, J; De Bleye, C; Dumont, E; Cailletaud, J; Sacré, P-Y; Evrard, B; Hubert, Ph; Ziemons, E

2017-08-15

Since the Food and Drug Administration (FDA) published a guidance based on the Process Analytical Technology (PAT) approach, real-time analyses during manufacturing processes are in real expansion. In this study, in-line Raman spectroscopic analyses were performed during a Hot-Melt Extrusion (HME) process to determine the Active Pharmaceutical Ingredient (API) content in real-time. The method was validated based on a univariate and a multivariate approach and the analytical performances of the obtained models were compared. Moreover, on one hand, in-line data were correlated with the real API concentration present in the sample quantified by a previously validated off-line confocal Raman microspectroscopic method. On the other hand, in-line data were also treated in function of the concentration based on the weighing of the components in the prepared mixture. The importance of developing quantitative methods based on the use of a reference method was thus highlighted. The method was validated according to the total error approach fixing the acceptance limits at ±15% and the α risk at ±5%. This method reaches the requirements of the European Pharmacopeia norms for the uniformity of content of single-dose preparations. The validation proves that future results will be in the acceptance limits with a previously defined probability. Finally, the in-line validated method was compared with the off-line one to demonstrate its ability to be used in routine analyses. Copyright © 2017 Elsevier B.V. All rights reserved.

Risk factor assessment to anticipate performance in the National Developmental Screening Test in children from a disadvantaged area.

PubMed

Montes, Alejandro; Pazos, Gustavo

2016-02-01

Identifying children at risk of failing the National Developmental Screening Test by combining prevalences of children suspected of having inapparent developmental disorders (IDDs) and associated risk factors (RFs) would allow to save resources. 1. To estimate the prevalence of children suspected of having IDDs. 2. To identify associated RFs. 3. To assess three methods developed based on observed RFs and propose a pre-screening procedure. The National Developmental Screening Test was administered to 60 randomly selected children aged between 2 and 4 years old from a socioeconomically disadvantaged area from Puerto Madryn. Twenty-four biological and socioenvironmental outcome measures were assessed in order to identify potential RFs using bivariate and multivariate analyses. The likelihood of failing the screening test was estimated as follows: 1. a multivariate logistic regression model was developed; 2. a relationship was established between the number of RFs present in each child and the percentage of children who failed the test; 3. these two methods were combined. The prevalence of children suspected of having IDDs was 55.0% (95% confidence interval: 42.4%-67.6%). Six RFs were initially identified using the bivariate approach. Three of them (maternal education, number of health checkups and Z scores for height-for-age, and maternal age) were included in the logistic regression model, which has a greater explanatory power. The third method included in the assessment showed greater sensitivity and specificity (85% and 79%, respectively). The estimated prevalence of children suspected of having IDDs was four times higher than the national standards. Seven RFs were identified. Combining the analysis of risk factor accumulation and a multivariate model provides a firm basis for developing a sensitive, specific and practical pre-screening procedure for socioeconomically disadvantaged areas. Sociedad Argentina de Pediatría.

Discrimination of soft tissues using laser-induced breakdown spectroscopy in combination with k nearest neighbors (kNN) and support vector machine (SVM) classifiers

NASA Astrophysics Data System (ADS)

Li, Xiaohui; Yang, Sibo; Fan, Rongwei; Yu, Xin; Chen, Deying

2018-06-01

In this paper, discrimination of soft tissues using laser-induced breakdown spectroscopy (LIBS) in combination with multivariate statistical methods is presented. Fresh pork fat, skin, ham, loin and tenderloin muscle tissues are manually cut into slices and ablated using a 1064 nm pulsed Nd:YAG laser. Discrimination analyses between fat, skin and muscle tissues, and further between highly similar ham, loin and tenderloin muscle tissues, are performed based on the LIBS spectra in combination with multivariate statistical methods, including principal component analysis (PCA), k nearest neighbors (kNN) classification, and support vector machine (SVM) classification. Performances of the discrimination models, including accuracy, sensitivity and specificity, are evaluated using 10-fold cross validation. The classification models are optimized to achieve best discrimination performances. The fat, skin and muscle tissues can be definitely discriminated using both kNN and SVM classifiers, with accuracy of over 99.83%, sensitivity of over 0.995 and specificity of over 0.998. The highly similar ham, loin and tenderloin muscle tissues can also be discriminated with acceptable performances. The best performances are achieved with SVM classifier using Gaussian kernel function, with accuracy of 76.84%, sensitivity of over 0.742 and specificity of over 0.869. The results show that the LIBS technique assisted with multivariate statistical methods could be a powerful tool for online discrimination of soft tissues, even for tissues of high similarity, such as muscles from different parts of the animal body. This technique could be used for discrimination of tissues suffering minor clinical changes, thus may advance the diagnosis of early lesions and abnormalities.

Relationship Between Central and Peripheral Atherosclerosis and Left Ventricular Dysfunction in a Community Population

PubMed Central

Tsao, Connie W.; Gona, Philimon; Salton, Carol; Murabito, Joanne M.; Oyama, Noriko; Danias, Peter G.; O’Donnell, Christopher J.; Manning, Warren J.; Yeon, Susan B.

2011-01-01

We aimed to determine the relationships between resting left ventricular (LV) wall motion abnormalities (WMAs), aortic plaque, and PAD in a community cohort. 1726 Framingham Heart Study Offspring Cohort participants (806 males, 65±9 years) underwent cardiovascular magnetic resonance with quantification of aortic plaque volume and assessment of regional LV systolic function. Claudication, lower extremity revascularization, and ankle-brachial index (ABI) were recorded at Examination 7. WMAs were associated with greater aortic plaque burden, decreased ABI, and claudication in age- and sex-adjusted analyses (all p<0.001), which were not significant after adjustment for cardiovascular risk factors. In age- and sex-adjusted analyses, both the presence (p<0.001) and volume of aortic plaque were associated with decreased ABI (p<0.001). After multivariable adjustment, ABI≤0.9 or prior revascularization was associated with a three-fold odds of aortic plaque (p=0.0083). Plaque volume significantly increased with decreasing ABI in multivariable-adjusted analyses (p<0.0001). In this free-living population, associations of WMAs with aortic plaque burden and clinical measures of PAD were attenuated after adjustment for coronary heart disease risk factors. Aortic plaque volume and ABI remained strongly negatively correlated after multivariable adjustment. Our findings suggest that the association between coronary heart disease and non-coronary atherosclerosis is explained by cardiovascular risk factors. Aortic atherosclerosis and PAD remain strongly associated after multivariable adjustment suggesting shared mechanisms beyond those captured by traditional risk factors. PMID:21708875

Factors associated with utilization of long-acting and permanent contraceptive methods among women who have decided not to have more children in Gondar city.

PubMed

Zenebe, Chernet Baye; Adefris, Mulat; Yenit, Melaku Kindie; Gelaw, Yalemzewod Assefa

2017-09-06

Despite the fact that long acting family planning methods reduce population growth and improve maternal health, their utilization remains poor. Therefore, this study assessed the prevalence of long acting and permanent family planning method utilization and associated factors among women in reproductive age groups who have decided not to have more children in Gondar city, northwest Ethiopia. An institution based cross-sectional study was conducted from August to October, 2015. Three hundred seventeen women who have decided not to have more children were selected consecutively into the study. A structured and pretested questionnaire was used to collect data. Both bivariate and multi-variable logistic regressions analyses were used to identify factors associated with utilization of long acting and permanent family planning methods. The multi-variable logistic regression analysis was used to investigate factors associated with the utilization of long acting and permanent family planning methods. The Adjusted Odds Ratio (AOR) with the corresponding 95% Confidence Interval (CI) was used to show the strength of associations, and variables with a P-value of <0.05 were considered statistically significant. In this study, the overall prevalence of long acting and permanent contraceptive (LAPCM) method utilization was 34.7% (95% CI: 29.5-39.9). According to the multi-variable logistic regression analysis, utilization of long acting and permanent contraceptive methods was significantly associated with women who had secondary school, (AOR: 2279, 95% CI: 1.17, 4.44), college, and above education (AOR: 2.91, 95% CI: 1.36, 6.24), history of previous utilization (AOR: 3.02, 95% CI: 1.69, 5.38), and information about LAPCM (AOR: 8.85, 95% CI: 2.04, 38.41). In this study the prevalence of long acting and permanent family planning method utilization among women who have decided not to have more children was high compared with previous studies conducted elsewhere. Advanced educational status, previous utilization of LAPCM, and information on LAPCM were significantly associated with the utilization of LAPCM. As a result, strengthening behavioral change communication channels to make information accessible is highly recommended.

Clinical Trial Participation and Time to Treatment Among Adolescents and Young Adults With Cancer: Does Age at Diagnosis or Insurance Make a Difference?

PubMed Central

Parsons, Helen M.; Harlan, Linda C.; Seibel, Nita L.; Stevens, Jennifer L.; Keegan, Theresa H.M.

2011-01-01

Purpose Because adolescent and young adult (AYA) patients with cancer have experienced variable improvement in survival over the past two decades, enhancing the quality and timeliness of cancer care in this population has emerged as a priority area. To identify current trends in AYA care, we examined patterns of clinical trial participation, time to treatment, and provider characteristics in a population-based sample of AYA patients with cancer. Methods Using the National Cancer Institute Patterns of Care Study, we used multivariate logistic regression to evaluate demographic and provider characteristics associated with clinical trial enrollment and time to treatment among 1,358 AYA patients with cancer (age 15 to 39 years) identified through the Surveillance, Epidemiology, and End Results Program. Results In our study, 14% of patients age 15 to 39 years had enrolled onto a clinical trial; participation varied by type of cancer, with the highest participation in those diagnosed with acute lymphoblastic leukemia (37%) and sarcoma (32%). Multivariate analyses demonstrated that uninsured, older patients and those treated by nonpediatric oncologists were less likely to enroll onto clinical trials. Median time from pathologic confirmation to first treatment was 3 days, but this varied by race/ethnicity and cancer site. In multivariate analyses, advanced cancer stage and outpatient treatment alone were associated with longer time from pathologic confirmation to treatment. Conclusion Our study identified factors associated with low clinical trial participation in AYA patients with cancer. These findings support the continued need to improve access to clinical trials and innovative treatments for this population, which may ultimately translate into improved survival. PMID:21931022

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

PubMed Central

2013-01-01

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

Quality of life in Chronic Pancreatitis is determined by constant pain, disability/unemployment, current smoking and associated co-morbidities

PubMed Central

Machicado, Jorge D.; Amann, Stephen T; Anderson, Michelle A.; Abberbock, Judah; Sherman, Stuart; Conwell, Darwin; Cote, Gregory A.; Singh, Vikesh K.; Lewis, Michele; Alkaade, Samer; Sandhu, Bimaljit S.; Guda, Nalini M.; Muniraj, Thiruvengadam; Tang, Gong; Baillie, John; Brand, Randall; Gardner, Timothy B.; Gelrud, Andres; Forsmark, Christopher E.; Banks, Peter A.; Slivka, Adam; Wilcox, C. Mel; Whitcomb, David C.; Yadav, Dhiraj

2018-01-01

Background Chronic pancreatitis (CP) has a profound independent effect on quality of life (QOL). Our aim was to identify factors that impact the QOL in CP patients. Methods We used data on 1,024 CP patients enrolled in the three NAPS2 studies. Information on demographics, risk factors, co-morbidities, disease phenotype and treatments was obtained from responses to structured questionnaires. Physical (PCS) and mental (MCS) component summary scores generated using responses to the Short Form-12 (SF-12) survey were used to assess QOL at enrollment. Multivariable linear regression models determined independent predictors of QOL. Results Mean PCS and MCS scores were 36.7±11.7 and 42.4±12.2, respectively. Significant (p<0.05) negative impact on PCS scores in multivariable analyses was noted due to constant mild-moderate pain with episodes of severe pain or constant severe pain (10 points), constant mild-moderate pain (5.2), pain-related disability/unemployment (5.1), current smoking (2.9 points) and medical co-morbidities. Significant (p<0.05) negative impact on MCS scores was related to constant pain irrespective of severity (6.8-6.9 points), current smoking (3.9 points) and pain-related disability/unemployment (2.4 points). In women, disability/unemployment resulted in an additional reduction 3.7 point reduction in MCS score. Final multivariable models explained 27% and 18% of the variance in PCS and MCS scores, respectively. Etiology, disease duration, pancreatic morphology, diabetes, exocrine insufficiency and prior endotherapy/pancreatic surgery had no significant independent effect on QOL. Conclusion Constant pain, pain-related disability/unemployment, current smoking, and concurrent co-morbidities significantly affect the QOL in CP. Further research is needed to identify factors impacting QOL not explained by our analyses. PMID:28244497

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

Wang, C. L.; Funk, L. L.; Riedel, R. A.

3He gas based neutron linear-position-sensitive detectors (LPSDs) have been applied for many neutron scattering instruments. Traditional Pulse-Height Analysis (PHA) for Neutron-Gamma Discrimination (NGD) resulted in the neutron-gamma efficiency ratio on the orders of 10 5-10 6. The NGD ratios of 3He detectors need to be improved for even better scientific results from neutron scattering. Digital Signal Processing (DSP) analyses of waveforms were proposed for obtaining better NGD ratios, based on features extracted from rise-time, pulse amplitude, charge integration, a simplified Wiener filter, and the cross-correlation between individual and template waveforms of neutron and gamma events. Fisher linear discriminant analysis (FLDA)more » and three multivariate analyses (MVAs) of the features were performed. The NGD ratios are improved by about 10 2-10 3 times compared with the traditional PHA method. Finally, our results indicate the NGD capabilities of 3He tube detectors can be significantly improved with subspace-learning based methods, which may result in a reduced data-collection time and better data quality for further data reduction.« less

Comparative Robustness of Recent Methods for Analyzing Multivariate Repeated Measures Designs

ERIC Educational Resources Information Center

Seco, Guillermo Vallejo; Gras, Jaime Arnau; Garcia, Manuel Ato

2007-01-01

This study evaluated the robustness of two recent methods for analyzing multivariate repeated measures when the assumptions of covariance homogeneity and multivariate normality are violated. Specifically, the authors' work compares the performance of the modified Brown-Forsythe (MBF) procedure and the mixed-model procedure adjusted by the…

Socioeconomic Correlates of Contraceptive Use among the Ethnic Tribal Women of Bangladesh: Does Sex Preference Matter?

PubMed

Kamal, S M Mostafa; Hassan, Che Hashim

2013-06-01

To examine the relationship between socioeconomic factors affecting contraceptive use among tribal women of Bangladesh with focusing on son preference over daughter. The study used data gathered through a cross sectional survey on four tribal communities resided in the Rangamati Hill District of the Chittagong Hill Tracts, Bangladesh. A multistage random sampling procedure was applied to collect data from 865 currently married women of whom 806 women were currently married, non-pregnant and had at least one living child, which are the basis of this study. The information was recorded in a pre-structured questionnaire. Simple cross tabulation, chi-square tests and logistic regression analyses were performed to analyzing data. The contraceptive prevalence rate among the study tribal women was 73%. The multivariate analyses yielded quantitatively important and reliable estimates of likelihood of contraceptive use. Findings revealed that after controlling for other variables, the likelihood of contraceptive use was found not to be significant among women with at least one son than those who had only daughters, indicating no preference of son over daughter. Multivariate logistic regression analysis suggests that home visitations by family planning workers, tribal identity, place of residence, husband's education, and type of family, television ownership, electricity connection in the household and number of times married are important determinants of any contraceptive method use among the tribal women. The contraceptive use rate among the disadvantaged tribal women was more than that of the national level. Door-step delivery services of modern methods should be reached and available targeting the poor and remote zones.

Incidence and course of adolescent deliberate self-harm in Victoria, Australia, and Washington State, USA

PubMed Central

Heerde, Jessica A.; Toumbourou, John W.; Hemphill, Sheryl A.; Herrenkohl, Todd I.; Patton, George C.; Catalano, Richard F.

2015-01-01

Purpose There have been few longitudinal studies of deliberate self-harm (DSH) in adolescents. This cross-national longitudinal study outlines risk and protective factors for DSH incidence and persistence. Methods Seventh and ninth grade students (average ages 13 and 15 years) were recruited as state-representative cohorts, surveyed and then followed-up 12-months later (N = 3,876), using the same methods in Washington State and Victoria, Australia. The retention rate was 99% in both states at follow-up. A range of risk and protective factors for DSH were examined using multivariate analyses. Results The prevalence of DSH in the past year was 1.53% in grade 7 and .91% in grade 9 for males and 4.12% and 1.34% for grade 7 and 9 females, with similar rates across states. In multivariate analyses, incident DSH was lower in Washington State (OR .67, 95% CI .45, 1.00) relative to Victoria 12-months later. Risk factors for incident DSH included being female (OR 1.93, CI 1.35, 2.76), high depressive symptoms (OR 3.52, CI 2.37, 5.21), antisocial behavior (OR 2.42, CI 1.46, 4.00), and lifetime (OR 1.85, CI 1.11, 3.08) and past month (OR 2.70, CI 1.57, 4.64) alcohol use relative to never using alcohol. Conclusions Much self-harm in adolescents resolves over the course of 12 months. Young people who self-harm have high rates of other health risk behaviors associated with family and peer risks that may all be targets for preventive intervention. PMID:26499859

Assessment and statistics of surgically induced astigmatism.

PubMed

Naeser, Kristian

2008-05-01

The aim of the thesis was to develop methods for assessment of surgically induced astigmatism (SIA) in individual eyes, and in groups of eyes. The thesis is based on 12 peer-reviewed publications, published over a period of 16 years. In these publications older and contemporary literature was reviewed(1). A new method (the polar system) for analysis of SIA was developed. Multivariate statistical analysis of refractive data was described(2-4). Clinical validation studies were performed. The description of a cylinder surface with polar values and differential geometry was compared. The main results were: refractive data in the form of sphere, cylinder and axis may define an individual patient or data set, but are unsuited for mathematical and statistical analyses(1). The polar value system converts net astigmatisms to orthonormal components in dioptric space. A polar value is the difference in meridional power between two orthogonal meridians(5,6). Any pair of polar values, separated by an arch of 45 degrees, characterizes a net astigmatism completely(7). The two polar values represent the net curvital and net torsional power over the chosen meridian(8). The spherical component is described by the spherical equivalent power. Several clinical studies demonstrated the efficiency of multivariate statistical analysis of refractive data(4,9-11). Polar values and formal differential geometry describe astigmatic surfaces with similar concepts and mathematical functions(8). Other contemporary methods, such as Long's power matrix, Holladay's and Alpins' methods, Zernike(12) and Fourier analyses(8), are correlated to the polar value system. In conclusion, analysis of SIA should be performed with polar values or other contemporary component systems. The study was supported by Statens Sundhedsvidenskabeligt Forskningsråd, Cykelhandler P. Th. Rasmussen og Hustrus Mindelegat, Hotelejer Carl Larsen og Hustru Nicoline Larsens Mindelegat, Landsforeningen til Vaern om Synet, Forskningsinitiativet for Arhus Amt, Alcon Denmark, and Desirée and Niels Ydes Fond.

Improving the Prognostic Ability through Better Use of Standard Clinical Data - The Nottingham Prognostic Index as an Example

PubMed Central

Winzer, Klaus-Jürgen; Buchholz, Anika; Schumacher, Martin; Sauerbrei, Willi

2016-01-01

Background Prognostic factors and prognostic models play a key role in medical research and patient management. The Nottingham Prognostic Index (NPI) is a well-established prognostic classification scheme for patients with breast cancer. In a very simple way, it combines the information from tumor size, lymph node stage and tumor grade. For the resulting index cutpoints are proposed to classify it into three to six groups with different prognosis. As not all prognostic information from the three and other standard factors is used, we will consider improvement of the prognostic ability using suitable analysis approaches. Methods and Findings Reanalyzing overall survival data of 1560 patients from a clinical database by using multivariable fractional polynomials and further modern statistical methods we illustrate suitable multivariable modelling and methods to derive and assess the prognostic ability of an index. Using a REMARK type profile we summarize relevant steps of the analysis. Adding the information from hormonal receptor status and using the full information from the three NPI components, specifically concerning the number of positive lymph nodes, an extended NPI with improved prognostic ability is derived. Conclusions The prognostic ability of even one of the best established prognostic index in medicine can be improved by using suitable statistical methodology to extract the full information from standard clinical data. This extended version of the NPI can serve as a benchmark to assess the added value of new information, ranging from a new single clinical marker to a derived index from omics data. An established benchmark would also help to harmonize the statistical analyses of such studies and protect against the propagation of many false promises concerning the prognostic value of new measurements. Statistical methods used are generally available and can be used for similar analyses in other diseases. PMID:26938061

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

Sole, Claudio V., E-mail: csole@iram.cl; School of Medicine, Complutense University, Madrid; Calvo, Felipe A.

Purpose: To assess long-term outcomes and toxicity of intraoperative electron-beam radiation therapy (IOERT) in the management of pediatric patients with Ewing sarcomas (EWS) and rhabdomyosarcomas (RMS). Methods and Materials: Seventy-one sarcoma (EWS n=37, 52%; RMS n=34, 48%) patients underwent IOERT for primary (n=46, 65%) or locally recurrent sarcomas (n=25, 35%) from May 1983 to November 2012. Local control (LC), overall survival (OS), and disease-free survival were estimated using Kaplan-Meier methods. For survival outcomes, potential associations were assessed in univariate and multivariate analyses using the Cox proportional hazards model. Results: After a median follow-up of 72 months (range, 4-310 months), 10-year LC, disease-freemore » survival, and OS was 74%, 57%, and 68%, respectively. In multivariate analysis after adjustment for other covariates, disease status (P=.04 and P=.05) and R1 margin status (P<.01 and P=.04) remained significantly associated with LC and OS. Nine patients (13%) reported severe chronic toxicity events (all grade 3). Conclusions: A multimodal IOERT-containing approach is a well-tolerated component of treatment for pediatric EWS and RMS patients, allowing reduction or substitution of external beam radiation exposure while maintaining high local control rates.« less

Panic disorder and agoraphobia: A direct comparison of their multivariate comorbidity patterns.

PubMed

Greene, Ashley L; Eaton, Nicholas R

2016-01-15

Scientific debate has long surrounded whether agoraphobia is a severe consequence of panic disorder or a frequently comorbid diagnosis. Multivariate comorbidity investigations typically treat these diagnoses as fungible in structural models, assuming both are manifestations of the fear-subfactor in the internalizing-externalizing model. No studies have directly compared these disorders' multivariate associations, which could clarify their conceptualization in classification and comorbidity research. In a nationally representative sample (N=43,093), we examined the multivariate comorbidity of panic disorder (1) without agoraphobia, (2) with agoraphobia, and (3) regardless of agoraphobia; and (4) agoraphobia without panic. We conducted exploratory and confirmatory factor analyses of these and 10 other lifetime DSM-IV diagnoses in a nationally representative sample (N=43,093). Differing bivariate and multivariate relations were found. Panic disorder without agoraphobia was largely a distress disorder, related to emotional disorders. Agoraphobia without panic was largely a fear disorder, related to phobias. When considered jointly, concomitant agoraphobia and panic was a fear disorder, and when panic was assessed without regard to agoraphobia (some individuals had agoraphobia while others did not) it was a mixed distress and fear disorder. Diagnoses were obtained from comprehensively trained lay interviewers, not clinicians and analyses used DSM-IV diagnoses (rather than DSM-5). These findings support the conceptualization of agoraphobia as a distinct diagnostic entity and the independent classification of both disorders in DSM-5, suggesting future multivariate comorbidity studies should not assume various panic/agoraphobia diagnoses are invariably fear disorders. Copyright © 2015 Elsevier B.V. All rights reserved.

Single-cell analysis of population context advances RNAi screening at multiple levels

PubMed Central

Snijder, Berend; Sacher, Raphael; Rämö, Pauli; Liberali, Prisca; Mench, Karin; Wolfrum, Nina; Burleigh, Laura; Scott, Cameron C; Verheije, Monique H; Mercer, Jason; Moese, Stefan; Heger, Thomas; Theusner, Kristina; Jurgeit, Andreas; Lamparter, David; Balistreri, Giuseppe; Schelhaas, Mario; De Haan, Cornelis A M; Marjomäki, Varpu; Hyypiä, Timo; Rottier, Peter J M; Sodeik, Beate; Marsh, Mark; Gruenberg, Jean; Amara, Ali; Greber, Urs; Helenius, Ari; Pelkmans, Lucas

2012-01-01

Isogenic cells in culture show strong variability, which arises from dynamic adaptations to the microenvironment of individual cells. Here we study the influence of the cell population context, which determines a single cell's microenvironment, in image-based RNAi screens. We developed a comprehensive computational approach that employs Bayesian and multivariate methods at the single-cell level. We applied these methods to 45 RNA interference screens of various sizes, including 7 druggable genome and 2 genome-wide screens, analysing 17 different mammalian virus infections and four related cell physiological processes. Analysing cell-based screens at this depth reveals widespread RNAi-induced changes in the population context of individual cells leading to indirect RNAi effects, as well as perturbations of cell-to-cell variability regulators. We find that accounting for indirect effects improves the consistency between siRNAs targeted against the same gene, and between replicate RNAi screens performed in different cell lines, in different labs, and with different siRNA libraries. In an era where large-scale RNAi screens are increasingly performed to reach a systems-level understanding of cellular processes, we show that this is often improved by analyses that account for and incorporate the single-cell microenvironment. PMID:22531119

High coffee consumption and different brewing methods in relation to postmenopausal endometrial cancer risk in the Norwegian women and cancer study: a population-based prospective study.

PubMed

Gavrilyuk, Oxana; Braaten, Tonje; Skeie, Guri; Weiderpass, Elisabete; Dumeaux, Vanessa; Lund, Eiliv

2014-03-25

Coffee and its compounds have been proposed to inhibit endometrial carcinogenesis. Studies in the Norwegian population can be especially interesting due to the high coffee consumption and increasing incidence of endometrial cancer in the country. A total of 97 926 postmenopausal Norwegian women from the population-based prospective Norwegian Women and Cancer (NOWAC) Study, were included in the present analysis. We evaluated the general association between total coffee consumption and endometrial cancer risk as well as the possible impact of brewing method. Multivariate Cox regression analysis was used to estimate risks, and heterogeneity tests were performed to compare brewing methods. During an average of 10.9 years of follow-up, 462 incident endometrial cancer cases were identified. After multivariate adjustment, significant risk reduction was found among participants who drank ≥8 cups/day of coffee with a hazard ratio of 0.52 (95% confidence interval, CI 0.34-0.79). However, we did not observe a significant dose-response relationship. No significant heterogeneity in risk was found when comparing filtered and boiled coffee brewing methods. A reduction in endometrial cancer risk was observed in subgroup analyses among participants who drank ≥8 cups/day and had a body mass index ≥25 kg/m2, and in current smokers. These data suggest that in this population with high coffee consumption, endometrial cancer risk decreases in women consuming ≥8 cups/day, independent of brewing method.

Impact of tumor deposits on the prognosis and chemotherapy efficacy in stage III colorectal cancer patients with different lymph node status: a retrospective cohort study in China.

PubMed

Li, Xiao; An, Bang; Zhao, Qi; Qi, Jianni; Wang, Wenwen; Zhang, Di; Li, Zhen; Qin, Chengyong

2018-06-21

The goal was to determine whether tumor deposits (TDs) had effects on the overall survival (OS), cancer-specific survival (CSS), disease-free survival (DFS) and responses to chemotherapy in advanced colorectal cancer (CRC) patients with different lymph node (N) stages. The retrospective cohort study recruited 1,455 stage III CRC patients diagnosed at a single institution between January 2010 and July 2016. Patients were divided into TDs negative and positive groups. Based on whether they accepted chemotherapy, patients were further divided into chemotherapy and non-chemotherapy groups. Kaplan-Meier methods, univariate and multivariate analyses, and subset analyses based on the N stage were performed to compare the OS, CSS and DFS between different groups. Multivariate Cox analyses showed that TDs were independent prognostic markers for the OS (adjusted HR=1.929, 95% CI: 1.339-2.777), CSS (adjusted HR=1.789, 95% CI: 1.165-2.748) and DFS (adjusted HR=2.179, 95% CI: 1.612-2.944) in all N stages combined. In addition, subset analyses based on the N stage further demonstrated that TDs were independent risk factors for the OS (P=0.012), CSS (P=0.010) and DFS (P<0.001) in patients with the N1a, 1 b stages, and for the OS (P=0.023) and DFS (P<0.001) in patients with the N2a, 2 b stages. Furthermore, the OS, CSS and DFS in the TDs negative group could be extended significantly after the administration of chemotherapy, whereas patients with positive TDs lost the DFS benefit from chemotherapy. Stage III CRC patients with positive TDs had a poor prognosis, and they did not display a DFS benefit from chemotherapy. TDs had adverse effects on the OS and DFS in patients with the N1a, 1 b and N2a, 2 b stages, providing evidence for the feasibility of the new TNM category method. Copyright © 2018. Published by Elsevier Ltd.

Multivariate Statistical Analysis of MSL APXS Bulk Geochemical Data

NASA Astrophysics Data System (ADS)

Hamilton, V. E.; Edwards, C. S.; Thompson, L. M.; Schmidt, M. E.

2014-12-01

We apply cluster and factor analyses to bulk chemical data of 130 soil and rock samples measured by the Alpha Particle X-ray Spectrometer (APXS) on the Mars Science Laboratory (MSL) rover Curiosity through sol 650. Multivariate approaches such as principal components analysis (PCA), cluster analysis, and factor analysis compliment more traditional approaches (e.g., Harker diagrams), with the advantage of simultaneously examining the relationships between multiple variables for large numbers of samples. Principal components analysis has been applied with success to APXS, Pancam, and Mössbauer data from the Mars Exploration Rovers. Factor analysis and cluster analysis have been applied with success to thermal infrared (TIR) spectral data of Mars. Cluster analyses group the input data by similarity, where there are a number of different methods for defining similarity (hierarchical, density, distribution, etc.). For example, without any assumptions about the chemical contributions of surface dust, preliminary hierarchical and K-means cluster analyses clearly distinguish the physically adjacent rock targets Windjana and Stephen as being distinctly different than lithologies observed prior to Curiosity's arrival at The Kimberley. In addition, they are separated from each other, consistent with chemical trends observed in variation diagrams but without requiring assumptions about chemical relationships. We will discuss the variation in cluster analysis results as a function of clustering method and pre-processing (e.g., log transformation, correction for dust cover) and implications for interpreting chemical data. Factor analysis shares some similarities with PCA, and examines the variability among observed components of a dataset so as to reveal variations attributable to unobserved components. Factor analysis has been used to extract the TIR spectra of components that are typically observed in mixtures and only rarely in isolation; there is the potential for similar results with data from APXS. These techniques offer new ways to understand the chemical relationships between the materials interrogated by Curiosity, and potentially their relation to materials observed by APXS instruments on other landed missions.

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.

Modeling the Drift Towards Sex Role Deviance.

ERIC Educational Resources Information Center

James, Jennifer; Vitaliano, Peter Paul

The interrelationships of deviant life experiences and current status, i.e., prostitution versus non-prostitution, were investigated by the application of multivariate analyses. Variables were studied involving early home life, pregnancy history, sexual history, and criminal involvement. Based on the analyses, three models were developed that…

Synergism in work site adoption of employee assistance programs and health promotion activities.

PubMed

Blum, T C; Roman, P M; Patrick, L

1990-05-01

As workplaces increasingly adopt proactive programs directed toward employee health issues, the interrelation between different programs becomes an important issue. Of interest here is the "synergy" in patterns of program adoption between employee assistance programs (EAPs) and health promotion activities (HPAs). We utilize the 1985 National Survey of Worksite Health Promotion Activities (N = 1358) for analyses of the dual presence of EAPs and HPAs, and in multivariate analyses we consider factors affecting such dual presence. The data suggest that synergy occurs, with EAP adoption appearing to influence HPA adoption to a greater extent than the reverse. In multivariate analyses, synergy is confirmed by the finding that, among a variety of relevant organizational characteristics, EAP presence and HPA presence are the best predictors of each other's presence. The analyses also indicate that there is minimal commonality in program ingredients across organizations reporting the presence of HPAs. Implications of the data for the future development of these two programming strategies are discussed.

