Multivariate longitudinal data analysis with censored and intermittent missing responses.

PubMed

Lin, Tsung-I; Lachos, Victor H; Wang, Wan-Lun

2018-05-08

The multivariate linear mixed model (MLMM) has emerged as an important analytical tool for longitudinal data with multiple outcomes. However, the analysis of multivariate longitudinal data could be complicated by the presence of censored measurements because of a detection limit of the assay in combination with unavoidable missing values arising when subjects miss some of their scheduled visits intermittently. This paper presents a generalization of the MLMM approach, called the MLMM-CM, for a joint analysis of the multivariate longitudinal data with censored and intermittent missing responses. A computationally feasible expectation maximization-based procedure is developed to carry out maximum likelihood estimation within the MLMM-CM framework. Moreover, the asymptotic standard errors of fixed effects are explicitly obtained via the information-based method. We illustrate our methodology by using simulated data and a case study from an AIDS clinical trial. Experimental results reveal that the proposed method is able to provide more satisfactory performance as compared with the traditional MLMM approach. Copyright © 2018 John Wiley & Sons, Ltd.

A Multivariate Methodological Workflow for the Analysis of FTIR Chemical Mapping Applied on Historic Paint Stratigraphies

PubMed Central

Sciutto, Giorgia; Oliveri, Paolo; Catelli, Emilio; Bonacini, Irene

2017-01-01

In the field of applied researches in heritage science, the use of multivariate approach is still quite limited and often chemometric results obtained are often underinterpreted. Within this scenario, the present paper is aimed at disseminating the use of suitable multivariate methodologies and proposes a procedural workflow applied on a representative group of case studies, of considerable importance for conservation purposes, as a sort of guideline on the processing and on the interpretation of this FTIR data. Initially, principal component analysis (PCA) is performed and the score values are converted into chemical maps. Successively, the brushing approach is applied, demonstrating its usefulness for a deep understanding of the relationships between the multivariate map and PC score space, as well as for the identification of the spectral bands mainly involved in the definition of each area localised within the score maps. PMID:29333162

Multivariate fault isolation of batch processes via variable selection in partial least squares discriminant analysis.

PubMed

Yan, Zhengbing; Kuang, Te-Hui; Yao, Yuan

2017-09-01

In recent years, multivariate statistical monitoring of batch processes has become a popular research topic, wherein multivariate fault isolation is an important step aiming at the identification of the faulty variables contributing most to the detected process abnormality. Although contribution plots have been commonly used in statistical fault isolation, such methods suffer from the smearing effect between correlated variables. In particular, in batch process monitoring, the high autocorrelations and cross-correlations that exist in variable trajectories make the smearing effect unavoidable. To address such a problem, a variable selection-based fault isolation method is proposed in this research, which transforms the fault isolation problem into a variable selection problem in partial least squares discriminant analysis and solves it by calculating a sparse partial least squares model. As different from the traditional methods, the proposed method emphasizes the relative importance of each process variable. Such information may help process engineers in conducting root-cause diagnosis. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

An Extension of Dominance Analysis to Canonical Correlation Analysis

ERIC Educational Resources Information Center

Huo, Yan; Budescu, David V.

2009-01-01

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

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.

Multivariate Statistical Analysis: a tool for groundwater quality assessment in the hidrogeologic region of the Ring of Cenotes, Yucatan, Mexico.

NASA Astrophysics Data System (ADS)

Ye, M.; Pacheco Castro, R. B.; Pacheco Avila, J.; Cabrera Sansores, A.

2014-12-01

The karstic aquifer of Yucatan is a vulnerable and complex system. The first fifteen meters of this aquifer have been polluted, due to this the protection of this resource is important because is the only source of potable water of the entire State. Through the assessment of groundwater quality we can gain some knowledge about the main processes governing water chemistry as well as spatial patterns which are important to establish protection zones. In this work multivariate statistical techniques are used to assess the groundwater quality of the supply wells (30 to 40 meters deep) in the hidrogeologic region of the Ring of Cenotes, located in Yucatan, Mexico. Cluster analysis and principal component analysis are applied in groundwater chemistry data of the study area. Results of principal component analysis show that the main sources of variation in the data are due sea water intrusion and the interaction of the water with the carbonate rocks of the system and some pollution processes. The cluster analysis shows that the data can be divided in four clusters. The spatial distribution of the clusters seems to be random, but is consistent with sea water intrusion and pollution with nitrates. The overall results show that multivariate statistical analysis can be successfully applied in the groundwater quality assessment of this karstic aquifer.

Risk Factors for Central Serous Chorioretinopathy: Multivariate Approach in a Case-Control Study.

PubMed

Chatziralli, Irini; Kabanarou, Stamatina A; Parikakis, Efstratios; Chatzirallis, Alexandros; Xirou, Tina; Mitropoulos, Panagiotis

2017-07-01

The purpose of this prospective study was to investigate the potential risk factors associated independently with central serous retinopathy (CSR) in a Greek population, using multivariate approach. Participants in the study were 183 consecutive patients diagnosed with CSR and 183 controls, matched for age. All participants underwent complete ophthalmological examination and information regarding their sociodemographic, clinical, medical and ophthalmological history were recorded, so as to assess potential risk factors for CSR. Univariate and multivariate analysis was performed. Univariate analysis showed that male sex, high educational status, high income, alcohol consumption, smoking, hypertension, coronary heart disease, obstructive sleep apnea, autoimmune disorders, H. pylori infection, type A personality and stress, steroid use, pregnancy and hyperopia were associated with CSR, while myopia was found to protect from CSR. In multivariate analysis, alcohol consumption, hypertension, coronary heart disease and autoimmune disorders lost their significance, while the remaining factors were all independently associated with CSR. It is important to take into account the various risk factors for CSR, so as to define vulnerable groups and to shed light into the pathogenesis of the disease.

Multivariate analysis of cytokine profiles in pregnancy complications.

PubMed

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

2018-03-01

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

Characterization of Interfacial Chemistry of Adhesive/Dentin Bond Using FTIR Chemical Imaging With Univariate and Multivariate Data Processing

PubMed Central

Wang, Yong; Yao, Xiaomei; Parthasarathy, Ranganathan

2008-01-01

Fourier transform infrared (FTIR) chemical imaging can be used to investigate molecular chemical features of the adhesive/dentin interfaces. However, the information is not straightforward, and is not easily extracted. The objective of this study was to use multivariate analysis methods, principal component analysis and fuzzy c-means clustering, to analyze spectral data in comparison with univariate analysis. The spectral imaging data collected from both the adhesive/healthy dentin and adhesive/caries-affected dentin specimens were used and compared. The univariate statistical methods such as mapping of intensities of specific functional group do not always accurately identify functional group locations and concentrations due to more or less band overlapping in adhesive and dentin. Apart from the ease with which information can be extracted, multivariate methods highlight subtle and often important changes in the spectra that are difficult to observe using univariate methods. The results showed that the multivariate methods gave more satisfactory, interpretable results than univariate methods and were conclusive in showing that they can discriminate and classify differences between healthy dentin and caries-affected dentin within the interfacial regions. It is demonstrated that the multivariate FTIR imaging approaches can be used in the rapid characterization of heterogeneous, complex structure. PMID:18980198

The potential use of cuticular hydrocarbons and multivariate analysis to age empty puparial cases of Calliphora vicina and Lucilia sericata.

PubMed

Moore, Hannah E; Pechal, Jennifer L; Benbow, M Eric; Drijfhout, Falko P

2017-05-16

Cuticular hydrocarbons (CHC) have been successfully used in the field of forensic entomology for identifying and ageing forensically important blowfly species, primarily in the larval stages. However in older scenes where all other entomological evidence is no longer present, Calliphoridae puparial cases can often be all that remains and therefore being able to establish the age could give an indication of the PMI. This paper examined the CHCs present in the lipid wax layer of insects, to determine the age of the cases over a period of nine months. The two forensically important species examined were Calliphora vicina and Lucilia sericata. The hydrocarbons were chemically extracted and analysed using Gas Chromatography - Mass Spectrometry. Statistical analysis was then applied in the form of non-metric multidimensional scaling analysis (NMDS), permutational multivariate analysis of variance (PERMANOVA) and random forest models. This study was successful in determining age differences within the empty cases, which to date, has not been establish by any other technique.

Distributions of Characteristic Roots in Multivariate Analysis

DTIC Science & Technology

1976-07-01

stiidied by various authors, have been briefly discussed. Such distributional ies of four test criteria and a few less important ones which are...functions h. -nots have further been discussed in view of the power comparisons made in co. ion wich tests of three multivariate hypotheses. In addition...one- sample case has also been considered in terms of distributional aspects of the ch. roots and criteria for tests of two hypotheses on the

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

PubMed

Sornborger, Andrew T; Lauderdale, James D

2016-11-01

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

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

NASA Astrophysics Data System (ADS)

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

2013-02-01

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

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

PubMed

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

2011-02-01

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

Parameters Selection for Bivariate Multiscale Entropy Analysis of Postural Fluctuations in Fallers and Non-Fallers Older Adults.

PubMed

Ramdani, Sofiane; Bonnet, Vincent; Tallon, Guillaume; Lagarde, Julien; Bernard, Pierre Louis; Blain, Hubert

2016-08-01

Entropy measures are often used to quantify the regularity of postural sway time series. Recent methodological developments provided both multivariate and multiscale approaches allowing the extraction of complexity features from physiological signals; see "Dynamical complexity of human responses: A multivariate data-adaptive framework," in Bulletin of Polish Academy of Science and Technology, vol. 60, p. 433, 2012. The resulting entropy measures are good candidates for the analysis of bivariate postural sway signals exhibiting nonstationarity and multiscale properties. These methods are dependant on several input parameters such as embedding parameters. Using two data sets collected from institutionalized frail older adults, we numerically investigate the behavior of a recent multivariate and multiscale entropy estimator; see "Multivariate multiscale entropy: A tool for complexity analysis of multichannel data," Physics Review E, vol. 84, p. 061918, 2011. We propose criteria for the selection of the input parameters. Using these optimal parameters, we statistically compare the multivariate and multiscale entropy values of postural sway data of non-faller subjects to those of fallers. These two groups are discriminated by the resulting measures over multiple time scales. We also demonstrate that the typical parameter settings proposed in the literature lead to entropy measures that do not distinguish the two groups. This last result confirms the importance of the selection of appropriate input parameters.

Discrimination between Bacillus and Alicyclobacillus isolates in apple juice by Fourier transform infrared spectroscopy and multivariate analysis.

PubMed

Al-Holy, Murad A; Lin, Mengshi; Alhaj, Omar A; Abu-Goush, Mahmoud H

2015-02-01

Alicyclobacillus is a causative agent of spoilage in pasteurized and heat-treated apple juice products. Differentiating between this genus and the closely related Bacillus is crucially important. In this study, Fourier transform infrared spectroscopy (FT-IR) was used to identify and discriminate between 4 Alicyclobacillus strains and 4 Bacillus isolates inoculated individually into apple juice. Loading plots over the range of 1350 and 1700 cm(-1) reflected the most distinctive biochemical features of Bacillus and Alicyclobacillus. Multivariate statistical methods (for example, principal component analysis and soft independent modeling of class analogy) were used to analyze the spectral data. Distinctive separation of spectral samples was observed. This study demonstrates that FT-IR spectroscopy in combination with multivariate analysis could serve as a rapid and effective tool for fruit juice industry to differentiate between Bacillus and Alicyclobacillus and to distinguish between species belonging to these 2 genera. © 2015 Institute of Food Technologists®

Influence of the Rh (D) blood group system on graft survival in renal transplantation.

PubMed

Bryan, C F; Mitchell, S I; Lin, H M; Nelson, P W; Shield, C F; Luger, A M; Pierce, G E; Ross, G; Warady, B A; Aeder, M I; Helling, T S; Landreneau, M D; Harrell, K M

1998-02-27

The Rh (D) blood group system has not traditionally been considered to be a clinically relevant histocompatibility barrier in transplantation since conflicting results of its clinical importance have been reported. We analyzed 786 consecutive primary cadaveric renal transplants performed by transplant centers in our Organ Procurement Organization (OPO) between 1990 and 1997. We also analyzed United Network for Organ Sharing (UNOS) data on 26,469 kidney transplants done from April 1994 to June 1996. Multivariate analysis revealed that Rh identity between the recipient and donor was significantly related to better graft outcome (risk ratio, 0.43; 95% confidence interval, 0.30 to 0.61; P=0.0001). Multivariate analysis of the UNOS data revealed that the Rh -/- group may have a positive influence on graft survival with a risk ratio of 0.43 (P=0.14). Multivariate analysis of primary cadaveric renal allografts performed within the Midwest Organ Bank OPO indicates that Rh (D) is a clinically relevant histocompatibility barrier that influences 7-year graft survival.

Multivariate analysis of prognostic factors in synovial sarcoma.

PubMed

Koh, Kyoung Hwan; Cho, Eun Yoon; Kim, Dong Wook; Seo, Sung Wook

2009-11-01

Many studies have described the diversity of synovial sarcoma in terms of its biological characteristics and clinical features. Moreover, much effort has been expended on the identification of prognostic factors because of unpredictable behaviors of synovial sarcomas. However, with the exception of tumor size, published results have been inconsistent. We attempted to identify independent risk factors using survival analysis. Forty-one consecutive patients with synovial sarcoma were prospectively followed from January 1997 to March 2008. Overall and progression-free survival for age, sex, tumor size, tumor location, metastasis at presentation, histologic subtype, chemotherapy, radiation therapy, and resection margin were analyzed, and standard multivariate Cox proportional hazard regression analysis was used to evaluate potential prognostic factors. Tumor size (>5 cm), nonlimb-based tumors, metastasis at presentation, and a monophasic subtype were associated with poorer overall survival. Multivariate analysis showed metastasis at presentation and monophasic tumor subtype affected overall survival. For the progression-free survival, monophasic subtype was found to be only 1 prognostic factor. The study confirmed that histologic subtype is the single most important independent prognostic factors of synovial sarcoma regardless of tumor stage.

Simultaneous determination of rifampicin, isoniazid and pyrazinamide in tablet preparations by multivariate spectrophotometric calibration.

PubMed

Goicoechea, H C; Olivieri, A C

1999-08-01

The use of multivariate spectrophotometric calibration is presented for the simultaneous determination of the active components of tablets used in the treatment of pulmonary tuberculosis. The resolution of ternary mixtures of rifampicin, isoniazid and pyrazinamide has been accomplished by using partial least squares (PLS-1) regression analysis. Although the components show an important degree of spectral overlap, they have been simultaneously determined with high accuracy and precision, rapidly and with no need of nonaqueous solvents for dissolving the samples. No interference has been observed from the tablet excipients. A comparison is presented with the related multivariate method of classical least squares (CLS) analysis, which is shown to yield less reliable results due to the severe spectral overlap among the studied compounds. This is highlighted in the case of isoniazid, due to the small absorbances measured for this component.

Multi-Fault Diagnosis of Rolling Bearings via Adaptive Projection Intrinsically Transformed Multivariate Empirical Mode Decomposition and High Order Singular Value Decomposition

PubMed Central

Lv, Yong; Song, Gangbing

2018-01-01

Rolling bearings are important components in rotary machinery systems. In the field of multi-fault diagnosis of rolling bearings, the vibration signal collected from single channels tends to miss some fault characteristic information. Using multiple sensors to collect signals at different locations on the machine to obtain multivariate signal can remedy this problem. The adverse effect of a power imbalance between the various channels is inevitable, and unfavorable for multivariate signal processing. As a useful, multivariate signal processing method, Adaptive-projection has intrinsically transformed multivariate empirical mode decomposition (APIT-MEMD), and exhibits better performance than MEMD by adopting adaptive projection strategy in order to alleviate power imbalances. The filter bank properties of APIT-MEMD are also adopted to enable more accurate and stable intrinsic mode functions (IMFs), and to ease mode mixing problems in multi-fault frequency extractions. By aligning IMF sets into a third order tensor, high order singular value decomposition (HOSVD) can be employed to estimate the fault number. The fault correlation factor (FCF) analysis is used to conduct correlation analysis, in order to determine effective IMFs; the characteristic frequencies of multi-faults can then be extracted. Numerical simulations and the application of multi-fault situation can demonstrate that the proposed method is promising in multi-fault diagnoses of multivariate rolling bearing signal. PMID:29659510

Multi-Fault Diagnosis of Rolling Bearings via Adaptive Projection Intrinsically Transformed Multivariate Empirical Mode Decomposition and High Order Singular Value Decomposition.

PubMed

Yuan, Rui; Lv, Yong; Song, Gangbing

2018-04-16

Rolling bearings are important components in rotary machinery systems. In the field of multi-fault diagnosis of rolling bearings, the vibration signal collected from single channels tends to miss some fault characteristic information. Using multiple sensors to collect signals at different locations on the machine to obtain multivariate signal can remedy this problem. The adverse effect of a power imbalance between the various channels is inevitable, and unfavorable for multivariate signal processing. As a useful, multivariate signal processing method, Adaptive-projection has intrinsically transformed multivariate empirical mode decomposition (APIT-MEMD), and exhibits better performance than MEMD by adopting adaptive projection strategy in order to alleviate power imbalances. The filter bank properties of APIT-MEMD are also adopted to enable more accurate and stable intrinsic mode functions (IMFs), and to ease mode mixing problems in multi-fault frequency extractions. By aligning IMF sets into a third order tensor, high order singular value decomposition (HOSVD) can be employed to estimate the fault number. The fault correlation factor (FCF) analysis is used to conduct correlation analysis, in order to determine effective IMFs; the characteristic frequencies of multi-faults can then be extracted. Numerical simulations and the application of multi-fault situation can demonstrate that the proposed method is promising in multi-fault diagnoses of multivariate rolling bearing signal.

Alternatives for using multivariate regression to adjust prospective payment rates

PubMed Central

Sheingold, Steven H.

1990-01-01

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

Fast classification of hazelnut cultivars through portable infrared spectroscopy and chemometrics

NASA Astrophysics Data System (ADS)

Manfredi, Marcello; Robotti, Elisa; Quasso, Fabio; Mazzucco, Eleonora; Calabrese, Giorgio; Marengo, Emilio

2018-01-01

The authentication and traceability of hazelnuts is very important for both the consumer and the food industry, to safeguard the protected varieties and the food quality. This study investigates the use of a portable FTIR spectrometer coupled to multivariate statistical analysis for the classification of raw hazelnuts. The method discriminates hazelnuts from different origins/cultivars based on differences of the signal intensities of their IR spectra. The multivariate classification methods, namely principal component analysis (PCA) followed by linear discriminant analysis (LDA) and partial least square discriminant analysis (PLS-DA), with or without variable selection, allowed a very good discrimination among the groups, with PLS-DA coupled to variable selection providing the best results. Due to the fast analysis, high sensitivity, simplicity and no sample preparation, the proposed analytical methodology could be successfully used to verify the cultivar of hazelnuts, and the analysis can be performed quickly and directly on site.

Analysis of laser printer and photocopier toners by spectral properties and chemometrics

NASA Astrophysics Data System (ADS)

Verma, Neha; Kumar, Raj; Sharma, Vishal

2018-05-01

The use of printers to generate falsified documents has become a common practice in today's world. The examination and identification of the printed matter in the suspected documents (civil or criminal cases) may provide important information about the authenticity of the document. In the present study, a total number of 100 black toner samples both from laser printers and photocopiers were examined using diffuse reflectance UV-Vis Spectroscopy. The present research is divided into two parts; visual discrimination and discrimination by using multivariate analysis. A comparison between qualitative and quantitative analysis showed that multivariate analysis (Principal component analysis) provides 99.59%pair-wise discriminating power for laser printer toners while 99.84% pair-wise discriminating power for photocopier toners. The overall results obtained confirm the applicability of UV-Vis spectroscopy and chemometrics, in the nondestructive analysis of toner printed documents while enhancing their evidential value for forensic applications.

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.

Application of Concepts from Cross-Recurrence Analysis in Speech Production: An Overview and Comparison with Other Nonlinear Methods

ERIC Educational Resources Information Center

Lancia, Leonardo; Fuchs, Susanne; Tiede, Mark

2014-01-01

Purpose: The aim of this article was to introduce an important tool, cross-recurrence analysis, to speech production applications by showing how it can be adapted to evaluate the similarity of multivariate patterns of articulatory motion. The method differs from classical applications of cross-recurrence analysis because no phase space…

Multivariate analysis of early and late nest sites of Abert's Towhees

Treesearch

Deborah M. Finch

1985-01-01

Seasonal variation in nest site selection by the Abert's towhee (Pipilo aberti) was studied in honey mesquite (Prosopis glandulosa) habitat along the lower Colorado River from March to July, 1981. Stepwise discriminant function analysis identified nest vegetation type, nest direction, and nest height as the three most important variables that characterized the...

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

PubMed

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

2018-02-27

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

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

PubMed Central

Ye, Lanhan; Song, Kunlin; Shen, Tingting

2018-01-01

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

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

PubMed

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

2013-01-01

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

Interpretability of Multivariate Brain Maps in Linear Brain Decoding: Definition, and Heuristic Quantification in Multivariate Analysis of MEG Time-Locked Effects.

PubMed

Kia, Seyed Mostafa; Vega Pons, Sandro; Weisz, Nathan; Passerini, Andrea

2016-01-01

Brain decoding is a popular multivariate approach for hypothesis testing in neuroimaging. Linear classifiers are widely employed in the brain decoding paradigm to discriminate among experimental conditions. Then, the derived linear weights are visualized in the form of multivariate brain maps to further study spatio-temporal patterns of underlying neural activities. It is well known that the brain maps derived from weights of linear classifiers are hard to interpret because of high correlations between predictors, low signal to noise ratios, and the high dimensionality of neuroimaging data. Therefore, improving the interpretability of brain decoding approaches is of primary interest in many neuroimaging studies. Despite extensive studies of this type, at present, there is no formal definition for interpretability of multivariate brain maps. As a consequence, there is no quantitative measure for evaluating the interpretability of different brain decoding methods. In this paper, first, we present a theoretical definition of interpretability in brain decoding; we show that the interpretability of multivariate brain maps can be decomposed into their reproducibility and representativeness. Second, as an application of the proposed definition, we exemplify a heuristic for approximating the interpretability in multivariate analysis of evoked magnetoencephalography (MEG) responses. Third, we propose to combine the approximated interpretability and the generalization performance of the brain decoding into a new multi-objective criterion for model selection. Our results, for the simulated and real MEG data, show that optimizing the hyper-parameters of the regularized linear classifier based on the proposed criterion results in more informative multivariate brain maps. More importantly, the presented definition provides the theoretical background for quantitative evaluation of interpretability, and hence, facilitates the development of more effective brain decoding algorithms in the future.

Interpretability of Multivariate Brain Maps in Linear Brain Decoding: Definition, and Heuristic Quantification in Multivariate Analysis of MEG Time-Locked Effects

PubMed Central

Kia, Seyed Mostafa; Vega Pons, Sandro; Weisz, Nathan; Passerini, Andrea

2017-01-01

Brain decoding is a popular multivariate approach for hypothesis testing in neuroimaging. Linear classifiers are widely employed in the brain decoding paradigm to discriminate among experimental conditions. Then, the derived linear weights are visualized in the form of multivariate brain maps to further study spatio-temporal patterns of underlying neural activities. It is well known that the brain maps derived from weights of linear classifiers are hard to interpret because of high correlations between predictors, low signal to noise ratios, and the high dimensionality of neuroimaging data. Therefore, improving the interpretability of brain decoding approaches is of primary interest in many neuroimaging studies. Despite extensive studies of this type, at present, there is no formal definition for interpretability of multivariate brain maps. As a consequence, there is no quantitative measure for evaluating the interpretability of different brain decoding methods. In this paper, first, we present a theoretical definition of interpretability in brain decoding; we show that the interpretability of multivariate brain maps can be decomposed into their reproducibility and representativeness. Second, as an application of the proposed definition, we exemplify a heuristic for approximating the interpretability in multivariate analysis of evoked magnetoencephalography (MEG) responses. Third, we propose to combine the approximated interpretability and the generalization performance of the brain decoding into a new multi-objective criterion for model selection. Our results, for the simulated and real MEG data, show that optimizing the hyper-parameters of the regularized linear classifier based on the proposed criterion results in more informative multivariate brain maps. More importantly, the presented definition provides the theoretical background for quantitative evaluation of interpretability, and hence, facilitates the development of more effective brain decoding algorithms in the future. PMID:28167896

Multivariate and geo-spatial approach for seawater quality of Chidiyatappu Bay, south Andaman Islands, India.

PubMed

Jha, Dilip Kumar; Vinithkumar, Nambali Valsalan; Sahu, Biraja Kumar; Dheenan, Palaiya Sukumaran; Das, Apurba Kumar; Begum, Mehmuna; Devi, Marimuthu Prashanthi; Kirubagaran, Ramalingam

2015-07-15

Chidiyatappu Bay is one of the least disturbed marine environments of Andaman & Nicobar Islands, the union territory of India. Oceanic flushing from southeast and northwest direction is prevalent in this bay. Further, anthropogenic activity is minimal in the adjoining environment. Considering the pristine nature of this bay, seawater samples collected from 12 sampling stations covering three seasons were analyzed. Principal Component Analysis (PCA) revealed 69.9% of total variance and exhibited strong factor loading for nitrite, chlorophyll a and phaeophytin. In addition, analysis of variance (ANOVA-one way), regression analysis, box-whisker plots and Geographical Information System based hot spot analysis further simplified and supported multivariate results. The results obtained are important to establish reference conditions for comparative study with other similar ecosystems in the region. Copyright © 2015 Elsevier Ltd. All rights reserved.

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.

Esophageal wall dose-surface maps do not improve the predictive performance of a multivariable NTCP model for acute esophageal toxicity in advanced stage NSCLC patients treated with intensity-modulated (chemo-)radiotherapy.

PubMed

Dankers, Frank; Wijsman, Robin; Troost, Esther G C; Monshouwer, René; Bussink, Johan; Hoffmann, Aswin L

2017-05-07

In our previous work, a multivariable normal-tissue complication probability (NTCP) model for acute esophageal toxicity (AET) Grade  ⩾2 after highly conformal (chemo-)radiotherapy for non-small cell lung cancer (NSCLC) was developed using multivariable logistic regression analysis incorporating clinical parameters and mean esophageal dose (MED). Since the esophagus is a tubular organ, spatial information of the esophageal wall dose distribution may be important in predicting AET. We investigated whether the incorporation of esophageal wall dose-surface data with spatial information improves the predictive power of our established NTCP model. For 149 NSCLC patients treated with highly conformal radiation therapy esophageal wall dose-surface histograms (DSHs) and polar dose-surface maps (DSMs) were generated. DSMs were used to generate new DSHs and dose-length-histograms that incorporate spatial information of the dose-surface distribution. From these histograms dose parameters were derived and univariate logistic regression analysis showed that they correlated significantly with AET. Following our previous work, new multivariable NTCP models were developed using the most significant dose histogram parameters based on univariate analysis (19 in total). However, the 19 new models incorporating esophageal wall dose-surface data with spatial information did not show improved predictive performance (area under the curve, AUC range 0.79-0.84) over the established multivariable NTCP model based on conventional dose-volume data (AUC  =  0.84). For prediction of AET, based on the proposed multivariable statistical approach, spatial information of the esophageal wall dose distribution is of no added value and it is sufficient to only consider MED as a predictive dosimetric parameter.

Esophageal wall dose-surface maps do not improve the predictive performance of a multivariable NTCP model for acute esophageal toxicity in advanced stage NSCLC patients treated with intensity-modulated (chemo-)radiotherapy

NASA Astrophysics Data System (ADS)

Dankers, Frank; Wijsman, Robin; Troost, Esther G. C.; Monshouwer, René; Bussink, Johan; Hoffmann, Aswin L.

2017-05-01

In our previous work, a multivariable normal-tissue complication probability (NTCP) model for acute esophageal toxicity (AET) Grade  ⩾2 after highly conformal (chemo-)radiotherapy for non-small cell lung cancer (NSCLC) was developed using multivariable logistic regression analysis incorporating clinical parameters and mean esophageal dose (MED). Since the esophagus is a tubular organ, spatial information of the esophageal wall dose distribution may be important in predicting AET. We investigated whether the incorporation of esophageal wall dose-surface data with spatial information improves the predictive power of our established NTCP model. For 149 NSCLC patients treated with highly conformal radiation therapy esophageal wall dose-surface histograms (DSHs) and polar dose-surface maps (DSMs) were generated. DSMs were used to generate new DSHs and dose-length-histograms that incorporate spatial information of the dose-surface distribution. From these histograms dose parameters were derived and univariate logistic regression analysis showed that they correlated significantly with AET. Following our previous work, new multivariable NTCP models were developed using the most significant dose histogram parameters based on univariate analysis (19 in total). However, the 19 new models incorporating esophageal wall dose-surface data with spatial information did not show improved predictive performance (area under the curve, AUC range 0.79-0.84) over the established multivariable NTCP model based on conventional dose-volume data (AUC  =  0.84). For prediction of AET, based on the proposed multivariable statistical approach, spatial information of the esophageal wall dose distribution is of no added value and it is sufficient to only consider MED as a predictive dosimetric parameter.

Variable Neighborhood Search Heuristics for Selecting a Subset of Variables in Principal Component Analysis

ERIC Educational Resources Information Center

Brusco, Michael J.; Singh, Renu; Steinley, Douglas

2009-01-01

The selection of a subset of variables from a pool of candidates is an important problem in several areas of multivariate statistics. Within the context of principal component analysis (PCA), a number of authors have argued that subset selection is crucial for identifying those variables that are required for correct interpretation of the…

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

PubMed

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

2017-03-22

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

Water quality analysis of the Rapur area, Andhra Pradesh, South India using multivariate techniques

NASA Astrophysics Data System (ADS)

Nagaraju, A.; Sreedhar, Y.; Thejaswi, A.; Sayadi, Mohammad Hossein

2017-10-01

The groundwater samples from Rapur area were collected from different sites to evaluate the major ion chemistry. The large number of data can lead to difficulties in the integration, interpretation, and representation of the results. Two multivariate statistical methods, hierarchical cluster analysis (HCA) and factor analysis (FA), were applied to evaluate their usefulness to classify and identify geochemical processes controlling groundwater geochemistry. Four statistically significant clusters were obtained from 30 sampling stations. This has resulted two important clusters viz., cluster 1 (pH, Si, CO3, Mg, SO4, Ca, K, HCO3, alkalinity, Na, Na + K, Cl, and hardness) and cluster 2 (EC and TDS) which are released to the study area from different sources. The application of different multivariate statistical techniques, such as principal component analysis (PCA), assists in the interpretation of complex data matrices for a better understanding of water quality of a study area. From PCA, it is clear that the first factor (factor 1), accounted for 36.2% of the total variance, was high positive loading in EC, Mg, Cl, TDS, and hardness. Based on the PCA scores, four significant cluster groups of sampling locations were detected on the basis of similarity of their water quality.

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

PubMed

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

2013-05-01

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

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

PubMed

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

2015-11-01

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

A Dynamic Intrusion Detection System Based on Multivariate Hotelling's T2 Statistics Approach for Network Environments

PubMed Central

Avalappampatty Sivasamy, Aneetha; Sundan, Bose

2015-01-01

The ever expanding communication requirements in today's world demand extensive and efficient network systems with equally efficient and reliable security features integrated for safe, confident, and secured communication and data transfer. Providing effective security protocols for any network environment, therefore, assumes paramount importance. Attempts are made continuously for designing more efficient and dynamic network intrusion detection models. In this work, an approach based on Hotelling's T2 method, a multivariate statistical analysis technique, has been employed for intrusion detection, especially in network environments. Components such as preprocessing, multivariate statistical analysis, and attack detection have been incorporated in developing the multivariate Hotelling's T2 statistical model and necessary profiles have been generated based on the T-square distance metrics. With a threshold range obtained using the central limit theorem, observed traffic profiles have been classified either as normal or attack types. Performance of the model, as evaluated through validation and testing using KDD Cup'99 dataset, has shown very high detection rates for all classes with low false alarm rates. Accuracy of the model presented in this work, in comparison with the existing models, has been found to be much better. PMID:26357668

A Dynamic Intrusion Detection System Based on Multivariate Hotelling's T2 Statistics Approach for Network Environments.

PubMed

Sivasamy, Aneetha Avalappampatty; Sundan, Bose

2015-01-01

The ever expanding communication requirements in today's world demand extensive and efficient network systems with equally efficient and reliable security features integrated for safe, confident, and secured communication and data transfer. Providing effective security protocols for any network environment, therefore, assumes paramount importance. Attempts are made continuously for designing more efficient and dynamic network intrusion detection models. In this work, an approach based on Hotelling's T(2) method, a multivariate statistical analysis technique, has been employed for intrusion detection, especially in network environments. Components such as preprocessing, multivariate statistical analysis, and attack detection have been incorporated in developing the multivariate Hotelling's T(2) statistical model and necessary profiles have been generated based on the T-square distance metrics. With a threshold range obtained using the central limit theorem, observed traffic profiles have been classified either as normal or attack types. Performance of the model, as evaluated through validation and testing using KDD Cup'99 dataset, has shown very high detection rates for all classes with low false alarm rates. Accuracy of the model presented in this work, in comparison with the existing models, has been found to be much better.

An enhanced cluster analysis program with bootstrap significance testing for ecological community analysis

USGS Publications Warehouse

McKenna, J.E.

2003-01-01

The biosphere is filled with complex living patterns and important questions about biodiversity and community and ecosystem ecology are concerned with structure and function of multispecies systems that are responsible for those patterns. Cluster analysis identifies discrete groups within multivariate data and is an effective method of coping with these complexities, but often suffers from subjective identification of groups. The bootstrap testing method greatly improves objective significance determination for cluster analysis. The BOOTCLUS program makes cluster analysis that reliably identifies real patterns within a data set more accessible and easier to use than previously available programs. A variety of analysis options and rapid re-analysis provide a means to quickly evaluate several aspects of a data set. Interpretation is influenced by sampling design and a priori designation of samples into replicate groups, and ultimately relies on the researcher's knowledge of the organisms and their environment. However, the BOOTCLUS program provides reliable, objectively determined groupings of multivariate data.

Multivariate Models of Parent-Late Adolescent Gender Dyads: The Importance of Parenting Processes in Predicting Adjustment

ERIC Educational Resources Information Center

McKinney, Cliff; Renk, Kimberly

2008-01-01

Although parent-adolescent interactions have been examined, relevant variables have not been integrated into a multivariate model. As a result, this study examined a multivariate model of parent-late adolescent gender dyads in an attempt to capture important predictors in late adolescents' important and unique transition to adulthood. The sample…

Igloo-Plot: a tool for visualization of multidimensional datasets.

PubMed

Kuntal, Bhusan K; Ghosh, Tarini Shankar; Mande, Sharmila S

2014-01-01

Advances in science and technology have resulted in an exponential growth of multivariate (or multi-dimensional) datasets which are being generated from various research areas especially in the domain of biological sciences. Visualization and analysis of such data (with the objective of uncovering the hidden patterns therein) is an important and challenging task. We present a tool, called Igloo-Plot, for efficient visualization of multidimensional datasets. The tool addresses some of the key limitations of contemporary multivariate visualization and analysis tools. The visualization layout, not only facilitates an easy identification of clusters of data-points having similar feature compositions, but also the 'marker features' specific to each of these clusters. The applicability of the various functionalities implemented herein is demonstrated using several well studied multi-dimensional datasets. Igloo-Plot is expected to be a valuable resource for researchers working in multivariate data mining studies. Igloo-Plot is available for download from: http://metagenomics.atc.tcs.com/IglooPlot/. Copyright © 2014 Elsevier Inc. All rights reserved.

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

PubMed

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

1995-10-01

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

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

NASA Astrophysics Data System (ADS)

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

2014-12-01

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

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.

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

PubMed

Lifshits, A M

1979-01-01

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

Detecting synchronization clusters in multivariate time series via coarse-graining of Markov chains.

PubMed

Allefeld, Carsten; Bialonski, Stephan

2007-12-01

Synchronization cluster analysis is an approach to the detection of underlying structures in data sets of multivariate time series, starting from a matrix R of bivariate synchronization indices. A previous method utilized the eigenvectors of R for cluster identification, analogous to several recent attempts at group identification using eigenvectors of the correlation matrix. All of these approaches assumed a one-to-one correspondence of dominant eigenvectors and clusters, which has however been shown to be wrong in important cases. We clarify the usefulness of eigenvalue decomposition for synchronization cluster analysis by translating the problem into the language of stochastic processes, and derive an enhanced clustering method harnessing recent insights from the coarse-graining of finite-state Markov processes. We illustrate the operation of our method using a simulated system of coupled Lorenz oscillators, and we demonstrate its superior performance over the previous approach. Finally we investigate the question of robustness of the algorithm against small sample size, which is important with regard to field applications.

Multivariate analysis in thoracic research.

PubMed

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

2015-03-01

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

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

PubMed

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

2014-11-01

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

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.

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.

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

ERIC Educational Resources Information Center

Grochowalski, Joseph H.

2015-01-01

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

Characterization of the volatile components in green tea by IRAE-HS-SPME/GC-MS combined with multivariate analysis.

PubMed

Yang, Yan-Qin; Yin, Hong-Xu; Yuan, Hai-Bo; Jiang, Yong-Wen; Dong, Chun-Wang; Deng, Yu-Liang

2018-01-01

In the present work, a novel infrared-assisted extraction coupled to headspace solid-phase microextraction (IRAE-HS-SPME) followed by gas chromatography-mass spectrometry (GC-MS) was developed for rapid determination of the volatile components in green tea. The extraction parameters such as fiber type, sample amount, infrared power, extraction time, and infrared lamp distance were optimized by orthogonal experimental design. Under optimum conditions, a total of 82 volatile compounds in 21 green tea samples from different geographical origins were identified. Compared with classical water-bath heating, the proposed technique has remarkable advantages of considerably reducing the analytical time and high efficiency. In addition, an effective classification of green teas based on their volatile profiles was achieved by partial least square-discriminant analysis (PLS-DA) and hierarchical clustering analysis (HCA). Furthermore, the application of a dual criterion based on the variable importance in the projection (VIP) values of the PLS-DA models and on the category from one-way univariate analysis (ANOVA) allowed the identification of 12 potential volatile markers, which were considered to make the most important contribution to the discrimination of the samples. The results suggest that IRAE-HS-SPME/GC-MS technique combined with multivariate analysis offers a valuable tool to assess geographical traceability of different tea varieties.

Characterization of the volatile components in green tea by IRAE-HS-SPME/GC-MS combined with multivariate analysis

PubMed Central

Yin, Hong-Xu; Yuan, Hai-Bo; Jiang, Yong-Wen; Dong, Chun-Wang; Deng, Yu-Liang

2018-01-01

In the present work, a novel infrared-assisted extraction coupled to headspace solid-phase microextraction (IRAE-HS-SPME) followed by gas chromatography-mass spectrometry (GC-MS) was developed for rapid determination of the volatile components in green tea. The extraction parameters such as fiber type, sample amount, infrared power, extraction time, and infrared lamp distance were optimized by orthogonal experimental design. Under optimum conditions, a total of 82 volatile compounds in 21 green tea samples from different geographical origins were identified. Compared with classical water-bath heating, the proposed technique has remarkable advantages of considerably reducing the analytical time and high efficiency. In addition, an effective classification of green teas based on their volatile profiles was achieved by partial least square-discriminant analysis (PLS-DA) and hierarchical clustering analysis (HCA). Furthermore, the application of a dual criterion based on the variable importance in the projection (VIP) values of the PLS-DA models and on the category from one-way univariate analysis (ANOVA) allowed the identification of 12 potential volatile markers, which were considered to make the most important contribution to the discrimination of the samples. The results suggest that IRAE-HS-SPME/GC-MS technique combined with multivariate analysis offers a valuable tool to assess geographical traceability of different tea varieties. PMID:29494626

Application of multivariate statistical techniques in microbial ecology

PubMed Central

Paliy, O.; Shankar, V.

2016-01-01

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

Multivariate analysis of PRISMA optimized TLC image for predicting antioxidant activity and identification of contributing compounds from Pereskia bleo.

PubMed

Sharif, K M; Rahman, M M; Azmir, J; Khatib, A; Sabina, E; Shamsudin, S H; Zaidul, I S M

2015-12-01

Multivariate analysis of thin-layer chromatography (TLC) images was modeled to predict antioxidant activity of Pereskia bleo leaves and to identify the contributing compounds of the activity. TLC was developed in optimized mobile phase using the 'PRISMA' optimization method and the image was then converted to wavelet signals and imported for multivariate analysis. An orthogonal partial least square (OPLS) model was developed consisting of a wavelet-converted TLC image and 2,2-diphynyl-picrylhydrazyl free radical scavenging activity of 24 different preparations of P. bleo as the x- and y-variables, respectively. The quality of the constructed OPLS model (1 + 1 + 0) with one predictive and one orthogonal component was evaluated by internal and external validity tests. The validated model was then used to identify the contributing spot from the TLC plate that was then analyzed by GC-MS after trimethylsilyl derivatization. Glycerol and amine compounds were mainly found to contribute to the antioxidant activity of the sample. An alternative method to predict the antioxidant activity of a new sample of P. bleo leaves has been developed. Copyright © 2015 John Wiley & Sons, Ltd.

Multivariate approach to quantitative analysis of Aphis gossypii Glover (Hemiptera: Aphididae) and their natural enemy populations at different cotton spacings.

PubMed

Malaquias, José B; Ramalho, Francisco S; Dos S Dias, Carlos T; Brugger, Bruno P; S Lira, Aline Cristina; Wilcken, Carlos F; Pachú, Jéssica K S; Zanuncio, José C

2017-02-09

The relationship between pests and natural enemies using multivariate analysis on cotton in different spacing has not been documented yet. Using multivariate approaches is possible to optimize strategies to control Aphis gossypii at different crop spacings because the possibility of a better use of the aphid sampling strategies as well as the conservation and release of its natural enemies. The aims of the study were (i) to characterize the temporal abundance data of aphids and its natural enemies using principal components, (ii) to analyze the degree of correlation between the insects and between groups of variables (pests and natural enemies), (iii) to identify the main natural enemies responsible for regulating A. gossypii populations, and (iv) to investigate the similarities in arthropod occurrence patterns at different spacings of cotton crops over two seasons. High correlations in the occurrence of Scymnus rubicundus with aphids are shown through principal component analysis and through the important role the species plays in canonical correlation analysis. Clustering the presence of apterous aphids matches the pattern verified for Chrysoperla externa at the three different spacings between rows. Our results indicate that S. rubicundus is the main candidate to regulate the aphid populations in all spacings studied.

Multivariate approach to quantitative analysis of Aphis gossypii Glover (Hemiptera: Aphididae) and their natural enemy populations at different cotton spacings

PubMed Central

Malaquias, José B.; Ramalho, Francisco S.; dos S. Dias, Carlos T.; Brugger, Bruno P.; S. Lira, Aline Cristina; Wilcken, Carlos F.; Pachú, Jéssica K. S.; Zanuncio, José C.

