Evaluation of statistical protocols for quality control of ecosystem carbon dioxide fluxes

Treesearch

Jorge F. Perez-Quezada; Nicanor Z. Saliendra; William E. Emmerich; Emilio A. Laca

2007-01-01

The process of quality control of micrometeorological and carbon dioxide (CO2) flux data can be subjective and may lack repeatability, which would undermine the results of many studies. Multivariate statistical methods and time series analysis were used together and independently to detect and replace outliers in CO2 flux...

Evaluation of Facility Management by Multivariate Statistics - Factor Analysis

NASA Astrophysics Data System (ADS)

Singovszki, Miloš; Vranayová, Zuzana

2013-06-01

Facility management is evolving, there is no exact than other sciences, although its development is fast forward. The knowledge and practical skills in facility management is not replaced, on the contrary, they complement each other. The existing low utilization of science in the field of facility management is mainly caused by the management of support activities are many variables and prevailing immediate reaction to the extraordinary situation arising from motives of those who have substantial experience and years of proven experience. Facility management is looking for a system that uses organized knowledge and will form the basis, which grows from a wide range of disciplines. Significant influence on its formation as a scientific discipline is the "structure, which follows strategy". The paper deals evaluate technology building as part of an facility management by multivariate statistic - factor analysis.

Multivariate analysis, mass balance techniques, and statistical tests as tools in igneous petrology: application to the Sierra de las Cruces volcanic range (Mexican Volcanic Belt).

PubMed

Velasco-Tapia, Fernando

2014-01-01

Magmatic processes have usually been identified and evaluated using qualitative or semiquantitative geochemical or isotopic tools based on a restricted number of variables. However, a more complete and quantitative view could be reached applying multivariate analysis, mass balance techniques, and statistical tests. As an example, in this work a statistical and quantitative scheme is applied to analyze the geochemical features for the Sierra de las Cruces (SC) volcanic range (Mexican Volcanic Belt). In this locality, the volcanic activity (3.7 to 0.5 Ma) was dominantly dacitic, but the presence of spheroidal andesitic enclaves and/or diverse disequilibrium features in majority of lavas confirms the operation of magma mixing/mingling. New discriminant-function-based multidimensional diagrams were used to discriminate tectonic setting. Statistical tests of discordancy and significance were applied to evaluate the influence of the subducting Cocos plate, which seems to be rather negligible for the SC magmas in relation to several major and trace elements. A cluster analysis following Ward's linkage rule was carried out to classify the SC volcanic rocks geochemical groups. Finally, two mass-balance schemes were applied for the quantitative evaluation of the proportion of the end-member components (dacitic and andesitic magmas) in the comingled lavas (binary mixtures).

Multiple Versus Single Set Validation of Multivariate Models to Avoid Mistakes.

PubMed

Harrington, Peter de Boves

2018-01-02

Validation of multivariate models is of current importance for a wide range of chemical applications. Although important, it is neglected. The common practice is to use a single external validation set for evaluation. This approach is deficient and may mislead investigators with results that are specific to the single validation set of data. In addition, no statistics are available regarding the precision of a derived figure of merit (FOM). A statistical approach using bootstrapped Latin partitions is advocated. This validation method makes an efficient use of the data because each object is used once for validation. It was reviewed a decade earlier but primarily for the optimization of chemometric models this review presents the reasons it should be used for generalized statistical validation. Average FOMs with confidence intervals are reported and powerful, matched-sample statistics may be applied for comparing models and methods. Examples demonstrate the problems with single validation sets.

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

Parsimonious Use of Indicators for Evaluating Sustainability Systems with Multivariate Statistical Analyses

EPA Science Inventory

Indicators are commonly used for evaluating relative sustainability for competing products and processes. When a set of indicators is chosen for a particular system of study, it is important to ensure that they are variable independently of each other. Often the number of indicat...

Geographic variation in forest composition and precipitation predict the synchrony of forest insect outbreaks

Treesearch

Kyle J. Haynes; Andrew M. Liebhold; Ottar N. Bjørnstad; Andrew J. Allstadt; Randall S. Morin

2018-01-01

Evaluating the causes of spatial synchrony in population dynamics in nature is notoriously difficult due to a lack of data and appropriate statistical methods. Here, we use a recently developed method, a multivariate extension of the local indicators of spatial autocorrelation statistic, to map geographic variation in the synchrony of gypsy moth outbreaks. Regression...

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.

Exploring the Replicability of a Study's Results: Bootstrap Statistics for the Multivariate Case.

ERIC Educational Resources Information Center

Thompson, Bruce

1995-01-01

Use of the bootstrap method in a canonical correlation analysis to evaluate the replicability of a study's results is illustrated. More confidence may be vested in research results that replicate. (SLD)

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

PubMed Central

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

2016-01-01

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

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

PubMed

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

2015-01-01

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

Multivariate Analysis, Mass Balance Techniques, and Statistical Tests as Tools in Igneous Petrology: Application to the Sierra de las Cruces Volcanic Range (Mexican Volcanic Belt)

PubMed Central

Velasco-Tapia, Fernando

2014-01-01

Magmatic processes have usually been identified and evaluated using qualitative or semiquantitative geochemical or isotopic tools based on a restricted number of variables. However, a more complete and quantitative view could be reached applying multivariate analysis, mass balance techniques, and statistical tests. As an example, in this work a statistical and quantitative scheme is applied to analyze the geochemical features for the Sierra de las Cruces (SC) volcanic range (Mexican Volcanic Belt). In this locality, the volcanic activity (3.7 to 0.5 Ma) was dominantly dacitic, but the presence of spheroidal andesitic enclaves and/or diverse disequilibrium features in majority of lavas confirms the operation of magma mixing/mingling. New discriminant-function-based multidimensional diagrams were used to discriminate tectonic setting. Statistical tests of discordancy and significance were applied to evaluate the influence of the subducting Cocos plate, which seems to be rather negligible for the SC magmas in relation to several major and trace elements. A cluster analysis following Ward's linkage rule was carried out to classify the SC volcanic rocks geochemical groups. Finally, two mass-balance schemes were applied for the quantitative evaluation of the proportion of the end-member components (dacitic and andesitic magmas) in the comingled lavas (binary mixtures). PMID:24737994

Statistical Evaluation of Time Series Analysis Techniques

NASA Technical Reports Server (NTRS)

Benignus, V. A.

1973-01-01

The performance of a modified version of NASA's multivariate spectrum analysis program is discussed. A multiple regression model was used to make the revisions. Performance improvements were documented and compared to the standard fast Fourier transform by Monte Carlo techniques.

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

NASA Technical Reports Server (NTRS)

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

1984-01-01

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

[Analysis of variance of repeated data measured by water maze with SPSS].

PubMed

Qiu, Hong; Jin, Guo-qin; Jin, Ru-feng; Zhao, Wei-kang

2007-01-01

To introduce the method of analyzing repeated data measured by water maze with SPSS 11.0, and offer a reference statistical method to clinical and basic medicine researchers who take the design of repeated measures. Using repeated measures and multivariate analysis of variance (ANOVA) process of the general linear model in SPSS and giving comparison among different groups and different measure time pairwise. Firstly, Mauchly's test of sphericity should be used to judge whether there were relations among the repeatedly measured data. If any (P

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

PubMed

Adams, Dean C; Collyer, Michael L

2018-01-01

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

Multivariate statistics applied to the reaction of common bean plants to parasitism by Meloidogyne javanica.

PubMed

Santos, L N S; Cabral, P D S; Neves, G A R; Alves, F R; Teixeira, M B; Cunha, F N; Silva, N F

2017-03-16

The availability of common bean cultivars tolerant to Meloidogyne javanica is limited in Brazil. Thus, the present study aimed to evaluate the reactions of 33 common bean genotypes (23 landrace, 8 commercial, 1 susceptible standard and 1 resistant standard) to M. javanica, employing multivariate statistics to discriminate the reaction of the genotypes. The experiment was conducted in a greenhouse using a completely randomized design with seven replicates. The seeds were sown in 1-L pots containing autoclaved soil and sand in a 1:1 ratio (v:v). On day 19, after emergence of the seedlings, the plants were treated with inoculum containing 4000 eggs + second-stage juveniles (J2). At 60 days after inoculation, the seedlings were evaluated based on biometric and parasitism-related traits, such as number of galls, final nematode population per root system, reproduction factor, and percent reduction in the reproduction factor of the nematode (%RRF). The data were subjected to analysis of variance using the F-test. The Mahalanobis generalized distance was used to obtain the dissimilarity matrix, and the average linkage between groups was used for clustering. The use of multivariate statistics allowed groups to be separated according to the resistance levels of genotypes, as observed in the %RRF. The landrace genotypes FORT-09, FORT-17, FORT-31, FORT-32, FORT-34 and FORT-36 presented resistance to M. javanica; thus, these genotypes can be considered potential sources of resistance.

Evaluation of dissolution profile similarity - Comparison between the f2, the multivariate statistical distance and the f2 bootstrapping methods.

PubMed

Paixão, Paulo; Gouveia, Luís F; Silva, Nuno; Morais, José A G

2017-03-01

A simulation study is presented, evaluating the performance of the f 2 , the model-independent multivariate statistical distance and the f 2 bootstrap methods in the ability to conclude similarity between two dissolution profiles. Different dissolution profiles, based on the Noyes-Whitney equation and ranging from theoretical f 2 values between 100 and 40, were simulated. Variability was introduced in the dissolution model parameters in an increasing order, ranging from a situation complying with the European guidelines requirements for the use of the f 2 metric to several situations where the f 2 metric could not be used anymore. Results have shown that the f 2 is an acceptable metric when used according to the regulatory requirements, but loses its applicability when variability increases. The multivariate statistical distance presented contradictory results in several of the simulation scenarios, which makes it an unreliable metric for dissolution profile comparisons. The bootstrap f 2 , although conservative in its conclusions is an alternative suitable method. Overall, as variability increases, all of the discussed methods reveal problems that can only be solved by increasing the number of dosage form units used in the comparison, which is usually not practical or feasible. Additionally, experimental corrective measures may be undertaken in order to reduce the overall variability, particularly when it is shown that it is mainly due to the dissolution assessment instead of being intrinsic to the dosage form. Copyright © 2016. Published by Elsevier B.V.

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

PubMed

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

2014-10-15

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

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

Treesearch

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

2006-01-01

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

Multivariate statistical process control of a continuous pharmaceutical twin-screw granulation and fluid bed drying process.

PubMed

Silva, A F; Sarraguça, M C; Fonteyne, M; Vercruysse, J; De Leersnyder, F; Vanhoorne, V; Bostijn, N; Verstraeten, M; Vervaet, C; Remon, J P; De Beer, T; Lopes, J A

2017-08-07

A multivariate statistical process control (MSPC) strategy was developed for the monitoring of the ConsiGma™-25 continuous tablet manufacturing line. Thirty-five logged variables encompassing three major units, being a twin screw high shear granulator, a fluid bed dryer and a product control unit, were used to monitor the process. The MSPC strategy was based on principal component analysis of data acquired under normal operating conditions using a series of four process runs. Runs with imposed disturbances in the dryer air flow and temperature, in the granulator barrel temperature, speed and liquid mass flow and in the powder dosing unit mass flow were utilized to evaluate the model's monitoring performance. The impact of the imposed deviations to the process continuity was also evaluated using Hotelling's T 2 and Q residuals statistics control charts. The influence of the individual process variables was assessed by analyzing contribution plots at specific time points. Results show that the imposed disturbances were all detected in both control charts. Overall, the MSPC strategy was successfully developed and applied. Additionally, deviations not associated with the imposed changes were detected, mainly in the granulator barrel temperature control. Copyright © 2017 Elsevier B.V. All rights reserved.

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

PubMed Central

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

2015-01-01

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

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…

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

PubMed

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

2010-11-01

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

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.

Time management in acute vertebrobasilar occlusion.