Chronic pruritus: evaluation of patient needs and treatment goals with a special regard to differences according to pruritus classification and sex.

PubMed

Steinke, S; Bruland, P; Blome, C; Osada, N; Dugas, M; Fritz, F; Augustin, M; Ständer, S

2017-02-01

Chronic pruritus (CP) is present in approximately one-third of all dermatological patients. Diagnostics and treatment are challenging and impair patients' quality of life. To analyse therapeutic needs in terms of the importance of treatment goals in a large sample of patients with CP. Routine data of 2747 patients with CP were analysed with descriptive methods and significance tests (univariate and multivariate variance analyses). The importance of 27 need items was measured using the Patient Needs Questionnaire of the Patient Benefit Index. The most important needs were to find a clear diagnosis and treatment, to no longer experience itching and to have confidence in the therapy, which were quite or very important to > 90% of the patients. The least important goals concerned a normal working or sex life. Nine needs related mostly to disease and psychological symptoms, and some social needs differed in importance between sexes (P ≤ 0·05). Patients with pruritus on inflamed skin or with chronic scratch lesions judged more than half of all needs as more important than did patients with pruritus on noninflamed skin (P ≤ 0·05). In the multivariate model, age, pruritus intensity and quality of life had a significant effect on the importance of therapeutic needs besides sex and pruritus classification. Patients with CP present high levels of various therapeutic needs with differences by sex and clinical phenotype. The most important needs can be addressed through medical activities such as appropriate itch medication and a trustful doctor-patient relationship. © 2016 British Association of Dermatologists.

Transmission clustering among newly diagnosed HIV patients in Chicago, 2008 to 2011: using phylogenetics to expand knowledge of regional HIV transmission patterns

PubMed Central

Lubelchek, Ronald J.; Hoehnen, Sarah C.; Hotton, Anna L.; Kincaid, Stacey L.; Barker, David E.; French, Audrey L.

2014-01-01

Introduction HIV transmission cluster analyses can inform HIV prevention efforts. We describe the first such assessment for transmission clustering among HIV patients in Chicago. Methods We performed transmission cluster analyses using HIV pol sequences from newly diagnosed patients presenting to Chicago’s largest HIV clinic between 2008 and 2011. We compared sequences via progressive pairwise alignment, using neighbor joining to construct an un-rooted phylogenetic tree. We defined clusters as >2 sequences among which each sequence had at least one partner within a genetic distance of ≤ 1.5%. We used multivariable regression to examine factors associated with clustering and used geospatial analysis to assess geographic proximity of phylogenetically clustered patients. Results We compared sequences from 920 patients; median age 35 years; 75% male; 67% Black, 23% Hispanic; 8% had a Rapid Plasma Reagin (RPR) titer ≥ 1:16 concurrent with their HIV diagnosis. We had HIV transmission risk data for 54%; 43% identified as men who have sex with men (MSM). Phylogenetic analysis demonstrated 123 patients (13%) grouped into 26 clusters, the largest having 20 members. In multivariable regression, age < 25, Black race, MSM status, male gender, higher HIV viral load, and RPR ≥ 1:16 associated with clustering. We did not observe geographic grouping of genetically clustered patients. Discussion Our results demonstrate high rates of HIV transmission clustering, without local geographic foci, among young Black MSM in Chicago. Applied prospectively, phylogenetic analyses could guide prevention efforts and help break the cycle of transmission. PMID:25321182

The Religiosity/Spirituality of Latina Breast Cancer Survivors and Influence on Health-Related Quality of Life

PubMed Central

Wildes, Kimberly A.; Miller, Alexander R.; de Majors, Sandra San Miguel; Ramirez, Amelie G.

2010-01-01

Objective The study evaluated the association of religiosity/spirituality (R/S) and health-related quality of life (HRQOL) among Latina breast cancer survivors (BCS) in order to determine whether R/S would be positively correlated with HRQOL and whether R/S would significantly influence HRQOL. Methods The cross-sectional study utilized self-report data from 117 Latina BCS survivors. R/S was measured with the Systems of Belief Inventory - 15 Revised (SBI-15R) and HRQOL was measured with the Functional Assessment of Cancer Therapy – General (FACT-G). Analyses included calculation of descriptive statistics, t-tests, bivariate correlations, and multivariate analyses. Results Latina BCS had very high levels of R/S and generally good HRQOL. The SBI-15R total score was positively correlated with FACT-G social well-being (r=0.266, p=0.005), relationship with doctor (r=0.219, p=0.020), and functional well-being (r=0.216, p=0.022). Multivariate analyses revealed that SBI-15R was a significant predictor of FACT-G functional well-being (p=0.041) and satisfaction with the relationship with the doctor (p=0.050), where higher levels of R/S predicted higher levels of well-being. Conclusions Latina BCS had very high levels of R/S, which were significantly, positively correlated with dimensions of HRQOL (social well-being, functional well-being, relationship with doctor). Further, these high levels of R/S predicted better functional well-being and satisfaction with the patient-doctor relationship while controlling for potentially confounding variables. Implications are discussed. PMID:19034922

A Bayesian approach to estimating variance components within a multivariate generalizability theory framework.

PubMed

Jiang, Zhehan; Skorupski, William

2017-12-12

In many behavioral research areas, multivariate generalizability theory (mG theory) has been typically used to investigate the reliability of certain multidimensional assessments. However, traditional mG-theory estimation-namely, using frequentist approaches-has limits, leading researchers to fail to take full advantage of the information that mG theory can offer regarding the reliability of measurements. Alternatively, Bayesian methods provide more information than frequentist approaches can offer. This article presents instructional guidelines on how to implement mG-theory analyses in a Bayesian framework; in particular, BUGS code is presented to fit commonly seen designs from mG theory, including single-facet designs, two-facet crossed designs, and two-facet nested designs. In addition to concrete examples that are closely related to the selected designs and the corresponding BUGS code, a simulated dataset is provided to demonstrate the utility and advantages of the Bayesian approach. This article is intended to serve as a tutorial reference for applied researchers and methodologists conducting mG-theory studies.

Characterizing backcountry camping impacts in Great Smoky Mountains National Park

USGS Publications Warehouse

Leung, Y.-F.; Marion, J.L.

1999-01-01

This investigates resource impacts on backcounty campsites in the Great Smoky Mountains National Park, USA. Study objectives were to enhance our understanding of camping impacts and to improve campsite impact assessment procedures by means of multivariate techniques. Three-hundred and eight campsites at designated backcountry campgrounds, and 69 additional unofficial campsites were assessed. Factor analysis of 195 established campsites on eight impact indicator variables revealed three dimensions of campsite impact: area disturbance, soil and groundcover damage, and tree-related damage. Four distinctive backcountry campsite types were identified, three of which were derived from cluster analyses of factor scores. These four backcountry campsite types characterize the intensity and areal extent of resource impacts, and they vary in locational and environmental attributes. At an aggregate level, different campsite types contributed unequally to the cumulative level of impact. The dimensional structure and typology developed in this study demonstrates that campsite impacts can be viewed and examined holistically with the use of multivariate methods. Implications for assessment procedures, management and further research are discussed.

Environmental quality assessment of Grand Harbour (Valletta, Maltese Islands): a case study of a busy harbour in the Central Mediterranean Sea.

PubMed

Romeo, Teresa; D'Alessandro, Michela; Esposito, Valentina; Scotti, Gianfranco; Berto, Daniela; Formalewicz, Malgorzata; Noventa, Seta; Giuliani, Silvia; Macchia, Simona; Sartori, Davide; Mazzola, Angelo; Andaloro, Franco; Giacobbe, Salvatore; Deidun, Alan; Renzi, Monia

2015-12-01

Contamination levels by plastic debris, trace elements and persistent organic pollutants were assessed and related to macrobenthic diversity within soft bottoms of Grand Harbour (Malta, Central Mediterranean). Sediment toxicity was evaluated by ecotoxicological method, deploying Bacteria (Vibrio fischeri), Echinodermata (Paracentrotus lividus) and Crustacea (Corophium orientale). Univariate analysis (Pearson's test) was used to test relationships between biodiversity indices, pollutants and grain size. A multivariate approach (PERMANOVA) was applied to investigate for any significant differences among sampling stations concerning plastic abundances and to test the relationship between infaunal abundances and pollutant concentrations (the BIOENV test). Significant differences in the plastic abundances were found between sampling stations. The lowest value for Shannon-Wiener biodiversity index was associated to the highest sediment pollution level. Multivariate analyses suggest that MBT and TBT were factors that most influenced macrozoobenthic abundance and biodiversity. The bivalve Corbula gibba and the introduced polychaete Monticellina dorsobranchialis were the most abundant found species.

Clinical outcomes in cystic fibrosis patients with Trichosporon respiratory infection.

PubMed

Esther, Charles R; Plongla, Rongpong; Kerr, Alan; Lin, Feng-Chang; Gilligan, Peter

2016-09-01

Relationships between clinical outcomes and novel respiratory pathogens such as Trichosporon are not well understood. Respiratory cultures from CF patients were screened for novel pathogens Trichosporon and Chryseobacterium as well as other pathogens over 28months. Relationships between microbiologic and clinical data were assessed using univariate and multivariate methods. Of 4934 respiratory cultures from 474 CF patients, 37 cultures from 10 patients were Trichosporon positive. Patients with positive Trichosproron cultures had a greater decline in FEV1 over time (-3.9%/year vs. -1.3%/year, p<0.05), whereas Chryseobacterium did not influence lung function. These findings were confirmed in multivariate analyses that included age, gender, and other common pathogens as confounders. Treatment of Trichosporon infected patients was associated with improved lung function. Trichosporon can be recovered from a small but clinically meaningful fraction of CF patients. The presence of Trichosporon, but not Chryseobacterium, is associated with greater declines in lung function. Copyright © 2016 European Cystic Fibrosis Society. Published by Elsevier B.V. All rights reserved.

Mentorship Programs in Radiation Oncology Residency Training Programs: A Critical Unmet Need

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

Dhami, Gurleen; Gao, Wendy; Gensheimer, Michael F.

Purpose: To conduct a nationwide survey to evaluate the current status of resident mentorship in radiation oncology. Methods and Materials: An anonymous electronic questionnaire was sent to all residents and recent graduates at US Accreditation Council for Graduate Medical Education–accredited radiation oncology residency programs, identified in the member directory of the Association of Residents in Radiation Oncology. Factors predictive of having a mentor and satisfaction with the mentorship experience were identified using univariate and multivariate analyses. Results: The survey response rate was 25%, with 85% of respondents reporting that mentorship plays a critical role in residency training, whereas only 53%more » had a current mentor. Larger programs (≥10 faculty, P=.004; and ≥10 residents, P<.001) were more likely to offer a formal mentorship program, which makes it more likely for residents to have an active mentor (88% vs 44%). Residents in a formal mentoring program reported being more satisfied with the overall mentorship experience (univariate odds ratio 8.77, P<.001; multivariate odds ratio 5, P<.001). On multivariate analysis, women were less likely to be satisfied with the mentorship experience. Conclusions: This is the first survey focusing on the status of residency mentorship in radiation oncology. Our survey highlights the unmet need for mentorship in residency programs.« less

Redo surgery risk in patients with cardiac prosthetic valve dysfunction

PubMed Central

Maciejewski, Marek; Piestrzeniewicz, Katarzyna; Bielecka-Dąbrowa, Agata; Piechowiak, Monika; Jaszewski, Ryszard

2011-01-01

Introduction The aim of the study was to analyse the risk factors of early and late mortality in patients undergoing the first reoperation for prosthetic valve dysfunction. Material and methods A retrospective observational study was performed in 194 consecutive patients (M = 75, F = 119; mean age 53.2 ±11 years) with a mechanical prosthetic valve (n = 103 cases; 53%) or bioprosthesis (91; 47%). Univariate and multivariate Cox statistical analysis was performed to determine risk factors of early and late mortality. Results The overall early mortality was 18.6%: 31.4% in patients with symptoms of NYHA functional class III-IV and 3.4% in pts in NYHA class I-II. Multivariate analysis identified symptoms of NYHA class III-IV and endocarditis as independent predictors of early mortality. The overall late mortality (> 30 days) was 8.2% (0.62% year/patient). Multivariate analysis identified age at the time of reoperation as a strong independent predictor of late mortality. Conclusions Reoperation in patients with prosthetic valves, performed urgently, especially in patients with symptoms of NYHA class III-IV or in the case of endocarditis, bears a high mortality rate. Risk of planned reoperation, mostly in patients with symptoms of NYHA class I-II, does not differ from the risk of the first operation. PMID:22291767

Multivariate Quantitative Chemical Analysis

NASA Technical Reports Server (NTRS)

Kinchen, David G.; Capezza, Mary

1995-01-01

Technique of multivariate quantitative chemical analysis devised for use in determining relative proportions of two components mixed and sprayed together onto object to form thermally insulating foam. Potentially adaptable to other materials, especially in process-monitoring applications in which necessary to know and control critical properties of products via quantitative chemical analyses of products. In addition to chemical composition, also used to determine such physical properties as densities and strengths.

Causal diagrams and multivariate analysis III: confound it!

PubMed

Jupiter, Daniel C

2015-01-01

This commentary concludes my series concerning inclusion of variables in multivariate analyses. We take up the issues of confounding and effect modification and summarize the work we have thus far done. Finally, we provide a rough algorithm to help guide us through the maze of possibilities that we have outlined. Copyright © 2015 American College of Foot and Ankle Surgeons. Published by Elsevier Inc. All rights reserved.

Measuring watershed runoff capability with ERTS data. [Washita River Basin, Oklahoma

NASA Technical Reports Server (NTRS)

Blanchard, B. J.

1974-01-01

Parameters of most equations used to predict runoff from an ungaged area are based on characteristics of the watershed and subject to the biases of a hydrologist. Digital multispectral scanner, MSS, data from ERTS was reduced with the aid of computer programs and a Dicomed display. Multivariate analyses of the MSS data indicate that discrimination between watersheds with different runoff capabilities is possible using ERTS data. Differences between two visible bands of MSS data can be used to more accurately evaluate the parameters than present subjective methods, thus reducing construction cost due to overdesign of flood detention structures.

Atmospheric water vapour over oceans from SSM/I measurements

NASA Technical Reports Server (NTRS)

Schluessel, Peter; Emery, William J.

1990-01-01

A statistical retrieval technique is developed to derive the atmospheric water vapor column content from the Special Sensor Microwave/Imager (SSM/I) measurements. The radiometer signals are simulated by means of radiative-transfer calculations for a large set of atmospheric/oceanic situations. These simulated responses are subsequently summarized by multivariate analyses, giving water-vapor coefficients and error estimates. Radiative-transfer calculations show that the SSM/I microwave imager can detect atmospheric water vapor structures with an accuracy from 0.145 to 0.17 g/sq cm. The accuracy of the method is confirmed by globally distributed match-ups with radiosonde measurements.

ENSO related variability in the Southern Hemisphere, 1948-2000

NASA Astrophysics Data System (ADS)

Ribera, Pedro; Mann, Michael E.

2003-01-01

The spatiotemporal evolution of Southern Hemisphere climate variability is diagnosed based on the use of the NCEP reanalysis (1948-2000) dataset. Using the MTM-SVD analysis method, significant narrowband variability is isolated from the multi-variate dataset. It is found that the ENSO signal exhibits statistically significant behavior at quasiquadrennial (3-6 yr) timescales for the full time-period. A significant quasibiennial (2-3 yr) timescales emerges only for the latter half of period. Analyses of the spatial evolution of the two reconstructed signals shed additional light on linkages between low and high-latitude Southern Hemisphere climate anomalies.

The relationship between physical appearance concerns, disgust, and anti-fat prejudice.

PubMed

O'Brien, Kerry S; Daníelsdóttir, Sigrún; Ólafsson, Ragnar P; Hansdóttir, Ingunn; Fridjónsdóttir, Thorarna G; Jónsdóttir, Halla

2013-09-01

This study examined relationships between physical appearance concerns (fear of fat, body image disturbance; BIDQ), disgust, and anti-fat prejudice (dislike, blame), and tested whether disgust mediates relationships between physical appearance concerns and anti-fat prejudice. Participants (N=1649; age=28 years) provided demographic data and completed measures of anti-fat prejudice, tendency to feel disgust, and physical appearance concerns. Univariate, multivariate, and mediation analyses were conducted. Univariate and multivariate associations were found between fear of fat, BIDQ, disgust, and anti-fat prejudice for women. For women only, mediation analyses showed that disgust partially mediated relationships between physical appearance concerns and dislike of fat people. For men, univariate and multivariate relationships were found between fear of fat, and dislike and blame of fat people, but disgust was not related to anti-fat prejudice. Newer constructs centering on physical appearance concerns and disgust appear promising candidates for understanding anti-fat prejudice. Copyright © 2013 Elsevier Ltd. All rights reserved.

The Classification of Ground Roasted Decaffeinated Coffee Using UV-VIS Spectroscopy and SIMCA Method

NASA Astrophysics Data System (ADS)

Yulia, M.; Asnaning, A. R.; Suhandy, D.

2018-05-01

In this work, an investigation on the classification between decaffeinated and non- decaffeinated coffee samples using UV-VIS spectroscopy and SIMCA method was investigated. Total 200 samples of ground roasted coffee were used (100 samples for decaffeinated coffee and 100 samples for non-decaffeinated coffee). After extraction and dilution, the spectra of coffee samples solution were acquired using a UV-VIS spectrometer (Genesys™ 10S UV-VIS, Thermo Scientific, USA) in the range of 190-1100 nm. The multivariate analyses of the spectra were performed using principal component analysis (PCA) and soft independent modeling of class analogy (SIMCA). The SIMCA model showed that the classification between decaffeinated and non-decaffeinated coffee samples was detected with 100% sensitivity and specificity.

Antimicrobial susceptibility of Escherichia coli F4, Pasteurella multocida, and Streptococcus suis isolates from a diagnostic veterinary laboratory and recommendations for a surveillance system

PubMed Central

Glass-Kaastra, Shiona K.; Pearl, David L.; Reid-Smith, Richard J.; McEwen, Beverly; Slavic, Durda; McEwen, Scott A.; Fairles, Jim

2014-01-01

Antimicrobial susceptibility data on Escherichia coli F4, Pasteurella multocida, and Streptococcus suis isolates from Ontario swine (January 1998 to October 2010) were acquired from a comprehensive diagnostic veterinary laboratory in Ontario, Canada. In relation to the possible development of a surveillance system for antimicrobial resistance, data were assessed for ease of management, completeness, consistency, and applicability for temporal and spatial statistical analyses. Limited farm location data precluded spatial analyses and missing demographic data limited their use as predictors within multivariable statistical models. Changes in the standard panel of antimicrobials used for susceptibility testing reduced the number of antimicrobials available for temporal analyses. Data consistency and quality could improve over time in this and similar diagnostic laboratory settings by encouraging complete reporting with sample submission and by modifying database systems to limit free-text data entry. These changes could make more statistical methods available for disease surveillance and cluster detection. PMID:24688133

Antimicrobial susceptibility of Escherichia coli F4, Pasteurella multocida, and Streptococcus suis isolates from a diagnostic veterinary laboratory and recommendations for a surveillance system.

PubMed

Glass-Kaastra, Shiona K; Pearl, David L; Reid-Smith, Richard J; McEwen, Beverly; Slavic, Durda; McEwen, Scott A; Fairles, Jim

2014-04-01

Antimicrobial susceptibility data on Escherichia coli F4, Pasteurella multocida, and Streptococcus suis isolates from Ontario swine (January 1998 to October 2010) were acquired from a comprehensive diagnostic veterinary laboratory in Ontario, Canada. In relation to the possible development of a surveillance system for antimicrobial resistance, data were assessed for ease of management, completeness, consistency, and applicability for temporal and spatial statistical analyses. Limited farm location data precluded spatial analyses and missing demographic data limited their use as predictors within multivariable statistical models. Changes in the standard panel of antimicrobials used for susceptibility testing reduced the number of antimicrobials available for temporal analyses. Data consistency and quality could improve over time in this and similar diagnostic laboratory settings by encouraging complete reporting with sample submission and by modifying database systems to limit free-text data entry. These changes could make more statistical methods available for disease surveillance and cluster detection.

Statistical Analyses of Raw Material Data for MTM45-1/CF7442A-36% RW: CMH Cure Cycle

NASA Technical Reports Server (NTRS)

Coroneos, Rula; Pai, Shantaram, S.; Murthy, Pappu

2013-01-01

This report describes statistical characterization of physical properties of the composite material system MTM45-1/CF7442A, which has been tested and is currently being considered for use on spacecraft structures. This composite system is made of 6K plain weave graphite fibers in a highly toughened resin system. This report summarizes the distribution types and statistical details of the tests and the conditions for the experimental data generated. These distributions will be used in multivariate regression analyses to help determine material and design allowables for similar material systems and to establish a procedure for other material systems. Additionally, these distributions will be used in future probabilistic analyses of spacecraft structures. The specific properties that are characterized are the ultimate strength, modulus, and Poisson??s ratio by using a commercially available statistical package. Results are displayed using graphical and semigraphical methods and are included in the accompanying appendixes.

Detection and identification of extra virgin olive oil adulteration by GC-MS combined with chemometrics.

PubMed

Yang, Yang; Ferro, Miguel Duarte; Cavaco, Isabel; Liang, Yizeng

2013-04-17

In this study, an analytical method for the detection and identification of extra virgin olive oil adulteration with four types of oils (corn, peanut, rapeseed, and sunflower oils) was proposed. The variables under evaluation included 22 fatty acids and 6 other significant parameters (the ratio of linoleic/linolenic acid, oleic/linoleic acid, total saturated fatty acids (SFAs), polyunsaturated fatty acids (PUFAs), monounsaturated fatty acids (MUFAs), MUFAs/PUFAs). Univariate analyses followed by multivariate analyses were applied to the adulteration investigation. As a result, the univariate analyses demonstrated that higher contents of eicosanoic acid, docosanoic acid, tetracosanoic acid, and SFAs were the peculiarities of peanut adulteration and higher levels of linolenic acid, 11-eicosenoic acid, erucic acid, and nervonic acid the characteristics of rapeseed adulteration. Then, PLS-LDA made the detection of adulteration effective with a 1% detection limit and 90% prediction ability; a Monte Carlo tree identified the type of adulteration with 85% prediction ability.

Investigation of trace elements in ancient pottery from Jenini, Brong Ahafo region, Ghana by INAA and Compton suppression spectrometry

NASA Astrophysics Data System (ADS)

Nyarko, B. J. B.; Bredwa-Mensah, Y.; Serfor-Armah, Y.; Dampare, S. B.; Akaho, E. H. K.; Osae, S.; Perbi, A.; Chatt, A.

2007-10-01

Concentrations of trace elements in ancient pottery excavated from Jenini in the Brong Ahafo region of Ghana were determined using instrumental neutron activation analysis (INAA) in conjunction with both conventional and Compton suppression counting. Jenini was a slave Camp of Samory Toure during the indigenous slavery and the Trans-Atlantic slave trade. Pottery fragments found during the excavation of the grave tombs of the slaves who died in the slave camps were analysed. In all, 26 trace elements were determined in 40 pottery fragments. These elemental concentrations were processed using multivariate statistical methods, cluster, factor and discriminant analyses in order to determine similarities and correlation between the various samples. The suitability of the two counting systems for determination of trace elements in pottery objects has been evaluated.

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

EPA Science Inventory

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

Matrix metalloproteinase-2 gene variants and abdominal aortic aneurysm.

PubMed

Smallwood, L; Warrington, N; Allcock, R; van Bockxmeer, F; Palmer, L J; Iacopetta, B; Golledge, J; Norman, P E

2009-08-01

To investigate associations between two polymorphisms of the matrix metalloproteinase-2 gene (MMP2) and the incidence and progression of abdominal aortic aneurysm (AAA). Cases and controls were recruited from a trial of screening for AAAs. The association between two variants of MMP2 (-1360C>T, and +649C>T) in men with AAA (n=678) and in controls (n=659) was examined using multivariate analyses. The association with AAA expansion (n=638) was also assessed. In multivariate analyses with adjustments for multiple testing, no association between either SNP and AAA presence or expansion was detected. MMP2 -1360C>T and +649C>T variants are not risk factors for AAA.

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

PubMed

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

2017-01-01

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

Relationship between central and peripheral atherosclerosis and left ventricular dysfunction in a community population.

PubMed

Tsao, Connie W; Gona, Philimon; Salton, Carol; Murabito, Joanne M; Oyama, Noriko; Danias, Peter G; O'Donnell, Christopher J; Manning, Warren J; Yeon, Susan B

2011-08-01

We aimed to determine the relationships between resting left ventricular (LV) wall motion abnormalities (WMAs), aortic plaque, and peripheral artery disease (PAD) in a community cohort. A total of 1726 Framingham Heart Study Offspring Cohort participants (806 males, 65 ± 9 years) underwent cardiovascular magnetic resonance with quantification of aortic plaque volume and assessment of regional left ventricular systolic function. Claudication, lower extremity revascularization, and ankle-brachial index (ABI) were recorded at the most contemporaneous examination visit. WMAs were associated with greater aortic plaque burden, decreased ABI, and claudication in age- and sex-adjusted analyses (all p < 0.001), which were not significant after adjustment for cardiovascular risk factors. In age- and sex-adjusted analyses, both the presence (p < 0.001) and volume of aortic plaque were associated with decreased ABI (p < 0.001). After multivariable adjustment, an ABI ≤ 0.9 or prior revascularization was associated with a threefold odds of aortic plaque (p = 0.0083). Plaque volume significantly increased with decreasing ABI in multivariable-adjusted analyses (p < 0.0001). In this free-living population, associations of WMAs with aortic plaque burden and clinical measures of PAD were attenuated after adjustment for coronary heart disease risk factors. Aortic plaque volume and ABI remained strongly negatively correlated after multivariable adjustment. Our findings suggest that the association between coronary heart disease and non-coronary atherosclerosis is explained by cardiovascular risk factors. Aortic atherosclerosis and PAD remain strongly associated after multivariable adjustment, suggesting shared mechanisms beyond those captured by traditional risk factors.

Is the prognostic significance of O6-methylguanine- DNA methyltransferase promoter methylation equally important in glioblastomas of patients from different continents? A systematic review with meta-analysis.

PubMed

Meng, Wei; Jiang, Yangyang; Ma, Jie

2017-01-01

O6-methylguanine-DNA methyltransferase (MGMT) is an independent predictor of therapeutic response and potential prognosis in patients with glioblastoma multiforme (GBM). However, its significance of clinical prognosis in different continents still needs to be explored. To explore the effects of MGMT promoter methylation on both progression-free survival (PFS) and overall survival (OS) among GBM patients from different continents, a systematic review of published studies was conducted. A total of 5103 patients from 53 studies were involved in the systematic review and the total percentage of MGMT promoter methylation was 45.53%. Of these studies, 16 studies performed univariate analyses and 17 performed multivariate analyses of MGMT promoter methylation on PFS. The pooled hazard ratio (HR) estimated for PFS was 0.55 (95% CI 0.50, 0.60) by univariate analysis and 0.43 (95% CI 0.38, 0.48) by multivariate analysis. The effect of MGMT promoter methylation on OS was explored in 30 studies by univariate analysis and in 30 studies by multivariate analysis. The combined HR was 0.48 (95% CI 0.44, 0.52) and 0.42 (95% CI 0.38, 0.45), respectively. In each subgroup divided by areas, the prognostic significance still remained highly significant. The proportion of methylation in each group was in inverse proportion to the corresponding HR in the univariate and multivariate analyses of PFS. However, from the perspective of OS, compared with data from Europe and the US, higher methylation rates in Asia did not bring better returns.

High coffee consumption and different brewing methods in relation to postmenopausal endometrial cancer risk in the Norwegian Women and Cancer Study: a population-based prospective study

PubMed Central

2014-01-01

Background Coffee and its compounds have been proposed to inhibit endometrial carcinogenesis. Studies in the Norwegian population can be especially interesting due to the high coffee consumption and increasing incidence of endometrial cancer in the country. Methods A total of 97 926 postmenopausal Norwegian women from the population-based prospective Norwegian Women and Cancer (NOWAC) Study, were included in the present analysis. We evaluated the general association between total coffee consumption and endometrial cancer risk as well as the possible impact of brewing method. Multivariate Cox regression analysis was used to estimate risks, and heterogeneity tests were performed to compare brewing methods. Results During an average of 10.9 years of follow-up, 462 incident endometrial cancer cases were identified. After multivariate adjustment, significant risk reduction was found among participants who drank ≥8 cups/day of coffee with a hazard ratio of 0.52 (95% confidence interval, CI 0.34-0.79). However, we did not observe a significant dose-response relationship. No significant heterogeneity in risk was found when comparing filtered and boiled coffee brewing methods. A reduction in endometrial cancer risk was observed in subgroup analyses among participants who drank ≥8 cups/day and had a body mass index ≥25 kg/m2, and in current smokers. Conclusions These data suggest that in this population with high coffee consumption, endometrial cancer risk decreases in women consuming ≥8 cups/day, independent of brewing method. PMID:24666820

Determination of Diethyl Phthalate and Polyhexamethylene Guanidine in Surrogate Alcohol from Russia

PubMed Central

Monakhova, Yulia B.; Kuballa, Thomas; Leitz, Jenny; Lachenmeier, Dirk W.

2011-01-01

Analytical methods based on spectroscopic techniques were developed and validated for the determination of diethyl phthalate (DEP) and polyhexamethylene guanidine (PHMG), which may occur in unrecorded alcohol. Analysis for PHMG was based on UV-VIS spectrophotometry after derivatization with Eosin Y and 1H NMR spectroscopy of the DMSO extract. Analysis of DEP was performed with direct UV-VIS and 1H NMR methods. Multivariate curve resolution and spectra computation methods were used to confirm the presence of PHMG and DEP in the investigated beverages. Of 22 analysed alcohol samples, two contained DEP or PHMG. 1H NMR analysis also revealed the presence of signals of hawthorn extract in three medicinal alcohols used as surrogate alcohol. The simple and cheap UV-VIS methods can be used for rapid screening of surrogate alcohol samples for impurities, while 1H NMR is recommended for specific confirmatory analysis if required. PMID:21647285

Determination of diethyl phthalate and polyhexamethylene guanidine in surrogate alcohol from Russia.

PubMed

Monakhova, Yulia B; Kuballa, Thomas; Leitz, Jenny; Lachenmeier, Dirk W

2011-01-01

Analytical methods based on spectroscopic techniques were developed and validated for the determination of diethyl phthalate (DEP) and polyhexamethylene guanidine (PHMG), which may occur in unrecorded alcohol. Analysis for PHMG was based on UV-VIS spectrophotometry after derivatization with Eosin Y and (1)H NMR spectroscopy of the DMSO extract. Analysis of DEP was performed with direct UV-VIS and (1)H NMR methods. Multivariate curve resolution and spectra computation methods were used to confirm the presence of PHMG and DEP in the investigated beverages. Of 22 analysed alcohol samples, two contained DEP or PHMG. (1)H NMR analysis also revealed the presence of signals of hawthorn extract in three medicinal alcohols used as surrogate alcohol. The simple and cheap UV-VIS methods can be used for rapid screening of surrogate alcohol samples for impurities, while (1)H NMR is recommended for specific confirmatory analysis if required.

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.