2017-01-01

The relationship between pests and natural enemies using multivariate analysis on cotton in different spacing has not been documented yet. Using multivariate approaches is possible to optimize strategies to control Aphis gossypii at different crop spacings because the possibility of a better use of the aphid sampling strategies as well as the conservation and release of its natural enemies. The aims of the study were (i) to characterize the temporal abundance data of aphids and its natural enemies using principal components, (ii) to analyze the degree of correlation between the insects and between groups of variables (pests and natural enemies), (iii) to identify the main natural enemies responsible for regulating A. gossypii populations, and (iv) to investigate the similarities in arthropod occurrence patterns at different spacings of cotton crops over two seasons. High correlations in the occurrence of Scymnus rubicundus with aphids are shown through principal component analysis and through the important role the species plays in canonical correlation analysis. Clustering the presence of apterous aphids matches the pattern verified for Chrysoperla externa at the three different spacings between rows. Our results indicate that S. rubicundus is the main candidate to regulate the aphid populations in all spacings studied. PMID:28181503

Multivariate approach to quantitative analysis of Aphis gossypii Glover (Hemiptera: Aphididae) and their natural enemy populations at different cotton spacings

NASA Astrophysics Data System (ADS)

Malaquias, José B.; Ramalho, Francisco S.; Dos S. Dias, Carlos T.; Brugger, Bruno P.; S. Lira, Aline Cristina; Wilcken, Carlos F.; Pachú, Jéssica K. S.; Zanuncio, José C.

2017-02-01

The relationship between pests and natural enemies using multivariate analysis on cotton in different spacing has not been documented yet. Using multivariate approaches is possible to optimize strategies to control Aphis gossypii at different crop spacings because the possibility of a better use of the aphid sampling strategies as well as the conservation and release of its natural enemies. The aims of the study were (i) to characterize the temporal abundance data of aphids and its natural enemies using principal components, (ii) to analyze the degree of correlation between the insects and between groups of variables (pests and natural enemies), (iii) to identify the main natural enemies responsible for regulating A. gossypii populations, and (iv) to investigate the similarities in arthropod occurrence patterns at different spacings of cotton crops over two seasons. High correlations in the occurrence of Scymnus rubicundus with aphids are shown through principal component analysis and through the important role the species plays in canonical correlation analysis. Clustering the presence of apterous aphids matches the pattern verified for Chrysoperla externa at the three different spacings between rows. Our results indicate that S. rubicundus is the main candidate to regulate the aphid populations in all spacings studied.

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.

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

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.

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

PubMed Central

Williams, L. Keoki; Buu, Anne

2017-01-01

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

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.

Trend Detection and Bivariate Frequency Analysis for Nonstrationary Rainfall Data

NASA Astrophysics Data System (ADS)

Joo, K.; Kim, H.; Shin, J. Y.; Heo, J. H.

2017-12-01

Multivariate frequency analysis has been developing for hydro-meteorological data such as rainfall, flood, and drought. Particularly, the copula has been used as a useful tool for multivariate probability model which has no limitation on deciding marginal distributions. The time-series rainfall data can be characterized to rainfall event by inter-event time definition (IETD) and each rainfall event has a rainfall depth and rainfall duration. In addition, nonstationarity in rainfall event has been studied recently due to climate change and trend detection of rainfall event is important to determine the data has nonstationarity or not. With the rainfall depth and duration of a rainfall event, trend detection and nonstationary bivariate frequency analysis has performed in this study. 62 stations from Korea Meteorological Association (KMA) over 30 years of hourly recorded data used in this study and the suitability of nonstationary copula for rainfall event has examined by the goodness-of-fit test.

Advanced spectral methods for climatic time series

USGS Publications Warehouse

Ghil, M.; Allen, M.R.; Dettinger, M.D.; Ide, K.; Kondrashov, D.; Mann, M.E.; Robertson, A.W.; Saunders, A.; Tian, Y.; Varadi, F.; Yiou, P.

2002-01-01

The analysis of univariate or multivariate time series provides crucial information to describe, understand, and predict climatic variability. The discovery and implementation of a number of novel methods for extracting useful information from time series has recently revitalized this classical field of study. Considerable progress has also been made in interpreting the information so obtained in terms of dynamical systems theory. In this review we describe the connections between time series analysis and nonlinear dynamics, discuss signal- to-noise enhancement, and present some of the novel methods for spectral analysis. The various steps, as well as the advantages and disadvantages of these methods, are illustrated by their application to an important climatic time series, the Southern Oscillation Index. This index captures major features of interannual climate variability and is used extensively in its prediction. Regional and global sea surface temperature data sets are used to illustrate multivariate spectral methods. Open questions and further prospects conclude the review.

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.

A comparison of hyperspectral reflectance and fluorescence imaging techniques for detection of contaminants on leafy greens

USDA-ARS?s Scientific Manuscript database

Ensuring the supply of safe, contaminant free fresh fruit and vegetables is of importance to consumers, suppliers and governments worldwide. In this study, three hyperspectral imaging (HSI) configurations coupled with two multivariate image analysis techniques are compared for detection of fecal con...

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

PubMed

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

2015-07-30

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

Influence factors and forecast of carbon emission in China: structure adjustment for emission peak

NASA Astrophysics Data System (ADS)

Wang, B.; Cui, C. Q.; Li, Z. P.

2018-02-01

This paper introduced Principal Component Analysis and Multivariate Linear Regression Model to verify long-term balance relationships between Carbon Emissions and the impact factors. The integrated model of improved PCA and multivariate regression analysis model is attainable to figure out the pattern of carbon emission sources. Main empirical results indicate that among all selected variables, the role of energy consumption scale was largest. GDP and Population follow and also have significant impacts on carbon emission. Industrialization rate and fossil fuel proportion, which is the indicator of reflecting the economic structure and energy structure, have a higher importance than the factor of urbanization rate and the dweller consumption level of urban areas. In this way, some suggestions are put forward for government to achieve the peak of carbon emissions.

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

NASA Technical Reports Server (NTRS)

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

1987-01-01

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

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

PubMed

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

2012-02-01

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

Multivariate 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…

Combination of multivariate curve resolution and multivariate classification techniques for comprehensive high-performance liquid chromatography-diode array absorbance detection fingerprints analysis of Salvia reuterana extracts.

PubMed

Hakimzadeh, Neda; Parastar, Hadi; Fattahi, Mohammad

2014-01-24

In this study, multivariate curve resolution (MCR) and multivariate classification methods are proposed to develop a new chemometric strategy for comprehensive analysis of high-performance liquid chromatography-diode array absorbance detection (HPLC-DAD) fingerprints of sixty Salvia reuterana samples from five different geographical regions. Different chromatographic problems occurred during HPLC-DAD analysis of S. reuterana samples, such as baseline/background contribution and noise, low signal-to-noise ratio (S/N), asymmetric peaks, elution time shifts, and peak overlap are handled using the proposed strategy. In this way, chromatographic fingerprints of sixty samples are properly segmented to ten common chromatographic regions using local rank analysis and then, the corresponding segments are column-wise augmented for subsequent MCR analysis. Extended multivariate curve resolution-alternating least squares (MCR-ALS) is used to obtain pure component profiles in each segment. In general, thirty-one chemical components were resolved using MCR-ALS in sixty S. reuterana samples and the lack of fit (LOF) values of MCR-ALS models were below 10.0% in all cases. Pure spectral profiles are considered for identification of chemical components by comparing their resolved spectra with the standard ones and twenty-four components out of thirty-one components were identified. Additionally, pure elution profiles are used to obtain relative concentrations of chemical components in different samples for multivariate classification analysis by principal component analysis (PCA) and k-nearest neighbors (kNN). Inspection of the PCA score plot (explaining 76.1% of variance accounted for three PCs) showed that S. reuterana samples belong to four clusters. The degree of class separation (DCS) which quantifies the distance separating clusters in relation to the scatter within each cluster is calculated for four clusters and it was in the range of 1.6-5.8. These results are then confirmed by kNN. In addition, according to the PCA loading plot and kNN dendrogram of thirty-one variables, five chemical constituents of luteolin-7-o-glucoside, salvianolic acid D, rosmarinic acid, lithospermic acid and trijuganone A are identified as the most important variables (i.e., chemical markers) for clusters discrimination. Finally, the effect of different chemical markers on samples differentiation is investigated using counter-propagation artificial neural network (CP-ANN) method. It is concluded that the proposed strategy can be successfully applied for comprehensive analysis of chromatographic fingerprints of complex natural samples. Copyright © 2013 Elsevier B.V. All rights reserved.

Impact of loneliness and depression on mortality: results from the Longitudinal Ageing Study Amsterdam.

PubMed

Holwerda, Tjalling J; van Tilburg, Theo G; Deeg, Dorly J H; Schutter, Natasja; Van, Rien; Dekker, Jack; Stek, Max L; Beekman, Aartjan T F; Schoevers, Robert A

2016-08-01

Loneliness is highly prevalent among older people, has serious health consequences and is an important predictor of mortality. Loneliness and depression may unfavourably interact with each other over time but data on this topic are scarce. To determine whether loneliness is associated with excess mortality after 19 years of follow-up and whether the joint effect with depression confers further excess mortality. Different aspects of loneliness were measured with the De Jong Gierveld scale and depression with the Centre for Epidemiologic Studies Depression Scale in a cohort of 2878 people aged 55-85 with 19 years of follow-up. Excess mortality hypotheses were tested with Kaplan-Meier and Cox proportional hazard analyses controlling for potential confounders. At follow-up loneliness and depression were associated with excess mortality in older men and women in bivariate analysis but not in multivariate analysis. In multivariate analysis, severe depression was associated with excess mortality in men who were lonely but not in women. Loneliness and depression are important predictors of early death in older adults. Severe depression has a strong association with excess mortality in older men who were lonely, indicating a lethal combination in this group. © The Royal College of Psychiatrists 2016.

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

PubMed

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

2015-01-01

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

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

PubMed Central

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

2015-01-01

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

Structural changes in cross-border liabilities: A multidimensional approach

NASA Astrophysics Data System (ADS)

Araújo, Tanya; Spelta, Alessandro

2014-01-01

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

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®

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

PubMed Central

Bujkiewicz, Sylwia; Riley, Richard D

2016-01-01

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

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

PubMed

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

2015-10-01

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

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.

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

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

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

2013-11-15

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

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.

Visualizing frequent patterns in large multivariate time series

NASA Astrophysics Data System (ADS)

Hao, M.; Marwah, M.; Janetzko, H.; Sharma, R.; Keim, D. A.; Dayal, U.; Patnaik, D.; Ramakrishnan, N.

2011-01-01

The detection of previously unknown, frequently occurring patterns in time series, often called motifs, has been recognized as an important task. However, it is difficult to discover and visualize these motifs as their numbers increase, especially in large multivariate time series. To find frequent motifs, we use several temporal data mining and event encoding techniques to cluster and convert a multivariate time series to a sequence of events. Then we quantify the efficiency of the discovered motifs by linking them with a performance metric. To visualize frequent patterns in a large time series with potentially hundreds of nested motifs on a single display, we introduce three novel visual analytics methods: (1) motif layout, using colored rectangles for visualizing the occurrences and hierarchical relationships of motifs in a multivariate time series, (2) motif distortion, for enlarging or shrinking motifs as appropriate for easy analysis and (3) motif merging, to combine a number of identical adjacent motif instances without cluttering the display. Analysts can interactively optimize the degree of distortion and merging to get the best possible view. A specific motif (e.g., the most efficient or least efficient motif) can be quickly detected from a large time series for further investigation. We have applied these methods to two real-world data sets: data center cooling and oil well production. The results provide important new insights into the recurring patterns.

Factors Influencing the Appearance of Oxaliplatin-Induced Allergy.

PubMed

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

2017-01-01

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

Multivariate meta-analysis: Potential and promise

PubMed Central

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

2011-01-01

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

Multivariate Longitudinal Analysis with Bivariate Correlation Test

PubMed Central

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

2016-01-01

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

Multivariate Longitudinal Analysis with Bivariate Correlation Test.

PubMed

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

2016-01-01

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

Peculiarities of data interpretation upon direct tissue analysis by Fourier transform ion cyclotron resonance mass spectrometry.

PubMed

Chagovets, Vtaliy; Kononikhin, Aleksey; Starodubtseva, Nataliia; Kostyukevich, Yury; Popov, Igor; Frankevich, Vladimir; Nikolaev, Eugene

2016-01-01

The importance of high-resolution mass spectrometry for the correct data interpretation of a direct tissue analysis is demonstrated with an example of its clinical application for an endometriosis study. Multivariate analysis of the data discovers lipid species differentially expressed in different tissues under investigation. High-resolution mass spectrometry allows unambiguous separation of peaks with close masses that correspond to proton and sodium adducts of phosphatidylcholines and to phosphatidylcholines differing in double bond number.

Multilingualism and fMRI: Longitudinal Study of Second Language Acquisition

PubMed Central

Andrews, Edna; Frigau, Luca; Voyvodic-Casabo, Clara; Voyvodic, James; Wright, John

2013-01-01

BOLD fMRI is often used for the study of human language. However, there are still very few attempts to conduct longitudinal fMRI studies in the study of language acquisition by measuring auditory comprehension and reading. The following paper is the first in a series concerning a unique longitudinal study devoted to the analysis of bi- and multilingual subjects who are: (1) already proficient in at least two languages; or (2) are acquiring Russian as a second/third language. The focus of the current analysis is to present data from the auditory sections of a set of three scans acquired from April, 2011 through April, 2012 on a five-person subject pool who are learning Russian during the study. All subjects were scanned using the same protocol for auditory comprehension on the same General Electric LX 3T Signa scanner in Duke University Hospital. Using a multivariate analysis of covariance (MANCOVA) for statistical analysis, proficiency measurements are shown to correlate significantly with scan results in the Russian conditions over time. The importance of both the left and right hemispheres in language processing is discussed. Special attention is devoted to the importance of contextualizing imaging data with corresponding behavioral and empirical testing data using a multivariate analysis of variance. This is the only study to date that includes: (1) longitudinal fMRI data with subject-based proficiency and behavioral data acquired in the same time frame; and (2) statistical modeling that demonstrates the importance of covariate language proficiency data for understanding imaging results of language acquisition. PMID:24961428

Multilingualism and fMRI: Longitudinal Study of Second Language Acquisition.

PubMed

Andrews, Edna; Frigau, Luca; Voyvodic-Casabo, Clara; Voyvodic, James; Wright, John

2013-05-28

BOLD fMRI is often used for the study of human language. However, there are still very few attempts to conduct longitudinal fMRI studies in the study of language acquisition by measuring auditory comprehension and reading. The following paper is the first in a series concerning a unique longitudinal study devoted to the analysis of bi- and multilingual subjects who are: (1) already proficient in at least two languages; or (2) are acquiring Russian as a second/third language. The focus of the current analysis is to present data from the auditory sections of a set of three scans acquired from April, 2011 through April, 2012 on a five-person subject pool who are learning Russian during the study. All subjects were scanned using the same protocol for auditory comprehension on the same General Electric LX 3T Signa scanner in Duke University Hospital. Using a multivariate analysis of covariance (MANCOVA) for statistical analysis, proficiency measurements are shown to correlate significantly with scan results in the Russian conditions over time. The importance of both the left and right hemispheres in language processing is discussed. Special attention is devoted to the importance of contextualizing imaging data with corresponding behavioral and empirical testing data using a multivariate analysis of variance. This is the only study to date that includes: (1) longitudinal fMRI data with subject-based proficiency and behavioral data acquired in the same time frame; and (2) statistical modeling that demonstrates the importance of covariate language proficiency data for understanding imaging results of language acquisition.

Geometrical Representations in the Learning of Two-Variable Functions

ERIC Educational Resources Information Center

Trigueros, Maria; Martinez-Planell, Rafael

2010-01-01

This study is part of a project concerned with the analysis of how students work with two-variable functions. This is of fundamental importance given the role of multivariable functions in mathematics and its applications. The portion of the project we report here concentrates on investigating the relationship between students' notion of subsets…

Classification of broiler breast fillets according to storage and to freeze-thaw treatment using near infrared spectroscopy and multivariate analysis

USDA-ARS?s Scientific Manuscript database

Visible/near-infrared (NIR) spectroscopy has shown potential for successfully classifying broiler breast fillets according to their texture properties. Freshness and shelf life are also important quality characteristics of boneless skinless chicken breast products in the marketplace. This study deal...

Measures of dependence for multivariate Lévy distributions

NASA Astrophysics Data System (ADS)

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

2001-02-01

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

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

PubMed

Tan, Chao; Zhao, Jia; Dong, Feng

2015-03-01

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

SurvMicro: assessment of miRNA-based prognostic signatures for cancer clinical outcomes by multivariate survival analysis.

PubMed

Aguirre-Gamboa, Raul; Trevino, Victor

2014-06-01

MicroRNAs (miRNAs) play a key role in post-transcriptional regulation of mRNA levels. Their function in cancer has been studied by high-throughput methods generating valuable sources of public information. Thus, miRNA signatures predicting cancer clinical outcomes are emerging. An important step to propose miRNA-based biomarkers before clinical validation is their evaluation in independent cohorts. Although it can be carried out using public data, such task is time-consuming and requires a specialized analysis. Therefore, to aid and simplify the evaluation of prognostic miRNA signatures in cancer, we developed SurvMicro, a free and easy-to-use web tool that assesses miRNA signatures from publicly available miRNA profiles using multivariate survival analysis. SurvMicro is composed of a wide and updated database of >40 cohorts in different tissues and a web tool where survival analysis can be done in minutes. We presented evaluations to portray the straightforward functionality of SurvMicro in liver and lung cancer. To our knowledge, SurvMicro is the only bioinformatic tool that aids the evaluation of multivariate prognostic miRNA signatures in cancer. SurvMicro and its tutorial are freely available at http://bioinformatica.mty.itesm.mx/SurvMicro. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Integrated Multivariate Analysis with Nondetects for the Development of Human Sewage Source-Tracking Tools Using Bacteriophages of Enterococcus faecalis.

PubMed

Wangkahad, Bencharong; Mongkolsuk, Skorn; Sirikanchana, Kwanrawee

2017-02-21

We developed sewage-specific microbial source tracking (MST) tools using enterococci bacteriophages and evaluated their performance with univariate and multivariate analyses involving data below detection limits. Newly isolated Enterococci faecalis bacterial strains AIM06 (DSM100702) and SR14 (DSM100701) demonstrated 100% specificity and 90% sensitivity to human sewage without detecting 68 animal manure pooled samples of cats, chickens, cows, dogs, ducks, pigs, and pigeons. AIM06 and SR14 bacteriophages were present in human sewage at 2-4 orders of magnitude. A principal component analysis confirmed the importance of both phages as main water quality parameters. The phages presented only in the polluted water, as classified by a cluster analysis, and at median concentrations of 1.71 × 10 2 and 4.27 × 10 2 PFU/100 mL, respectively, higher than nonhost specific RYC2056 phages and sewage-specific KS148 phages (p < 0.05). Interestingly, AIM06 and SR14 phages exhibited significant correlations with each other and with total coliforms, E. coli, enterococci, and biochemical oxygen demand (Kendall's tau = 0.348 to 0.605, p < 0.05), a result supporting their roles as water quality indicators. This research demonstrates the multiregional applicability of enterococci hosts in MST application and highlights the significance of multivariate analysis with nondetects in evaluating the performance of new MST host strains.

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

PubMed

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

2016-01-05

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

Multi-country health surveys: are the analyses misleading?

PubMed

Masood, Mohd; Reidpath, Daniel D

2014-05-01

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

Vegetation characteristics important to common songbirds in east Texas

USGS Publications Warehouse

Conner, Richard N.; Dickson, James G.; Locke, Brian A.; Segelquist, Charles A.

1983-01-01

Multivariate studies of breeding bird communities have used principal component analysis (PCA) or several-group (three or more groups) discriminant function analysis (DFA) to ordinate bird species on vegetational continua (Cody 1968, James 1971, Whitmore 1975). In community studies, high resolution of habitat requirements for individual species is not always possible with either PCA or several-group DFA. When habitat characteristics of several species are examined with a DFA the resultant axes optimally discriminate among all species simultaneously. Hence, the characteristics assigned to a particular species reflect in part the presence of other species in the analyses. A better resolution of each species' habitat requirements may be obtained from a two-group DFA, wherein habitats selected by a species are discriminated from all other available habitats. Analyses using two-group DFAs to compare habitat used by a species with habitat unused by the same species have the potential to provide an optimal frame of reference from which to examine habitat variables (Martinka 1972, Conner and Adkisson 1976, Whitmore 1981). Mathematically (DFA) it is possible to maximally separate two groups of multivariate observations with a single axis (Harner and whitmore 1977). A line drawn in three or n-dimensional space can easily be positioned to intersect two multivariate means (centroids). If three or more centroids for species are analyzed simultaneously, a single line can no longer intersect all centroids unless a perfectly linear relationship exists for the species being examined. The probability of such an occurrence is extremely low. Thus, a high degree of resolution can be realized when a two-group DFA is used to determine habitat parameters important to individual species. We have used two-group DFA to identify vegetation variable important to 12 common species of songbirds in East Texas.

Multivariate Regression Analysis and Slaughter Livestock,

DTIC Science & Technology

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

Principal Angle Enrichment Analysis (PAEA): Dimensionally Reduced Multivariate Gene Set Enrichment Analysis Tool

PubMed Central

Clark, Neil R.; Szymkiewicz, Maciej; Wang, Zichen; Monteiro, Caroline D.; Jones, Matthew R.; Ma’ayan, Avi

2016-01-01

Gene set analysis of differential expression, which identifies collectively differentially expressed gene sets, has become an important tool for biology. The power of this approach lies in its reduction of the dimensionality of the statistical problem and its incorporation of biological interpretation by construction. Many approaches to gene set analysis have been proposed, but benchmarking their performance in the setting of real biological data is difficult due to the lack of a gold standard. In a previously published work we proposed a geometrical approach to differential expression which performed highly in benchmarking tests and compared well to the most popular methods of differential gene expression. As reported, this approach has a natural extension to gene set analysis which we call Principal Angle Enrichment Analysis (PAEA). PAEA employs dimensionality reduction and a multivariate approach for gene set enrichment analysis. However, the performance of this method has not been assessed nor its implementation as a web-based tool. Here we describe new benchmarking protocols for gene set analysis methods and find that PAEA performs highly. The PAEA method is implemented as a user-friendly web-based tool, which contains 70 gene set libraries and is freely available to the community. PMID:26848405

Principal Angle Enrichment Analysis (PAEA): Dimensionally Reduced Multivariate Gene Set Enrichment Analysis Tool.

PubMed

Clark, Neil R; Szymkiewicz, Maciej; Wang, Zichen; Monteiro, Caroline D; Jones, Matthew R; Ma'ayan, Avi

2015-11-01

Gene set analysis of differential expression, which identifies collectively differentially expressed gene sets, has become an important tool for biology. The power of this approach lies in its reduction of the dimensionality of the statistical problem and its incorporation of biological interpretation by construction. Many approaches to gene set analysis have been proposed, but benchmarking their performance in the setting of real biological data is difficult due to the lack of a gold standard. In a previously published work we proposed a geometrical approach to differential expression which performed highly in benchmarking tests and compared well to the most popular methods of differential gene expression. As reported, this approach has a natural extension to gene set analysis which we call Principal Angle Enrichment Analysis (PAEA). PAEA employs dimensionality reduction and a multivariate approach for gene set enrichment analysis. However, the performance of this method has not been assessed nor its implementation as a web-based tool. Here we describe new benchmarking protocols for gene set analysis methods and find that PAEA performs highly. The PAEA method is implemented as a user-friendly web-based tool, which contains 70 gene set libraries and is freely available to the community.

Multivariate Statistical Analysis of Water Quality data in Indian River Lagoon, Florida

NASA Astrophysics Data System (ADS)

Sayemuzzaman, M.; Ye, M.

2015-12-01

The Indian River Lagoon, is part of the longest barrier island complex in the United States, is a region of particular concern to the environmental scientist because of the rapid rate of human development throughout the region and the geographical position in between the colder temperate zone and warmer sub-tropical zone. Thus, the surface water quality analysis in this region always brings the newer information. In this present study, multivariate statistical procedures were applied to analyze the spatial and temporal water quality in the Indian River Lagoon over the period 1998-2013. Twelve parameters have been analyzed on twelve key water monitoring stations in and beside the lagoon on monthly datasets (total of 27,648 observations). The dataset was treated using cluster analysis (CA), principle component analysis (PCA) and non-parametric trend analysis. The CA was used to cluster twelve monitoring stations into four groups, with stations on the similar surrounding characteristics being in the same group. The PCA was then applied to the similar groups to find the important water quality parameters. The principal components (PCs), PC1 to PC5 was considered based on the explained cumulative variances 75% to 85% in each cluster groups. Nutrient species (phosphorus and nitrogen), salinity, specific conductivity and erosion factors (TSS, Turbidity) were major variables involved in the construction of the PCs. Statistical significant positive or negative trends and the abrupt trend shift were detected applying Mann-Kendall trend test and Sequential Mann-Kendall (SQMK), for each individual stations for the important water quality parameters. Land use land cover change pattern, local anthropogenic activities and extreme climate such as drought might be associated with these trends. This study presents the multivariate statistical assessment in order to get better information about the quality of surface water. Thus, effective pollution control/management of the surface waters can be undertaken.

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

PubMed

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

2012-07-06

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

Multivariate evaluation of the effectiveness of treatment efficacy of cypermethrin against sea lice (Lepeophtheirus salmonis) in Atlantic salmon (Salmo salar)

PubMed Central

2013-01-01

Background The sea louse Lepeophtheirus salmonis is the most important ectoparasite of farmed Atlantic salmon (Salmo salar) in Norwegian aquaculture. Control of sea lice is primarily dependent on the use of delousing chemotherapeutants, which are both expensive and toxic to other wildlife. The method most commonly used for monitoring treatment effectiveness relies on measuring the percentage reduction in the mobile stages of Lepeophtheirus salmonis only. However, this does not account for changes in the other sea lice stages and may result in misleading or incomplete interpretation regarding the effectiveness of treatment. With the aim of improving the evaluation of delousing treatments, we explored multivariate analyses of bath treatments using the topical pyrethroid, cypermethrin, in salmon pens at five Norwegian production sites. Results Conventional univariate analysis indicated reductions of over 90% in mobile stages at all sites. In contrast, multivariate analyses indicated differing treatment effectiveness between sites (p-value < 0.01) based on changes in the proportion and abundance of the chalimus and PAAM (pre-adult and adult males) stages. Low water temperatures and shortened intervals between sampling after treatment may account for the differences in the composition of chalimus and PAAM stage groups following treatment. Using multivariate analysis, such factors could be separated from those which were attributable to inadequate treatment or chemotherapeutant failure. Conclusions Multivariate analyses for evaluation of treatment effectiveness against multiple life cycle stages of L. salmonis yield additional information beyond that derivable from univariate methods. This can aid in the identification of causes of apparent treatment failure in salmon aquaculture. PMID:24354936

Application of multivariate statistical techniques in microbial ecology.

PubMed

Paliy, O; Shankar, V

2016-03-01

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

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

PubMed

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

2018-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2018-06-01

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

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

PubMed

Dinç, Erdal; Ozdemir, Abdil

2005-01-01

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

Health Related Quality of Life among Insulin-Dependent Diabetics: Disease-Related and Psychosocial Correlates.

ERIC Educational Resources Information Center

Aalto, Anna-Mari; Uutela, Antti; Aro, Arja R.

1997-01-01

The associations of health and psychosocial factors with the Health Related Quality of Life Questionnaire were examined in adult type 1 diabetic patients (N=385). The most important factors from multivariate analysis were self-efficacy and diabetes-related social support, especially among those in good physical condition. Diabetes-specific factors…

HIV Testing Behavior among Pacific Islanders in Southern California: Exploring the Importance of Race/Ethnicity, Knowledge, and Domestic Violence

ERIC Educational Resources Information Center

Takahashi, Lois M.; Kim, Anna J.; Sablan-Santos, Lola; Quitugua, Lourdes Flores; Lepule, Jonathan; Maguadog, Tony; Perez, Rose; Young, Steve; Young, Louise

2011-01-01

This article presents an analysis of a 2008 community needs assessment survey of a convenience sample of 179 Pacific Islander respondents in southern California; the needs assessment focused on HIV knowledge, HIV testing behavior, and experience with intimate partner/relationship violence. Multivariate logistic regression results indicated that…

Multivariate analysis: A statistical approach for computations

NASA Astrophysics Data System (ADS)

Michu, Sachin; Kaushik, Vandana

2014-10-01

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

Multivariate Cluster Analysis.

ERIC Educational Resources Information Center

McRae, Douglas J.

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

Evaluation of drinking quality of groundwater through multivariate techniques in urban area.

PubMed

Das, Madhumita; Kumar, A; Mohapatra, M; Muduli, S D

2010-07-01

Groundwater is a major source of drinking water in urban areas. Because of the growing threat of debasing water quality due to urbanization and development, monitoring water quality is a prerequisite to ensure its suitability for use in drinking. But analysis of a large number of properties and parameter to parameter basis evaluation of water quality is not feasible in a regular interval. Multivariate techniques could streamline the data without much loss of information to a reasonably manageable data set. In this study, using principal component analysis, 11 relevant properties of 58 water samples were grouped into three statistical factors. Discriminant analysis identified "pH influence" as the most distinguished factor and pH, Fe, and NO₃⁻ as the most discriminating variables and could be treated as water quality indicators. These were utilized to classify the sampling sites into homogeneous clusters that reflect location-wise importance of specific indicator/s for use to monitor drinking water quality in the whole study area.

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

Multielement analysis of Canadian wines by inductively coupled plasma mass spectrometry (ICP-MS) and multivariate statistics.

PubMed

Taylor, Vivien F; Longerich, Henry P; Greenough, John D

2003-02-12

Trace element fingerprints were deciphered for wines from Canada's two major wine-producing regions, the Okanagan Valley and the Niagara Peninsula, for the purpose of examining differences in wine element composition with region of origin and identifying elements important to determining provenance. Analysis by ICP-MS allowed simultaneous determination of 34 trace elements in wine (Li, Be, Mg, Al, P, Cl, Ca, Ti, V, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Br, Rb, Sr, Mo, Ag, Cd, Sb, I, Cs, Ba, La, Ce, Tl, Pb, Bi, Th, and U) at low levels of detection, and patterns in trace element concentrations were deciphered by multivariate statistical analysis. The two regions were discriminated with 100% accuracy using 10 of these elements. Differences in soil chemistry between the Niagara and Okanagan vineyards were evident, without a good correlation between soil and wine composition. The element Sr was found to be a good indicator of provenance and has been reported in fingerprinting studies of other regions.

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

PubMed

Bena, Antonella; Giraudo, Massimiliano

2013-01-01

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

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

PubMed

Nojima, Masanori; Tokunaga, Mutsumi; Nagamura, Fumitaka

2018-05-05

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

Jaundice: an important, poorly recognized risk factor for diminished survival in patients with adenocarcinoma of the head of the pancreas

PubMed Central

Strasberg, Steven M; Gao, Feng; Sanford, Dominic; Linehan, David C; Hawkins, William G; Fields, Ryan; Carpenter, Danielle H; Brunt, Elizabeth M; Phillips, Carolyn

2014-01-01

Objectives: Jaundice impairs cellular immunity, an important defence against the dissemination of cancer. Jaundice is a common mode of presentation in pancreatic head adenocarcinoma. The purpose of this study was to determine whether there is an association between preoperative jaundice and survival in patients who have undergone resection of such tumours. Methods: Thirty possible survival risk factors were evaluated in a database of over 400 resected patients. Univariate analysis was used to determine odds ratio for death. All factors for which a P-value of <0.30 was obtained were entered into a multivariate analysis using the Cox model with backward selection. Results: Preoperative jaundice, age, positive node status, poor differentiation and lymphatic invasion were significant indicators of poor outcome in multivariate analysis. Absence of jaundice was a highly favourable prognostic factor. Interaction emerged between jaundice and nodal status. The benefit conferred by the absence of jaundice was restricted to patients in whom negative node status was present. Five-year overall survival in this group was 66%. Jaundiced patients who underwent preoperative stenting had a survival advantage. Conclusions: Preoperative jaundice is a negative risk factor in adenocarcinoma of the pancreas. Additional studies are required to determine the exact mechanism for this effect. PMID:23600768

Shell shape variation of queen conch Strombus gigas (Mesograstropoda: Strombidae) from Southwest Caribbean.

PubMed

Márquez, Edna Judith; Restrepo-Escobar, Natalia; Montoya-Herrera, Francisco Luis

2016-12-01

The endangered species Strombus gigas is a marine gastropod of significant economic importance through the Greater Caribbean region. In contrast to phenotypic plasticity, the role of genetics on shell variations in S. gigas has not been addressed so far, despite its importance in evolution, management and conservation of this species. This work used geometric morphometrics to investigate the phenotypic variation of 219 shells of S. gigas from eight sites of the Colombian Southwest Caribbean. Differences in mean size between sexes and among sites were contrasted by analysis of variance. Allometry was tested by multivariate regression and the hypothesis of common slope was contrasted by covariance multivariate analysis. Differences in the shell shape among sites were analyzed by principal component analysis. Sexual size dimorphism was not significant, whereas sexual shape dimorphism was significant and variable across sites. Differences in the shell shape among sites were concordant with genetic differences based on microsatellite data, supporting its genetic background. Besides, differences in the shell shape between populations genetically similar suggest a role of phenotypic plasticity in the morphometric variation of the shell shape. These outcomes evidence the role of genetic background and phenotypic plasticity in the shell shape of S. gigas. Thus, geometric morphometrics of shell shape may constitute a complementary tool to explore the genetic diversity of this species.

Reduced miR-300 expression predicts poor prognosis in patients with laryngeal squamous cell carcinoma.

PubMed

He, F-Y; Liu, H-J; Guo, Q; Sheng, J-L

2017-02-01

miR-300 has been demonstrated to play an important role in the progression of several tumors, but its role in tumorigenesis of laryngeal squamous cell carcinoma (LSCC) is still unclear. The purpose of this study was to explore miR-300 expression in LSCC patients and analyze its association with clinicopathological factors and prognosis. In the present study, we measured the expression level of miR-300 in LSCC tissues by RT-PCR. Associations between miRNA-300 expressions and various clinicopathological characteristics were analyzed. Patient survival and their differences were determined by Kaplan-Meier method and log-rank test. The univariate and multivariate analysis were performed using the Cox proportional hazard analysis. miR-300 expression was significantly increased in LSCC tissues compared with that in adjacent non-cancerous tissues (p < 0.01). In addition, lymph node metastasis (p = 0.004) and TNM stage (p = 0.001) were obvious influence factors for the expression of miR-300. More importantly, Kaplan-Meier analysis showed that LSCC patients with low miR-300 expression tended to have shorter overall survival (p < 0.001). Finally, multivariate analysis revealed that miR-300 expression was an independent prognostic factor for LSCC patients. Our results pointed to miR-300 as a powerful prognostic marker in LSCC and as a novel target for tumor-suppressive therapy.

A Baseline for the Multivariate Comparison of Resting-State Networks

PubMed Central

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

2011-01-01

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

Oral pathology follow-up by means of micro-Raman spectroscopy on tissue and blood serum samples: an application of wavelet and multivariate data analysis

NASA Astrophysics Data System (ADS)

Delfino, I.; Camerlingo, C.; Zenone, F.; Perna, G.; Capozzi, V.; Cirillo, N.; Gaeta, G. M.; De Mol, E.; Lepore, M.

2009-02-01

Pemphigus vulgaris (PV) is a potentially fatal autoimmune disease that cause blistering of the skin and oral cavity. It is characterized by disruption of cell-cell adhesion within the suprabasal layers of epithelium, a phenomenon termed acantholysis Patients with PV develop IgG autoantibodies against normal constituents of the intercellular substance of keratinocytes. The mechanisms by which such autoantibodies induce blisters are not clearly understood. The qualitative analysis of such effects provides important clues in the search for a specific diagnosis, and the quantitative analysis of biochemical abnormalities is important in measuring the extent of the disease process, designing therapy and evaluating the efficacy of treatment. Improved diagnostic techniques could permit the recognition of more subtle forms of disease and reveal incipient lesions clinically unapparent, so that progression of potentially severe forms could be reversed with appropriate treatment. In this paper, we report the results of our micro-Raman spectroscopy study on tissue and blood serum samples from ill, recovered and under therapy PV patients. The complexity of the differences among their characteristic Raman spectra has required a specific strategy to obtain reliable information on the illness stage of the patients For this purpose, wavelet techniques and advanced multivariate analysis methods have been developed and applied to the experimental Raman spectra. Promising results have been obtained.

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

PubMed

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

2016-12-01

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

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

PubMed

Bonetti, Jennifer; Quarino, Lawrence

2014-05-01

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

Psychosocial distress in patients with thyroid cancer.

PubMed

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

2015-04-01

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

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

PubMed Central

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

2012-01-01

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

Analyzing Multiple Outcomes in Clinical Research Using Multivariate Multilevel Models

PubMed Central

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

2014-01-01

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

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.

Factors affecting plant species composition of hedgerows: relative importance and hierarchy

NASA Astrophysics Data System (ADS)

Deckers, Bart; Hermy, Martin; Muys, Bart

2004-07-01

Although there has been a clear quantitative and qualitative decline in traditional hedgerow network landscapes during last century, hedgerows are crucial for the conservation of rural biodiversity, functioning as an important habitat, refuge and corridor for numerous species. To safeguard this conservation function, insight in the basic organizing principles of hedgerow plant communities is needed. The vegetation composition of 511 individual hedgerows situated within an ancient hedgerow network landscape in Flanders, Belgium was recorded, in combination with a wide range of explanatory variables, including a selection of spatial variables. Non-parametric statistics in combination with multivariate data analysis techniques were used to study the effect of individual explanatory variables. Next, variables were grouped in five distinct subsets and the relative importance of these variable groups was assessed by two related variation partitioning techniques, partial regression and partial canonical correspondence analysis, taking into account explicitly the existence of intercorrelations between variables of different factor groups. Most explanatory variables affected significantly hedgerow species richness and composition. Multivariate analysis showed that, besides adjacent land use, hedgerow management, soil conditions, hedgerow type and origin, the role of other factors such as hedge dimensions, intactness, etc., could certainly not be neglected. Furthermore, both methods revealed the same overall ranking of the five distinct factor groups. Besides a predominant impact of abiotic environmental conditions, it was found that management variables and structural aspects have a relatively larger influence on the distribution of plant species in hedgerows than their historical background or spatial configuration.

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

Assessment of cardio-respiratory interactions in preterm infants by bivariate autoregressive modeling and surrogate data analysis.

PubMed

Indic, Premananda; Bloch-Salisbury, Elisabeth; Bednarek, Frank; Brown, Emery N; Paydarfar, David; Barbieri, Riccardo

2011-07-01

Cardio-respiratory interactions are weak at the earliest stages of human development, suggesting that assessment of their presence and integrity may be an important indicator of development in infants. Despite the valuable research devoted to infant development, there is still a need for specifically targeted standards and methods to assess cardiopulmonary functions in the early stages of life. We present a new methodological framework for the analysis of cardiovascular variables in preterm infants. Our approach is based on a set of mathematical tools that have been successful in quantifying important cardiovascular control mechanisms in adult humans, here specifically adapted to reflect the physiology of the developing cardiovascular system. We applied our methodology in a study of cardio-respiratory responses for 11 preterm infants. We quantified cardio-respiratory interactions using specifically tailored multivariate autoregressive analysis and calculated the coherence as well as gain using causal approaches. The significance of the interactions in each subject was determined by surrogate data analysis. The method was tested in control conditions as well as in two different experimental conditions; with and without use of mild mechanosensory intervention. Our multivariate analysis revealed a significantly higher coherence, as confirmed by surrogate data analysis, in the frequency range associated with eupneic breathing compared to the other ranges. Our analysis validates the models behind our new approaches, and our results confirm the presence of cardio-respiratory coupling in early stages of development, particularly during periods of mild mechanosensory intervention, thus encouraging further application of our approach. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

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

NASA Technical Reports Server (NTRS)

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

1980-01-01

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

Methods for presentation and display of multivariate data

NASA Technical Reports Server (NTRS)

Myers, R. H.

1981-01-01

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

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

ERIC Educational Resources Information Center

Warne, Russell T.

2014-01-01

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

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

PubMed

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

2014-09-01

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

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

PubMed

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

2017-02-01

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

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

PubMed

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

2011-12-01

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

Prognostic importance of DNA ploidy in non-endometrioid, high-risk endometrial carcinomas.

PubMed

Sorbe, Bengt

2016-03-01

The present study investigated the predictive and prognostic impact of DNA ploidy together with other well-known prognostic factors in a series of non-endometrioid, high-risk endometrial carcinomas. From a complete consecutive series of 4,543 endometrial carcinomas of International Federation of Gynecology and Obstetrics (FIGO) stages I-IV, 94 serous carcinomas, 48 clear cell carcinomas and 231 carcinosarcomas were selected as a non-endometrioid, high-risk group for further studies regarding prognosis. The impact of DNA ploidy, as assessed by flow cytometry, was of particular focus. The age of the patients, FIGO stage, depth of myometrial infiltration and tumor expression of p53 were also included in the analyses (univariate and multivariate). In the complete series of cases, the recurrence rate was 37%, and the 5-year overall survival rate was 39% with no difference between the three histological subtypes. The primary cure rate (78%) was also similar for all tumor types studied. DNA ploidy was a significant predictive factor (on univariate analysis) for primary tumor cure rate, and a prognostic factor for survival rate (on univariate and multivariate analyses). The predictive and prognostic impact of DNA ploidy was higher in carcinosarcomas than in serous and clear cell carcinomas. In the majority of multivariate analyses, FIGO stage and depth of myometrial infiltration were the most important predictive (tumor recurrence) and prognostic (survival rate) factors. DNA ploidy status is a less important predictive and prognostic factor in non-endometrioid, high-risk endometrial carcinomas than in the common endometrioid carcinomas, in which FIGO and nuclear grade also are highly significant and important factors.

Multivariate research in areas of phosphorus cast-iron brake shoes manufacturing using the statistical analysis and the multiple regression equations

NASA Astrophysics Data System (ADS)

Kiss, I.; Cioată, V. G.; Alexa, V.; Raţiu, S. A.

2017-05-01

The braking system is one of the most important and complex subsystems of railway vehicles, especially when it comes for safety. Therefore, installing efficient safe brakes on the modern railway vehicles is essential. Nowadays is devoted attention to solving problems connected with using high performance brake materials and its impact on thermal and mechanical loading of railway wheels. The main factor that influences the selection of a friction material for railway applications is the performance criterion, due to the interaction between the brake block and the wheel produce complex thermos-mechanical phenomena. In this work, the investigated subjects are the cast-iron brake shoes, which are still widely used on freight wagons. Therefore, the cast-iron brake shoes - with lamellar graphite and with a high content of phosphorus (0.8-1.1%) - need a special investigation. In order to establish the optimal condition for the cast-iron brake shoes we proposed a mathematical modelling study by using the statistical analysis and multiple regression equations. Multivariate research is important in areas of cast-iron brake shoes manufacturing, because many variables interact with each other simultaneously. Multivariate visualization comes to the fore when researchers have difficulties in comprehending many dimensions at one time. Technological data (hardness and chemical composition) obtained from cast-iron brake shoes were used for this purpose. In order to settle the multiple correlation between the hardness of the cast-iron brake shoes, and the chemical compositions elements several model of regression equation types has been proposed. Because a three-dimensional surface with variables on three axes is a common way to illustrate multivariate data, in which the maximum and minimum values are easily highlighted, we plotted graphical representation of the regression equations in order to explain interaction of the variables and locate the optimal level of each variable for maximal response. For the calculation of the regression coefficients, dispersion and correlation coefficients, the software Matlab was used.

Multivariate Analysis of Genotype-Phenotype Association.