PubMed

Kamper, Lars; Rybacki, Konrad; Mansour, Michael; Winkler, Sven B; Kempkes, Udo; Haage, Patrick

2009-03-01

Acute vertebrobasilar occlusion (VBO) is associated with a high risk of stroke and death. Although local thrombolysis may achieve recanalization and improve outcome, mortality is still between 35% and 75%. However, without recanalization the chance of a good outcome is extremely poor, with mortality rates of 80-90%. Early treatment is a fundamental factor, but detailed studies of the exact time management of the diagnostic and interventional workflow are still lacking. Data on 18 patients were retrospectively evaluated. Time periods between symptom onset, admission to hospital, time of diagnosis, and beginning of intervention were correlated with postinterventional neurological status. The Glasgow Coma Scale and National Institute of Health Stroke Scale (NIHSS) were used to examine patients before and after local thrombolysis. Additionally, multivariate statistics were applied to reveal similarities between patients with neurological improvement. Primary recanalization was achieved in 77% of patients. The overall mortality was 55%. Major complications were intracranial hemorrhage and peripheral embolism. The time period from symptom onset to intervention showed a strong correlation with the postinterventional NIHSS as well as the patient's age, with the best results in a 4-h interval. Multivariate statistics revealed similarities among the patients. Evaluation of time management in acute VBO by multivariate statistics is a helpful tool for definition of similarities in this patient group. Similarly to the door-to-balloon time for acute coronary interventions, the chances for a good outcome depend on a short time interval between symptom onset and intervention. While the only manipulable time period starts with hospital admission, our results emphasize the necessity of efficient intrahospital workflow.

A multi-analyte serum test for the detection of non-small cell lung cancer

PubMed Central

Farlow, E C; Vercillo, M S; Coon, J S; Basu, S; Kim, A W; Faber, L P; Warren, W H; Bonomi, P; Liptay, M J; Borgia, J A

2010-01-01

Background: In this study, we appraised a wide assortment of biomarkers previously shown to have diagnostic or prognostic value for non-small cell lung cancer (NSCLC) with the intent of establishing a multi-analyte serum test capable of identifying patients with lung cancer. Methods: Circulating levels of 47 biomarkers were evaluated against patient cohorts consisting of 90 NSCLC and 43 non-cancer controls using commercial immunoassays. Multivariate statistical methods were used on all biomarkers achieving statistical relevance to define an optimised panel of diagnostic biomarkers for NSCLC. The resulting biomarkers were fashioned into a classification algorithm and validated against serum from a second patient cohort. Results: A total of 14 analytes achieved statistical relevance upon evaluation. Multivariate statistical methods then identified a panel of six biomarkers (tumour necrosis factor-α, CYFRA 21-1, interleukin-1ra, matrix metalloproteinase-2, monocyte chemotactic protein-1 and sE-selectin) as being the most efficacious for diagnosing early stage NSCLC. When tested against a second patient cohort, the panel successfully classified 75 of 88 patients. Conclusions: Here, we report the development of a serum algorithm with high specificity for classifying patients with NSCLC against cohorts of various ‘high-risk' individuals. A high rate of false positives was observed within the cohort in which patients had non-neoplastic lung nodules, possibly as a consequence of the inflammatory nature of these conditions. PMID:20859284

Effect of sexual steroids on boar kinematic sperm subpopulations.

PubMed

Ayala, E M E; Aragón, M A

2017-11-01

Here, we show the effects of sexual steroids, progesterone, testosterone, or estradiol on motility parameters of boar sperm. Sixteen commercial seminal doses, four each of four adult boars, were analyzed using computer assisted sperm analysis (CASA). Mean values of motility parameters were analyzed by bivariate and multivariate statistics. Principal component analysis (PCA), followed by hierarchical clustering, was applied on data of motility parameters, provided automatically as intervals by the CASA system. Effects of sexual steroids were described in the kinematic subpopulations identified from multivariate statistics. Mean values of motility parameters were not significantly changed after addition of sexual steroids. Multivariate graphics showed that sperm subpopulations were not sensitive to the addition of either testosterone or estradiol, but sperm subpopulations responsive to progesterone were found. Distribution of motility parameters were wide in controls but sharpened at distinct concentrations of progesterone. We conclude that kinematic sperm subpopulations responsive to progesterone are present in boar semen, and these subpopulations are masked in evaluations of mean values of motility parameters. © 2017 International Society for Advancement of Cytometry. © 2017 International Society for Advancement of Cytometry.

Analyzing Faculty Salaries When Statistics Fail.

ERIC Educational Resources Information Center

Simpson, William A.

The role played by nonstatistical procedures, in contrast to multivariant statistical approaches, in analyzing faculty salaries is discussed. Multivariant statistical methods are usually used to establish or defend against prima facia cases of gender and ethnic discrimination with respect to faculty salaries. These techniques are not applicable,…

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.

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

USGS Publications Warehouse

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

2003-01-01

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

Multivariate Relationships between Statistics Anxiety and Motivational Beliefs

ERIC Educational Resources Information Center

Baloglu, Mustafa; Abbassi, Amir; Kesici, Sahin

2017-01-01

In general, anxiety has been found to be associated with motivational beliefs and the current study investigated multivariate relationships between statistics anxiety and motivational beliefs among 305 college students (60.0% women). The Statistical Anxiety Rating Scale, the Motivated Strategies for Learning Questionnaire, and a set of demographic…

Evaluation and comparison of statistical methods for early temporal detection of outbreaks: A simulation-based study

PubMed Central

Le Strat, Yann

2017-01-01

The objective of this paper is to evaluate a panel of statistical algorithms for temporal outbreak detection. Based on a large dataset of simulated weekly surveillance time series, we performed a systematic assessment of 21 statistical algorithms, 19 implemented in the R package surveillance and two other methods. We estimated false positive rate (FPR), probability of detection (POD), probability of detection during the first week, sensitivity, specificity, negative and positive predictive values and F1-measure for each detection method. Then, to identify the factors associated with these performance measures, we ran multivariate Poisson regression models adjusted for the characteristics of the simulated time series (trend, seasonality, dispersion, outbreak sizes, etc.). The FPR ranged from 0.7% to 59.9% and the POD from 43.3% to 88.7%. Some methods had a very high specificity, up to 99.4%, but a low sensitivity. Methods with a high sensitivity (up to 79.5%) had a low specificity. All methods had a high negative predictive value, over 94%, while positive predictive values ranged from 6.5% to 68.4%. Multivariate Poisson regression models showed that performance measures were strongly influenced by the characteristics of time series. Past or current outbreak size and duration strongly influenced detection performances. PMID:28715489

On the Power of Multivariate Latent Growth Curve Models to Detect Correlated Change

ERIC Educational Resources Information Center

Hertzog, Christopher; Lindenberger, Ulman; Ghisletta, Paolo; Oertzen, Timo von

2006-01-01

We evaluated the statistical power of single-indicator latent growth curve models (LGCMs) to detect correlated change between two variables (covariance of slopes) as a function of sample size, number of longitudinal measurement occasions, and reliability (measurement error variance). Power approximations following the method of Satorra and Saris…

Comparing and combining biomarkers as principle surrogates for time-to-event clinical endpoints.

PubMed

Gabriel, Erin E; Sachs, Michael C; Gilbert, Peter B

2015-02-10

Principal surrogate endpoints are useful as targets for phase I and II trials. In many recent trials, multiple post-randomization biomarkers are measured. However, few statistical methods exist for comparison of or combination of biomarkers as principal surrogates, and none of these methods to our knowledge utilize time-to-event clinical endpoint information. We propose a Weibull model extension of the semi-parametric estimated maximum likelihood method that allows for the inclusion of multiple biomarkers in the same risk model as multivariate candidate principal surrogates. We propose several methods for comparing candidate principal surrogates and evaluating multivariate principal surrogates. These include the time-dependent and surrogate-dependent true and false positive fraction, the time-dependent and the integrated standardized total gain, and the cumulative distribution function of the risk difference. We illustrate the operating characteristics of our proposed methods in simulations and outline how these statistics can be used to evaluate and compare candidate principal surrogates. We use these methods to investigate candidate surrogates in the Diabetes Control and Complications Trial. Copyright © 2014 John Wiley & Sons, Ltd.

Síndrome metabólico y otros factores asociados a gonartrosis.

PubMed

Charles-Lozoya, Sergio; Treviño-Báez, Joaquín Darío; Ramos-Rivera, Jesús Alejandro; Rangel-Flores, Jesús María; Tamez-Montes, Juan Carlos; Brizuela-Ventura, Jesús Miguel

2017-01-01

To evaluate whether an association exists between gonarthrosis and metabolic syndrome X (MS) as well as other potential risk factors. Comparative cross-sectional study of 310 patients evaluated by pathology of knee grouped in patients with gonarthrosis and without it. Sociodemographic, anthropometric and laboratory data was obtained. Gonarthrosis was defined as a ≥ 2 score in Kellgren-Lawrence radiological scale, and MS was assessed using the International Diabetes Federation criteria. Odds ratio and logistic regression were used for bivariate and multivariate analysis respectively. The prevalence of MS in patients who had gonarthrosis was 79.9%, statistically higher than in patients without gonarthrosis (p = 0.001). Other factors that had a statistically higher frequency in this group included diabetes mellitus (p = 0.02) and hypertension (p = 0.02). Multivariate analysis revealed MS had an association with a higher prevalence of gonarthrosis (p = 0.003), while high density lipoproteins (p = 0.02) was associated with a lower prevalence. MS and its related alterations are associated to gonarthrosis; their adequate control could prevent patients from developing the disease. Copyright: © 2017 SecretarÍa de Salud

Expression of p53, p21 and cyclin D1 in penile cancer: p53 predicts poor prognosis.

PubMed

Gunia, Sven; Kakies, Christoph; Erbersdobler, Andreas; Hakenberg, Oliver W; Koch, Stefan; May, Matthias

2012-03-01

To evaluate the role of p53, p21 and cyclin D1 expression in patients with penile cancer (PC). Paraffin-embedded tissues from PC specimens from six pathology departments were subjected to a central histopathological review performed by one pathologist. The tissue microarray technique was used for immunostaining which was evaluated by two independent pathologists and correlated with cancer-specific survival (CSS). κ-statistics were used to assess interobserver variability. Uni- and multivariable Cox proportional hazards analysis was applied to assess the independent effects of several prognostic factors on CSS over a median of 32 months (IQR 6-66 months). Specimens and clinical data from 110 men treated surgically for primary PC were collected. p53 staining was positive in 30 and negative in 62 specimens. κ-statistics showed substantial interobserver reproducibility of p53 staining evaluation (κ=0.73; p<0.001). The 5-year CSS rate for the entire study cohort was 74%. Five-year CSS was 84% in p53-negative and 51% in p53-positive PC patients (p=0.003). Multivariable analysis showed p53 (HR=3.20; p=0.041) and pT-stage (HR=4.29; p<0.001) as independent significant prognostic factors for CSS. Cyclin D1 and p21 expression were not correlated with survival. However, incorporating p21 into a multivariable Cox model did contribute to improved model quality for predicting CSS. In patients with PC, the expression of p53 in the primary tumour specimen can be reproducibly assessed and is negatively associated with cancer specific survival.

Temporal and spatial assessment of river surface water quality using multivariate statistical techniques: a study in Can Tho City, a Mekong Delta area, Vietnam.

PubMed

Phung, Dung; Huang, Cunrui; Rutherford, Shannon; Dwirahmadi, Febi; Chu, Cordia; Wang, Xiaoming; Nguyen, Minh; Nguyen, Nga Huy; Do, Cuong Manh; Nguyen, Trung Hieu; Dinh, Tuan Anh Diep

2015-05-01

The present study is an evaluation of temporal/spatial variations of surface water quality using multivariate statistical techniques, comprising cluster analysis (CA), principal component analysis (PCA), factor analysis (FA) and discriminant analysis (DA). Eleven water quality parameters were monitored at 38 different sites in Can Tho City, a Mekong Delta area of Vietnam from 2008 to 2012. Hierarchical cluster analysis grouped the 38 sampling sites into three clusters, representing mixed urban-rural areas, agricultural areas and industrial zone. FA/PCA resulted in three latent factors for the entire research location, three for cluster 1, four for cluster 2, and four for cluster 3 explaining 60, 60.2, 80.9, and 70% of the total variance in the respective water quality. The varifactors from FA indicated that the parameters responsible for water quality variations are related to erosion from disturbed land or inflow of effluent from sewage plants and industry, discharges from wastewater treatment plants and domestic wastewater, agricultural activities and industrial effluents, and contamination by sewage waste with faecal coliform bacteria through sewer and septic systems. Discriminant analysis (DA) revealed that nephelometric turbidity units (NTU), chemical oxygen demand (COD) and NH₃ are the discriminating parameters in space, affording 67% correct assignation in spatial analysis; pH and NO₂ are the discriminating parameters according to season, assigning approximately 60% of cases correctly. The findings suggest a possible revised sampling strategy that can reduce the number of sampling sites and the indicator parameters responsible for large variations in water quality. This study demonstrates the usefulness of multivariate statistical techniques for evaluation of temporal/spatial variations in water quality assessment and management.

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

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.