Lymphopenia Association With Gross Tumor Volume and Lung V5 and Its Effects on Non-Small Cell Lung Cancer Patient Outcomes

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

Tang, Chad; Liao, Zhongxing, E-mail: zliao@mdanderson.org; Gomez, Daniel

2014-08-01

Purpose: Radiation therapy (RT) can both suppress and stimulate the immune system. We sought to investigate the mechanisms underlying radiation-induced lymphopenia and its associations with patient outcomes in non-small cell lung cancer (NSCLC). Methods and Materials: Subjects consisted of 711 patients who had received definitive RT for NSCLC. A lymphocyte nadir was calculated as the minimum lymphocyte value measured during definitive RT. Associations between gross tumor volumes (GTVs) and lung dose-volume histogram (DVH) parameters with lymphocyte nadirs were assessed with Spearman correlation coefficients. Relationships between lymphocyte nadirs with overall survival (OS) and event free survival (EFS) were evaluated with Kaplan-Meiermore » analysis and compared with log-rank test results. Multivariate regressions were conducted with linear and Cox regression analyses. All variables were analyzed as continuous if possible. Results: Larger GTVs were correlated with lower lymphocyte nadirs regardless of concurrent chemotherapy receipt (with concurrent: r = −0.26, P<.0001; without: r = −0.48, P<.0001). Analyses of lung DVH parameters revealed significant correlations at lower doses (lung V5-V10: P<.0001) that incrementally decreased and became nonsignificant at higher doses (lung V60-V70: P>.05). Of note, no significant associations were detected between GTV and lung DVH parameters with total leukocyte, neutrophil, or monocyte nadirs during RT or with lymphocyte count prior to RT. Multivariate analysis revealed larger GTV (P<.0001), receipt of concurrent chemotherapy (P<.0001), twice-daily radiation fractionation (P=.02), and stage III disease (P=.05) to be associated with lower lymphocyte nadirs. On univariate analysis, patients with higher lymphocyte nadirs exhibited significantly improved OS (hazard ratio [HR] = 0.51 per 10{sup 3} lymphocytes/μL, P=.01) and EFS (HR = 0.46 per 10{sup 3} lymphocytes/μL, P<.0001). These differences held on multivariate analyses, controlling for common disease and treatment characteristics including GTV. Conclusions: Lower lymphocyte nadirs during definitive RT were associated with larger GTVs and worse patient outcomes.« less

Inhalant Use, Inhalant-Use Disorders, and Antisocial Behavior: Findings From the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC)*

PubMed Central

Howard, Matthew O.; Perron, Brian E.; Vaughn, Michael G.; Bender, Kimberly A.; Garland, Eric

2010-01-01

Objective: Few studies have explored the topography of antisocial behavior in a nationally representative sample of inhalant users. We examined (a) the lifetime prevalence of 20 childhood and adult antisocial behaviors in inhalant users with inhalant-use disorders (IUD+) and without IUDs (IUD−); (b) the nature and strength of associations between inhalant use, IUDs, and specific antisocial behaviors in multi-variate analyses; and (c) the relationships between inhalant use, IUDs, and antisocial behaviors in a national sample of adults with antisocial personality disorder. Method: The National Epidemiologic Survey on Alcohol and Related Conditions was a multistage national survey of 43,093 U.S. residents. Respondents completed a structured psychiatric interview. Results: IUD+ and IUD− respondents were significantly younger and more likely to be unemployed, to be male, to have never married, and to report family and personal histories of alcohol and drug problems than inhalant nonusers. Family histories of alcohol problems and personal histories of drug problems were significantly more prevalent among IUD+ respondents, compared with IUD− respondents. In bivariate analyses, IUD+ and IUD− respondents evidenced significantly higher lifetime levels of all childhood and adult antisocial behaviors than inhalant nonusers. IUD+ respondents were significantly more likely than their IUD− counterparts to report bullying behavior, starting physical fights, using dangerous weapons, physical cruelty to people, staying out all night without permission, running away, and frequent truancy in childhood, as well as greater deceitfulness, impulsivity, irritability/aggressiveness, recklessness, and irresponsibility in adulthood. Multivari-ate analyses indicated that IUD+ respondents had a significantly elevated risk for childhood and adult antisocial behaviors, compared with inhalant nonusers, with the strongest effects for using dangerous weapons, physical cruelty to animals, and physical cruelty to people. Similarly, IUD+ respondents differed significantly from their IUD− counterparts primarily across measures of interpersonal violence. Among persons with antisocial personality disorder, inhalant use and IUDs were associated with greater antisocial behavior, albeit with fewer and weaker effects. Conclusions: Respondents with IUDs had pervasively elevated levels of antisocial conduct, including diverse forms of early-onset and inter-personally violent behavior. PMID:20230717

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.

A new multivariate zero-adjusted Poisson model with applications to biomedicine.

PubMed

Liu, Yin; Tian, Guo-Liang; Tang, Man-Lai; Yuen, Kam Chuen

2018-05-25

Recently, although advances were made on modeling multivariate count data, existing models really has several limitations: (i) The multivariate Poisson log-normal model (Aitchison and Ho, ) cannot be used to fit multivariate count data with excess zero-vectors; (ii) The multivariate zero-inflated Poisson (ZIP) distribution (Li et al., 1999) cannot be used to model zero-truncated/deflated count data and it is difficult to apply to high-dimensional cases; (iii) The Type I multivariate zero-adjusted Poisson (ZAP) distribution (Tian et al., 2017) could only model multivariate count data with a special correlation structure for random components that are all positive or negative. In this paper, we first introduce a new multivariate ZAP distribution, based on a multivariate Poisson distribution, which allows the correlations between components with a more flexible dependency structure, that is some of the correlation coefficients could be positive while others could be negative. We then develop its important distributional properties, and provide efficient statistical inference methods for multivariate ZAP model with or without covariates. Two real data examples in biomedicine are used to illustrate the proposed methods. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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.

Measures of Social Deprivation That Predict Health Care Access and Need within a Rational Area of Primary Care Service Delivery

PubMed Central

Butler, Danielle C; Petterson, Stephen; Phillips, Robert L; Bazemore, Andrew W

2013-01-01

Objective To develop a measure of social deprivation that is associated with health care access and health outcomes at a novel geographic level, primary care service area. Data Sources/Study Setting Secondary analysis of data from the Dartmouth Atlas, AMA Masterfile, National Provider Identifier data, Small Area Health Insurance Estimates, American Community Survey, Area Resource File, and Behavioural Risk Factor Surveillance System. Data were aggregated to primary care service areas (PCSAs). Study Design Social deprivation variables were selected from literature review and international examples. Factor analysis was used. Correlation and multivariate analyses were conducted between index, health outcomes, and measures of health care access. The derived index was compared with poverty as a predictor of health outcomes. Data Collection/Extraction Methods Variables not available at the PCSA level were estimated at block level, then aggregated to PCSA level. Principal Findings Our social deprivation index is positively associated with poor access and poor health outcomes. This pattern holds in multivariate analyses controlling for other measures of access. A multidimensional measure of deprivation is more strongly associated with health outcomes than a measure of poverty alone. Conclusions This geographic index has utility for identifying areas in need of assistance and is timely for revision of 35-year-old provider shortage and geographic underservice designation criteria used to allocate federal resources. PMID:22816561

Comparative assessment of essential and heavy metals in fruits from different geographical origins.

PubMed

Grembecka, Małgorzata; Szefer, Piotr

2013-11-01

The aim of this investigation was to estimate and compare essential and heavy metals contents in 98 commercially available fresh fruits from different geographic regions using multivariate techniques. The concentrations of 12 elements (calcium, magnesium, potassium, sodium, phophorus, cobalt (Co), manganese, iron, chromium (Cr), nickel (Ni), zinc and copper) were determined using flame atomic absorption spectrometry with deuterium-background correction. Phosphorus was determined in the form of phosphomolybdate by a spectrophotometric method. Reliability of the procedure was checked by analysis of the certified reference materials tea (NCS DC 73351), cabbage (IAEA-359) and spinach leaves (NIST-1570). Recoveries of the elements analysed varied between 85.5 and 103%, and precisions for the reference materials were 0.13-6.08%. Based on recommended dietary allowance and adequate intake estimated for essential elements, it was concluded that accessory fruits such as pineapples, raspberries and strawberries supply organism with the highest amounts of bioelements. Although accessory fruits were also found to be the greatest source of Ni among all the analysed fruits, in all the fruits Ni was more abundant than Cr and Co. Significant correlation coefficients (p < 0.001, p < 0.01 and p < 0.05) were found between concentrations of some metals in fresh fruits. Application of ANOVA Kruskal-Wallis test and multivariate techniques such as factor analysis and cluster analysis enabled us to differentiate particular botanical families and types of fruits.

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

PubMed Central

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

2002-01-01

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

Children and Adolescents Unvaccinated Against Measles: Geographic Clustering, Parents' Beliefs, and Missed Opportunities

PubMed Central

Marcuse, Edgar K.; Seward, Jane F.; Zhao, Zhen; Orenstein, Walter A.

2015-01-01

Objective We evaluated the extent to which children and adolescents were not vaccinated against measles (“unvaccinated”), clustering within U.S. counties, and factors associated with unvaccination, including parents' vaccine-related beliefs and missed opportunities. Methods We analyzed data from the 2010–2013 National Immunization Survey (NIS) and NIS-Teen Survey of households with 19- to 35-month-old children and 13- to 17-year-old adolescents, respectively. We used provider-reported vaccination histories to assess measles vaccination status. Results In 2013, 7.5% of children and 4.5% of adolescents were unvaccinated against measles. Four-fifths (80.0%) of unvaccinated children lived in counties containing 41.9% of the nation's children, and 80.0% of unvaccinated adolescents lived in counties containing 30.4% of the nation's adolescents. Multivariable statistical analyses found that 74.6% of children who were unvaccinated against measles missed being vaccinated for reasons other than parents' negative vaccine-related beliefs, and 89.6% could be deemed as having at least one missed opportunity for being vaccinated against measles because they were administered at least one dose of other recommended vaccines after 12 months of age. Among adolescents, multivariable analyses found that only demographic factors, not vaccine-related parental beliefs, were independently associated with being unvaccinated. Conclusions Reasons other than negative vaccine-related beliefs, including missed opportunities, accounted for the vast majority of unvaccinated children and adolescents. PMID:26327727

Relationship between alcohol intake, body fat, and physical activity – a population-based study

PubMed Central

Liangpunsakul, Suthat; Crabb, David W.; Qi, Rong

2010-01-01

Objectives Aside from fat, ethanol is the macronutrient with the highest energy density. Whether the energy derived from ethanol affects the body composition and fat mass is debatable. We investigated the relationship between alcohol intake, body composition, and physical activity in the US population using the third National Health and Nutrition Examination Survey (NHANES III). Methods Ten thousand five hundred and fifty subjects met eligible criteria and constituted our study cohort. Estimated percent body fat and resting metabolic rate were calculated based on the sum of the skinfolds. Multivariate regression analyses were performed accounting for the study sampling weight. Results In both genders, moderate and hazardous alcohol drinkers were younger (p<0.05), had significantly lower BMI (P<0.01) and body weight (p<0.01) than controls, non drinkers. Those with hazardous alcohol consumption had significantly less physical activity compared to those with no alcohol use and moderate drinkers in both genders. Female had significantly higher percent body fat than males. In the multivariate linear regression analyses, the levels of alcohol consumption were found to be an independent predictor associated with lower percent body fat only in male subjects. Conclusions Our results showed that alcoholics are habitually less active and that alcohol drinking is an independent predictor of lower percent body fat especially in male alcoholics. PMID:20696406

A Call for Conducting Multivariate Mixed Analyses

ERIC Educational Resources Information Center

Onwuegbuzie, Anthony J.

2016-01-01

Several authors have written methodological works that provide an introductory- and/or intermediate-level guide to conducting mixed analyses. Although these works have been useful for beginning and emergent mixed researchers, with very few exceptions, works are lacking that describe and illustrate advanced-level mixed analysis approaches. Thus,…

Concordance of Results from Randomized and Observational Analyses within the Same Study: A Re-Analysis of the Women’s Health Initiative Limited-Access Dataset

PubMed Central

Bolland, Mark J.; Grey, Andrew; Gamble, Greg D.; Reid, Ian R.

2015-01-01

Background Observational studies (OS) and randomized controlled trials (RCTs) often report discordant results. In the Women’s Health Initiative Calcium and Vitamin D (WHI CaD) RCT, women were randomly assigned to CaD or placebo, but were permitted to use personal calcium and vitamin D supplements, creating a unique opportunity to compare results from randomized and observational analyses within the same study. Methods WHI CaD was a 7-year RCT of 1g calcium/400IU vitamin D daily in 36,282 post-menopausal women. We assessed the effects of CaD on cardiovascular events, death, cancer and fracture in a randomized design- comparing CaD with placebo in 43% of women not using personal calcium or vitamin D supplements- and in a observational design- comparing women in the placebo group (44%) using personal calcium and vitamin D supplements with non-users. Incidence was assessed using Cox proportional hazards models, and results from the two study designs deemed concordant if the absolute difference in hazard ratios was ≤0.15. We also compared results from WHI CaD to those from the WHI Observational Study(WHI OS), which used similar methodology for analyses and recruited from the same population. Results In WHI CaD, for myocardial infarction and stroke, results of unadjusted and 6/8 covariate-controlled observational analyses (age-adjusted, multivariate-adjusted, propensity-adjusted, propensity-matched) were not concordant with the randomized design results. For death, hip and total fracture, colorectal and total cancer, unadjusted and covariate-controlled observational results were concordant with randomized results. For breast cancer, unadjusted and age-adjusted observational results were concordant with randomized results, but only 1/3 other covariate-controlled observational results were concordant with randomized results. Multivariate-adjusted results from WHI OS were concordant with randomized WHI CaD results for only 4/8 endpoints. Conclusions Results of randomized analyses in WHI CaD were concordant with observational analyses for 5/8 endpoints in WHI CaD and 4/8 endpoints in WHI OS. PMID:26440516

On the Numerical Formulation of Parametric Linear Fractional Transformation (LFT) Uncertainty Models for Multivariate Matrix Polynomial Problems

NASA Technical Reports Server (NTRS)

Belcastro, Christine M.

1998-01-01

Robust control system analysis and design is based on an uncertainty description, called a linear fractional transformation (LFT), which separates the uncertain (or varying) part of the system from the nominal system. These models are also useful in the design of gain-scheduled control systems based on Linear Parameter Varying (LPV) methods. Low-order LFT models are difficult to form for problems involving nonlinear parameter variations. This paper presents a numerical computational method for constructing and LFT model for a given LPV model. The method is developed for multivariate polynomial problems, and uses simple matrix computations to obtain an exact low-order LFT representation of the given LPV system without the use of model reduction. Although the method is developed for multivariate polynomial problems, multivariate rational problems can also be solved using this method by reformulating the rational problem into a polynomial form.

Bivariate versus multivariate smart spectrophotometric calibration methods for the simultaneous determination of a quaternary mixture of mosapride, pantoprazole and their degradation products.

PubMed

Hegazy, M A; Yehia, A M; Moustafa, A A

2013-05-01

The ability of bivariate and multivariate spectrophotometric methods was demonstrated in the resolution of a quaternary mixture of mosapride, pantoprazole and their degradation products. The bivariate calibrations include bivariate spectrophotometric method (BSM) and H-point standard addition method (HPSAM), which were able to determine the two drugs, simultaneously, but not in the presence of their degradation products, the results showed that simultaneous determinations could be performed in the concentration ranges of 5.0-50.0 microg/ml for mosapride and 10.0-40.0 microg/ml for pantoprazole by bivariate spectrophotometric method and in the concentration ranges of 5.0-45.0 microg/ml for both drugs by H-point standard addition method. Moreover, the applied multivariate calibration methods were able for the determination of mosapride, pantoprazole and their degradation products using concentration residuals augmented classical least squares (CRACLS) and partial least squares (PLS). The proposed multivariate methods were applied to 17 synthetic samples in the concentration ranges of 3.0-12.0 microg/ml mosapride, 8.0-32.0 microg/ml pantoprazole, 1.5-6.0 microg/ml mosapride degradation products and 2.0-8.0 microg/ml pantoprazole degradation products. The proposed bivariate and multivariate calibration methods were successfully applied to the determination of mosapride and pantoprazole in their pharmaceutical preparations.

Weekly Low-Dose Docetaxel-Based Chemoradiotherapy for Locally Advanced Oropharyngeal or Hypopharyngeal Carcinoma: A Retrospective, Single-Institution Study

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

Fukada, Junichi, E-mail: fukada@sc.itc.keio.ac.j; Shigematsu, Naoyuki; Takeda, Atsuya

2010-02-01

Purpose: To retrospectively assess the efficacy, toxicity, and prognostic factors of weekly low-dose docetaxel-based chemoradiotherapy for Stage III/IV oropharyngeal or hypopharyngeal carcinoma. Methods and Materials: Between 2001 and 2005, 72 consecutive patients with locally advanced oropharyngeal or hypopharyngeal carcinoma were treated with concurrent chemoradiotherapy (CCR; radiation at 60 Gy plus weekly docetaxel [10 mg/m{sup 2}]). Thirty of these patients also received neoadjuvant chemotherapy (NAC; docetaxel, cisplatin, and 5-fluorouracil) before concurrent chemoradiotherapy. Survival was calculated according to the Kaplan-Meier method. The prognostic factors were evaluated by univariate and multivariate analyses. Results: The median follow-up was 33 months, with overall survival, disease-freemore » survival, and locoregional control rates at 3 years of 59%, 45%, and 52%, respectively. Thirty-six patients (50%) experienced more than one Grade 3 to 4 acute toxicity. Grade 3 mucositis occurred in 32 patients (44%), Grade 4 laryngeal edema in 1 (1%). Grade >=3 severe hematologic toxicity was observed in only 2 patients (3%). Grade 3 dysphagia occurred as a late complication in 2 patients (3%). Multivariate analyses identified age, T stage, hemoglobin level, and completion of weekly docetaxel, but not NAC, as significant factors determining disease-free survival. Conclusions: Docetaxel is an active agent used in both concurrent and sequential chemoradiotherapy regimens. Mucositis was the major acute toxicity, but this was well tolerated in most subjects. Anemia was the most significant prognostic factor determining survival. Further studies are warranted to investigate the optimal protocol for integrating docetaxel into first-line chemoradiotherapy regimens, as well as the potential additive impact of NAC.« less

Linear Accelerator-Based Radiosurgery Alone for Arteriovenous Malformation: More Than 12 Years of Observation

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

Matsuo, Takayuki, E-mail: takayuki@nagasaki-u.ac.jp; Kamada, Kensaku; Izumo, Tsuyoshi

Purpose: Although radiosurgery is an accepted treatment method for intracranial arteriovenous malformations (AVMs), its long-term therapeutic effects have not been sufficiently evaluated, and many reports of long-term observations are from gamma-knife facilities. Furthermore, there are few reported results of treatment using only linear accelerator (LINAC)-based radiosurgery (LBRS). Methods and Materials: Over a period of more than 12 years, we followed the long-term results of LBRS treatment performed in 51 AVM patients. Results: The actuarial obliteration rates, after a single radiosurgery session, at 3, 5, 10, and 15 years were 46.9%, 54.0%, 64.4%, and 68.0%, respectively; when subsequent radiosurgeries were included, themore » rates were 46.9%, 61.3%, 74.2%, and 90.3%, respectively. Obliteration rates were significantly related to target volumes ≥4 cm{sup 3}, marginal doses ≥12 Gy, Spetzler-Martin grades (1 vs other), and AVM scores ≥1.5; multivariate analyses revealed a significant difference for target volumes ≥4 cm{sup 3}. The postprocedural actuarial symptomatic radiation injury rates, after a single radiation surgery session, at 5, 10, and 15 years were 12.3%, 16.8%, and 19.1%, respectively. Volumes ≥4 cm{sup 3}, location (lobular or other), AVM scores ≥1.5, and the number of radiosurgery were related to radiation injury incidence; multivariate analyses revealed significant differences associated with volumes ≥4 cm{sup 3} and location (lobular or other). Conclusions: Positive results can be obtained with LBRS when performed with a target volume ≤4 cm{sup 3}, an AVM score ≤1.5, and ≥12 Gy radiation. Bleeding and radiation injuries may appear even 10 years after treatment, necessitating long-term observation.« less

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.

Toward high value sensing: monolayer-protected metal nanoparticles in multivariable gas and vapor sensors.

PubMed

Potyrailo, Radislav A

2017-08-29

For detection of gases and vapors in complex backgrounds, "classic" analytical instruments are an unavoidable alternative to existing sensors. Recently a new generation of sensors, known as multivariable sensors, emerged with a fundamentally different perspective for sensing to eliminate limitations of existing sensors. In multivariable sensors, a sensing material is designed to have diverse responses to different gases and vapors and is coupled to a multivariable transducer that provides independent outputs to recognize these diverse responses. Data analytics tools provide rejection of interferences and multi-analyte quantitation. This review critically analyses advances of multivariable sensors based on ligand-functionalized metal nanoparticles also known as monolayer-protected nanoparticles (MPNs). These MPN sensing materials distinctively stand out from other sensing materials for multivariable sensors due to their diversity of gas- and vapor-response mechanisms as provided by organic and biological ligands, applicability of these sensing materials for broad classes of gas-phase compounds such as condensable vapors and non-condensable gases, and for several principles of signal transduction in multivariable sensors that result in non-resonant and resonant electrical sensors as well as material- and structure-based photonic sensors. Such features should allow MPN multivariable sensors to be an attractive high value addition to existing analytical instrumentation.

Reprint of: Relationship between cataract severity and socioeconomic status.

PubMed

Wesolosky, Jason D; Rudnisky, Christopher J

2015-06-01

To determine the relationship between cataract severity and socioeconomic status (SES). Retrospective, observational case series. A total of 1350 eyes underwent phacoemulsification cataract extraction by a single surgeon using an Alcon Infiniti system. Cataract severity was measured using phaco time in seconds. SES was measured using area-level aggregate census data: median income, education, proportion of common-law couples, and employment rate. Preoperative best corrected visual acuity was obtained and converted to logarithm of the minimum angle of resolution values. For patients undergoing bilateral surgery, the generalized estimating equation was used to account for the correlation between eyes. Univariate analyses were performed using simple regression, and multivariate analyses were performed to account for variables with significant relationships (p < 0.05) on univariate testing. Sensitivity analyses were performed to assess the effect of including patient age in the controlled analyses. Multivariate analyses demonstrated that cataracts were more severe when the median income was lower (p = 0.001) and the proportion of common-law couples living in a patient's community (p = 0.012) and the unemployment rate (p = 0.002) were higher. These associations persisted even when controlling for patient age. Patients of lower SES have more severe cataracts. Copyright © 2015. Published by Elsevier Inc.

An online spaced-education game for global continuing medical education: a randomized trial.

PubMed

Kerfoot, B Price; Baker, Harley

2012-07-01

To assess the efficacy of a "spaced-education" game as a method of continuing medical education (CME) among physicians across the globe. The efficacy of educational games for the CME has yet to be established. We created a novel online educational game by incorporating game mechanics into "spaced education" (SE), an evidence-based method of online CME. This 34-week randomized trial enrolled practicing urologists across the globe. The SE game consisted of 40 validated multiple-choice questions and explanations on urology clinical guidelines. Enrollees were randomized to 2 cohorts: cohort A physicians were sent 2 questions via an automated e-mail system every 2 days, and cohort B physicians were sent 4 questions every 4 days. Adaptive game mechanics re-sent the questions in 12 or 24 days if answered incorrectly and correctly, respectively. Questions expired if not answered on time (appointment dynamic). Physicians retired questions by answering each correctly twice-in-a-row (progression dynamic). Competition was fostered by posting relative performance among physicians. Main outcome measures were baseline scores (percentage of questions answered correctly upon initial presentation) and completion scores (percentage of questions retired). A total of 1470 physicians from 63 countries enrolled. Median baseline score was 48% (interquartile range [IQR] 17) and, in multivariate analyses, was found to vary significantly by region (Cohen dmax = 0.31, P = 0.001) and age (dmax = 0.41, P < 0.001). Median completion score was 98% (IQR 25) and varied significantly by age (dmax = 0.21, P < 0.001) and American Board of Urology certification (d = 0.10, P = 0.033) but not by region (multivariate analyses). Question clustering reduced physicians' performance (d = 0.43, P < 0.001). Seventy-six percent of enrollees (1111/1470) requested to participate in future SE games. An online SE game can substantially improve guidelines knowledge and is a well-accepted method of global CME delivery.

Radiotherapy is associated with significant improvement in local and regional control in Merkel cell carcinoma

PubMed Central

2012-01-01

Introduction Merkel cell carcinoma (MCC) is a rare tumour of skin. This study is a retrospective audit of patients with MCC from St Vincent’s and Mater Hospital, Sydney, Australia. The aim of this study was to investigate the influence of radiotherapy (RT) on the local and regional control of MCC lesions and survival of patients with MCC. Method The data bases in anatomical pathology, RT and surgery. We searched for patients having a diagnosis of MCC between 1996 and 2007. Patient, tumour and treatment characteristics were collected and analysed. Univariate survival analysis of categorical variables was conducted with the Kaplan-Meier method together with the Log-Rank test for statistical significance. Continuous variables were assessed using the Cox regression method. Multivariate analysis was performed for significant univariate results. Results Sixty seven patients were found. Sixty two who were stage I-III and were treated with radical intent were analysed. 68% were male. The median age was 74 years. Forty-two cases (68%) were stage I or II, and 20 cases (32%) were stage III. For the subset of 42 stage I and II patients, those that had RT to their primary site had a 2-year local recurrence free survival of 89% compared with 36% for patients not receiving RT (p<0.001). The cumulative 2-year regional recurrence free survival for patients having adjuvant regional RT was 84% compared with 43% for patients not receiving this treatment (p<0.001). Immune status at initial surgery was a significant predictor for OS and MCCSS. In a multivariate analysis combining macroscopic size (mm) and immune status at initial surgery, only immune status remained a significant predictor of overall survival (HR=2.096, 95% CI: 1.002-4.385, p=0.049). Conclusions RT is associated with significant improvement in local and regional control in Merkel cell carcinoma. Immunosuppression is an important factor in overall survival. PMID:23075308

Classical least squares multivariate spectral analysis

DOEpatents

Haaland, David M.

2002-01-01

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

Multivariate analysis: A statistical approach for computations

NASA Astrophysics Data System (ADS)

Michu, Sachin; Kaushik, Vandana

2014-10-01

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

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.

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.

Factors Affecting Female Teachers' Attitudes toward Help-Seeking or Help-Avoidance in Coping with Behavioral Problems

ERIC Educational Resources Information Center

Inbar-Furst, Hagit; Gumpel, Thomas P.

2015-01-01

Questionnaires were given to 392 elementary school teachers to examine help-seeking or help-avoidance in dealing with classroom behavioral problems. Scale validity was examined through a series of exploratory and confirmatory factor analyses. Using a series of multivariate regression analyses and structural equation modeling, we identified…

Reporting Practices and Use of Quantitative Methods in Canadian Journal Articles in Psychology.

PubMed

Counsell, Alyssa; Harlow, Lisa L

2017-05-01

With recent focus on the state of research in psychology, it is essential to assess the nature of the statistical methods and analyses used and reported by psychological researchers. To that end, we investigated the prevalence of different statistical procedures and the nature of statistical reporting practices in recent articles from the four major Canadian psychology journals. The majority of authors evaluated their research hypotheses through the use of analysis of variance (ANOVA), t -tests, and multiple regression. Multivariate approaches were less common. Null hypothesis significance testing remains a popular strategy, but the majority of authors reported a standardized or unstandardized effect size measure alongside their significance test results. Confidence intervals on effect sizes were infrequently employed. Many authors provided minimal details about their statistical analyses and less than a third of the articles presented on data complications such as missing data and violations of statistical assumptions. Strengths of and areas needing improvement for reporting quantitative results are highlighted. The paper concludes with recommendations for how researchers and reviewers can improve comprehension and transparency in statistical reporting.

Application of ¹H NMR for the characterisation and authentication of ''Tonda Gentile Trilobata" hazelnuts from Piedmont (Italy).

PubMed

Caligiani, Augusta; Coisson, Jean Daniel; Travaglia, Fabiano; Acquotti, Domenico; Palla, Gerardo; Palla, Luigi; Arlorio, Marco

2014-04-01

The Italian hazelnut (Corylus avellana L.) cultivar "Tonda Gentile Trilobata" (TGT) is covered by protected geographical indication "Nocciola Piemonte" and is well-known as the best-suited hazelnut for the industrial transformation into roasted kernel. The hazelnut cultivar identification is primarily based on morphological characteristics, so there is the need for more objective analytical methods for high quality hazelnut authentication. This study reports the (1)H NMR fingerprinting of raw and roasted hazelnut, with the aim of obtaining hazelnut classification based on their spectroscopic pattern. (1)H NMR analyses were carried out on polar extracts of TGT and other cultivars: the data were analysed with multivariate statistical methods. Results showed that (1)H NMR combined with chemometrics is useful to characterise the hazelnuts as a function of the cultivars, both on raw and roasted form. The classification models allowed identifying molecular markers useful to distinguish TGT from other types, among these trigonelline, amino acids and an unidentified orto-disubstituted aromatic compound. Copyright © 2013 Elsevier Ltd. All rights reserved.

Using a latent variable model with non-constant factor loadings to examine PM2.5 constituents related to secondary inorganic aerosols.

PubMed

Zhang, Zhenzhen; O'Neill, Marie S; Sánchez, Brisa N

2016-04-01

Factor analysis is a commonly used method of modelling correlated multivariate exposure data. Typically, the measurement model is assumed to have constant factor loadings. However, from our preliminary analyses of the Environmental Protection Agency's (EPA's) PM 2.5 fine speciation data, we have observed that the factor loadings for four constituents change considerably in stratified analyses. Since invariance of factor loadings is a prerequisite for valid comparison of the underlying latent variables, we propose a factor model that includes non-constant factor loadings that change over time and space using P-spline penalized with the generalized cross-validation (GCV) criterion. The model is implemented using the Expectation-Maximization (EM) algorithm and we select the multiple spline smoothing parameters by minimizing the GCV criterion with Newton's method during each iteration of the EM algorithm. The algorithm is applied to a one-factor model that includes four constituents. Through bootstrap confidence bands, we find that the factor loading for total nitrate changes across seasons and geographic regions.

Practice-based evidence study design for comparative effectiveness research.

PubMed

Horn, Susan D; Gassaway, Julie

2007-10-01

To describe a new, rigorous, comprehensive practice-based evidence for clinical practice improvement (PBE-CPI) study methodology, and compare its features, advantages, and disadvantages to those of randomized controlled trials and sophisticated statistical methods for comparative effectiveness research. PBE-CPI incorporates natural variation within data from routine clinical practice to determine what works, for whom, when, and at what cost. It uses the knowledge of front-line caregivers, who develop study questions and define variables as part of a transdisciplinary team. Its comprehensive measurement framework provides a basis for analyses of significant bivariate and multivariate associations between treatments and outcomes, controlling for patient differences, such as severity of illness. PBE-CPI studies can uncover better practices more quickly than randomized controlled trials or sophisticated statistical methods, while achieving many of the same advantages. We present examples of actionable findings from PBE-CPI studies in postacute care settings related to comparative effectiveness of medications, nutritional support approaches, incontinence products, physical therapy activities, and other services. Outcomes improved when practices associated with better outcomes in PBE-CPI analyses were adopted in practice.

Multielement geochemistry identifies the spatial pattern of soil and sediment contamination in an urban parkland, Western Australia.

PubMed

Rate, Andrew W

2018-06-15

Urban environments are dynamic and highly heterogeneous, and multiple additions of potential contaminants are likely on timescales which are short relative to natural processes. The likely sources and location of soil or sediment contamination in urban environment should therefore be detectable using multielement geochemical composition combined with rigorously applied multivariate statistical techniques. Soil, wetland sediment, and street dust was sampled along intersecting transects in Robertson Park in metropolitan Perth, Western Australia. Samples were analysed for near-total concentrations of multiple elements (including Cd, Ce, Co, Cr, Cu, Fe, Gd, La, Mn, Nd, Ni, Pb, Y, and Zn), as well as pH, and electrical conductivity. Samples at some locations within Robertson Park had high concentrations of potentially toxic elements (Pb above Health Investigation Limits; As, Ba, Cu, Mn, Ni, Pb, V, and Zn above Ecological Investigation Limits). However, these concentrations carry low risk due to the main land use as recreational open space, the low proportion of samples exceeding guideline values, and a tendency for the highest concentrations to be located within the less accessible wetland basin. The different spatial distributions of different groups of contaminants was consistent with different inputs of contaminants related to changes in land use and technology over the history of the site. Multivariate statistical analyses reinforced the spatial information, with principal component analysis identifying geochemical associations of elements which were also spatially related. A multivariate linear discriminant model was able to discriminate samples into a-priori types, and could predict sample type with 84% accuracy based on multielement composition. The findings suggest substantial advantages of characterising a site using multielement and multivariate analyses, an approach which could benefit investigations of other sites of concern. Copyright © 2018 Elsevier B.V. All rights reserved.