PubMed

Mitteroecker, Philipp; Cheverud, James M; Pavlicev, Mihaela

2016-04-01

With the advent of modern imaging and measurement technology, complex phenotypes are increasingly represented by large numbers of measurements, which may not bear biological meaning one by one. For such multivariate phenotypes, studying the pairwise associations between all measurements and all alleles is highly inefficient and prevents insight into the genetic pattern underlying the observed phenotypes. We present a new method for identifying patterns of allelic variation (genetic latent variables) that are maximally associated-in terms of effect size-with patterns of phenotypic variation (phenotypic latent variables). This multivariate genotype-phenotype mapping (MGP) separates phenotypic features under strong genetic control from less genetically determined features and thus permits an analysis of the multivariate structure of genotype-phenotype association, including its dimensionality and the clustering of genetic and phenotypic variables within this association. Different variants of MGP maximize different measures of genotype-phenotype association: genetic effect, genetic variance, or heritability. In an application to a mouse sample, scored for 353 SNPs and 11 phenotypic traits, the first dimension of genetic and phenotypic latent variables accounted for >70% of genetic variation present in all 11 measurements; 43% of variation in this phenotypic pattern was explained by the corresponding genetic latent variable. The first three dimensions together sufficed to account for almost 90% of genetic variation in the measurements and for all the interpretable genotype-phenotype association. Each dimension can be tested as a whole against the hypothesis of no association, thereby reducing the number of statistical tests from 7766 to 3-the maximal number of meaningful independent tests. Important alleles can be selected based on their effect size (additive or nonadditive effect on the phenotypic latent variable). This low dimensionality of the genotype-phenotype map has important consequences for gene identification and may shed light on the evolvability of organisms. Copyright © 2016 by the Genetics Society of America.

Estimating the net effect of progesterone elevation on the day of hCG on live birth rates after IVF: a cohort analysis of 3296 IVF cycles.

PubMed

Venetis, Christos A; Kolibianakis, Efstratios M; Bosdou, Julia K; Lainas, George T; Sfontouris, Ioannis A; Tarlatzis, Basil C; Lainas, Tryfon G

2015-03-01

What is the proper way of assessing the effect of progesterone elevation (PE) on the day of hCG on live birth in women undergoing fresh embryo transfer after in vitro fertilization (IVF) using GnRH analogues and gonadotrophins? This study indicates that a multivariable approach, where the effect of the most important confounders is controlled for, can lead to markedly different results regarding the association between PE on the day of hCG and live birth rates after IVF when compared with the bivariate analysis that has been typically used in the relevant literature up to date. PE on the day of hCG is associated with decreased pregnancy rates in fresh IVF cycles. Evidence for this comes from observational studies that mostly failed to control for potential confounders. This is a retrospective analysis of a cohort of fresh IVF/intracytoplasmic sperm injection cycles (n = 3296) performed in a single IVF centre during the period 2001-2013. Patients in whom ovarian stimulation was performed with gonadotrophins and GnRH analogues. Natural cycles and cycles where stimulation involved the administration of clomiphene were excluded. In order to reflect routine clinical practice, no other exclusion criteria were imposed on this dataset. The primary outcome measure for this study was live birth defined as the delivery of a live infant after 24 weeks of gestation. We compared the association between PE on the day of hCG (defined as P > 1.5 ng/ml) and live birth rates calculated by simple bivariate analyses with that derived from multivariable logistic regression. The multivariable analysis controlled for female age, number of oocytes retrieved, number of embryos transferred, developmental stage of embryos at transfer (cleavage versus blastocyst), whether at least one good-quality embryo was transferred, the woman's body mass index, the total dose of FSH administered during ovarian stimulation and the type of GnRH analogues used (agonists versus antagonists) during ovarian stimulation. In addition, an interaction analysis was performed in order to assess whether the ovarian response (<6, 6-18, >18 oocytes) has a moderating effect on the association of PE on the day of hCG with live birth rates after IVF. Live birth rates were not significantly different between cycles with and those without PE when a bivariate analysis was performed [odds ratio (OR): 0.78, 95% confidence interval (CI): 0.56-1.09]. However, when a multivariable analysis was performed, controlling for the effect of the aforementioned confounders, live birth rates (OR: 0.68, 95% CI: 0.48-0.97) were significantly decreased in the group with PE on the day of hCG. The number of oocytes retrieved was the most potent confounder, causing a 29.4% reduction in the OR for live birth between the two groups compared. Furthermore, a moderating effect of ovarian response on the association between PE and live birth rates was not supported in the present analysis since no interaction was detected between PE and the type of ovarian response (<6, 6-18, >18 oocytes). This is a retrospective analysis of data collected during a 12-year period, and although the effect of the most important confounders was controlled for in the multivariable analysis, the presence of residual bias cannot be excluded. This analysis highlights the need for a multivariable approach when researchers or clinicians aim to evaluate the impact of PE on pregnancy rates in their own clinical setting. Failure to do so might explain why many past studies have failed to identify the detrimental effect of PE in fresh IVF cycles. None. © The Author 2015. Published by Oxford University Press on behalf of the European Society of Human Reproduction and Embryology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Long-term outcome of pronation-external rotation ankle fractures treated with syndesmotic screws only.

PubMed

Lambers, Kaj T A; van den Bekerom, Michel P J; Doornberg, Job N; Stufkens, Sjoerd A S; van Dijk, C Niek; Kloen, Peter

2013-09-04

There is sparse information in the literature on the outcome of Maisonneuve-type pronation-external rotation ankle fractures treated with syndesmotic screws. The primary aim of this study was to determine the long-term results of such treatment of these fractures as indicated by standardized patient-based and physician-based outcome measures. The secondary aim was to identify predictors of the outcome with use of bivariate and multivariate statistical analysis. Fifty patients with pronation-external rotation (predominantly Maisonneuve) fractures were treated with open reduction and internal fixation of the syndesmosis utilizing only one or two screws. The results were evaluated at a mean of twenty-one years after the fracture utilizing three standardized outcomes instruments: (1) the Foot and Ankle Ability Measure (FAAM), (2) the American Orthopaedic Foot & Ankle Society (AOFAS) ankle-hindfoot scale, and (3) the Center for Epidemiologic Studies-Depression (CES-D) Scale. Osteoarthritis was graded according to the van Dijk and revised Takakura radiographic scoring systems. Bivariate and multivariate analyses were performed to identify predictors of long-term outcome. Forty-four (92%) of forty-eighty patients had good or excellent AOFAS scores, and forty-four (90%) of forty-nine had good or excellent FAAM scores. Arthrodesis for severe osteoarthritis was performed in two patients. Radiographic evidence of osteoarthritis was observed in twenty-four (49%) of forty-nine patients. Multivariate analysis identified pain as the most important independent predictor of long-term ankle function as indicated by the AOFAS and FAAM scores, explaining 91% and 53% of the variation in scores, respectively. Analysis of pain as the dependent variable in bivariate analyses revealed that depression, ankle range of motion, and a subsequent surgery were significantly correlated with higher pain scores. No firm conclusions could be drawn after multivariate analysis of predictors of pain. Long-term functional outcomes at a mean of twenty-one years after pronation-external rotation ankle fractures treated with one or two syndesmotic screws were good to excellent in the great majority of patients despite substantial radiographic evidence of osteoarthritis in one-half of the patients. The most important predictor of long-term functional outcome was patient-reported pain rather than physician-reported function or posttraumatic osteoarthritis. There was no significant association between radiographic signs of posttraumatic osteoarthritis and perceived pain in the present series.

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

PubMed

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

2014-08-01

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

p Ka determinations of xanthene derivates in aqueous solutions by multivariate analysis applied to UV-Vis spectrophotometric data

NASA Astrophysics Data System (ADS)

Batistela, Vagner Roberto; Pellosi, Diogo Silva; de Souza, Franciane Dutra; da Costa, Willian Ferreira; de Oliveira Santin, Silvana Maria; de Souza, Vagner Roberto; Caetano, Wilker; de Oliveira, Hueder Paulo Moisés; Scarminio, Ieda Spacino; Hioka, Noboru

2011-09-01

Xanthenes form to an important class of dyes which are widely used. Most of them present three acid-base groups: two phenolic sites and one carboxylic site. Therefore, the p Ka determination and the attribution of each group to the corresponding p Ka value is a very important feature. Attempts to obtain reliable p Ka through the potentiometry titration and the electronic absorption spectrophotometry using the first and second orders derivative failed. Due to the close p Ka values allied to strong UV-Vis spectral overlap, multivariate analysis, a powerful chemometric method, is applied in this work. The determination was performed for eosin Y, erythrosin B, and bengal rose B, and also for other synthesized derivatives such as 2-(3,6-dihydroxy-9-acridinyl) benzoic acid, 2,4,5,7-tetranitrofluorescein, eosin methyl ester, and erythrosin methyl ester in water. These last two compounds (esters) permitted to attribute the p Ka of the phenolic group, which is not easily recognizable for some investigated dyes. Besides the p Ka determination, the chemometry allowed for estimating the electronic spectrum of some prevalent protolytic species and the substituents effects evaluation.

Nest-site selection analysis of hooded crane (Grus monacha) in Northeastern China based on a multivariate ensemble model.

PubMed

Jiao, Shengwu; Guo, Yumin; Huettmann, Falk; Lei, Guangchun

2014-07-01

Avian nest-site selection is an important research and management subject. The hooded crane (Grus monacha) is a vulnerable (VU) species according to the IUCN Red List. Here, we present the first long-term Chinese legacy nest data for this species (1993-2010) with publicly available metadata. Further, we provide the first study that reports findings on multivariate nest habitat preference using such long-term field data for this species. Our work was carried out in Northeastern China, where we found and measured 24 nests and 81 randomly selected control plots and their environmental parameters in a vast landscape. We used machine learning (stochastic boosted regression trees) to quantify nest selection. Our analysis further included varclust (R Hmisc) and (TreenNet) to address statistical correlations and two-way interactions. We found that from an initial list of 14 measured field variables, water area (+), water depth (+) and shrub coverage (-) were the main explanatory variables that contributed to hooded crane nest-site selection. Agricultural sites played a smaller role in the selection of these nests. Our results are important for the conservation management of cranes all over East Asia and constitute a defensible and quantitative basis for predictive models.

Comparative evaluation of spectroscopic models using different multivariate statistical tools in a multicancer scenario

NASA Astrophysics Data System (ADS)

Ghanate, A. D.; Kothiwale, S.; Singh, S. P.; Bertrand, Dominique; Krishna, C. Murali

2011-02-01

Cancer is now recognized as one of the major causes of morbidity and mortality. Histopathological diagnosis, the gold standard, is shown to be subjective, time consuming, prone to interobserver disagreement, and often fails to predict prognosis. Optical spectroscopic methods are being contemplated as adjuncts or alternatives to conventional cancer diagnostics. The most important aspect of these approaches is their objectivity, and multivariate statistical tools play a major role in realizing it. However, rigorous evaluation of the robustness of spectral models is a prerequisite. The utility of Raman spectroscopy in the diagnosis of cancers has been well established. Until now, the specificity and applicability of spectral models have been evaluated for specific cancer types. In this study, we have evaluated the utility of spectroscopic models representing normal and malignant tissues of the breast, cervix, colon, larynx, and oral cavity in a broader perspective, using different multivariate tests. The limit test, which was used in our earlier study, gave high sensitivity but suffered from poor specificity. The performance of other methods such as factorial discriminant analysis and partial least square discriminant analysis are at par with more complex nonlinear methods such as decision trees, but they provide very little information about the classification model. This comparative study thus demonstrates not just the efficacy of Raman spectroscopic models but also the applicability and limitations of different multivariate tools for discrimination under complex conditions such as the multicancer scenario.

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

PubMed Central

2011-01-01

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

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

PubMed

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

2017-01-01

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

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

PubMed

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

2018-04-26

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

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.

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

PubMed

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

2009-08-01

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

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.

Determinants of Paramedic Response Readiness for CBRNE Threats

PubMed Central

Jones, Alison; Smith, George; Nelson, Jenny; Agho, Kingsley; Taylor, Melanie; Raphael, Beverley

2010-01-01

Paramedics play a pivotal role in the response to major emergencies. Recent evidence indicates that their confidence and willingness to respond to chemical, biological, radiological, nuclear, and explosives-related (CBRNE) incidents differs from that relating to their “routine” emergency work. To further investigate the factors underpinning their readiness to respond to CBRNE incidents, paramedics in New South Wales (NSW), Australia, were asked to complete a validated online survey instrument. Univariate and multivariate analyses were performed to examine associated factors determining readiness. The sample of 663 respondents was weighted to reflect the NSW paramedic population as a whole. The univariate analysis indicated that gender, length of service, deployment concern, perceived personal resilience, CBRNE training, and incident experience were significantly associated with perceived CBRNE response readiness. In the initial multivariate analysis, significantly higher response readiness was associated with male gender, university education, and greater length of service (10-15 years). In the final multivariate model, the combined effect of training/incident experience negated the significant effects observed in the initial model and, importantly, showed that those with recent training reported higher readiness, irrespective of incident experience. Those with lower concern regarding CBRNE deployment and those with higher personal resilience were significantly more likely to report higher readiness (Adjusted Relative Risk [ARR] = 0.91, 95% CI: 0.84-0.99; ARR = 1.40, 95% CI: 1.11-1.72, respectively). These findings will assist emergency medical planners in recognizing occupational and dispositional factors associated with enhanced CBRNE readiness and highlight the important role of training in redressing potential readiness differences associated with these factors. PMID:20569060

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

ERIC Educational Resources Information Center

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

2009-01-01

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

Dangers in Using Analysis of Covariance Procedures.

ERIC Educational Resources Information Center

Campbell, Kathleen T.

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

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

PubMed

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

2017-04-01

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

Automated pre-processing and multivariate vibrational spectra analysis software for rapid results in clinical settings

NASA Astrophysics Data System (ADS)

Bhattacharjee, T.; Kumar, P.; Fillipe, L.

2018-02-01

Vibrational spectroscopy, especially FTIR and Raman, has shown enormous potential in disease diagnosis, especially in cancers. Their potential for detecting varied pathological conditions are regularly reported. However, to prove their applicability in clinics, large multi-center multi-national studies need to be undertaken; and these will result in enormous amount of data. A parallel effort to develop analytical methods, including user-friendly software that can quickly pre-process data and subject them to required multivariate analysis is warranted in order to obtain results in real time. This study reports a MATLAB based script that can automatically import data, preprocess spectra— interpolation, derivatives, normalization, and then carry out Principal Component Analysis (PCA) followed by Linear Discriminant Analysis (LDA) of the first 10 PCs; all with a single click. The software has been verified on data obtained from cell lines, animal models, and in vivo patient datasets, and gives results comparable to Minitab 16 software. The software can be used to import variety of file extensions, asc, .txt., .xls, and many others. Options to ignore noisy data, plot all possible graphs with PCA factors 1 to 5, and save loading factors, confusion matrices and other parameters are also present. The software can provide results for a dataset of 300 spectra within 0.01 s. We believe that the software will be vital not only in clinical trials using vibrational spectroscopic data, but also to obtain rapid results when these tools get translated into clinics.

Benthic algae of benchmark streams in agricultural areas of eastern Wisconsin

USGS Publications Warehouse

Scudder, Barbara C.; Stewart, Jana S.

2001-01-01

Multivariate analyses indicated multiple scales of environmental factors affect algae. Although two-way indicator species analysis (TWINSPAN), detrended correspondence analysis (DCA), and canonical correspondence analysis (CCA) generally separated sites according to RHU, only DCA ordination indicated a separation of sites according to ecoregion. Environmental variables con-elated with DCA axes 1 and 2 and therefore indicated as important explanatory factors for algal distribution and abundance were factors related to stream size, basin land use/cover, geomorphology, hydrogeology, and riparian disturbance. CCA analyses with a more limited set of environmental variables indicated that pH, average width of natural riparian vegetation (segment scale), basin land use/cover and Q/Q2 were the most important variables affecting the distribution and relative abundance of benthic algae at the 20 benchmark streams,

Foot anthropometry and morphology phenomena.

PubMed

Agić, Ante; Nikolić, Vasilije; Mijović, Budimir

2006-12-01

Foot structure description is important for many reasons. The foot anthropometric morphology phenomena are analyzed together with hidden biomechanical functionality in order to fully characterize foot structure and function. For younger Croatian population the scatter data of the individual foot variables were interpolated by multivariate statistics. Foot structure descriptors are influenced by many factors, as a style of life, race, climate, and things of the great importance in human society. Dominant descriptors are determined by principal component analysis. Some practical recommendation and conclusion for medical, sportswear and footwear practice are highlighted.

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

PubMed

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

2016-11-01

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

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

PubMed

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

2016-07-01

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

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

PubMed Central

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

2016-01-01

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

The joint return period analysis of natural disasters based on monitoring and statistical modeling of multidimensional hazard factors.

PubMed

Liu, Xueqin; Li, Ning; Yuan, Shuai; Xu, Ning; Shi, Wenqin; Chen, Weibin

2015-12-15

As a random event, a natural disaster has the complex occurrence mechanism. The comprehensive analysis of multiple hazard factors is important in disaster risk assessment. In order to improve the accuracy of risk analysis and forecasting, the formation mechanism of a disaster should be considered in the analysis and calculation of multi-factors. Based on the consideration of the importance and deficiencies of multivariate analysis of dust storm disasters, 91 severe dust storm disasters in Inner Mongolia from 1990 to 2013 were selected as study cases in the paper. Main hazard factors from 500-hPa atmospheric circulation system, near-surface meteorological system, and underlying surface conditions were selected to simulate and calculate the multidimensional joint return periods. After comparing the simulation results with actual dust storm events in 54years, we found that the two-dimensional Frank Copula function showed the better fitting results at the lower tail of hazard factors and that three-dimensional Frank Copula function displayed the better fitting results at the middle and upper tails of hazard factors. However, for dust storm disasters with the short return period, three-dimensional joint return period simulation shows no obvious advantage. If the return period is longer than 10years, it shows significant advantages in extreme value fitting. Therefore, we suggest the multivariate analysis method may be adopted in forecasting and risk analysis of serious disasters with the longer return period, such as earthquake and tsunami. Furthermore, the exploration of this method laid the foundation for the prediction and warning of other nature disasters. Copyright © 2015 Elsevier B.V. All rights reserved.

Groundwater quality assessment of urban Bengaluru using multivariate statistical techniques

NASA Astrophysics Data System (ADS)

Gulgundi, Mohammad Shahid; Shetty, Amba

2018-03-01

Groundwater quality deterioration due to anthropogenic activities has become a subject of prime concern. The objective of the study was to assess the spatial and temporal variations in groundwater quality and to identify the sources in the western half of the Bengaluru city using multivariate statistical techniques. Water quality index rating was calculated for pre and post monsoon seasons to quantify overall water quality for human consumption. The post-monsoon samples show signs of poor quality in drinking purpose compared to pre-monsoon. Cluster analysis (CA), principal component analysis (PCA) and discriminant analysis (DA) were applied to the groundwater quality data measured on 14 parameters from 67 sites distributed across the city. Hierarchical cluster analysis (CA) grouped the 67 sampling stations into two groups, cluster 1 having high pollution and cluster 2 having lesser pollution. Discriminant analysis (DA) was applied to delineate the most meaningful parameters accounting for temporal and spatial variations in groundwater quality of the study area. Temporal DA identified pH as the most important parameter, which discriminates between water quality in the pre-monsoon and post-monsoon seasons and accounts for 72% seasonal assignation of cases. Spatial DA identified Mg, Cl and NO3 as the three most important parameters discriminating between two clusters and accounting for 89% spatial assignation of cases. Principal component analysis was applied to the dataset obtained from the two clusters, which evolved three factors in each cluster, explaining 85.4 and 84% of the total variance, respectively. Varifactors obtained from principal component analysis showed that groundwater quality variation is mainly explained by dissolution of minerals from rock water interactions in the aquifer, effect of anthropogenic activities and ion exchange processes in water.

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.

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.

Logistic regression analysis of factors associated with avascular necrosis of the femoral head following femoral neck fractures in middle-aged and elderly patients.

PubMed

Ai, Zi-Sheng; Gao, You-Shui; Sun, Yuan; Liu, Yue; Zhang, Chang-Qing; Jiang, Cheng-Hua

2013-03-01

Risk factors for femoral neck fracture-induced avascular necrosis of the femoral head have not been elucidated clearly in middle-aged and elderly patients. Moreover, the high incidence of screw removal in China and its effect on the fate of the involved femoral head require statistical methods to reflect their intrinsic relationship. Ninety-nine patients older than 45 years with femoral neck fracture were treated by internal fixation between May 1999 and April 2004. Descriptive analysis, interaction analysis between associated factors, single factor logistic regression, multivariate logistic regression, and detailed interaction analysis were employed to explore potential relationships among associated factors. Avascular necrosis of the femoral head was found in 15 cases (15.2 %). Age × the status of implants (removal vs. maintenance) and gender × the timing of reduction were interactive according to two-factor interactive analysis. Age, the displacement of fractures, the quality of reduction, and the status of implants were found to be significant factors in single factor logistic regression analysis. Age, age × the status of implants, and the quality of reduction were found to be significant factors in multivariate logistic regression analysis. In fine interaction analysis after multivariate logistic regression analysis, implant removal was the most important risk factor for avascular necrosis in 56-to-85-year-old patients, with a risk ratio of 26.00 (95 % CI = 3.076-219.747). The middle-aged and elderly have less incidence of avascular necrosis of the femoral head following femoral neck fractures treated by cannulated screws. The removal of cannulated screws can induce a significantly high incidence of avascular necrosis of the femoral head in elderly patients, while a high-quality reduction is helpful to reduce avascular necrosis.

Differential use of fresh water environments by wintering waterfowl of coastal Texas

USGS Publications Warehouse

White, D.H.; James, D.

1978-01-01

A comparative study of the environmental relationships among 14 species of wintering waterfowl was conducted at the Welder Wildlife Foundation, San Patricia County, near Sinton, Texas during the fall and early winter of 1973. Measurements of 20 environmental factors (social, vegetational, physical, and chemical) were subjected to multivariate statistical methods to determine certain niche characteristics and environmental relationships of waterfowl wintering in the aquatic community.....Each waterfowl species occupied a unique realized niche by responding to distinct combinations of environmental factors identified by principal component analysis. One percent confidence ellipses circumscribing the mean scores plotted for the first and second principal components gave an indication of relative niche width for each species. The waterfowl environments were significantly different interspecifically and water depth at feeding site and % emergent vegetation were most important in the separation. This was shown by subjecting the transformed data to multivariate analysis of variance with an associated step-down procedure. The species were distributed along a community cline extending from shallow water with abundant emergent vegetation to open deep water with little emergent vegetation of any kind. Four waterfowl subgroups were significantly separated along the cline, as indicated by one-way analysis of variance with Duncan?s multiple range test. Clumping of the bird species toward the middle of the available habitat hyperspace was shown in a plot of the principal component scores for the random samples and individual species.....Naturally occurring relationships among waterfowl were clarified using principal comcomponent analysis and related multivariate procedures. These techniques may prove useful in wetland management for particular groups of waterfowl based on habitat preferences.

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

PubMed Central

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

2013-01-01

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

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

PubMed Central

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

2012-01-01

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

High performance computing enabling exhaustive analysis of higher order single nucleotide polymorphism interaction in Genome Wide Association Studies.

PubMed

Goudey, Benjamin; Abedini, Mani; Hopper, John L; Inouye, Michael; Makalic, Enes; Schmidt, Daniel F; Wagner, John; Zhou, Zeyu; Zobel, Justin; Reumann, Matthias

2015-01-01

Genome-wide association studies (GWAS) are a common approach for systematic discovery of single nucleotide polymorphisms (SNPs) which are associated with a given disease. Univariate analysis approaches commonly employed may miss important SNP associations that only appear through multivariate analysis in complex diseases. However, multivariate SNP analysis is currently limited by its inherent computational complexity. In this work, we present a computational framework that harnesses supercomputers. Based on our results, we estimate a three-way interaction analysis on 1.1 million SNP GWAS data requiring over 5.8 years on the full "Avoca" IBM Blue Gene/Q installation at the Victorian Life Sciences Computation Initiative. This is hundreds of times faster than estimates for other CPU based methods and four times faster than runtimes estimated for GPU methods, indicating how the improvement in the level of hardware applied to interaction analysis may alter the types of analysis that can be performed. Furthermore, the same analysis would take under 3 months on the currently largest IBM Blue Gene/Q supercomputer "Sequoia" at the Lawrence Livermore National Laboratory assuming linear scaling is maintained as our results suggest. Given that the implementation used in this study can be further optimised, this runtime means it is becoming feasible to carry out exhaustive analysis of higher order interaction studies on large modern GWAS.

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

ERIC Educational Resources Information Center

Gaotlhobogwe, Michael; Laugharne, Janet; Durance, Isabelle

2011-01-01

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

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

PubMed

Thulin, M

2016-09-10

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

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.

Factors related to clinical pregnancy after vitrified-warmed embryo transfer: a retrospective and multivariate logistic regression analysis of 2313 transfer cycles.

PubMed

Shi, Wenhao; Zhang, Silin; Zhao, Wanqiu; Xia, Xue; Wang, Min; Wang, Hui; Bai, Haiyan; Shi, Juanzi

2013-07-01

What factors does multivariate logistic regression show to be significantly associated with the likelihood of clinical pregnancy in vitrified-warmed embryo transfer (VET) cycles? Assisted hatching (AH) and if the reason to freeze embryos was to avoid the risk of ovarian hyperstimulation syndrome (OHSS) were significantly positively associated with a greater likelihood of clinical pregnancy. Single factor analysis has shown AH, number of embryos transferred and the reason of freezing for OHSS to be positively and damaged blastomere to be negatively significantly associated with the chance of clinical pregnancy after VET. It remains unclear what factors would be significant after multivariate analysis. The study was a retrospective analysis of 2313 VET cycles from 1481 patients performed between January 2008 and April 2012. A multivariate logistic regression analysis was performed to identify the factors to affect clinical pregnancy outcome of VET. There were 22 candidate variables selected based on clinical experiences and the literature. With the thresholds of α entry = α removal= 0.05 for both variable entry and variable removal, eight variables were chosen to contribute the multivariable model by the bootstrap stepwise variable selection algorithm (n = 1000). Eight variables were age at controlled ovarian hyperstimulation (COH), reason for freezing, AH, endometrial thickness, damaged blastomere, number of embryos transferred, number of good-quality embryos, and blood presence on transfer catheter. A descriptive comparison of the relative importance was accomplished by the proportion of explained variation (PEV). Among the reasons for freezing, the OHSS group showed a higher OR than the surplus embryo group when compared with other reasons for VET groups (OHSS versus Other, OR: 2.145; CI: 1.4-3.286; Surplus embryos versus Other, OR: 1.152; CI: 0.761-1.743) and high PEV (marginal 2.77%, P = 0.2911; partial 1.68%; CI of area under receptor operator characteristic curve (ROC): 0.5576-0.6000). AH also showed a high OR (OR: 2.105, CI: 1.554-2.85) and high PEV (marginal 1.97%; partial 1.02%; CI of area under ROC: 0.5344-0.5647). The number of good-quality embryos showed the highest marginal PEV and partial PEV (marginal 3.91%, partial 2.28%; CI of area under ROC: 0.5886-0.6343). This was a retrospective multivariate analysis of the data obtained in 5 years from a single IVF center. Repeated cycles in the same woman were treated as independent observations, which could introduce bias. Results are based on clinical pregnancy and not live births. Prospective analysis of a larger data set from a multicenter study based on live births is necessary to confirm the findings. Paying attention to the quality of embryos, the number of good embryos, AH and the reasons for freezing that are associated with clinical pregnancy after VET will assist the improvement of success rates.

Combining fibre optic Raman spectroscopy and tactile resonance measurement for tissue characterization

NASA Astrophysics Data System (ADS)

Candefjord, Stefan; Nyberg, Morgan; Jalkanen, Ville; Ramser, Kerstin; Lindahl, Olof A.

2010-12-01

Tissue characterization is fundamental for identification of pathological conditions. Raman spectroscopy (RS) and tactile resonance measurement (TRM) are two promising techniques that measure biochemical content and stiffness, respectively. They have potential to complement the golden standard--histological analysis. By combining RS and TRM, complementary information about tissue content can be obtained and specific drawbacks can be avoided. The aim of this study was to develop a multivariate approach to compare RS and TRM information. The approach was evaluated on measurements at the same points on porcine abdominal tissue. The measurement points were divided into five groups by multivariate analysis of the RS data. A regression analysis was performed and receiver operating characteristic (ROC) curves were used to compare the RS and TRM data. TRM identified one group efficiently (area under ROC curve 0.99). The RS data showed that the proportion of saturated fat was high in this group. The regression analysis showed that stiffness was mainly determined by the amount of fat and its composition. We concluded that RS provided additional, important information for tissue identification that was not provided by TRM alone. The results are promising for development of a method combining RS and TRM for intraoperative tissue characterization.

Population structure of the Korean gizzard shad, Konosirus punctatus (Clupeiformes, Clupeidae) using multivariate morphometric analysis

NASA Astrophysics Data System (ADS)

Myoung, Se Hun; Kim, Jin-Koo

2016-03-01

The gizzard shad, Konosirus punctatus, is one of the most important fish species in Korea, China, Japan and Taiwan, and therefore the implementation of an appropriate population structure analysis is both necessary and fitting. In order to clarify the current distribution range for the two lineages of the Korean gizzard shad (Myoung and Kim 2014), we conducted a multivariate morphometric analysis by locality and lineage. We analyzed 17 morphometric and 5 meristic characters of 173 individuals, which were sampled from eight localities in the East Sea, the Yellow Sea and the Korean Strait. Unlike population genetics studies, the canonical discriminant analysis (CDA) results showed that the two morphotypes were clearly segregated by the center value "0" of CAN1, of which morphotype A occurred from the Yellow Sea to the western Korean Strait with negative values, and morphotype B occurred from the East Sea to the eastern Korean Strait with positive values even though there exists an admixture zone in the eastern Korean Strait. Further studies using more sensitive markers such as microsatellite DNA are required in order to define the true relationship between the two lineages.

Prevalence and risk factors for scrub typhus in South India.

PubMed

Trowbridge, Paul; P, Divya; Premkumar, Prasanna S; Varghese, George M

2017-05-01

To determine the prevalence and risk factors of scrub typhus in Tamil Nadu, South India. We performed a clustered seroprevalence study of the areas around Vellore. All participants completed a risk factor survey, with seropositive and seronegative participants acting as cases and controls, respectively, in a risk factor analysis. After univariate analysis, variables found to be significant underwent multivariate analysis. Of 721 people participating in this study, 31.8% tested seropositive. By univariate analysis, after accounting for clustering, having a house that was clustered with other houses, having a fewer rooms in a house, having fewer people living in a household, defecating outside, female sex, age >60 years, shorter height, lower weight, smaller body mass index and smaller mid-upper arm circumference were found to be significantly associated with seropositivity. After multivariate regression modelling, living in a house clustered with other houses, female sex and age >60 years were significantly associated with scrub typhus exposure. Overall, scrub typhus is much more common than previously thought. Previously described individual environmental and habitual risk factors seem to have less importance in South India, perhaps because of the overall scrub typhus-conducive nature of the environment in this region. © 2017 John Wiley & Sons Ltd.

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

PubMed

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

2016-09-01

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

A systematic uncertainty analysis for liner impedance eduction technology

NASA Astrophysics Data System (ADS)

Zhou, Lin; Bodén, Hans

2015-11-01

The so-called impedance eduction technology is widely used for obtaining acoustic properties of liners used in aircraft engines. The measurement uncertainties for this technology are still not well understood though it is essential for data quality assessment and model validation. A systematic framework based on multivariate analysis is presented in this paper to provide 95 percent confidence interval uncertainty estimates in the process of impedance eduction. The analysis is made using a single mode straightforward method based on transmission coefficients involving the classic Ingard-Myers boundary condition. The multivariate technique makes it possible to obtain an uncertainty analysis for the possibly correlated real and imaginary parts of the complex quantities. The results show that the errors in impedance results at low frequency mainly depend on the variability of transmission coefficients, while the mean Mach number accuracy is the most important source of error at high frequencies. The effect of Mach numbers used in the wave dispersion equation and in the Ingard-Myers boundary condition has been separated for comparison of the outcome of impedance eduction. A local Mach number based on friction velocity is suggested as a way to reduce the inconsistencies found when estimating impedance using upstream and downstream acoustic excitation.

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

PubMed

Arvanitoyannis, Ioannis S; Vlachos, Antonios

2007-01-01

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

Multivariate 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.

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

PubMed

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

2012-04-01

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

Multivariate analysis to determine the factors affecting the attitudes toward organ donation of healthcare assistants in Spanish and Mexican healthcare centers.

PubMed

Ríos, A; López-Navas, A; Ayala-García, M A; Sebastián, M; Febrero, B; Ramírez, E J; Muñoz, G; Palacios, G; Rodríguez, J S; Martínez, M A; Nieto, A; Martínez-Alarcón, L; Ramis, G; Ramírez, P; Parrilla, P

2012-01-01

Healthcare assistants are an important group of workers who can influence public opinion. Their attitudes toward organ donation may influence public awareness of healthcare matters; negative attitudes toward donation and transplantation could have a negative impact on public attitudes. Our objective was analyze the attitudes of healthcare assistants, in Spanish and Mexican healthcare centers toward organ donation and determine factors affecting them using a multivariate analysis. As part of the "International Collaborative Donor Project," 32 primary care centers and 4 hospitals were selected in Spain and 5 hospitals in Mexico. A randomized sample of healthcare assistants was stratified according to healthcare services. Attitudes were evaluated using a validated questionnaire of the psychosocial aspects of donation, which was self-completed anonymously by the respondent. Statistical analysis used the chi-square test, Student t test, and logistic regression analysis. Of 532 respondents, 66% in favored donation and 34% were against it or undecided. Upon multivariate analysis, the following variables had the most weight: 1) country of origin (Mexicans were more in favor than Spanish; odds ratio [OR]) = 1.964; P = .014); 2) a partner with a favorable attitude (OR = 2.597; P = .013); 3) not being concerned about possible bodily mutilation after donation (OR = 2.631; P = .006); 4) preference for options apart from burial for handling the body after death (OR = 4.694; P < .001) and 5) accepting an autopsy if one was needed (OR = 3.584; P < .001). The attitudes of healthcare assistants toward organ donation varied considerably according to the respondent's country of origin. The psycho-social profile of a person with a positive attitude to donation was similar to that described within the general public. Copyright © 2012 Elsevier Inc. All rights reserved.

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

PubMed Central

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

2015-01-01

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

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

PubMed

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

2016-05-01

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

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.

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Label-free Chemical Imaging of Fungal Spore Walls by Raman Microscopy and Multivariate Curve Resolution Analysis

PubMed Central

Noothalapati, Hemanth; Sasaki, Takahiro; Kaino, Tomohiro; Kawamukai, Makoto; Ando, Masahiro; Hamaguchi, Hiro-o; Yamamoto, Tatsuyuki

2016-01-01

Fungal cell walls are medically important since they represent a drug target site for antifungal medication. So far there is no method to directly visualize structurally similar cell wall components such as α-glucan, β-glucan and mannan with high specificity, especially in a label-free manner. In this study, we have developed a Raman spectroscopy based molecular imaging method and combined multivariate curve resolution analysis to enable detection and visualization of multiple polysaccharide components simultaneously at the single cell level. Our results show that vegetative cell and ascus walls are made up of both α- and β-glucans while spore wall is exclusively made of α-glucan. Co-localization studies reveal the absence of mannans in ascus wall but are distributed primarily in spores. Such detailed picture is believed to further enhance our understanding of the dynamic spore wall architecture, eventually leading to advancements in drug discovery and development in the near future. PMID:27278218

Microwave-Assisted Extraction of Phenolic Compounds from Almond Skin Byproducts (Prunus amygdalus): A Multivariate Analysis Approach.

PubMed

Valdés, Arantzazu; Vidal, Lorena; Beltrán, Ana; Canals, Antonio; Garrigós, María Carmen

2015-06-10

A microwave-assisted extraction (MAE) procedure to isolate phenolic compounds from almond skin byproducts was optimized. A three-level, three-factor Box-Behnken design was used to evaluate the effect of almond skin weight, microwave power, and irradiation time on total phenolic content (TPC) and antioxidant activity (DPPH). Almond skin weight was the most important parameter in the studied responses. The best extraction was achieved using 4 g, 60 s, 100 W, and 60 mL of 70% (v/v) ethanol. TPC, antioxidant activity (DPPH, FRAP), and chemical composition (HPLC-DAD-ESI-MS/MS) were determined by using the optimized method from seven different almond cultivars. Successful discrimination was obtained for all cultivars by using multivariate linear discriminant analysis (LDA), suggesting the influence of cultivar type on polyphenol content and antioxidant activity. The results show the potential of almond skin as a natural source of phenolics and the effectiveness of MAE for the reutilization of these byproducts.

PGI chicory (Cichorium intybus L.) traceability by means of HRMAS-NMR spectroscopy: a preliminary study.

PubMed

Ritota, Mena; Casciani, Lorena; Valentini, Massimiliano

2013-05-01

Analytical traceability of PGI and PDO foods (Protected Geographical Indication and Protected Denomination Origin respectively) is one of the most challenging tasks of current applied research. Here we proposed a metabolomic approach based on the combination of (1)H high-resolution magic angle spinning-nuclear magnetic resonance (HRMAS-NMR) spectroscopy with multivariate analysis, i.e. PLS-DA, as a reliable tool for the traceability of Italian PGI chicories (Cichorium intybus L.), i.e. Radicchio Rosso di Treviso and Radicchio Variegato di Castelfranco, also known as red and red-spotted, respectively. The metabolic profile was gained by means of HRMAS-NMR, and multivariate data analysis allowed us to build statistical models capable of providing clear discrimination among the two varieties and classification according to the geographical origin. Based on Variable Importance in Projection values, the molecular markers for classifying the different types of red chicories analysed were found accounting for both the cultivar and the place of origin. © 2012 Society of Chemical Industry.

On Models for Binomial Data with Random Numbers of Trials

PubMed Central

Comulada, W. Scott; Weiss, Robert E.

2010-01-01

Summary A binomial outcome is a count s of the number of successes out of the total number of independent trials n = s + f, where f is a count of the failures. The n are random variables not fixed by design in many studies. Joint modeling of (s, f) can provide additional insight into the science and into the probability π of success that cannot be directly incorporated by the logistic regression model. Observations where n = 0 are excluded from the binomial analysis yet may be important to understanding how π is influenced by covariates. Correlation between s and f may exist and be of direct interest. We propose Bayesian multivariate Poisson models for the bivariate response (s, f), correlated through random effects. We extend our models to the analysis of longitudinal and multivariate longitudinal binomial outcomes. Our methodology was motivated by two disparate examples, one from teratology and one from an HIV tertiary intervention study. PMID:17688514

A tool for classifying individuals with chronic back pain: using multivariate pattern analysis with functional magnetic resonance imaging data.

PubMed

Callan, Daniel; Mills, Lloyd; Nott, Connie; England, Robert; England, Shaun

2014-01-01

Chronic pain is one of the most prevalent health problems in the world today, yet neurological markers, critical to diagnosis of chronic pain, are still largely unknown. The ability to objectively identify individuals with chronic pain using functional magnetic resonance imaging (fMRI) data is important for the advancement of diagnosis, treatment, and theoretical knowledge of brain processes associated with chronic pain. The purpose of our research is to investigate specific neurological markers that could be used to diagnose individuals experiencing chronic pain by using multivariate pattern analysis with fMRI data. We hypothesize that individuals with chronic pain have different patterns of brain activity in response to induced pain. This pattern can be used to classify the presence or absence of chronic pain. The fMRI experiment consisted of alternating 14 seconds of painful electric stimulation (applied to the lower back) with 14 seconds of rest. We analyzed contrast fMRI images in stimulation versus rest in pain-related brain regions to distinguish between the groups of participants: 1) chronic pain and 2) normal controls. We employed supervised machine learning techniques, specifically sparse logistic regression, to train a classifier based on these contrast images using a leave-one-out cross-validation procedure. We correctly classified 92.3% of the chronic pain group (N = 13) and 92.3% of the normal control group (N = 13) by recognizing multivariate patterns of activity in the somatosensory and inferior parietal cortex. This technique demonstrates that differences in the pattern of brain activity to induced pain can be used as a neurological marker to distinguish between individuals with and without chronic pain. Medical, legal and business professionals have recognized the importance of this research topic and of developing objective measures of chronic pain. This method of data analysis was very successful in correctly classifying each of the two groups.

A Tool for Classifying Individuals with Chronic Back Pain: Using Multivariate Pattern Analysis with Functional Magnetic Resonance Imaging Data

PubMed Central

Callan, Daniel; Mills, Lloyd; Nott, Connie; England, Robert; England, Shaun

2014-01-01

Chronic pain is one of the most prevalent health problems in the world today, yet neurological markers, critical to diagnosis of chronic pain, are still largely unknown. The ability to objectively identify individuals with chronic pain using functional magnetic resonance imaging (fMRI) data is important for the advancement of diagnosis, treatment, and theoretical knowledge of brain processes associated with chronic pain. The purpose of our research is to investigate specific neurological markers that could be used to diagnose individuals experiencing chronic pain by using multivariate pattern analysis with fMRI data. We hypothesize that individuals with chronic pain have different patterns of brain activity in response to induced pain. This pattern can be used to classify the presence or absence of chronic pain. The fMRI experiment consisted of alternating 14 seconds of painful electric stimulation (applied to the lower back) with 14 seconds of rest. We analyzed contrast fMRI images in stimulation versus rest in pain-related brain regions to distinguish between the groups of participants: 1) chronic pain and 2) normal controls. We employed supervised machine learning techniques, specifically sparse logistic regression, to train a classifier based on these contrast images using a leave-one-out cross-validation procedure. We correctly classified 92.3% of the chronic pain group (N = 13) and 92.3% of the normal control group (N = 13) by recognizing multivariate patterns of activity in the somatosensory and inferior parietal cortex. This technique demonstrates that differences in the pattern of brain activity to induced pain can be used as a neurological marker to distinguish between individuals with and without chronic pain. Medical, legal and business professionals have recognized the importance of this research topic and of developing objective measures of chronic pain. This method of data analysis was very successful in correctly classifying each of the two groups. PMID:24905072

Seasonal assessment and apportionment of surface water pollution using multivariate statistical methods: Sinos River, southern Brazil.

PubMed

Alves, Darlan Daniel; Riegel, Roberta Plangg; de Quevedo, Daniela Müller; Osório, Daniela Montanari Migliavacca; da Costa, Gustavo Marques; do Nascimento, Carlos Augusto; Telöken, Franko

2018-06-08

Assessment of surface water quality is an issue of currently high importance, especially in polluted rivers which provide water for treatment and distribution as drinking water, as is the case of the Sinos River, southern Brazil. Multivariate statistical techniques allow a better understanding of the seasonal variations in water quality, as well as the source identification and source apportionment of water pollution. In this study, the multivariate statistical techniques of cluster analysis (CA), principal component analysis (PCA), and positive matrix factorization (PMF) were used, along with the Kruskal-Wallis test and Spearman's correlation analysis in order to interpret a water quality data set resulting from a monitoring program conducted over a period of almost two years (May 2013 to April 2015). The water samples were collected from the raw water inlet of the municipal water treatment plant (WTP) operated by the Water and Sewage Services of Novo Hamburgo (COMUSA). CA allowed the data to be grouped into three periods (autumn and summer (AUT-SUM); winter (WIN); spring (SPR)). Through the PCA, it was possible to identify that the most important parameters in contribution to water quality variations are total coliforms (TCOLI) in SUM-AUT, water level (WL), water temperature (WT), and electrical conductivity (EC) in WIN and color (COLOR) and turbidity (TURB) in SPR. PMF was applied to the complete data set and enabled the source apportionment water pollution through three factors, which are related to anthropogenic sources, such as the discharge of domestic sewage (mostly represented by Escherichia coli (ECOLI)), industrial wastewaters, and agriculture runoff. The results provided by this study demonstrate the contribution provided by the use of integrated statistical techniques in the interpretation and understanding of large data sets of water quality, showing also that this approach can be used as an efficient methodology to optimize indicators for water quality assessment.