Instrumental Neutron Activation Analysis and Multivariate Statistics for Pottery Provenance

NASA Astrophysics Data System (ADS)

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

2004-06-01

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

Multivariate postprocessing techniques for probabilistic hydrological forecasting

NASA Astrophysics Data System (ADS)

Hemri, Stephan; Lisniak, Dmytro; Klein, Bastian

2016-04-01

Hydrologic ensemble forecasts driven by atmospheric ensemble prediction systems need statistical postprocessing in order to account for systematic errors in terms of both mean and spread. Runoff is an inherently multivariate process with typical events lasting from hours in case of floods to weeks or even months in case of droughts. This calls for multivariate postprocessing techniques that yield well calibrated forecasts in univariate terms and ensure a realistic temporal dependence structure at the same time. To this end, the univariate ensemble model output statistics (EMOS; Gneiting et al., 2005) postprocessing method is combined with two different copula approaches that ensure multivariate calibration throughout the entire forecast horizon. These approaches comprise ensemble copula coupling (ECC; Schefzik et al., 2013), which preserves the dependence structure of the raw ensemble, and a Gaussian copula approach (GCA; Pinson and Girard, 2012), which estimates the temporal correlations from training observations. Both methods are tested in a case study covering three subcatchments of the river Rhine that represent different sizes and hydrological regimes: the Upper Rhine up to the gauge Maxau, the river Moselle up to the gauge Trier, and the river Lahn up to the gauge Kalkofen. The results indicate that both ECC and GCA are suitable for modelling the temporal dependences of probabilistic hydrologic forecasts (Hemri et al., 2015). References Gneiting, T., A. E. Raftery, A. H. Westveld, and T. Goldman (2005), Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation, Monthly Weather Review, 133(5), 1098-1118, DOI: 10.1175/MWR2904.1. Hemri, S., D. Lisniak, and B. Klein, Multivariate postprocessing techniques for probabilistic hydrological forecasting, Water Resources Research, 51(9), 7436-7451, DOI: 10.1002/2014WR016473. Pinson, P., and R. Girard (2012), Evaluating the quality of scenarios of short-term wind power generation, Applied Energy, 96, 12-20, DOI: 10.1016/j.apenergy.2011.11.004. Schefzik, R., T. L. Thorarinsdottir, and T. Gneiting (2013), Uncertainty quantification in complex simulation models using ensemble copula coupling, Statistical Science, 28, 616-640, DOI: 10.1214/13-STS443.

Single-variant and multi-variant trend tests for genetic association with next-generation sequencing that are robust to sequencing error.

PubMed

Kim, Wonkuk; Londono, Douglas; Zhou, Lisheng; Xing, Jinchuan; Nato, Alejandro Q; Musolf, Anthony; Matise, Tara C; Finch, Stephen J; Gordon, Derek

2012-01-01

As with any new technology, next-generation sequencing (NGS) has potential advantages and potential challenges. One advantage is the identification of multiple causal variants for disease that might otherwise be missed by SNP-chip technology. One potential challenge is misclassification error (as with any emerging technology) and the issue of power loss due to multiple testing. Here, we develop an extension of the linear trend test for association that incorporates differential misclassification error and may be applied to any number of SNPs. We call the statistic the linear trend test allowing for error, applied to NGS, or LTTae,NGS. This statistic allows for differential misclassification. The observed data are phenotypes for unrelated cases and controls, coverage, and the number of putative causal variants for every individual at all SNPs. We simulate data considering multiple factors (disease mode of inheritance, genotype relative risk, causal variant frequency, sequence error rate in cases, sequence error rate in controls, number of loci, and others) and evaluate type I error rate and power for each vector of factor settings. We compare our results with two recently published NGS statistics. Also, we create a fictitious disease model based on downloaded 1000 Genomes data for 5 SNPs and 388 individuals, and apply our statistic to those data. We find that the LTTae,NGS maintains the correct type I error rate in all simulations (differential and non-differential error), while the other statistics show large inflation in type I error for lower coverage. Power for all three methods is approximately the same for all three statistics in the presence of non-differential error. Application of our statistic to the 1000 Genomes data suggests that, for the data downloaded, there is a 1.5% sequence misclassification rate over all SNPs. Finally, application of the multi-variant form of LTTae,NGS shows high power for a number of simulation settings, although it can have lower power than the corresponding single-variant simulation results, most probably due to our specification of multi-variant SNP correlation values. In conclusion, our LTTae,NGS addresses two key challenges with NGS disease studies; first, it allows for differential misclassification when computing the statistic; and second, it addresses the multiple-testing issue in that there is a multi-variant form of the statistic that has only one degree of freedom, and provides a single p value, no matter how many loci. Copyright © 2013 S. Karger AG, Basel.

Single variant and multi-variant trend tests for genetic association with next generation sequencing that are robust to sequencing error

PubMed Central

Kim, Wonkuk; Londono, Douglas; Zhou, Lisheng; Xing, Jinchuan; Nato, Andrew; Musolf, Anthony; Matise, Tara C.; Finch, Stephen J.; Gordon, Derek

2013-01-01

As with any new technology, next generation sequencing (NGS) has potential advantages and potential challenges. One advantage is the identification of multiple causal variants for disease that might otherwise be missed by SNP-chip technology. One potential challenge is misclassification error (as with any emerging technology) and the issue of power loss due to multiple testing. Here, we develop an extension of the linear trend test for association that incorporates differential misclassification error and may be applied to any number of SNPs. We call the statistic the linear trend test allowing for error, applied to NGS, or LTTae,NGS. This statistic allows for differential misclassification. The observed data are phenotypes for unrelated cases and controls, coverage, and the number of putative causal variants for every individual at all SNPs. We simulate data considering multiple factors (disease mode of inheritance, genotype relative risk, causal variant frequency, sequence error rate in cases, sequence error rate in controls, number of loci, and others) and evaluate type I error rate and power for each vector of factor settings. We compare our results with two recently published NGS statistics. Also, we create a fictitious disease model, based on downloaded 1000 Genomes data for 5 SNPs and 388 individuals, and apply our statistic to that data. We find that the LTTae,NGS maintains the correct type I error rate in all simulations (differential and non-differential error), while the other statistics show large inflation in type I error for lower coverage. Power for all three methods is approximately the same for all three statistics in the presence of non-differential error. Application of our statistic to the 1000 Genomes data suggests that, for the data downloaded, there is a 1.5% sequence misclassification rate over all SNPs. Finally, application of the multi-variant form of LTTae,NGS shows high power for a number of simulation settings, although it can have lower power than the corresponding single variant simulation results, most probably due to our specification of multi-variant SNP correlation values. In conclusion, our LTTae,NGS addresses two key challenges with NGS disease studies; first, it allows for differential misclassification when computing the statistic; and second, it addresses the multiple-testing issue in that there is a multi-variant form of the statistic that has only one degree of freedom, and provides a single p-value, no matter how many loci. PMID:23594495

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.

Robust multivariate nonparametric tests for detection of two-sample location shift in clinical trials

PubMed Central

Jiang, Xuejun; Guo, Xu; Zhang, Ning; Wang, Bo

2018-01-01

This article presents and investigates performance of a series of robust multivariate nonparametric tests for detection of location shift between two multivariate samples in randomized controlled trials. The tests are built upon robust estimators of distribution locations (medians, Hodges-Lehmann estimators, and an extended U statistic) with both unscaled and scaled versions. The nonparametric tests are robust to outliers and do not assume that the two samples are drawn from multivariate normal distributions. Bootstrap and permutation approaches are introduced for determining the p-values of the proposed test statistics. Simulation studies are conducted and numerical results are reported to examine performance of the proposed statistical tests. The numerical results demonstrate that the robust multivariate nonparametric tests constructed from the Hodges-Lehmann estimators are more efficient than those based on medians and the extended U statistic. The permutation approach can provide a more stringent control of Type I error and is generally more powerful than the bootstrap procedure. The proposed robust nonparametric tests are applied to detect multivariate distributional difference between the intervention and control groups in the Thai Healthy Choices study and examine the intervention effect of a four-session motivational interviewing-based intervention developed in the study to reduce risk behaviors among youth living with HIV. PMID:29672555

Feasibility Study on the Use of On-line Multivariate Statistical Process Control for Safeguards Applications in Natural Uranium Conversion Plants

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

Ladd-Lively, Jennifer L

2014-01-01

The objective of this work was to determine the feasibility of using on-line multivariate statistical process control (MSPC) for safeguards applications in natural uranium conversion plants. Multivariate statistical process control is commonly used throughout industry for the detection of faults. For safeguards applications in uranium conversion plants, faults could include the diversion of intermediate products such as uranium dioxide, uranium tetrafluoride, and uranium hexafluoride. This study was limited to a 100 metric ton of uranium (MTU) per year natural uranium conversion plant (NUCP) using the wet solvent extraction method for the purification of uranium ore concentrate. A key component inmore » the multivariate statistical methodology is the Principal Component Analysis (PCA) approach for the analysis of data, development of the base case model, and evaluation of future operations. The PCA approach was implemented through the use of singular value decomposition of the data matrix where the data matrix represents normal operation of the plant. Component mole balances were used to model each of the process units in the NUCP. However, this approach could be applied to any data set. The monitoring framework developed in this research could be used to determine whether or not a diversion of material has occurred at an NUCP as part of an International Atomic Energy Agency (IAEA) safeguards system. This approach can be used to identify the key monitoring locations, as well as locations where monitoring is unimportant. Detection limits at the key monitoring locations can also be established using this technique. Several faulty scenarios were developed to test the monitoring framework after the base case or normal operating conditions of the PCA model were established. In all of the scenarios, the monitoring framework was able to detect the fault. Overall this study was successful at meeting the stated objective.« less

Integrated environmental monitoring and multivariate data analysis-A case study.

PubMed

Eide, Ingvar; Westad, Frank; Nilssen, Ingunn; de Freitas, Felipe Sales; Dos Santos, Natalia Gomes; Dos Santos, Francisco; Cabral, Marcelo Montenegro; Bicego, Marcia Caruso; Figueira, Rubens; Johnsen, Ståle

2017-03-01

The present article describes integration of environmental monitoring and discharge data and interpretation using multivariate statistics, principal component analysis (PCA), and partial least squares (PLS) regression. The monitoring was carried out at the Peregrino oil field off the coast of Brazil. One sensor platform and 3 sediment traps were placed on the seabed. The sensors measured current speed and direction, turbidity, temperature, and conductivity. The sediment trap samples were used to determine suspended particulate matter that was characterized with respect to a number of chemical parameters (26 alkanes, 16 PAHs, N, C, calcium carbonate, and Ba). Data on discharges of drill cuttings and water-based drilling fluid were provided on a daily basis. The monitoring was carried out during 7 campaigns from June 2010 to October 2012, each lasting 2 to 3 months due to the capacity of the sediment traps. The data from the campaigns were preprocessed, combined, and interpreted using multivariate statistics. No systematic difference could be observed between campaigns or traps despite the fact that the first campaign was carried out before drilling, and 1 of 3 sediment traps was located in an area not expected to be influenced by the discharges. There was a strong covariation between suspended particulate matter and total N and organic C suggesting that the majority of the sediment samples had a natural and biogenic origin. Furthermore, the multivariate regression showed no correlation between discharges of drill cuttings and sediment trap or turbidity data taking current speed and direction into consideration. Because of this lack of correlation with discharges from the drilling location, a more detailed evaluation of chemical indicators providing information about origin was carried out in addition to numerical modeling of dispersion and deposition. The chemical indicators and the modeling of dispersion and deposition support the conclusions from the multivariate statistics. Integr Environ Assess Manag 2017;13:387-395. © 2016 SETAC. © 2016 SETAC.

[Rank distributions in community ecology from the statistical viewpoint].

PubMed

Maksimov, V N

2004-01-01

Traditional statistical methods for definition of empirical functions of abundance distribution (population, biomass, production, etc.) of species in a community are applicable for processing of multivariate data contained in the above quantitative indices of the communities. In particular, evaluation of moments of distribution suffices for convolution of the data contained in a list of species and their abundance. At the same time, the species should be ranked in the list in ascending rather than descending population and the distribution models should be analyzed on the basis of the data on abundant species only.

A Hierarchical Multivariate Bayesian Approach to Ensemble Model output Statistics in Atmospheric Prediction

DTIC Science & Technology

2017-09-01

efficacy of statistical post-processing methods downstream of these dynamical model components with a hierarchical multivariate Bayesian approach to...Bayesian hierarchical modeling, Markov chain Monte Carlo methods , Metropolis algorithm, machine learning, atmospheric prediction 15. NUMBER OF PAGES...scale processes. However, this dissertation explores the efficacy of statistical post-processing methods downstream of these dynamical model components

Multivariate meta-analysis: potential and promise.