An Exploratory Study of Fatigue and Physical Activity in Canadian Thyroid Cancer Patients.

PubMed

Alhashemi, Ahmad; Jones, Jennifer M; Goldstein, David P; Mina, Daniel Santa; Thabane, Lehana; Sabiston, Catherine M; Chang, Eugene K; Brierley, James D; Sawka, Anna M

2017-09-01

Fatigue is common among cancer survivors, but fatigue in thyroid cancer (TC) survivors may be under-appreciated. This study investigated the severity and prevalence of moderate and severe fatigue in TC survivors. Potential predictive factors, including physical activity, were explored. A cross-sectional, written, self-administered TC patient survey and retrospective chart review were performed in an outpatient academic Endocrinology clinic in Toronto, Canada. The primary outcome measure was the global fatigue score measured by the Brief Fatigue Inventory (BFI). Physical activity was evaluated using the International Physical Activity Questionnaire-7 day (IPAQ-7). Predictors of BFI global fatigue score were explored in univariate analyses and a multivariable linear regression model. The response rate was 63.1% (205/325). Three-quarters of the respondents were women (152/205). The mean age was 52.5 years, and the mean time since first TC surgery was 6.8 years. The mean global BFI score was 3.5 (standard deviation 2.4) out of 10 (10 is worst). The prevalence of moderate-severe fatigue (global BFI score 4.1-10 out of 10) was 41.4% (84/203). Individuals who were unemployed or unable to work due to disability reported significantly higher levels of fatigue compared to the rest of the study population, in uni-and multivariable analyses. Furthermore, increased physical activity was associated with reduced fatigue in uni- and multivariable analyses. Other socio-demographic, disease, or biochemical variables were not significantly associated with fatigue in the multivariable model. Moderate or severe fatigue was reported in about 4/10 TC survivors. Independent predictors of worse fatigue included unemployment and reduced physical activity.

Angiotensin-converting Enzyme Inhibitor and Statin Medication Use and Incident Mobility Limitation in Community Older Adults. The Health, Aging and Body Composition Study

PubMed Central

Gray, Shelly L.; Boudreau, Robert M.; Newman, Anne B.; Studenski, Stephanie A.; Shorr, Ronald I; Bauer, Douglas C.; Simonsick, Eleanor M.; Hanlon, Joseph T

2012-01-01

Objective Angiotensin-converting enzyme (ACE) inhibitors and statin medications have been proposed as potential agents to prevent or delay physical disability; yet limited research has evaluated whether such use in older community dwelling adults is associated with a lower risk of incident mobility limitation. Design Longitudinal cohort study Setting Health, Aging and Body Composition (Health ABC) Participants 3055 participants who were well functioning at baseline (e.g., no mobility limitations). Measurements Summated standardized daily doses (low, medium and high) and duration of ACE inhibitor and statin use was computed. Mobility limitation (two consecutive self-reports of having any difficulty walking 1/4 mile or climbing 10 steps without resting) was assessed every 6 months after baseline. Multivariable Cox proportional hazard analyses were conducted adjusting for demographics, health status, and health behaviors. Results At baseline, ACE inhibitors and statins were used by 15.2% and 12.9%, respectively and both increased to over 25% by year 6. Over 6.5 years of follow-up, 49.8% had developed mobility limitation. In separate multivariable models, neither ACE inhibitor (multivariate hazard ratio [HR] 0.95; 95% confidence interval [CI] 0.82–1.09) nor statin use (multivariate HR 1.02; 95% CI 0.87–1.17) was associated with a lower risk for mobility limitation. Similar findings were seen in analyses examining dose- and duration-response relationships and sensitivity analyses restricted to those with hypertension. Conclusions These findings indicate that ACE inhibitors and statins widely prescribed to treat hypertension and hypercholesterolemia, respectively do not lower risk of mobility limitation, an important life quality indicator. PMID:22092102

Fathers' Depression Related to Positive and Negative Parenting Behaviors With 1-Year-Old Children

PubMed Central

Davis, Matthew M.; Freed, Gary L.; Clark, Sarah J.

2011-01-01

OBJECTIVE: To examine the associations between depression in fathers of 1-year-old children and specific positive and negative parenting behaviors discussed by pediatric providers at well-child visits. METHODS: We performed a cross-sectional secondary analysis by using interview data from 1746 fathers of 1-year-old children in the Fragile Families and Child Wellbeing Study. Positive parenting behaviors included fathers' reports of playing games, singing songs, and reading stories to their children ≥3 days in a typical week. Negative parenting behavior included fathers' reports of spanking their 1-year-old children in the previous month. Depression was assessed by using the World Health Organization Composite International Diagnostic Interview Short Form. Weighted bivariate and multivariate analyses of parenting behaviors were performed while controlling for demographics and paternal substance abuse. RESULTS: Overall, 7% of fathers had depression. In bivariate analyses, depressed fathers were more likely than nondepressed fathers to report spanking their 1-year-old children in the previous month (41% compared with 13%; P < .01). In multivariate analyses, depressed fathers were less likely to report reading to their children ≥3 days in a typical week (adjusted odds ratio: 0.38 [95% confidence interval: 0.15–0.98]) and much more likely to report spanking (adjusted odds ratio: 3.92 [95% confidence interval: 1.23–12.5]). Seventy-seven percent of depressed fathers reported talking to their children's doctor in the previous year. CONCLUSIONS: Paternal depression is associated with parenting behaviors relevant to well-child visits. Pediatric providers should consider screening fathers for depression, discussing specific parenting behaviors (eg, reading to children and appropriate discipline), and referring for treatment if appropriate. PMID:21402627

Identifying Children At Risk for Being Bullies in the US

PubMed Central

Shetgiri, Rashmi; Lin, Hua; Flores, Glenn

2012-01-01

Objective To identify risk factors associated with the highest and lowest prevalence of bullying perpetration among US children. Methods Using the 2001–2002 Health Behavior in School-Aged Children, a nationally-representative survey of US children in 6th–10th grades, bivariate analyses were conducted to identify factors associated with any (≥ once or twice), moderate (≥ two-three times/month), and frequent (≥ weekly) bullying. Stepwise multivariable analyses identified risk factors associated with bullying. Recursive partitioning analysis (RPA) identified risk factors which, in combination, identify students with the highest and lowest bullying prevalence. Results The prevalence of any bullying in the 13,710 students was 37.3%, moderate bullying was 12.6%, and frequent bullying was 6.6%. Characteristics associated with bullying were similar in the multivariable analyses and RPA clusters. In RPA, the highest prevalence of any bullying (67%) accrued in children with a combination of fighting and weapon-carrying. Students who carry weapons, smoke, and drink alcohol more than 5–6 days weekly were at highest risk for moderate bullying (61%). Those who carry weapons, smoke, drink > once daily, have above-average academic performance, moderate/high family affluence, and feel irritable or bad-tempered daily were at highest risk for frequent bullying (68%). Conclusions Risk clusters for any, moderate, and frequent bullying differ. Children who fight and carry weapons are at highest risk of any bullying. Weapon-carrying, smoking, and alcohol use are included in the highest risk clusters for moderate and frequent bullying. Risk-group categories may be useful to providers in identifying children at highest risks for bullying and in targeting interventions. PMID:22989731

Predictors of axillary lymph node metastases in women with early breast cancer in Singapore.

PubMed

Tan, L G L; Tan, Y Y; Heng, D; Chan, M Y

2005-12-01

The presence of axillary lymph node metastases is an important prognostic factor in breast cancer. Sentinel lymph node biopsy (SLNB) is an emerging method for the staging of the axilla. It is hoped that with SLNB, the morbidity from axillary lymph node dissection (ALND) can be avoided without compromising the staging and management of early breast cancer. However, only patients found to be SLNB negative benefit from this procedure, as those with positive SLNB may still require ALND. Our objective is to study the various clinico-pathological factors to find predictive factors for axillary lymph node involvement in early breast cancer. It is hoped that with these factors, we will be better able to identify groups of patients most likely to benefit from SLNB. A retrospective study of 380 early breast cancer cases (stage T1 and T2, N0, N1, M0) in women treated in the Department of General Surgery, Tan Tock Seng Hospital, between January 1999 and August 2002, was conducted. Incidence of nodal metastases was correlated with clinico-pathological factors, and analysed by univariate and multivariate analyses. Approximately 35 percent of the 380 cases of early breast cancer had nodal metastases. Multivariate analyses revealed four independent predictors of node positivity: tumour size (p-value equals 0.0001), presence of lymphovascular invasion (p-value is less than 0.0001), tumours with histology other than invasive ductal or lobular carcinoma (p-value equals 0.04), and presence of progesterone receptors (p-value equals 0.05). We have found independent preoperative predictive factors in our local population for the presence of nodal metastases. This information can aid patient selection for SLNB and improve patient counselling.

Relationship between water quality and macro-scale parameters (land use, erosion, geology, and population density) in the Siminehrood River Basin.

PubMed

Bostanmaneshrad, Farshid; Partani, Sadegh; Noori, Roohollah; Nachtnebel, Hans-Peter; Berndtsson, Ronny; Adamowski, Jan Franklin

2018-10-15

To date, few studies have investigated the simultaneous effects of macro-scale parameters (MSPs) such as land use, population density, geology, and erosion layers on micro-scale water quality variables (MSWQVs). This research focused on an evaluation of the relationship between MSPs and MSWQVs in the Siminehrood River Basin, Iran. In addition, we investigated the importance of water particle travel time (hydrological distance) on this relationship. The MSWQVs included 13 physicochemical and biochemical parameters observed at 15 stations during three seasons. Primary screening was performed by utilizing three multivariate statistical analyses (Pearson's correlation, cluster and discriminant analyses) in seven series of observed data. These series included three separate seasonal data, three two-season data, and aggregated three-season data for investigation of relationships between MSPs and MSWQVs. Coupled data (pairs of MSWQVs and MSPs) repeated in at least two out of three statistical analyses were selected for final screening. The primary screening results demonstrated significant relationships between land use and phosphorus, total solids and turbidity, erosion levels and electrical conductivity, and erosion and total solids. Furthermore, water particle travel time effects were considered through three geographical pattern definitions of distance for each MSP by using two weighting methods. To find effective MSP factors on MSWQVs, a multivariate linear regression analysis was employed. Then, preliminary equations that estimated MSWQVs were developed. The preliminary equations were modified to adaptive equations to obtain the final models. The final models indicated that a new metric, referred to as hydrological distance, provided better MSWQV estimation and water quality prediction compared to the National Sanitation Foundation Water Quality Index. Crown Copyright © 2018. Published by Elsevier B.V. All rights reserved.

The pattern of choosing dialysis modality and related mortality outcomes in Korea: a national population-based study

PubMed Central

Kim, Hyung Jong; Park, Jung Tak; Han, Seung Hyeok; Yoo, Tae-Hyun; Park, Hyeong-Cheon; Kang, Shin-Wook; Kim, Kyoung Hoon; Ryu, Dong-Ryeol; Kim, Hyunwook

2017-01-01

Background/Aims Since comorbidities are major determinants of modality choice, and also interact with dialysis modality on mortality outcomes, we examined the pattern of modality choice according to comorbidities and then evaluated how such choices affected mortality in incident dialysis patients. Methods We analyzed 32,280 incident dialysis patients in Korea. Patterns in initial dialysis choice were assessed by multivariate logistic regression analyses. Multivariate Poisson regression analyses were performed to evaluate the effects of interactions between comorbidities and dialysis modality on mortality and to quantify these interactions using the synergy factor. Results Prior histories of myocardial infarction (p = 0.031), diabetes (p = 0.001), and congestive heart failure (p = 0.003) were independent factors favoring the initiation with peritoneal dialysis (PD), but were associated with increased mortality with PD. In contrast, a history of cerebrovascular disease and 1-year increase in age favored initiation with hemodialysis (HD) and were related to a survival benefit with HD (p < 0.001, both). While favoring initiation with HD, having Medical Aid (p = 0.001) and male gender (p = 0.047) were related to increased mortality with HD. Furthermore, although the severity of comorbidities did not inf luence dialysis modality choice, mortality in incident PD patients was significantly higher compared to that in HD patients as the severity of comorbidities increased (p for trend < 0.001). Conclusions Some comorbidities exerted independent effects on initial choice of dialysis modality, but this choice did not always lead to the best results. Further analyses of the pattern of choosing dialysis modality according to baseline comorbid conditions and related consequent mortality outcomes are needed. PMID:28651309

Patient-reported safety incidents in older patients with long-term conditions: a large cross-sectional study

PubMed Central

Panagioti, Maria; Blakeman, Thomas; Hann, Mark; Bower, Peter

2017-01-01

Background Increasing evidence suggests that patient safety is a serious concern for older patients with long-term conditions. Despite this, there is a lack of research on safety incidents encountered by this patient group. In this study, we sought to examine patient reports of safety incidents and factors associated with reports of safety incidents in older patients with long-term conditions. Methods The baseline cross-sectional data from a longitudinal cohort study were analysed. Older patients (n=3378 aged 65 years and over) with a long-term condition registered in general practices were included in the study. The main outcome was patient-reported safety incidents including availability and appropriateness of medical tests and prescription of wrong types or doses of medication. Binary univariate and multivariate logistic regression analyses were undertaken to examine factors associated with patient-reported safety incidents. Results Safety incidents were reported by 11% of the patients. Four factors were significantly associated with patient-reported safety incidents in multivariate analyses. The experience of multiple long-term conditions (OR=1.09, 95% CI 1.05 to 1.13), a probable diagnosis of depression (OR=1.36, 95% CI 1.06 to 1.74) and greater relational continuity of care (OR=1.28, 95% CI 1.08 to 1.52) were associated with increased odds for patient-reported safety incidents. Perceived greater support and involvement in self-management was associated with lower odds for patient-reported safety incidents (OR=0.95, 95% CI 0.93 to 0.97). Conclusions We found that older patients with multimorbidity and depression are more likely to report experiences of patient safety incidents. Improving perceived support and involvement of patients in their care may help prevent patient-reported safety incidents. PMID:28559454

Adverse obstetric outcomes in pregnant women with uterine fibroids in China: A multicenter survey involving 112,403 deliveries

PubMed Central

Zhao, Rong; Wang, Xin; Zou, Liying; Li, Guanghui; Chen, Yi; Li, Changdong; Zhang, Weiyuan

2017-01-01

Objective To estimate the association between uterine fibroids and adverse obstetric outcomes. Methods This was a retrospective cross-sectional study of 112,403 deliveries from 14 provinces and 39 different hospitals in 2011 in mainland China. We compared pregnancy outcomes in women with and without uterine fibroids who underwent detailed second trimester obstetric ultrasonography during 18 to 22 weeks. Obstetric outcomes include cesarean delivery, breech presentation, preterm delivery, placenta previa, placental abruption, premature rupture of membranes and neonatal birthweight. Univariate analyses and multivariate logistic regression analyses were performed. Results Of 112,403 women who underwent routine obstetric survey, 3,012 (2.68%) women were identified with at least 1 fibroid. By univariate and multivariate analyses, the presence of uterine fibroids was significantly associated with cesarean delivery (Adjusted odds radio [AOR] 1.8, 95% confidence interval [CI] 1.7–2.0), breech presentation (AOR 1.3, 95% CI 1.2–1.5) and postpartum hemorrhage (AOR 1.2, 95% CI 1.1–1.4). The size of uterine fibroids and location in uterus had important effect on the mode of delivery. The rates of PPH were significantly higher with increasing size of the uterine fibroid (P<0.001). And the location of fibroid (intramural, submucosal or subserosal) also have a statistically significant impact on the risk of PPH (5.6% [subserosal] vs 4.7% [submucosal] vs 8.6% [intramural]). Conclusion Pregnant women with uterine fibroids are at increased risk for cesarean delivery, breech presentation and postpartum hemorrhage. And different characteristics of uterine fibroids affect obstetric outcomes through different ways. Such detailed information may be useful in risk-stratifying pregnant women with fibroids. PMID:29136018

Comparison of FIB-4 index and aspartate aminotransferase to platelet ratio index on carcinogenesis in chronic hepatitis B treated with entecavir

PubMed Central

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

Aims: We sought to compare the effects of FIB-4 index and aspartate aminotransferase to platelet ratio index (APRI) on hepatocellular carcinoma (HCC) incidence in chronic hepatitis B (CHB) patients undergoing entecavir (ETV) therapy. Patient and methods: A total of 338 nucleosides analogue therapy naïve CHB patients initially treated with ETV were analyzed. The optimal cutoff points in each continuous variable were determined by receiver operating curve (ROC) analysis. The effects of FIB-4 index and APRI on HCC incidence were compared using time-dependent ROC analysis and factors linked to HCC incidence were also examined using univariate and multivariate analyses. Results: There were 215 males and 123 females with the median age of 52 years and the median baseline HBV-DNA level of 6.6 log copies/ml. The median follow-up interval after the initiation of ETV therapy was 4.99 years. During the follow-up period, 33 patients (9.8%) developed HCC. The 3-, 5- 7-year cumulative HCC incidence rates in all cases were 4.4%, 9.2% and 13.5%, respectively. In the multivariate analysis, FIB-4 index revealed to be an independent predictor associated with HCC incidence, while APRI was not. In the time-dependent ROC analyses for all cases and for all subgroups analyses stratified by viral status or cirrhosis status, all area under the ROCs in each time point (2-, 3-, 4-, 5-, 6-, and 7-year) of FIB-4 index were higher than those of APRI. Conclusion: FIB-4 index rather than APRI can be a useful predictor associated with HCC development for CHB patients undergoing ETV therapy. PMID:28243319

Domestic Violence, Unwanted Pregnancy and Pregnancy Termination among Urban Women of Bangladesh

PubMed Central

2013-01-01

Objective This paper explores the relationship between domestic violence against women inflicted by husbands, unwanted pregnancy and pregnancy termination of Bangladeshi urban women. Materials and methods The study used the nationally representative 2007 Bangladesh Demographic and Health Survey (BDHS) data. The BDHS covered a representative sample of 10,996 ever married women from rural and urban areas. The BDHS used a separate module to collect information from women regarding domestic violence. The survey gathered information of domestic violence from 1,013 urban women which are the basis of the study. Simple cross tabulation, bivariate and multivariate statistical analyses were performed to analyzing data. Results Overall, the lifetime prevalence of domestic violence was 47.5%. Of the most recent pregnancies, 15.6% were unwanted and 16.0% of the women terminated pregnancy in their marital life. The multivariate binary logistic regression analyses yielded quantitatively important and reliable estimate of unwanted pregnancy and pregnancy termination. The regression analyses yielded significantly (p < 0.05) increased risk of unwanted pregnancy only for physical violence (OR = 2.35, 95% CI = 1.28-4.32) and for both physical and sexual violence (OR = 2.27, 95% CI = 1.02-5.28), and higher risk of pregnancy termination for only physical violence (OR = 1.41, 95% CI = 0.95-2.10) and for both physical and sexual violence (OR = 1.81, 95% CI = 1.07-3.04) than women who were never abused. Current age, higher parity and early marriage are also important determinants of unwanted pregnancy and pregnancy termination. Conclusion Violence against women inflicted by husbands is commonplace in Bangladesh. Any strategy to reduce the burden of unwanted pregnancy and induced abortion should include prevention of violence against women and strengthening women's sexual and reproductive health. PMID:24971097

Landscape genetics of leaf-toed geckos in the tropical dry forest of northern Mexico.

PubMed

Blair, Christopher; Jiménez Arcos, Victor H; Mendez de la Cruz, Fausto R; Murphy, Robert W

2013-01-01

Habitat fragmentation due to both natural and anthropogenic forces continues to threaten the evolution and maintenance of biological diversity. This is of particular concern in tropical regions that are experiencing elevated rates of habitat loss. Although less well-studied than tropical rain forests, tropical dry forests (TDF) contain an enormous diversity of species and continue to be threatened by anthropogenic activities including grazing and agriculture. However, little is known about the processes that shape genetic connectivity in species inhabiting TDF ecosystems. We adopt a landscape genetic approach to understanding functional connectivity for leaf-toed geckos (Phyllodactylus tuberculosus) at multiple sites near the northernmost limit of this ecosystem at Alamos, Sonora, Mexico. Traditional analyses of population genetics are combined with multivariate GIS-based landscape analyses to test hypotheses on the potential drivers of spatial genetic variation. Moderate levels of within-population diversity and substantial levels of population differentiation are revealed by FST and Dest. Analyses using structure suggest the occurrence of from 2 to 9 genetic clusters depending on the model used. Landscape genetic analysis suggests that forest cover, stream connectivity, undisturbed habitat, slope, and minimum temperature of the coldest period explain more genetic variation than do simple Euclidean distances. Additional landscape genetic studies throughout TDF habitat are required to understand species-specific responses to landscape and climate change and to identify common drivers. We urge researchers interested in using multivariate distance methods to test for, and report, significant correlations among predictor matrices that can impact results, particularly when adopting least-cost path approaches. Further investigation into the use of information theoretic approaches for model selection is also warranted.

Authentication of commercial spices based on the similarities between gas chromatographic fingerprints.

PubMed

Matsushita, Takaya; Zhao, Jing Jing; Igura, Noriyuki; Shimoda, Mitsuya

2018-06-01

A simple and solvent-free method was developed for the authentication of commercial spices. The similarities between gas chromatographic fingerprints were measured using similarity indices and multivariate data analyses, as morphological differentiation between dried powders and small spice particles was challenging. The volatile compounds present in 11 spices (i.e. allspice, anise, black pepper, caraway, clove, coriander, cumin, dill, fennel, star anise, and white pepper) were extracted by headspace solid-phase microextraction, and analysed by gas chromatography-mass spectrometry. The largest 10 peaks were selected from each total ion chromatogram, and a total of 65 volatiles were tentatively identified. The similarity indices (i.e. the congruence coefficients) were calculated using the data matrices of the identified compound relative peak areas to differentiate between two sets of fingerprints. Where pairs of similar fingerprints produced high congruence coefficients (>0.80), distinctive volatile markers were employed to distinguish between these samples. In addition, hierarchical cluster analysis and principal component analysis were performed to visualise the similarity among fingerprints, and the analysed spices were grouped and characterised according to their distinctive major components. This method is suitable for screening unknown spices, and can therefore be employed to evaluate the quality and authenticity of various spices. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

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

Predictors of Cervical Cancer Screening for Rarely or Never Screened Rural Appalachian Women

PubMed Central

Hatcher, Jennifer; Studts, Christina R.; Dignan, Mark; Turner, Lisa M.; Schoenberg, Nancy E.

2011-01-01

Background and Purpose Women who have not had a Papanicolaou test in five years or more have increased risk of developing invasive cervical cancer. This study compares Appalachian women whose last screening was more than one year ago but less than five years ago with those not screened for the previous five years or more. Methods Using PRECEDE/PROCEED as a guide, factors related to obtaining Pap tests were examined using cross-sectional data from 345 Appalachian Kentucky women. Bivariate and multivariate analyses were conducted to identify predictors of screening. Results Thirty-four percent of participants were rarely- or never-screened. In multiple logistic regression analyses, several factors increased those odds, including belief that cervical cancer has symptoms, and not having a regular source of medical care. Conclusion The findings from this study may lead to the development of effective intervention and policies that increase cervical cancer screening in this population. PMID:21317514

The Role of Positive Alcohol Expectancies in Underage Binge Drinking Among College Students

PubMed Central

McBride, Nicole M.; Barrett, Blake; Moore, Kathleen A.; Schonfeld, Lawrence

2014-01-01

Objective This study explored associations between positive alcohol expectancies, demographics, as well as academic status and binge drinking among underage college students. Participants A sample of 1,553 underage college students at three public universities and one college in the southeast who completed the Core Alcohol and Drug Survey in the spring 2013 semester. Methods A series of bivariate analyses and logistic regression models were used to examine associations between demographic and academic status variables as well as positive alcohol expectancies with self-reported binge drinking. Positive alcohol expectancies were examined in multivariable models via two factors derived from principal component analyses. Results Students who endorsed higher agreement of these two emergent factors (Sociability; Sexuality) were more likely to report an occurrence of binge drinking in the past two weeks. Conclusions Study results document associations between positive alcohol expectancies and binge drinking among underage students; implications for prevention and treatment are discussed. PMID:24678848

Applying of Factor Analyses for Determination of Trace Elements Distribution in Water from River Vardar and Its Tributaries, Macedonia/Greece

PubMed Central

Popov, Stanko Ilić; Stafilov, Trajče; Šajn, Robert; Tănăselia, Claudiu; Bačeva, Katerina

2014-01-01

A systematic study was carried out to investigate the distribution of fifty-six elements in the water samples from river Vardar (Republic of Macedonia and Greece) and its major tributaries. The samples were collected from 27 sampling sites. Analyses were performed by mass spectrometry with inductively coupled plasma (ICP-MS) and atomic emission spectrometry with inductively coupled plasma (ICP-AES). Cluster and R mode factor analysis (FA) was used to identify and characterise element associations and four associations of elements were determined by the method of multivariate statistics. Three factors represent the associations of elements that occur in the river water naturally while Factor 3 represents an anthropogenic association of the elements (Cd, Ga, In, Pb, Re, Tl, Cu, and Zn) introduced in the river waters from the waste waters from the mining and metallurgical activities in the country. PMID:24587756

Applying of factor analyses for determination of trace elements distribution in water from Vardar and its tributaries, Macedonia/Greece.

PubMed

Popov, Stanko Ilić; Stafilov, Trajče; Sajn, Robert; Tănăselia, Claudiu; Bačeva, Katerina

2014-01-01

A systematic study was carried out to investigate the distribution of fifty-six elements in the water samples from river Vardar (Republic of Macedonia and Greece) and its major tributaries. The samples were collected from 27 sampling sites. Analyses were performed by mass spectrometry with inductively coupled plasma (ICP-MS) and atomic emission spectrometry with inductively coupled plasma (ICP-AES). Cluster and R mode factor analysis (FA) was used to identify and characterise element associations and four associations of elements were determined by the method of multivariate statistics. Three factors represent the associations of elements that occur in the river water naturally while Factor 3 represents an anthropogenic association of the elements (Cd, Ga, In, Pb, Re, Tl, Cu, and Zn) introduced in the river waters from the waste waters from the mining and metallurgical activities in the country.

Peritoneal Dialysate Glucose Load and Systemic Glucose Metabolism in Non-Diabetics: Results from the GLOBAL Fluid Cohort Study

PubMed Central

Chess, James; Do, Jun-Young; Noh, Hyunjin; Lee, Hi-Bahl; Kim, Yong-Lim; Summers, Angela; Williams, Paul Ford; Davison, Sara; Dorval, Marc

2016-01-01

Background and Objectives Glucose control is a significant predictor of mortality in diabetic peritoneal dialysis (PD) patients. During PD, the local toxic effects of intra-peritoneal glucose are well recognized, but despite large amounts of glucose being absorbed, the systemic effects of this in non-diabetic patients are not clear. We sought to clarify whether dialysate glucose has an effect upon systemic glucose metabolism. Methods and Materials We analysed the Global Fluid Study cohort, a prospective, observational cohort study initiated in 2002. A subset of 10 centres from 3 countries with high data quality were selected (368 incident and 272 prevalent non-diabetic patients), with multilevel, multivariable analysis of the reciprocal of random glucose levels, and a stratified-by-centre Cox survival analysis. Results The median follow up was 5.6 and 6.4 years respectively in incident and prevalent patients. On multivariate analysis, serum glucose increased with age (β = -0.007, 95%CI -0.010, -0.004) and decreased with higher serum sodium (β = 0.002, 95%CI 0.0005, 0.003) in incident patients and increased with dialysate glucose (β = -0.0002, 95%CI -0.0004, -0.00006) in prevalent patients. Levels suggested undiagnosed diabetes in 5.4% of prevalent patients. Glucose levels predicted death in unadjusted analyses of both incident and prevalent groups but in an adjusted survival analysis they did not (for random glucose 6–10 compared with <6, Incident group HR 0.92, 95%CI 0.58, 1.46, Prevalent group HR 1.42, 95%CI 0.86, 2.34). Conclusions In prevalent non-diabetic patients, random glucose levels at a diabetic level are under-recognised and increase with dialysate glucose load. Random glucose levels predict mortality in unadjusted analyses, but this association has not been proven in adjusted analyses. PMID:27249020

National Income Inequality and Declining GDP Growth Rates Are Associated with Increases in HIV Diagnoses among People Who Inject Drugs in Europe: A Panel Data Analysis

PubMed Central

Nikolopoulos, Georgios K.; Fotiou, Anastasios; Kanavou, Eleftheria; Richardson, Clive; Detsis, Marios; Pharris, Anastasia; Suk, Jonathan E.; Semenza, Jan C.; Costa-Storti, Claudia; Paraskevis, Dimitrios; Sypsa, Vana; Malliori, Melpomeni-Minerva; Friedman, Samuel R.; Hatzakis, Angelos

2015-01-01

Background There is sparse evidence that demonstrates the association between macro-environmental processes and drug-related HIV epidemics. The present study explores the relationship between economic, socio-economic, policy and structural indicators, and increases in reported HIV infections among people who inject drugs (PWID) in the European Economic Area (EEA). Methods We used panel data (2003–2012) for 30 EEA countries. Statistical analyses included logistic regression models. The dependent variable was taking value 1 if there was an outbreak (significant increase in the national rate of HIV diagnoses in PWID) and 0 otherwise. Explanatory variables included the growth rate of Gross Domestic Product (GDP), the share of the population that is at risk for poverty, the unemployment rate, the Eurostat S80/S20 ratio, the Gini coefficient, the per capita government expenditure on health and social protection, and variables on drug control policy and drug-using population sizes. Lags of one to three years were investigated. Findings In multivariable analyses, using two-year lagged values, we found that a 1% increase of GDP was associated with approximately 30% reduction in the odds of an HIV outbreak. In GDP-adjusted analyses with three-year lagged values, the effect of the national income inequality on the likelihood of an HIV outbreak was significant [S80/S20 Odds Ratio (OR) = 3.89; 95% Confidence Interval (CI): 1.15 to 13.13]. Generally, the multivariable analyses produced similar results across three time lags tested. Interpretation Given the limitations of ecological research, we found that declining economic growth and increasing national income inequality were associated with an elevated probability of a large increase in the number of HIV diagnoses among PWID in EEA countries during the last decade. HIV prevention may be more effective if developed within national and European-level policy contexts that promote income equality, especially among vulnerable groups. PMID:25875598

Splenectomy Causes 10-Fold Increased Risk of Portal Venous System Thrombosis in Liver Cirrhosis Patients.