Characterization of exopolymers of aquatic bacteria by pyrolysis-mass spectrometry

NASA Technical Reports Server (NTRS)

Ford, T.; Sacco, E.; Black, J.; Kelley, T.; Goodacre, R.; Berkeley, R. C.; Mitchell, R.

1991-01-01

Exopolymers from a diverse collection of marine and freshwater bacteria were characterized by pyrolysis-mass spectrometry (Py-MS). Py-MS provides spectra of pyrolysis fragments that are characteristic of the original material. Analysis of the spectra by multivariate statistical techniques (principal component and canonical variate analysis) separated these exopolymers into distinct groups. Py-MS clearly distinguished characteristic fragments, which may be derived from components responsible for functional differences between polymers. The importance of these distinctions and the relevance of pyrolysis information to exopolysaccharide function in aquatic bacteria is discussed.

Quantifying uncertainty in high-resolution coupled hydrodynamic-ecosystem models

NASA Astrophysics Data System (ADS)

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

2007-01-01

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

The Multivariate Temporal Response Function (mTRF) Toolbox: A MATLAB Toolbox for Relating Neural Signals to Continuous Stimuli.

PubMed

Crosse, Michael J; Di Liberto, Giovanni M; Bednar, Adam; Lalor, Edmund C

2016-01-01

Understanding how brains process sensory signals in natural environments is one of the key goals of twenty-first century neuroscience. While brain imaging and invasive electrophysiology will play key roles in this endeavor, there is also an important role to be played by noninvasive, macroscopic techniques with high temporal resolution such as electro- and magnetoencephalography. But challenges exist in determining how best to analyze such complex, time-varying neural responses to complex, time-varying and multivariate natural sensory stimuli. There has been a long history of applying system identification techniques to relate the firing activity of neurons to complex sensory stimuli and such techniques are now seeing increased application to EEG and MEG data. One particular example involves fitting a filter-often referred to as a temporal response function-that describes a mapping between some feature(s) of a sensory stimulus and the neural response. Here, we first briefly review the history of these system identification approaches and describe a specific technique for deriving temporal response functions known as regularized linear regression. We then introduce a new open-source toolbox for performing this analysis. We describe how it can be used to derive (multivariate) temporal response functions describing a mapping between stimulus and response in both directions. We also explain the importance of regularizing the analysis and how this regularization can be optimized for a particular dataset. We then outline specifically how the toolbox implements these analyses and provide several examples of the types of results that the toolbox can produce. Finally, we consider some of the limitations of the toolbox and opportunities for future development and application.

The Multivariate Temporal Response Function (mTRF) Toolbox: A MATLAB Toolbox for Relating Neural Signals to Continuous Stimuli

PubMed Central

Crosse, Michael J.; Di Liberto, Giovanni M.; Bednar, Adam; Lalor, Edmund C.

2016-01-01

Understanding how brains process sensory signals in natural environments is one of the key goals of twenty-first century neuroscience. While brain imaging and invasive electrophysiology will play key roles in this endeavor, there is also an important role to be played by noninvasive, macroscopic techniques with high temporal resolution such as electro- and magnetoencephalography. But challenges exist in determining how best to analyze such complex, time-varying neural responses to complex, time-varying and multivariate natural sensory stimuli. There has been a long history of applying system identification techniques to relate the firing activity of neurons to complex sensory stimuli and such techniques are now seeing increased application to EEG and MEG data. One particular example involves fitting a filter—often referred to as a temporal response function—that describes a mapping between some feature(s) of a sensory stimulus and the neural response. Here, we first briefly review the history of these system identification approaches and describe a specific technique for deriving temporal response functions known as regularized linear regression. We then introduce a new open-source toolbox for performing this analysis. We describe how it can be used to derive (multivariate) temporal response functions describing a mapping between stimulus and response in both directions. We also explain the importance of regularizing the analysis and how this regularization can be optimized for a particular dataset. We then outline specifically how the toolbox implements these analyses and provide several examples of the types of results that the toolbox can produce. Finally, we consider some of the limitations of the toolbox and opportunities for future development and application. PMID:27965557

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

DTIC Science & Technology

1993-06-18

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

Multivariate analysis for scanning tunneling spectroscopy data

NASA Astrophysics Data System (ADS)

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

2018-01-01

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

NIR and Py-mbms coupled with multivariate data analysis as a high-throughput biomass characterization technique: a review

PubMed Central

Xiao, Li; Wei, Hui; Himmel, Michael E.; Jameel, Hasan; Kelley, Stephen S.

2014-01-01

Optimizing the use of lignocellulosic biomass as the feedstock for renewable energy production is currently being developed globally. Biomass is a complex mixture of cellulose, hemicelluloses, lignins, extractives, and proteins; as well as inorganic salts. Cell wall compositional analysis for biomass characterization is laborious and time consuming. In order to characterize biomass fast and efficiently, several high through-put technologies have been successfully developed. Among them, near infrared spectroscopy (NIR) and pyrolysis-molecular beam mass spectrometry (Py-mbms) are complementary tools and capable of evaluating a large number of raw or modified biomass in a short period of time. NIR shows vibrations associated with specific chemical structures whereas Py-mbms depicts the full range of fragments from the decomposition of biomass. Both NIR vibrations and Py-mbms peaks are assigned to possible chemical functional groups and molecular structures. They provide complementary information of chemical insight of biomaterials. However, it is challenging to interpret the informative results because of the large amount of overlapping bands or decomposition fragments contained in the spectra. In order to improve the efficiency of data analysis, multivariate analysis tools have been adapted to define the significant correlations among data variables, so that the large number of bands/peaks could be replaced by a small number of reconstructed variables representing original variation. Reconstructed data variables are used for sample comparison (principal component analysis) and for building regression models (partial least square regression) between biomass chemical structures and properties of interests. In this review, the important biomass chemical structures measured by NIR and Py-mbms are summarized. The advantages and disadvantages of conventional data analysis methods and multivariate data analysis methods are introduced, compared and evaluated. This review aims to serve as a guide for choosing the most effective data analysis methods for NIR and Py-mbms characterization of biomass. PMID:25147552

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.

Ecological prediction with nonlinear multivariate time-frequency functional data models

USGS Publications Warehouse

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

2013-01-01

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

Comparison between imported versus domestic drug-eluting stents in China: A large single-center data.

PubMed

Liu, Ru; Gao, Zhan; Chen, Jue; Gao, Lijian; Song, Lei; Qiao, Shubin; Yang, Yuejin; Gao, Runlin; Xu, Bo; Yuan, Jinqing

2017-08-01

In recent years, most drug-eluting stents (DESs) were domestically produced in China, but how domestic DESs perform compared to imported DESs was still unknown. A total of 9011 consecutive cases with DESs implantation in a single center throughout 2013 were prospectively collected. Two-year clinical outcomes were evaluated between patients implanted with imported and domestic DESs. During 2-year follow-up, the rates of all-cause death, cardiac death, myocardial infarction, stroke, and stent thrombosis were not significantly different between two groups. However, the rate of revascularization was significantly higher in domestic DES group, shown as higher rates of overall revascularization, target vessel revascularization (TVR), and target lesion revascularization (TLR) (9.7% vs 6.4%, P < 0.001; 5.6% vs 3.2%, P < 0.001; 4.5% vs 2.2%, P < 0.001, respectively). Accordingly, major adverse cardiac events (MACE) rate was significantly higher in domestic DES group (12.1% vs 8.5%, P < 0.001). Multivariable Cox regression analysis indicated that domestic DES was an independent risk factor of MACE (HR [95%CI]: 1.22 [1.05-1.41]), overall revascularization (HR [95%CI]: 1.29 [1.09-1.53]), TVR (HR [95%CI]: 1.54 [1.22-1.94]), and TLR (HR [95%CI]: 1.85 [1.41-2.42]). After propensity score matching, the rates of overall revascularization, TVR, and TLR were still significantly higher in domestic DES group, and domestic DES was still predictive of overall revascularization, TVR, and TLR in multivariate Cox regression analysis. Domestic DESs showed the same safety as imported DESs in this real-world cohort. But, patients implanted with domestic DESs had a higher risk of revascularization than imported DESs. © 2017, Wiley Periodicals, Inc.

Multivariate Analysis of Schools and Educational Policy.

ERIC Educational Resources Information Center

Kiesling, Herbert J.

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

Using video and theater to increase knowledge and change attitudes-Why are gorillas important to the world and to Congo?

PubMed

Breuer, Thomas; Mavinga, Franck Barrel; Evans, Ron; Lukas, Kristen E

2017-10-01

Applying environmental education in primate range countries is an important long-term activity to stimulate pro-conservation behavior. Within captive settings, mega-charismatic species, such as great apes are often used to increase knowledge and positively influence attitudes of visitors. Here, we evaluate the effectiveness of a short-term video and theater program developed for a Western audience and adapted to rural people living in two villages around Nouabalé-Ndoki National Park, Republic of Congo. We assessed the knowledge gain and attitude change using oral evaluation in the local language (N = 111). Overall pre-program knowledge about Western gorillas (Gorilla gorilla) was high. Detailed multivariate analysis of pre-program knowledge revealed differences in knowledge between two villages and people with different jobs while attitudes largely were similar between groups. The short-term education program was successful in raising knowledge, particularly of those people with less pre-program knowledge. We also noted an overall significant attitude improvement. Our data indicate short-term education programs are useful in quickly raising knowledge as well improving attitudes. Furthermore, education messages need to be clearly adapted to the daily livelihood realities of the audience, and multi-variate analysis can help to identify potential target groups for education programs. © 2017 Wiley Periodicals, Inc.

A multivariate analysis of genetic constraints to life history evolution in a wild population of red deer.

PubMed

Walling, Craig A; Morrissey, Michael B; Foerster, Katharina; Clutton-Brock, Tim H; Pemberton, Josephine M; Kruuk, Loeske E B

2014-12-01

Evolutionary theory predicts that genetic constraints should be widespread, but empirical support for their existence is surprisingly rare. Commonly applied univariate and bivariate approaches to detecting genetic constraints can underestimate their prevalence, with important aspects potentially tractable only within a multivariate framework. However, multivariate genetic analyses of data from natural populations are challenging because of modest sample sizes, incomplete pedigrees, and missing data. Here we present results from a study of a comprehensive set of life history traits (juvenile survival, age at first breeding, annual fecundity, and longevity) for both males and females in a wild, pedigreed, population of red deer (Cervus elaphus). We use factor analytic modeling of the genetic variance-covariance matrix ( G: ) to reduce the dimensionality of the problem and take a multivariate approach to estimating genetic constraints. We consider a range of metrics designed to assess the effect of G: on the deflection of a predicted response to selection away from the direction of fastest adaptation and on the evolvability of the traits. We found limited support for genetic constraint through genetic covariances between traits, both within sex and between sexes. We discuss these results with respect to other recent findings and to the problems of estimating these parameters for natural populations. Copyright © 2014 Walling et al.

A Multivariate Analysis of Genetic Constraints to Life History Evolution in a Wild Population of Red Deer

PubMed Central

Walling, Craig A.; Morrissey, Michael B.; Foerster, Katharina; Clutton-Brock, Tim H.; Pemberton, Josephine M.; Kruuk, Loeske E. B.

2014-01-01

Evolutionary theory predicts that genetic constraints should be widespread, but empirical support for their existence is surprisingly rare. Commonly applied univariate and bivariate approaches to detecting genetic constraints can underestimate their prevalence, with important aspects potentially tractable only within a multivariate framework. However, multivariate genetic analyses of data from natural populations are challenging because of modest sample sizes, incomplete pedigrees, and missing data. Here we present results from a study of a comprehensive set of life history traits (juvenile survival, age at first breeding, annual fecundity, and longevity) for both males and females in a wild, pedigreed, population of red deer (Cervus elaphus). We use factor analytic modeling of the genetic variance–covariance matrix (G) to reduce the dimensionality of the problem and take a multivariate approach to estimating genetic constraints. We consider a range of metrics designed to assess the effect of G on the deflection of a predicted response to selection away from the direction of fastest adaptation and on the evolvability of the traits. We found limited support for genetic constraint through genetic covariances between traits, both within sex and between sexes. We discuss these results with respect to other recent findings and to the problems of estimating these parameters for natural populations. PMID:25278555

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.

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

NASA Technical Reports Server (NTRS)

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

1993-01-01

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

Multivariate pattern dependence

PubMed Central

Saxe, Rebecca

2017-01-01

When we perform a cognitive task, multiple brain regions are engaged. Understanding how these regions interact is a fundamental step to uncover the neural bases of behavior. Most research on the interactions between brain regions has focused on the univariate responses in the regions. However, fine grained patterns of response encode important information, as shown by multivariate pattern analysis. In the present article, we introduce and apply multivariate pattern dependence (MVPD): a technique to study the statistical dependence between brain regions in humans in terms of the multivariate relations between their patterns of responses. MVPD characterizes the responses in each brain region as trajectories in region-specific multidimensional spaces, and models the multivariate relationship between these trajectories. We applied MVPD to the posterior superior temporal sulcus (pSTS) and to the fusiform face area (FFA), using a searchlight approach to reveal interactions between these seed regions and the rest of the brain. Across two different experiments, MVPD identified significant statistical dependence not detected by standard functional connectivity. Additionally, MVPD outperformed univariate connectivity in its ability to explain independent variance in the responses of individual voxels. In the end, MVPD uncovered different connectivity profiles associated with different representational subspaces of FFA: the first principal component of FFA shows differential connectivity with occipital and parietal regions implicated in the processing of low-level properties of faces, while the second and third components show differential connectivity with anterior temporal regions implicated in the processing of invariant representations of face identity. PMID:29155809

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

NASA Astrophysics Data System (ADS)

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

2018-03-01

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

A need for a standardization in anaerobic digestion experiments? Let's get some insight from meta-analysis and multivariate analysis.

PubMed

Lavergne, Céline; Jeison, David; Ortega, Valentina; Chamy, Rolando; Donoso-Bravo, Andrés

2018-09-15

An important variability in the experimental results in anaerobic digestion lab test has been reported. This study presents a meta-analysis coupled with multivariate analysis aiming to assess the impact of this experimental variability in batch and continuous operation at mesophilic and thermophilic anaerobic digestion of waste activated sludge. An analysis of variance showed that there was no significant difference between mesophilic and thermophilic conditions in both continuous and batch conditions. Concerning the operation mode, the values of methane yield were significantly higher in batch experiment than in continuous reactors. According to the PCA, for both cases, the methane yield is positive correlated to the temperature rises. Interestingly, in the batch experiments, the higher the volatile solids in the substrate was, the lowest was the methane production, which is correlated to experimental flaws when setting up those tests. In continuous mode, unlike the batch test, the methane yield is strongly (positively) correlated to the organic content of the substrate. Experimental standardization, above all, in batch conditions are urgently necessary or move to continuous experiments for reporting results. The modeling can also be a source of disturbance in batch test. Copyright © 2018 Elsevier Ltd. All rights reserved.

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

PubMed

Ferreira, Ana P; Tobyn, Mike

2015-01-01

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

Prognostic significance of biomarkers in predicting outcome in patients with coronary artery disease and left ventricular dysfunction: results of the biomarker substudy of the Surgical Treatment for Ischemic Heart Failure trials.

PubMed

Feldman, Arthur M; Mann, Douglas L; She, Lilin; Bristow, Michael R; Maisel, Alan S; McNamara, Dennis M; Walsh, Ryan; Lee, Dorellyn L; Wos, Stanislaw; Lang, Irene; Wells, Gretchen; Drazner, Mark H; Schmedtje, John F; Pauly, Daniel F; Sueta, Carla A; Di Maio, Michael; Kron, Irving L; Velazquez, Eric J; Lee, Kerry L

2013-05-01

Patients with heart failure and coronary artery disease often undergo coronary artery bypass grafting, but assessment of the risk of an adverse outcome in these patients is difficult. To evaluate the ability of biomarkers to contribute independent prognostic information in these patients, we measured levels in patients enrolled in the biomarker substudies of the Surgical Treatment for Ischemic Heart Failure (STICH) trials. Patients in STICH Hypothesis 1 were randomized to medical therapy or coronary artery bypass grafting, whereas those in STICH Hypothesis 2 were randomized to coronary artery bypass grafting or coronary artery bypass grafting with left ventricular reconstruction. In substudy patients assigned to STICH Hypothesis 1 (n=606), plasma levels of soluble tumor necrosis factor-α receptor-1 (sTNFR-1) and brain natriuretic peptide (BNP) were highly predictive of the primary outcome variable of mortality by univariate analysis (BNP: χ(2)=40.6; P<0.0001 and sTNFR-1: χ(2)=38.9; P<0.0001). When considered in the context of multivariable analysis, both BNP and sTNFR-1 contributed independent prognostic information beyond the information provided by a large array of clinical factors independent of treatment assignment. Consistent results were seen when assessing the predictive value of BNP and sTNFR-1 in patients assigned to STICH Hypothesis 2 (n=626). Both plasma levels of BNP (χ(2)=30.3) and sTNFR-1 (χ(2)=45.5) were highly predictive in univariate analysis (P<0.0001) and in multivariable analysis for the primary end point of death or cardiac hospitalization. In multivariable analysis, the prognostic information contributed by BNP (χ(2)=6.0; P=0.049) and sTNFR-1 (χ(2)=8.8; P=0.003) remained statistically significant even after accounting for other clinical information. Although the biomarkers added little discriminatory improvement to the clinical factors (increase in c-index ≤0.1), net reclassification improvement for the primary end points was 0.29 for BNP and 0.21 for sTNFR-1 in the Hypothesis 1 cohort, and 0.15 for BNP and 0.30 for sTNFR-1 in the Hypothesis 2 cohort, reflecting important predictive improvement. Elevated levels of sTNFR-1 and BNP are strongly associated with outcomes, independent of therapy, in 2 large and independent studies, thus providing important cross-validation for the prognostic importance of these 2 biomarkers.

The Prognostic Significance of Biomarkers in Predicting Outcome in Patients With Coronary Artery Disease and Left Ventricular Dysfunction: Results of the Biomarker Sub-Study of the Surgical Treatment for Ischemic Heart Failure (STICH) Trials

PubMed Central

Feldman, Arthur M.; Mann, Douglas L.; She, Lilin; Bristow, Michael R.; Maisel, Alan S.; McNamara, Dennis M.; Walsh, Ryan; Lee, Dorellyn L.; Wos, Stanislaw; Lang, Irene; Wells, Gretchen; Drazner, Mark H.; Schmedtje, John F.; Pauly, Daniel F.; Sueta, Carla A.; Di Maio, Michael; Kron, Irving L.; Velazquez, Eric J.; Lee, Kerry L.

2013-01-01

Background Patients with heart failure and coronary artery disease often undergo coronary artery bypass grafting (CABG) but assessment of the risk of an adverse outcome in these patients is difficult. To evaluate the ability of biomarkers to contribute independent prognostic information in these patients, we measured levels in patients enrolled in the Biomarker Sub-studies of the Surgical Treatment for Ischemic Heart Failure (STICH) trials. Patients in STICH Hypothesis 1 were randomized to medical therapy or CABG whereas those in STICH Hypothesis 2 were randomized to CABG or CABG with left ventricular reconstruction. Methods and Results In sub-study patients assigned to STICH Hypothesis 1 (n=606), plasma levels of sTNFR-1 and BNP were highly predictive of the primary outcome variable of mortality by univariate analysis (BNP χ2=40.6; p<0.0001: sTNFR-1 χ2=38,9; p<0.0001). When considered in the context of multivariable analysis, both BNP and sTNFR-1 contributed independent prognostic information beyond the information provided by a large array of clinical factors independent of treatment assignment. Consistent results were seen when assessing the predictive value of BNP and sTNFR-1 in patients assigned to STICH Hypothesis 2 (n=626). Both plasma levels of BNP (χ2=30.3) and sTNFR-1 (χ2=45.5) were highly predictive in univariate analysis (p<0.0001) as well as in multivariable analysis for the primary endpoint of death or cardiac hospitalization. In multivariable analysis, the prognostic information contributed by BNP (χ2=6.0; p=0.049) and sTNFR-1 (χ2=8.8; p=0.003) remained statistically significant even after accounting for other clinical information. Although the biomarkers added little discriminatory improvement to the clinical factors (increase in c-index ≤ 0.1), Net Reclassification Improvement (NRI) for the primary endpoints was 0.29 for BNP and 0.21 for sTNFR-1in the Hypothesis 1 cohort, and 0.15 for BNP and 0.30 for sTNFR-1 in the Hypothesis 2 cohort, reflecting important predictive improvement. Conclusions Elevated levels of sTNFR-1 and BNP are strongly associated with outcomes, independent of therapy, in two large and independent studies, thus providing important cross-validation for the prognostic importance of these two biomarkers. PMID:23584092

Multicausal systems ask for multicausal approaches: A network perspective on subjective well-being in individuals with autism spectrum disorder.

PubMed

Deserno, Marie K; Borsboom, Denny; Begeer, Sander; Geurts, Hilde M

2017-11-01

Given the heterogeneity of autism spectrum disorder, an important limitation of much autism spectrum disorder research is that outcome measures are statistically modeled as separate dependent variables. Often, their multivariate structure is either ignored or treated as a nuisance. This study aims to lift this limitation by applying network analysis to explicate the multivariate pattern of risk and success factors for subjective well-being in autism spectrum disorder. We estimated a network structure for 27 potential factors in 2341 individuals with autism spectrum disorder to assess the centrality of specific life domains and their importance for well-being. The data included both self- and proxy-reported information. We identified social satisfaction and societal contribution as the strongest direct paths to subjective well-being. The results suggest that an important contribution to well-being lies in resources that allow the individual to engage in social relations, which influence well-being directly. Factors most important in determining the network's structure include self-reported IQ, living situation, level of daily activity, and happiness. Number of family members with autism spectrum disorder and openness about one's diagnosis are least important of all factors for subjective well-being. These types of results can serve as a roadmap for interventions directed at improving the well-being of individuals with autism spectrum disorder.

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

PubMed

Schneider, Astrid; Hommel, Gerhard; Blettner, Maria

2010-11-01

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

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

PubMed

Graffelman, Jan; van Eeuwijk, Fred

2005-12-01

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

Multivariate probability distribution for sewer system vulnerability assessment under data-limited conditions.

PubMed

Del Giudice, G; Padulano, R; Siciliano, D

2016-01-01

The lack of geometrical and hydraulic information about sewer networks often excludes the adoption of in-deep modeling tools to obtain prioritization strategies for funds management. The present paper describes a novel statistical procedure for defining the prioritization scheme for preventive maintenance strategies based on a small sample of failure data collected by the Sewer Office of the Municipality of Naples (IT). Novelty issues involve, among others, considering sewer parameters as continuous statistical variables and accounting for their interdependences. After a statistical analysis of maintenance interventions, the most important available factors affecting the process are selected and their mutual correlations identified. Then, after a Box-Cox transformation of the original variables, a methodology is provided for the evaluation of a vulnerability map of the sewer network by adopting a joint multivariate normal distribution with different parameter sets. The goodness-of-fit is eventually tested for each distribution by means of a multivariate plotting position. The developed methodology is expected to assist municipal engineers in identifying critical sewers, prioritizing sewer inspections in order to fulfill rehabilitation requirements.

Application of kernel principal component analysis and computational machine learning to exploration of metabolites strongly associated with diet.

PubMed

Shiokawa, Yuka; Date, Yasuhiro; Kikuchi, Jun

2018-02-21

Computer-based technological innovation provides advancements in sophisticated and diverse analytical instruments, enabling massive amounts of data collection with relative ease. This is accompanied by a fast-growing demand for technological progress in data mining methods for analysis of big data derived from chemical and biological systems. From this perspective, use of a general "linear" multivariate analysis alone limits interpretations due to "non-linear" variations in metabolic data from living organisms. Here we describe a kernel principal component analysis (KPCA)-incorporated analytical approach for extracting useful information from metabolic profiling data. To overcome the limitation of important variable (metabolite) determinations, we incorporated a random forest conditional variable importance measure into our KPCA-based analytical approach to demonstrate the relative importance of metabolites. Using a market basket analysis, hippurate, the most important variable detected in the importance measure, was associated with high levels of some vitamins and minerals present in foods eaten the previous day, suggesting a relationship between increased hippurate and intake of a wide variety of vegetables and fruits. Therefore, the KPCA-incorporated analytical approach described herein enabled us to capture input-output responses, and should be useful not only for metabolic profiling but also for profiling in other areas of biological and environmental systems.

Instrumental Neutron Activation Analysis and Multivariate Statistics for Pottery Provenance

NASA Astrophysics Data System (ADS)

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

2004-06-01

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

A Study of Effects of MultiCollinearity in the Multivariable Analysis

PubMed Central

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

2015-01-01

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

A Study of Effects of MultiCollinearity in the Multivariable Analysis.

PubMed

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

2014-10-01

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

Staging research of human lung cancer tissues by high-resolution magic angle spinning proton nuclear magnetic resonance spectroscopy (HRMAS 1 H NMR) and multivariate data analysis.

PubMed

Chen, Wenxue; Lu, Shaohua; Wang, Guifang; Chen, Fener; Bai, Chunxue

2017-10-01

High-resolution magic-angle spinning proton nuclear magnetic resonance (HRMAS 1 H NMR) spectroscopy technique was employed to analyze the metabonomic characterizations of lung cancer tissues in hope to identify potential diagnostic biomarkers for malignancy detection and staging research of lung tissues. HRMAS 1 H NMR spectroscopy technique can rapidly provide important information for accurate diagnosis and staging of cancer tissues owing to its noninvasive nature and limited requirement for the samples, and thus has been acknowledged as an excellent tool to investigate tissue metabolism and provide a more realistic insight into the metabonomics of tissues when combined with multivariate data analysis (MVDA) such as component analysis and orthogonal partial least squares-discriminant analysis in particular. HRMAS 1 H NMR spectra displayed the metabonomic differences of 32 lung cancer tissues at the different stages from 32 patients. The significant changes (P < 0.05) of some important metabolites such as lipids, aspartate and choline-containing compounds in cancer tissues at the different stages had been identified. Furthermore, the combination of HRMAS 1 H NMR spectroscopy and MVDA might potentially and precisely provided for a high sensitivity, specificity, prediction accuracy in the positive identification of the staging for the cancer tissues in contrast with the pathological data in clinic. This study highlighted the potential of metabonomics in clinical settings so that the techniques might be further exploited for the diagnosis and staging prediction of lung cancer in future. © 2016 John Wiley & Sons Australia, Ltd.

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

PubMed

Olswold, Curtis; de Andrade, Mariza

2003-12-31

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

The importance of histopathological and clinical variables in predicting the evolution of colon cancer.

PubMed

Diculescu, Mircea; Iacob, Răzvan; Iacob, Speranţa; Croitoru, Adina; Becheanu, Gabriel; Popeneciu, Valentin

2002-09-01

It has been a consensus that prognostic factors should always be taken into account before planning treatment in colorectal cancer. A 5 year prospective study was conducted, in order to assess the importance of several histopathological and clinical prognostic variables in the prediction of evolution in colon cancer. Some of the factors included in the analysis are still subject to dispute by different authors. 46 of 53 screened patients qualified to enter the study and underwent a potentially curative resection of the tumor, followed, when necessary, by adjuvant chemotherapy. Univariate and multivariate analyses were carried out in order to identify independent prognostic indicators. The endpoint of the study was considered the recurrence of the tumor or the detection of metastases. 65.2% of the patients had a good evolution during the follow up period. Multivariate survival analysis performed by Cox proportional hazard model identified 3 independent prognostic factors: Dukes stage (p = 0.00002), the grade of differentiation (p = 0.0009) and the weight loss index, representing the weight loss of the patient divided by the number of months when it was actually lost (p = 0.02). Age under 40 years, sex, microscopic aspect of the tumor, tumor location, anemia degree were not identified by our analysis as having prognostic importance. Histopathological factors continue to be the most valuable source of information regarding the possible evolution of patients with colorectal cancer. Individual clinical symptoms or biological parameters such as erytrocyte sedimentation rate or hemoglobin level are of little or no prognostic value. More research is required relating to the impact of a performance status index (which could include also weight loss index) as another reliable prognostic variable.

Multivariate frequency domain analysis of protein dynamics

NASA Astrophysics Data System (ADS)

Matsunaga, Yasuhiro; Fuchigami, Sotaro; Kidera, Akinori

2009-03-01

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

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

Socioeconomic status and prevalence of self-reported diabetes among adults in Tehran: results from a large population-based cross-sectional study (Urban HEART-2).

PubMed

Asadi-Lari, M; Khosravi, A; Nedjat, S; Mansournia, M A; Majdzadeh, R; Mohammad, K; Vaez-Mahdavi, M R; Faghihzadeh, S; Haeri Mehrizi, A A; Cheraghian, B

2016-05-01

Diabetes mellitus is an important public health challenge worldwide. The prevalence of type 2 diabetes varies across countries. The aim of this study is to estimate the prevalence of type 2 diabetes and to determine related factors including socioeconomic factors in a large random sample of Tehran population in 2011. In this cross-sectional study, 91,814 individuals aged over 20 years were selected randomly based on a multistage, cluster sampling. All participants were interviewed by trained personnel using standard questionnaires. Prevalence and Townsend deprivation indexes were calculated. Principal component analysis (PCA) was used to construct wealth index. Logistic regression model was used in multivariate analysis. The estimated prevalence of self-reported diabetes was 4.98 % overall, 4.76 %in men and 5.19 % in women (P < 0.003). In multivariate analysis, age, marital status (married and divorced/widow) and BMI were positively associated with the prevalence of self-reported diabetes. Of the socioeconomic variables, educational level and wealth status were negatively and Townsend Index was positively associated with diabetes. Our study findings highlight low reported prevalence of diabetes among adults in Tehran. Subjects with low socioeconomic status (SES) had a higher prevalence of type 2 diabetes. Weight gain and obesity were the most important risk factors associated with type 2 diabetes. Wealth index and educational level were better socioeconomic indicators for presenting the inequality in diabetes prevalence in relation to Townsend deprivation index.

Phospholipids fatty acids of drinking water reservoir sedimentary microbial community: Structure and function responses to hydrostatic pressure and other physico-chemical properties.

PubMed

Chai, Bei-Bei; Huang, Ting-Lin; Zhao, Xiao-Guang; Li, Ya-Jiao

2015-07-01

Microbial communities in three drinking water reservoirs, with different depth in Xi'an city, were quantified by phospholipids fatty acids analysis and multivariate statistical analysis was employed to interpret their response to different hydrostatic pressure and other physico-chemical properties of sediment and overlying water. Principle component analyses of sediment characteristics parameters showed that hydrostatic pressure was the most important effect factor to differentiate the overlying water quality from three drinking water reservoirs from each other. NH4+ content in overlying water was positive by related to hydrostatic pressure, while DO in water-sediment interface and sediment OC in sediment were negative by related with it. Three drinking water reservoir sediments were characterized by microbial communities dominated by common and facultative anaerobic Gram-positive bacteria, as well as, by sulfur oxidizing bacteria. Hydrostatic pressure and physico-chemical properties of sediments (such as sediment OC, sediment TN and sediment TP) were important effect factors to microbial community structure, especially hydrostatic pressure. It is also suggested that high hydrostatic pressure and low dissolved oxygen concentration stimulated Gram-positive and sulfate-reducing bacteria (SRB) bacterial population in drinking water reservoir sediment. This research supplied a successful application of phospholipids fatty acids and multivariate analysis to investigate microbial community composition response to different environmental factors. Thus, few physico-chemical factors can be used to estimate composition microbial of community as reflected by phospholipids fatty acids, which is difficult to detect.

Brain galanin system genes interact with life stresses in depression-related phenotypes

PubMed Central

Juhasz, Gabriella; Hullam, Gabor; Eszlari, Nora; Gonda, Xenia; Antal, Peter; Anderson, Ian Muir; Hökfelt, Tomas G. M.; Deakin, J. F. William; Bagdy, Gyorgy

2014-01-01

Galanin is a stress-inducible neuropeptide and cotransmitter in serotonin and norepinephrine neurons with a possible role in stress-related disorders. Here we report that variants in genes for galanin (GAL) and its receptors (GALR1, GALR2, GALR3), despite their disparate genomic loci, conferred increased risk of depression and anxiety in people who experienced childhood adversity or recent negative life events in a European white population cohort totaling 2,361 from Manchester, United Kingdom and Budapest, Hungary. Bayesian multivariate analysis revealed a greater relevance of galanin system genes in highly stressed subjects compared with subjects with moderate or low life stress. Using the same method, the effect of the galanin system genes was stronger than the effect of the well-studied 5-HTTLPR polymorphism in the serotonin transporter gene (SLC6A4). Conventional multivariate analysis using general linear models demonstrated that interaction of galanin system genes with life stressors explained more variance (1.7%, P = 0.005) than the life stress-only model. This effect replicated in independent analysis of the Manchester and Budapest subpopulations, and in males and females. The results suggest that the galanin pathway plays an important role in the pathogenesis of depression in humans by increasing the vulnerability to early and recent psychosocial stress. Correcting abnormal galanin function in depression could prove to be a novel target for drug development. The findings further emphasize the importance of modeling environmental interaction in finding new genes for depression. PMID:24706871

Ecological correlates of the non-indigenous parasitoid assemblage associated with a Hawaiian endemic moth.

PubMed

Kaufman, Leyla V; Wright, Mark G

2011-08-01

Understanding what ecological factors might predispose indigenous habitats to invasion by invasive species is an important aspect of conservation and invasive species management, particularly when biological control is considered for suppression of the invasive species. This study seeks to identify ecological factors that might play a role in determining the structure of the parasitoid assemblage associated with caterpillars of the endemic Hawaiian moth Udea stellata (Crambidae). Parasitoids were reared from field-collected U. stellata larvae at 18 locations. Fourteen environmental variables were measured at each site. Two multivariate analyses, principal component analysis (PCA) and partial redundancy analysis (RDA), were used to analyze the parasitoid assemblage across a range of habitats varying in environmental characteristics. The PCA analysis showed that the occurrence of some species were highly correlated, and associated with less disturbed sites, whereas other species were associated with sites of medium and high levels of disturbance. The RDA analysis showed that only three of the measured environmental variables (U. stellata density, elevation, and level of habitat disturbance) significantly explained variability in the parasitoid assemblage among sites. There was greater parasitoid species richness associated with U. stellata larvae at higher elevation sites with a lower degree of habitat disturbance by exotic vegetation. The purposely introduced parasitoid species were associated with the non-target moth at sites located at higher elevations with low levels of disturbance. Multivariate analysis has the potential to provide valuable insights into the identification of important environmental factors that mediate parasitoid assemblage structure and level of parasitism on a particular target or non-target species, and therefore facilitate identification of suitable target habitats or susceptible non-target habitats.

Linking Spatial Variations in Water Quality with Water and Land Management using Multivariate Techniques.

PubMed

Wan, Yongshan; Qian, Yun; Migliaccio, Kati White; Li, Yuncong; Conrad, Cecilia

2014-03-01

Most studies using multivariate techniques for pollution source evaluation are conducted in free-flowing rivers with distinct point and nonpoint sources. This study expanded on previous research to a managed "canal" system discharging into the Indian River Lagoon, Florida, where water and land management is the single most important anthropogenic factor influencing water quality. Hydrometric and land use data of four drainage basins were uniquely integrated into the analysis of 25 yr of monthly water quality data collected at seven stations to determine the impact of water and land management on the spatial variability of water quality. Cluster analysis (CA) classified seven monitoring stations into four groups (CA groups). All water quality parameters identified by discriminant analysis showed distinct spatial patterns among the four CA groups. Two-step principal component analysis/factor analysis (PCA/FA) was conducted with (i) water quality data alone and (ii) water quality data in conjunction with rainfall, flow, and land use data. The results indicated that PCA/FA of water quality data alone was unable to identify factors associated with management activities. The addition of hydrometric and land use data into PCA/FA revealed close associations of nutrients and color with land management and storm-water retention in pasture and citrus lands; total suspended solids, turbidity, and NO + NO with flow and Lake Okeechobee releases; specific conductivity with supplemental irrigation supply; and dissolved O with wetland preservation. The practical implication emphasizes the importance of basin-specific land and water management for ongoing pollutant loading reduction and ecosystem restoration programs. Copyright © by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America, Inc.

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

Socio-economic Correlates of Malnutrition among Married Women in Bangladesh.

PubMed

Mostafa Kamal, S M; Md Aynul, Islam

2010-12-01

This paper examines the prevalence and socio-economic correlates of malnutrition among ever married non-pregnant women of reproductive age of Bangladesh using a nationally representative weighted sample of 10,145. Body mass index was used to measure nutritional status. Both bivariate and multivariate statistical analyses were employed to assess the relationship between socio-economic characteristics and women's nutritional status. Overall, 28.5% of the women were found to be underweight. The fixed effect multivariate binary logistic regression analysis yielded significantly increased risk of underweight for the young, currently working, non-Muslim, rural residents, widowed, divorced or separated women. Significant wide variations of malnourishment prevailed in the administrative regions of the country. Wealth index and women's education were the most important determinants of underweight. The multivariate logistic regression analysis revealed that the risk of being underweight was almost seven times higher (OR=6.76, 95% CI=5.20-8.80) among women with no formal education as compared to those with higher education and the likelihood of underweight was significantly (p<0.001) 5.2 times (OR=5.23, 95% CI=4.51-6.07) in the poorest as compared to their richest counterparts. Poverty alleviation programmes should be strengthened targeting the poor. Effective policies, information and health education programmes for women are required to ensure adequate access to health services and for them to understand the components of a healthy diet.

Association Between Treatment at High-Volume Facilities and Improved Overall Survival in Soft Tissue Sarcomas.

PubMed

Venigalla, Sriram; Nead, Kevin T; Sebro, Ronnie; Guttmann, David M; Sharma, Sonam; Simone, Charles B; Levin, William P; Wilson, Robert J; Weber, Kristy L; Shabason, Jacob E

2018-03-15

Soft tissue sarcomas (STS) are rare malignancies that require complex multidisciplinary management. Therefore, facilities with high sarcoma case volume may demonstrate superior outcomes. We hypothesized that STS treatment at high-volume (HV) facilities would be associated with improved overall survival (OS). Patients aged ≥18 years with nonmetastatic STS treated with surgery and radiation therapy at a single facility from 2004 through 2013 were identified from the National Cancer Database. Facilities were dichotomized into HV and low-volume (LV) cohorts based on total case volume over the study period. OS was assessed using multivariable Cox regression with propensity score-matching. Patterns of care were assessed using multivariable logistic regression analysis. Of 9025 total patients, 1578 (17%) and 7447 (83%) were treated at HV and LV facilities, respectively. On multivariable analysis, high educational attainment, larger tumor size, higher grade, and negative surgical margins were statistically significantly associated with treatment at HV facilities; conversely, black race and non-metropolitan residence were negative predictors of treatment at HV facilities. On propensity score-matched multivariable analysis, treatment at HV facilities versus LV facilities was associated with improved OS (hazard ratio, 0.87, 95% confidence interval, 0.80-0.95; P = .001). Older age, lack of insurance, greater comorbidity, larger tumor size, higher tumor grade, and positive surgical margins were associated with statistically significantly worse OS. In this observational cohort study using the National Cancer Database, receipt of surgery and radiation therapy at HV facilities was associated with improved OS in patients with STS. Potential sociodemographic disparities limit access to care at HV facilities for certain populations. Our findings highlight the importance of receipt of care at HV facilities for patients with STS and warrant further study into improving access to care at HV facilities. Copyright © 2017 Elsevier Inc. All rights reserved.

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

PubMed

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

2014-04-01

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

Biostatistics Series Module 10: Brief Overview of Multivariate Methods.

PubMed

Hazra, Avijit; Gogtay, Nithya

2017-01-01

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

Information spreading by a combination of MEG source estimation and multivariate pattern classification.

PubMed

Sato, Masashi; Yamashita, Okito; Sato, Masa-Aki; Miyawaki, Yoichi

2018-01-01

To understand information representation in human brain activity, it is important to investigate its fine spatial patterns at high temporal resolution. One possible approach is to use source estimation of magnetoencephalography (MEG) signals. Previous studies have mainly quantified accuracy of this technique according to positional deviations and dispersion of estimated sources, but it remains unclear how accurately MEG source estimation restores information content represented by spatial patterns of brain activity. In this study, using simulated MEG signals representing artificial experimental conditions, we performed MEG source estimation and multivariate pattern analysis to examine whether MEG source estimation can restore information content represented by patterns of cortical current in source brain areas. Classification analysis revealed that the corresponding artificial experimental conditions were predicted accurately from patterns of cortical current estimated in the source brain areas. However, accurate predictions were also possible from brain areas whose original sources were not defined. Searchlight decoding further revealed that this unexpected prediction was possible across wide brain areas beyond the original source locations, indicating that information contained in the original sources can spread through MEG source estimation. This phenomenon of "information spreading" may easily lead to false-positive interpretations when MEG source estimation and classification analysis are combined to identify brain areas that represent target information. Real MEG data analyses also showed that presented stimuli were able to be predicted in the higher visual cortex at the same latency as in the primary visual cortex, also suggesting that information spreading took place. These results indicate that careful inspection is necessary to avoid false-positive interpretations when MEG source estimation and multivariate pattern analysis are combined.

Factors influencing pre-operative urinary calcium excretion in primary hyperparathyroidism.

PubMed

Kaderli, Reto M; Riss, Philipp; Geroldinger, Angelika; Selberherr, Andreas; Scheuba, Christian; Niederle, Bruno

2017-07-01

Normal or elevated 24-hour urinary calcium (Ca) excretion is a diagnostic marker in primary hyperparathyroidism (PHPT). It is used to distinguish familial hypocalciuric hypercalcaemia (FHH) from PHPT by calculating the Ca/creatinine clearance ratio (CCCR). The variance of CCCR in patients with PHPT is considerable. The aim of this study was to analyse the parameters affecting CCCR in patients with PHPT. The data were collected prospectively. Patients with sporadic PHPT undergoing successful surgery were included in a retrospective analysis. The analysis covered 381 patients with pre-operative workup 2 days before removal of a solitary parathyroid adenoma. The impact of serum Ca and 25-hydroxyvitamin D3 (25-OH D3) on CCCR. The coefficient of determination (R 2 ) in the multivariable model for CCCR consisting of age, Ca, 25-OH D3, 1,25-dihydroxyvitamin D3 (1,25-(OH)2 D3), testosterone (separately for males and females), intact parathyroid hormone (iPTH) and osteocalcin was 25.8%. The only significant parameters in the multivariable analysis were 1,25-(OH)2 D3 and osteocalcin with a drop in R 2 of 15.4% (P<.001) and 2.4% (P=.006), respectively. Bone mineral densities at the lumbar spine, distal radius and left femoral neck were not associated with CCCR (r=-.08, r=-.10 and r=-0.09). In multivariable analysis, 1,25-(OH)2 D3 and osteocalcin were the only factors correlating with CCCR. Vitamin D3 replacement may therefore impair the diagnostic value of CCCR and increase the importance of close monitoring of urinary Ca excretion during treatment. © 2017 John Wiley & Sons Ltd.