PubMed

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

2011-09-10

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

Multivariate meta-analysis: Potential and promise

PubMed Central

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

2011-01-01

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

Cognitive Complaints After Breast Cancer Treatments: Examining the Relationship With Neuropsychological Test Performance

PubMed Central

2013-01-01

Background Cognitive complaints are reported frequently after breast cancer treatments. Their association with neuropsychological (NP) test performance is not well-established. Methods Early-stage, posttreatment breast cancer patients were enrolled in a prospective, longitudinal, cohort study prior to starting endocrine therapy. Evaluation included an NP test battery and self-report questionnaires assessing symptoms, including cognitive complaints. Multivariable regression models assessed associations among cognitive complaints, mood, treatment exposures, and NP test performance. Results One hundred eighty-nine breast cancer patients, aged 21–65 years, completed the evaluation; 23.3% endorsed higher memory complaints and 19.0% reported higher executive function complaints (>1 SD above the mean for healthy control sample). Regression modeling demonstrated a statistically significant association of higher memory complaints with combined chemotherapy and radiation treatments (P = .01), poorer NP verbal memory performance (P = .02), and higher depressive symptoms (P < .001), controlling for age and IQ. For executive functioning complaints, multivariable modeling controlling for age, IQ, and other confounds demonstrated statistically significant associations with better NP visual memory performance (P = .03) and higher depressive symptoms (P < .001), whereas combined chemotherapy and radiation treatment (P = .05) approached statistical significance. Conclusions About one in five post–adjuvant treatment breast cancer patients had elevated memory and/or executive function complaints that were statistically significantly associated with domain-specific NP test performances and depressive symptoms; combined chemotherapy and radiation treatment was also statistically significantly associated with memory complaints. These results and other emerging studies suggest that subjective cognitive complaints in part reflect objective NP performance, although their etiology and biology appear to be multifactorial, motivating further transdisciplinary research. PMID:23606729

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

PubMed

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

2014-01-01

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

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

USDA-ARS?s Scientific Manuscript database

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

Health and human rights: a statistical measurement framework using household survey data in Uganda.

PubMed

Wesonga, Ronald; Owino, Abraham; Ssekiboobo, Agnes; Atuhaire, Leonard; Jehopio, Peter

2015-05-03

Health is intertwined with human rights as is clearly reflected in the right to life. Promotion of health practices in the context of human rights can be accomplished if there is a better understanding of the level of human rights observance. In this paper, we evaluate and present an appraisal for a possibility of applying household survey to study the determinants of health and human rights and also derive the probability that human rights are observed; an important ingredient into the national planning framework. Data from the Uganda National Governance Baseline Survey were used. A conceptual framework for predictors of a hybrid dependent variable was developed and both bivariate and multivariate statistical techniques employed. Multivariate post estimation computations were derived after evaluations of the significance of coefficients of health and human rights predictors. Findings, show that household characteristics of respondents considered in this study were statistically significant (p < 0.05) to provide a reliable assessment of human rights observance. For example, a unit increase of respondents' schooling levels results in an increase of about 34% level of positively assessing human rights observance. Additionally, the study establishes, through the three models presented, that household assessment of health and human rights observance was 20% which also represents how much of the entire continuum of human rights is demanded. Findings propose important evidence for monitoring and evaluation of health in the context human rights using household survey data. They provide a benchmark for health and human rights assessments with a focus on international and national development plans to achieve socio-economic transformation and health in society.

Analysis of the procedures used to evaluate suicide crime scenes in Brazil: a statistical approach to interpret reports.

PubMed

Bruni, Aline Thaís; Velho, Jesus Antonio; Ferreira, Arthur Serra Lopes; Tasso, Maria Júlia; Ferrari, Raíssa Santos; Yoshida, Ricardo Luís; Dias, Marcos Salvador; Leite, Vitor Barbanti Pereira

2014-08-01

This study uses statistical techniques to evaluate reports on suicide scenes; it utilizes 80 reports from different locations in Brazil, randomly collected from both federal and state jurisdictions. We aimed to assess a heterogeneous group of cases in order to obtain an overall perspective of the problem. We evaluated variables regarding the characteristics of the crime scene, such as the detected traces (blood, instruments and clothes) that were found and we addressed the methodology employed by the experts. A qualitative approach using basic statistics revealed a wide distribution as to how the issue was addressed in the documents. We examined a quantitative approach involving an empirical equation and we used multivariate procedures to validate the quantitative methodology proposed for this empirical equation. The methodology successfully identified the main differences in the information presented in the reports, showing that there is no standardized method of analyzing evidences. Copyright © 2014 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

Metal and physico-chemical variations at a hydroelectric reservoir analyzed by Multivariate Analyses and Artificial Neural Networks: environmental management and policy/decision-making tools.

PubMed

Cavalcante, Y L; Hauser-Davis, R A; Saraiva, A C F; Brandão, I L S; Oliveira, T F; Silveira, A M

2013-01-01

This paper compared and evaluated seasonal variations in physico-chemical parameters and metals at a hydroelectric power station reservoir by applying Multivariate Analyses and Artificial Neural Networks (ANN) statistical techniques. A Factor Analysis was used to reduce the number of variables: the first factor was composed of elements Ca, K, Mg and Na, and the second by Chemical Oxygen Demand. The ANN showed 100% correct classifications in training and validation samples. Physico-chemical analyses showed that water pH values were not statistically different between the dry and rainy seasons, while temperature, conductivity, alkalinity, ammonia and DO were higher in the dry period. TSS, hardness and COD, on the other hand, were higher during the rainy season. The statistical analyses showed that Ca, K, Mg and Na are directly connected to the Chemical Oxygen Demand, which indicates a possibility of their input into the reservoir system by domestic sewage and agricultural run-offs. These statistical applications, thus, are also relevant in cases of environmental management and policy decision-making processes, to identify which factors should be further studied and/or modified to recover degraded or contaminated water bodies. Copyright © 2012 Elsevier B.V. All rights reserved.

Multivariate statistical characterization of charged and uncharged domain walls in multiferroic hexagonal YMnO3 single crystal visualized by a spherical aberration-corrected STEM.

PubMed

Matsumoto, Takao; Ishikawa, Ryo; Tohei, Tetsuya; Kimura, Hideo; Yao, Qiwen; Zhao, Hongyang; Wang, Xiaolin; Chen, Dapeng; Cheng, Zhenxiang; Shibata, Naoya; Ikuhara, Yuichi

2013-10-09

A state-of-the-art spherical aberration-corrected STEM was fully utilized to directly visualize the multiferroic domain structure in a hexagonal YMnO3 single crystal at atomic scale. With the aid of multivariate statistical analysis (MSA), we obtained unbiased and quantitative maps of ferroelectric domain structures with atomic resolution. Such a statistical image analysis of the transition region between opposite polarizations has confirmed atomically sharp transitions of ferroelectric polarization both in antiparallel (uncharged) and tail-to-tail 180° (charged) domain boundaries. Through the analysis, a correlated subatomic image shift of Mn-O layers with that of Y layers, exhibiting a double-arc shape of reversed curvatures, have been elucidated. The amount of image shift in Mn-O layers along the c-axis is statistically significant as small as 0.016 nm, roughly one-third of the evident image shift of 0.048 nm in Y layers. Interestingly, a careful analysis has shown that such a subatomic image shift in Mn-O layers vanishes at the tail-to-tail 180° domain boundaries. Furthermore, taking advantage of the annular bright field (ABF) imaging technique combined with MSA, the tilting of MnO5 bipyramids, the very core mechanism of multiferroicity of the material, is evaluated.

Is math anxiety in the secondary classroom limiting physics mastery? A study of math anxiety and physics performance

NASA Astrophysics Data System (ADS)

Mercer, Gary J.

This quantitative study examined the relationship between secondary students with math anxiety and physics performance in an inquiry-based constructivist classroom. The Revised Math Anxiety Rating Scale was used to evaluate math anxiety levels. The results were then compared to the performance on a physics standardized final examination. A simple correlation was performed, followed by a multivariate regression analysis to examine effects based on gender and prior math background. The correlation showed statistical significance between math anxiety and physics performance. The regression analysis showed statistical significance for math anxiety, physics performance, and prior math background, but did not show statistical significance for math anxiety, physics performance, and gender.

A Civilian/Military Trauma Institute: National Trauma Coordinating Center

DTIC Science & Technology

2015-12-01

zip codes was used in “proximity to violence” analysis. Data were analyzed using SPSS (version 20.0, SPSS Inc., Chicago, IL). Multivariable linear...number of adverse events and serious events was not statistically higher in one group, the incidence of deep venous thrombosis (DVT) was statistically ...subjects the lack of statistical difference on multivariate analysis may be related to an underpowered sample size. It was recommended that the

A new test of multivariate nonlinear causality

PubMed Central

Bai, Zhidong; Jiang, Dandan; Lv, Zhihui; Wong, Wing-Keung; Zheng, Shurong

2018-01-01

The multivariate nonlinear Granger causality developed by Bai et al. (2010) (Mathematics and Computers in simulation. 2010; 81: 5-17) plays an important role in detecting the dynamic interrelationships between two groups of variables. Following the idea of Hiemstra-Jones (HJ) test proposed by Hiemstra and Jones (1994) (Journal of Finance. 1994; 49(5): 1639-1664), they attempt to establish a central limit theorem (CLT) of their test statistic by applying the asymptotical property of multivariate U-statistic. However, Bai et al. (2016) (2016; arXiv: 1701.03992) revisit the HJ test and find that the test statistic given by HJ is NOT a function of U-statistics which implies that the CLT neither proposed by Hiemstra and Jones (1994) nor the one extended by Bai et al. (2010) is valid for statistical inference. In this paper, we re-estimate the probabilities and reestablish the CLT of the new test statistic. Numerical simulation shows that our new estimates are consistent and our new test performs decent size and power. PMID:29304085

A new test of multivariate nonlinear causality.

PubMed

Bai, Zhidong; Hui, Yongchang; Jiang, Dandan; Lv, Zhihui; Wong, Wing-Keung; Zheng, Shurong

2018-01-01

The multivariate nonlinear Granger causality developed by Bai et al. (2010) (Mathematics and Computers in simulation. 2010; 81: 5-17) plays an important role in detecting the dynamic interrelationships between two groups of variables. Following the idea of Hiemstra-Jones (HJ) test proposed by Hiemstra and Jones (1994) (Journal of Finance. 1994; 49(5): 1639-1664), they attempt to establish a central limit theorem (CLT) of their test statistic by applying the asymptotical property of multivariate U-statistic. However, Bai et al. (2016) (2016; arXiv: 1701.03992) revisit the HJ test and find that the test statistic given by HJ is NOT a function of U-statistics which implies that the CLT neither proposed by Hiemstra and Jones (1994) nor the one extended by Bai et al. (2010) is valid for statistical inference. In this paper, we re-estimate the probabilities and reestablish the CLT of the new test statistic. Numerical simulation shows that our new estimates are consistent and our new test performs decent size and power.

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…

Application of multivariate Gaussian detection theory to known non-Gaussian probability density functions

NASA Astrophysics Data System (ADS)

Schwartz, Craig R.; Thelen, Brian J.; Kenton, Arthur C.

1995-06-01

A statistical parametric multispectral sensor performance model was developed by ERIM to support mine field detection studies, multispectral sensor design/performance trade-off studies, and target detection algorithm development. The model assumes target detection algorithms and their performance models which are based on data assumed to obey multivariate Gaussian probability distribution functions (PDFs). The applicability of these algorithms and performance models can be generalized to data having non-Gaussian PDFs through the use of transforms which convert non-Gaussian data to Gaussian (or near-Gaussian) data. An example of one such transform is the Box-Cox power law transform. In practice, such a transform can be applied to non-Gaussian data prior to the introduction of a detection algorithm that is formally based on the assumption of multivariate Gaussian data. This paper presents an extension of these techniques to the case where the joint multivariate probability density function of the non-Gaussian input data is known, and where the joint estimate of the multivariate Gaussian statistics, under the Box-Cox transform, is desired. The jointly estimated multivariate Gaussian statistics can then be used to predict the performance of a target detection algorithm which has an associated Gaussian performance model.

Impact of statistical learning methods on the predictive power of multivariate normal tissue complication probability models.