PubMed

Qi, Xingshun; Han, Guohong; Ye, Chun; Zhang, Yongguo; Dai, Junna; Peng, Ying; Deng, Han; Li, Jing; Hou, Feifei; Ning, Zheng; Zhao, Jiancheng; Zhang, Xintong; Wang, Ran; Guo, Xiaozhong

2016-07-19

BACKGROUND Portal venous system thrombosis (PVST) is a life-threatening complication of liver cirrhosis. We conducted a retrospective study to comprehensively analyze the prevalence and risk factors of PVST in liver cirrhosis. MATERIAL AND METHODS All cirrhotic patients without malignancy admitted between June 2012 and December 2013 were eligible if they underwent contrast-enhanced CT or MRI scans. Independent predictors of PVST in liver cirrhosis were calculated in multivariate analyses. Subgroup analyses were performed according to the severity of PVST (any PVST, main portal vein [MPV] thrombosis >50%, and clinically significant PVST) and splenectomy. Odds ratios (ORs) and 95% confidence intervals (CIs) were reported. RESULTS Overall, 113 cirrhotic patients were enrolled. The prevalence of PVST was 16.8% (19/113). Splenectomy (any PVST: OR=11.494, 95%CI=2.152-61.395; MPV thrombosis >50%: OR=29.987, 95%CI=3.247-276.949; clinically significant PVST: OR=40.415, 95%CI=3.895-419.295) and higher hemoglobin (any PVST: OR=0.974, 95%CI=0.953-0.996; MPV thrombosis >50%: OR=0.936, 95%CI=0.895-0.980; clinically significant PVST: OR=0.935, 95%CI=0.891-0.982) were the independent predictors of PVST. The prevalence of PVST was 13.3% (14/105) after excluding splenectomy. Higher hemoglobin was the only independent predictor of MPV thrombosis >50% (OR=0.952, 95%CI=0.909-0.997). No independent predictors of any PVST or clinically significant PVST were identified in multivariate analyses. Additionally, PVST patients who underwent splenectomy had a significantly higher proportion of clinically significant PVST but lower MELD score than those who did not undergo splenectomy. In all analyses, the in-hospital mortality was not significantly different between cirrhotic patient with and without PVST. CONCLUSIONS Splenectomy may increase by at least 10-fold the risk of PVST in liver cirrhosis independent of severity of liver dysfunction.

Splenectomy Causes 10-Fold Increased Risk of Portal Venous System Thrombosis in Liver Cirrhosis Patients

PubMed Central

Qi, Xingshun; Han, Guohong; Ye, Chun; Zhang, Yongguo; Dai, Junna; Peng, Ying; Deng, Han; Li, Jing; Hou, Feifei; Ning, Zheng; Zhao, Jiancheng; Zhang, Xintong; Wang, Ran; Guo, Xiaozhong

2016-01-01

Background Portal venous system thrombosis (PVST) is a life-threatening complication of liver cirrhosis. We conducted a retrospective study to comprehensively analyze the prevalence and risk factors of PVST in liver cirrhosis. Material/Methods All cirrhotic patients without malignancy admitted between June 2012 and December 2013 were eligible if they underwent contrast-enhanced CT or MRI scans. Independent predictors of PVST in liver cirrhosis were calculated in multivariate analyses. Subgroup analyses were performed according to the severity of PVST (any PVST, main portal vein [MPV] thrombosis >50%, and clinically significant PVST) and splenectomy. Odds ratios (ORs) and 95% confidence intervals (CIs) were reported. Results Overall, 113 cirrhotic patients were enrolled. The prevalence of PVST was 16.8% (19/113). Splenectomy (any PVST: OR=11.494, 95%CI=2.152–61.395; MPV thrombosis >50%: OR=29.987, 95%CI=3.247–276.949; clinically significant PVST: OR=40.415, 95%CI=3.895–419.295) and higher hemoglobin (any PVST: OR=0.974, 95%CI=0.953–0.996; MPV thrombosis >50%: OR=0.936, 95%CI=0.895–0.980; clinically significant PVST: OR=0.935, 95%CI=0.891–0.982) were the independent predictors of PVST. The prevalence of PVST was 13.3% (14/105) after excluding splenectomy. Higher hemoglobin was the only independent predictor of MPV thrombosis >50% (OR=0.952, 95%CI=0.909–0.997). No independent predictors of any PVST or clinically significant PVST were identified in multivariate analyses. Additionally, PVST patients who underwent splenectomy had a significantly higher proportion of clinically significant PVST but lower MELD score than those who did not undergo splenectomy. In all analyses, the in-hospital mortality was not significantly different between cirrhotic patient with and without PVST. Conclusions Splenectomy may increase by at least 10-fold the risk of PVST in liver cirrhosis independent of severity of liver dysfunction. PMID:27432511

Statistical limitations in functional neuroimaging. I. Non-inferential methods and statistical models.

PubMed Central

Petersson, K M; Nichols, T E; Poline, J B; Holmes, A P

1999-01-01

Functional neuroimaging (FNI) provides experimental access to the intact living brain making it possible to study higher cognitive functions in humans. In this review and in a companion paper in this issue, we discuss some common methods used to analyse FNI data. The emphasis in both papers is on assumptions and limitations of the methods reviewed. There are several methods available to analyse FNI data indicating that none is optimal for all purposes. In order to make optimal use of the methods available it is important to know the limits of applicability. For the interpretation of FNI results it is also important to take into account the assumptions, approximations and inherent limitations of the methods used. This paper gives a brief overview over some non-inferential descriptive methods and common statistical models used in FNI. Issues relating to the complex problem of model selection are discussed. In general, proper model selection is a necessary prerequisite for the validity of the subsequent statistical inference. The non-inferential section describes methods that, combined with inspection of parameter estimates and other simple measures, can aid in the process of model selection and verification of assumptions. The section on statistical models covers approaches to global normalization and some aspects of univariate, multivariate, and Bayesian models. Finally, approaches to functional connectivity and effective connectivity are discussed. In the companion paper we review issues related to signal detection and statistical inference. PMID:10466149

Mathematical Formulation of Multivariate Euclidean Models for Discrimination Methods.

ERIC Educational Resources Information Center

Mullen, Kenneth; Ennis, Daniel M.

1987-01-01

Multivariate models for the triangular and duo-trio methods are described, and theoretical methods are compared to a Monte Carlo simulation. Implications are discussed for a new theory of multidimensional scaling which challenges the traditional assumption that proximity measures and perceptual distances are monotonically related. (Author/GDC)

Assessment of alternative disposal methods to reduce greenhouse gas emissions from municipal solid waste in India.

PubMed

Yedla, Sudhakar; Sindhu, N T

2016-06-01

Open dumping, the most commonly practiced method of solid waste disposal in Indian cities, creates serious environment and economic challenges, and also contributes significantly to greenhouse gas emissions. The present article attempts to analyse and identify economically effective ways to reduce greenhouse gas emissions from municipal solid waste. The article looks at the selection of appropriate methods for the control of methane emissions. Multivariate functional models are presented, based on theoretical considerations as well as the field measurements to forecast the greenhouse gas mitigation potential for all the methodologies under consideration. Economic feasibility is tested by calculating the unit cost of waste disposal for the respective disposal process. The purpose-built landfill system proposed by Yedla and Parikh has shown promise in controlling greenhouse gas and saving land. However, these studies show that aerobic composting offers the optimal method, both in terms of controlling greenhouse gas emissions and reducing costs, mainly by requiring less land than other methods. © The Author(s) 2016.

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.

Factors associated with seasonal influenza vaccination in pregnant women.

PubMed

Henninger, Michelle L; Irving, Stephanie A; Thompson, Mark; Avalos, Lyndsay Ammon; Ball, Sarah W; Shifflett, Pat; Naleway, Allison L

2015-05-01

This observational study followed a cohort of pregnant women during the 2010-2011 influenza season to determine factors associated with vaccination. Participants were 1105 pregnant women who completed a survey assessing health beliefs related to vaccination upon enrollment and were then followed to determine vaccination status by the end of the 2010-2011 influenza season. We conducted univariate and multivariate analyses to explore factors associated with vaccination status and a factor analysis of survey items to identify health beliefs associated with vaccination. Sixty-three percent (n=701) of the participants were vaccinated. In the univariate analyses, multiple factors were associated with vaccination status, including maternal age, race, marital status, educational level, and gravidity. Factor analysis identified two health belief factors associated with vaccination: participant's positive views (factor 1) and negative views (factor 2) of influenza vaccination. In a multivariate logistic regression model, factor 1 was associated with increased likelihood of vaccination (adjusted odds ratio [aOR]=2.18; 95% confidence interval [CI]=1.72-2.78), whereas factor 2 was associated with decreased likelihood of vaccination (aOR=0.36; 95% CI=0.28-0.46). After controlling for the two health belief factors in multivariate analyses, demographic factors significant in univariate analyses were no longer significant. Women who received a provider recommendation were about three times more likely to be vaccinated (aOR=3.14; 95% CI=1.99-4.96). Pregnant women's health beliefs about vaccination appear to be more important than demographic and maternal factors previously associated with vaccination status. Provider recommendation remains one of the most critical factors influencing vaccination during pregnancy.

Comparing patients with spinal cord infarction and cerebral infarction: clinical characteristics, and short-term outcome

PubMed Central

Naess, Halvor; Romi, Fredrik

2011-01-01

Background: To compare the clinical characteristics, and short-term outcome of spinal cord infarction and cerebral infarction. Methods: Risk factors, concomitant diseases, neurological deficits on admission, and short-term outcome were registered among 28 patients with spinal cord infarction and 1075 patients with cerebral infarction admitted to the Department of Neurology, Haukeland University Hospital, Bergen, Norway. Multivariate analyses were performed with location of stroke (cord or brain), neurological deficits on admission, and short-term outcome (both Barthel Index [BI] 1 week after symptom onset and discharge home or to other institution) as dependent variables. Results: Multivariate analysis showed that patients with spinal cord infarction were younger, more often female, and less afflicted by hypertension and cardiac disease than patients with cerebral infarction. Functional score (BI) was lower among patients with spinal cord infarctions 1 week after onset of symptoms (P < 0.001). Odds ratio for being discharged home was 5.5 for patients with spinal cord infarction compared to cerebral infarction after adjusting for BI scored 1 week after onset (P = 0.019). Conclusion: Patients with spinal cord infarction have a risk factor profile that differs significantly from that of patients with cerebral infarction, although there are some parallels to cerebral infarction caused by atherosclerosis. Patients with spinal cord infarction were more likely to be discharged home when adjusting for early functional level on multivariate analysis. PMID:21915166

Identification of the isomers using principal component analysis (PCA) method

NASA Astrophysics Data System (ADS)

Kepceoǧlu, Abdullah; Gündoǧdu, Yasemin; Ledingham, Kenneth William David; Kilic, Hamdi Sukur

2016-03-01

In this work, we have carried out a detailed statistical analysis for experimental data of mass spectra from xylene isomers. Principle Component Analysis (PCA) was used to identify the isomers which cannot be distinguished using conventional statistical methods for interpretation of their mass spectra. Experiments have been carried out using a linear TOF-MS coupled to a femtosecond laser system as an energy source for the ionisation processes. We have performed experiments and collected data which has been analysed and interpreted using PCA as a multivariate analysis of these spectra. This demonstrates the strength of the method to get an insight for distinguishing the isomers which cannot be identified using conventional mass analysis obtained through dissociative ionisation processes on these molecules. The PCA results dependending on the laser pulse energy and the background pressure in the spectrometers have been presented in this work.

Validation of an automated tractography method for the optic radiations as a biomarker of visual acuity in neurofibromatosis-associated optic pathway glioma.

PubMed

de Blank, Peter; Fisher, Michael J; Gittleman, Haley; Barnholtz-Sloan, Jill S; Badve, Chaitra; Berman, Jeffrey I

2018-01-01

Fractional anisotropy (FA) of the optic radiations has been associated with vision deficit in multiple intrinsic brain pathologies including NF1 associated optic pathway glioma, but hand-drawn regions of interest used in previous tractography methods limit consistency of this potential biomarker. We created an automated method to identify white matter tracts in the optic radiations and compared this method to previously reported hand-drawn tractography. Automated tractography of the optic radiation using probabilistic streamline fiber tracking between the lateral geniculate nucleus of the thalamus and the occipital cortex was compared to the hand-drawn method between regions of interest posterior to Meyer's loop and anterior to tract branching near the calcarine cortex. Reliability was assessed by two independent raters in a sample of 20 healthy child controls. Among 50 children with NF1-associated optic pathway glioma, the association of FA and visual acuity deficit was compared for both tractography methods. Hand-drawn tractography methods required 2.6±0.9min/participant; automated methods were performed in <1min of operator time for all participants. Cronbach's alpha was 0.83 between two independent raters for FA in hand-drawn tractography, but repeated automated tractography resulted in identical FA values (Cronbach's alpha=1). On univariate and multivariate analyses, FA was similarly associated with visual acuity loss using both methods. Receiver operator characteristic curves of both multivariate models demonstrated that both automated and hand-drawn tractography methods were equally able to distinguish normal from abnormal visual acuity. Automated tractography of the optic radiations offers a fast, reliable and consistent method of tract identification that is not reliant on operator time or expertise. This method of tract identification may be useful as DTI is developed as a potential biomarker for visual acuity. Copyright © 2017 Elsevier Inc. All rights reserved.

Use of proxy measures in estimating socioeconomic inequalities in malaria prevalence.

PubMed

Somi, Masha F; Butler, James R; Vahid, Farshid; Njau, Joseph D; Kachur, S P; Abdulla, Salim

2008-03-01

To present and compare socioeconomic status (SES) rankings of households using consumption and an asset-based index as two alternative measures of SES; and to compare and evaluate the performance of these two measures in multivariate analyses of the socioeconomic gradient in malaria prevalence. Data for the study come from a survey of 557 households in 25 study villages in Tanzania in 2004. Household SES was determined using consumption and an asset-based index calculated using Principal Components Analysis on a set of household variables. In multivariate analyses of malaria prevalence, we also used two other measures of disease prevalence: parasitaemia and self-report of malaria or fever in the 2 weeks before interview. Household rankings based on the two measures of SES differ substantially. In multivariate analyses, there was a statistically significant negative association between both measures of SES and parasitaemia but not between either measure of SES and self-reported malaria. Age of individual, use of a mosquito net, and wall construction were negatively and significantly associated with parasitaemia, whilst roof construction was positively associated with parasitaemia. Only age remained significant when malaria self-report was used as the measure of disease prevalence. An asset index is an effective alternative to consumption in measuring the socioeconomic gradient in malaria parasitaemia, but self-report may be an unreliable measure of malaria prevalence for this purpose.

Impact of Gender on 30-Day Complications After Primary Total Joint Arthroplasty.

PubMed

Robinson, Jonathan; Shin, John I; Dowdell, James E; Moucha, Calin S; Chen, Darwin D

2017-08-01

Impact of gender on 30-day complications has been investigated in other surgical procedures but has not yet been studied in total hip arthroplasty (THA) or total knee arthroplasty (TKA). Patients who received THA or TKA from 2012 to 2014 were identified in the National Surgical Quality Improvement Program database. Patients were divided into 2 groups based on gender. Bivariate and multivariate analyses were performed to assess associations between gender and patient factors and complications after THA or TKA and to assess whether gender was an independent risk factor. THA patients consisted of 45.1% male and 54.9% female. In a multivariate analysis, female gender was found to be a protective factor for mortality, sepsis, cardiovascular complications, unplanned reintubation, and renal complications and as an independent risk factor for urinary tract infection, blood transfusion, and nonhome discharge after THA. TKA patients consisted of 36.7% male and 62.3% female. Multivariate analysis revealed female gender as a protective factor for sepsis, cardiovascular complications, and renal complications and as an independent risk factor for urinary tract infection, blood transfusion, and nonhome discharge after TKA. There are discrepancies in the THA or TKA complications based on gender, and the multivariate analyses confirmed gender as an independent risk factor for certain complications. Physicians should be mindful of patient's gender for better risk stratification and informed consent. Copyright © 2017 Elsevier Inc. All rights reserved.

Low health literacy predicts decline in physical function among older adults: findings from the LitCog cohort study

PubMed Central

Smith, Samuel G; O'Conor, Rachel; Curtis, Laura M; Waite, Katie; Deary, Ian J; Paasche-Orlow, Michael; Wolf, Michael S

2015-01-01

Background Limited health literacy is associated with worse physical function in cross-sectional studies. We aimed to determine if health literacy is a risk factor for decline in physical function among older adults. Methods A longitudinal cohort of 529 community-dwelling American adults aged 55–74 years were recruited from an academic general internal medicine clinic and federally qualified health centres in 2008–2011. Health literacy (Newest Vital Sign), age, gender, race, education, chronic conditions, body mass index, alcohol consumption, smoking status and exercise frequency were included in multivariable analyses. The 10-item PROMIS (Patient-Reported Outcomes Measurement Information System) physical function scale was assessed at baseline and follow-up (mean=3.2 years, SD=0.39). Results Nearly half of the sample (48.2%) had either marginal (25.5%) or low health literacy (22.7%). Average physical function at baseline was 83.2 (SD=16.6) of 100, and health literacy was associated with poorer baseline physical function in multivariable analysis (p=0.004). At follow-up, physical function declined to 81.9 (SD=17.3; p=0.006) and 20.5% experienced a meaningful decline (>0.5 SD of baseline score). In multivariable analyses, participants with marginal (OR 2.62; 95%CI 1.38 to 4.95; p=0.003) and low (OR 2.57; 95%CI 1.22 to 5.44; p=0.013) health literacy were more likely to experience meaningful decline in physical function than the adequate health literacy group. Entering cognitive abilities to these models did not substantially attenuate effect sizes. Health literacy attenuated the relationship between black race and decline in physical function by 32.6%. Conclusions Lower health literacy increases the risk of exhibiting faster physical decline over time among older adults. Strategies that reduce literacy disparities should be designed and evaluated. PMID:25573701

Assessment of metals bioavailability to vegetables under field conditions using DGT, single extractions and multivariate statistics

PubMed Central

2012-01-01

Background The metals bioavailability in soils is commonly assessed by chemical extractions; however a generally accepted method is not yet established. In this study, the effectiveness of Diffusive Gradients in Thin-films (DGT) technique and single extractions in the assessment of metals bioaccumulation in vegetables, and the influence of soil parameters on phytoavailability were evaluated using multivariate statistics. Soil and plants grown in vegetable gardens from mining-affected rural areas, NW Romania, were collected and analysed. Results Pseudo-total metal content of Cu, Zn and Cd in soil ranged between 17.3-146 mg kg-1, 141–833 mg kg-1 and 0.15-2.05 mg kg-1, respectively, showing enriched contents of these elements. High degrees of metals extractability in 1M HCl and even in 1M NH4Cl were observed. Despite the relatively high total metal concentrations in soil, those found in vegetables were comparable to values typically reported for agricultural crops, probably due to the low concentrations of metals in soil solution (Csoln) and low effective concentrations (CE), assessed by DGT technique. Among the analysed vegetables, the highest metal concentrations were found in carrots roots. By applying multivariate statistics, it was found that CE, Csoln and extraction in 1M NH4Cl, were better predictors for metals bioavailability than the acid extractions applied in this study. Copper transfer to vegetables was strongly influenced by soil organic carbon (OC) and cation exchange capacity (CEC), while pH had a higher influence on Cd transfer from soil to plants. Conclusions The results showed that DGT can be used for general evaluation of the risks associated to soil contamination with Cu, Zn and Cd in field conditions. Although quantitative information on metals transfer from soil to vegetables was not observed. PMID:23079133

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

PubMed Central

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

2014-01-01

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

Using Statistical Process Control for detecting anomalies in multivariate spatiotemporal Earth Observations

NASA Astrophysics Data System (ADS)

Flach, Milan; Mahecha, Miguel; Gans, Fabian; Rodner, Erik; Bodesheim, Paul; Guanche-Garcia, Yanira; Brenning, Alexander; Denzler, Joachim; Reichstein, Markus

2016-04-01

The number of available Earth observations (EOs) is currently substantially increasing. Detecting anomalous patterns in these multivariate time series is an important step in identifying changes in the underlying dynamical system. Likewise, data quality issues might result in anomalous multivariate data constellations and have to be identified before corrupting subsequent analyses. In industrial application a common strategy is to monitor production chains with several sensors coupled to some statistical process control (SPC) algorithm. The basic idea is to raise an alarm when these sensor data depict some anomalous pattern according to the SPC, i.e. the production chain is considered 'out of control'. In fact, the industrial applications are conceptually similar to the on-line monitoring of EOs. However, algorithms used in the context of SPC or process monitoring are rarely considered for supervising multivariate spatio-temporal Earth observations. The objective of this study is to exploit the potential and transferability of SPC concepts to Earth system applications. We compare a range of different algorithms typically applied by SPC systems and evaluate their capability to detect e.g. known extreme events in land surface processes. Specifically two main issues are addressed: (1) identifying the most suitable combination of data pre-processing and detection algorithm for a specific type of event and (2) analyzing the limits of the individual approaches with respect to the magnitude, spatio-temporal size of the event as well as the data's signal to noise ratio. Extensive artificial data sets that represent the typical properties of Earth observations are used in this study. Our results show that the majority of the algorithms used can be considered for the detection of multivariate spatiotemporal events and directly transferred to real Earth observation data as currently assembled in different projects at the European scale, e.g. http://baci-h2020.eu/index.php/ and http://earthsystemdatacube.net/. Known anomalies such as the Russian heatwave are detected as well as anomalies which are not detectable with univariate methods.

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.

Sequence of Radiotherapy and Chemotherapy in Breast Cancer After Breast-Conserving Surgery

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

Jobsen, Jan J., E-mail: J.Jobsen@mst.nl; Palen, Job van der; Department of Research Methodology, Measurement and Data Analysis, Faculty of Behavioural Science, University of Twente

2012-04-01

Purpose: The optimal sequence of radiotherapy and chemotherapy in breast-conserving therapy is unknown. Methods and Materials: From 1983 through 2007, a total of 641 patients with 653 instances of breast-conserving therapy (BCT), received both chemotherapy and radiotherapy and are the basis of this analysis. Patients were divided into three groups. Groups A and B comprised patients treated before 2005, Group A radiotherapy first and Group B chemotherapy first. Group C consisted of patients treated from 2005 onward, when we had a fixed sequence of radiotherapy first, followed by chemotherapy. Results: Local control did not show any differences among the threemore » groups. For distant metastasis, no difference was shown between Groups A and B. Group C, when compared with Group A, showed, on univariate and multivariate analyses, a significantly better distant metastasis-free survival. The same was noted for disease-free survival. With respect to disease-specific survival, no differences were shown on multivariate analysis among the three groups. Conclusion: Radiotherapy, as an integral part of the primary treatment of BCT, should be administered first, followed by adjuvant chemotherapy.« less

A multivariate distance-based analytic framework for microbial interdependence association test in longitudinal study.

PubMed

Zhang, Yilong; Han, Sung Won; Cox, Laura M; Li, Huilin

2017-12-01

Human microbiome is the collection of microbes living in and on the various parts of our body. The microbes living on our body in nature do not live alone. They act as integrated microbial community with massive competing and cooperating and contribute to our human health in a very important way. Most current analyses focus on examining microbial differences at a single time point, which do not adequately capture the dynamic nature of the microbiome data. With the advent of high-throughput sequencing and analytical tools, we are able to probe the interdependent relationship among microbial species through longitudinal study. Here, we propose a multivariate distance-based test to evaluate the association between key phenotypic variables and microbial interdependence utilizing the repeatedly measured microbiome data. Extensive simulations were performed to evaluate the validity and efficiency of the proposed method. We also demonstrate the utility of the proposed test using a well-designed longitudinal murine experiment and a longitudinal human study. The proposed methodology has been implemented in the freely distributed open-source R package and Python code. © 2017 WILEY PERIODICALS, INC.

Loneliness in senior housing communities.

PubMed

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

2018-05-23

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

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.

Density of Indoor Tanning Facilities in 116 Large U.S. Cities

PubMed Central

Hoerster, Katherine D.; Garrow, Rebecca L.; Mayer, Joni A.; Clapp, Elizabeth J.; Weeks, John R.; Woodruff, Susan I.; Sallis, James F.; Slymen, Donald J.; Patel, Minal R.; Sybert, Stephanie A.

2009-01-01

Background U.S. adolescents and young adults are using indoor tanning at high rates, even though it has been linked to both melanoma and squamous cell cancer. Because the availability of commercial indoor tanning facilities may influence use, data are needed on the number and density of such facilities. Methods In March 2006, commercial indoor tanning facilities in 116 large U.S. cities were identified, and the number and density (per 100,000 population) were computed for each city. Bivariate and multivariate analyses conducted in 2008 tested the association between tanning-facility density and selected geographic, climatologic, demographic, and legislative variables. Results Mean facility number and density across cities were 41.8 (SD=30.8) and 11.8 (SD=6.0), respectively. In multivariate analysis, cities with higher percentages of whites and lower ultraviolet (UV)index scores had significantly higher facility densities than those with lower percentages of whites and higher UV index scores. Conclusions These data indicate that commercial indoor tanning is widely available in the urban U.S., and this availability may help explain the high usage of indoor tanning. PMID:19215849

Multivariate analyses of crater parameters and the classification of craters

NASA Technical Reports Server (NTRS)

Siegal, B. S.; Griffiths, J. C.

1974-01-01

Multivariate analyses were performed on certain linear dimensions of six genetic types of craters. A total of 320 craters, consisting of laboratory fluidization craters, craters formed by chemical and nuclear explosives, terrestrial maars and other volcanic craters, and terrestrial meteorite impact craters, authenticated and probable, were analyzed in the first data set in terms of their mean rim crest diameter, mean interior relief, rim height, and mean exterior rim width. The second data set contained an additional 91 terrestrial craters of which 19 were of experimental percussive impact and 28 of volcanic collapse origin, and which was analyzed in terms of mean rim crest diameter, mean interior relief, and rim height. Principal component analyses were performed on the six genetic types of craters. Ninety per cent of the variation in the variables can be accounted for by two components. Ninety-nine per cent of the variation in the craters formed by chemical and nuclear explosives is explained by the first component alone.

Hepatectomy As A First Choice Treatment For Liver Metastasis From Gastric Cancer: A Single Center Experience.

PubMed

Sakamoto, Hirohiko; Amikura, Katsumi; Tanaka, Yoichi; Kawashima, Yoshiyuki

2014-05-01

Indication of hepatectomy for liver metastases from gastric cancer (LMGC) is still controversial despite many papers favoring surgery. The aim of this study is to claim that we should accept hepatectomy as first choice treatment for LMGC. It is important to have a consensus on this matter for surgeons to treat LMGC properly. Fifty three patients undergoing hepatectomy for LMGC from 1990 through 2010 were retrospectively analysed for survival and prognostic factors. Analyses were made on size, multiplicity, synchronicity and positive surgical margin as liver metastasis factors. Serosal invasion, node metastasis, histological differentiation and UICC stage were analysed as primary site factors. Multivariate analysis was performed for those positive for univariate analysis. Cumulative 5 year survival rate was 27%. Multiplicity, positive margin and node metastasis (N > 2) yielded significant difference on univariate analysis. On multivariate analysis multiplicity and node metastasis (N > 2) were significant. Hepatectomy for LMGC is potentially curative and should be regarded as first choice. Solitary and N < 3 are good prognostic factors.

Social support network characteristics and sexual risk taking among a racially/ethnically diverse sample of young, urban men who have sex with men.

PubMed

Kapadia, F; Siconolfi, D E; Barton, S; Olivieri, B; Lombardo, L; Halkitis, P N

2013-06-01

Associations between social support network characteristics and sexual risk among racially/ethnically diverse young men who have sex with men (YMSM) were examined using egocentric network data from a prospective cohort study of YMSM (n = 501) recruited in New York City. Bivariate and multivariable logistic regression analyses examined associations between social support network characteristics and sexual risk taking behaviors in Black, Hispanic/Latino, and White YMSM. Bivariate analyses indicated key differences in network size, composition, communication frequency and average relationship duration by race/ethnicity. In multivariable analyses, controlling for individual level sociodemographic, psychosocial and relationship factors, having a sexual partner in one's social support network was associated with unprotected sexual behavior for both Hispanic/Latino (AOR = 3.90) and White YMSM (AOR = 4.93). Further examination of key network characteristics across racial/ethnic groups are warranted in order to better understand the extant mechanisms for provision of HIV prevention programming to racially/ethnically diverse YMSM at risk for HIV.

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.

Physical, social and emotional function after work accidents: a medicolegal perspective.

PubMed

Holtedahl, Robin; Veiersted, Kaj Bo

2007-01-01

The aim of this study was to analyse social and functional consequences of work accidents in a group of workers' compensation claimants who had been referred from the National Insurance Administration for a medicolegal assessment. The injured workers were evaluated on average 3 years after their accidents. Their medical records were analysed, and each injury was scored according to the Abbreviated Injury Scale (AIS). Participants completed the Short Form Questionnaire (SF-36). Factors relating to outcome on SF-36 were analysed using univariate and multivariate methods. 191 claimants returned the SF-36 (62%), 83% of the respondents had an AIS score of less than two, 33% reported working full time. Compared to population-based norms, the respondents reported significantly reduced health on all eight scales of SF-36. Better health and function was mainly associated with a higher level of education and more serious injuries. The extent of social support in the workplace after the accident was only partly related to outcome. The importance of psychosocial factors when making injury assessments in a medicolegal setting is highlighted.

Low Serum Levels of Uric Acid are Associated With Development of Poststroke Depression.

PubMed

Gu, Yingying; Han, Bin; Wang, Liping; Chang, Yaling; Zhu, Lin; Ren, Wenwei; Yan, Mengjiao; Zhang, Xiangyang; He, Jincai

2015-11-01

Poststroke depression (PSD) is a frequent complication of stroke that has been associated with poorer outcome of stroke patients. This study sought to examine the possible association between serum uric acid levels and the development of PSD.We recruited 196 patients with acute ischemic stroke and 100 healthy volunteers. Serum uric acid levels were tested by uricase-PAP method within 24 hr after admission. Neuropsychological evaluations were conducted at 3-month poststroke. The 17-item Hamilton Depression Scale was used to assess depressive symptoms. Diagnosis of PSD was made in accordance with DSM-IV criteria for depression. Multivariate analyses were conducted using logistic regression models.Fifty-six patients (28.6%) were diagnosed as having PSD at 3 months. PSD patients showed significantly lower levels of uric acid at baseline as compared to non-PSD patients (237.02 ± 43.43 vs 309.10 ± 67.44 μmol/L, t = -8.86, P < 0.001). In multivariate analyses, uric acid levels (≤239.0 and ≥328.1 μmol/L) were independently associated with the development of PSD (OR, 7.76; 95% confidence interval [CI], 2.56-23.47, P < 0.001 and OR, 0.05; 95% CI, 0.01-0.43, P = 0.01, respectively) after adjustment for possible variables.Serum uric acid levels at admission are found to be correlated with PSD and may predict its development at 3 months after stroke.

Community Attitudes About Discussing Sexual Health: Assessing Public Opinion of Local STD Prevention Campaigns

PubMed Central

Thomas, Rosalind; Bekan Homawoo, Brigitte; McClamroch, Kristi; Wise, Benjamin; Coles, F. Bruce

2013-01-01

Objectives We assessed public views about the acceptability of and need for sexually transmitted disease (STD) and sexual health-related educational messaging in local campaigns. Methods A 28-item state-added module was included in the 2008 New York Behavioral Risk Factor Surveillance System survey (n=3,751). Respondents rated acceptability of venues/dissemination channels and messaging and agreement with attitudinal/need statements. Additional data were analyzed from a separate state survey with individual county samples (n=36,257). We conducted univariate, bivariate, and multivariable modeling analyses. Results Each venue was acceptable to more than three-quarters of respondents (range: 79% for billboards to 95% for teaching STD prevention in high school). All message areas were acceptable to at least 85% of respondents (acceptability rating range: 85% to 97%). More than 70% agreed that there is a need for more open discussion about STDs. Bivariate analyses identified areas where messaging tailored to specific subgroups may be helpful (e.g., 26% of white people, 44% of African Americans, and 45% of Hispanic people agreed with the statement, “I need ideas about how to talk to my partner about protection from STDs”). Little geographic variation was seen. Results of multivariable modeling on opposition showed limited interaction effects. Conclusion These data provide key information about current community norms and reflect the public's approval for hearing and seeing more about sexual health and STDs in a range of public forums. PMID:23450887

Race and weight change in US women: the roles of socioeconomic and marital status.