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

PubMed Central

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

2013-01-01

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

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

PubMed

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

2013-12-28

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

Information spreading by a combination of MEG source estimation and multivariate pattern classification

PubMed Central

Sato, Masashi; Yamashita, Okito; Sato, Masa-aki

2018-01-01

To understand information representation in human brain activity, it is important to investigate its fine spatial patterns at high temporal resolution. One possible approach is to use source estimation of magnetoencephalography (MEG) signals. Previous studies have mainly quantified accuracy of this technique according to positional deviations and dispersion of estimated sources, but it remains unclear how accurately MEG source estimation restores information content represented by spatial patterns of brain activity. In this study, using simulated MEG signals representing artificial experimental conditions, we performed MEG source estimation and multivariate pattern analysis to examine whether MEG source estimation can restore information content represented by patterns of cortical current in source brain areas. Classification analysis revealed that the corresponding artificial experimental conditions were predicted accurately from patterns of cortical current estimated in the source brain areas. However, accurate predictions were also possible from brain areas whose original sources were not defined. Searchlight decoding further revealed that this unexpected prediction was possible across wide brain areas beyond the original source locations, indicating that information contained in the original sources can spread through MEG source estimation. This phenomenon of “information spreading” may easily lead to false-positive interpretations when MEG source estimation and classification analysis are combined to identify brain areas that represent target information. Real MEG data analyses also showed that presented stimuli were able to be predicted in the higher visual cortex at the same latency as in the primary visual cortex, also suggesting that information spreading took place. These results indicate that careful inspection is necessary to avoid false-positive interpretations when MEG source estimation and multivariate pattern analysis are combined. PMID:29912968

Application of Maxent Multivariate Analysis to Define Reptile Species Distributions and Changes Related to Climate Change

DTIC Science & Technology

2016-06-01

species of importance to the military, such as birds and gophers. ERDC/CERL TR-16-6 88 References Cited Buhlmann, Kurt A ., Thomas S.B. Akre , John...As a re- sult, it can be shown with a high degree of assurance that the majority of a reptile’s range can be delineated with just a few bioclimatic...the reptiles as a group ............................................... 86 9.2 Recommendations

Self-Critical, and Robust, Procedures for the Analysis of Multivariate Normal Data.

DTIC Science & Technology

1982-06-01

Influence Functions The influence function is the most important tt of qual- itative zobustness since many other robustness characteristics of an estimator...may be derived from it. The influence function characterizes the (asymptotic) response of an estimator to an additional observation as a function of...the influence function be bounded. It is also advantageous, in our opinion, if the influence functions are re-descending to zero. The influence function for

A Framework and Algorithms for Multivariate Time Series Analytics (MTSA): Learning, Monitoring, and Recommendation

ERIC Educational Resources Information Center

Ngan, Chun-Kit

2013-01-01

Making decisions over multivariate time series is an important topic which has gained significant interest in the past decade. A time series is a sequence of data points which are measured and ordered over uniform time intervals. A multivariate time series is a set of multiple, related time series in a particular domain in which domain experts…

Multivariate Feature Selection of Image Descriptors Data for Breast Cancer with Computer-Assisted Diagnosis

PubMed Central

Galván-Tejada, Carlos E.; Zanella-Calzada, Laura A.; Galván-Tejada, Jorge I.; Celaya-Padilla, José M.; Gamboa-Rosales, Hamurabi; Garza-Veloz, Idalia; Martinez-Fierro, Margarita L.

2017-01-01

Breast cancer is an important global health problem, and the most common type of cancer among women. Late diagnosis significantly decreases the survival rate of the patient; however, using mammography for early detection has been demonstrated to be a very important tool increasing the survival rate. The purpose of this paper is to obtain a multivariate model to classify benign and malignant tumor lesions using a computer-assisted diagnosis with a genetic algorithm in training and test datasets from mammography image features. A multivariate search was conducted to obtain predictive models with different approaches, in order to compare and validate results. The multivariate models were constructed using: Random Forest, Nearest centroid, and K-Nearest Neighbor (K-NN) strategies as cost function in a genetic algorithm applied to the features in the BCDR public databases. Results suggest that the two texture descriptor features obtained in the multivariate model have a similar or better prediction capability to classify the data outcome compared with the multivariate model composed of all the features, according to their fitness value. This model can help to reduce the workload of radiologists and present a second opinion in the classification of tumor lesions. PMID:28216571

Multivariate Feature Selection of Image Descriptors Data for Breast Cancer with Computer-Assisted Diagnosis.

PubMed

Galván-Tejada, Carlos E; Zanella-Calzada, Laura A; Galván-Tejada, Jorge I; Celaya-Padilla, José M; Gamboa-Rosales, Hamurabi; Garza-Veloz, Idalia; Martinez-Fierro, Margarita L

2017-02-14

Breast cancer is an important global health problem, and the most common type of cancer among women. Late diagnosis significantly decreases the survival rate of the patient; however, using mammography for early detection has been demonstrated to be a very important tool increasing the survival rate. The purpose of this paper is to obtain a multivariate model to classify benign and malignant tumor lesions using a computer-assisted diagnosis with a genetic algorithm in training and test datasets from mammography image features. A multivariate search was conducted to obtain predictive models with different approaches, in order to compare and validate results. The multivariate models were constructed using: Random Forest, Nearest centroid, and K-Nearest Neighbor (K-NN) strategies as cost function in a genetic algorithm applied to the features in the BCDR public databases. Results suggest that the two texture descriptor features obtained in the multivariate model have a similar or better prediction capability to classify the data outcome compared with the multivariate model composed of all the features, according to their fitness value. This model can help to reduce the workload of radiologists and present a second opinion in the classification of tumor lesions.

Multivariate time series analysis of neuroscience data: some challenges and opportunities.

PubMed

Pourahmadi, Mohsen; Noorbaloochi, Siamak

2016-04-01

Neuroimaging data may be viewed as high-dimensional multivariate time series, and analyzed using techniques from regression analysis, time series analysis and spatiotemporal analysis. We discuss issues related to data quality, model specification, estimation, interpretation, dimensionality and causality. Some recent research areas addressing aspects of some recurring challenges are introduced. Copyright © 2015 Elsevier Ltd. All rights reserved.

Revealing hidden spectral information of chlorine and sulfur in data of a mobile Laser-induced Breakdown Spectroscopy system using chemometrics

NASA Astrophysics Data System (ADS)

Gottlieb, C.; Millar, S.; Günther, T.; Wilsch, G.

2017-06-01

For the damage assessment of reinforced concrete structures the quantified ingress profiles of harmful species like chlorides, sulfates and alkali need to be determined. In order to provide on-site analysis of concrete a fast and reliable method is necessary. Low transition probabilities as well as the high ionization energies for chlorine and sulfur in the near-infrared range makes the detection of Cl I and S I in low concentrations a difficult task. For the on-site analysis a mobile LIBS-system (λ = 1064 nm, Epulse ≤ 3 mJ, τ = 1.5 ns) with an automated scanner has been developed at BAM. Weak chlorine and sulfur signal intensities do not allow classical univariate analysis for process data derived from the mobile system. In order to improve the analytical performance multivariate analysis like PLS-R will be presented in this work. A comparison to standard univariate analysis will be carried out and results covering important parameters like detection and quantification limits (LOD, LOQ) as well as processing variances will be discussed (Allegrini and Olivieri, 2014 [1]; Ostra et al., 2008 [2]). It will be shown that for the first time a low cost mobile system is capable of providing reproducible chlorine and sulfur analysis on concrete by using a low sensitive system in combination with multivariate evaluation.

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

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

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

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

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

DOE PAGES

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

2017-12-28

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

Data analysis techniques

NASA Technical Reports Server (NTRS)

Park, Steve

1990-01-01

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

Chemical structure of wood charcoal by infrared spectroscopy and multivariate analysis

Treesearch

Nicole Labbe; David Harper; Timothy Rials; Thomas Elder

2006-01-01

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

Multivariate relationships between groundwater chemistry and toxicity in an urban aquifer.

PubMed

Dewhurst, Rachel E; Wells, N Claire; Crane, Mark; Callaghan, Amanda; Connon, Richard; Mather, John D

2003-11-01

Multivariate statistical methods were used to investigate the causes of toxicity and controls on groundwater chemistry from 274 boreholes in an urban area (London) of the United Kingdom. The groundwater was alkaline to neutral, and chemistry was dominated by calcium, sodium, and sulfate. Contaminants included fuels, solvents, and organic compounds derived from landfill material. The presence of organic material in the aquifer caused decreases in dissolved oxygen, sulfate and nitrate concentrations, and increases in ferrous iron and ammoniacal nitrogen concentrations. Pearson correlations between toxicity results and the concentration of individual analytes indicated that concentrations of ammoniacal nitrogen, dissolved oxygen, ferrous iron, and hydrocarbons were important where present. However, principal component and regression analysis suggested no significant correlation between toxicity and chemistry over the whole area. Multidimensional scaling was used to investigate differences in sites caused by historical use, landfill gas status, or position within the sample area. Significant differences were observed between sites with different historical land use and those with different gas status. Examination of the principal component matrix revealed that these differences are related to changes in the importance of reduced chemical species.

Multivariate cross-classification: applying machine learning techniques to characterize abstraction in neural representations

PubMed Central

Kaplan, Jonas T.; Man, Kingson; Greening, Steven G.

2015-01-01

Here we highlight an emerging trend in the use of machine learning classifiers to test for abstraction across patterns of neural activity. When a classifier algorithm is trained on data from one cognitive context, and tested on data from another, conclusions can be drawn about the role of a given brain region in representing information that abstracts across those cognitive contexts. We call this kind of analysis Multivariate Cross-Classification (MVCC), and review several domains where it has recently made an impact. MVCC has been important in establishing correspondences among neural patterns across cognitive domains, including motor-perception matching and cross-sensory matching. It has been used to test for similarity between neural patterns evoked by perception and those generated from memory. Other work has used MVCC to investigate the similarity of representations for semantic categories across different kinds of stimulus presentation, and in the presence of different cognitive demands. We use these examples to demonstrate the power of MVCC as a tool for investigating neural abstraction and discuss some important methodological issues related to its application. PMID:25859202

Multivariate analysis: greater insights into complex systems

USDA-ARS?s Scientific Manuscript database

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

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

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

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

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

[Risk indicators associated with the consumption of illicit drugs by schoolchildren in a community in the south of Brazil].

PubMed

Backes, Dirce Stein; Zanatta, Fabrício Batistin; Costenaro, Regina Santini; Rangel, Rosiane Filipin; Vidal, Janice; Kruel, Cristina Saling; de Mattos, Karen Mallo

2014-03-01

This study sought to identify the risk indicators associated with the consumption of illicit drugs by schoolchildren in public schools in a community in the south of Brazil. This is a non-experimental cross-sectional study conducted with 535 students of primary schoolchildren from six public schools. Data were collected using a questionnaire between October 2011 and March 2012. The results were presented by simple and relative distribution of frequency and odds ratio (OR) and the 95% reliability intervals were calculated to verify the association between the dependent and independent variables. Multivariate analysis was also performed using the question "have you ever used illicit drugs?" Univariate analysis revealed an association between family income, color, period in which the child studied, failure to pass annual tests, use of methods of prevention, smoking habit and knowing someone who uses drugs with the fact of having experimented with the use of illicit drugs. After multivariate analysis, the smoking habit was the only indicator significantly associated with the question of having made use of illicit drugs. The results indicate that the smoking habit is an important indicator of the predictive risk for the use of illicit drugs.

Multivariate statistical techniques for the evaluation of surface water quality of the Himalayan foothills streams, Pakistan

NASA Astrophysics Data System (ADS)

Malik, Riffat Naseem; Hashmi, Muhammad Zaffar

2017-10-01

Himalayan foothills streams, Pakistan play an important role in living water supply and irrigation of farmlands; thus, the water quality is closely related to public health. Multivariate techniques were applied to check spatial and seasonal trends, and metals contamination sources of the Himalayan foothills streams, Pakistan. Grab surface water samples were collected from different sites (5-15 cm water depth) in pre-washed polyethylene containers. Fast Sequential Atomic Absorption Spectrophotometer (Varian FSAA-240) was used to measure the metals concentration. Concentrations of Ni, Cu, and Mn were high in pre-monsoon season than the post-monsoon season. Cluster analysis identified impaired, moderately impaired and least impaired clusters based on water parameters. Discriminant function analysis indicated spatial variability in water was due to temperature, electrical conductivity, nitrates, iron and lead whereas seasonal variations were correlated with 16 physicochemical parameters. Factor analysis identified municipal and poultry waste, automobile activities, surface runoff, and soil weathering as major sources of contamination. Levels of Mn, Cr, Fe, Pb, Cd, Zn and alkalinity were above the WHO and USEPA standards for surface water. The results of present study will help to higher authorities for the management of the Himalayan foothills streams.

Population differences in the postcrania of modern South Africans and the implications for ancestry estimation.

PubMed

Liebenberg, Leandi; L'Abbé, Ericka N; Stull, Kyra E

2015-12-01

The cranium is widely recognized as the most important skeletal element to use when evaluating population differences and estimating ancestry. However, the cranium is not always intact or available for analysis, which emphasizes the need for postcranial alternatives. The purpose of this study was to quantify postcraniometric differences among South Africans that can be used to estimate ancestry. Thirty-nine standard measurements from 11 postcranial bones were collected from 360 modern black, white and coloured South Africans; the sex and ancestry distribution were equal. Group differences were explored with analysis of variance (ANOVA) and Tukey's honestly significant difference (HSD) test. Linear and flexible discriminant analysis (LDA and FDA, respectively) were conducted with bone models as well as numerous multivariate subsets to identify the model and method that yielded the highest correct classifications. Leave-one-out (LDA) and k-fold (k=10; FDA) cross-validation with equal priors were used for all models. ANOVA and Tukey's HSD results reveal statistically significant differences between at least two of the three groups for the majority of the variables, with varying degrees of group overlap. Bone models, which consisted of all measurements per bone, resulted in low accuracies that ranged from 46% to 63% (LDA) and 41% to 66% (FDA). In contrast, the multivariate subsets, which consisted of different variable combinations from all elements, achieved accuracies as high as 85% (LDA) and 87% (FDA). Thus, when using a multivariate approach, the postcranial skeleton can distinguish among three modern South African groups with high accuracy. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Discrimination of irradiated MOX fuel from UOX fuel by multivariate statistical analysis of simulated activities of gamma-emitting isotopes

NASA Astrophysics Data System (ADS)

Åberg Lindell, M.; Andersson, P.; Grape, S.; Hellesen, C.; Håkansson, A.; Thulin, M.

2018-03-01

This paper investigates how concentrations of certain fission products and their related gamma-ray emissions can be used to discriminate between uranium oxide (UOX) and mixed oxide (MOX) type fuel. Discrimination of irradiated MOX fuel from irradiated UOX fuel is important in nuclear facilities and for transport of nuclear fuel, for purposes of both criticality safety and nuclear safeguards. Although facility operators keep records on the identity and properties of each fuel, tools for nuclear safeguards inspectors that enable independent verification of the fuel are critical in the recovery of continuity of knowledge, should it be lost. A discrimination methodology for classification of UOX and MOX fuel, based on passive gamma-ray spectroscopy data and multivariate analysis methods, is presented. Nuclear fuels and their gamma-ray emissions were simulated in the Monte Carlo code Serpent, and the resulting data was used as input to train seven different multivariate classification techniques. The trained classifiers were subsequently implemented and evaluated with respect to their capabilities to correctly predict the classes of unknown fuel items. The best results concerning successful discrimination of UOX and MOX-fuel were acquired when using non-linear classification techniques, such as the k nearest neighbors method and the Gaussian kernel support vector machine. For fuel with cooling times up to 20 years, when it is considered that gamma-rays from the isotope 134Cs can still be efficiently measured, success rates of 100% were obtained. A sensitivity analysis indicated that these methods were also robust.

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

PubMed

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

2017-12-01

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

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.

Fighting for Intelligence: A Brief Overview of the Academic Work of John L. Horn

PubMed Central

McArdle, John J.; Hofer, Scott M.

2015-01-01

John L. Horn (1928–2006) was a pioneer in multivariate thinking and the application of multivariate methods to research on intelligence and personality. His key works on individual differences in the methodological areas of factor analysis and the substantive areas of cognition are reviewed here. John was also our mentor, teacher, colleague, and friend. We overview John Horn’s main contributions to the field of intelligence by highlighting 3 issues about his methods of factor analysis and 3 of his substantive debates about intelligence. We first focus on Horn’s methodological demonstrations describing (a) the many uses of simulated random variables in exploratory factor analysis; (b) the exploratory uses of confirmatory factor analysis; and (c) the key differences between states, traits, and trait-changes. On a substantive basis, John believed that there were important individual differences among people in terms of cognition and personality. These sentiments led to his intellectual battles about (d) Spearman’s g theory of a unitary intelligence, (e) Guilford’s multifaceted model of intelligence, and (f) the Schaie and Baltes approach to defining the lack of decline of intelligence earlier in the life span. We conclude with a summary of John Horn’s unique approaches to dealing with common issues. PMID:26246642

Comparative study of different approaches for multivariate image analysis in HPTLC fingerprinting of natural products such as plant resin.

PubMed

Ristivojević, Petar; Trifković, Jelena; Vovk, Irena; Milojković-Opsenica, Dušanka

2017-01-01

Considering the introduction of phytochemical fingerprint analysis, as a method of screening the complex natural products for the presence of most bioactive compounds, use of chemometric classification methods, application of powerful scanning and image capturing and processing devices and algorithms, advancement in development of novel stationary phases as well as various separation modalities, high-performance thin-layer chromatography (HPTLC) fingerprinting is becoming attractive and fruitful field of separation science. Multivariate image analysis is crucial in the light of proper data acquisition. In a current study, different image processing procedures were studied and compared in detail on the example of HPTLC chromatograms of plant resins. In that sense, obtained variables such as gray intensities of pixels along the solvent front, peak area and mean values of peak were used as input data and compared to obtained best classification models. Important steps in image analysis, baseline removal, denoising, target peak alignment and normalization were pointed out. Numerical data set based on mean value of selected bands and intensities of pixels along the solvent front proved to be the most convenient for planar-chromatographic profiling, although required at least the basic knowledge on image processing methodology, and could be proposed for further investigation in HPLTC fingerprinting. Copyright © 2016 Elsevier B.V. All rights reserved.

Methods for spectral image analysis by exploiting spatial simplicity

DOEpatents

Keenan, Michael R.

2010-05-25

Several full-spectrum imaging techniques have been introduced in recent years that promise to provide rapid and comprehensive chemical characterization of complex samples. One of the remaining obstacles to adopting these techniques for routine use is the difficulty of reducing the vast quantities of raw spectral data to meaningful chemical information. Multivariate factor analysis techniques, such as Principal Component Analysis and Alternating Least Squares-based Multivariate Curve Resolution, have proven effective for extracting the essential chemical information from high dimensional spectral image data sets into a limited number of components that describe the spectral characteristics and spatial distributions of the chemical species comprising the sample. There are many cases, however, in which those constraints are not effective and where alternative approaches may provide new analytical insights. For many cases of practical importance, imaged samples are "simple" in the sense that they consist of relatively discrete chemical phases. That is, at any given location, only one or a few of the chemical species comprising the entire sample have non-zero concentrations. The methods of spectral image analysis of the present invention exploit this simplicity in the spatial domain to make the resulting factor models more realistic. Therefore, more physically accurate and interpretable spectral and abundance components can be extracted from spectral images that have spatially simple structure.

Methods for spectral image analysis by exploiting spatial simplicity

DOEpatents

Keenan, Michael R.

2010-11-23

Several full-spectrum imaging techniques have been introduced in recent years that promise to provide rapid and comprehensive chemical characterization of complex samples. One of the remaining obstacles to adopting these techniques for routine use is the difficulty of reducing the vast quantities of raw spectral data to meaningful chemical information. Multivariate factor analysis techniques, such as Principal Component Analysis and Alternating Least Squares-based Multivariate Curve Resolution, have proven effective for extracting the essential chemical information from high dimensional spectral image data sets into a limited number of components that describe the spectral characteristics and spatial distributions of the chemical species comprising the sample. There are many cases, however, in which those constraints are not effective and where alternative approaches may provide new analytical insights. For many cases of practical importance, imaged samples are "simple" in the sense that they consist of relatively discrete chemical phases. That is, at any given location, only one or a few of the chemical species comprising the entire sample have non-zero concentrations. The methods of spectral image analysis of the present invention exploit this simplicity in the spatial domain to make the resulting factor models more realistic. Therefore, more physically accurate and interpretable spectral and abundance components can be extracted from spectral images that have spatially simple structure.

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

PubMed

Moazzez, Ashkan; de Virgilio, Christian

2016-10-01

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

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

PubMed Central

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

2013-01-01

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

Extracting galactic structure parameters from multivariated density estimation

NASA Technical Reports Server (NTRS)

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

1992-01-01

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

Clinical factors and the decision to transfuse chronic dialysis patients.

PubMed

Whitman, Cynthia B; Shreay, Sanatan; Gitlin, Matthew; van Oijen, Martijn G H; Spiegel, Brennan M R

2013-11-01

Red blood cell transfusion was previously the principle therapy for anemia in CKD but became less prevalent after the introduction of erythropoiesis-stimulating agents. This study used adaptive choice-based conjoint analysis to identify preferences and predictors of transfusion decision-making in CKD. A computerized adaptive choice-based conjoint survey was administered between June and August of 2012 to nephrologists, internists, and hospitalists listed in the American Medical Association Masterfile. The survey quantified the relative importance of 10 patient attributes, including hemoglobin levels, age, occult blood in stool, severity of illness, eligibility for transplant, iron indices, erythropoiesis-stimulating agents, cardiovascular disease, and functional status. Triggers of transfusions in common dialysis scenarios were studied, and based on adaptive choice-based conjoint-derived preferences, relative importance by performing multivariable regression to identify predictors of transfusion preferences was assessed. A total of 350 providers completed the survey (n=305 nephrologists; mean age=46 years; 21% women). Of 10 attributes assessed, absolute hemoglobin level was the most important driver of transfusions, accounting for 29% of decision-making, followed by functional status (16%) and cardiovascular comorbidities (12%); 92% of providers transfused when hemoglobin was 7.5 g/dl, independent of other factors. In multivariable regression, Veterans Administration providers were more likely to transfuse at 8.0 g/dl (odds ratio, 5.9; 95% confidence interval, 1.9 to 18.4). Although transplant eligibility explained only 5% of decision-making, nephrologists were five times more likely to value it as important compared with non-nephrologists (odds ratio, 5.2; 95% confidence interval, 2.4 to 11.1). Adaptive choice-based conjoint analysis was useful in predicting influences on transfusion decisions. Hemoglobin level, functional status, and cardiovascular comorbidities most strongly influenced transfusion decision-making, but preference variations were observed among subgroups.

MULTIVARIATE CURVE RESOLUTION OF NMR SPECTROSCOPY METABONOMIC DATA

EPA Science Inventory

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

Characterizing multivariate decoding models based on correlated EEG spectral features

PubMed Central

McFarland, Dennis J.

2013-01-01

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

Drunk driving detection based on classification of multivariate time series.

PubMed

Li, Zhenlong; Jin, Xue; Zhao, Xiaohua

2015-09-01

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

Factors predicting recurrence of chronic subdural haematoma: the influence of intraoperative irrigation and low-molecular-weight heparin thromboprophylaxis.

PubMed

Tahsim-Oglou, Yasemin; Beseoglu, Kerim; Hänggi, Daniel; Stummer, Walter; Steiger, Hans-Jakob

2012-06-01

Burr-hole drainage has become the accepted treatment of choice for chronic subdural haematoma (cSDH), although still burdened with a major recurrence rate. The current analysis was initiated to determine management-related risk factors for recurrence, i.e. postoperative low-molecular-weight heparin thromboprophylaxis, and the importance of rinsing the subdural space. Two-hundred and forty-seven patients with computerised tomography (CT) defined symptomatic cSDH were managed by two burr-hole trepanations and drainage between January 2005 and November 2008. Postoperative thromboprophylaxis with 40 mg enoxaparine daily was given only during the first half of the study period. For the current analysis the amount of rinsing fluid, postoperative low-dose thromboprophylaxis, as well as age and gender, bilaterality, preoperative and postoperative blood coagulation studies, platelet counts and decrease of subdural fluid on early postoperative CT, were recorded and correlated with recurrence. Statistical calculation was done by univariate and multivariate analysis. A total of 62 of 247 patients needed revision surgery for recurrence (25.1 %). Recurrence rates were significantly lower in the patients treated without postoperative enoxaparine (18.84 %) than in the group with postoperative low-dose enoxaparine thromboprophylaxis (32.11 %) and enoxaparine was administered in a higher proportion of the patients suffering recurrence (P = 0.013). A median intraoperative irrigation volume of 863 ml saline was used in the patients suffering recurrence and 1,500 ml in patients without recurrence (P < 0.001). The median age was slightly higher in the patients suffering from recurrence. Male gender predominated in both groups but was slightly more pronounced in the recurrence group. Preoperative and postoperative platelet counts and plasmatic coagulation indices did not differ significantly between the groups. Relative residual subdural fluid collection on early postoperative CT remained larger in patients finally suffering recurrence (P = 0.03). Multivariate analysis confirmed a small amount of rinsing fluid, male gender and the use of enoxaparine as the most important risk factors for recurrence, although that latter factor did not reach statistical significance in the multivariate analysis. The investigation provides evidence that copious intraoperative irrigation and avoidance of postoperative low-molecular-weight heparin thromboprophylaxis may reduce the recurrence rate of cSDH.

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

PubMed Central

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

2015-01-01

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

The use of IRMS, (1)H NMR and chemical analysis to characterise Italian and imported Tunisian olive oils.

PubMed

Camin, Federica; Pavone, Anita; Bontempo, Luana; Wehrens, Ron; Paolini, Mauro; Faberi, Angelo; Marianella, Rosa Maria; Capitani, Donatella; Vista, Silvia; Mannina, Luisa

2016-04-01

Isotope Ratio Mass Spectrometry (IRMS), (1)H Nuclear Magnetic Resonance ((1)H NMR), conventional chemical analysis and chemometric elaboration were used to assess quality and to define and confirm the geographical origin of 177 Italian PDO (Protected Denomination of Origin) olive oils and 86 samples imported from Tunisia. Italian olive oils were richer in squalene and unsaturated fatty acids, whereas Tunisian olive oils showed higher δ(18)O, δ(2)H, linoleic acid, saturated fatty acids β-sitosterol, sn-1 and 3 diglyceride values. Furthermore, all the Tunisian samples imported were of poor quality, with a K232 and/or acidity values above the limits established for extra virgin olive oils. By combining isotopic composition with (1)H NMR data using a multivariate statistical approach, a statistical model able to discriminate olive oil from Italy and those imported from Tunisia was obtained, with an optimal differentiation ability arriving at around 98%. Copyright © 2015 Elsevier Ltd. All rights reserved.

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

ERIC Educational Resources Information Center

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

2016-01-01

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

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 meta-analysis using individual participant data

PubMed Central

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

2016-01-01

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

An Application of Discriminant Analysis to the Selection of Software Cost Estimating Models.

DTIC Science & Technology

1984-09-01

the PRICE S Users Manual (29:111-25) was used with a slight modification. Based on the experience and advice of Captain Joe Dean, Electronic System...this study, and EXP is the expansion factor listed in the PRICE S User’s Manual . Another important factor needing explanation is development cost...coefficients and a unique constant. According to the SPSS manual (26:445) "Under the assumption of a multivariate normal distribution, the

Tobacco and alcohol use in adolescents with unplanned pregnancies: relation with family structure, tobacco and alcohol use at home and by friends.

PubMed

Francisco, Vazquez-Nava; Carlos, Vazquez-Rodríguez; Eliza, Vazquez-Rodriguez; Octelina, Castillo-Ruiz; Maria, Iribar Ibabe

2016-03-01

Recent publications show that smoking and alcohol use among adolescents with unplanned pregnancy is increasing and the causes need to be further studied. To determine the association between living in a non-intact family household and the presence of smokers and consumers of alcoholic beverages in the adolescents' environment with smoking and consuming alcoholic beverages in adolescents with unplanned pregnancies. A cross-sectional study was carried out among 785 pregnant adolescents, aged 13-19 years. Data was collected by trained interviewers using a self-administered questionnaire. The association was determined using multivariate logistic regression analysis. In adolescents with unplanned pregnancies, the prevalence of active smoking was 21.2% and of alcohol consumption, 41.5%. The percentage of smoking at home was 57.4% and alcohol consumption, 77.5%. Approximately, 80.3% of adolescents with unplanned pregnancies had friends who smoked and 90.6% consumed alcoholic beverages. Multivariate logistic regression analysis shows that having friends who smoke or who consume alcoholic beverages is the most important risk factor for substance use in adolescents with unplanned pregnancies. Smoking and alcohol consumption at home are not associated with smoking in adolescents with unplanned pregnancies. Socializing with friends who smoke and/or consume alcoholic beverages constitutes the most important risk factor for substance use among adolescents with unplanned pregnancies.

Comparative artificial neural network and partial least squares models for analysis of Metronidazole, Diloxanide, Spiramycin and Cliquinol in pharmaceutical preparations.

PubMed

Elkhoudary, Mahmoud M; Abdel Salam, Randa A; Hadad, Ghada M

2014-09-15

Metronidazole (MNZ) is a widely used antibacterial and amoebicide drug. Therefore, it is important to develop a rapid and specific analytical method for the determination of MNZ in mixture with Spiramycin (SPY), Diloxanide (DIX) and Cliquinol (CLQ) in pharmaceutical preparations. This work describes simple, sensitive and reliable six multivariate calibration methods, namely linear and nonlinear artificial neural networks preceded by genetic algorithm (GA-ANN) and principle component analysis (PCA-ANN) as well as partial least squares (PLS) either alone or preceded by genetic algorithm (GA-PLS) for UV spectrophotometric determination of MNZ, SPY, DIX and CLQ in pharmaceutical preparations with no interference of pharmaceutical additives. The results manifest the problem of nonlinearity and how models like ANN can handle it. Analytical performance of these methods was statistically validated with respect to linearity, accuracy, precision and specificity. The developed methods indicate the ability of the previously mentioned multivariate calibration models to handle and solve UV spectra of the four components' mixtures using easy and widely used UV spectrophotometer. Copyright © 2014 Elsevier B.V. All rights reserved.

[Referral to internal medicine for alcoholism: influence on follow-up care].

PubMed

Avila, P; Marcos, M; Avila, J J; Laso, F J

2008-11-01

The problem of high rates of patient drop-out in alcohol treatment programs is frequently reported in the literature. Our aim was to investigate if internal medicine referral could improve abstinence and retention rates in a cohort of alcoholic patients. A retrospective observational study was conducted comparing 200 alcoholic patients attending a psychiatric unit (group 1) with 100 patients attending both this unit and an internal medicine unit (group 2). We collected sociodemographic and clinical variables and analysed differences regarding abstinence and retention rates by means of univariate and multivariate analysis. At 3 and 12 months follow-up, group 2 patients had higher retention and abstinence rates than group 1 patients. Multivariate analysis including potential confounding variables showed that independent predictors of one-year retention were internal medicine referral and being married. Independent predictors of one-year abstinence were being married, age > 44 years and receipt of drug treatment. The higher retention rate found among patients referred to Internal Medicine specialists, a result that has not been previously reported to the best of our knowledge, emphasizes the importance of a multidisciplinary team approach in the treatment of alcoholism.

Comparative multivariate analysis of biometric traits of West African Dwarf and Red Sokoto goats.

PubMed

Yakubu, Abdulmojeed; Salako, Adebowale E; Imumorin, Ikhide G

2011-03-01

The population structure of 302 randomly selected West African Dwarf (WAD) and Red Sokoto (RS) goats was examined using multivariate morphometric analyses. This was to make the case for conservation, rational management and genetic improvement of these two most important Nigerian goat breeds. Fifteen morphometric measurements were made on each individual animal. RS goats were superior (P<0.05) to the WAD for the body size and skeletal proportions investigated. The phenotypic variability between the two breeds was revealed by their mutual responses in the principal components. While four principal components were extracted for WAD goats, three components were obtained for their RS counterparts with variation in the loading traits of each component for each breed. The Mahalanobis distance of 72.28 indicated a high degree of spatial racial separation in morphology between the genotypes. The Ward's option of the cluster analysis consolidated the morphometric distinctness of the two breeds. Application of selective breeding to genetic improvement would benefit from the detected phenotypic differentiation. Other implications for management and conservation of the goats are highlighted.

Correlates of HIV knowledge and Sexual risk behaviors among Female Military Personnel

PubMed Central

Essien, E. James; Monjok, Emmanuel; Chen, Hua; Abughosh, Susan; Ekong, Ernest; Peters, Ronald J.; Holmes, Laurens; Holstad, Marcia M.; Mgbere, Osaro

2010-01-01

Objective Uniformed services personnel are at an increased risk of HIV infection. We examined the HIV/AIDS knowledge and sexual risk behaviors among female military personnel to determine the correlates of HIV risk behaviors in this population. Method The study used a cross-sectional design to examine HIV/AIDS knowledge and sexual risk behaviors in a sample of 346 females drawn from two military cantonments in Southwestern Nigeria. Data was collected between 2006 and 2008. Using bivariate analysis and multivariate logistic regression, HIV/AIDS knowledge and sexual behaviors were described in relation to socio-demographic characteristics of the participants. Results Multivariate logistic regression analysis revealed that level of education and knowing someone with HIV/AIDS were significant (p<0.05) predictors of HIV knowledge in this sample. HIV prevention self-efficacy was significantly (P<0.05) predicted by annual income and race/ethnicity. Condom use attitudes were also significantly (P<0.05) associated with number of children, annual income, and number of sexual partners. Conclusion Data indicates the importance of incorporating these predictor variables into intervention designs. PMID:20387111

Detection of Butter Adulteration with Lard by Employing (1)H-NMR Spectroscopy and Multivariate Data Analysis.

PubMed

Fadzillah, Nurrulhidayah Ahmad; Man, Yaakob bin Che; Rohman, Abdul; Rosman, Arieff Salleh; Ismail, Amin; Mustafa, Shuhaimi; Khatib, Alfi

2015-01-01

The authentication of food products from the presence of non-allowed components for certain religion like lard is very important. In this study, we used proton Nuclear Magnetic Resonance ((1)H-NMR) spectroscopy for the analysis of butter adulterated with lard by simultaneously quantification of all proton bearing compounds, and consequently all relevant sample classes. Since the spectra obtained were too complex to be analyzed visually by the naked eyes, the classification of spectra was carried out.The multivariate calibration of partial least square (PLS) regression was used for modelling the relationship between actual value of lard and predicted value. The model yielded a highest regression coefficient (R(2)) of 0.998 and the lowest root mean square error calibration (RMSEC) of 0.0091% and root mean square error prediction (RMSEP) of 0.0090, respectively. Cross validation testing evaluates the predictive power of the model. PLS model was shown as good models as the intercept of R(2)Y and Q(2)Y were 0.0853 and -0.309, respectively.

Comparative artificial neural network and partial least squares models for analysis of Metronidazole, Diloxanide, Spiramycin and Cliquinol in pharmaceutical preparations

NASA Astrophysics Data System (ADS)

Elkhoudary, Mahmoud M.; Abdel Salam, Randa A.; Hadad, Ghada M.

2014-09-01

Metronidazole (MNZ) is a widely used antibacterial and amoebicide drug. Therefore, it is important to develop a rapid and specific analytical method for the determination of MNZ in mixture with Spiramycin (SPY), Diloxanide (DIX) and Cliquinol (CLQ) in pharmaceutical preparations. This work describes simple, sensitive and reliable six multivariate calibration methods, namely linear and nonlinear artificial neural networks preceded by genetic algorithm (GA-ANN) and principle component analysis (PCA-ANN) as well as partial least squares (PLS) either alone or preceded by genetic algorithm (GA-PLS) for UV spectrophotometric determination of MNZ, SPY, DIX and CLQ in pharmaceutical preparations with no interference of pharmaceutical additives. The results manifest the problem of nonlinearity and how models like ANN can handle it. Analytical performance of these methods was statistically validated with respect to linearity, accuracy, precision and specificity. The developed methods indicate the ability of the previously mentioned multivariate calibration models to handle and solve UV spectra of the four components’ mixtures using easy and widely used UV spectrophotometer.

Hybrid least squares multivariate spectral analysis methods

DOEpatents

Haaland, David M.

2002-01-01

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

Multivariate generalized multifactor dimensionality reduction to detect gene-gene interactions

PubMed Central

2013-01-01

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

Chemometric and multivariate statistical analysis of time-of-flight secondary ion mass spectrometry spectra from complex Cu-Fe sulfides.

PubMed

Kalegowda, Yogesh; Harmer, Sarah L

2012-03-20

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) spectra of mineral samples are complex, comprised of large mass ranges and many peaks. Consequently, characterization and classification analysis of these systems is challenging. In this study, different chemometric and statistical data evaluation methods, based on monolayer sensitive TOF-SIMS data, have been tested for the characterization and classification of copper-iron sulfide minerals (chalcopyrite, chalcocite, bornite, and pyrite) at different flotation pulp conditions (feed, conditioned feed, and Eh modified). The complex mass spectral data sets were analyzed using the following chemometric and statistical techniques: principal component analysis (PCA); principal component-discriminant functional analysis (PC-DFA); soft independent modeling of class analogy (SIMCA); and k-Nearest Neighbor (k-NN) classification. PCA was found to be an important first step in multivariate analysis, providing insight into both the relative grouping of samples and the elemental/molecular basis for those groupings. For samples exposed to oxidative conditions (at Eh ~430 mV), each technique (PCA, PC-DFA, SIMCA, and k-NN) was found to produce excellent classification. For samples at reductive conditions (at Eh ~ -200 mV SHE), k-NN and SIMCA produced the most accurate classification. Phase identification of particles that contain the same elements but a different crystal structure in a mixed multimetal mineral system has been achieved.

Psycho-Cognitive Intervention for ASD from Cross-Species Behavioral Analyses of Infants, Chicks and Common Marmosets.

PubMed

Koshiba, Mamiko; Karino, Genta; Mimura, Koki; Nakamura, Shun; Yui, Kunio; Kunikata, Tetsuya; Yamanouchi, Hideo

2016-01-01

Educational treatment to support social development of children with autism spectrum disorder (ASD) is an important topic in developmental psychiatry. However, it remains difficult to objectively quantify the socio-emotional development of ASD children. To address this problem, we developed a novel analytical method that assesses subjects' complex behaviors using multivariate analysis, 'Behavior Output analysis for Quantitative Emotional State Translation' (BOUQUET). Here, we examine the potential for psycho-cognitive ASD therapy based on comparative evaluations of clinical (human) and experimental (animal) models. Our observations of ASD children (vs. their normally developing siblings) and the domestic chick in socio-sensory deprivation models show the importance of unimodal sensory stimulation, particularly important for tactile- and auditory-biased socialization. Identifying psycho-cognitive elements in early neural development, human newborn infants in neonatal intensive care unit as well as a New World monkey, the common marmoset, also prompted us to focus on the development of voluntary movement against gravity. In summary, striking behavioral similarities between children with ASD and domestic chicks' socio-sensory deprivation models support the role of multimodal sensory-motor integration as a prerequisite step for normal development of socio-emotional and psycho-cognitive functions. Data obtained in the common marmoset model also suggest that switching from primitive anti-gravity reflexes to complex voluntary movement may be a critical milestone for psycho-cognitive development. Combining clinical findings with these animal models, and using multivariate integrative analyses may facilitate the development of effective interventions to improve social functions in infants and in children with neurodevelopmental disorders.

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

DTIC Science & Technology

1982-12-20

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

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

NASA Astrophysics Data System (ADS)

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

2008-05-01

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

SUGGESTIONS FOR OPTIMIZED PLANNING OF MULTIVARIATE MONITORING OF ATMOSPHERIC POLLUTION

EPA Science Inventory

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

Multivariate Meta-Analysis Using Individual Participant Data

ERIC Educational Resources Information Center

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

2015-01-01

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

Bayesian inference for multivariate meta-analysis Box-Cox transformation models for individual patient data with applications to evaluation of cholesterol lowering drugs

PubMed Central

Kim, Sungduk; Chen, Ming-Hui; Ibrahim, Joseph G.; Shah, Arvind K.; Lin, Jianxin

2013-01-01

In this paper, we propose a class of Box-Cox transformation regression models with multidimensional random effects for analyzing multivariate responses for individual patient data (IPD) in meta-analysis. Our modeling formulation uses a multivariate normal response meta-analysis model with multivariate random effects, in which each response is allowed to have its own Box-Cox transformation. Prior distributions are specified for the Box-Cox transformation parameters as well as the regression coefficients in this complex model, and the Deviance Information Criterion (DIC) is used to select the best transformation model. Since the model is quite complex, a novel Monte Carlo Markov chain (MCMC) sampling scheme is developed to sample from the joint posterior of the parameters. This model is motivated by a very rich dataset comprising 26 clinical trials involving cholesterol lowering drugs where the goal is to jointly model the three dimensional response consisting of Low Density Lipoprotein Cholesterol (LDL-C), High Density Lipoprotein Cholesterol (HDL-C), and Triglycerides (TG) (LDL-C, HDL-C, TG). Since the joint distribution of (LDL-C, HDL-C, TG) is not multivariate normal and in fact quite skewed, a Box-Cox transformation is needed to achieve normality. In the clinical literature, these three variables are usually analyzed univariately: however, a multivariate approach would be more appropriate since these variables are correlated with each other. A detailed analysis of these data is carried out using the proposed methodology. PMID:23580436

Bayesian inference for multivariate meta-analysis Box-Cox transformation models for individual patient data with applications to evaluation of cholesterol-lowering drugs.

PubMed

Kim, Sungduk; Chen, Ming-Hui; Ibrahim, Joseph G; Shah, Arvind K; Lin, Jianxin

2013-10-15

In this paper, we propose a class of Box-Cox transformation regression models with multidimensional random effects for analyzing multivariate responses for individual patient data in meta-analysis. Our modeling formulation uses a multivariate normal response meta-analysis model with multivariate random effects, in which each response is allowed to have its own Box-Cox transformation. Prior distributions are specified for the Box-Cox transformation parameters as well as the regression coefficients in this complex model, and the deviance information criterion is used to select the best transformation model. Because the model is quite complex, we develop a novel Monte Carlo Markov chain sampling scheme to sample from the joint posterior of the parameters. This model is motivated by a very rich dataset comprising 26 clinical trials involving cholesterol-lowering drugs where the goal is to jointly model the three-dimensional response consisting of low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C), and triglycerides (TG) (LDL-C, HDL-C, TG). Because the joint distribution of (LDL-C, HDL-C, TG) is not multivariate normal and in fact quite skewed, a Box-Cox transformation is needed to achieve normality. In the clinical literature, these three variables are usually analyzed univariately; however, a multivariate approach would be more appropriate because these variables are correlated with each other. We carry out a detailed analysis of these data by using the proposed methodology. Copyright © 2013 John Wiley & Sons, Ltd.