PubMed

Xu, Cheng-Jian; van der Schaaf, Arjen; Schilstra, Cornelis; Langendijk, Johannes A; van't Veld, Aart A

2012-03-15

To study the impact of different statistical learning methods on the prediction performance of multivariate normal tissue complication probability (NTCP) models. In this study, three learning methods, stepwise selection, least absolute shrinkage and selection operator (LASSO), and Bayesian model averaging (BMA), were used to build NTCP models of xerostomia following radiotherapy treatment for head and neck cancer. Performance of each learning method was evaluated by a repeated cross-validation scheme in order to obtain a fair comparison among methods. It was found that the LASSO and BMA methods produced models with significantly better predictive power than that of the stepwise selection method. Furthermore, the LASSO method yields an easily interpretable model as the stepwise method does, in contrast to the less intuitive BMA method. The commonly used stepwise selection method, which is simple to execute, may be insufficient for NTCP modeling. The LASSO method is recommended. Copyright © 2012 Elsevier Inc. All rights reserved.

Inferring Instantaneous, Multivariate and Nonlinear Sensitivities for the Analysis of Feedback Processes in a Dynamical System: Lorenz Model Case Study

NASA Technical Reports Server (NTRS)

Aires, Filipe; Rossow, William B.; Hansen, James E. (Technical Monitor)

2001-01-01

A new approach is presented for the analysis of feedback processes in a nonlinear dynamical system by observing its variations. The new methodology consists of statistical estimates of the sensitivities between all pairs of variables in the system based on a neural network modeling of the dynamical system. The model can then be used to estimate the instantaneous, multivariate and nonlinear sensitivities, which are shown to be essential for the analysis of the feedbacks processes involved in the dynamical system. The method is described and tested on synthetic data from the low-order Lorenz circulation model where the correct sensitivities can be evaluated analytically.

Gap Shape Classification using Landscape Indices and Multivariate Statistics

PubMed Central

Wu, Chih-Da; Cheng, Chi-Chuan; Chang, Che-Chang; Lin, Chinsu; Chang, Kun-Cheng; Chuang, Yung-Chung

2016-01-01

This study proposed a novel methodology to classify the shape of gaps using landscape indices and multivariate statistics. Patch-level indices were used to collect the qualified shape and spatial configuration characteristics for canopy gaps in the Lienhuachih Experimental Forest in Taiwan in 1998 and 2002. Non-hierarchical cluster analysis was used to assess the optimal number of gap clusters and canonical discriminant analysis was used to generate the discriminant functions for canopy gap classification. The gaps for the two periods were optimally classified into three categories. In general, gap type 1 had a more complex shape, gap type 2 was more elongated and gap type 3 had the largest gaps that were more regular in shape. The results were evaluated using Wilks’ lambda as satisfactory (p < 0.001). The agreement rate of confusion matrices exceeded 96%. Differences in gap characteristics between the classified gap types that were determined using a one-way ANOVA showed a statistical significance in all patch indices (p = 0.00), except for the Euclidean nearest neighbor distance (ENN) in 2002. Taken together, these results demonstrated the feasibility and applicability of the proposed methodology to classify the shape of a gap. PMID:27901127

Gap Shape Classification using Landscape Indices and Multivariate Statistics.

PubMed

Wu, Chih-Da; Cheng, Chi-Chuan; Chang, Che-Chang; Lin, Chinsu; Chang, Kun-Cheng; Chuang, Yung-Chung

2016-11-30

This study proposed a novel methodology to classify the shape of gaps using landscape indices and multivariate statistics. Patch-level indices were used to collect the qualified shape and spatial configuration characteristics for canopy gaps in the Lienhuachih Experimental Forest in Taiwan in 1998 and 2002. Non-hierarchical cluster analysis was used to assess the optimal number of gap clusters and canonical discriminant analysis was used to generate the discriminant functions for canopy gap classification. The gaps for the two periods were optimally classified into three categories. In general, gap type 1 had a more complex shape, gap type 2 was more elongated and gap type 3 had the largest gaps that were more regular in shape. The results were evaluated using Wilks' lambda as satisfactory (p < 0.001). The agreement rate of confusion matrices exceeded 96%. Differences in gap characteristics between the classified gap types that were determined using a one-way ANOVA showed a statistical significance in all patch indices (p = 0.00), except for the Euclidean nearest neighbor distance (ENN) in 2002. Taken together, these results demonstrated the feasibility and applicability of the proposed methodology to classify the shape of a gap.

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

Overweight and Obesity: Prevalence and Correlates in a Large Clinical Sample of Children with Autism Spectrum Disorder

ERIC Educational Resources Information Center

Zuckerman, Katharine E.; Hill, Alison P.; Guion, Kimberly; Voltolina, Lisa; Fombonne, Eric

2014-01-01

Autism Spectrum Disorders (ASDs) and childhood obesity (OBY) are rising public health concerns. This study aimed to evaluate the prevalence of overweight (OWT) and OBY in a sample of 376 Oregon children with ASD, and to assess correlates of OWT and OBY in this sample. We used descriptive statistics, bivariate, and focused multivariate analyses to…

MIDAS: Regionally linear multivariate discriminative statistical mapping.

PubMed

Varol, Erdem; Sotiras, Aristeidis; Davatzikos, Christos

2018-07-01

Statistical parametric maps formed via voxel-wise mass-univariate tests, such as the general linear model, are commonly used to test hypotheses about regionally specific effects in neuroimaging cross-sectional studies where each subject is represented by a single image. Despite being informative, these techniques remain limited as they ignore multivariate relationships in the data. Most importantly, the commonly employed local Gaussian smoothing, which is important for accounting for registration errors and making the data follow Gaussian distributions, is usually chosen in an ad hoc fashion. Thus, it is often suboptimal for the task of detecting group differences and correlations with non-imaging variables. Information mapping techniques, such as searchlight, which use pattern classifiers to exploit multivariate information and obtain more powerful statistical maps, have become increasingly popular in recent years. However, existing methods may lead to important interpretation errors in practice (i.e., misidentifying a cluster as informative, or failing to detect truly informative voxels), while often being computationally expensive. To address these issues, we introduce a novel efficient multivariate statistical framework for cross-sectional studies, termed MIDAS, seeking highly sensitive and specific voxel-wise brain maps, while leveraging the power of regional discriminant analysis. In MIDAS, locally linear discriminative learning is applied to estimate the pattern that best discriminates between two groups, or predicts a variable of interest. This pattern is equivalent to local filtering by an optimal kernel whose coefficients are the weights of the linear discriminant. By composing information from all neighborhoods that contain a given voxel, MIDAS produces a statistic that collectively reflects the contribution of the voxel to the regional classifiers as well as the discriminative power of the classifiers. Critically, MIDAS efficiently assesses the statistical significance of the derived statistic by analytically approximating its null distribution without the need for computationally expensive permutation tests. The proposed framework was extensively validated using simulated atrophy in structural magnetic resonance imaging (MRI) and further tested using data from a task-based functional MRI study as well as a structural MRI study of cognitive performance. The performance of the proposed framework was evaluated against standard voxel-wise general linear models and other information mapping methods. The experimental results showed that MIDAS achieves relatively higher sensitivity and specificity in detecting group differences. Together, our results demonstrate the potential of the proposed approach to efficiently map effects of interest in both structural and functional data. Copyright © 2018. Published by Elsevier Inc.

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.

Assessment of metals bioavailability to vegetables under field conditions using DGT, single extractions and multivariate statistics

PubMed Central

2012-01-01

Background The metals bioavailability in soils is commonly assessed by chemical extractions; however a generally accepted method is not yet established. In this study, the effectiveness of Diffusive Gradients in Thin-films (DGT) technique and single extractions in the assessment of metals bioaccumulation in vegetables, and the influence of soil parameters on phytoavailability were evaluated using multivariate statistics. Soil and plants grown in vegetable gardens from mining-affected rural areas, NW Romania, were collected and analysed. Results Pseudo-total metal content of Cu, Zn and Cd in soil ranged between 17.3-146 mg kg-1, 141–833 mg kg-1 and 0.15-2.05 mg kg-1, respectively, showing enriched contents of these elements. High degrees of metals extractability in 1M HCl and even in 1M NH4Cl were observed. Despite the relatively high total metal concentrations in soil, those found in vegetables were comparable to values typically reported for agricultural crops, probably due to the low concentrations of metals in soil solution (Csoln) and low effective concentrations (CE), assessed by DGT technique. Among the analysed vegetables, the highest metal concentrations were found in carrots roots. By applying multivariate statistics, it was found that CE, Csoln and extraction in 1M NH4Cl, were better predictors for metals bioavailability than the acid extractions applied in this study. Copper transfer to vegetables was strongly influenced by soil organic carbon (OC) and cation exchange capacity (CEC), while pH had a higher influence on Cd transfer from soil to plants. Conclusions The results showed that DGT can be used for general evaluation of the risks associated to soil contamination with Cu, Zn and Cd in field conditions. Although quantitative information on metals transfer from soil to vegetables was not observed. PMID:23079133

The Effect of the Multivariate Box-Cox Transformation on the Power of MANOVA.

ERIC Educational Resources Information Center

Kirisci, Levent; Hsu, Tse-Chi

Most of the multivariate statistical techniques rely on the assumption of multivariate normality. The effects of non-normality on multivariate tests are assumed to be negligible when variance-covariance matrices and sample sizes are equal. Therefore, in practice, investigators do not usually attempt to remove non-normality. In this simulation…

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

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

PubMed Central

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

2003-01-01

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

Multivariate statistical process control in product quality review assessment - A case study.

PubMed

Kharbach, M; Cherrah, Y; Vander Heyden, Y; Bouklouze, A

2017-11-01

According to the Food and Drug Administration and the European Good Manufacturing Practices (GMP) guidelines, Annual Product Review (APR) is a mandatory requirement in GMP. It consists of evaluating a large collection of qualitative or quantitative data in order to verify the consistency of an existing process. According to the Code of Federal Regulation Part 11 (21 CFR 211.180), all finished products should be reviewed annually for the quality standards to determine the need of any change in specification or manufacturing of drug products. Conventional Statistical Process Control (SPC) evaluates the pharmaceutical production process by examining only the effect of a single factor at the time using a Shewhart's chart. It neglects to take into account the interaction between the variables. In order to overcome this issue, Multivariate Statistical Process Control (MSPC) can be used. Our case study concerns an APR assessment, where 164 historical batches containing six active ingredients, manufactured in Morocco, were collected during one year. Each batch has been checked by assaying the six active ingredients by High Performance Liquid Chromatography according to European Pharmacopoeia monographs. The data matrix was evaluated both by SPC and MSPC. The SPC indicated that all batches are under control, while the MSPC, based on Principal Component Analysis (PCA), for the data being either autoscaled or robust scaled, showed four and seven batches, respectively, out of the Hotelling T 2 95% ellipse. Also, an improvement of the capability of the process is observed without the most extreme batches. The MSPC can be used for monitoring subtle changes in the manufacturing process during an APR assessment. Copyright © 2017 Académie Nationale de Pharmacie. Published by Elsevier Masson SAS. All rights reserved.

Statistical analysis of multivariate atmospheric variables. [cloud cover

NASA Technical Reports Server (NTRS)

Tubbs, J. D.

1979-01-01

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

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

PubMed

Hastings, Gareth D; Rubin, Alan

2015-01-01

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

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

NASA Astrophysics Data System (ADS)

Wicker, James Eric

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

Multivariate mixed linear model analysis of longitudinal data: an information-rich statistical technique for analyzing disease resistance data

USDA-ARS?s Scientific Manuscript database

The mixed linear model (MLM) is currently among the most advanced and flexible statistical modeling techniques and its use in tackling problems in plant pathology has begun surfacing in the literature. The longitudinal MLM is a multivariate extension that handles repeatedly measured data, such as r...

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…

Use of the Köppen-Trewartha climate classification to evaluate climatic refugia in statistically derived ecoregions for the People’s Republic of China

Treesearch

B. Baker; Henry Diaz; William Hargrove; Forrest Hoffman

2010-01-01

Changes in climate as projected by state-of-the-art climate models are likely to result in novel combinations of climate and topo-edaphic factors that will have substantial impacts on the distribution and persistence of natural vegetation and animal species. We have used multivariate techniques to quantify some of these changes; the...

Additives and salts for dye-sensitized solar cells electrolytes: what is the best choice?