PubMed Central

Kahn, H S; Williamson, D F; Stevens, J A

1991-01-01

BACKGROUND. The prevalence of overweight among Black women in the US is higher than among White women, but the causes are unknown. METHODS. We examined the weight change for 514 Black and 2,770 White women who entered the first Health and Nutrtion Examination Survey (1971-75) at ages 25-44 years and were weighed again a decade later. We used multivariate analyses to estimate the weight-change effectgs associated with race, family income, education, and marital change. RESULTS. After multiple adjustments, Black race, education below college level, and becoming married during the follow-up interval were each independently associated with an increased mean weight change. Using multivariate logistic analyses, Black race was not independently associated with an increased risk of major weight gain (change greater than or equal to +13 kg), but it was associated with a reduced likelihood of major weight loss (change less than or equal to -7 kg) (odds ratio - 0.64 [95% CI -0.41, 0.97])]. Very low family income was independently associated with the likelihood of both major weight gain (OR - 1.71 [95% CI - 1.15, 2.55]) and major weight loss (OR - 1.86 [95% CI - 1.18, 2.95]). CONCLUSIONS. Amont US women, Black race is independently associated with a reduced likelihood of major weight loss, but not with major weight gain. Women at greatest risk of weight gain are those with education below college level, those entering marriage, and those with very low family income. PMID:2036117

Computed tomography predictors of hepatocellular carcinoma tumour necrosis after chemoembolization

PubMed Central

Bryant, Mary K; Dorn, David P; Zarzour, Jessica; Smith, J Kevin; Redden, David T; Saddekni, Souheil; Aal, Ahmed Kamel Abdel; Gray, Stephen H; Eckhoff, Devin E; DuBay, Derek A

2014-01-01

Background Radiographical features associated with a favourable response to trans-arterial chemoembolization (TACE) are poorly defined for patients with hepatocellular carcinoma (HCC). Methods From 2008 to 2012, all first TACE interventions for HCC performed at the University of Alabama at Birmingham (UAB) were retrospectively reviewed. Only patients with a pre-TACE and a post-TACE computed tomography (CT) scan were included in the analyses (n = 115). HCC tumour response to TACE was quantified via the the modified Response Evaluation Criteria in Solid Tumors (mRECIST) criteria. Univariate and multivariable analyses were constructed. Results The index HCC tumours experienced a > 90% or complete tumour necrosis in 59/115 (51%) of patients after the first TACE intervention. On univariate analysis, smaller tumour size, peripheral tumour location and arterial enhancement were associated with a > 90% or complete tumour necrosis, whereas, only smaller tumour size [odds ratio (OR) 0.62; 95% confidence interval (CI) 0.48, 0.81] and peripheral location (OR 6.91; 95% CI 1.75, 27.29) were significant on multivariable analysis. There was a trend towards improved survival in the patients that experienced a > 90% or complete tumour necrosis (P = 0.08). Conclusions Peripherally located smaller HCC tumours are most likely to experience a > 90% or complete tumour necrosis after TACE. Surprisingly, arterial-phase enhancement and portal venous-phase washout were not significantly predictive of TACE-induced tumour necrosis. The TACE response was not statistically associated with improved survival. PMID:23980917

Determination of hydrazine in drinking water: Development and multivariate optimization of a rapid and simple solid phase microextraction-gas chromatography-triple quadrupole mass spectrometry protocol.

PubMed

Gionfriddo, Emanuela; Naccarato, Attilio; Sindona, Giovanni; Tagarelli, Antonio

2014-07-04

In this work, the capabilities of solid phase microextraction were exploited in a fully optimized SPME-GC-QqQ-MS analytical approach for hydrazine assay. A rapid and easy method was obtained by a simple derivatization reaction with propyl chloroformate and pyridine carried out directly in water samples, followed by automated SPME analysis in the same vial without further sample handling. The affinity of the different derivatized compounds obtained towards five commercially available SPME coatings was evaluated, in order to achieve the best extraction efficiency. GC analyses were carried out using a GC-QqQ-MS instrument in selected reaction monitoring (SRM) acquisition mode which has allowed the achievement of high specificity by selecting appropriate precursor-product ion couples improving the capability in analyte identification. The multivariate approach of experimental design was crucial in order to optimize derivatization reaction, SPME process and tandem mass spectrometry parameters. Accuracy of the proposed protocol, tested at 60, 200 and 800 ng L(-1), provided satisfactory values (114.2%, 83.6% and 98.6%, respectively), whereas precision (RSD%) at the same concentration levels were of 10.9%, 7.9% and 7.7% respectively. Limit of detection and quantification of 4.4 and 8.3 ng L(-1) were obtained. The reliable application of the proposed protocol to real drinking water samples confirmed its capability to be used as analytical tool for routine analyses. Copyright © 2014 Elsevier B.V. All rights reserved.

Prevalence and Determinants of Physical Activity and Fluid Intake in Kidney Transplant Recipients

PubMed Central

Gordon, Elisa J.; Prohaska, Thomas R.; Gallant, Mary P.; Sehgal, Ashwini R.; Strogatz, David; Conti, David; Siminoff, Laura A.

2009-01-01

Background and Significance Self-care for kidney transplantation is recommended to maintain kidney function. Little is known about levels of self-care practices, and demographic, psychosocial, and health-related correlates. Aim We investigated patients’ self-reported exercise and fluid intake, demographic and psychosocial factors associated with these self-care practices, and health-related quality of life. Methods Eighty-eight of 158 kidney recipients from two academic medical centers completed a semi-structured interview and surveys 2 months post-transplant. Results Most patients were sedentary (76%) with a quarter exercising either regularly (11%) or not at current recommendations (13%). One third (35%) reported drinking the recommended three liters of fluid daily. Multivariate analyses indicated that private insurance, high self-efficacy, and better physical functioning were significantly associated with engaging in physical activity (p<0.05); while male gender, private insurance, high self-efficacy, and not attributing oneself responsible for transplant success were significant predictors of adherence to fluid intake (p<0.05). Despite the significance of these predictors, models for physical activity and fluid intake explained 10–15% of the overall variance in these behaviors. Multivariate analyses indicated that younger age, high value of exercise, and higher social functioning significantly (p<0.05) predicted high self-efficacy for physical activity, while being married significantly (p<0.05) predicted high self-efficacy for fluid intake. Conclusion Identifying patients at risk of inadequate self-care practice is essential for educating patients about the importance of self-care. PMID:19925468

Geographic disparities in Healthy Eating Index scores (HEI-2005 and 2010) by residential property values: Findings from Seattle Obesity Study (SOS).

PubMed

Drewnowski, Adam; Aggarwal, Anju; Cook, Andrea; Stewart, Orion; Moudon, Anne Vernez

2016-02-01

Higher socioeconomic status (SES) has been linked with higher-quality diets. New GIS methods allow for geographic mapping of diet quality at a very granular level. To examine the geographic distribution of two measures of diet quality: Healthy Eating Index (HEI 2005 and HEI 2010) in relation to residential property values in Seattle-King County. The Seattle Obesity Study (SOS) collected data from a population-based sample of King County adults in 2008-09. Socio-demographic data were obtained by 20-min telephone survey. Dietary data were obtained from food frequency questionnaires (FFQs). Home addresses were geocoded to the tax parcel and residential property values were obtained from the King County tax assessor. Multivariable regression analyses using 1116 adults tested associations between SES variables and diet quality measured (HEI scores). Residential property values, education, and incomes were associated with higher HEI scores in bivariate analyses. Property values were not collinear with either education or income. In adjusted multivariable models, education and residential property were better associated with HEI, compared to than income. Mapping of HEI-2005 and HEI-2010 at the census block level illustrated the geographic distribution of diet quality across Seattle-King County. The use of residential property values, an objective measure of SES, allowed for the first visual exploration of diet quality at high spatial resolution: the census block level. Copyright © 2015 Elsevier Inc. All rights reserved.

Forgotten marriages? Measuring the reliability of marriage histories

PubMed Central

Chae, Sophia

2016-01-01

BACKGROUND Marriage histories are a valuable data source for investigating nuptiality. While researchers typically acknowledge the problems associated with their use, it is unknown to what extent these problems occur and how marriage analyses are affected. OBJECTIVE This paper seeks to investigate the quality of marriage histories by measuring levels of misreporting, examining the characteristics associated with misreporting, and assessing whether misreporting biases marriage indicators. METHODS Using data from the Malawi Longitudinal Study of Families and Health (MLSFH), I compare marriage histories reported by the same respondents at two different points in time. I investigate whether respondents consistently report their spouses (by name), status of marriage, and dates of marriage. I use multivariate regression models to investigate the characteristics associated with misreporting. Finally, I examine whether misreporting marriages and marriage dates affects marriage indicators. RESULTS Results indicate that 28.3% of men and 17.9% of women omitted at least one marriage in one of the survey waves. Multivariate regression models show that misreporting is not random: marriage, individual, interviewer, and survey characteristics are associated with marriage omission and marriage date inconsistencies. Misreporting also affects marriage indicators. CONCLUSIONS This is the first study of its kind to examine the reliability of marriage histories collected in the context of Sub-Saharan Africa. Although marriage histories are frequently used to study marriage dynamics, until now no knowledge has existed on the degree of misreporting. Misreporting in marriage histories is shown to be non-negligent and could potentially affect analyses. PMID:27152090

Effects of freezing on white perch Morone americana (Gmelin, 1789): Implications for multivariate morphometrics

USGS Publications Warehouse

Kocovsky, Patrick

2016-01-01

This study tested the hypothesis that duration of freezing differentially affects whole-body morphometrics of a derived teleost. Whole-body morphometrics are frequently analyzed to test hypotheses of different species, or stocks within a species, of fishes. Specimens used for morphometric analyses are typically fixed or preserved prior to analysis, yet little research has been done on how fixation or preservation methods or duration of preservation of specimens might affect outcomes of multivariate statistical analyses of differences in shape. To determine whether whole-body morphometrics changed as a result of freezing, 23 whole-body morphometrics of age-1 white perch (Morone americana) from western Lake Erie (n = 211) were analyzed immediately after capture, after being held on ice overnight, and after freezing for 100 or 200 days. Discriminant function analysis revealed that all four groups differed significantly from one another (P < 0.0001). The first canonical axis reflected long-axis morphometrics, where there was a clear pattern of positive translation along this axis with duration of preservation. Re-classification analysis demonstrated fish were typically assigned to their original preservation class except for fish frozen 100 days, which assigned mostly to frozen 200 days. Morphometric comparisons using frozen fish must be done on fish frozen for identical periods of time to avoid biases related to the length of time they were frozen. Similar experiments should be conducted on other species and also using formalin- and alcohol-preserved specimens.

Complications Following Primary and Revision Transsphenoidal Surgeries for Pituitary Tumors

PubMed Central

Krings, James G.; Kallogjeri, Dorina; Wineland, Andre; Nepple, Kenneth G.; Piccirillo, Jay F.; Getz, Anne E.

2014-01-01

Objective This study aimed to determine the incidence of major complications following both primary and revision transsphenoidal pituitary surgery. Major complications included endocrinopathic, skull base, orbital, hemorrhagic and thromboembolic complications, respiratory failure, and death. Secondarily, this study aimed to examine factors associated with the occurrence of complications. Study Design Retrospective cohort analysis of California and Florida all-payer databases from 2005-2008. Methods The major complication rate following both primary and revision transsphenoidal pituitary surgery was calculated. Bivariate analyses were performed to investigate the relationship of patient characteristics with complication occurrence, and a multivariate model was constructed to determine risk factors associated with these complications. Results 5,277 primary cases and 192 revision cases met inclusion criteria. There was a non-significant absolute difference of 3.09% (95% CI −11.00 to 16.14) between the rate of complications following primary (n=443; 8.39%) and revision (n=22; 11.46%) surgeries. Multivariate analyses showed that patients with Medicare (OR=1.74; 95% CI 1.17 to 2.61), Medicaid (OR=2.13; 95% CI 1.59 to 2.86), or a malignant neoplasm (OR=3.10; 95% CI 1.62 to 5.93) were more likely to have complications. Conclusions The rate of major complications following transsphenoidal pituitary surgery is lower than earlier retrospective reports. The overall complication rate following revision surgery was not significantly different from primary surgery. Insurance status and a diagnosis of a malignant neoplasm were associated with a higher rate of complications. PMID:25263939

Simulating Multivariate Nonnormal Data Using an Iterative Algorithm

ERIC Educational Resources Information Center

Ruscio, John; Kaczetow, Walter

2008-01-01

Simulating multivariate nonnormal data with specified correlation matrices is difficult. One especially popular method is Vale and Maurelli's (1983) extension of Fleishman's (1978) polynomial transformation technique to multivariate applications. This requires the specification of distributional moments and the calculation of an intermediate…

Network Analysis of Associations between Serum Interferon Alpha Activity, Autoantibodies, and Clinical Features in Systemic Lupus Erythematosus

PubMed Central

Weckerle, Corinna E.; Franek, Beverly S.; Kelly, Jennifer A.; Kumabe, Marissa; Mikolaitis, Rachel A.; Green, Stephanie L.; Utset, Tammy O.; Jolly, Meenakshi; James, Judith A.; Harley, John B.; Niewold, Timothy B.

2010-01-01

Background Interferon-alpha (IFN-α) is a primary pathogenic factor in systemic lupus erythematosus (SLE), and high IFN-α levels may be associated with particular clinical manifestations. The prevalence of individual clinical and serologic features differs significantly by ancestry. We used multivariate and network analyses to detect associations between clinical and serologic disease manifestations and serum IFN-α activity in a large diverse SLE cohort. Methods 1089 SLE patients were studied (387 African-American, 186 Hispanic-American, and 516 European-American). Presence or absence of ACR clinical criteria for SLE, autoantibodies, and serum IFN-α activity data were analyzed in univariate and multivariate models. Iterative multivariate logistic regression was performed in each background separately to establish the network of associations between variables that were independently significant following Bonferroni correction. Results In all ancestral backgrounds, high IFN-α activity was associated with anti-Ro and anti-dsDNA antibodies (p-values 4.6×10−18 and 2.9 × 10−16 respectively). Younger age, non-European ancestry, and anti-RNP were also independently associated with increased serum IFN-α activity (p≤6.7×10−4). We found 14 unique associations between variables in network analysis, and only 7 of these associations were shared by more than one ancestral background. Associations between clinical criteria were different in different ancestral backgrounds, while autoantibody-IFN-α relationships were similar across backgrounds. IFN-α activity and autoantibodies were not associated with ACR clinical features in multivariate models. Conclusions Serum IFN-α activity was strongly and consistently associated with autoantibodies, and not independently associated with clinical features in SLE. IFN-α may be more relevant to humoral tolerance and initial pathogenesis than later clinical disease manifestations. PMID:21162028

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

Pretreatment Health Behaviors Predict Survival Among Patients With Head and Neck Squamous Cell Carcinoma

PubMed Central

Duffy, Sonia A.; Ronis, David L.; McLean, Scott; Fowler, Karen E.; Gruber, Stephen B.; Wolf, Gregory T.; Terrell, Jeffrey E.

2009-01-01

Purpose Our prior work has shown that the health behaviors of head and neck cancer patients are interrelated and are associated with quality of life; however, other than smoking, the relationship between health behaviors and survival is unclear. Patients and Methods A prospective cohort study was conducted to determine the relationship between five pretreatment health behaviors (smoking, alcohol, diet, physical activity, and sleep) and all-cause survival among 504 head and neck cancer patients. Results Smoking status was the strongest predictor of survival, with both current smokers (hazard ratio [HR] = 2.4; 95% CI, 1.3 to 4.4) and former smokers (HR = 2.0; 95% CI, 1.2 to 3.5) showing significant associations with poor survival. Problem drinking was associated with survival in the univariate analysis (HR = 1.4; 95% CI, 1.0 to 2.0) but lost significance when controlling for other factors. Low fruit intake was negatively associated with survival in the univariate analysis only (HR = 1.6; 95% CI, 1.1 to 2.1), whereas vegetable intake was not significant in either univariate or multivariate analyses. Although physical activity was associated with survival in the univariate analysis (HR = 0.95; 95% CI, 0.93 to 0.97), it was not significant in the multivariate model. Sleep was not significantly associated with survival in either univariate or multivariate analysis. Control variables that were also independently associated with survival in the multivariate analysis were age, education, tumor site, cancer stage, and surgical treatment. Conclusion Variation in selected pretreatment health behaviors (eg, smoking, fruit intake, and physical activity) in this population is associated with variation in survival. PMID:19289626

Impacts of Rising Health Care Costs on Families with Employment-Based Private Insurance: A National Analysis with State Fixed Effects

PubMed Central

Yu, Hao; Dick, Andrew W

2012-01-01

Background Given the rapid growth of health care costs, some experts were concerned with erosion of employment-based private insurance (EBPI). This empirical analysis aims to quantify the concern. Methods Using the National Health Account, we generated a cost index to represent state-level annual cost growth. We merged it with the 1996–2003 Medical Expenditure Panel Survey. The unit of analysis is the family. We conducted both bivariate and multivariate logistic analyses. Results The bivariate analysis found a significant inverse association between the cost index and the proportion of families receiving an offer of EBPI. The multivariate analysis showed that the cost index was significantly negatively associated with the likelihood of receiving an EBPI offer for the entire sample and for families in the first, second, and third quartiles of income distribution. The cost index was also significantly negatively associated with the proportion of families with EBPI for the entire year for each family member (EBPI-EYEM). The multivariate analysis confirmed significance of the relationship for the entire sample, and for families in the second and third quartiles of income distribution. Among the families with EBPI-EYEM, there was a positive relationship between the cost index and this group's likelihood of having out-of-pocket expenditures exceeding 10 percent of family income. The multivariate analysis confirmed significance of the relationship for the entire group and for families in the second and third quartiles of income distribution. Conclusions Rising health costs reduce EBPI availability and enrollment, and the financial protection provided by it, especially for middle-class families. PMID:22417314

Multiset canonical correlations analysis and multispectral, truly multitemporal remote sensing data.

PubMed

Nielsen, Allan Aasbjerg

2002-01-01

This paper describes two- and multiset canonical correlations analysis (CCA) for data fusion, multisource, multiset, or multitemporal exploratory data analysis. These techniques transform multivariate multiset data into new orthogonal variables called canonical variates (CVs) which, when applied in remote sensing, exhibit ever-decreasing similarity (as expressed by correlation measures) over sets consisting of 1) spectral variables at fixed points in time (R-mode analysis), or 2) temporal variables with fixed wavelengths (T-mode analysis). The CVs are invariant to linear and affine transformations of the original variables within sets which means, for example, that the R-mode CVs are insensitive to changes over time in offset and gain in a measuring device. In a case study, CVs are calculated from Landsat Thematic Mapper (TM) data with six spectral bands over six consecutive years. Both Rand T-mode CVs clearly exhibit the desired characteristic: they show maximum similarity for the low-order canonical variates and minimum similarity for the high-order canonical variates. These characteristics are seen both visually and in objective measures. The results from the multiset CCA R- and T-mode analyses are very different. This difference is ascribed to the noise structure in the data. The CCA methods are related to partial least squares (PLS) methods. This paper very briefly describes multiset CCA-based multiset PLS. Also, the CCA methods can be applied as multivariate extensions to empirical orthogonal functions (EOF) techniques. Multiset CCA is well-suited for inclusion in geographical information systems (GIS).

Development of a quantitative multivariable radiographic method to evaluate anatomic changes associated with laminitis in the forefeet of donkeys.

PubMed

Collins, Simon N; Dyson, Sue J; Murray, Rachel C; Newton, J Richard; Burden, Faith; Trawford, Andrew F

2012-08-01

To establish and validate an objective method of radiographic diagnosis of anatomic changes in laminitic forefeet of donkeys on the basis of data from a comprehensive series of radiographic measurements. 85 donkeys with and 85 without forelimb laminitis for baseline data determination; a cohort of 44 donkeys with and 18 without forelimb laminitis was used for validation analyses. For each donkey, lateromedial radiographic views of 1 weight-bearing forelimb were obtained; images from 11 laminitic and 2 nonlaminitic donkeys were excluded (motion artifact) from baseline data determination. Data from an a priori selection of 19 measurements of anatomic features of laminitic and nonlaminitic donkey feet were analyzed by use of a novel application of multivariate statistical techniques. The resultant diagnostic models were validated in a blinded manner with data from the separate cohort of laminitic and nonlaminitic donkeys. Data were modeled, and robust statistical rules were established for the diagnosis of anatomic changes within laminitic donkey forefeet. Component 1 scores ≤ -3.5 were indicative of extreme anatomic change, and scores from -2.0 to 0.0 denoted modest change. Nonlaminitic donkeys with a score from 0.5 to 1.0 should be considered as at risk for laminitis. Results indicated that the radiographic procedures evaluated can be used for the identification, assessment, and monitoring of anatomic changes associated with laminitis. Screening assessments by use of this method may enable early detection of mild anatomic change and identification of at-risk donkeys.

New strategy for determination of anthocyanins, polyphenols and antioxidant capacity of Brassica oleracea liquid extract using infrared spectroscopies and multivariate regression

NASA Astrophysics Data System (ADS)

de Oliveira, Isadora R. N.; Roque, Jussara V.; Maia, Mariza P.; Stringheta, Paulo C.; Teófilo, Reinaldo F.

2018-04-01

A new method was developed to determine the antioxidant properties of red cabbage extract (Brassica oleracea) by mid (MID) and near (NIR) infrared spectroscopies and partial least squares (PLS) regression. A 70% (v/v) ethanolic extract of red cabbage was concentrated to 9° Brix and further diluted (12 to 100%) in water. The dilutions were used as external standards for the building of PLS models. For the first time, this strategy was applied for building multivariate regression models. Reference analyses and spectral data were obtained from diluted extracts. The determinate properties were total and monomeric anthocyanins, total polyphenols and antioxidant capacity by ABTS (2,2-azino-bis(3-ethyl-benzothiazoline-6-sulfonate)) and DPPH (2,2-diphenyl-1-picrylhydrazyl) methods. Ordered predictors selection (OPS) and genetic algorithm (GA) were used for feature selection before PLS regression (PLS-1). In addition, a PLS-2 regression was applied to all properties simultaneously. PLS-1 models provided more predictive models than did PLS-2 regression. PLS-OPS and PLS-GA models presented excellent prediction results with a correlation coefficient higher than 0.98. However, the best models were obtained using PLS and variable selection with the OPS algorithm and the models based on NIR spectra were considered more predictive for all properties. Then, these models provided a simple, rapid and accurate method for determination of red cabbage extract antioxidant properties and its suitability for use in the food industry.

Evaluation of the efficiency of continuous wavelet transform as processing and preprocessing algorithm for resolution of overlapped signals in univariate and multivariate regression analyses; an application to ternary and quaternary mixtures

NASA Astrophysics Data System (ADS)

Hegazy, Maha A.; Lotfy, Hayam M.; Mowaka, Shereen; Mohamed, Ekram Hany

2016-07-01

Wavelets have been adapted for a vast number of signal-processing applications due to the amount of information that can be extracted from a signal. In this work, a comparative study on the efficiency of continuous wavelet transform (CWT) as a signal processing tool in univariate regression and a pre-processing tool in multivariate analysis using partial least square (CWT-PLS) was conducted. These were applied to complex spectral signals of ternary and quaternary mixtures. CWT-PLS method succeeded in the simultaneous determination of a quaternary mixture of drotaverine (DRO), caffeine (CAF), paracetamol (PAR) and p-aminophenol (PAP, the major impurity of paracetamol). While, the univariate CWT failed to simultaneously determine the quaternary mixture components and was able to determine only PAR and PAP, the ternary mixtures of DRO, CAF, and PAR and CAF, PAR, and PAP. During the calculations of CWT, different wavelet families were tested. The univariate CWT method was validated according to the ICH guidelines. While for the development of the CWT-PLS model a calibration set was prepared by means of an orthogonal experimental design and their absorption spectra were recorded and processed by CWT. The CWT-PLS model was constructed by regression between the wavelet coefficients and concentration matrices and validation was performed by both cross validation and external validation sets. Both methods were successfully applied for determination of the studied drugs in pharmaceutical formulations.

Remote-sensing data processing with the multivariate regression analysis method for iron mineral resource potential mapping: a case study in the Sarvian area, central Iran

NASA Astrophysics Data System (ADS)

Mansouri, Edris; Feizi, Faranak; Jafari Rad, Alireza; Arian, Mehran

2018-03-01

This paper uses multivariate regression to create a mathematical model for iron skarn exploration in the Sarvian area, central Iran, using multivariate regression for mineral prospectivity mapping (MPM). The main target of this paper is to apply multivariate regression analysis (as an MPM method) to map iron outcrops in the northeastern part of the study area in order to discover new iron deposits in other parts of the study area. Two types of multivariate regression models using two linear equations were employed to discover new mineral deposits. This method is one of the reliable methods for processing satellite images. ASTER satellite images (14 bands) were used as unique independent variables (UIVs), and iron outcrops were mapped as dependent variables for MPM. According to the results of the probability value (p value), coefficient of determination value (R2) and adjusted determination coefficient (Radj2), the second regression model (which consistent of multiple UIVs) fitted better than other models. The accuracy of the model was confirmed by iron outcrops map and geological observation. Based on field observation, iron mineralization occurs at the contact of limestone and intrusive rocks (skarn type).

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.

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.

Relationship between cataract severity and socioeconomic status.

PubMed

Wesolosky, Jason D; Rudnisky, Christopher J

2013-12-01

To determine the relationship between cataract severity and socioeconomic status (SES). Retrospective, observational case series. A total of 1350 eyes underwent phacoemulsification cataract extraction by a single surgeon using an Alcon Infiniti system. Cataract severity was measured using phaco time in seconds. SES was measured using area-level aggregate census data: median income, education, proportion of common-law couples, and employment rate. Preoperative best corrected visual acuity was obtained and converted to logarithm of the minimum angle of resolution values. For patients undergoing bilateral surgery, the generalized estimating equation was used to account for the correlation between eyes. Univariate analyses were performed using simple regression, and multivariate analyses were performed to account for variables with significant relationships (p < 0.05) on univariate testing. Sensitivity analyses were performed to assess the effect of including patient age in the controlled analyses. Multivariate analyses demonstrated that cataracts were more severe when the median income was lower (p = 0.001) and the proportion of common-law couples living in a patient's community (p = 0.012) and the unemployment rate (p = 0.002) were higher. These associations persisted even when controlling for patient age. Patients of lower SES have more severe cataracts. Copyright © 2013 Canadian Ophthalmological Society. Published by Elsevier Inc. All rights reserved.

Multicollinearity in spatial genetics: separating the wheat from the chaff using commonality analyses.

PubMed

Prunier, J G; Colyn, M; Legendre, X; Nimon, K F; Flamand, M C

2015-01-01

Direct gradient analyses in spatial genetics provide unique opportunities to describe the inherent complexity of genetic variation in wildlife species and are the object of many methodological developments. However, multicollinearity among explanatory variables is a systemic issue in multivariate regression analyses and is likely to cause serious difficulties in properly interpreting results of direct gradient analyses, with the risk of erroneous conclusions, misdirected research and inefficient or counterproductive conservation measures. Using simulated data sets along with linear and logistic regressions on distance matrices, we illustrate how commonality analysis (CA), a detailed variance-partitioning procedure that was recently introduced in the field of ecology, can be used to deal with nonindependence among spatial predictors. By decomposing model fit indices into unique and common (or shared) variance components, CA allows identifying the location and magnitude of multicollinearity, revealing spurious correlations and thus thoroughly improving the interpretation of multivariate regressions. Despite a few inherent limitations, especially in the case of resistance model optimization, this review highlights the great potential of CA to account for complex multicollinearity patterns in spatial genetics and identifies future applications and lines of research. We strongly urge spatial geneticists to systematically investigate commonalities when performing direct gradient analyses. © 2014 John Wiley & Sons Ltd.

Multivariate statistical analysis of low-voltage EDS spectrum images

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

Anderson, I.M.

1998-03-01

Whereas energy-dispersive X-ray spectrometry (EDS) has been used for compositional analysis in the scanning electron microscope for 30 years, the benefits of using low operating voltages for such analyses have been explored only during the last few years. This paper couples low-voltage EDS with two other emerging areas of characterization: spectrum imaging and multivariate statistical analysis. The specimen analyzed for this study was a finished Intel Pentium processor, with the polyimide protective coating stripped off to expose the final active layers.

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.

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

Antioxidant capacity of cornelian cherry (Cornus mas L.) - comparison between permanganate reducing antioxidant capacity and other antioxidant methods.

PubMed

Popović, Boris M; Stajner, Dubravka; Slavko, Kevrešan; Sandra, Bijelić

2012-09-15

Ethanol extracts (80% in water) of 10 cornelian cherry (Cornus mas L.) genotypes were studied for antioxidant properties, using methods including DPPH(), ()NO, O(2)(-) and ()OH antiradical powers, FRAP, total phenolic and anthocyanin content (TPC and ACC) and also one relatively new, permanganate method (permanganate reducing antioxidant capacity-PRAC). Lipid peroxidation (LP) was also determined as an indicator of oxidative stress. The data from different procedures were compared and analysed by multivariate techniques (correlation matrix calculation and principal component analysis (PCA)). Significant positive correlations were obtained between TPC, ACC and DPPH(), ()NO, O(2)(-), and ()OH antiradical powers, and also between PRAC and TPC, ACC and FRAP. PCA found two major clusters of cornelian cherry, based on antiradical power, FRAP and PRAC and also on chemical composition. Chemometric evaluation showed close interdependence between PRAC method and FRAP and ACC. There was a huge variation between C. mas genotypes in terms of antioxidant activity. Copyright © 2012 Elsevier Ltd. All rights reserved.

Test-retest stability of the Task and Ego Orientation Questionnaire.

PubMed

Lane, Andrew M; Nevill, Alan M; Bowes, Neal; Fox, Kenneth R

2005-09-01

Establishing stability, defined as observing minimal measurement error in a test-retest assessment, is vital to validating psychometric tools. Correlational methods, such as Pearson product-moment, intraclass, and kappa are tests of association or consistency, whereas stability or reproducibility (regarded here as synonymous) assesses the agreement between test-retest scores. Indexes of reproducibility using the Task and Ego Orientation in Sport Questionnaire (TEOSQ; Duda & Nicholls, 1992) were investigated using correlational (Pearson product-moment, intraclass, and kappa) methods, repeated measures multivariate analysis of variance, and calculating the proportion of agreement within a referent value of +/-1 as suggested by Nevill, Lane, Kilgour, Bowes, and Whyte (2001). Two hundred thirteen soccer players completed the TEOSQ on two occasions, 1 week apart. Correlation analyses indicated a stronger test-retest correlation for the Ego subscale than the Task subscale. Multivariate analysis of variance indicated stability for ego items but with significant increases in four task items. The proportion of test-retest agreement scores indicated that all ego items reported relatively poor stability statistics with test-retest scores within a range of +/-1, ranging from 82.7-86.9%. By contrast, all task items showed test-retest difference scores ranging from 92.5-99%, although further analysis indicated that four task subscale items increased significantly. Findings illustrated that correlational methods (Pearson product-moment, intraclass, and kappa) are influenced by the range in scores, and calculating the proportion of agreement of test-retest differences with a referent value of +/-1 could provide additional insight into the stability of the questionnaire. It is suggested that the item-by-item proportion of agreement method proposed by Nevill et al. (2001) should be used to supplement existing methods and could be especially helpful in identifying rogue items in the initial stages of psychometric questionnaire validation.

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

PubMed

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

2016-04-01

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

Diversity pattern in Sesamum mutants selected for a semi-arid cropping system.

PubMed

Murty, B R; Oropeza, F

1989-02-01

Due to the complex requirements of moisture stress, substantial genetic diversity with a wide array of character combinations and effective simultaneous selection for several variables is necessary for improving the productivity and adaptation of a component crop in order for it to fit into a cropping system under semi-arid tropical conditions. Sesamum indicum L. is grown in Venezuela after rice/sorghum/or maize under such conditions. A mutation breeding program was undertaken using six locally adapted varieties to develop genotypes suitable for the above system. The diversity pattern for nine variables was assessed by multivariate analysis in 301 M4 progenies. Analysis of the characteristic roots and principal components in three methods of selection, i.e., M2 bulks (A), individual plant selection throughout (B), and selection in M3 for single variable (C), revealed differences in the pattern of variation between varieties, selection methods, and varieties x methods interactions. Method B was superior to the others and gave 17 of the 21 best M5 progenies. 'Piritu' and 'CF' varieties yielded the most productive progenies in M5 and M6. Diversity was large and selection was effective for such developmental traits as earliness and synchrony, combined with multiple disease resistance, which could be related to their importance by multivariate analyses. Considerable differences in the variety of character combinations among the high yielding. M5 progenies of 'CF' and 'Piritu' suggested possible further yield improvement. The superior response of 'Piritu' and 'CF' over other varieties in yield and adaptation was due to major changes in plant type and character associations. Multilocation testing of M5 generations revealed that the mutant progenies had a 40%-100% yield superiority over the parents; this was combined with earliness, synchrony, and multiple disease resistance, and was confirmed in the M6 generation grown on a commercial scale. This study showed that multivariate analysis is an effective tool for assessing diversity patterns, choice of appropriate variety, and selection methodology in order to make rapid progress in meeting the complex requirements of semi-arid cropping systems.