The Effects of Geography and Spatial Behavior on Health Care Utilization among the Residents of a Rural Region

PubMed Central

Arcury, Thomas A; Gesler, Wilbert M; Preisser, John S; Sherman, Jill; Spencer, John; Perin, Jamie

2005-01-01

Objective This analysis determines the importance of geography and spatial behavior as predisposing and enabling factors in rural health care utilization, controlling for demographic, social, cultural, and health status factors. Data Sources A survey of 1,059 adults in 12 rural Appalachian North Carolina counties. Study Design This cross-sectional study used a three-stage sampling design stratified by county and ethnicity. Preliminary analysis of health services utilization compared weighted proportions of number of health care visits in the previous 12 months for regular check-up care, chronic care, and acute care across geographic, sociodemographic, cultural, and health variables. Multivariable logistic models identified independent correlates of health services utilization. Data Collection Methods Respondents answered standard survey questions. They located places in which they engaged health related and normal day-to-day activities; these data were entered into a geographic information system for analysis. Principal Findings Several geographic and spatial behavior factors, including having a driver's license, use of provided rides, and distance for regular care, were significantly related to health care utilization for regular check-up and chronic care in the bivariate analysis. In the multivariate model, having a driver's license and distance for regular care remained significant, as did several predisposing (age, gender, ethnicity), enabling (household income), and need (physical and mental health measures, number of conditions). Geographic measures, as predisposing and enabling factors, were related to regular check-up and chronic care, but not to acute care visits. Conclusions These results show the importance of geographic and spatial behavior factors in rural health care utilization. They also indicate continuing inequity in rural health care utilization that must be addressed in public policy. PMID:15663706

Study and interpretation of chemical composition of rainwater in selected urban and rural locations in India using multivariate analysis

NASA Astrophysics Data System (ADS)

Chakraborty, Bidisha; Gupta, Abhik

2018-04-01

Rainwater is an important untapped resource for all water managers and can be collected and used personally for all uses and simultaneously diverted to ground for recharge of depleting aquifers. Rain water is the most purest form of water until it is contaminated by the atmospheric pollution. Evaluation of rainwater quality analysis is also essential for non-potable applications and to match quality to specific uses. Rainwater quality analysis is, therefore, carried out to understand the problems of rainwater contamination with various pollutants. Rainwater samples were collected from the pre-monsoon season of March 2010 to post-monsoon of October 2013, from seven sampling sites namely Irongmara, Badarpur, Bongaigaon, Dolaigaon, BGR Township, Kolkata and Kharagpur, which characterised typical suburban, urban and industrialised locations respectively. A total of 943 samples were collected during this period from the sampling sites, taking utmost care in sampling and storage were analysed for heavy metals determination. Results for pH, EC, Pb, Cd, Ni, Zn, Cr and Co were reported in this study. The samples were collected using PVC bottles. The highest concentration of elements was observed at the beginning of the rainfall season when large amounts of dust accumulated in the atmosphere scavenged by rain. The values of pH in rainwater samples were relatively within the World Health Organization (WHO) standard for drinking water. Multivariate statistical analysis especially varimax rotation was applied to bring to focus the hidden yet important variables which influence the rainwater quality. It is also observed that rainwater contamination may not be restricted to industrial areas alone but vehicular emission may also contribute significantly in certain areas.

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

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

Loveday, D.L.; Craggs, C.

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

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

PubMed

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

2017-04-01

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

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.

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

ERIC Educational Resources Information Center

Bejar, Isaac I.

1981-01-01

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

A Multivariate Descriptive Model of Motivation for Orthodontic Treatment.

ERIC Educational Resources Information Center

Hackett, Paul M. W.; And Others

1993-01-01

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

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

ERIC Educational Resources Information Center

Grundmann, Matthias

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

Univariate Analysis of Multivariate Outcomes in Educational Psychology.

ERIC Educational Resources Information Center

Hubble, L. M.

1984-01-01

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

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

ERIC Educational Resources Information Center

Warner, Rebecca M.

2007-01-01

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

Evaluation of Meterorite Amono Acid Analysis Data Using Multivariate Techniques

NASA Technical Reports Server (NTRS)

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

1999-01-01

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

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

EPA Science Inventory

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

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

PubMed

Ma, Yan; Mazumdar, Madhu

2011-10-30

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

Interpreting support vector machine models for multivariate group wise analysis in neuroimaging

PubMed Central

Gaonkar, Bilwaj; Shinohara, Russell T; Davatzikos, Christos

2015-01-01

Machine learning based classification algorithms like support vector machines (SVMs) have shown great promise for turning a high dimensional neuroimaging data into clinically useful decision criteria. However, tracing imaging based patterns that contribute significantly to classifier decisions remains an open problem. This is an issue of critical importance in imaging studies seeking to determine which anatomical or physiological imaging features contribute to the classifier’s decision, thereby allowing users to critically evaluate the findings of such machine learning methods and to understand disease mechanisms. The majority of published work addresses the question of statistical inference for support vector classification using permutation tests based on SVM weight vectors. Such permutation testing ignores the SVM margin, which is critical in SVM theory. In this work we emphasize the use of a statistic that explicitly accounts for the SVM margin and show that the null distributions associated with this statistic are asymptotically normal. Further, our experiments show that this statistic is a lot less conservative as compared to weight based permutation tests and yet specific enough to tease out multivariate patterns in the data. Thus, we can better understand the multivariate patterns that the SVM uses for neuroimaging based classification. PMID:26210913

Input-output oriented computation algorithms for the control of large flexible structures

NASA Technical Reports Server (NTRS)

Minto, K. D.

1989-01-01

An overview is given of work in progress aimed at developing computational algorithms addressing two important aspects in the control of large flexible space structures; namely, the selection and placement of sensors and actuators, and the resulting multivariable control law design problem. The issue of sensor/actuator set selection is particularly crucial to obtaining a satisfactory control design, as clearly a poor choice will inherently limit the degree to which good control can be achieved. With regard to control law design, the researchers are driven by concerns stemming from the practical issues associated with eventual implementation of multivariable control laws, such as reliability, limit protection, multimode operation, sampling rate selection, processor throughput, etc. Naturally, the burden imposed by dealing with these aspects of the problem can be reduced by ensuring that the complexity of the compensator is minimized. Our approach to these problems is based on extensions to input/output oriented techniques that have proven useful in the design of multivariable control systems for aircraft engines. In particular, researchers are exploring the use of relative gain analysis and the condition number as a means of quantifying the process of sensor/actuator selection and placement for shape control of a large space platform.

Assessing admixture by multivariate analyses of phenotypic differentiation in the Algerian goat livestock.

PubMed

Ouchene-Khelifi, Nadjet-Amina; Ouchene, Nassim; Maftah, Abderrahman; Da Silva, Anne Blondeau; Lafri, Mohamed

2015-10-01

In Algeria, goat research has been largely neglected, in spite of the economic importance of this domestic species for rural livelihoods. Goat farming is traditional and cross-breeding practices are current. The phenotypic variability of the four main native breeds (Arabia, Makatia, M'zabite and Kabyle), and of two exotic breeds (Alpine and Saanen), was investigated for the first time, using multivariate discriminant analysis. A total of 892 females were sampled in a large area, including the cradle of the native breeds, and phenotyped with 23 quantitative measures and 10 qualitative traits. Our results suggested that cross-breeding practices have ever led to critical consequences, particularly for Makatia and M'zabite. The information reported in this study has to be carefully considered in order to establish governmental plan able to prevent the genetic dilution of the Algerian goat livestock.

A multivariate twin study of early literacy in Japanese Kana

PubMed Central

Fujisawa, Keiko K.; Wadsworth, Sally J.; Kakihana, Shinichiro; Olson, Richard K.; DeFries, John C.; Byrne, Brian; Ando, Juko

2013-01-01

This first Japanese twin study of early literacy development investigated the extent to which genetic and environmental factors influence individual differences in prereading skills in 238 pairs of twins at 42 months of age. Twin pairs were individually tested on measures of phonological awareness, kana letter name/sound knowledge, receptive vocabulary, visual perception, nonword repetition, and digit span. Results obtained from univariate behavioral-genetic analyses yielded little evidence for genetic influences, but substantial shared-environmental influences, for all measures. Phenotypic confirmatory factor analysis suggested three correlated factors: phonological awareness, letter name/sound knowledge, and general prereading skills. Multivariate behavioral genetic analyses confirmed relatively small genetic and substantial shared environmental influences on the factors. The correlations among the three factors were mostly attributable to shared environment. Thus, shared environmental influences play an important role in the early reading development of Japanese children. PMID:23997545

Genetic and environmental influences on female sexual orientation, childhood gender typicality and adult gender identity.

PubMed

Burri, Andrea; Cherkas, Lynn; Spector, Timothy; Rahman, Qazi

2011-01-01

Human sexual orientation is influenced by genetic and non-shared environmental factors as are two important psychological correlates--childhood gender typicality (CGT) and adult gender identity (AGI). However, researchers have been unable to resolve the genetic and non-genetic components that contribute to the covariation between these traits, particularly in women. Here we performed a multivariate genetic analysis in a large sample of British female twins (N = 4,426) who completed a questionnaire assessing sexual attraction, CGT and AGI. Univariate genetic models indicated modest genetic influences on sexual attraction (25%), AGI (11%) and CGT (31%). For the multivariate analyses, a common pathway model best fitted the data. This indicated that a single latent variable influenced by a genetic component and common non-shared environmental component explained the association between the three traits but there was substantial measurement error. These findings highlight common developmental factors affecting differences in sexual orientation.

Compulsive buying: Earlier illicit drug use, impulse buying, depression, and adult ADHD symptoms.

PubMed

Brook, Judith S; Zhang, Chenshu; Brook, David W; Leukefeld, Carl G

2015-08-30

This longitudinal study examined the association between psychosocial antecedents, including illicit drug use, and adult compulsive buying (CB) across a 29-year time period from mean age 14 to mean age 43. Participants originally came from a community-based random sample of residents in two upstate New York counties. Multivariate linear regression analysis was used to study the relationship between the participant's earlier psychosocial antecedents and adult CB in the fifth decade of life. The results of the multivariate linear regression analyses showed that gender (female), earlier adult impulse buying (IB), depressive mood, illicit drug use, and concurrent ADHD symptoms were all significantly associated with adult CB at mean age 43. It is important that clinicians treating CB in adults should consider the role of drug use, symptoms of ADHD, IB, depression, and family factors in CB. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Compulsive Buying: Earlier Illicit Drug Use, Impulse Buying, Depression, and Adult ADHD Symptoms

PubMed Central

Brook, Judith S.; Zhang, Chenshu; Brook, David W.; Leukefeld, Carl G.

2015-01-01

This longitudinal study examined the association between psychosocial antecedents, including illicit drug use, and adult compulsive buying (CB) across a 29-year time period from mean age 14 to mean age 43. Participants originally came from a community-based random sample of residents in two upstate New York counties. Multivariate linear regression analysis was used to study the relationship between the participant’s earlier psychosocial antecedents and adult CB in the fifth decade of life. The results of the multivariate linear regression analyses showed that gender (female), earlier adult impulse buying (IB), depressive mood, illicit drug use, and concurrent ADHD symptoms were all significantly associated with adult CB at mean age 43. It is important that clinicians treating CB in adults should consider the role of drug use, symptoms of ADHD, IB, depression, and family factors in CB. PMID:26165963

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

PubMed

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

2001-03-01

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

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.

Evaluation of genetic diversity among soybean (Glycine max) genotypes using univariate and multivariate analysis.

PubMed

Oliveira, M M; Sousa, L B; Reis, M C; Silva Junior, E G; Cardoso, D B O; Hamawaki, O T; Nogueira, A P O

2017-05-31

The genetic diversity study has paramount importance in breeding programs; hence, it allows selection and choice of the parental genetic divergence, which have the agronomic traits desired by the breeder. This study aimed to characterize the genetic divergence between 24 soybean genotypes through their agronomic traits, using multivariate clustering methods to select the potential genitors for the promising hybrid combinations. Six agronomic traits evaluated were number of days to flowering and maturity, plant height at flowering and maturity, insertion height of the first pod, and yield. The genetic divergence evaluated by multivariate analysis that esteemed first the Mahalanobis' generalized distance (D 2 ), then the clustering using Tocher's optimization methods, and then the unweighted pair group method with arithmetic average (UPGMA). Tocher's optimization method and the UPGMA agreed with the groups' constitution between each other, the formation of eight distinct groups according Tocher's method and seven distinct groups using UPGMA. The trait number of days for flowering (45.66%) was the most efficient to explain dissimilarity between genotypes, and must be one of the main traits considered by the breeder in the moment of genitors choice in soybean-breeding programs. The genetic variability allowed the identification of dissimilar genotypes and with superior performances. The hybridizations UFU 18 x UFUS CARAJÁS, UFU 15 x UFU 13, and UFU 13 x UFUS CARAJÁS are promising to obtain superior segregating populations, which enable the development of more productive genotypes.

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

PubMed

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

2013-11-01

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

The Pathways for Intelligible Speech: Multivariate and Univariate Perspectives

PubMed Central

Evans, S.; Kyong, J.S.; Rosen, S.; Golestani, N.; Warren, J.E.; McGettigan, C.; Mourão-Miranda, J.; Wise, R.J.S.; Scott, S.K.

2014-01-01

An anterior pathway, concerned with extracting meaning from sound, has been identified in nonhuman primates. An analogous pathway has been suggested in humans, but controversy exists concerning the degree of lateralization and the precise location where responses to intelligible speech emerge. We have demonstrated that the left anterior superior temporal sulcus (STS) responds preferentially to intelligible speech (Scott SK, Blank CC, Rosen S, Wise RJS. 2000. Identification of a pathway for intelligible speech in the left temporal lobe. Brain. 123:2400–2406.). A functional magnetic resonance imaging study in Cerebral Cortex used equivalent stimuli and univariate and multivariate analyses to argue for the greater importance of bilateral posterior when compared with the left anterior STS in responding to intelligible speech (Okada K, Rong F, Venezia J, Matchin W, Hsieh IH, Saberi K, Serences JT,Hickok G. 2010. Hierarchical organization of human auditory cortex: evidence from acoustic invariance in the response to intelligible speech. 20: 2486–2495.). Here, we also replicate our original study, demonstrating that the left anterior STS exhibits the strongest univariate response and, in decoding using the bilateral temporal cortex, contains the most informative voxels showing an increased response to intelligible speech. In contrast, in classifications using local “searchlights” and a whole brain analysis, we find greater classification accuracy in posterior rather than anterior temporal regions. Thus, we show that the precise nature of the multivariate analysis used will emphasize different response profiles associated with complex sound to speech processing. PMID:23585519

Characterizing multivariate decoding models based on correlated EEG spectral features.

PubMed

McFarland, Dennis J

2013-07-01

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

Time Series Model Identification by Estimating Information.

DTIC Science & Technology

1982-11-01

principle, Applications of Statistics, P. R. Krishnaiah , ed., North-Holland: Amsterdam, 27-41. Anderson, T. W. (1971). The Statistical Analysis of Time Series...E. (1969). Multiple Time Series Modeling, Multivariate Analysis II, edited by P. Krishnaiah , Academic Press: New York, 389-409. Parzen, E. (1981...Newton, H. J. (1980). Multiple Time Series Modeling, II Multivariate Analysis - V, edited by P. Krishnaiah , North Holland: Amsterdam, 181-197. Shibata, R

Genomic Analysis of Complex Microbial Communities in Wounds

DTIC Science & Technology

2012-01-01

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

Application of reiteration of Hankel singular value decomposition in quality control

NASA Astrophysics Data System (ADS)

Staniszewski, Michał; Skorupa, Agnieszka; Boguszewicz, Łukasz; Michalczuk, Agnieszka; Wereszczyński, Kamil; Wicher, Magdalena; Konopka, Marek; Sokół, Maria; Polański, Andrzej

2017-07-01

Medical centres are obliged to store past medical records, including the results of quality assurance (QA) tests of the medical equipment, which is especially useful in checking reproducibility of medical devices and procedures. Analysis of multivariate time series is an important part of quality control of NMR data. In this work we proposean anomaly detection tool based on Reiteration of Hankel Singular Value Decomposition method. The presented method was compared with external software and authors obtained comparable results.

[The applied of computer for analysis on contraceptive efficacy of IUD of rural women in Guangdong province].

PubMed

Jia, G H

1989-12-01

This paper discussed the usage and effect of IUD-type O use for rural married women in Guangdong province. The continuation rate of IUD-type O is 71.7 per cent 100 women in one year. The main problem for failure was expulsion. This paper have used a combination of univariate and multivariate analytic methods. On the whole, the important factors were number of gravid and parity, number of induced abortion and medical technical level etc.

Ethnic differences in self reported health in Malmö in southern Sweden

PubMed Central

Lindstrom, M; Sundquist, J; Ostergren, P

2001-01-01

STUDY OBJECTIVE—The aim of this study was to investigate ethnic differences in self reported health in the city of Malmö, Sweden, and whether these differences could be explained by psychosocial and economic conditions.
DESIGN/SETTING/PARTICIPANTS—The public health survey in Malmö 1994 was a cross sectional study. A total of 5600 people aged 20-80 years completed a postal questionnaire. The participation rate was 71%. The population was categorised according to country of origin: born in Sweden, other Western countries, Yugoslavia, Poland, Arabic speaking countries and all other countries. The multivariate analysis was performed using a logistic regression model in order to investigate the importance of possible confounders on the differences by country of origin in self reported health. Finally, variables measuring psychosocial and economic conditions were introduced into the model.
MAIN RESULTS—The odds ratios of having poor self reported health were significantly higher among men born in other Western countries, Yugoslavia, Arabic speaking countries and in the category all other countries, as well as among women born in Yugoslavia, Poland and all other countries, compared with men and women born in Sweden. The multivariate analysis including age and education did not change these results. A huge reduction of the odds ratios was observed for men and women born in Yugoslavia, Arabic speaking countries and all other countries, and for women born in Poland after the introduction of the social network, social support and economic factors into the multivariate model.
CONCLUSIONS—There were significant ethnic group differences in self reported health. These differences were greatly reduced by psychosocial and economic factors, which suggest that these factors may be important determinants of self rated health in certain minority groups.


Keywords: self reported health; social network; social support PMID:11154248

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

Wong, Jonathan; Xu, Beibei; Moores Cancer Center, University of California San Diego, La Jolla, California

Purpose/Objective: Palliative radiation therapy represents an important treatment option among patients with advanced cancer, although research shows decreased use among older patients. This study evaluated age-related patterns of palliative radiation use among an elderly Medicare population. Methods and Materials: We identified 63,221 patients with metastatic lung, breast, prostate, or colorectal cancer diagnosed between 2000 and 2007 from the Surveillance, Epidemiology, and End Results (SEER)-Medicare linked database. Receipt of palliative radiation therapy was extracted from Medicare claims. Multivariate Poisson regression analysis determined residual age-related disparity in the receipt of palliative radiation therapy after controlling for confounding covariates including age-related differences inmore » patient and demographic covariates, length of life, and patient preferences for aggressive cancer therapy. Results: The use of radiation decreased steadily with increasing patient age. Forty-two percent of patients aged 66 to 69 received palliative radiation therapy. Rates of palliative radiation decreased to 38%, 32%, 24%, and 14% among patients aged 70 to 74, 75 to 79, 80 to 84, and over 85, respectively. Multivariate analysis found that confounding covariates attenuated these findings, although the decreased relative rate of palliative radiation therapy among the elderly remained clinically and statistically significant. On multivariate analysis, compared to patients 66 to 69 years old, those aged 70 to 74, 75 to 79, 80 to 84, and over 85 had a 7%, 15%, 25%, and 44% decreased rate of receiving palliative radiation, respectively (all P<.0001). Conclusions: Age disparity with palliative radiation therapy exists among older cancer patients. Further research should strive to identify barriers to palliative radiation among the elderly, and extra effort should be made to give older patients the opportunity to receive this quality of life-enhancing treatment at the end of life.« less

Urinary bladder cancer treated with radical cystectomy: perioperative parameters and early complications prospectively registered in a national population-based database.

PubMed

Jerlström, Tomas; Gårdmark, Truls; Carringer, Malcolm; Holmäng, Sten; Liedberg, Fredrik; Hosseini, Abolfazl; Malmström, Per-Uno; Ljungberg, Börje; Hagberg, Oskar; Jahnson, Staffan

2014-08-01

Cystectomy combined with pelvic lymph-node dissection and urinary diversion entails high morbidity and mortality. Improvements are needed, and a first step is to collect information on the current situation. In 2011, this group took the initiative to start a population-based database in Sweden (population 9.5 million in 2011) with prospective registration of patients and complications until 90 days after cystectomy. This article reports findings from the first year of registration. Participation was voluntary, and data were reported by local urologists or research nurses. Perioperative parameters and early complications classified according to the modified Clavien system were registered, and selected variables of possible importance for complications were analysed by univariate and multivariate logistic regression. During 2011, 285 (65%) of 435 cystectomies performed in Sweden were registered in the database, the majority reported by the seven academic centres. Median blood loss was 1000 ml, operating time 318 min, and length of hospital stay 15 days. Any complications were registered for 103 patients (36%). Clavien grades 1-2 and 3-5 were noted in 19% and 15%, respectively. Thirty-seven patients (13%) were reoperated on at least once. In logistic regression analysis elevated risk of complications was significantly associated with operating time exceeding 318 min in both univariate and multivariate analysis, and with age 76-89 years only in multivariate analysis. It was feasible to start a national population-based registry of radical cystectomies for bladder cancer. The evaluation of the first year shows an increased risk of complications in patients with longer operating time and higher age. The results agree with some previously published series but should be interpreted with caution considering the relatively low coverage, which is expected to be higher in the future.

Searching for forcing signatures in decadal patterns of shoreline change

NASA Astrophysics Data System (ADS)

Burningham, H.; French, J.

2016-12-01

Analysis of shoreline position at spatial scales of the order 10 - 100 km and at a multi-decadal time-scale has the potential to reveal regional coherence (or lack of) in the primary controls on shoreline tendencies and trends. Such information is extremely valuable for the evaluation of climate forcing on coastal behaviour. Segmenting a coast into discrete behaviour units based on these types of analyses is often subjective, however, and in the context of pervasive human interventions and alongshore variability in ocean climate, determining the most important controls on shoreline dynamics can be challenging. Multivariate analyses provide one means to resolve common behaviours across shoreline position datasets, thereby underpinning a more objective evaluation of possible coupling between shorelines at different scales. In an analysis of the Suffolk coast (eastern England) we explore the use of multivariate statistics to understand and classify mesoscale coastal behaviour. Suffolk comprises a relatively linear shoreline that shifts from east-facing in the north to southeast-facing in the south. Although primarily formed of a beach foreshore backed by cliffs or shingle barrier, the shoreline is punctuated at 3 locations by narrow tidal inlets with offset entrances that imply a persistent north to south sediment transport direction. Tidal regime decreases south to north from mesotidal (3.6m STR) to microtidal (1.9m STR), and the bimodal wave climate (northeast and southwest modes) presents complex local-scale variability in nearshore conditions. Shorelines exhibit a range of decadal behaviours from rapid erosion (up to 4m/yr) to quasi-stability that cannot be directly explained by the spatial organisation of contemporary landforms or coastal defences. A multivariate statistical approach to shoreline change analysis helps to define the key modes of change and determine the most likely forcing factors.

Association between thoracic aortic disease and inguinal hernia.

PubMed

Olsson, Christian; Eriksson, Per; Franco-Cereceda, Anders

2014-08-21

The study hypothesis was that thoracic aortic disease (TAD) is associated with a higher-than-expected prevalence of inguinal hernia. Such an association has been reported for abdominal aortic aneurysm (AAA) and hernia. Unlike AAA, TAD is not necessarily detectable with clinical examination or ultrasound, and there are no population-based screening programs for TAD. Therefore, conditions associated with TAD, such as inguinal hernia, are of particular clinical relevance. The prevalence of inguinal hernia in subjects with TAD was determined from nation-wide register data and compared to a non-TAD group (patients with isolated aortic stenosis). Groups were balanced using propensity score matching. Multivariable statistical analysis (logistic regression) was performed to identify variables independently associated with hernia. Hernia prevalence was 110 of 750 (15%) in subjects with TAD versus 29 of 301 (9.6%) in non-TAD, P=0.03. This statistically significant difference remained after propensity score matching: 21 of 159 (13%) in TAD versus 14 of 159 (8.9%) in non-TAD, P<0.001. Variables independently associated with hernia in multivariable analysis were male sex (odds ratio [OR] with 95% confidence interval [95% CI]) 3.4 (2.1 to 5.4), P<0.001; increased age, OR 1.02/year (1.004 to 1.04), P=0.014; and TAD, OR 1.8 (1.1 to 2.8), P=0.015. The prevalence of inguinal hernia (15%) in TAD is higher than expected in a general population and higher in TAD, compared to non-TAD. TAD is independently associated with hernia in multivariable analysis. Presence or history of hernia may be of importance in detecting TAD, and the association warrants further study. © 2014 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell.

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

DOE PAGES

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

2007-11-10

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

Hybrid least squares multivariate spectral analysis methods

DOEpatents

Haaland, David M.

2004-03-23

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

Evaluating Measurement of Dynamic Constructs: Defining a Measurement Model of Derivatives

PubMed Central

Estabrook, Ryne

2015-01-01

While measurement evaluation has been embraced as an important step in psychological research, evaluating measurement structures with longitudinal data is fraught with limitations. This paper defines and tests a measurement model of derivatives (MMOD), which is designed to assess the measurement structure of latent constructs both for analyses of between-person differences and for the analysis of change. Simulation results indicate that MMOD outperforms existing models for multivariate analysis and provides equivalent fit to data generation models. Additional simulations show MMOD capable of detecting differences in between-person and within-person factor structures. Model features, applications and future directions are discussed. PMID:24364383

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

USGS Publications Warehouse

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

1998-01-01

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

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

Treesearch

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

1996-01-01

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

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

ERIC Educational Resources Information Center

Tchumtchoua, Sylvie; Dey, Dipak K.

2012-01-01

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

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

PubMed Central

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

2003-01-01

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

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

PubMed

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

2012-11-01

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

Multivariate meta-analysis using individual participant data.

PubMed

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

2015-06-01

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

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

PubMed

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

2017-01-01

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

Multivariate analysis of longitudinal rates of change.

PubMed

Bryan, Matthew; Heagerty, Patrick J

2016-12-10

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

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

PubMed

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

2018-02-01

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

Simultaneous Evaluation of Life Cycle Dynamics between a Host Paramecium and the Endosymbionts of Paramecium bursaria Using Capillary Flow Cytometry.

PubMed

Takahashi, Toshiyuki

2016-08-17

Endosymbioses are driving forces underlying cell evolution. The endosymbiosis exhibited by Paramecium bursaria is an excellent model with which to study symbiosis. A single-cell microscopic analysis of P. bursaria reveals that endosymbiont numbers double when the host is in the division phase. Consequently, endosymbionts must arrange their cell cycle schedule if the culture-condition-dependent change delays the generation time of P. bursaria. However, it remains poorly understood whether endosymbionts keep pace with the culture-condition-dependent behaviors of P. bursaria, or not. Using microscopy and flow cytometry, this study investigated the life cycle behaviors occurring between endosymbionts and the host. To establish a connection between the host cell cycle and endosymbionts comprehensively, multivariate analysis was applied. The multivariate analysis revealed important information related to regulation between the host and endosymbionts. Results show that dividing endosymbionts underwent transition smoothly from the division phase to interphase, when the host was in the logarithmic phase. In contrast, endosymbiont division stagnated when the host was in the stationary phase. This paper explains that endosymbionts fine-tune their cell cycle pace with their host and that a synchronous life cycle between the endosymbionts and the host is guaranteed in the symbiosis of P. bursaria.

Simultaneous Evaluation of Life Cycle Dynamics between a Host Paramecium and the Endosymbionts of Paramecium bursaria Using Capillary Flow Cytometry

PubMed Central

Takahashi, Toshiyuki

2016-01-01

Endosymbioses are driving forces underlying cell evolution. The endosymbiosis exhibited by Paramecium bursaria is an excellent model with which to study symbiosis. A single-cell microscopic analysis of P. bursaria reveals that endosymbiont numbers double when the host is in the division phase. Consequently, endosymbionts must arrange their cell cycle schedule if the culture-condition-dependent change delays the generation time of P. bursaria. However, it remains poorly understood whether endosymbionts keep pace with the culture-condition-dependent behaviors of P. bursaria, or not. Using microscopy and flow cytometry, this study investigated the life cycle behaviors occurring between endosymbionts and the host. To establish a connection between the host cell cycle and endosymbionts comprehensively, multivariate analysis was applied. The multivariate analysis revealed important information related to regulation between the host and endosymbionts. Results show that dividing endosymbionts underwent transition smoothly from the division phase to interphase, when the host was in the logarithmic phase. In contrast, endosymbiont division stagnated when the host was in the stationary phase. This paper explains that endosymbionts fine-tune their cell cycle pace with their host and that a synchronous life cycle between the endosymbionts and the host is guaranteed in the symbiosis of P. bursaria. PMID:27531180

Fludarabine Melphalan reduced-intensity conditioning allotransplanation provides similar disease control in lymphoid and myeloid malignancies: analysis of 344 patients.

PubMed

Bryant, A; Nivison-Smith, I; Pillai, E S; Kennedy, G; Kalff, A; Ritchie, D; George, B; Hertzberg, M; Patil, S; Spencer, A; Fay, K; Cannell, P; Berkahn, L; Doocey, R; Spearing, R; Moore, J

2014-01-01

This was an Australasian Bone Marrow Transplant Recipient Registry (ABMTRR)-based retrospective study assessing the outcome of Fludarabine Melphalan (FluMel) reduced-intensity conditioning between 1998 and 2008. Median follow-up was 3.4 years. There were 344 patients with a median age of 54 years (18-68). In all, 234 patients had myeloid malignancies, with AML (n=166) being the commonest indication. There were 110 lymphoid patients with non-hodgkins lymphoma (NHL) (n=64) the main indication. TRM at day 100 was 14% with no significant difference between the groups. OS and disease-free survival (DFS) were similar between myeloid and lymphoid patients (57 and 50% at 3 years, respectively). There was no difference in cumulative incidence of relapse or GVHD between groups. Multivariate analysis revealed four significant adverse risk factors for DFS: donor other than HLA-identical sibling donor, not in remission at transplant, previous autologous transplant and recipient CMV positive. Chronic GVHD was associated with improved DFS in multivariate analysis predominantly due to a marked reduction in relapse (HR:0.44, P=0.003). This study confirms that FluMel provides durable and equivalent remissions in both myeloid and lymphoid malignancies. Disease stage and chronic GVHD remain important determinants of outcome for FluMel allografting.

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

PubMed

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

2017-05-15

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

H. Pylori as a predictor of marginal ulceration: A nationwide analysis.

PubMed

Schulman, Allison R; Abougergi, Marwan S; Thompson, Christopher C

2017-03-01

Helicobacter pylori has been implicated as a risk factor for development of marginal ulceration following gastric bypass, although studies have been small and yielded conflicting results. This study sought to determine the relationship between H. pylori infection and development of marginal ulceration following bariatric surgery in a nationwide analysis. This was a retrospective cohort study using the 2012 Nationwide Inpatient Sample (NIS) database. Discharges with ICD-9-CM code indicating marginal ulceration and a secondary ICD-9-CM code for bariatric surgery were included. Primary outcome was incidence of marginal ulceration. A stepwise forward selection model was used to build the multivariate logistic regression model based on known risk factors. A P value of 0.05 was considered significant. There were 253,765 patients who met inclusion criteria. Prevalence of marginal ulceration was 3.90%. Of those patients found to have marginal ulceration, 31.20% of patients were H. pylori-positive. Final multivariate regression analysis revealed that H. pylori was the strongest independent predictor of marginal ulceration. H. pylori is an independent predictor of marginal ulceration using a large national database. Preoperative testing for and eradication of H. pylori prior to bariatric surgery may be an important preventive measure to reduce the incidence of ulcer development. © 2017 The Obesity Society.

Multivariate statistical analysis of a high rate biofilm process treating kraft mill bleach plant effluent.

PubMed

Goode, C; LeRoy, J; Allen, D G

2007-01-01

This study reports on a multivariate analysis of the moving bed biofilm reactor (MBBR) wastewater treatment system at a Canadian pulp mill. The modelling approach involved a data overview by principal component analysis (PCA) followed by partial least squares (PLS) modelling with the objective of explaining and predicting changes in the BOD output of the reactor. Over two years of data with 87 process measurements were used to build the models. Variables were collected from the MBBR control scheme as well as upstream in the bleach plant and in digestion. To account for process dynamics, a variable lagging approach was used for variables with significant temporal correlations. It was found that wood type pulped at the mill was a significant variable governing reactor performance. Other important variables included flow parameters, faults in the temperature or pH control of the reactor, and some potential indirect indicators of biomass activity (residual nitrogen and pH out). The most predictive model was found to have an RMSEP value of 606 kgBOD/d, representing a 14.5% average error. This was a good fit, given the measurement error of the BOD test. Overall, the statistical approach was effective in describing and predicting MBBR treatment performance.

The lexical development of children with hearing impairment and associated factors.

PubMed

Penna, Leticia Macedo; Lemos, Stela Maris Aguiar; Alves, Cláudia Regina Lindgren

2014-01-01

This study aimed at analyzing the association between the lexical development of children with hearing impairment and their psychosocial and socioeconomic characteristics and medical history. An analytic transversal study was conducted in an Auditive Health Attention Service. One hundred and ten children from 6 to 10 years old using hearing aids and presenting hearing loss that ranged from light to deep levels were evaluated. All children were subjected to oral, written language and auditory perception tests. Parents answered a structured questionnaire to collect data from their medical history and socioeconomic status, and questionnaires about the features of the family environment and psychosocial characteristics. Multivariate analysis was performed by logistic regression, being the initial model composed by variables with p<0,20 in the univariate analysis. In the final model, we adopted a significance level of 5%. The final model of the multivariate analysis showed an association between the performance on the vocabulary test and the results of phonemic discrimination test (OR=0.81; 95%CI 0.73-0.89). The results show the importance of stimulating the auditory processing, particularly the phonemic discrimination skill, throughout the rehabilitation process of children with hearing impairment. This stimulation can enhance lexical development and minimize the metalanguage and learning difficulties often observed in these children.

Characterization of cytochrome c as marker for retinal cell degeneration by uv/vis spectroscopic imaging

NASA Astrophysics Data System (ADS)

Hollmach, Julia; Schweizer, Julia; Steiner, Gerald; Knels, Lilla; Funk, Richard H. W.; Thalheim, Silko; Koch, Edmund

2011-07-01

Retinal diseases like age-related macular degeneration have become an important cause of visual loss depending on increasing life expectancy and lifestyle habits. Due to the fact that no satisfying treatment exists, early diagnosis and prevention are the only possibilities to stop the degeneration. The protein cytochrome c (cyt c) is a suitable marker for degeneration processes and apoptosis because it is a part of the respiratory chain and involved in the apoptotic pathway. The determination of the local distribution and oxidative state of cyt c in living cells allows the characterization of cell degeneration processes. Since cyt c exhibits characteristic absorption bands between 400 and 650 nm wavelength, uv/vis in situ spectroscopic imaging was used for its characterization in retinal ganglion cells. The large amount of data, consisting of spatial and spectral information, was processed by multivariate data analysis. The challenge consists in the identification of the molecular information of cyt c. Baseline correction, principle component analysis (PCA) and cluster analysis (CA) were performed in order to identify cyt c within the spectral dataset. The combination of PCA and CA reveals cyt c and its oxidative state. The results demonstrate that uv/vis spectroscopic imaging in conjunction with sophisticated multivariate methods is a suitable tool to characterize cyt c under in situ conditions.

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

PubMed

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

2017-10-01

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

Link between perceived smoking behaviour at school and students smoking status: a large survey among Italian adolescents.

PubMed

Backhaus, I; D'Egidio, V; Grassucci, D; Gelardini, M; Ardizzone, C; La Torre, G

2017-10-01

To investigate a possible link between sociodemographic factors, the perception of smoking habits at school and smoking status of Italian adolescents attending secondary school. The study was a cross-sectional study. An anonymous online survey was employed to gather information on age, gender, smoking status and to examine the perception of smoking behaviour on the school premises. Chi-squared and Kruskal-Wallis tests were performed for the univariate analysis and logistic and multinomial regressions for the multivariate analysis. The statistical analyses included 1889 students. Univariate analysis showed significant differences concerning knowledge between smoker and non-smoker concerning the harmfulness of smoking (P < 0.001). According to the multivariate analysis smokers had a higher perception of teacher, principal or janitor smoking at school (odds ratio: 1.54 [95% confidence interval 1.26-1.89]). Students older than 19 years most often begin smoking because their friends smoke compared with younger students (adjusted odds ratio: 1.18 [95% confidence interval 0.48-2.89]). School environment and behaviour of role models play a crucial part in student smoking. To prevent and reduce youth tobacco smoking, not merely the presence of preventive measures is important but greater attention needs to be placed on the enforcement of smoking policies. Copyright © 2017 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.

Cocaine dependence and thalamic functional connectivity: a multivariate pattern analysis.

PubMed

Zhang, Sheng; Hu, Sien; Sinha, Rajita; Potenza, Marc N; Malison, Robert T; Li, Chiang-Shan R

2016-01-01

Cocaine dependence is associated with deficits in cognitive control. Previous studies demonstrated that chronic cocaine use affects the activity and functional connectivity of the thalamus, a subcortical structure critical for cognitive functioning. However, the thalamus contains nuclei heterogeneous in functions, and it is not known how thalamic subregions contribute to cognitive dysfunctions in cocaine dependence. To address this issue, we used multivariate pattern analysis (MVPA) to examine how functional connectivity of the thalamus distinguishes 100 cocaine-dependent participants (CD) from 100 demographically matched healthy control individuals (HC). We characterized six task-related networks with independent component analysis of fMRI data of a stop signal task and employed MVPA to distinguish CD from HC on the basis of voxel-wise thalamic connectivity to the six independent components. In an unbiased model of distinct training and testing data, the analysis correctly classified 72% of subjects with leave-one-out cross-validation (p < 0.001), superior to comparison brain regions with similar voxel counts (p < 0.004, two-sample t test). Thalamic voxels that form the basis of classification aggregate in distinct subclusters, suggesting that connectivities of thalamic subnuclei distinguish CD from HC. Further, linear regressions provided suggestive evidence for a correlation of the thalamic connectivities with clinical variables and performance measures on the stop signal task. Together, these findings support thalamic circuit dysfunction in cognitive control as an important neural marker of cocaine dependence.

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

DTIC Science & Technology

1980-07-08

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

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

DTIC Science & Technology

2015-01-01

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

Multivariate optimum interpolation of surface pressure and winds over oceans

NASA Technical Reports Server (NTRS)

Bloom, S. C.

1984-01-01

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

Nonlinear multivariate and time series analysis by neural network methods

NASA Astrophysics Data System (ADS)

Hsieh, William W.

2004-03-01

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

The Importance of Factors Related to Nurse Retention: Using the Baptist Health Nurse Retention Questionnaire, Part 2.

PubMed

Bugajski, Andrew; Lengerich, Alex; Marchese, Matthew; Hall, Brittany; Yackzan, Susan; Davies, Claire; Brockopp, Dorothy

2017-06-01

The purpose of this study was to examine the importance of factors related to nurse retention. Retaining nurses within the healthcare system is a challenge for hospital administrators. Understanding factors important to nurse retention is essential. Responses of nurses (n = 279) to the Baptist Health Nurse Retention Questionnaire (BHNRQ) at a 391-bed Magnet® redesignated community hospital were analyzed to explore differences in importance scores of bedside nurses. The results demonstrate that each of the 12 items on the BHNRQ was moderately to highly important. A multivariate analysis of variance based on generation, degree, unit, and experience revealed no significant differences on subscale scores (nursing practice, management, and staffing). Themes derived from the comment section on the BHNRQ were consistent with quantitative findings. Clinical and managerial competence, engagement with their employees, and presence on the unit are keys to retaining a satisfied nursing workforce.

Comparative study on fast classification of brick samples by combination of principal component analysis and linear discriminant analysis using stand-off and table-top laser-induced breakdown spectroscopy

NASA Astrophysics Data System (ADS)

Vítková, Gabriela; Prokeš, Lubomír; Novotný, Karel; Pořízka, Pavel; Novotný, Jan; Všianský, Dalibor; Čelko, Ladislav; Kaiser, Jozef

2014-11-01

Focusing on historical aspect, during archeological excavation or restoration works of buildings or different structures built from bricks it is important to determine, preferably in-situ and in real-time, the locality of bricks origin. Fast classification of bricks on the base of Laser-Induced Breakdown Spectroscopy (LIBS) spectra is possible using multivariate statistical methods. Combination of principal component analysis (PCA) and linear discriminant analysis (LDA) was applied in this case. LIBS was used to classify altogether the 29 brick samples from 7 different localities. Realizing comparative study using two different LIBS setups - stand-off and table-top it is shown that stand-off LIBS has a big potential for archeological in-field measurements.

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

PubMed

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

2009-05-30

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

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

PubMed

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

2009-01-01

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

PYCHEM: a multivariate analysis package for python.

PubMed

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

2006-10-15

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

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

PubMed

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

2017-12-01

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

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

PubMed Central

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

2016-01-01

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

Kernel canonical-correlation Granger causality for multiple time series

NASA Astrophysics Data System (ADS)

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

2011-04-01

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

Multivariate geometry as an approach to algal community analysis

USGS Publications Warehouse

Allen, T.F.H.; Skagen, S.

1973-01-01

Multivariate analyses are put in the context of more usual approaches to phycological investigations. The intuitive common-sense involved in methods of ordination, classification and discrimination are emphasised by simple geometric accounts which avoid jargon and matrix algebra. Warnings are given that artifacts result from technique abuses by the naive or over-enthusiastic. An analysis of a simple periphyton data set is presented as an example of the approach. Suggestions are made as to situations in phycological investigations, where the techniques could be appropriate. The discipline is reprimanded for its neglect of the multivariate approach.

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.

Analysis of Exhaled Breath Volatile Organic Compounds in Inflammatory Bowel Disease: A Pilot Study.

PubMed

Hicks, Lucy C; Huang, Juzheng; Kumar, Sacheen; Powles, Sam T; Orchard, Timothy R; Hanna, George B; Williams, Horace R T

2015-09-01

Distinguishing between the inflammatory bowel diseases [IBD], Crohn's disease [CD] and ulcerative colitis [UC], is important for determining management and prognosis. Selected ion flow tube mass spectrometry [SIFT-MS] may be used to analyse volatile organic compounds [VOCs] in exhaled breath: these may be altered in disease states, and distinguishing breath VOC profiles can be identified. The aim of this pilot study was to identify, quantify, and analyse VOCs present in the breath of IBD patients and controls, potentially providing insights into disease pathogenesis and complementing current diagnostic algorithms. SIFT-MS breath profiling of 56 individuals [20 UC, 18 CD, and 18 healthy controls] was undertaken. Multivariate analysis included principal components analysis and partial least squares discriminant analysis with orthogonal signal correction [OSC-PLS-DA]. Receiver operating characteristic [ROC] analysis was performed for each comparative analysis using statistically significant VOCs. OSC-PLS-DA modelling was able to distinguish both CD and UC from healthy controls and from one other with good sensitivity and specificity. ROC analysis using combinations of statistically significant VOCs [dimethyl sulphide, hydrogen sulphide, hydrogen cyanide, ammonia, butanal, and nonanal] gave integrated areas under the curve of 0.86 [CD vs healthy controls], 0.74 [UC vs healthy controls], and 0.83 [CD vs UC]. Exhaled breath VOC profiling was able to distinguish IBD patients from controls, as well as to separate UC from CD, using both multivariate and univariate statistical techniques. Copyright © 2015 European Crohn’s and Colitis Organisation (ECCO). Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Tracking Problem Solving by Multivariate Pattern Analysis and Hidden Markov Model Algorithms

ERIC Educational Resources Information Center

Anderson, John R.