NASA Astrophysics Data System (ADS)

Bella, Federico; Sacco, Adriano; Pugliese, Diego; Laurenti, Marco; Bianco, Stefano

2014-10-01

A multivariate chemometric approach is proposed for the first time for performance optimization of I-/I3- liquid electrolytes for dye-sensitized solar cells (DSSCs). Over the years the system composed by iodide/triiodide redox shuttle dissolved in organic solvent has been enriched with the addition of different specific cations and chemical compounds to improve the photoelectrochemical behavior of the cell. However, usually such additives act favorably with respect to some of the cell parameters and negatively to others. Moreover, the combined action of different compounds often yields contradictory results, and from the literature it is not possible to identify an optimal recipe. We report here a systematic work, based on a multivariate experimental design, to statistically and quantitatively evaluate the effect of different additives on the photovoltaic performances of the device. The effect of cation size in iodine salts, the iodine/iodide ratio in the electrolyte and the effect of type and concentration of additives are mutually evaluated by means of a Design of Experiment (DoE) approach. Through this statistical method, the optimization of the overall parameters is demonstrated with a limited number of experimental trials. A 25% improvement on the photovoltaic conversion efficiency compared with that obtained with a commercial electrolyte is demonstrated.

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

Treesearch

Donald W. Seegrist

1975-01-01

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

Study of the questionnaire of the Polytechnic University of Valencia (UPV) teaching staff, using students opinion survey. Statistical treatment

NASA Astrophysics Data System (ADS)

Martinez Gomez, Monica

Quality improvement of university institutions represents the most important challenge in the next years, and the potential tool to achieve it is based on the institutional evaluation in general, and specially the evaluation of the teaching performance. The opinion questionnaire from the students is the most generalised tool used to evaluate the teaching performance at Spanish universities. The general objective of this thesis is to develop a statistical methodology suitable to extract, analyse and interpret the information contained in the Questionnaire of Teaching Evaluation from Student Opinion (CEDA) of the UPV, aimed at optimising its practical use. The study is centred in the application of different multivariate techniques and has been structured in three parts: (1) Evaluation of the reliability, validity and dimensionality of the tool. The multivariate method used for this purpose is the Factorial Analysis. (2) Determination of the capacity of the questionnaire to identify different profiles of lecturers based on the quality perceived by students. This target is conducted with different multivariate classification techniques: hierarchical cluster analysis, non-hierarchical and two-stage analysis. Moreover, those items that best discriminate among the teaching typologies obtained are identified in the questionnaire. (3) Identification of the teaching typologies according to different descriptive characteristics referent to the subject and lecturer, with the use of decision trees. Once identified these typologies, a new discriminant analysis is conducted aimed at identifying those items that best characterise each typology. Finally, a study is carried out with the classification method SIMCA (Soft Independent Modelling of Class Analogy) in order to determine the discriminant loading of every item among the identified teaching typologies, allowing the identification of those that best distinguish the different classes obtained. With the combined use of the proposed techniques, it is expected to optimise the use of CEDA as a measuring tool and an indicator of the teaching quality at the university, that would allow the introduction of actions for the continuous improvement in the teaching processes of the UPV.

Risk of false decision on conformity of a multicomponent material when test results of the components' content are correlated.

PubMed

Kuselman, Ilya; Pennecchi, Francesca R; da Silva, Ricardo J N B; Hibbert, D Brynn

2017-11-01

The probability of a false decision on conformity of a multicomponent material due to measurement uncertainty is discussed when test results are correlated. Specification limits of the components' content of such a material generate a multivariate specification interval/domain. When true values of components' content and corresponding test results are modelled by multivariate distributions (e.g. by multivariate normal distributions), a total global risk of a false decision on the material conformity can be evaluated based on calculation of integrals of their joint probability density function. No transformation of the raw data is required for that. A total specific risk can be evaluated as the joint posterior cumulative function of true values of a specific batch or lot lying outside the multivariate specification domain, when the vector of test results, obtained for the lot, is inside this domain. It was shown, using a case study of four components under control in a drug, that the correlation influence on the risk value is not easily predictable. To assess this influence, the evaluated total risk values were compared with those calculated for independent test results and also with those assuming much stronger correlation than that observed. While the observed statistically significant correlation did not lead to a visible difference in the total risk values in comparison to the independent test results, the stronger correlation among the variables caused either the total risk decreasing or its increasing, depending on the actual values of the test results. Copyright © 2017 Elsevier B.V. All rights reserved.

Atrial Electrogram Fractionation Distribution before and after Pulmonary Vein Isolation in Human Persistent Atrial Fibrillation-A Retrospective Multivariate Statistical Analysis.

PubMed

Almeida, Tiago P; Chu, Gavin S; Li, Xin; Dastagir, Nawshin; Tuan, Jiun H; Stafford, Peter J; Schlindwein, Fernando S; Ng, G André

2017-01-01

Purpose: Complex fractionated atrial electrograms (CFAE)-guided ablation after pulmonary vein isolation (PVI) has been used for persistent atrial fibrillation (persAF) therapy. This strategy has shown suboptimal outcomes due to, among other factors, undetected changes in the atrial tissue following PVI. In the present work, we investigate CFAE distribution before and after PVI in patients with persAF using a multivariate statistical model. Methods: 207 pairs of atrial electrograms (AEGs) were collected before and after PVI respectively, from corresponding LA regions in 18 persAF patients. Twelve attributes were measured from the AEGs, before and after PVI. Statistical models based on multivariate analysis of variance (MANOVA) and linear discriminant analysis (LDA) have been used to characterize the atrial regions and AEGs. Results: PVI significantly reduced CFAEs in the LA (70 vs. 40%; P < 0.0001). Four types of LA regions were identified, based on the AEGs characteristics: (i) fractionated before PVI that remained fractionated after PVI (31% of the collected points); (ii) fractionated that converted to normal (39%); (iii) normal prior to PVI that became fractionated (9%) and; (iv) normal that remained normal (21%). Individually, the attributes failed to distinguish these LA regions, but multivariate statistical models were effective in their discrimination ( P < 0.0001). Conclusion: Our results have unveiled that there are LA regions resistant to PVI, while others are affected by it. Although, traditional methods were unable to identify these different regions, the proposed multivariate statistical model discriminated LA regions resistant to PVI from those affected by it without prior ablation information.

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 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.

Performance evaluation of a hybrid-passive landfill leachate treatment system using multivariate statistical techniques

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

Wallace, Jack, E-mail: jack.wallace@ce.queensu.ca; Champagne, Pascale, E-mail: champagne@civil.queensu.ca; Monnier, Anne-Charlotte, E-mail: anne-charlotte.monnier@insa-lyon.fr

Highlights: • Performance of a hybrid passive landfill leachate treatment system was evaluated. • 33 Water chemistry parameters were sampled for 21 months and statistically analyzed. • Parameters were strongly linked and explained most (>40%) of the variation in data. • Alkalinity, ammonia, COD, heavy metals, and iron were criteria for performance. • Eight other parameters were key in modeling system dynamics and criteria. - Abstract: A pilot-scale hybrid-passive treatment system operated at the Merrick Landfill in North Bay, Ontario, Canada, treats municipal landfill leachate and provides for subsequent natural attenuation. Collected leachate is directed to a hybrid-passive treatment system,more » followed by controlled release to a natural attenuation zone before entering the nearby Little Sturgeon River. The study presents a comprehensive evaluation of the performance of the system using multivariate statistical techniques to determine the interactions between parameters, major pollutants in the leachate, and the biological and chemical processes occurring in the system. Five parameters (ammonia, alkalinity, chemical oxygen demand (COD), “heavy” metals of interest, with atomic weights above calcium, and iron) were set as criteria for the evaluation of system performance based on their toxicity to aquatic ecosystems and importance in treatment with respect to discharge regulations. System data for a full range of water quality parameters over a 21-month period were analyzed using principal components analysis (PCA), as well as principal components (PC) and partial least squares (PLS) regressions. PCA indicated a high degree of association for most parameters with the first PC, which explained a high percentage (>40%) of the variation in the data, suggesting strong statistical relationships among most of the parameters in the system. Regression analyses identified 8 parameters (set as independent variables) that were most frequently retained for modeling the five criteria parameters (set as dependent variables), on a statistically significant level: conductivity, dissolved oxygen (DO), nitrite (NO{sub 2}{sup −}), organic nitrogen (N), oxidation reduction potential (ORP), pH, sulfate and total volatile solids (TVS). The criteria parameters and the significant explanatory parameters were most important in modeling the dynamics of the passive treatment system during the study period. Such techniques and procedures were found to be highly valuable and could be applied to other sites to determine parameters of interest in similar naturalized engineered systems.« 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.

Detection of cervical lesions by multivariate analysis of diffuse reflectance spectra: a clinical study.

PubMed

Prabitha, Vasumathi Gopala; Suchetha, Sambasivan; Jayanthi, Jayaraj Lalitha; Baiju, Kamalasanan Vijayakumary; Rema, Prabhakaran; Anuraj, Koyippurath; Mathews, Anita; Sebastian, Paul; Subhash, Narayanan

2016-01-01

Diffuse reflectance (DR) spectroscopy is a non-invasive, real-time, and cost-effective tool for early detection of malignant changes in squamous epithelial tissues. The present study aims to evaluate the diagnostic power of diffuse reflectance spectroscopy for non-invasive discrimination of cervical lesions in vivo. A clinical trial was carried out on 48 sites in 34 patients by recording DR spectra using a point-monitoring device with white light illumination. The acquired data were analyzed and classified using multivariate statistical analysis based on principal component analysis (PCA) and linear discriminant analysis (LDA). Diagnostic accuracies were validated using random number generators. The receiver operating characteristic (ROC) curves were plotted for evaluating the discriminating power of the proposed statistical technique. An algorithm was developed and used to classify non-diseased (normal) from diseased sites (abnormal) with a sensitivity of 72 % and specificity of 87 %. While low-grade squamous intraepithelial lesion (LSIL) could be discriminated from normal with a sensitivity of 56 % and specificity of 80 %, and high-grade squamous intraepithelial lesion (HSIL) from normal with a sensitivity of 89 % and specificity of 97 %, LSIL could be discriminated from HSIL with 100 % sensitivity and specificity. The areas under the ROC curves were 0.993 (95 % confidence interval (CI) 0.0 to 1) and 1 (95 % CI 1) for the discrimination of HSIL from normal and HSIL from LSIL, respectively. The results of the study show that DR spectroscopy could be used along with multivariate analytical techniques as a non-invasive technique to monitor cervical disease status in real time.

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.

Full Bayesian evaluation of the safety effects of reducing the posted speed limit in urban residential area.

PubMed

Islam, Md Tazul; El-Basyouny, Karim

2015-07-01

Full Bayesian (FB) before-after evaluation is a newer approach than the empirical Bayesian (EB) evaluation in traffic safety research. While a number of earlier studies have conducted univariate and multivariate FB before-after safety evaluations and compared the results with the EB method, often contradictory conclusions have been drawn. To this end, the objectives of the current study were to (i) perform a before-after safety evaluation using both the univariate and multivariate FB methods in order to enhance our understanding of these methodologies, (ii) perform the EB evaluation and compare the results with those of the FB methods and (iii) apply the FB and EB methods to evaluate the safety effects of reducing the urban residential posted speed limit (PSL) for policy recommendation. In addition to three years of crash data for both the before and after periods, traffic volume, road geometry and other relevant data for both the treated and reference sites were collected and used. According to the model goodness-of-fit criteria, the current study found that the multivariate FB model for crash severities outperformed the univariate FB models. Moreover, in terms of statistical significance of the safety effects, the EB and FB methods led to opposite conclusions when the safety effects were relatively small with high standard deviation. Therefore, caution should be taken in drawing conclusions from the EB method. Based on the FB method, the PSL reduction was found effective in reducing crashes of all severities and thus is recommended for improving safety on urban residential collector roads. Copyright © 2015 Elsevier Ltd. All rights reserved.

Multivariate objective response detectors (MORD): statistical tools for multichannel EEG analysis during rhythmic stimulation.

PubMed

Felix, Leonardo Bonato; Miranda de Sá, Antonio Mauricio Ferreira Leite; Infantosi, Antonio Fernando Catelli; Yehia, Hani Camille

2007-03-01

The presence of cerebral evoked responses can be tested by using objective response detectors. They are statistical tests that provide a threshold above which responses can be assumed to have occurred. The detection power depends on the signal-to-noise ratio (SNR) of the response and the amount of data available. However, the correlation within the background noise could also affect the power of such detectors. For a fixed SNR, the detection can only be improved at the expense of using a longer stretch of signal. This can constitute a limitation, for instance, in monitored surgeries. Alternatively, multivariate objective response detection (MORD) could be used. This work applies two MORD techniques (multiple coherence and multiple component synchrony measure) to EEG data collected during intermittent photic stimulation. They were evaluated throughout Monte Carlo simulations, which also allowed verifying that correlation in the background reduces the detection rate. Considering the N EEG derivations as close as possible to the primary visual cortex, if N = 4, 6 or 8, multiple coherence leads to a statistically significant higher detection rate in comparison with multiple component synchrony measure. With the former, the best performance was obtained with six signals (O1, O2, T5, T6, P3 and P4).