On the road again: concurrency and condom use among Uganda truck drivers.

PubMed

Costenbader, Elizabeth C; Lancaster, Kathryn; Bufumbo, Leonard; Akol, Angela; Guest, Greg

2015-01-01

Long-distance truck drivers have been shown to be a critical population in the spread of HIV in Africa. In 2009, surveys with 385 Ugandan long-distance truck drivers measured concurrency point prevalence with two methods; it ranged from 37.4% (calendar-method) to 50.1% (direct question). The majority (84%) of relationships reported were long-term resulting in a long duration of overlap (average of 58 months) across concurrent partnerships. Only 7% of these men reported using any condoms with their spouses during the past month. Among all non-spousal relationships, duration of relationship was the factor most strongly associated with engaging in unprotected sex in the past month in a multivariable analyses controlling for partner and relationship characteristics. Innovative intervention programs for these men and their partners are needed that address the realities of truck drivers' lifestyles.

Study on discrimination of oral cancer from normal using blood plasma based on fluorescence steady and excited state at excitation wavelength 280 nm

NASA Astrophysics Data System (ADS)

Rekha, Pachaiappan; Aruna, Prakasa Rao; Ganesan, Singaravelu

2016-03-01

Many research works based on fluorescence spectroscopy have proven its potential in the diagnosis of various diseases using the spectral signatures of the native key fluorophores such as tryptophan, tyrosine, collagen, NADH, FAD and porphyrin. These fluorophores distribution, concentration and their conformation may be changed depending upon the pathological and metabolic conditions of cells and tissues. In this study, we have made an attempt to characterize the blood plasma of normal subject and oral cancer patients by native fluorescence spectroscopy at 280 nm excitation. Further, the fluorescence data were analyzed by employing the multivariate statistical method - linear discriminant analyses (LDA) using leaves one out cross validation method. The results illustrate the potential of fluorescence spectroscopy technique in the diagnosis of oral cancer using blood plasma.

Calibrated Multivariate Regression with Application to Neural Semantic Basis Discovery.

PubMed

Liu, Han; Wang, Lie; Zhao, Tuo

2015-08-01

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

A 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

Multivariate analyses of tinnitus complaint and change in tinnitus complaint: a masker study.

PubMed

Jakes, S; Stephens, S D

1987-11-01

Multivariate statistical techniques were used to re-analyse the data from the recent DHSS multi-centre masker study. These analyses were undertaken to three ends. First, to clarify and attempt to replicate the previously found factor structure of complaints about tinnitus. Secondly, to attempt to identify common factors in the change or improvement measures pre- and post-masker treatment. Thirdly, to identify predictors of any such outcome factors. Two complaint factors were identified; 'Distress' and 'intrusiveness'. A series of analyses were conducted on change measures using different numbers of subjects and variables. When only semantic differential scales were used, the change factors were very similar to the complaint factors noted above. When variables measuring other aspects of improvement were included, several other factors were identified. These included; 'tinnitus helped', 'masking effects', 'residual inhibition' and 'matched loudness'. Twenty-five conceptually distinct predictors of outcome were identified. These predictor variables were quite different for different outcome factors. For example, high-frequency hearing loss was a predictor of tinnitus being helped by the masker, and a low frequency match and a low masking threshold predicted therapeutic success on residual inhibition. Decrease in matched loudness was predicted by louder tinnitus initially.

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.

Correlation between smoking habit and surgical outcomes on viral-associated hepatocellular carcinomas.

PubMed

Kai, Keita; Komukai, Sho; Koga, Hiroki; Yamaji, Koutaro; Ide, Takao; Kawaguchi, Atsushi; Aishima, Shinichi; Noshiro, Hirokazu

2018-01-07

To investigate the association between smoking habits and surgical outcomes in hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) (B-HCC) and hepatitis C virus (HCV)-related HCC (C-HCC) and clarify the clinicopathological features associated with smoking status in B-HCC and C-HCC patients. We retrospectively examined the cases of the 341 consecutive patients with viral-associated HCC (C-HCC, n = 273; B-HCC, n = 68) who underwent curative surgery for their primary lesion. We categorized smoking status at the time of surgery into never, ex- and current smoker. We analyzed the B-HCC and C-HCC groups' clinicopathological features and surgical outcomes, i.e ., disease-free survival (DFS), overall survival (OS), and disease-specific survival (DSS). Univariate and multivariate analyses were performed using a Cox proportional hazards regression model. We also performed subset analyses in both patient groups comparing the current smokers to the other patients. The multivariate analysis in the C-HCC group revealed that current-smoker status was significantly correlated with both OS ( P = 0.0039) and DSS ( P = 0.0416). In the B-HCC patients, no significant correlation was observed between current-smoker status and DFS, OS, or DSS in the univariate or multivariate analyses. The subset analyses comparing the current smokers to the other patients in both the C-HCC and B-HCC groups revealed that the current smokers developed HCC at significantly younger ages than the other patients irrespective of viral infection status. A smoking habit is significantly correlated with the overall and disease-specific survivals of patients with C-HCC. In contrast, the B-HCC patients showed a weak association between smoking status and surgical outcomes.

Assessing the effects of habitat patches ensuring propagule supply and different costs inclusion in marine spatial planning through multivariate analyses.

PubMed

Appolloni, L; Sandulli, R; Vetrano, G; Russo, G F

2018-05-15

Marine Protected Areas are considered key tools for conservation of coastal ecosystems. However, many reserves are characterized by several problems mainly related to inadequate zonings that often do not protect high biodiversity and propagule supply areas precluding, at the same time, economic important zones for local interests. The Gulf of Naples is here employed as a study area to assess the effects of inclusion of different conservation features and costs in reserve design process. In particular eight scenarios are developed using graph theory to identify propagule source patches and fishing and exploitation activities as costs-in-use for local population. Scenarios elaborated by MARXAN, software commonly used for marine conservation planning, are compared using multivariate analyses (MDS, PERMANOVA and PERMDISP) in order to assess input data having greatest effects on protected areas selection. MARXAN is heuristic software able to give a number of different correct results, all of them near to the best solution. Its outputs show that the most important areas to be protected, in order to ensure long-term habitat life and adequate propagule supply, are mainly located around the Gulf islands. In addition through statistical analyses it allowed us to prove that different choices on conservation features lead to statistically different scenarios. The presence of propagule supply patches forces MARXAN to select almost the same areas to protect decreasingly different MARXAN results and, thus, choices for reserves area selection. The multivariate analyses applied here to marine spatial planning proved to be very helpful allowing to identify i) how different scenario input data affect MARXAN and ii) what features have to be taken into account in study areas characterized by peculiar biological and economic interests. Copyright © 2018 Elsevier Ltd. All rights reserved.

Empirical study of the dependence of the results of multivariable flexible survival analyses on model selection strategy.

PubMed

Binquet, C; Abrahamowicz, M; Mahboubi, A; Jooste, V; Faivre, J; Bonithon-Kopp, C; Quantin, C

2008-12-30

Flexible survival models, which avoid assumptions about hazards proportionality (PH) or linearity of continuous covariates effects, bring the issues of model selection to a new level of complexity. Each 'candidate covariate' requires inter-dependent decisions regarding (i) its inclusion in the model, and representation of its effects on the log hazard as (ii) either constant over time or time-dependent (TD) and, for continuous covariates, (iii) either loglinear or non-loglinear (NL). Moreover, 'optimal' decisions for one covariate depend on the decisions regarding others. Thus, some efficient model-building strategy is necessary.We carried out an empirical study of the impact of the model selection strategy on the estimates obtained in flexible multivariable survival analyses of prognostic factors for mortality in 273 gastric cancer patients. We used 10 different strategies to select alternative multivariable parametric as well as spline-based models, allowing flexible modeling of non-parametric (TD and/or NL) effects. We employed 5-fold cross-validation to compare the predictive ability of alternative models.All flexible models indicated significant non-linearity and changes over time in the effect of age at diagnosis. Conventional 'parametric' models suggested the lack of period effect, whereas more flexible strategies indicated a significant NL effect. Cross-validation confirmed that flexible models predicted better mortality. The resulting differences in the 'final model' selected by various strategies had also impact on the risk prediction for individual subjects.Overall, our analyses underline (a) the importance of accounting for significant non-parametric effects of covariates and (b) the need for developing accurate model selection strategies for flexible survival analyses. Copyright 2008 John Wiley & Sons, Ltd.

Intention stability assessed using residual change scores moderates the intention-behaviour association: a prospective cohort study.

PubMed

Rowan, Alicia A; McDermott, Máirtín S; Allen, Mark S

2017-12-01

Intention stability is considered to be one of the key pre-requisites for a strong association between intention and behaviour. It has been claimed, however, that studies examining the moderating impact of intention stability may be invalid, as they have relied on statistically inferior methods. Residual change scores have been suggested as a more appropriate method of measuring change (or lack thereof) in constructs. The aim of the current study, therefore, is to test whether intention stability, calculated using residual change scores, moderates the intention-physical activity behaviour association. A total of 163 participants (124 women, 39 men) completed questionnaires online at three time points separated by 14 day intervals. The moderating impact of intention stability was assessed using multiple linear regression followed up using simple slope analyses to identify the direction of any effect. The interaction of intention and intention stability was found to significantly improve the overall model fit. Intentions had a stronger positive association with behaviour when intentions were more stable than when they were more unstable. However, sensitivity analyses revealed that the association was not robust and reduced to non-significant with the removal of potential multivariate outliers. Future research should use residual change scores as the preferred method of assessing intention stability.

Unbiased profiling of volatile organic compounds in the headspace of Allium plants using an in-tube extraction device.

PubMed

Kusano, Miyako; Kobayashi, Makoto; Iizuka, Yumiko; Fukushima, Atsushi; Saito, Kazuki

2016-02-29

Plants produce and emit important volatile organic compounds (VOCs), which have an essential role in biotic and abiotic stress responses and in plant-plant and plant-insect interactions. In order to study the bouquets from plants qualitatively and quantitatively, a comprehensive, analytical method yielding reproducible results is required. We applied in-tube extraction (ITEX) and solid-phase microextraction (SPME) for studying the emissions of Allium plants. The collected HS samples were analyzed by gas chromatography-time-of-flight-mass spectrometry (GC-TOF-MS), and the results were subjected to multivariate analysis. In case of ITEX-method Allium cultivars released more than 300 VOCs, out of which we provisionally identified 50 volatiles. We also used the VOC profiles of Allium samples to discriminate among groups of A. fistulosum, A. chinense (rakkyo), and A. tuberosum (Oriental garlic). As we found 12 metabolite peaks including dipropyl disulphide with significant changes in A. chinense and A. tuberosum when compared to the control cultivar, these metabolite peaks can be used for chemotaxonomic classification of A. chinense, tuberosum, and A. fistulosum. Compared to SPME-method our ITEX-based VOC profiling technique contributes to automatic and reproducible analyses. Hence, it can be applied to high-throughput analyses such as metabolite profiling.

Glycated Hemoglobin Independently Predicts Stroke Recurrence within One Year after Acute First-Ever Non-Cardioembolic Strokes Onset in A Chinese Cohort Study

PubMed Central

Wu, Shuolin; Shi, Yuzhi; Wang, Chunxue; Jia, Qian; Zhang, Ning; Zhao, Xingquan; Liu, Gaifen; Wang, Yilong; Liu, Liping; Wang, Yongjun

2013-01-01

Objective Hyperglycemia is related to stroke. Glycated hemoglobin (HbA1c) can reflect pre-stroke glycaemia status. However, the information on the direct association between HbA1c and recurrence after non-cardioembolic acute ischemic strokes is rare and there is no consistent conclusion. Methods The ACROSS-China database comprised of 2186 consecutive first-ever acute ischemic stroke patients with baseline HbA1c values. After excluding patients who died from non-stroke recurrence and patients lost to follow up, 1817 and 1540 were eligible for 3-month and 1-year analyses, respectively. Multivariate Cox regression was performed to evaluate the associations between HbA1c and 3-month and 1-year stroke recurrence. Results The HbA1c values at admission were divided into 4 levels by quartiles: Q1 (<5.5%); Q2 (5.5 to <6.1%); Q3 (6.1% to <7.2%); and Q4 (≥7.2%). The cumulative recurrence rates were 8.3% and 11.0% for 3 months and 1 year, respectively. In multivariate analyses, when compared with Q1, the adjusted hazard ratios (AHRs) were 2.83 (95% confidence interval (CI) 1.28-6.26) in Q3 and 3.71(95% CI 1.68-8.21) in Q4 for 3-month stroke recurrence; 3.30 (95% CI 1.31-8.34) in Q3 and 3.35 (95% CI 1.36-8.21) in Q4 for 1-year stroke recurrence. Adding fasting plasma glucose in the multivariate analyses did not modify the association: AHRs were 2.75 (95% CI 1.24-6.11) in Q3 and 3.67 (95% CI 1.59-8.53) in Q4 for 3-month analysis; AHRs were 3.08 (95% CI 1.10-8.64) in Q3 and 3.31(95% CI 1.35-8.14) in Q4 for 1-year analysis. Conclusions A higher “normal” HbA1c level reflecting pre-stroke glycaemia status independently predicts stroke recurrence within one year after non-cardioembolic acute ischemic stroke onset. HbA1c is recommended as a routine test in acute ischemic stroke patients. PMID:24236195

Testing Mean Differences among Groups: Multivariate and Repeated Measures Analysis with Minimal Assumptions

PubMed Central

Bathke, Arne C.; Friedrich, Sarah; Pauly, Markus; Konietschke, Frank; Staffen, Wolfgang; Strobl, Nicolas; Höller, Yvonne

2018-01-01

ABSTRACT To date, there is a lack of satisfactory inferential techniques for the analysis of multivariate data in factorial designs, when only minimal assumptions on the data can be made. Presently available methods are limited to very particular study designs or assume either multivariate normality or equal covariance matrices across groups, or they do not allow for an assessment of the interaction effects across within-subjects and between-subjects variables. We propose and methodologically validate a parametric bootstrap approach that does not suffer from any of the above limitations, and thus provides a rather general and comprehensive methodological route to inference for multivariate and repeated measures data. As an example application, we consider data from two different Alzheimer’s disease (AD) examination modalities that may be used for precise and early diagnosis, namely, single-photon emission computed tomography (SPECT) and electroencephalogram (EEG). These data violate the assumptions of classical multivariate methods, and indeed classical methods would not have yielded the same conclusions with regards to some of the factors involved. PMID:29565679

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.

Meta-Analytic Structural Equation Modeling (MASEM): Comparison of the Multivariate Methods

ERIC Educational Resources Information Center

Zhang, Ying

2011-01-01

Meta-analytic Structural Equation Modeling (MASEM) has drawn interest from many researchers recently. In doing MASEM, researchers usually first synthesize correlation matrices across studies using meta-analysis techniques and then analyze the pooled correlation matrix using structural equation modeling techniques. Several multivariate methods of…

Multivariate analyses of Erzgebirge granite and rhyolite composition: Implications for classification of granites and their genetic relations

USGS Publications Warehouse

Forster, H.-J.; Davis, J.C.; Tischendorf, G.; Seltmann, R.

1999-01-01

High-precision major, minor and trace element analyses for 44 elements have been made of 329 Late Variscan granitic and rhyolitic rocks from the Erzgebirge metallogenic province of Germany. The intrusive histories of some of these granites are not completely understood and exposures of rock are not adequate to resolve relationships between what apparently are different plutons. Therefore, it is necessary to turn to chemical analyses to decipher the evolution of the plutons and their relationships. A new classification of Erzgebirge plutons into five major groups of granites, based on petrologic interpretations of geochemical and mineralogical relationships (low-F biotite granites; low-F two-mica granites; high-F, high-P2O5 Li-mica granites; high-F, low-P2O5 Li-mica granites; high-F, low-P2O5 biotite granites) was tested by multivariate techniques. Canonical analyses of major elements, minor elements, trace elements and ratio variables all distinguish the groups with differing amounts of success. Univariate ANOVA's, in combination with forward-stepwise and backward-elimination canonical analyses, were used to select ten variables which were most effective in distinguishing groups. In a biplot, groups form distinct clusters roughly arranged along a quadratic path. Within groups, individual plutons tend to be arranged in patterns possibly reflecting granitic evolution. Canonical functions were used to classify samples of rhyolites of unknown association into the five groups. Another canonical analysis was based on ten elements traditionally used in petrology and which were important in the new classification of granites. Their biplot pattern is similar to that from statistically chosen variables but less effective at distinguishing the five groups of granites. This study shows that multivariate statistical techniques can provide significant insight into problems of granitic petrogenesis and may be superior to conventional procedures for petrological interpretation.

External validity of a hierarchical dimensional model of child and adolescent psychopathology: Tests using confirmatory factor analyses and multivariate behavior genetic analyses.

PubMed

Waldman, Irwin D; Poore, Holly E; van Hulle, Carol; Rathouz, Paul J; Lahey, Benjamin B

2016-11-01

Several recent studies of the hierarchical phenotypic structure of psychopathology have identified a General psychopathology factor in addition to the more expected specific Externalizing and Internalizing dimensions in both youth and adult samples and some have found relevant unique external correlates of this General factor. We used data from 1,568 twin pairs (599 MZ & 969 DZ) age 9 to 17 to test hypotheses for the underlying structure of youth psychopathology and the external validity of the higher-order factors. Psychopathology symptoms were assessed via structured interviews of caretakers and youth. We conducted phenotypic analyses of competing structural models using Confirmatory Factor Analysis and used Structural Equation Modeling and multivariate behavior genetic analyses to understand the etiology of the higher-order factors and their external validity. We found that both a General factor and specific Externalizing and Internalizing dimensions are necessary for characterizing youth psychopathology at both the phenotypic and etiologic levels, and that the 3 higher-order factors differed substantially in the magnitudes of their underlying genetic and environmental influences. Phenotypically, the specific Externalizing and Internalizing dimensions were slightly negatively correlated when a General factor was included, which reflected a significant inverse correlation between the nonshared environmental (but not genetic) influences on Internalizing and Externalizing. We estimated heritability of the general factor of psychopathology for the first time. Its moderate heritability suggests that it is not merely an artifact of measurement error but a valid construct. The General, Externalizing, and Internalizing factors differed in their relations with 3 external validity criteria: mother's smoking during pregnancy, parent's harsh discipline, and the youth's association with delinquent peers. Multivariate behavior genetic analyses supported the external validity of the 3 higher-order factors by suggesting that the General, Externalizing, and Internalizing factors were correlated with peer delinquency and parent's harsh discipline for different etiologic reasons. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

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.

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…

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.

A Hierarchical Multivariate Bayesian Approach to Ensemble Model output Statistics in Atmospheric Prediction

DTIC Science & Technology

2017-09-01

efficacy of statistical post-processing methods downstream of these dynamical model components with a hierarchical multivariate Bayesian approach to...Bayesian hierarchical modeling, Markov chain Monte Carlo methods , Metropolis algorithm, machine learning, atmospheric prediction 15. NUMBER OF PAGES...scale processes. However, this dissertation explores the efficacy of statistical post-processing methods downstream of these dynamical model components

Development of methodology for identification the nature of the polyphenolic extracts by FTIR associated with multivariate analysis

NASA Astrophysics Data System (ADS)

Grasel, Fábio dos Santos; Ferrão, Marco Flôres; Wolf, Carlos Rodolfo

2016-01-01

Tannins are polyphenolic compounds of complex structures formed by secondary metabolism in several plants. These polyphenolic compounds have different applications, such as drugs, anti-corrosion agents, flocculants, and tanning agents. This study analyses six different type of polyphenolic extracts by Fourier transform infrared spectroscopy (FTIR) combined with multivariate analysis. Through both principal component analysis (PCA) and hierarchical cluster analysis (HCA), we observed well-defined separation between condensed (quebracho and black wattle) and hydrolysable (valonea, chestnut, myrobalan, and tara) tannins. For hydrolysable tannins, it was also possible to observe the formation of two different subgroups between samples of chestnut and valonea and between samples of tara and myrobalan. Among all samples analysed, the chestnut and valonea showed the greatest similarity, indicating that these extracts contain equivalent chemical compositions and structure and, therefore, similar properties.

CpG island methylator phenotype identifies high risk patients among microsatellite stable BRAF mutated colorectal cancers

PubMed Central

Vedeld, Hege Marie; Merok, Marianne; Jeanmougin, Marine; Danielsen, Stine A.; Honne, Hilde; Presthus, Gro Kummeneje; Svindland, Aud; Sjo, Ole H.; Hektoen, Merete; Eknæs, Mette; Nesbakken, Arild; Lothe, Ragnhild A.

2017-01-01

The prognostic value of CpG island methylator phenotype (CIMP) in colorectal cancer remains unsettled. We aimed to assess the prognostic value of this phenotype analyzing a total of 1126 tumor samples obtained from two Norwegian consecutive colorectal cancer series. CIMP status was determined by analyzing the 5‐markers CAGNA1G, IGF2, NEUROG1, RUNX3 and SOCS1 by quantitative methylation specific PCR (qMSP). The effect of CIMP on time to recurrence (TTR) and overall survival (OS) were determined by uni‐ and multivariate analyses. Subgroup analyses were conducted according to MSI and BRAF mutation status, disease stage, and also age at time of diagnosis (<60, 60‐74, ≥75 years). Patients with CIMP positive tumors demonstrated significantly shorter TTR and worse OS compared to those with CIMP negative tumors (multivariate hazard ratio [95% CI] 1.86 [1.31‐2.63] and 1.89 [1.34‐2.65], respectively). In stratified analyses, CIMP tumors showed significantly worse outcome among patients with microsatellite stable (MSS, P < 0.001), and MSS BRAF mutated tumors (P < 0.001), a finding that persisted in patients with stage II, III or IV disease, and that remained significant in multivariate analysis (P < 0.01). Consistent results were found for all three age groups. To conclude, CIMP is significantly associated with inferior outcome for colorectal cancer patients, and can stratify the poor prognostic patients with MSS BRAF mutated tumors. PMID:28542846

Predictors of Upper-Extremity Physical Function in Older Adults.

PubMed

Hermanussen, Hugo H; Menendez, Mariano E; Chen, Neal C; Ring, David; Vranceanu, Ana-Maria

2016-10-01

Little is known about the influence of habitual participation in physical exercise and diet on upper-extremity physical function in older adults. To assess the relationship of general physical exercise and diet to upper-extremity physical function and pain intensity in older adults. A cohort of 111 patients 50 or older completed a sociodemographic survey, the Rapid Assessment of Physical Activity (RAPA), an 11-point ordinal pain intensity scale, a Mediterranean diet questionnaire, and three Patient- Reported Outcomes Measurement Information System (PROMIS) based questionnaires: Pain Interference to measure inability to engage in activities due to pain, Upper-Extremity Physical Function, and Depression. Multivariable linear regression modeling was used to characterize the association of physical activity, diet, depression, and pain interference to pain intensity and upper-extremity function. Higher general physical activity was associated with higher PROMIS Upper-Extremity Physical Function and lower pain intensity in bivariate analyses. Adherence to the Mediterranean diet did not correlate with PROMIS Upper-Extremity Physical Function or pain intensity in bivariate analysis. In multivariable analyses factors associated with higher PROMIS Upper-Extremity Physical Function were male sex, non-traumatic diagnosis and PROMIS Pain Interference, with the latter accounting for most of the observed variability (37%). Factors associated with greater pain intensity in multivariable analyses included fewer years of education and higher PROMIS Pain Interference. General physical activity and diet do not seem to be as strongly or directly associated with upper-extremity physical function as pain interference.

Missed or Delayed Medical Care Appointments by Older Users of Nonemergency Medical Transportation

PubMed Central

MacLeod, Kara E.; Ragland, David R.; Prohaska, Thomas R.; Smith, Matthew Lee; Irmiter, Cheryl; Satariano, William A.

2015-01-01

Purpose of the Study: This study identified factors associated with canceling nonemergency medical transportation appointments among older adult Medicaid patients. Design and Methods: Data from 125,913 trips for 2,913 Delaware clients were examined. Mediation analyses, as well as, multivariate logistic regressions were conducted. Results: Over half of canceled trips were attributed to client reasons (e.g., no show, refusal). Client characteristics (e.g., race, sex, functional status) were associated with cancelations; however, these differed based on the cancelation reason. Regularly scheduled trips were less likely to be canceled. Implications: The evolving American health care system may increase service availability. Additional policies can improve service accessibility and overcome utilization barriers. PMID:24558264

Influence of scary beliefs about the Tuskegee Syphilis Study on willingness to participate in research

PubMed Central

Davis, Jenna L.; Green, B. Lee; Katz, Ralph V.

2013-01-01

Objectives To assess whether scary/alarming beliefs about details on the Tuskegee Syphilis Study (TSS) are associated with willingness and/or fear to participate in biomedical research. Methods Scary beliefs about TSS were examined for 565 Black and White adults who had heard of the TSS. Multivariate analyses by race were used to measure association. Results No association between scary beliefs and willingness or fear to participate in research was found (P>0.05). Conclusions These findings provide additional evidence that awareness or detailed knowledge about the TSS does not appear today to be a major factor influencing Blacks’ willingness to participate in research. PMID:22924230

A revision of chiggers of the minuta species-group (Acari: Trombiculidae: Neotrombicula Hirst, 1925) using multivariate morphometrics.

PubMed

Stekolnikov, Alexandr A; Klimov, Pavel B

2010-09-01

We revise chiggers belonging to the minuta-species group (genus Neotrombicula Hirst, 1925) from the Palaearctic using size-free multivariate morphometrics. This approach allowed us to resolve several diagnostic problems. We show that the widely distributed Neotrombicula scrupulosa Kudryashova, 1993 forms three spatially and ecologically isolated groups different from each other in size or shape (morphometric property) only: specimens from the Caucasus are distinct from those from Asia in shape, whereas the Asian specimens from plains and mountains are different from each other in size. We developed a multivariate classification model to separate three closely related species: N. scrupulosa, N. lubrica Kudryashova, 1993 and N. minuta Schluger, 1966. This model is based on five shape variables selected from an initial 17 variables by a best subset analysis using a custom size-correction subroutine. The variable selection procedure slightly improved the predictive power of the model, suggesting that it not only removed redundancy but also reduced 'noise' in the dataset. The overall classification accuracy of this model is 96.2, 96.2 and 95.5%, as estimated by internal validation, external validation and jackknife statistics, respectively. Our analyses resulted in one new synonymy: N. dimidiata Stekolnikov, 1995 is considered to be a synonym of N. lubrica. Both N. scrupulosa and N. lubrica are recorded from new localities. A key to species of the minuta-group incorporating results from our multivariate analyses is presented.

Multivariate outcome prediction in traumatic brain injury with focus on laboratory values.

PubMed

Nelson, David W; Rudehill, Anders; MacCallum, Robert M; Holst, Anders; Wanecek, Michael; Weitzberg, Eddie; Bellander, Bo-Michael

2012-11-20

Traumatic brain injury (TBI) is a major cause of morbidity and mortality. Identifying factors relevant to outcome can provide a better understanding of TBI pathophysiology, in addition to aiding prognostication. Many common laboratory variables have been related to outcome but may not be independent predictors in a multivariate setting. In this study, 757 patients were identified in the Karolinska TBI database who had retrievable early laboratory variables. These were analyzed towards a dichotomized Glasgow Outcome Scale (GOS) with logistic regression and relevance vector machines, a non-linear machine learning method, univariately and controlled for the known important predictors in TBI outcome: age, Glasgow Coma Score (GCS), pupil response, and computed tomography (CT) score. Accuracy was assessed with Nagelkerke's pseudo R². Of the 18 investigated laboratory variables, 15 were found significant (p<0.05) towards outcome in univariate analyses. In contrast, when adjusting for other predictors, few remained significant. Creatinine was found an independent predictor of TBI outcome. Glucose, albumin, and osmolarity levels were also identified as predictors, depending on analysis method. A worse outcome related to increasing osmolarity may warrant further study. Importantly, hemoglobin was not found significant when adjusted for post-resuscitation GCS as opposed to an admission GCS, and timing of GCS can thus have a major impact on conclusions. In total, laboratory variables added an additional 1.3-4.4% to pseudo R².

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.

Machine learning methods applied on dental fear and behavior management problems in children.

PubMed

Klingberg, G; Sillén, R; Norén, J G

1999-08-01

The etiologies of dental fear and dental behavior management problems in children were investigated in a database of information on 2,257 Swedish children 4-6 and 9-11 years old. The analyses were performed using computerized inductive techniques within the field of artificial intelligence. The database held information regarding dental fear levels and behavior management problems, which were defined as outcomes, i.e. dependent variables. The attributes, i.e. independent variables, included data on dental health and dental treatments, information about parental dental fear, general anxiety, socioeconomic variables, etc. The data contained both numerical and discrete variables. The analyses were performed using an inductive analysis program (XpertRule Analyser, Attar Software Ltd, Lancashire, UK) that presents the results in a hierarchic diagram called a knowledge tree. The importance of the different attributes is represented by their position in this diagram. The results show that inductive methods are well suited for analyzing multifactorial and complex relationships in large data sets, and are thus a useful complement to multivariate statistical techniques. The knowledge trees for the two outcomes, dental fear and behavior management problems, were very different from each other, suggesting that the two phenomena are not equivalent. Dental fear was found to be more related to non-dental variables, whereas dental behavior management problems seemed connected to dental variables.

Comparison of baseline removal methods for laser-induced breakdown spectroscopy of geological samples

NASA Astrophysics Data System (ADS)

Dyar, M. Darby; Giguere, Stephen; Carey, CJ; Boucher, Thomas

2016-12-01

This project examines the causes, effects, and optimization of continuum removal in laser-induced breakdown spectroscopy (LIBS) to produce the best possible prediction accuracy of elemental composition in geological samples. We compare prediction accuracy resulting from several different techniques for baseline removal, including asymmetric least squares (ALS), adaptive iteratively reweighted penalized least squares (Air-PLS), fully automatic baseline correction (FABC), continuous wavelet transformation, median filtering, polynomial fitting, the iterative thresholding Dietrich method, convex hull/rubber band techniques, and a newly-developed technique for Custom baseline removal (BLR). We assess the predictive performance of these methods using partial least-squares analysis for 13 elements of geological interest, expressed as the weight percentages of SiO2, Al2O3, TiO2, FeO, MgO, CaO, Na2O, K2O, and the parts per million concentrations of Ni, Cr, Zn, Mn, and Co. We find that previously published methods for baseline subtraction generally produce equivalent prediction accuracies for major elements. When those pre-existing methods are used, automated optimization of their adjustable parameters is always necessary to wring the best predictive accuracy out of a data set; ideally, it should be done for each individual variable. The new technique of Custom BLR produces significant improvements in prediction accuracy over existing methods across varying geological data sets, instruments, and varying analytical conditions. These results also demonstrate the dual objectives of the continuum removal problem: removing a smooth underlying signal to fit individual peaks (univariate analysis) versus using feature selection to select only those channels that contribute to best prediction accuracy for multivariate analyses. Overall, the current practice of using generalized, one-method-fits-all-spectra baseline removal results in poorer predictive performance for all methods. The extra steps needed to optimize baseline removal for each predicted variable and empower multivariate techniques with the best possible input data for optimal prediction accuracy are shown to be well worth the slight increase in necessary computations and complexity.