2012-01-01

Multivariate pattern analysis can be combined with Hidden Markov Model algorithms to track the second-by-second thinking as people solve complex problems. Two applications of this methodology are illustrated with a data set taken from children as they interacted with an intelligent tutoring system for algebra. The first "mind reading" application…

Functional Path Analysis as a Multivariate Technique in Developing a Theory of Participation in Adult Education.

ERIC Educational Resources Information Center

Martin, James L.

This paper reports on attempts by the author to construct a theoretical framework of adult education participation using a theory development process and the corresponding multivariate statistical techniques. Two problems are identified: the lack of theoretical framework in studying problems, and the limiting of statistical analysis to univariate…

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

ERIC Educational Resources Information Center

Finch, W. Holmes

2016-01-01

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

Web-Based Tools for Modelling and Analysis of Multivariate Data: California Ozone Pollution Activity

ERIC Educational Resources Information Center

Dinov, Ivo D.; Christou, Nicolas

2011-01-01

This article presents a hands-on web-based activity motivated by the relation between human health and ozone pollution in California. This case study is based on multivariate data collected monthly at 20 locations in California between 1980 and 2006. Several strategies and tools for data interrogation and exploratory data analysis, model fitting…

Bias and Precision of Measures of Association for a Fixed-Effect Multivariate Analysis of Variance Model

ERIC Educational Resources Information Center

Kim, Soyoung; Olejnik, Stephen

2005-01-01

The sampling distributions of five popular measures of association with and without two bias adjusting methods were examined for the single factor fixed-effects multivariate analysis of variance model. The number of groups, sample sizes, number of outcomes, and the strength of association were manipulated. The results indicate that all five…

Multivariate analysis of climate along the southern coast of Alaska—some forestry implications.

Treesearch

Wilbur A. Farr; John S. Hard

1987-01-01

A multivariate analysis of climate was used to delineate 10 significantly different groups of climatic stations along the southern coast of Alaska based on latitude, longitude, seasonal temperatures and precipitation, frost-free periods, and total number of growing degree days. The climatic stations were too few to delineate this rugged, mountainous region into...

The Emerging Field of Quantitative Blood Metabolomics for Biomarker Discovery in Critical Illnesses

PubMed Central

Serkova, Natalie J.; Standiford, Theodore J.

2011-01-01

Metabolomics, a science of systems biology, is the global assessment of endogenous metabolites within a biologic system and represents a “snapshot” reading of gene function, enzyme activity, and the physiological landscape. Metabolite detection, either individual or grouped as a metabolomic profile, is usually performed in cells, tissues, or biofluids by either nuclear magnetic resonance spectroscopy or mass spectrometry followed by sophisticated multivariate data analysis. Because loss of metabolic homeostasis is common in critical illness, the metabolome could have many applications, including biomarker and drug target identification. Metabolomics could also significantly advance our understanding of the complex pathophysiology of acute illnesses, such as sepsis and acute lung injury/acute respiratory distress syndrome. Despite this potential, the clinical community is largely unfamiliar with the field of metabolomics, including the methodologies involved, technical challenges, and, most importantly, clinical uses. Although there is evidence of successful preclinical applications, the clinical usefulness and application of metabolomics in critical illness is just beginning to emerge, the advancement of which hinges on linking metabolite data to known and validated clinically relevant indices. In addition, other important aspects, such as patient selection, sample collection, and processing, as well as the needed multivariate data analysis, have to be taken into consideration before this innovative approach to biomarker discovery can become a reliable tool in the intensive care unit. The purpose of this review is to begin to familiarize clinicians with the field of metabolomics and its application for biomarker discovery in critical illnesses such as sepsis. PMID:21680948

The Plant Ionome Revisited by the Nutrient Balance Concept

PubMed Central

Parent, Serge-Étienne; Parent, Léon Etienne; Egozcue, Juan José; Rozane, Danilo-Eduardo; Hernandes, Amanda; Lapointe, Line; Hébert-Gentile, Valérie; Naess, Kristine; Marchand, Sébastien; Lafond, Jean; Mattos, Dirceu; Barlow, Philip; Natale, William

2013-01-01

Tissue analysis is commonly used in ecology and agronomy to portray plant nutrient signatures. Nutrient concentration data, or ionomes, belong to the compositional data class, i.e., multivariate data that are proportions of some whole, hence carrying important numerical properties. Statistics computed across raw or ordinary log-transformed nutrient data are intrinsically biased, hence possibly leading to wrong inferences. Our objective was to present a sound and robust approach based on a novel nutrient balance concept to classify plant ionomes. We analyzed leaf N, P, K, Ca, and Mg of two wild and six domesticated fruit species from Canada, Brazil, and New Zealand sampled during reproductive stages. Nutrient concentrations were (1) analyzed without transformation, (2) ordinary log-transformed as commonly but incorrectly applied in practice, (3) additive log-ratio (alr) transformed as surrogate to stoichiometric rules, and (4) converted to isometric log-ratios (ilr) arranged as sound nutrient balance variables. Raw concentration and ordinary log transformation both led to biased multivariate analysis due to redundancy between interacting nutrients. The alr- and ilr-transformed data provided unbiased discriminant analyses of plant ionomes, where wild and domesticated species formed distinct groups and the ionomes of species and cultivars were differentiated without numerical bias. The ilr nutrient balance concept is preferable to alr, because the ilr technique projects the most important interactions between nutrients into a convenient Euclidean space. This novel numerical approach allows rectifying historical biases and supervising phenotypic plasticity in plant nutrition studies. PMID:23526060

Amino acid substitutions in the hepatitis C virus core region predict hepatocarcinogenesis following eradication of HCV RNA by all-oral direct-acting antiviral regimens.

PubMed

Ogata, Fumihiro; Akuta, Norio; Kobayashi, Masahiro; Fujiyama, Shunichiro; Kawamura, Yusuke; Sezaki, Hitomi; Hosaka, Tetsuya; Kobayashi, Mariko; Saitoh, Satoshi; Suzuki, Yoshiyuki; Suzuki, Fumitaka; Arase, Yasuji; Ikeda, Kenji; Kumada, Hiromitsu

2018-06-01

Impact of substitution of aa70 in the core region (Core aa70) in HCV genotype 1b (HCV-1b) on hepatocarcinogenesis following eradication of HCV RNA by direct-acting antiviral therapy is not clear. In a retrospective study, 533 patients with HCV-related chronic liver disease, with sustained virological response defined as negative HCV RNA at 12 weeks after cessation of direct-acting antiviral therapy, were examined to evaluate the relationship between Core aa70 substitution and hepatocarcinogenesis. Twelve patients developed hepatocellular carcinoma during the follow-up period. The cumulative hepatocarcinogenesis rates were 1.7% and 2.4% at the end of 1 and 2 years, respectively. Overall, multivariate analysis identified HCV subgroup (HCV-1b with Gln70(His70); P = 0.003) and age (>65 years; P = 0.049), as pretreatment predictors of hepatocarcinogenesis. In HCV-1b patients, multivariate analysis identified post-treatment Wisteria floribunda agglutinin positive Mac-2 binding protein (>1.8 COI; P = 0.042) and HCV subgroup (HCV-1b with Gln70(His70); P = 0.071), as predictors of hepatocarcinogenesis, including post-treatment parameter. In conclusion, Core aa70 substitution in HCV-1b at the start of direct-acting antiviral therapy is an important predictor of hepatocarcinogenesis following eradication of HCV RNA. This study emphasizes the importance of detection of Core aa70 substitution before initiating antiviral therapy. © 2018 Wiley Periodicals, Inc.

Analysis of HIV Correlated Factors in Chinese and Vietnamese Female Sex Workers in Hekou, Yunnan Province, a Chinese Border Region

PubMed Central

Wang, Junjie; Ding, Guowei; Zhu, Zhibin; Zhou, Chunlian; Wang, Ning

2015-01-01

Objectives To assess the prevalence and correlated factors of HIV-1 among Chinese and Vietnamese female sex workers (FSW) in the border county of Hekou, Yunnan province, China. Methods A cross-sectional survey was conducted collecting information on demographics, sexual behavior, medical history, and drug use. Blood samples were obtained to test for HIV/STIs. Multivariate logistic regression model was used to examine associations between factors and HIV-1 infection. Results Of 345 FSWs who participated in this study, 112 (32.5%) were Chinese and 233 (67.5) were Vietnamese. Vietnamese FSWs were significantly more likely to be HIV-1 positive (7.7%) compared with Chinese FSWs (0.9%) (p = 0.009). In multivariate analysis, sexual debut at age≤16 (OR 3.8: 95% CI: 1.4, 10.6), last client’s payment <150 RMB ($22 USD) (OR: 5.2, 95% CI; 1.7, 16.6), and HSV-2 (OR: 12.3; 95% CI: 1.6, 94.8) were significant for HIV-1 infection. Conclusions Differences in HIV prevalence in Vietnamese and Chinese FSWs may be indicative of differential risk. It is important to characterize the nature of trans-border transmission in order to gain a better understanding of the potential impact on the international HIV epidemic. Understanding the correlated factors for HIV in Vietnamese and Chinese FSWs is important for designing interventions for this vulnerable population. PMID:26053040

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.

Serotonin and aggressive motivation in crustaceans: altering the decision to retreat.

PubMed

Huber, R; Smith, K; Delago, A; Isaksson, K; Kravitz, E A

1997-05-27

In crustaceans, as in most animal species, the amine serotonin has been suggested to serve important roles in aggression. Here we show that injection of serotonin into the hemolymph of subordinate, freely moving animals results in a renewed willingness of these animals to engage the dominants in further agonistic encounters. By multivariate statistical analysis, we demonstrate that this reversal results principally from a reduction in the likelihood of retreat and an increase in the duration of fighting. Serotonin infusion does not alter other aspects of fighting behavior, including which animal initiates an encounter, how quickly fighting escalates, or which animal eventually retreats. Preliminary studies suggest that serotonin uptake plays an important role in this behavioral reversal.

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

PubMed

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

2007-06-01

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

MULTIVARIATE ANALYSES (CONONICAL CORRELATION AND PARTIAL LEAST SQUARE, PLS) TO MODEL AND ASSESS THE ASSOCIATION OF LANDSCAPE METRICS TO SURFACE WATER CHEMICAL AND BIOLOGICAL PROPERTIES USING SAVANNAH RIVER BASIN DATA.

EPA Science Inventory

Many multivariate methods are used in describing and predicting relation; each has its unique usage of categorical and non-categorical data. In multivariate analysis of variance (MANOVA), many response variables (y's) are related to many independent variables that are categorical...

Multivariate Density Estimation and Remote Sensing

NASA Technical Reports Server (NTRS)

Scott, D. W.

1983-01-01

Current efforts to develop methods and computer algorithms to effectively represent multivariate data commonly encountered in remote sensing applications are described. While this may involve scatter diagrams, multivariate representations of nonparametric probability density estimates are emphasized. The density function provides a useful graphical tool for looking at data and a useful theoretical tool for classification. This approach is called a thunderstorm data analysis.

Comprehensive drought characteristics analysis based on a nonlinear multivariate drought index

NASA Astrophysics Data System (ADS)

Yang, Jie; Chang, Jianxia; Wang, Yimin; Li, Yunyun; Hu, Hui; Chen, Yutong; Huang, Qiang; Yao, Jun

2018-02-01

It is vital to identify drought events and to evaluate multivariate drought characteristics based on a composite drought index for better drought risk assessment and sustainable development of water resources. However, most composite drought indices are constructed by the linear combination, principal component analysis and entropy weight method assuming a linear relationship among different drought indices. In this study, the multidimensional copulas function was applied to construct a nonlinear multivariate drought index (NMDI) to solve the complicated and nonlinear relationship due to its dependence structure and flexibility. The NMDI was constructed by combining meteorological, hydrological, and agricultural variables (precipitation, runoff, and soil moisture) to better reflect the multivariate variables simultaneously. Based on the constructed NMDI and runs theory, drought events for a particular area regarding three drought characteristics: duration, peak, and severity were identified. Finally, multivariate drought risk was analyzed as a tool for providing reliable support in drought decision-making. The results indicate that: (1) multidimensional copulas can effectively solve the complicated and nonlinear relationship among multivariate variables; (2) compared with single and other composite drought indices, the NMDI is slightly more sensitive in capturing recorded drought events; and (3) drought risk shows a spatial variation; out of the five partitions studied, the Jing River Basin as well as the upstream and midstream of the Wei River Basin are characterized by a higher multivariate drought risk. In general, multidimensional copulas provides a reliable way to solve the nonlinear relationship when constructing a comprehensive drought index and evaluating multivariate drought characteristics.

Multivariate analysis as a key tool in chemotaxonomy of brinjal eggplant, African eggplants and wild related species.

PubMed

Haliński, Łukasz P; Samuels, John; Stepnowski, Piotr

2017-12-01

The brinjal eggplant (Solanum melongena L.) is an important vegetable species worldwide, while African eggplants (S. aethiopicum L., S. macrocarpon L.) are indigenous vegetable species of local significance. Taxonomy of eggplants and their wild relatives is complicated and still unclear. Hence, the objective of the study was to clarify taxonomic position of cultivars and landraces of brinjal, its wild relatives and African eggplant species and their wild ancestors using chemotaxonomic markers and multivariate analysis techniques for data processing, with special attention paid to the recognition of markers characteristic for each group of the plants. The total of 34 accessions belonging to 9 species from genus Solanum L. were used in the study. Chemotaxonomic analysis was based on the profiles of cuticular n-alkanes and methylalkanes, obtained using gas chromatography-mass spectrometry and gas chromatography with flame ionization detector. Standard hierarchical cluster analysis (HCA) and principal component analysis (PCA) were used for the classification, while the latter and two-way HCA allowed to identify markers responsible for the clustering of the species. Cultivars, landraces and wild forms of S. melongena were practically identical in terms of their taxonomic position. The results confirmed high and statistically significant distinctiveness of all African eggplant species from the brinjal eggplant. The latter was characterized mostly by abundant long chain hydrocarbons in the range of 34-37 carbon atoms. The differences between both African eggplant species were, however, also statistically significant; S. aethiopicum displayed the highest contribution of 2-methylalkanes to the total cuticular hydrocarbons, while S. macrocarpon was characterized by elevated n-alkanes in the range of 25-32 carbon atoms. Wild ancestors of both African eggplant species were identical with their cultivated relatives. Concluding, high usefulness of the chemotaxonomic approach in classification of this important group of plants was confirmed. Copyright © 2017 Elsevier Ltd. All rights reserved.

Evaluation of cerebral maturation by visual and quantitative analysis of resting electroencephalography in children with primary nocturnal enuresis.

PubMed

Hallioğlu, O; Ozge, A; Comelekoglu, U; Topaloglu, A K; Kanik, A; Duzovali, O; Yilgor, E

2001-10-01

This study was undertaken to evaluate resting electroencephalographic (EEG) changes and their relations to cerebral maturation in children with primary nocturnal enuresis. Cerebral maturation is known to be important in the pathogenesis of this disorder. Twenty-five right-handed patients with primary nocturnal enuresis, aged 6 to 14 years, and 23 age- and sex-matched healthy children were included in this cross-sectional case-control study. The abnormalities detected using such techniques as hemispheral asymmetry, regional differences, and hyperventilation response in addition to visual and quantitative EEG analysis were examined statistically by multivariate analysis. A decrease in alpha activity in the left (dominant hemisphere) temporal lobe and in the frontal lobes bilaterally and an increase in delta activity in the right temporal region were observed. We concluded that insufficient cerebral maturation is an important factor in the pathogenesis of primary nocturnal enuresis, and EEG, as a noninvasive and inexpensive method, could be used in evaluating cerebral maturation.

Probabilistic, meso-scale flood loss modelling

NASA Astrophysics Data System (ADS)

Kreibich, Heidi; Botto, Anna; Schröter, Kai; Merz, Bruno

2016-04-01

Flood risk analyses are an important basis for decisions on flood risk management and adaptation. However, such analyses are associated with significant uncertainty, even more if changes in risk due to global change are expected. Although uncertainty analysis and probabilistic approaches have received increased attention during the last years, they are still not standard practice for flood risk assessments and even more for flood loss modelling. State of the art in flood loss modelling is still the use of simple, deterministic approaches like stage-damage functions. Novel probabilistic, multi-variate flood loss models have been developed and validated on the micro-scale using a data-mining approach, namely bagging decision trees (Merz et al. 2013). In this presentation we demonstrate and evaluate the upscaling of the approach to the meso-scale, namely on the basis of land-use units. The model is applied in 19 municipalities which were affected during the 2002 flood by the River Mulde in Saxony, Germany (Botto et al. submitted). The application of bagging decision tree based loss models provide a probability distribution of estimated loss per municipality. Validation is undertaken on the one hand via a comparison with eight deterministic loss models including stage-damage functions as well as multi-variate models. On the other hand the results are compared with official loss data provided by the Saxon Relief Bank (SAB). The results show, that uncertainties of loss estimation remain high. Thus, the significant advantage of this probabilistic flood loss estimation approach is that it inherently provides quantitative information about the uncertainty of the prediction. References: Merz, B.; Kreibich, H.; Lall, U. (2013): Multi-variate flood damage assessment: a tree-based data-mining approach. NHESS, 13(1), 53-64. Botto A, Kreibich H, Merz B, Schröter K (submitted) Probabilistic, multi-variable flood loss modelling on the meso-scale with BT-FLEMO. Risk Analysis.

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

ERIC Educational Resources Information Center

McKinney, Cliff; Renk, Kimberly

2011-01-01

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

A Framework for Establishing Standard Reference Scale of Texture by Multivariate Statistical Analysis Based on Instrumental Measurement and Sensory Evaluation.

PubMed

Zhi, Ruicong; Zhao, Lei; Xie, Nan; Wang, Houyin; Shi, Bolin; Shi, Jingye

2016-01-13

A framework of establishing standard reference scale (texture) is proposed by multivariate statistical analysis according to instrumental measurement and sensory evaluation. Multivariate statistical analysis is conducted to rapidly select typical reference samples with characteristics of universality, representativeness, stability, substitutability, and traceability. The reasonableness of the framework method is verified by establishing standard reference scale of texture attribute (hardness) with Chinese well-known food. More than 100 food products in 16 categories were tested using instrumental measurement (TPA test), and the result was analyzed with clustering analysis, principal component analysis, relative standard deviation, and analysis of variance. As a result, nine kinds of foods were determined to construct the hardness standard reference scale. The results indicate that the regression coefficient between the estimated sensory value and the instrumentally measured value is significant (R(2) = 0.9765), which fits well with Stevens's theory. The research provides reliable a theoretical basis and practical guide for quantitative standard reference scale establishment on food texture characteristics.

A Course in... Multivariable Control Methods.

ERIC Educational Resources Information Center

Deshpande, Pradeep B.

1988-01-01

Describes an engineering course for graduate study in process control. Lists four major topics: interaction analysis, multiloop controller design, decoupling, and multivariable control strategies. Suggests a course outline and gives information about each topic. (MVL)

Immigration and leisure-time physical inactivity: a population-based study.

PubMed

Lindström, M; Sundquist, J

2001-05-01

To investigate the relationship between migration status and sedentary leisure-time physical activity status in the city of Malmö, Sweden. The public health survey in 1994 is a cross-sectional study. A total of 5,600 individuals aged 20-80 completed a postal questionnaire. The response rate was 71%. The population was categorized according to country of birth. Multivariate analysis was performed using a logistic regression model to investigate the importance of possible confounders for the differences in sedentary leisure-time physical activity status. The prevalence of a sedentary leisure-time physical activity status was 18.1% among men and 26.7% among women. The odds ratio of a sedentary leisure-time physical activity status was significantly higher among men born in Arabic-speaking countries, in All other countries, and among women born in Yugoslavia, Poland, Arabic-speaking countries, and the category all other countries', compared to the reference group born in Sweden. The multivariate analysis including age, sex, and education did not alter these results. There were significant ethnic differences in leisure-time physical activity status. This is a CVD risk factor that could be affected by intervention programs aimed at specific ethnic subgroups of the population.

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%).

Quality of Acute Care for Patients With Urinary Stones in the United States.

PubMed

Scales, Charles D; Bergman, Jonathan; Carter, Stacey; Jack, Gregory; Saigal, Christopher S; Litwin, Mark S

2015-11-01

To describe guideline adherence for patients with suspected upper tract stones. We performed a cross-sectional analysis of visits recorded by the National Hospital Ambulatory Medical Care Survey (emergency department [ED] component) in 2007-2010 (most recent data). We assessed adherence to clinical guidelines for diagnostic laboratory testing, imaging, and pharmacologic therapy. Multivariable regression models controlled for important covariates. An estimated 4,956,444 ED visits for patients with suspected kidney stones occurred during the study period. Guideline adherence was highest for diagnostic imaging, with 3,122,229 (63%) visits providing optimal imaging. Complete guideline-based laboratory testing occurred in only 2 of every 5 visits. Pharmacologic therapy to facilitate stone passage was prescribed during only 17% of eligible visits. In multivariable analysis of guideline adherence, we found little variation by patient, provider, or facility characteristics. Guideline-recommended care was absent from a substantial proportion of acute care visits for patients with suspected kidney stones. These failures of care delivery likely increase costs and temporary disability. Targeted interventions to improve guideline adherence should be designed and evaluated to improve care for patients with symptomatic kidney stones. Published by Elsevier Inc.

Quality of Acute Care for Patients with Urinary Stones in the United States

PubMed Central

Scales, Charles D.; Bergman, Jonathan; Carter, Stacey; Jack, Gregory; Saigal, Christopher S.; Litwin, Mark S.

2015-01-01

Objective To describe guideline adherence for patients with suspected upper tract stones. Methods We performed a cross-sectional analysis of visits recorded by the National Hospital Ambulatory Medical Care Survey (ED component) in 2007–2010 (most recent data). We assessed adherence to clinical guidelines for diagnostic laboratory testing, imaging, and pharmacologic therapy. Multivariable regression models controlled for important covariates. Results An estimated 4,956,444 ED visits for patients with suspected kidney stones occurred during the study period. Guideline adherence was highest for diagnostic imaging, with 3,122,229 (63%) visits providing optimal imaging. Complete guideline-based laboratory testing occurred in only 2 of every 5 visits. Pharmacologic therapy to facilitate stone passage was prescribed during only 17% of eligible visits. In multivariable analysis of guideline adherence, we found little variation by patient, provider or facility characteristics. Conclusions Guideline-recommended care was absent from a substantial proportion of acute care visits for patients with suspected kidney stones. These failures of care delivery likely increase costs and temporary disability. Targeted interventions to improve guideline adherence should be designed and evaluated to improve care for patients with symptomatic kidney stones. PMID:26335495

Multiscale entropy analysis of biological signals: a fundamental bi-scaling law

PubMed Central

Gao, Jianbo; Hu, Jing; Liu, Feiyan; Cao, Yinhe

2015-01-01

Since introduced in early 2000, multiscale entropy (MSE) has found many applications in biosignal analysis, and been extended to multivariate MSE. So far, however, no analytic results for MSE or multivariate MSE have been reported. This has severely limited our basic understanding of MSE. For example, it has not been studied whether MSE estimated using default parameter values and short data set is meaningful or not. Nor is it known whether MSE has any relation with other complexity measures, such as the Hurst parameter, which characterizes the correlation structure of the data. To overcome this limitation, and more importantly, to guide more fruitful applications of MSE in various areas of life sciences, we derive a fundamental bi-scaling law for fractal time series, one for the scale in phase space, the other for the block size used for smoothing. We illustrate the usefulness of the approach by examining two types of physiological data. One is heart rate variability (HRV) data, for the purpose of distinguishing healthy subjects from patients with congestive heart failure, a life-threatening condition. The other is electroencephalogram (EEG) data, for the purpose of distinguishing epileptic seizure EEG from normal healthy EEG. PMID:26082711

Retro-regression--another important multivariate regression improvement.

PubMed

Randić, M

2001-01-01

We review the serious problem associated with instabilities of the coefficients of regression equations, referred to as the MRA (multivariate regression analysis) "nightmare of the first kind". This is manifested when in a stepwise regression a descriptor is included or excluded from a regression. The consequence is an unpredictable change of the coefficients of the descriptors that remain in the regression equation. We follow with consideration of an even more serious problem, referred to as the MRA "nightmare of the second kind", arising when optimal descriptors are selected from a large pool of descriptors. This process typically causes at different steps of the stepwise regression a replacement of several previously used descriptors by new ones. We describe a procedure that resolves these difficulties. The approach is illustrated on boiling points of nonanes which are considered (1) by using an ordered connectivity basis; (2) by using an ordering resulting from application of greedy algorithm; and (3) by using an ordering derived from an exhaustive search for optimal descriptors. A novel variant of multiple regression analysis, called retro-regression (RR), is outlined showing how it resolves the ambiguities associated with both "nightmares" of the first and the second kind of MRA.

A multivariate analysis of prognostic factors for melanoma patients with lesions greater than or equal to 3.65 mm in thickness. The importance of revealing alternative Cox models.

PubMed Central

Day, C L; Lew, R A; Mihm, M C; Sober, A J; Harris, M N; Kopf, A W; Fitzpatrick, T B; Harrist, T J; Golomb, F M; Postel, A; Hennessey, P; Gumport, S L; Raker, J W; Malt, R A; Cosimi, A B; Wood, W C; Roses, D F; Gorstein, F; Rigel, D; Friedman, R J; Mintzis, M M; Grier, R W

1982-01-01

Fourteen prognostic factors were examined in 79 patients with clinical Stage I melanoma greater than or equal to 3.65 mm in thickness. All nine patients with melanoma of the hands or feet died of melanoma. A Cox proportional hazards (multivariate) analysis of the remaining 70 patients showed that a combination of the following four variables best predicted bony or visceral metastases: 1) a nearly absent or minimal lymphocyte response at the base of the tumor, 2) histologic type other than superficial spreading melanoma, 3) location on the trunk, and 4) positive nodes or no initial node dissection. Ulceration and/or ulceration width were not useful in predicting outcome either singly or in combination with other variables. Patients with negative lymph nodes and primary tumors of the trunk, hands, and feet did not do better than patients with positive nodes at those sites. Conversely, non of 16 patients with negative lymph nodes and extremity melanomas (excluding the hands and feet) or head and neck melanomas developed visceral or bony metastases (i.e., five-year disease-free survival rate 100%). PMID:7055383

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

PubMed Central

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

2016-01-01

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

Evolution of the Max and Mlx networks in animals.

PubMed

McFerrin, Lisa G; Atchley, William R

2011-01-01

Transcription factors (TFs) are essential for the regulation of gene expression and often form emergent complexes to perform vital roles in cellular processes. In this paper, we focus on the parallel Max and Mlx networks of TFs because of their critical involvement in cell cycle regulation, proliferation, growth, metabolism, and apoptosis. A basic-helix-loop-helix-zipper (bHLHZ) domain mediates the competitive protein dimerization and DNA binding among Max and Mlx network members to form a complex system of cell regulation. To understand the importance of these network interactions, we identified the bHLHZ domain of Max and Mlx network proteins across the animal kingdom and carried out several multivariate statistical analyses. The presence and conservation of Max and Mlx network proteins in animal lineages stemming from the divergence of Metazoa indicate that these networks have ancient and essential functions. Phylogenetic analysis of the bHLHZ domain identified clear relationships among protein families with distinct points of radiation and divergence. Multivariate discriminant analysis further isolated specific amino acid changes within the bHLHZ domain that classify proteins, families, and network configurations. These analyses on Max and Mlx network members provide a model for characterizing the evolution of TFs involved in essential networks.

IGFBP6 Regulates Cell Apoptosis and Migration in Glioma.

PubMed

Bei, Yuanqi; Huang, Qingfeng; Shen, Jianhong; Shi, Jinlong; Shen, Chaoyan; Xu, Peng; Chang, Hao; Xia, Xiaojie; Xu, Li; Ji, Bin; Chen, JianGuo

2017-07-01

The insulin-like growth factor binding protein 6 (IGFBP6), as an inhibitor of IGF-II actions, plays an important role in inhibiting survival and migration of tumor cells. In our study, we intended to demonstrate the biological function of IGFBP6 in the development of glioma and its clinical significance. Firstly, Western blot and immunohistochemistry revealed that the expression of IGFBP6 inversely correlated with glioma grade. Secondly, multivariate analysis with the Cox proportional hazards model and Kaplan-Meier analysis indicated that IGFBP6 could be an independent prognostic factor for the survival of glioma patients. In addition, overexpression of IGFBP6 induced glioma cell apoptosis, and depletion of IGFBP6 had the opposite action. Finally, overexpression of IGFBP6 inhibited migration of glioma cells, and depletion of IGFBP6 had the opposite action. Together our findings suggest that IGFBP6 might be an important regulator and prognostic factor for glioma.

Resilience following spinal cord injury: A prospective controlled study investigating the influence of the provision of group cognitive behavior therapy during inpatient rehabilitation.

PubMed

Guest, Rebecca; Craig, Ashley; Nicholson Perry, Kathryn; Tran, Yvonne; Ephraums, Catherine; Hales, Alison; Dezarnaulds, Annalisa; Crino, Rocco; Middleton, James

2015-11-01

To examine change in resilience in people with spinal cord injury (SCI) when group cognitive behavior therapy (GCBT) was added to routine psychosocial rehabilitation (RPR). A prospective repeated-measures cohort design was used to determine the efficacy of the addition of GCBT (n = 50). The control group consisted of individuals receiving RPR, which included access to individual CBT (ICBT) when required (n = 38). Groups were assessed on 3 occasions: soon after admission, within 2 weeks of discharge, and 6-months postdischarge. Measures included sociodemographic, injury, and psychosocial factors. The outcome variable was resilience, considered an important outcome measure for recovery. To adjust for baseline differences in self-efficacy, depressive mood and anxiety between the 2 groups, these factors were entered into a repeated measures multivariate analysis of covariance (MANCOVA) as covariates. Latent class analysis was used to determine the best-fitting model of resilience trajectories for both groups. The MANCOVA indicated that the addition of GCBT to psychosocial rehabilitation did not result in improved resilience compared with the ICBT group. Trajectory data indicated over 60% were demonstrating acceptable resilience irrespective of group. Changes in resilience mean scores suggest the addition of GCBT adds little to resilience outcomes. Latent class modeling indicated both groups experienced similar trajectories of improvement and deterioration. Results highlight the importance of conducting multivariate modeling analysis that isolates subgroups of related cases over time to understand complex trajectories. Further research is needed to clarify individual differences in CBT intervention preference as well as other factors which impact on resilience. (c) 2015 APA, all rights reserved).

Impact of social capital on psychological distress and interaction with house destruction and displacement after the Great East Japan Earthquake of 2011.

PubMed

Tsuchiya, Naho; Nakaya, Naoki; Nakamura, Tomohiro; Narita, Akira; Kogure, Mana; Aida, Jun; Tsuji, Ichiro; Hozawa, Atsushi; Tomita, Hiroaki

2017-01-01

Social capital has been considered an important factor affecting mental-health outcomes, such as psychological distress in post-disaster settings. Although disaster-related house condition and displacement could affect both social capital and psychological distress, limited studies have investigated interactions. This study aimed to examine the association between social capital and psychological distress, taking into consideration the interaction of disaster-related house condition after the Great East Japan Earthquake of 2011. Using data from 3793 adults living in Shichigahama, Miyagi Prefecture, Japan, we examined the association between social capital measured by generalized trust and psychological distress measured by the Kessler 6 scale. We conducted stratified analysis to investigate an interaction of house destruction and displacement. Multivariate analyses taking into consideration the interaction were performed. In the crude analysis, low social capital (odds ratio [OR] 4.46; 95% confidence interval [CI], 3.27-6.07) and large-scale house destruction (OR 1.96; 95%CI, 1.47-2.62) were significantly associated with psychological distress. Stratified analyses detected an interaction with house destruction and displacement (P for interaction = 0.04). Multivariate analysis with interaction term revealed that individuals with low social capital, large-scale house damage, and displacement were at greater risk of psychological distress, corresponding to adjusted OR of 5.78 (95%CI, 3.48-9.60). In the post-disaster setting, low social capital increased the risk of psychological distress, especially among individuals who had large-scale house destruction. Among the participants with severe disaster damage, high social capital would play an important role in protecting mental health. © 2016 The Authors. Psychiatry and Clinical Neurosciences published by John Wiley & Sons Australia, Ltd on behalf of Japanese Society of Psychiatry and Neurology.

Independent Predictors of Prognosis Based on Oral Cavity Squamous Cell Carcinoma Surgical Margins.

PubMed

Buchakjian, Marisa R; Ginader, Timothy; Tasche, Kendall K; Pagedar, Nitin A; Smith, Brian J; Sperry, Steven M

2018-05-01

Objective To conduct a multivariate analysis of a large cohort of oral cavity squamous cell carcinoma (OCSCC) cases for independent predictors of local recurrence (LR) and overall survival (OS), with emphasis on the relationship between (1) prognosis and (2) main specimen permanent margins and intraoperative tumor bed frozen margins. Study Design Retrospective cohort study. Setting Tertiary academic head and neck cancer program. Subjects and Methods This study included 426 patients treated with OCSCC resection between 2005 and 2014 at University of Iowa Hospitals and Clinics. Patients underwent excision of OCSCC with intraoperative tumor bed frozen margin sampling and main specimen permanent margin assessment. Multivariate analysis of the data set to predict LR and OS was performed. Results Independent predictors of LR included nodal involvement, histologic grade, and main specimen permanent margin status. Specifically, the presence of a positive margin (odds ratio, 6.21; 95% CI, 3.3-11.9) or <1-mm/carcinoma in situ margin (odds ratio, 2.41; 95% CI, 1.19-4.87) on the main specimen was an independent predictor of LR, whereas intraoperative tumor bed margins were not predictive of LR on multivariate analysis. Similarly, independent predictors of OS on multivariate analysis included nodal involvement, extracapsular extension, and a positive main specimen margin. Tumor bed margins did not independently predict OS. Conclusion The main specimen margin is a strong independent predictor of LR and OS on multivariate analysis. Intraoperative tumor bed frozen margins do not independently predict prognosis. We conclude that emphasis should be placed on evaluating the main specimen margins when estimating prognosis after OCSCC resection.

[Frequent visitors to psychiatric emergency service: Demographical and clinical analysis].

PubMed

Schmoll, S; Boyer, L; Henry, J-M; Belzeaux, R

2015-04-01

Frequent visitors of psychiatric emergency wards are an important health care problem. Previous studies underlined that 2 % to 9 % of patients induce 15 % to 33 % of total clinical activity. Those patients have chronic and severe mental illness such as schizophrenia, associated with social and financial difficulties. The aim of this study was to describe demographic and clinical characteristics of frequent visitors to a psychiatric emergency ward in a French Academic hospital over 6years in comparison to non-frequent visitors. The study is based on a retrospective review of the psychiatric emergency wards' administrative and medical computer databases; data that included demographic, financial, clinical, and management information. During this 6-year study, the psychiatric ward recorded 16,754 care episodes for 8800 different patients. We compared frequent visitors with other visitors using univariate and multivariate analyses. Frequent visitors were defined by a number of visits greater than 2 of the mean standard deviation. Two percent of patients (n=192) had nine or more visits during the period. These patients caused 21 % of the total number of the visits. In the univariate analysis, the most significant reasons for referral in frequent visitors versus others (P<0.001) were: more frequent anxiety (37.6 % vs. 32.1 %), less frequent disruptive behavior (8.4 % vs. 12.9 %), depression (7.8 % vs. 17.2 %) and suicide attempt (4.5 % vs. 11.1 %). Factors associated with frequent visitors (P<0.001), after including all significant or confounding variables (multivariate analysis), were: schizophrenia and schizophrenia spectrum disorders (OR=29.5, IC: 11.4-76), DSM-IV cluster B personality disorders (OR=5.5, IC: 3.6-8.4), mental and behavioral disorders due to psychoactive substance use (OR=4.6, IC: 3.1-7), financial assistance through social government programs (OR range: 9.1-2.4, all significant) and being homeless (OR=2.7, IC: 1.8-4). Factors associated with non-frequent visitors were mood disorders (OR=0.07, IC: 0.03-0.19) and neurotic, stress-related, and somatoform disorders (OR=0.14, IC: 0.05-0.4). Sex and age were not significant in multivariate analysis. This study identifies significant demographic and clinical factors associated with frequent visits in psychiatric emergency ward in accordance with the large majority of previous studies. We found that psychotic disorders or schizophrenia were the main diagnosis of these patients. Moreover, precariousness (homeless, financial assistance) is an important demographic factor associated with recurrence. However, contrary to numerous studies, we found no effect of sex or age. Due to this important economical and clinical burden, more specific care and alternative solutions to emergency care have to be proposed to this population of patients. Copyright © 2013 L’Encéphale, Paris. Published by Elsevier Masson SAS. All rights reserved.

Copula Multivariate analysis of Gross primary production and its hydro-environmental driver; A BIOME-BGC model applied to the Antisana páramos

NASA Astrophysics Data System (ADS)

Minaya, Veronica; Corzo, Gerald; van der Kwast, Johannes; Galarraga, Remigio; Mynett, Arthur

2014-05-01

Simulations of carbon cycling are prone to uncertainties from different sources, which in general are related to input data, parameters and the model representation capacities itself. The gross carbon uptake in the cycle is represented by the gross primary production (GPP), which deals with the spatio-temporal variability of the precipitation and the soil moisture dynamics. This variability associated with uncertainty of the parameters can be modelled by multivariate probabilistic distributions. Our study presents a novel methodology that uses multivariate Copulas analysis to assess the GPP. Multi-species and elevations variables are included in a first scenario of the analysis. Hydro-meteorological conditions that might generate a change in the next 50 or more years are included in a second scenario of this analysis. The biogeochemical model BIOME-BGC was applied in the Ecuadorian Andean region in elevations greater than 4000 masl with the presence of typical vegetation of páramo. The change of GPP over time is crucial for climate scenarios of the carbon cycling in this type of ecosystem. The results help to improve our understanding of the ecosystem function and clarify the dynamics and the relationship with the change of climate variables. Keywords: multivariate analysis, Copula, BIOME-BGC, NPP, páramos

Multivariate Analysis of 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

Factors influencing knowledge on completion of treatment among TB patients under directly observed treatment strategy, in selected health facilities in Embu County, Kenya.

PubMed

Ndwiga, Joshua Muriuki; Kikuvi, Gideon; Omolo, Jared Odhiambo

2016-01-01

The World Health Organization (WHO) promotes the Directly Observed Treatment (DOT) strategy as the standard to increase adherence to Tuberculosis (TB) medication. However, cases of retreatment and Multi Drug Resistant continue to be reported in many parts of Kenya. This study sought to determine the factors influencing the completion of tuberculosis medication among TB patients in Embu County, Kenya. A descriptive cross-sectional study was conducted on a population of tuberculosis patients under DOT attending selected TB treatment clinics in Embu County, in Kenya. One hundred and forty TB patients interviewed within a period of 3 months. Data were analyzed using SPSS version 17.0 and included Bivariate and Multivariate Analysis. The level of significance was p≤ 0.05. The male and female participants were 61.4% and 38.6% respectively. The mean age of the respondents was 35±31.34-39.3 years. For the majority (52%) of the participants, the highest level of education was primary education. The unemployed participants formed the highest number of the respondent in the study (73%). The majorities (91.4%0) of the respondents were under the home-based DOT strategy (91.4%, 95% C.I: 85.5-95.5). Bivariate analysis using Chi-square showed that the level of education (p=0.003), patients feeling uncomfortable during supervision (p=0.01), and knowledge regarding the frequency of taking medication (p=0.004) were all significantly associated with knowledge regarding the importance of completion of medication. However, none of these factors was significant after multivariate analysis. Most participants did not know the importance of completion of medication. TB programs should come up with better ways to educate TB patients on the importance of supervision and treatment completion during the treatment of TB. The education programs should focus on influencing the attitudes of patients and creating awareness about the importance of treatment completion. The TB programs should be designed towards eliminating the factors influencing the completion of TB medication.

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

PubMed

Griswold, Cortland K

2015-12-21

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

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.

Dynamic Granger causality based on Kalman filter for evaluation of functional network connectivity in fMRI data

PubMed Central

Havlicek, Martin; Jan, Jiri; Brazdil, Milan; Calhoun, Vince D.

2015-01-01

Increasing interest in understanding dynamic interactions of brain neural networks leads to formulation of sophisticated connectivity analysis methods. Recent studies have applied Granger causality based on standard multivariate autoregressive (MAR) modeling to assess the brain connectivity. Nevertheless, one important flaw of this commonly proposed method is that it requires the analyzed time series to be stationary, whereas such assumption is mostly violated due to the weakly nonstationary nature of functional magnetic resonance imaging (fMRI) time series. Therefore, we propose an approach to dynamic Granger causality in the frequency domain for evaluating functional network connectivity in fMRI data. The effectiveness and robustness of the dynamic approach was significantly improved by combining a forward and backward Kalman filter that improved estimates compared to the standard time-invariant MAR modeling. In our method, the functional networks were first detected by independent component analysis (ICA), a computational method for separating a multivariate signal into maximally independent components. Then the measure of Granger causality was evaluated using generalized partial directed coherence that is suitable for bivariate as well as multivariate data. Moreover, this metric provides identification of causal relation in frequency domain, which allows one to distinguish the frequency components related to the experimental paradigm. The procedure of evaluating Granger causality via dynamic MAR was demonstrated on simulated time series as well as on two sets of group fMRI data collected during an auditory sensorimotor (SM) or auditory oddball discrimination (AOD) tasks. Finally, a comparison with the results obtained from a standard time-invariant MAR model was provided. PMID:20561919

Exploring the Structure of Library and Information Science Web Space Based on Multivariate Analysis of Social Tags

ERIC Educational Resources Information Center

Joo, Soohyung; Kipp, Margaret E. I.

2015-01-01

Introduction: This study examines the structure of Web space in the field of library and information science using multivariate analysis of social tags from the Website, Delicious.com. A few studies have examined mathematical modelling of tags, mainly examining tagging in terms of tripartite graphs, pattern tracing and descriptive statistics. This…

Multivariate Analysis of High Through-Put Adhesively Bonded Single Lap Joints: Experimental and Workflow Protocols

DTIC Science & Technology

2016-06-01

unlimited. v List of Tables Table 1 Single-lap-joint experimental parameters ..............................................7 Table 2 Survey ...Joints: Experimental and Workflow Protocols by Robert E Jensen, Daniel C DeSchepper, and David P Flanagan Approved for...TR-7696 ● JUNE 2016 US Army Research Laboratory Multivariate Analysis of High Through-Put Adhesively Bonded Single Lap Joints: Experimental

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

ERIC Educational Resources Information Center

Jackson, Dan; Rollins, Katie; Coughlin, Patrick

2014-01-01

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

Atomic-scale phase composition through multivariate statistical analysis of atom probe tomography data.