Multivariate statistical approach to estimate mixing proportions for unknown end members

USGS Publications Warehouse

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

2012-01-01

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

Ripening-dependent metabolic changes in the volatiles of pineapple (Ananas comosus (L.) Merr.) fruit: II. Multivariate statistical profiling of pineapple aroma compounds based on comprehensive two-dimensional gas chromatography-mass spectrometry.

PubMed

Steingass, Christof Björn; Jutzi, Manfred; Müller, Jenny; Carle, Reinhold; Schmarr, Hans-Georg

2015-03-01

Ripening-dependent changes of pineapple volatiles were studied in a nontargeted profiling analysis. Volatiles were isolated via headspace solid phase microextraction and analyzed by comprehensive 2D gas chromatography and mass spectrometry (HS-SPME-GC×GC-qMS). Profile patterns presented in the contour plots were evaluated applying image processing techniques and subsequent multivariate statistical data analysis. Statistical methods comprised unsupervised hierarchical cluster analysis (HCA) and principal component analysis (PCA) to classify the samples. Supervised partial least squares discriminant analysis (PLS-DA) and partial least squares (PLS) regression were applied to discriminate different ripening stages and describe the development of volatiles during postharvest storage, respectively. Hereby, substantial chemical markers allowing for class separation were revealed. The workflow permitted the rapid distinction between premature green-ripe pineapples and postharvest-ripened sea-freighted fruits. Volatile profiles of fully ripe air-freighted pineapples were similar to those of green-ripe fruits postharvest ripened for 6 days after simulated sea freight export, after PCA with only two principal components. However, PCA considering also the third principal component allowed differentiation between air-freighted fruits and the four progressing postharvest maturity stages of sea-freighted pineapples.

Arsenic health risk assessment in drinking water and source apportionment using multivariate statistical techniques in Kohistan region, northern Pakistan.

PubMed

Muhammad, Said; Tahir Shah, M; Khan, Sardar

2010-10-01

The present study was conducted in Kohistan region, where mafic and ultramafic rocks (Kohistan island arc and Indus suture zone) and metasedimentary rocks (Indian plate) are exposed. Water samples were collected from the springs, streams and Indus river and analyzed for physical parameters, anions, cations and arsenic (As(3+), As(5+) and arsenic total). The water quality in Kohistan region was evaluated by comparing the physio-chemical parameters with permissible limits set by Pakistan environmental protection agency and world health organization. Most of the studied parameters were found within their respective permissible limits. However in some samples, the iron and arsenic concentrations exceeded their permissible limits. For health risk assessment of arsenic, the average daily dose, hazards quotient (HQ) and cancer risk were calculated by using statistical formulas. The values of HQ were found >1 in the samples collected from Jabba, Dubair, while HQ values were <1 in rest of the samples. This level of contamination should have low chronic risk and medium cancer risk when compared with US EPA guidelines. Furthermore, the inter-dependence of physio-chemical parameters and pollution load was also calculated by using multivariate statistical techniques like one-way ANOVA, correlation analysis, regression analysis, cluster analysis and principle component analysis. Copyright © 2010 Elsevier Ltd. All rights reserved.

Materials Approach to Dissecting Surface Responses in the Attachment Stages of Biofouling Organisms

DTIC Science & Technology

2016-04-25

their settlement behavior in regards to the coating surfaces. 5) Multivariate statistical analysis was used to examine the effect (if any) of the...applied to glass rods and were deployed in the field to evaluate settlement preferences. Canonical Analysis of Principal Coordinates were applied to...the influence of coating surface properties on the patterns in settlement observed in the field in the extension of this work over the coming year

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.

Meta-analysis of quantitative pleiotropic traits for next-generation sequencing with multivariate functional linear models

PubMed Central

Chiu, Chi-yang; Jung, Jeesun; Chen, Wei; Weeks, Daniel E; Ren, Haobo; Boehnke, Michael; Amos, Christopher I; Liu, Aiyi; Mills, James L; Ting Lee, Mei-ling; Xiong, Momiao; Fan, Ruzong

2017-01-01

To analyze next-generation sequencing data, multivariate functional linear models are developed for a meta-analysis of multiple studies to connect genetic variant data to multiple quantitative traits adjusting for covariates. The goal is to take the advantage of both meta-analysis and pleiotropic analysis in order to improve power and to carry out a unified association analysis of multiple studies and multiple traits of complex disorders. Three types of approximate F -distributions based on Pillai–Bartlett trace, Hotelling–Lawley trace, and Wilks's Lambda are introduced to test for association between multiple quantitative traits and multiple genetic variants. Simulation analysis is performed to evaluate false-positive rates and power of the proposed tests. The proposed methods are applied to analyze lipid traits in eight European cohorts. It is shown that it is more advantageous to perform multivariate analysis than univariate analysis in general, and it is more advantageous to perform meta-analysis of multiple studies instead of analyzing the individual studies separately. The proposed models require individual observations. The value of the current paper can be seen at least for two reasons: (a) the proposed methods can be applied to studies that have individual genotype data; (b) the proposed methods can be used as a criterion for future work that uses summary statistics to build test statistics to meta-analyze the data. PMID:28000696

Meta-analysis of quantitative pleiotropic traits for next-generation sequencing with multivariate functional linear models.

PubMed

Chiu, Chi-Yang; Jung, Jeesun; Chen, Wei; Weeks, Daniel E; Ren, Haobo; Boehnke, Michael; Amos, Christopher I; Liu, Aiyi; Mills, James L; Ting Lee, Mei-Ling; Xiong, Momiao; Fan, Ruzong

2017-02-01

To analyze next-generation sequencing data, multivariate functional linear models are developed for a meta-analysis of multiple studies to connect genetic variant data to multiple quantitative traits adjusting for covariates. The goal is to take the advantage of both meta-analysis and pleiotropic analysis in order to improve power and to carry out a unified association analysis of multiple studies and multiple traits of complex disorders. Three types of approximate F -distributions based on Pillai-Bartlett trace, Hotelling-Lawley trace, and Wilks's Lambda are introduced to test for association between multiple quantitative traits and multiple genetic variants. Simulation analysis is performed to evaluate false-positive rates and power of the proposed tests. The proposed methods are applied to analyze lipid traits in eight European cohorts. It is shown that it is more advantageous to perform multivariate analysis than univariate analysis in general, and it is more advantageous to perform meta-analysis of multiple studies instead of analyzing the individual studies separately. The proposed models require individual observations. The value of the current paper can be seen at least for two reasons: (a) the proposed methods can be applied to studies that have individual genotype data; (b) the proposed methods can be used as a criterion for future work that uses summary statistics to build test statistics to meta-analyze the data.

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

Mathematics and statistics research department. Progress report, period ending June 30, 1981

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

Lever, W.E.; Kane, V.E.; Scott, D.S.

1981-09-01

This report is the twenty-fourth in the series of progress reports of the Mathematics and Statistics Research Department of the Computer Sciences Division, Union Carbide Corporation - Nuclear Division (UCC-ND). Part A records research progress in biometrics research, materials science applications, model evaluation, moving boundary problems, multivariate analysis, numerical linear algebra, risk analysis, and complementary areas. Collaboration and consulting with others throughout the UCC-ND complex are recorded in Part B. Included are sections on biology and health sciences, chemistry, energy, engineering, environmental sciences, health and safety research, materials sciences, safeguards, surveys, and uranium resource evaluation. Part C summarizes the variousmore » educational activities in which the staff was engaged. Part D lists the presentations of research results, and Part E records the staff's other professional activities during the report period.« less

Prediction of the presence of insulin resistance using general health checkup data in Japanese employees with metabolic risk factors.

PubMed

Takahara, Mitsuyoshi; Katakami, Naoto; Kaneto, Hideaki; Noguchi, Midori; Shimomura, Iichiro

2014-01-01

The aim of the current study was to develop a predictive model of insulin resistance using general health checkup data in Japanese employees with one or more metabolic risk factors. We used a database of 846 Japanese employees with one or more metabolic risk factors who underwent general health checkup and a 75-g oral glucose tolerance test (OGTT). Logistic regression models were developed to predict existing insulin resistance evaluated using the Matsuda index. The predictive performance of these models was assessed using the C statistic. The C statistics of body mass index (BMI), waist circumference and their combined use were 0.743, 0.732 and 0.749, with no significant differences. The multivariate backward selection model, in which BMI, the levels of plasma glucose, high-density lipoprotein (HDL) cholesterol, log-transformed triglycerides and log-transformed alanine aminotransferase and hypertension under treatment remained, had a C statistic of 0.816, with a significant difference compared to the combined use of BMI and waist circumference (p＜0.01). The C statistic was not significantly reduced when the levels of log-transformed triglycerides and log-transformed alanine aminotransferase and hypertension under treatment were simultaneously excluded from the multivariate model (p=0.14). On the other hand, further exclusion of any of the remaining three variables significantly reduced the C statistic (all p＜0.01). When predicting the presence of insulin resistance using general health checkup data in Japanese employees with metabolic risk factors, it is important to take into consideration the BMI and fasting plasma glucose and HDL cholesterol levels.

Hydrogeochemistry and water quality of the Kordkandi-Duzduzan plain, NW Iran: application of multivariate statistical analysis and PoS index.

PubMed

Soltani, Shahla; Asghari Moghaddam, Asghar; Barzegar, Rahim; Kazemian, Naeimeh; Tziritis, Evangelos

2017-08-18

Kordkandi-Duzduzan plain is one of the fertile plains of East Azarbaijan Province, NW of Iran. Groundwater is an important resource for drinking and agricultural purposes due to the lack of surface water resources in the region. The main objectives of the present study are to identify the hydrogeochemical processes and the potential sources of major, minor, and trace metals and metalloids such as Cr, Mn, Cd, Fe, Al, and As by using joint hydrogeochemical techniques and multivariate statistical analysis and to evaluate groundwater quality deterioration with the use of PoS environmental index. To achieve these objectives, 23 groundwater samples were collected in September 2015. Piper diagram shows that the mixed Ca-Mg-Cl is the dominant groundwater type, and some of the samples have Ca-HCO 3 , Ca-Cl, and Na-Cl types. Multivariate statistical analyses indicate that weathering and dissolution of different rocks and minerals, e.g., silicates, gypsum, and halite, ion exchange, and agricultural activities influence the hydrogeochemistry of the study area. The cluster analysis divides the samples into two distinct clusters which are completely different in EC (and its dependent variables such as Na + , K + , Ca 2+ , Mg 2+ , SO 4 2- , and Cl - ), Cd, and Cr variables according to the ANOVA statistical test. Based on the median values, the concentrations of pH, NO 3 - , SiO 2 , and As in cluster 1 are elevated compared with those of cluster 2, while their maximum values occur in cluster 2. According to the PoS index, the dominant parameter that controls quality deterioration is As, with 60% of contribution. Samples of lowest PoS values are located in the southern and northern parts (recharge area) while samples of the highest values are located in the discharge area and the eastern part.

Impact of livestock on a mosquito community (Diptera: Culicidae) in a Brazilian tropical dry forest.

PubMed

Santos, Cleandson Ferreira; Borges, Magno

2015-01-01

This study evaluated the effects of cattle removal on the Culicidae mosquito community structure in a tropical dry forest in Brazil. Culicidae were collected during dry and wet seasons in cattle presence and absence between August 2008 and October 2010 and assessed using multivariate statistical models. Cattle removal did not significantly alter Culicidae species richness and abundance. However, alterations were noted in Culicidae community composition. This is the first study to evaluate the impact of cattle removal on Culicidae community structure in Brazil and demonstrates the importance of assessing ecological parameters such as community species composition.

Systematic evaluation of serum and plasma collection on the endogenous metabolome.

PubMed

Zhou, Zhi; Chen, Yanhua; He, Jiuming; Xu, Jing; Zhang, Ruiping; Mao, Yan; Abliz, Zeper

2017-02-01

In metabolomics research, the use of different blood collection methods may influence endogenous metabolites. Ultra HPLC coupled with MS/MS was applied together with multivariate statistics to investigate metabolomics differences in serum and plasma samples handled by different anticoagulants. A total of 135 known representative metabolites were assessed for comprehensive evaluation of the effects of anticoagulants. Exogenous factors, including separation gel ingredients from the serum collection tubes and the anticoagulants, affected mass spectrometer detection. Heparin plasma yielded the best detection of different functional groups and is therefore the optimal blood specimen for metabolomics research, followed by potassium oxalate plasma.