Measuring multiple spike train synchrony.

PubMed

Kreuz, Thomas; Chicharro, Daniel; Andrzejak, Ralph G; Haas, Julie S; Abarbanel, Henry D I

2009-10-15

Measures of multiple spike train synchrony are essential in order to study issues such as spike timing reliability, network synchronization, and neuronal coding. These measures can broadly be divided in multivariate measures and averages over bivariate measures. One of the most recent bivariate approaches, the ISI-distance, employs the ratio of instantaneous interspike intervals (ISIs). In this study we propose two extensions of the ISI-distance, the straightforward averaged bivariate ISI-distance and the multivariate ISI-diversity based on the coefficient of variation. Like the original measure these extensions combine many properties desirable in applications to real data. In particular, they are parameter-free, time scale independent, and easy to visualize in a time-resolved manner, as we illustrate with in vitro recordings from a cortical neuron. Using a simulated network of Hindemarsh-Rose neurons as a controlled configuration we compare the performance of our methods in distinguishing different levels of multi-neuron spike train synchrony to the performance of six other previously published measures. We show and explain why the averaged bivariate measures perform better than the multivariate ones and why the multivariate ISI-diversity is the best performer among the multivariate methods. Finally, in a comparison against standard methods that rely on moving window estimates, we use single-unit monkey data to demonstrate the advantages of the instantaneous nature of our methods.

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

Multivariate Analyses of Rotator Cuff Pathologies in Shoulder Disability

PubMed Central

Henseler, Jan F.; Raz, Yotam; Nagels, Jochem; van Zwet, Erik W.; Raz, Vered; Nelissen, Rob G. H. H.

2015-01-01

Background Disability of the shoulder joint is often caused by a tear in the rotator cuff (RC) muscles. Four RC muscles coordinate shoulder movement and stability, among them the supraspinatus and infraspinatus muscle which are predominantly torn. The contribution of each RC muscle to tear pathology is not fully understood. We hypothesized that muscle atrophy and fatty infiltration, features of RC muscle degeneration, are predictive of superior humeral head translation and shoulder functional disability. Methods Shoulder features, including RC muscle surface area and fatty infiltration, superior humeral translation and RC tear size were obtained from a consecutive series of Magnetic Resonance Imaging with arthrography (MRA). We investigated patients with superior (supraspinatus, n = 39) and posterosuperior (supraspinatus and infraspinatus, n = 30) RC tears, and patients with an intact RC (n = 52) as controls. The individual or combinatorial contribution of RC measures to superior humeral translation, as a sign of RC dysfunction, was investigated with univariate or multivariate models, respectively. Results Using the univariate model the infraspinatus surface area and fatty infiltration in both the supraspinatus and infraspinatus had a significant contribution to RC dysfunction. With the multivariate model, however, the infraspinatus surface area only affected superior humeral translation (p<0.001) and discriminated between superior and posterosuperior tears. In contrast neither tear size nor fatty infiltration of the supraspinatus or infraspinatus contributed to superior humeral translation. Conclusion Our study reveals that infraspinatus atrophy has the strongest contribution to RC tear pathologies. This suggests a pivotal role for the infraspinatus in preventing shoulder disability. PMID:25710703

A strategy for simultaneous determination of fatty acid composition, fatty acid position, and position-specific isotope contents in triacylglycerol matrices by 13C-NMR.

PubMed

Merchak, Noelle; Silvestre, Virginie; Loquet, Denis; Rizk, Toufic; Akoka, Serge; Bejjani, Joseph

2017-01-01

Triacylglycerols, which are quasi-universal components of food matrices, consist of complex mixtures of molecules. Their site-specific 13 C content, their fatty acid profile, and their position on the glycerol moiety may significantly vary with the geographical, botanical, or animal origin of the sample. Such variables are valuable tracers for food authentication issues. The main objective of this work was to develop a new method based on a rapid and precise 13 C-NMR spectroscopy (using a polarization transfer technique) coupled with multivariate linear regression analyses in order to quantify the whole set of individual fatty acids within triacylglycerols. In this respect, olive oil samples were analyzed by means of both adiabatic 13 C-INEPT sequence and gas chromatography (GC). For each fatty acid within the studied matrix and for squalene as well, a multivariate prediction model was constructed using the deconvoluted peak areas of 13 C-INEPT spectra as predictors, and the data obtained by GC as response variables. This 13 C-NMR-based strategy, tested on olive oil, could serve as an alternative to the gas chromatographic quantification of individual fatty acids in other matrices, while providing additional compositional and isotopic information. Graphical abstract A strategy based on the multivariate linear regression of variables obtained by a rapid 13 C-NMR technique was developed for the quantification of individual fatty acids within triacylglycerol matrices. The conceived strategy was tested on olive oil.

A general framework for multivariate multi-index drought prediction based on Multivariate Ensemble Streamflow Prediction (MESP)

NASA Astrophysics Data System (ADS)

Hao, Zengchao; Hao, Fanghua; Singh, Vijay P.

2016-08-01

Drought is among the costliest natural hazards worldwide and extreme drought events in recent years have caused huge losses to various sectors. Drought prediction is therefore critically important for providing early warning information to aid decision making to cope with drought. Due to the complicated nature of drought, it has been recognized that the univariate drought indicator may not be sufficient for drought characterization and hence multivariate drought indices have been developed for drought monitoring. Alongside the substantial effort in drought monitoring with multivariate drought indices, it is of equal importance to develop a drought prediction method with multivariate drought indices to integrate drought information from various sources. This study proposes a general framework for multivariate multi-index drought prediction that is capable of integrating complementary prediction skills from multiple drought indices. The Multivariate Ensemble Streamflow Prediction (MESP) is employed to sample from historical records for obtaining statistical prediction of multiple variables, which is then used as inputs to achieve multivariate prediction. The framework is illustrated with a linearly combined drought index (LDI), which is a commonly used multivariate drought index, based on climate division data in California and New York in the United States with different seasonality of precipitation. The predictive skill of LDI (represented with persistence) is assessed by comparison with the univariate drought index and results show that the LDI prediction skill is less affected by seasonality than the meteorological drought prediction based on SPI. Prediction results from the case study show that the proposed multivariate drought prediction outperforms the persistence prediction, implying a satisfactory performance of multivariate drought prediction. The proposed method would be useful for drought prediction to integrate drought information from various sources for early drought warning.

An error bound for a discrete reduced order model of a linear multivariable system

NASA Technical Reports Server (NTRS)

Al-Saggaf, Ubaid M.; Franklin, Gene F.

1987-01-01

The design of feasible controllers for high dimension multivariable systems can be greatly aided by a method of model reduction. In order for the design based on the order reduction to include a guarantee of stability, it is sufficient to have a bound on the model error. Previous work has provided such a bound for continuous-time systems for algorithms based on balancing. In this note an L-infinity bound is derived for model error for a method of order reduction of discrete linear multivariable systems based on balancing.

Two time-series analyses of the impact of antibiotic consumption and alcohol-based hand disinfection on the incidences of nosocomial methicillin-resistant Staphylococcus aureus infection and Clostridium difficile infection.

PubMed

Kaier, Klaus; Hagist, Christian; Frank, Uwe; Conrad, Andreas; Meyer, Elisabeth

2009-04-01

To determine the impact of antibiotic consumption and alcohol-based hand disinfection on the incidences of nosocomial methicillin-resistant Staphylococcus aureus (MRSA) infection and Clostridium difficile infection (CDI). Two multivariate time-series analyses were performed that used as dependent variables the monthly incidences of nosocomial MRSA infection and CDI at the Freiburg University Medical Center during the period January 2003 through October 2007. The volume of alcohol-based hand rub solution used per month was quantified in liters per 1,000 patient-days. Antibiotic consumption was calculated in terms of the number of defined daily doses per 1,000 patient-days per month. The use of alcohol-based hand rub was found to have a significant impact on the incidence of nosocomial MRSA infection (P< .001). The multivariate analysis (R2=0.66) showed that a higher volume of use of alcohol-based hand rub was associated with a lower incidence of nosocomial MRSA infection. Conversely, a higher level of consumption of selected antimicrobial agents was associated with a higher incidence of nosocomial MRSA infection. This analysis showed this relationship was the same for the use of second-generation cephalosporins (P= .023), third-generation cephalosporins (P= .05), fluoroquinolones (P= .01), and lincosamides (P= .05). The multivariate analysis (R2=0.55) showed that a higher level of consumption of third-generation cephalosporins (P= .008), fluoroquinolones (P= .084), and/or macrolides (P= .007) was associated with a higher incidence of CDI. A correlation with use of alcohol-based hand rub was not detected. In 2 multivariate time-series analyses, we were able to show the impact of hand hygiene and antibiotic use on the incidence of nosocomial MRSA infection, but we found no association between hand hygiene and incidence of CDI.

Postcraniometric sex and ancestry estimation in South Africa: a validation study.

PubMed

Liebenberg, Leandi; Krüger, Gabriele C; L'Abbé, Ericka N; Stull, Kyra E

2018-05-24

With the acceptance of the Daubert criteria as the standards for best practice in forensic anthropological research, more emphasis is being placed on the validation of published methods. Methods, both traditional and novel, need to be validated, adjusted, and refined for optimal performance within forensic anthropological analyses. Recently, a custom postcranial database of modern South Africans was created for use in Fordisc 3.1. Classification accuracies of up to 85% for ancestry estimation and 98% for sex estimation were achieved using a multivariate approach. To measure the external validity and report more realistic performance statistics, an independent sample was tested. The postcrania from 180 black, white, and colored South Africans were measured and classified using the custom postcranial database. A decrease in accuracy was observed for both ancestry estimation (79%) and sex estimation (95%) of the validation sample. When incorporating both sex and ancestry simultaneously, the method achieved 70% accuracy, and 79% accuracy when sex-specific ancestry analyses were run. Classification matrices revealed that postcrania were more likely to misclassify as a result of ancestry rather than sex. While both sex and ancestry influence the size of an individual, sex differences are more marked in the postcranial skeleton and are therefore easier to identify. The external validity of the postcranial database was verified and therefore shown to be a useful tool for forensic casework in South Africa. While the classification rates were slightly lower than the original method, this is expected when a method is generalized.

An Alternative Method for Computing Mean and Covariance Matrix of Some Multivariate Distributions

ERIC Educational Resources Information Center

Radhakrishnan, R.; Choudhury, Askar

2009-01-01

Computing the mean and covariance matrix of some multivariate distributions, in particular, multivariate normal distribution and Wishart distribution are considered in this article. It involves a matrix transformation of the normal random vector into a random vector whose components are independent normal random variables, and then integrating…

The role of social support and parity in contraceptive use in Cambodia.

PubMed

Samandari, Ghazaleh; Speizer, Ilene S; O'Connell, Kathryn

2010-09-01

In Cambodia, unmet need for contraception is high. Studies suggest that social support and parity each play a role in contraceptive decision making. A representative sample of 706 married women aged 15-49 from two rural provinces in Cambodia who wished to delay childbirth were interviewed about their contraceptive use and their perceptions of their husband's, peers' and elders' support of contraception. Multivariate analyses examined associations between support measures and women's current use of modern methods, among all women and by parity. Overall, 43% of women were currently using a modern method. Women who believed that their husband had a positive attitude toward contraception were more likely than those who did not to use a method (odds ratio, 3.4), whereas women who were nervous about talking with their husband about contraception were less likely than others to use a method (0.6); these associations remained in analyses by parity. Among all women and high-parity women, those whose husband made the final decision about contraception were less likely than other women to use a method (0.6 and 0.4, respectively). Perceiving that most of one's peers practice contraception was strongly associated with method use among low-parity women (4.4). Among all groups, women who agreed that one should not practice contraception if an elder says not to had decreased odds of method use (0.5 each). To promote contraceptive use, family planning programs should focus on increasing men's approval of contraception, improving partner communication around family planning and bolstering women's confidence in their reproductive decision making.

Regression and multivariate models for predicting particulate matter concentration level.

PubMed

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

2018-01-01

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

Morphological analyses suggest a new taxonomic circumscription for Hymenaea courbaril L. (Leguminosae, Caesalpinioideae)

PubMed Central

Souza, Isys Mascarenhas; Funch, Ligia Silveira; de Queiroz, Luciano Paganucci

2014-01-01

Abstract Hymenaea is a genus of the Resin-producing Clade of the tribe Detarieae (Leguminosae: Caesalpinioideae) with 14 species. Hymenaea courbaril is the most widespread species of the genus, ranging from southern Mexico to southeastern Brazil. As currently circumscribed, Hymenaea courbaril is a polytypic species with six varieties: var. altissima, var. courbaril, var. longifolia, var. stilbocarpa, var. subsessilis, and var. villosa. These varieties are distinguishable mostly by traits related to leaflet shape and indumentation, and calyx indumentation. We carried out morphometric analyses of 14 quantitative (continuous) leaf characters in order to assess the taxonomy of Hymenaea courbaril under the Unified Species Concept framework. Cluster analysis used the Unweighted Pair Group Method with Arithmetic Mean (UPGMA) based on Bray-Curtis dissimilarity matrices. Principal Component Analyses (PCA) were carried out based on the same morphometric matrix. Two sets of Analyses of Similarity and Non Parametric Multivariate Analysis of Variance were carried out to evaluate statistical support (1) for the major groups recovered using UPGMA and PCA, and (2) for the varieties. All analyses recovered three major groups coincident with (1) var. altissima, (2) var. longifolia, and (3) all other varieties. These results, together with geographical and habitat information, were taken as evidence of three separate metapopulation lineages recognized here as three distinct species. Nomenclatural adjustments, including reclassifying formerly misapplied types, are proposed. PMID:25009440

Morphological analyses suggest a new taxonomic circumscription for Hymenaea courbaril L. (Leguminosae, Caesalpinioideae).

PubMed

Souza, Isys Mascarenhas; Funch, Ligia Silveira; de Queiroz, Luciano Paganucci

2014-01-01

Hymenaea is a genus of the Resin-producing Clade of the tribe Detarieae (Leguminosae: Caesalpinioideae) with 14 species. Hymenaea courbaril is the most widespread species of the genus, ranging from southern Mexico to southeastern Brazil. As currently circumscribed, Hymenaea courbaril is a polytypic species with six varieties: var. altissima, var. courbaril, var. longifolia, var. stilbocarpa, var. subsessilis, and var. villosa. These varieties are distinguishable mostly by traits related to leaflet shape and indumentation, and calyx indumentation. We carried out morphometric analyses of 14 quantitative (continuous) leaf characters in order to assess the taxonomy of Hymenaea courbaril under the Unified Species Concept framework. Cluster analysis used the Unweighted Pair Group Method with Arithmetic Mean (UPGMA) based on Bray-Curtis dissimilarity matrices. Principal Component Analyses (PCA) were carried out based on the same morphometric matrix. Two sets of Analyses of Similarity and Non Parametric Multivariate Analysis of Variance were carried out to evaluate statistical support (1) for the major groups recovered using UPGMA and PCA, and (2) for the varieties. All analyses recovered three major groups coincident with (1) var. altissima, (2) var. longifolia, and (3) all other varieties. These results, together with geographical and habitat information, were taken as evidence of three separate metapopulation lineages recognized here as three distinct species. Nomenclatural adjustments, including reclassifying formerly misapplied types, are proposed.

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.

Association between cardiovascular risk factors and carotid intima-media thickness in prepubertal Brazilian children.

PubMed

Gazolla, Fernanda Mussi; Neves Bordallo, Maria Alice; Madeira, Isabel Rey; de Miranda Carvalho, Cecilia Noronha; Vieira Monteiro, Alexandra Maria; Pinheiro Rodrigues, Nádia Cristina; Borges, Marcos Antonio; Collett-Solberg, Paulo Ferrez; Muniz, Bruna Moreira; de Oliveira, Cecilia Lacroix; Pinheiro, Suellen Martins; de Queiroz Ribeiro, Rebeca Mathias

2015-05-01

Early exposure to cardiovascular risk factors creates a chronic inflammatory state that could damage the endothelium followed by thickening of the carotid intima-media. To investigate the association of cardiovascular risk factors and thickening of the carotid intima. Media in prepubertal children. In this cross-sectional study, carotid intima-media thickness (cIMT) and cardiovascular risk factors were assessed in 129 prepubertal children aged from 5 to 10 year. Association was assessed by simple and multivariate logistic regression analyses. In simple logistic regression analyses, body mass index (BMI) z-score, waist circumference, and systolic blood pressure (SBP) were positively associated with increased left, right, and average cIMT, whereas diastolic blood pressure was positively associated only with increased left and average cIMT (p<0.05). In multivariate logistic regression analyses increased left cIMT was positively associated to BMI z-score and SBP, and increased average cIMT was only positively associated to SBP (p<0.05). BMI z-score and SBP were the strongest risk factors for increased cIMT.

Social support network characteristics and sexual risk taking among a racially/ethnically diverse sample of young, urban men who have sex with men

PubMed Central

Kapadia, F; Siconolfi, DE; Barton, S; Olivieri, B; Lombardo, L; Halkitis, PN

2013-01-01

Associations between social support network characteristics and sexual risk among racially/ethnically diverse young men who have sex with men (YMSM) were examined using egocentric network data from a prospective cohort study of YMSM (n=501) recruited in New York City. Bivariate and multivariable logistic regression analyses examined associations between social support network characteristics and sexual risk taking behaviors in Black, Hispanic/Latino, and White YMSM. Bivariate analyses indicated key differences in network size, composition, communication frequency and average relationship duration by race/ethnicity. In multivariable analyses, controlling for individual level sociodemographic, psychosocial and relationship factors, having a sexual partner in one’s social support network was associated with unprotected sexual behavior for both Hispanic/Latino (AOR=3.90) and White YMSM (AOR=4.93). Further examination of key network characteristics across racial/ethnic groups are warranted in order to better understand the extant mechanisms for provision of HIV prevention programming to racially/ethnically diverse YMSM at risk for HIV. PMID:23553346

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.

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.

Relevant Feature Set Estimation with a Knock-out Strategy and Random Forests

PubMed Central

Ganz, Melanie; Greve, Douglas N.; Fischl, Bruce; Konukoglu, Ender

2015-01-01

Group analysis of neuroimaging data is a vital tool for identifying anatomical and functional variations related to diseases as well as normal biological processes. The analyses are often performed on a large number of highly correlated measurements using a relatively smaller number of samples. Despite the correlation structure, the most widely used approach is to analyze the data using univariate methods followed by post-hoc corrections that try to account for the data’s multivariate nature. Although widely used, this approach may fail to recover from the adverse effects of the initial analysis when local effects are not strong. Multivariate pattern analysis (MVPA) is a powerful alternative to the univariate approach for identifying relevant variations. Jointly analyzing all the measures, MVPA techniques can detect global effects even when individual local effects are too weak to detect with univariate analysis. Current approaches are successful in identifying variations that yield highly predictive and compact models. However, they suffer from lessened sensitivity and instabilities in identification of relevant variations. Furthermore, current methods’ user-defined parameters are often unintuitive and difficult to determine. In this article, we propose a novel MVPA method for group analysis of high-dimensional data that overcomes the drawbacks of the current techniques. Our approach explicitly aims to identify all relevant variations using a “knock-out” strategy and the Random Forest algorithm. In evaluations with synthetic datasets the proposed method achieved substantially higher sensitivity and accuracy than the state-of-the-art MVPA methods, and outperformed the univariate approach when the effect size is low. In experiments with real datasets the proposed method identified regions beyond the univariate approach, while other MVPA methods failed to replicate the univariate results. More importantly, in a reproducibility study with the well-known ADNI dataset the proposed method yielded higher stability and power than the univariate approach. PMID:26272728

A new lizard species of the Phymaturus patagonicus group (Squamata: Liolaemini) from northern Patagonia, Neuquén, Argentina.

PubMed

Marín, Andrea González; Pérez, Cristian Hernán Fulvio; Minoli, Ignacio; Morando, Mariana; Avila, Luciano Javier

2016-06-10

The integrative taxonomy framework allows developing robust hypotheses of species limits based on the integration of results from different data sets and analytical methods. In this work, we test a candidate species hypothesis previously suggested based on molecular data, with geometric and traditional morphometrics analyses (multivariate and univariate). This new lizard species is part of the Phymaturus patagonicus group (payuniae clade) that is distributed in Neuquén and Mendoza provinces (Argentina). Our results showed that Phymaturus rahuensis sp. nov. differs from the other species of the payuniae clade by a higher number of midbody scales, and fewer supralabials scales, finger lamellae and toe lamellae. Also, its multidimensional spaces, both based on continuous lineal variables and geometric morphometrics (shape) characters, do not overlap with those of the other species in this clade. The results of the morphometric and geometric morphometric analyses presented here, coupled with previously published molecular data, represent three independent lines of evidence that support the diagnosis of this new taxon.

Assessing the impact of fishing in shallow rocky reefs: a multivariate approach to ecosystem management.

PubMed

Sangil, Carlos; Martín-García, Laura; Clemente, Sabrina

2013-11-15

In this paper we develop a tool to assess the impact of fishing on ecosystem functioning in shallow rocky reefs. The relationships between biological parameters (fishes, sea urchins, seaweeds), and fishing activities (fish traps, boats, land-based fishing, spearfishing) were tested in La Palma island (Canary Islands). Data from fishing activities and biological parameters were analyzed using principal component analyses. We produced two models using the first component of these analyses. This component was interpreted as a new variable that described the fishing pressure and the conservation status at each studied site. Subsequently the scores on the first axis were mapped using universal kriging methods and the models obtained were extrapolated across the whole island to display the expected fishing pressure and conservation status more widely. The fishing pressure and conservation status models were spatially related; zones where fishing pressure was high coincided with zones in the unhealthiest ecological state. Copyright © 2013 Elsevier Ltd. All rights reserved.

Evaluation of the Risk of Grade 3 Oral and Pharyngeal Dysphagia Using Atlas-Based Method and Multivariate Analyses of Individual Patient Dose Distributions

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

Otter, Sophie; Schick, Ulrike; Gulliford, Sarah

Purpose: The study aimed to apply the atlas of complication incidence (ACI) method to patients receiving radical treatment for head and neck squamous cell carcinomas (HNSCC), to generate constraints based on dose-volume histograms (DVHs), and to identify clinical and dosimetric parameters that predict the risk of grade 3 oral mucositis (g3OM) and pharyngeal dysphagia (g3PD). Methods and Materials: Oral and pharyngeal mucosal DVHs were generated for 253 patients who received radiation (RT) or chemoradiation (CRT). They were used to produce ACI for g3OM and g3PD. Multivariate analysis (MVA) of the effect of dosimetry, clinical, and patient-related variables was performed usingmore » logistic regression and bootstrapping. Receiver operating curve (ROC) analysis was also performed, and the Youden index was used to find volume constraints that discriminated between volumes that predicted for toxicity. Results: We derived statistically significant dose-volume constraints for g3OM over the range v28 to v70. Only 3 statistically significant constraints were derived for g3PD v67, v68, and v69. On MVA, mean dose to the oral mucosa predicted for g3OM and concomitant chemotherapy and mean dose to the inferior constrictor (IC) predicted for g3PD. Conclusions: We have used the ACI method to evaluate incidences of g3OM and g3PD and ROC analysis to generate constraints to predict g3OM and g3PD derived from entire individual patient DVHs. On MVA, the strongest predictors were radiation dose (for g3OM) and concomitant chemotherapy (for g3PD).« less

Women's Awareness of, Interest in, and Experiences with Long-acting Reversible and Permanent Contraception.

PubMed

Burns, Bridgit; Grindlay, Kate; Dennis, Amanda

2015-01-01

Long-acting reversible contraception (LARC) and sterilization are popular contraceptive methods. However, they have been associated with safety concerns and coercive practices. We aimed to understand women's opinions and experiences related to these methods, including whether the methods' fraught histories influence use or interest. Between May and July 2013, we conducted an online survey with a convenience sample of 520 women aged 14 to 45. We used quota sampling to ensure women of color were at least 60% of our sample. Descriptive statistics, χ(2) tests, and multivariable logistic regression were used to estimate participants' awareness of, interest in, and experiences with LARCs and sterilization. Overall, 30% of women reported current LARC use and 67% interest in future LARC use. Four percent reported sterilization use and 48% interest in future sterilization. In multivariate analyses, current LARC use was lower among Asian women versus White women (odds ratio [OR], 0.24), and interest in future use was higher among women aged 14 to 24 versus 35 to 45 (OR, 5.49). Interest in sterilization was higher among women aged 14 to 24 and 25 to 34 versus 35 to 45 (ORs, 3.29-3.66) and women with disabilities (OR, 1.64), and lower among Black compared with White women (OR, 0.41). Method misperceptions were evident, and concerns about contraceptive coercion were reported. Concerns about contraceptive coercion were not predominant reasons for noninterest in LARCs and sterilization, but were reported by some participants. Lower sterilization interest among Black women and higher sterilization interest among women with disabilities warrant further research. Efforts to address misperceptions about LARCs and sterilization, including their safety and efficacy, are needed. Copyright © 2015 Jacobs Institute of Women's Health. Published by Elsevier Inc. All rights reserved.

Influence of pay-for-performance programs on information technology use among child health providers: the devil is in the details.

PubMed

Menachemi, Nir; Struchen-Shellhorn, Wendy; Brooks, Robert G; Simpson, Lisa

2009-01-01

Pay-for-performance programs are used to promote improved health care quality, often through increased use of health information technology. However, little is known about whether pay-for-performance programs influence the adoption of health information technology, especially among child health providers. This study explored how various pay-for-performance compensation methods are related to health information technology use. Survey data from 1014 child health providers practicing in Florida were analyzed by using univariate and multivariate techniques. Questions asked about the adoption of electronic health records and personal digital assistants, as well as types of activities that affected child health provider compensation or income. The most common reported method to affect respondents' compensation was traditional productivity or billing (78%). Of the pay-for-performance-related methods of compensation, child health providers indicated that measures of clinical care (41%), patient surveys and experience (34%), the use of health information technology (29%), and quality bonuses or incentives (27%) were a major or minor factor in their compensation. In multivariate logistic regression analyses, only pay-for-performance programs that compensated directly for health information technology use were associated with an increased likelihood of electronic health record system adoption. Pay-for-performance programs linking measures of clinical quality to compensation were positively associated with personal digital assistant use among child health providers. Pay-for-performance programs that do not directly emphasize health information technology use do not influence the adoption of electronic health records among Florida physicians treating children. Understanding how different pay-for-performance compensation methods incentivize health information technology adoption is important for improving quality.

Recent Advances in Resting-State Electroencephalography Biomarkers for Autism Spectrum Disorder-A Review of Methodological and Clinical Challenges.

PubMed

Heunis, Tosca-Marie; Aldrich, Chris; de Vries, Petrus J

2016-08-01

Electroencephalography (EEG) has been used for almost a century to identify seizure-related disorders in humans, typically through expert interpretation of multichannel recordings. Attempts have been made to quantify EEG through frequency analyses and graphic representations. These "traditional" quantitative EEG analysis methods were limited in their ability to analyze complex and multivariate data and have not been generally accepted in clinical settings. There has been growing interest in identification of novel EEG biomarkers to detect early risk of autism spectrum disorder, to identify clinically meaningful subgroups, and to monitor targeted intervention strategies. Most studies to date have, however, used quantitative EEG approaches, and little is known about the emerging multivariate analytical methods or the robustness of candidate biomarkers in the context of the variability of autism spectrum disorder. Here, we present a targeted review of methodological and clinical challenges in the search for novel resting-state EEG biomarkers for autism spectrum disorder. Three primary novel methodologies are discussed: (1) modified multiscale entropy, (2) coherence analysis, and (3) recurrence quantification analysis. Results suggest that these methods may be able to classify resting-state EEG as "autism spectrum disorder" or "typically developing", but many signal processing questions remain unanswered. We suggest that the move to novel EEG analysis methods is akin to the progress in neuroimaging from visual inspection, through region-of-interest analysis, to whole-brain computational analysis. Novel resting-state EEG biomarkers will have to evaluate a range of potential demographic, clinical, and technical confounders including age, gender, intellectual ability, comorbidity, and medication, before these approaches can be translated into the clinical setting. Copyright © 2016 Elsevier Inc. All rights reserved.

Multivariate Curve Resolution Methods Illustrated Using Infrared Spectra of an Alcohol Dissolved in Carbon Tetrachloride

ERIC Educational Resources Information Center

Grung, Bjorn; Nodland, Egil; Forland, Geir Martin

2007-01-01

The analysis of the infrared spectra of an alcohol dissolved in carbon tetrachloride gives a better understanding of the various multivariate curve resolution methods. The resulting concentration profile is found to be very useful for calculating the degree of association and equilibrium constants of different compounds.

Piecewise multivariate modelling of sequential metabolic profiling data.

PubMed

Rantalainen, Mattias; Cloarec, Olivier; Ebbels, Timothy M D; Lundstedt, Torbjörn; Nicholson, Jeremy K; Holmes, Elaine; Trygg, Johan

2008-02-19

Modelling the time-related behaviour of biological systems is essential for understanding their dynamic responses to perturbations. In metabolic profiling studies, the sampling rate and number of sampling points are often restricted due to experimental and biological constraints. A supervised multivariate modelling approach with the objective to model the time-related variation in the data for short and sparsely sampled time-series is described. A set of piecewise Orthogonal Projections to Latent Structures (OPLS) models are estimated, describing changes between successive time points. The individual OPLS models are linear, but the piecewise combination of several models accommodates modelling and prediction of changes which are non-linear with respect to the time course. We demonstrate the method on both simulated and metabolic profiling data, illustrating how time related changes are successfully modelled and predicted. The proposed method is effective for modelling and prediction of short and multivariate time series data. A key advantage of the method is model transparency, allowing easy interpretation of time-related variation in the data. The method provides a competitive complement to commonly applied multivariate methods such as OPLS and Principal Component Analysis (PCA) for modelling and analysis of short time-series data.

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.

Optimizing RNA Extraction of Renal Papilla Biopsy Tissue in Kidney Stone Formers: A New Methodology for Genomic Study.

PubMed

Taguchi, Kazumi; Usawachintachit, Manint; Hamamoto, Shuzo; Unno, Rei; Tzou, David T; Sherer, Benjamin A; Wang, Yongmei; Okada, Atsushi; Stoller, Marshall L; Yasui, Takahiro; Chi, Thomas

2017-09-01

Endoscopic tools have provided versatile examination and treatment for kidney stone procedures. Despite endourologists researching urinary stone disease using endoscopes to collect tissue, this tissue collection method is limited. Endoscopically removed tissues are small in size, restricting the types of genome-based examination possible. We investigated a new method of renal papilla biopsy and RNA extraction to establish a genomic research methodology for kidney stone disease. We conducted a prospective multi-institutional study and collected renal papilla specimens from consecutive percutaneous nephrolithotomy and ureteroscopy (URS) cases performed for removal of upper urinary tract stones. Renal papilla tissue was extracted using ureteroscopic biopsy forceps after stone removal. RNA was extracted using two different extraction kits, and their quantity and quality were examined. Additionally, the impact of biopsy on surgical complications was compared between cases performed with and without biopsy by matched case-control analysis adjusted for age, gender, body mass index, bilaterality, and stone burden. A total of 90 biopsies from 49 patients were performed, and the median duration between specimen collection and RNA extraction was 61 days. Both univariate and multivariate analyses showed BIGopsy ® forceps usage significantly increased the total yield (p = 0.004) and quality (p = 0.001 for A260/280, p = 0.004 for A260/A230) of extracted RNA. Extraction using the RNeasy Micro Kit ® also improved A260/A230, whereas reduced RNA integrity number of extracted RNA by univariate and multivariate analyses (p = 0.002 and p < 0.001, respectively). Moreover, matched case-control study demonstrated that endoscopic renal papilla biopsy caused no significant surgical complications, including bleeding, decreased stone clearance and hematocrit, and renal dysfunction. Biopsies during URS imparted an average of 20 minutes of procedure time over nonbiopsy cases. We demonstrate a safe methodology for optimal RNA extraction of renal papilla tissue. This technique will accelerate advanced genomic studies for kidney stone formers by facilitating larger tissue yields.