PubMed

Keenan, Michael R; Smentkowski, Vincent S; Ulfig, Robert M; Oltman, Edward; Larson, David J; Kelly, Thomas F

2011-06-01

We demonstrate for the first time that multivariate statistical analysis techniques can be applied to atom probe tomography data to estimate the chemical composition of a sample at the full spatial resolution of the atom probe in three dimensions. Whereas the raw atom probe data provide the specific identity of an atom at a precise location, the multivariate results can be interpreted in terms of the probabilities that an atom representing a particular chemical phase is situated there. When aggregated to the size scale of a single atom (∼0.2 nm), atom probe spectral-image datasets are huge and extremely sparse. In fact, the average spectrum will have somewhat less than one total count per spectrum due to imperfect detection efficiency. These conditions, under which the variance in the data is completely dominated by counting noise, test the limits of multivariate analysis, and an extensive discussion of how to extract the chemical information is presented. Efficient numerical approaches to performing principal component analysis (PCA) on these datasets, which may number hundreds of millions of individual spectra, are put forward, and it is shown that PCA can be computed in a few seconds on a typical laptop computer.

Evaluation of the microscopic distribution of florfenicol in feed pellets for salmon by Fourier Transform infrared imaging and multivariate analysis.

PubMed

Bastidas, Camila Y; von Plessing, Carlos; Troncoso, José; Del P Castillo, Rosario

2018-04-15

Fourier Transform infrared imaging and multivariate analysis were used to identify, at the microscopic level, the presence of florfenicol (FF), a heavily-used antibiotic in the salmon industry, supplied to fishes in feed pellets for the treatment of salmonid rickettsial septicemia (SRS). The FF distribution was evaluated using Principal Component Analysis (PCA) and Augmented Multivariate Curve Resolution with Alternating Least Squares (augmented MCR-ALS) on the spectra obtained from images with pixel sizes of 6.25 μm × 6.25 μm and 1.56 μm × 1.56 μm, in different zones of feed pellets. Since the concentration of the drug was 3.44 mg FF/g pellet, this is the first report showing the powerful ability of the used of spectroscopic techniques and multivariate analysis, especially the augmented MCR-ALS, to describe the FF distribution in both the surface and inner parts of feed pellets at low concentration, in a complex matrix and at the microscopic level. The results allow monitoring the incorporation of the drug into the feed pellets. Copyright © 2018 Elsevier B.V. All rights reserved.

Risk factors for incidental durotomy during lumbar surgery: a retrospective study by multivariate analysis.

PubMed

Chen, Zhixiang; Shao, Peng; Sun, Qizhao; Zhao, Dong

2015-03-01

The purpose of the present study was to use a prospectively collected data to evaluate the rate of incidental durotomy (ID) during lumbar surgery and determine the associated risk factors by using univariate and multivariate analysis. We retrospectively reviewed 2184 patients who underwent lumbar surgery from January 1, 2009 to December 31, 2011 at a single hospital. Patients with ID (n=97) were compared with the patients without ID (n=2019). The influences of several potential risk factors that might affect the occurrence of ID were assessed using univariate and multivariate analyses. The overall incidence of ID was 4.62%. Univariate analysis demonstrated that older age, diabetes, lumbar central stenosis, posterior approach, revision surgery, prior lumber surgery and minimal invasive surgery are risk factors for ID during lumbar surgery. However, multivariate analysis identified older age, prior lumber surgery, revision surgery, and minimally invasive surgery as independent risk factors. Older age, prior lumber surgery, revision surgery, and minimal invasive surgery were independent risk factors for ID during lumbar surgery. These findings may guide clinicians making future surgical decisions regarding ID and aid in the patient counseling process to alleviate risks and complications. Copyright © 2015 Elsevier B.V. All rights reserved.

Linear models of coregionalization for multivariate lattice data: Order-dependent and order-free cMCARs.

PubMed

MacNab, Ying C

2016-08-01

This paper concerns with multivariate conditional autoregressive models defined by linear combination of independent or correlated underlying spatial processes. Known as linear models of coregionalization, the method offers a systematic and unified approach for formulating multivariate extensions to a broad range of univariate conditional autoregressive models. The resulting multivariate spatial models represent classes of coregionalized multivariate conditional autoregressive models that enable flexible modelling of multivariate spatial interactions, yielding coregionalization models with symmetric or asymmetric cross-covariances of different spatial variation and smoothness. In the context of multivariate disease mapping, for example, they facilitate borrowing strength both over space and cross variables, allowing for more flexible multivariate spatial smoothing. Specifically, we present a broadened coregionalization framework to include order-dependent, order-free, and order-robust multivariate models; a new class of order-free coregionalized multivariate conditional autoregressives is introduced. We tackle computational challenges and present solutions that are integral for Bayesian analysis of these models. We also discuss two ways of computing deviance information criterion for comparison among competing hierarchical models with or without unidentifiable prior parameters. The models and related methodology are developed in the broad context of modelling multivariate data on spatial lattice and illustrated in the context of multivariate disease mapping. The coregionalization framework and related methods also present a general approach for building spatially structured cross-covariance functions for multivariate geostatistics. © The Author(s) 2016.

Multivariate reference technique for quantitative analysis of fiber-optic tissue Raman spectroscopy.

PubMed

Bergholt, Mads Sylvest; Duraipandian, Shiyamala; Zheng, Wei; Huang, Zhiwei

2013-12-03

We report a novel method making use of multivariate reference signals of fused silica and sapphire Raman signals generated from a ball-lens fiber-optic Raman probe for quantitative analysis of in vivo tissue Raman measurements in real time. Partial least-squares (PLS) regression modeling is applied to extract the characteristic internal reference Raman signals (e.g., shoulder of the prominent fused silica boson peak (~130 cm(-1)); distinct sapphire ball-lens peaks (380, 417, 646, and 751 cm(-1))) from the ball-lens fiber-optic Raman probe for quantitative analysis of fiber-optic Raman spectroscopy. To evaluate the analytical value of this novel multivariate reference technique, a rapid Raman spectroscopy system coupled with a ball-lens fiber-optic Raman probe is used for in vivo oral tissue Raman measurements (n = 25 subjects) under 785 nm laser excitation powers ranging from 5 to 65 mW. An accurate linear relationship (R(2) = 0.981) with a root-mean-square error of cross validation (RMSECV) of 2.5 mW can be obtained for predicting the laser excitation power changes based on a leave-one-subject-out cross-validation, which is superior to the normal univariate reference method (RMSE = 6.2 mW). A root-mean-square error of prediction (RMSEP) of 2.4 mW (R(2) = 0.985) can also be achieved for laser power prediction in real time when we applied the multivariate method independently on the five new subjects (n = 166 spectra). We further apply the multivariate reference technique for quantitative analysis of gelatin tissue phantoms that gives rise to an RMSEP of ~2.0% (R(2) = 0.998) independent of laser excitation power variations. This work demonstrates that multivariate reference technique can be advantageously used to monitor and correct the variations of laser excitation power and fiber coupling efficiency in situ for standardizing the tissue Raman intensity to realize quantitative analysis of tissue Raman measurements in vivo, which is particularly appealing in challenging Raman endoscopic applications.

Causal diagrams and multivariate analysis II: precision work.

PubMed

Jupiter, Daniel C

2014-01-01

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

Multivariate optimum interpolation of surface pressure and surface wind over oceans

NASA Technical Reports Server (NTRS)

Bloom, S. C.; Baker, W. E.; Nestler, M. S.

1984-01-01

The present multivariate analysis method for surface pressure and winds incorporates ship wind observations into the analysis of surface pressure. For the specific case of 0000 GMT, on February 3, 1979, the additional data resulted in a global rms difference of 0.6 mb; individual maxima as larse as 5 mb occurred over the North Atlantic and East Pacific Oceans. These differences are noted to be smaller than the analysis increments to the first-guess fields.

The influence of television and video game use on attention and school problems: a multivariate analysis with other risk factors controlled.

PubMed

Ferguson, Christopher J

2011-06-01

Research on youth mental health has increasingly indicated the importance of multivariate analyses of multiple risk factors for negative outcomes. Television and video game use have often been posited as potential contributors to attention problems, but previous studies have not always been well-controlled or used well-validated outcome measures. The current study examines the multivariate nature of risk factors for attention problems symptomatic of attention deficit hyperactivity disorder and poor school performance. A predominantly Hispanic population of 603 children (ages 10-14) and their parents/guardians responded to multiple behavioral measures. Outcome measures included parent and child reported attention problem behaviors on the Child Behavior Checklist (CBCL) as well as poor school performance as measured by grade point average (GPA). Results found that internal factors such as male gender, antisocial traits, family environment and anxiety best predicted attention problems. School performance was best predicted by family income. Television and video game use, whether total time spent using, or exposure to violent content specifically, did not predict attention problems or GPA. Television and video game use do not appear to be significant predictors of childhood attention problems. Intervention and prevention efforts may be better spent on other risk factors. Copyright © 2010 Elsevier Ltd. All rights reserved.

Multiscale Characterization of PM2.5 in Southern Taiwan based on Noise-assisted Multivariate Empirical Mode Decomposition and Time-dependent Intrinsic Correlation

NASA Astrophysics Data System (ADS)

Hsiao, Y. R.; Tsai, C.

2017-12-01

As the WHO Air Quality Guideline indicates, ambient air pollution exposes world populations under threat of fatal symptoms (e.g. heart disease, lung cancer, asthma etc.), raising concerns of air pollution sources and relative factors. This study presents a novel approach to investigating the multiscale variations of PM2.5 in southern Taiwan over the past decade, with four meteorological influencing factors (Temperature, relative humidity, precipitation and wind speed),based on Noise-assisted Multivariate Empirical Mode Decomposition(NAMEMD) algorithm, Hilbert Spectral Analysis(HSA) and Time-dependent Intrinsic Correlation(TDIC) method. NAMEMD algorithm is a fully data-driven approach designed for nonlinear and nonstationary multivariate signals, and is performed to decompose multivariate signals into a collection of channels of Intrinsic Mode Functions (IMFs). TDIC method is an EMD-based method using a set of sliding window sizes to quantify localized correlation coefficients for multiscale signals. With the alignment property and quasi-dyadic filter bank of NAMEMD algorithm, one is able to produce same number of IMFs for all variables and estimates the cross correlation in a more accurate way. The performance of spectral representation of NAMEMD-HSA method is compared with Complementary Empirical Mode Decomposition/ Hilbert Spectral Analysis (CEEMD-HSA) and Wavelet Analysis. The nature of NAMAMD-based TDICC analysis is then compared with CEEMD-based TDIC analysis and the traditional correlation analysis.

The return period analysis of natural disasters with statistical modeling of bivariate joint probability distribution.

PubMed

Li, Ning; Liu, Xueqin; Xie, Wei; Wu, Jidong; Zhang, Peng

2013-01-01

New features of natural disasters have been observed over the last several years. The factors that influence the disasters' formation mechanisms, regularity of occurrence and main characteristics have been revealed to be more complicated and diverse in nature than previously thought. As the uncertainty involved increases, the variables need to be examined further. This article discusses the importance and the shortage of multivariate analysis of natural disasters and presents a method to estimate the joint probability of the return periods and perform a risk analysis. Severe dust storms from 1990 to 2008 in Inner Mongolia were used as a case study to test this new methodology, as they are normal and recurring climatic phenomena on Earth. Based on the 79 investigated events and according to the dust storm definition with bivariate, the joint probability distribution of severe dust storms was established using the observed data of maximum wind speed and duration. The joint return periods of severe dust storms were calculated, and the relevant risk was analyzed according to the joint probability. The copula function is able to simulate severe dust storm disasters accurately. The joint return periods generated are closer to those observed in reality than the univariate return periods and thus have more value in severe dust storm disaster mitigation, strategy making, program design, and improvement of risk management. This research may prove useful in risk-based decision making. The exploration of multivariate analysis methods can also lay the foundation for further applications in natural disaster risk analysis. © 2012 Society for Risk Analysis.

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

PubMed

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

2017-07-01

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

Analysis/forecast experiments with a multivariate statistical analysis scheme using FGGE data

NASA Technical Reports Server (NTRS)

Baker, W. E.; Bloom, S. C.; Nestler, M. S.

1985-01-01

A three-dimensional, multivariate, statistical analysis method, optimal interpolation (OI) is described for modeling meteorological data from widely dispersed sites. The model was developed to analyze FGGE data at the NASA-Goddard Laboratory of Atmospherics. The model features a multivariate surface analysis over the oceans, including maintenance of the Ekman balance and a geographically dependent correlation function. Preliminary comparisons are made between the OI model and similar schemes employed at the European Center for Medium Range Weather Forecasts and the National Meteorological Center. The OI scheme is used to provide input to a GCM, and model error correlations are calculated for forecasts of 500 mb vertical water mixing ratios and the wind profiles. Comparisons are made between the predictions and measured data. The model is shown to be as accurate as a successive corrections model out to 4.5 days.

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.

Clinical Factors and the Decision to Transfuse Chronic Dialysis Patients

PubMed Central

Whitman, Cynthia B.; Shreay, Sanatan; Gitlin, Matthew; van Oijen, Martijn G. H.

2013-01-01

Summary Background and objectives Red blood cell transfusion was previously the principle therapy for anemia in CKD but became less prevalent after the introduction of erythropoiesis-stimulating agents. This study used adaptive choice-based conjoint analysis to identify preferences and predictors of transfusion decision-making in CKD. Design, setting, participants, & measurements A computerized adaptive choice-based conjoint survey was administered between June and August of 2012 to nephrologists, internists, and hospitalists listed in the American Medical Association Masterfile. The survey quantified the relative importance of 10 patient attributes, including hemoglobin levels, age, occult blood in stool, severity of illness, eligibility for transplant, iron indices, erythropoiesis-stimulating agents, cardiovascular disease, and functional status. Triggers of transfusions in common dialysis scenarios were studied, and based on adaptive choice-based conjoint-derived preferences, relative importance by performing multivariable regression to identify predictors of transfusion preferences was assessed. Results A total of 350 providers completed the survey (n=305 nephrologists; mean age=46 years; 21% women). Of 10 attributes assessed, absolute hemoglobin level was the most important driver of transfusions, accounting for 29% of decision-making, followed by functional status (16%) and cardiovascular comorbidities (12%); 92% of providers transfused when hemoglobin was 7.5 g/dl, independent of other factors. In multivariable regression, Veterans Administration providers were more likely to transfuse at 8.0 g/dl (odds ratio, 5.9; 95% confidence interval, 1.9 to 18.4). Although transplant eligibility explained only 5% of decision-making, nephrologists were five times more likely to value it as important compared with non-nephrologists (odds ratio, 5.2; 95% confidence interval, 2.4 to11.1). Conclusions Adaptive choice-based conjoint analysis was useful in predicting influences on transfusion decisions. Hemoglobin level, functional status, and cardiovascular comorbidities most strongly influenced transfusion decision-making, but preference variations were observed among subgroups. PMID:23929931

Differentiation of aflatoxigenic and non-aflatoxigenic strains of Aspergilli by FT-IR spectroscopy.

PubMed

Atkinson, Curtis; Pechanova, Olga; Sparks, Darrell L; Brown, Ashli; Rodriguez, Jose M

2014-01-01

Fourier transform infrared spectroscopy (FT-IR) is a well-established and widely accepted methodology to identify and differentiate diverse microbial species. In this study, FT-IR was used to differentiate 20 strains of ubiquitous and agronomically important phytopathogens of Aspergillus flavus and Aspergillus parasiticus. By analyzing their spectral profiles via principal component and cluster analysis, differentiation was achieved between the aflatoxin-producing and nonproducing strains of both fungal species. This study thus indicates that FT-IR coupled to multivariate statistics can rapidly differentiate strains of Aspergilli based on their toxigenicity.

Spectral compression algorithms for the analysis of very large multivariate images

DOEpatents

Keenan, Michael R.

2007-10-16

A method for spectrally compressing data sets enables the efficient analysis of very large multivariate images. The spectral compression algorithm uses a factored representation of the data that can be obtained from Principal Components Analysis or other factorization technique. Furthermore, a block algorithm can be used for performing common operations more efficiently. An image analysis can be performed on the factored representation of the data, using only the most significant factors. The spectral compression algorithm can be combined with a spatial compression algorithm to provide further computational efficiencies.

A general program to compute the multivariable stability margin for systems with parametric uncertainty

NASA Technical Reports Server (NTRS)

Sanchez Pena, Ricardo S.; Sideris, Athanasios

1988-01-01

A computer program implementing an algorithm for computing the multivariable stability margin to check the robust stability of feedback systems with real parametric uncertainty is proposed. The authors present in some detail important aspects of the program. An example is presented using lateral directional control system.

A Simpli ed, General Approach to Simulating from Multivariate Copula Functions

Treesearch

Barry Goodwin

2012-01-01

Copulas have become an important analytic tool for characterizing multivariate distributions and dependence. One is often interested in simulating data from copula estimates. The process can be analytically and computationally complex and usually involves steps that are unique to a given parametric copula. We describe an alternative approach that uses \\probability{...

Esophageal cancer detection based on tissue surface-enhanced Raman spectroscopy and multivariate analysis

NASA Astrophysics Data System (ADS)

Feng, Shangyuan; Lin, Juqiang; Huang, Zufang; Chen, Guannan; Chen, Weisheng; Wang, Yue; Chen, Rong; Zeng, Haishan

2013-01-01

The capability of using silver nanoparticle based near-infrared surface enhanced Raman scattering (SERS) spectroscopy combined with principal component analysis (PCA) and linear discriminate analysis (LDA) to differentiate esophageal cancer tissue from normal tissue was presented. Significant differences in Raman intensities of prominent SERS bands were observed between normal and cancer tissues. PCA-LDA multivariate analysis of the measured tissue SERS spectra achieved diagnostic sensitivity of 90.9% and specificity of 97.8%. This exploratory study demonstrated great potential for developing label-free tissue SERS analysis into a clinical tool for esophageal cancer detection.

New multivariable capabilities of the INCA program

NASA Technical Reports Server (NTRS)

Bauer, Frank H.; Downing, John P.; Thorpe, Christopher J.

1989-01-01

The INteractive Controls Analysis (INCA) program was developed at NASA's Goddard Space Flight Center to provide a user friendly, efficient environment for the design and analysis of control systems, specifically spacecraft control systems. Since its inception, INCA has found extensive use in the design, development, and analysis of control systems for spacecraft, instruments, robotics, and pointing systems. The (INCA) program was initially developed as a comprehensive classical design analysis tool for small and large order control systems. The latest version of INCA, expected to be released in February of 1990, was expanded to include the capability to perform multivariable controls analysis and design.

Testing Mean Differences among Groups: Multivariate and Repeated Measures Analysis with Minimal Assumptions

PubMed Central

Bathke, Arne C.; Friedrich, Sarah; Pauly, Markus; Konietschke, Frank; Staffen, Wolfgang; Strobl, Nicolas; Höller, Yvonne

2018-01-01

ABSTRACT To date, there is a lack of satisfactory inferential techniques for the analysis of multivariate data in factorial designs, when only minimal assumptions on the data can be made. Presently available methods are limited to very particular study designs or assume either multivariate normality or equal covariance matrices across groups, or they do not allow for an assessment of the interaction effects across within-subjects and between-subjects variables. We propose and methodologically validate a parametric bootstrap approach that does not suffer from any of the above limitations, and thus provides a rather general and comprehensive methodological route to inference for multivariate and repeated measures data. As an example application, we consider data from two different Alzheimer’s disease (AD) examination modalities that may be used for precise and early diagnosis, namely, single-photon emission computed tomography (SPECT) and electroencephalogram (EEG). These data violate the assumptions of classical multivariate methods, and indeed classical methods would not have yielded the same conclusions with regards to some of the factors involved. PMID:29565679

Algogenic substances and metabolic status in work-related Trapezius Myalgia: a multivariate explorative study.

PubMed

Gerdle, Björn; Kristiansen, Jesper; Larsson, Britt; Saltin, Bengt; Søgaard, Karen; Sjøgaard, Gisela

2014-10-28

This study compares the levels of algesic substances between subjects with trapezius myalgia (TM) and healthy controls (CON) and explores the multivariate correlation pattern between these substances, pain, and metabolic status together with relative blood flow changes reported in our previous paper (Eur J Appl Physiol 108:657-669, 2010). 43 female workers with (TM) and 19 females without (CON) trapezius myalgia were - using microdialysis - compared for differences in interstitial concentrations of interleukin-6 (IL-6), bradykinin (BKN), serotonin (5-HT), lactate dehydrogenas (LDH), substance P, and N-terminal propeptide of procollagen type I (PINP) in the trapezius muscle at rest and during repetitive/stressful work. These data were also used in multivariate analyses together with previously presented data (Eur J Appl Physiol 108:657-669, 2010): trapezius muscle blood flow, metabolite accumulation, oxygenation, and pain development and sensitivity. Substance P was significantly elevated in TM (p=0.0068). No significant differences were found in the classical algesic substances (p: 0.432-0.926). The multivariate analysis showed that blood flow related variables, interstitial concentrations of metabolic (pyruvate), and algesic (BKN and K+) substances were important for the discrimination of the subjects to one of the two groups (R2: 0.19-0.31, p<0.05). Pain intensity was positively associated with levels of 5-HT and K+ and negatively associated with oxygenation indicators and IL-6 in TM (R2: 0.24, p<0.05). A negative correlation existed in TM between mechanical pain sensitivity of trapezius and BKN and IL-6 (R2: 0.26-0.39, p<0.05). The present study increased understanding alterations in the myalgic muscle. When considering the system-wide aspects, increased concentrations of lactate, pyruvate and K+ and decreased oxygenation characterized TM compared to CON. There are three major possible explanations for this finding: the workers with pain had relatively low severity of myalgia, metabolic alterations preceded detectable alterations in levels of algesics, or peripheral sensitization and other muscle alterations existed in TM. Only SP of the investigated algesic substances was elevated in TM. Several of the algesics were of importance for the levels of pain intensity and mechanical pain sensitivity in TM. These results indicate peripheral contribution to maintenance of central nociceptive and pain mechanisms and may be important to consider when designing treatments.

Development of multivariate exposure and fatal accident involvement rates for 1977

DOT National Transportation Integrated Search

1985-10-01

The need for multivariate accident involvement rates is often encounted in : accident analysis. The FARS (Fatal Accident Reporting System) files contain : records of fatal involvements characterized by many variables while NPTS : (National Personal T...

Bayesian multivariate hierarchical transformation models for ROC analysis.

PubMed

O'Malley, A James; Zou, Kelly H

2006-02-15

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

Bayesian multivariate hierarchical transformation models for ROC analysis

PubMed Central

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

2006-01-01

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

A Novel Approach to Detect Accelerated Aged and Surface-Mediated Degradation in Explosives by UPLC-ESI-MS.

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

Beppler, Christina L

2015-12-01

A new approach was created for studying energetic material degradation. This approach involved detecting and tentatively identifying non-volatile chemical species by liquid chromatography-mass spectrometry (LC-MS) with multivariate statistical data analysis that form as the CL-20 energetic material thermally degraded. Multivariate data analysis showed clear separation and clustering of samples based on sample group: either pristine or aged material. Further analysis showed counter-clockwise trends in the principal components analysis (PCA), a type of multivariate data analysis, Scores plots. These trends may indicate that there was a discrete shift in the chemical markers as the went from pristine to aged material, andmore » then again when the aged CL-20 mixed with a potentially incompatible material was thermally aged for 4, 6, or 9 months. This new approach to studying energetic material degradation should provide greater knowledge of potential degradation markers in these materials.« less

Complex numbers in chemometrics: examples from multivariate impedance measurements on lipid monolayers.

PubMed

Geladi, Paul; Nelson, Andrew; Lindholm-Sethson, Britta

2007-07-09

Electrical impedance gives multivariate complex number data as results. Two examples of multivariate electrical impedance data measured on lipid monolayers in different solutions give rise to matrices (16x50 and 38x50) of complex numbers. Multivariate data analysis by principal component analysis (PCA) or singular value decomposition (SVD) can be used for complex data and the necessary equations are given. The scores and loadings obtained are vectors of complex numbers. It is shown that the complex number PCA and SVD are better at concentrating information in a few components than the naïve juxtaposition method and that Argand diagrams can replace score and loading plots. Different concentrations of Magainin and Gramicidin A give different responses and also the role of the electrolyte medium can be studied. An interaction of Gramicidin A in the solution with the monolayer over time can be observed.

Multivariate Analysis of Fruit Antioxidant Activities of Blackberry Treated with 1-Methylcyclopropene or Vacuum Precooling

PubMed Central

Li, Jian; Ma, Guowei; Ma, Lin; Bao, Xiaolin; Li, Liping; Zhao, Qian

2018-01-01

Effects of 1-methylcyclopropene (1-MCP) and vacuum precooling on quality and antioxidant properties of blackberries (Rubus spp.) were evaluated using one-way analysis of variance, principal component analysis (PCA), partial least squares (PLS), and path analysis. Results showed that the activities of antioxidant enzymes were enhanced by both 1-MCP treatment and vacuum precooling. PCA could discriminate 1-MCP treated fruit and the vacuum precooled fruit and showed that the radical-scavenging activities in vacuum precooled fruit were higher than those in 1-MCP treated fruit. The scores of PCA showed that H2O2 content was the most important variables of blackberry fruit. PLSR results showed that peroxidase (POD) activity negatively correlated with H2O2 content. The results of path coefficient analysis indicated that glutathione (GSH) also had an indirect effect on H2O2 content. PMID:29487622

Social Cognitive and Planned Behavior Variables Associated with Stages of Change for Physical Activity in Spinal Cord Injury: A Multivariate Analysis

ERIC Educational Resources Information Center

Keegan, John; Ditchman, Nicole; Dutta, Alo; Chiu, Chung-Yi; Muller, Veronica; Chan, Fong; Kundu, Madan

2016-01-01

Purpose: To apply the constructs of social cognitive theory (SCT) and the theory of planned behavior (TPB) to understand the stages of change (SOC) for physical activities among individuals with a spinal cord injury (SCI). Method: Ex post facto design using multivariate analysis of variance (MANOVA). The participants were 144 individuals with SCI…

To See the World in a Grain of Sand: Recognizing the Origin of Sand Specimens by Diffuse Reflectance Infrared Fourier Transform Spectroscopy and Multivariate Exploratory Data Analysis

ERIC Educational Resources Information Center

Pezzolo, Alessandra De Lorenzi

2011-01-01

The diffuse reflectance infrared Fourier transform (DRIFT) spectra of sand samples exhibit features reflecting their composition. Basic multivariate analysis (MVA) can be used to effectively sort subsets of homogeneous specimens collected from nearby locations, as well as pointing out similarities in composition among sands of different origins.…

Testing key predictions of the associative account of mirror neurons in humans using multivariate pattern analysis.

PubMed

Oosterhof, Nikolaas N; Wiggett, Alison J; Cross, Emily S

2014-04-01

Cook et al. overstate the evidence supporting their associative account of mirror neurons in humans: most studies do not address a key property, action-specificity that generalizes across the visual and motor domains. Multivariate pattern analysis (MVPA) of neuroimaging data can address this concern, and we illustrate how MVPA can be used to test key predictions of their account.

Multivariate Quantitative Chemical Analysis

NASA Technical Reports Server (NTRS)

Kinchen, David G.; Capezza, Mary

1995-01-01

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

Robust LOD scores for variance component-based linkage analysis.

PubMed

Blangero, J; Williams, J T; Almasy, L

2000-01-01

The variance component method is now widely used for linkage analysis of quantitative traits. Although this approach offers many advantages, the importance of the underlying assumption of multivariate normality of the trait distribution within pedigrees has not been studied extensively. Simulation studies have shown that traits with leptokurtic distributions yield linkage test statistics that exhibit excessive Type I error when analyzed naively. We derive analytical formulae relating the deviation from the expected asymptotic distribution of the lod score to the kurtosis and total heritability of the quantitative trait. A simple correction constant yields a robust lod score for any deviation from normality and for any pedigree structure, and effectively eliminates the problem of inflated Type I error due to misspecification of the underlying probability model in variance component-based linkage analysis.

Forensic analysis of dyed textile fibers.

PubMed

Goodpaster, John V; Liszewski, Elisa A

2009-08-01

Textile fibers are a key form of trace evidence, and the ability to reliably associate or discriminate them is crucial for forensic scientists worldwide. While microscopic and instrumental analysis can be used to determine the composition of the fiber itself, additional specificity is gained by examining fiber color. This is particularly important when the bulk composition of the fiber is relatively uninformative, as it is with cotton, wool, or other natural fibers. Such analyses pose several problems, including extremely small sample sizes, the desire for nondestructive techniques, and the vast complexity of modern dye compositions. This review will focus on more recent methods for comparing fiber color by using chromatography, spectroscopy, and mass spectrometry. The increasing use of multivariate statistics and other data analysis techniques for the differentiation of spectra from dyed fibers will also be discussed.

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.

Compound effects of temperature and precipitation in making droughts more frequent in Marathwada, India

NASA Astrophysics Data System (ADS)

Mondal, A.; Zachariah, M.; Achutarao, K. M.; Otto, F. E. L.

2017-12-01

The Marathwada region in Maharashtra, India is known to suffer significantly from agrarian crisis including farmer suicides resulting from persistent droughts. Drought monitoring in India is commonly based on univariate indicators that consider the deficiency in precipitation alone. However, droughts may involve complex interplay of multiple physical variables, necessitating an integrated, multivariate approach to analyse their behaviour. In this study, we compare the behaviour of drought characteristics in Marathwada in the recent years as compared to the first half of the twentieth century, using a joint precipitation and temperature-based Multivariate Standardized Drought Index (MSDI). Drought events in the recent times are found to exhibit exceptional simultaneous anomalies of high temperature and precipitation deficits in this region, though studies on precipitation alone show that these events are within the range of historically observed variability. Additionally, we also develop multivariate copula-based Severity-Duration-Frequency (SDF) relationships for droughts in this region and compare their natures pre- and post- 1950. Based on multivariate return periods considering both temperature and precipitation anomalies, as well as the severity and duration of droughts, it is found that droughts have become more frequent in the post-1950 period. Based on precipitation alone, such an observation cannot be made. This emphasizes the sensitivity of droughts to temperature and underlines the importance of considering compound effects of temperature and precipitation in order to avoid an underestimation of drought risk. This observation-based analysis is the first step towards investigating the causal mechanisms of droughts, their evolutions and impacts in this region, particularly those influenced by anthropogenic climate change.

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.

Sensitive analytical method for simultaneous analysis of some vasoconstrictors with highly overlapped analytical signals

NASA Astrophysics Data System (ADS)

Nikolić, G. S.; Žerajić, S.; Cakić, M.

2011-10-01

Multivariate calibration method is a powerful mathematical tool that can be applied in analytical chemistry when the analytical signals are highly overlapped. The method with regression by partial least squares is proposed for the simultaneous spectrophotometric determination of adrenergic vasoconstrictors in decongestive solution containing two active components: phenyleprine hydrochloride and trimazoline hydrochloride. These sympathomimetic agents are that frequently associated in pharmaceutical formulations against the common cold. The proposed method, which is, simple and rapid, offers the advantages of sensitivity and wide range of determinations without the need for extraction of the vasoconstrictors. In order to minimize the optimal factors necessary to obtain the calibration matrix by multivariate calibration, different parameters were evaluated. The adequate selection of the spectral regions proved to be important on the number of factors. In order to simultaneously quantify both hydrochlorides among excipients, the spectral region between 250 and 290 nm was selected. A recovery for the vasoconstrictor was 98-101%. The developed method was applied to assay of two decongestive pharmaceutical preparations.

Genetic and Environmental Influences on Female Sexual Orientation, Childhood Gender Typicality and Adult Gender Identity

PubMed Central

Burri, Andrea; Cherkas, Lynn; Spector, Timothy; Rahman, Qazi

2011-01-01

Background Human sexual orientation is influenced by genetic and non-shared environmental factors as are two important psychological correlates – childhood gender typicality (CGT) and adult gender identity (AGI). However, researchers have been unable to resolve the genetic and non-genetic components that contribute to the covariation between these traits, particularly in women. Methodology/Principal Findings Here we performed a multivariate genetic analysis in a large sample of British female twins (N = 4,426) who completed a questionnaire assessing sexual attraction, CGT and AGI. Univariate genetic models indicated modest genetic influences on sexual attraction (25%), AGI (11%) and CGT (31%). For the multivariate analyses, a common pathway model best fitted the data. Conclusions/Significance This indicated that a single latent variable influenced by a genetic component and common non-shared environmental component explained the association between the three traits but there was substantial measurement error. These findings highlight common developmental factors affecting differences in sexual orientation. PMID:21760939

Usage of multivariate geostatistics in interpolation processes for meteorological precipitation maps

NASA Astrophysics Data System (ADS)

Gundogdu, Ismail Bulent

2017-01-01

Long-term meteorological data are very important both for the evaluation of meteorological events and for the analysis of their effects on the environment. Prediction maps which are constructed by different interpolation techniques often provide explanatory information. Conventional techniques, such as surface spline fitting, global and local polynomial models, and inverse distance weighting may not be adequate. Multivariate geostatistical methods can be more significant, especially when studying secondary variables, because secondary variables might directly affect the precision of prediction. In this study, the mean annual and mean monthly precipitations from 1984 to 2014 for 268 meteorological stations in Turkey have been used to construct country-wide maps. Besides linear regression, the inverse square distance and ordinary co-Kriging (OCK) have been used and compared to each other. Also elevation, slope, and aspect data for each station have been taken into account as secondary variables, whose use has reduced errors by up to a factor of three. OCK gave the smallest errors (1.002 cm) when aspect was included.

Business closure and relocation: a comparative analysis of the Loma Prieta earthquake and Hurricane Andrew.

PubMed

Wasileski, Gabriela; Rodríguez, Havidán; Diaz, Walter

2011-01-01

The occurrence of a number of large-scale disasters or catastrophes in recent years, including the Indian Ocean tsunami (2004), the Kashmir earthquake (2005), Hurricane Katrina (2005) and Hurricane Ike (2008), have raised our awareness regarding the devastating effects of disasters on human populations and the importance of developing mitigation and preparedness strategies to limit the consequences of such events. However, there is still a dearth of social science research focusing on the socio-economic impact of disasters on businesses in the United States. This paper contributes to this research literature by focusing on the impact of disasters on business closure and relocation through the use of multivariate logistic regression models, specifically focusing on the Loma Prieta earthquake (1989) and Hurricane Andrew (1992). Using a multivariate model, we examine how physical damage to the infrastructure, lifeline disruption and business characteristics, among others, impact business closure and relocation following major disasters. © 2011 The Author(s). Disasters © Overseas Development Institute, 2011.

Predicting major element mineral/melt equilibria - A statistical approach

NASA Technical Reports Server (NTRS)

Hostetler, C. J.; Drake, M. J.

1980-01-01

Empirical equations have been developed for calculating the mole fractions of NaO0.5, MgO, AlO1.5, SiO2, KO0.5, CaO, TiO2, and FeO in a solid phase of initially unknown identity given only the composition of the coexisting silicate melt. The approach involves a linear multivariate regression analysis in which solid composition is expressed as a Taylor series expansion of the liquid compositions. An internally consistent precision of approximately 0.94 is obtained, that is, the nature of the liquidus phase in the input data set can be correctly predicted for approximately 94% of the entries. The composition of the liquidus phase may be calculated to better than 5 mol % absolute. An important feature of this 'generalized solid' model is its reversibility; that is, the dependent and independent variables in the linear multivariate regression may be inverted to permit prediction of the composition of a silicate liquid produced by equilibrium partial melting of a polymineralic source assemblage.

Anima: Modular Workflow System for Comprehensive Image Data Analysis

PubMed Central

Rantanen, Ville; Valori, Miko; Hautaniemi, Sampsa

2014-01-01

Modern microscopes produce vast amounts of image data, and computational methods are needed to analyze and interpret these data. Furthermore, a single image analysis project may require tens or hundreds of analysis steps starting from data import and pre-processing to segmentation and statistical analysis; and ending with visualization and reporting. To manage such large-scale image data analysis projects, we present here a modular workflow system called Anima. Anima is designed for comprehensive and efficient image data analysis development, and it contains several features that are crucial in high-throughput image data analysis: programing language independence, batch processing, easily customized data processing, interoperability with other software via application programing interfaces, and advanced multivariate statistical analysis. The utility of Anima is shown with two case studies focusing on testing different algorithms developed in different imaging platforms and an automated prediction of alive/dead C. elegans worms by integrating several analysis environments. Anima is a fully open source and available with documentation at www.anduril.org/anima. PMID:25126541

Early and late fracture following extensive limb lengthening in patients with achondroplasia and hypochondroplasia.

PubMed

Kitoh, H; Mishima, K; Matsushita, M; Nishida, Y; Ishiguro, N

2014-09-01

Two types of fracture, early and late, have been reported following limb lengthening in patients with achondroplasia (ACH) and hypochondroplasia (HCH). We reviewed 25 patients with these conditions who underwent 72 segmental limb lengthening procedures involving the femur and/or tibia, between 2003 and 2011. Gender, age at surgery, lengthened segment, body mass index, the shape of the callus, the amount and percentage of lengthening and the healing index were evaluated to determine predictive factors for the occurrence of early (within three weeks after removal of the fixation pins) and late fracture (> three weeks after removal of the pins). The Mann‑Whitney U test and Pearson's chi-squared test for univariate analysis and stepwise regression model for multivariate analysis were used to identify the predictive factor for each fracture. Only one patient (two tibiae) was excluded from the analysis due to excessively slow formation of the regenerate, which required supplementary measures. A total of 24 patients with 70 limbs were included in the study. There were 11 early fractures in eight patients. The shape of the callus (lateral or central callus) was the only statistical variable related to the occurrence of early fracture in univariate and multivariate analyses. Late fracture was observed in six limbs and the mean time between removal of the fixation pins and fracture was 18.3 weeks (3.3 to 38.4). Lengthening of the tibia, larger healing index, and lateral or central callus were related to the occurrence of a late fracture in univariate analysis. A multivariate analysis demonstrated that the shape of the callus was the strongest predictor for late fracture (odds ratio: 19.3, 95% confidence interval: 2.91 to 128). Lateral or central callus had a significantly larger risk of fracture than fusiform, cylindrical, or concave callus. Radiological monitoring of the shape of the callus during distraction is important to prevent early and late fracture of lengthened limbs in patients with ACH or HCH. In patients with thin callus formation, some measures to stimulate bone formation should be considered as early as possible. ©2014 The British Editorial Society of Bone & Joint Surgery.

Implications of Supermarket Access, Neighborhood Walkability, and Poverty Rates for Diabetes Risk in an Employee Population

PubMed Central

Herrick, Cynthia J.; Yount, Byron W.; Eyler, Amy A.

2016-01-01

Objective Diabetes is a growing public health problem, and the environment in which people live and work may affect diabetes risk. The goal of this study was to examine the association between multiple aspects of environment and diabetes risk in an employee population. Design This was a retrospective cross-sectional analysis. Home environment variables were derived using employee zip code. Descriptive statistics were run on all individual and zip code level variables, stratified by diabetes risk and worksite. A multivariable logistic regression analysis was then conducted to determine the strongest associations with diabetes risk. Setting Data was collected from employee health fairs in a Midwestern health system 2009–2012. Subjects The dataset contains 25,227 unique individuals across four years of data. From this group, using an individual’s first entry into the database, 15,522 individuals had complete data for analysis. Results The prevalence of high diabetes risk in this population was 2.3%. There was significant variability in individual and zip code level variables across worksites. From the multivariable analysis, living in a zip code with higher percent poverty and higher walk score was positively associated with high diabetes risk, while living in a zip code with higher supermarket density was associated with a reduction in high diabetes risk. Conclusions Our study underscores the important relationship between poverty, home neighborhood environment, and diabetes risk, even in a relatively healthy employed population, and suggests a role for the employer in promoting health. PMID:26638995

Implications of supermarket access, neighbourhood walkability and poverty rates for diabetes risk in an employee population.

PubMed

Herrick, Cynthia J; Yount, Byron W; Eyler, Amy A

2016-08-01

Diabetes is a growing public health problem, and the environment in which people live and work may affect diabetes risk. The goal of the present study was to examine the association between multiple aspects of environment and diabetes risk in an employee population. This was a retrospective cross-sectional analysis. Home environment variables were derived using employees' zip code. Descriptive statistics were run on all individual- and zip-code-level variables, stratified by diabetes risk and worksite. A multivariable logistic regression analysis was then conducted to determine the strongest associations with diabetes risk. Data were collected from employee health fairs in a Midwestern health system, 2009-2012. The data set contains 25 227 unique individuals across four years of data. From this group, using an individual's first entry into the database, 15 522 individuals had complete data for analysis. The prevalence of high diabetes risk in this population was 2·3 %. There was significant variability in individual- and zip-code-level variables across worksites. From the multivariable analysis, living in a zip code with higher percentage of poverty and higher walk score was positively associated with high diabetes risk, while living in a zip code with higher supermarket density was associated with a reduction in high diabetes risk. Our study underscores the important relationship between poverty, home neighbourhood environment and diabetes risk, even in a relatively healthy employed population, and suggests a role for the employer in promoting health.

Multivariate analysis of variations in intrinsic foot musculature among hominoids.

PubMed

Oishi, Motoharu; Ogihara, Naomichi; Shimizu, Daisuke; Kikuchi, Yasuhiro; Endo, Hideki; Une, Yumi; Soeta, Satoshi; Amasaki, Hajime; Ichihara, Nobutsune

2018-05-01

Comparative analysis of the foot muscle architecture among extant great apes is important for understanding the evolution of the human foot and, hence, human habitual bipedal walking. However, to our knowledge, there is no previous report of a quantitative comparison of hominoid intrinsic foot muscle dimensions. In the present study, we quantitatively compared muscle dimensions of the hominoid foot by means of multivariate analysis. The foot muscle mass and physiological cross-sectional area (PCSA) of five chimpanzees, one bonobo, two gorillas, and six orangutans were obtained by our own dissections, and those of humans were taken from published accounts. The muscle mass and PCSA were respectively divided by the total mass and total PCSA of the intrinsic muscles of the entire foot for normalization. Variations in muscle architecture among human and extant great apes were quantified based on principal component analysis. Our results demonstrated that the muscle architecture of the orangutan was the most distinctive, having a larger first dorsal interosseous muscle and smaller abductor hallucis brevis muscle. On the other hand, the gorilla was found to be unique in having a larger abductor digiti minimi muscle. Humans were distinguished from extant great apes by a larger quadratus plantae muscle. The chimpanzee and the bonobo appeared to have very similar muscle architecture, with an intermediate position between the human and the orangutan. These differences (or similarities) in architecture of the intrinsic foot muscles among humans and great apes correspond well to the differences in phylogeny, positional behavior, and locomotion. © 2018 Anatomical Society.

Breast-feeding, water and sanitation, and childhood malnutrition in the Philippines.

PubMed

Magnani, R J; Mock, N B; Bertrand, W E; Clay, D C

1993-04-01

This study examines effects and interactions of socioeconomic status, access to water supply and sanitation, and breast-feeding practices in relation to child growth in two provincial cities in the Philippines. Multivariate analysis identified food expenditure per head, education of the household head and gender of the child as significant predictors of nutritional status. The duration of partial and full breast-feeding was negatively (though non-significantly) associated with growth. Sanitation facilities and breast-feeding are, however, important determinants during the first year of life. Among children over 1 year of age, socioeconomic variables and gender are the most important predictors. Breast-feeding is shown to provide more important health benefits for children in lower income households. The need for further studies on the causes of gender differences in nutritional status was apparent.