Quantification of proportions of different water sources in a mining operation.

PubMed

Scheiber, Laura; Ayora, Carlos; Vázquez-Suñé, Enric

2018-04-01

The water drained in mining operations (galleries, shafts, open pits) usually comes from different sources. Evaluating the contribution of these sources is very often necessary for water management. To determine mixing ratios, a conventional mass balance is often used. However, the presence of more than two sources creates uncertainties in mass balance applications. Moreover, the composition of the end-members is not commonly known with certainty and/or can vary in space and time. In this paper, we propose a powerful tool for solving such problems and managing groundwater in mining sites based on multivariate statistical analysis. This approach was applied to the Cobre Las Cruces mining complex, the largest copper mine in Europe. There, the open pit water is a mixture of three end-members: runoff (RO), basal Miocene (Mb) and Paleozoic (PZ) groundwater. The volume of water drained from the Miocene base aquifer must be determined and compensated via artificial recharging to comply with current regulations. Through multivariate statistical analysis of samples from a regional field campaign, the compositions of PZ and Mb end-members were firstly estimated, and then used for mixing calculations at the open pit scale. The runoff end-member was directly determined from samples collected in interception trenches inside the open pit. The application of multivariate statistical methods allowed the estimation of mixing ratios for the hydrological years 2014-2015 and 2015-2016. Open pit water proportions have changed from 15% to 7%, 41% to 36%, and 44% to 57% for runoff, Mb and PZ end-members, respectively. An independent estimation of runoff based on the curve method yielded comparable results. Copyright © 2017 Elsevier B.V. All rights reserved.

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

NASA Astrophysics Data System (ADS)

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

2018-06-01

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

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.

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

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…

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

PubMed

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

2015-01-01

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

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.

Using Statistical Process Control for detecting anomalies in multivariate spatiotemporal Earth Observations

NASA Astrophysics Data System (ADS)

Flach, Milan; Mahecha, Miguel; Gans, Fabian; Rodner, Erik; Bodesheim, Paul; Guanche-Garcia, Yanira; Brenning, Alexander; Denzler, Joachim; Reichstein, Markus

2016-04-01

The number of available Earth observations (EOs) is currently substantially increasing. Detecting anomalous patterns in these multivariate time series is an important step in identifying changes in the underlying dynamical system. Likewise, data quality issues might result in anomalous multivariate data constellations and have to be identified before corrupting subsequent analyses. In industrial application a common strategy is to monitor production chains with several sensors coupled to some statistical process control (SPC) algorithm. The basic idea is to raise an alarm when these sensor data depict some anomalous pattern according to the SPC, i.e. the production chain is considered 'out of control'. In fact, the industrial applications are conceptually similar to the on-line monitoring of EOs. However, algorithms used in the context of SPC or process monitoring are rarely considered for supervising multivariate spatio-temporal Earth observations. The objective of this study is to exploit the potential and transferability of SPC concepts to Earth system applications. We compare a range of different algorithms typically applied by SPC systems and evaluate their capability to detect e.g. known extreme events in land surface processes. Specifically two main issues are addressed: (1) identifying the most suitable combination of data pre-processing and detection algorithm for a specific type of event and (2) analyzing the limits of the individual approaches with respect to the magnitude, spatio-temporal size of the event as well as the data's signal to noise ratio. Extensive artificial data sets that represent the typical properties of Earth observations are used in this study. Our results show that the majority of the algorithms used can be considered for the detection of multivariate spatiotemporal events and directly transferred to real Earth observation data as currently assembled in different projects at the European scale, e.g. http://baci-h2020.eu/index.php/ and http://earthsystemdatacube.net/. Known anomalies such as the Russian heatwave are detected as well as anomalies which are not detectable with univariate methods.

Metabolic profiling of human lung cancer blood plasma using 1H NMR spectroscopy

NASA Astrophysics Data System (ADS)

Kokova, Daria; Dementeva, Natalia; Kotelnikov, Oleg; Ponomaryova, Anastasia; Cherdyntseva, Nadezhda; Kzhyshkowska, Juliya

2017-11-01

Lung cancer (both small cell and non-small cell) is the second most common cancer in both men and women. The article represents results of evaluating of the plasma metabolic profiles of 100 lung cancer patients and 100 controls to investigate significant metabolites using 400 MHz 1H NMR spectrometer. The results of multivariate statistical analysis show that a medium-field NMR spectrometer can obtain the data which are already sufficient for clinical metabolomics.

Investigation of Clinical Characteristics and Etiological Factors in Children with Molar Incisor Hypomineralization

PubMed Central

Giuca, Maria Rita; Cappè, Maria; Carli, Elisabetta; Lardani, Lisa

2018-01-01

Aim The purpose of the present study was to evaluate the clinical defects and etiological factors potentially involved in the onset of MIH in a pediatric sample. Methods 120 children, selected from the university dental clinic, were included: 60 children (25 boys and 35 girls; average age: 9.8 ± 1.8 years) with MIH formed the test group and 60 children (27 boys and 33 girls; average age: 10.1 ± 2 years) without MIH constituted the control group. Distribution and severity of MIH defects were evaluated, and a questionnaire was used to investigate the etiological variables; chi-square, univariate, and multivariate statistical tests were performed (significance level set at p < 0.05). Results A total of 186 molars and 98 incisors exhibited MIH defects: 55 molars and 75 incisors showed mild defects, 91 molars and 20 incisors had moderate lesions, and 40 molars and 3 incisors showed severe lesions. Univariate and multivariate statistical analysis showed a significant association (p < 0.05) between MIH and ear, nose, and throat (ENT) disorders and the antibiotics used during pregnancy (0.019). Conclusions Moderate defects were more frequent in the molars, while mild lesions were more frequent in the incisors. Antibiotics used during pregnancy and ENT may be directly involved in the etiology of MIH in children. PMID:29861729

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

PubMed

Kragelj, Borut

2016-03-01

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

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.

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.

Evaluation of the prediction precision capability of partial least squares regression approach for analysis of high alloy steel by laser induced breakdown spectroscopy

NASA Astrophysics Data System (ADS)

Sarkar, Arnab; Karki, Vijay; Aggarwal, Suresh K.; Maurya, Gulab S.; Kumar, Rohit; Rai, Awadhesh K.; Mao, Xianglei; Russo, Richard E.

2015-06-01

Laser induced breakdown spectroscopy (LIBS) was applied for elemental characterization of high alloy steel using partial least squares regression (PLSR) with an objective to evaluate the analytical performance of this multivariate approach. The optimization of the number of principle components for minimizing error in PLSR algorithm was investigated. The effect of different pre-treatment procedures on the raw spectral data before PLSR analysis was evaluated based on several statistical (standard error of prediction, percentage relative error of prediction etc.) parameters. The pre-treatment with "NORM" parameter gave the optimum statistical results. The analytical performance of PLSR model improved by increasing the number of laser pulses accumulated per spectrum as well as by truncating the spectrum to appropriate wavelength region. It was found that the statistical benefit of truncating the spectrum can also be accomplished by increasing the number of laser pulses per accumulation without spectral truncation. The constituents (Co and Mo) present in hundreds of ppm were determined with relative precision of 4-9% (2σ), whereas the major constituents Cr and Ni (present at a few percent levels) were determined with a relative precision of ~ 2%(2σ).

Self-Regulated Learning Strategies in Relation with Statistics Anxiety

ERIC Educational Resources Information Center

Kesici, Sahin; Baloglu, Mustafa; Deniz, M. Engin

2011-01-01

Dealing with students' attitudinal problems related to statistics is an important aspect of statistics instruction. Employing the appropriate learning strategies may have a relationship with anxiety during the process of statistics learning. Thus, the present study investigated multivariate relationships between self-regulated learning strategies…

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

PubMed Central

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

2017-01-01

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

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

PubMed

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

2017-12-01

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

[An evaluation of clinical characteristics and prognosis of brain-stem infarction in diabetics].

PubMed

Lu, Zheng-qi; Li, Hai-yan; Hu, Xue-qiang; Zhang, Bing-jun

2011-01-01

To analyze the relationship between diabetics and the onset, clinical outcomes and prognosis of brainstem infarction, and to evaluate the impact of diabetes on brainstem infarction. Compare 172 cases of acute brainstem infarction in patients with or without diabetes. Analyze the associated risk factors of patients with brain-stem infarction in diabetics by multi-variate logistic regression analysis. Compare the National Institutes of Health Stroke Scale (NIHSS) and Modified Rankin scale (mRS) Score, pathogenetic condition and the outcome of the two groups in different times. The systolic blood pressure (SBP), TG, LDL-C, apolipoprotein B (Apo B), glutamyl transpeptidase (γ-GT), fibrinogen (Fb), fasting blood glucose (FPG) and glycosylated hemoglobin(HbA1c)in diabetic group were higher than those in non-diabetic group, which was statistically significant (P < 0.05). From multi-variate logistic regression analysis, γ-GT, Apo B and FPG were the risk predictors of diabetes with brainstem infarction(OR = 1.017, 4.667 and 3.173, respectively), while HDL-C was protective (OR = 0.288). HbA1c was a risk predictor of severity for acute brainstem infarction (OR = 1.299), while Apo A was beneficial (OR = 0.212). Compared with brain-stem infarction in non-diabetic group, NIHSS score and intensive care therapy of diabetic groups on the admission had no statistically significance, while the NIHSS score on discharge and the outcome at 6 months' of follow-up were statistically significant. Diabetes is closely associated with brainstem infarction. Brainstem infarction with diabetes cause more rapid progression, poorer prognosis, higher rates of mortality as well as disability and higher recurrence rate of cerebral infarction.

Use of multivariate statistics to identify unreliable data obtained using CASA.

PubMed

Martínez, Luis Becerril; Crispín, Rubén Huerta; Mendoza, Maximino Méndez; Gallegos, Oswaldo Hernández; Martínez, Andrés Aragón

2013-06-01

In order to identify unreliable data in a dataset of motility parameters obtained from a pilot study acquired by a veterinarian with experience in boar semen handling, but without experience in the operation of a computer assisted sperm analysis (CASA) system, a multivariate graphical and statistical analysis was performed. Sixteen boar semen samples were aliquoted then incubated with varying concentrations of progesterone from 0 to 3.33 µg/ml and analyzed in a CASA system. After standardization of the data, Chernoff faces were pictured for each measurement, and a principal component analysis (PCA) was used to reduce the dimensionality and pre-process the data before hierarchical clustering. The first twelve individual measurements showed abnormal features when Chernoff faces were drawn. PCA revealed that principal components 1 and 2 explained 63.08% of the variance in the dataset. Values of principal components for each individual measurement of semen samples were mapped to identify differences among treatment or among boars. Twelve individual measurements presented low values of principal component 1. Confidence ellipses on the map of principal components showed no statistically significant effects for treatment or boar. Hierarchical clustering realized on two first principal components produced three clusters. Cluster 1 contained evaluations of the two first samples in each treatment, each one of a different boar. With the exception of one individual measurement, all other measurements in cluster 1 were the same as observed in abnormal Chernoff faces. Unreliable data in cluster 1 are probably related to the operator inexperience with a CASA system. These findings could be used to objectively evaluate the skill level of an operator of a CASA system. This may be particularly useful in the quality control of semen analysis using CASA systems.

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

PubMed

Counsell, Alyssa; Harlow, Lisa L

2017-05-01

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

Surface hardness evaluation of different composite resin materials: influence of sports and energy drinks immersion after a short-term period

PubMed Central

ERDEMİR, Ugur; YİLDİZ, Esra; EREN, Meltem Mert; OZEL, Sevda

2013-01-01

Objectives: This study evaluated the effect of sports and energy drinks on the surface hardness of different composite resin restorative materials over a 1-month period. Material and Methods: A total of 168 specimens: Compoglass F, Filtek Z250, Filtek Supreme, and Premise were prepared using a customized cylindrical metal mould and they were divided into six groups (N=42; n=7 per group). For the control groups, the specimens were stored in distilled water for 24 hours at 37º C and the water was renewed daily. For the experimental groups, the specimens were immersed in 5 mL of one of the following test solutions: Powerade, Gatorade, X-IR, Burn, and Red Bull, for two minutes daily for up to a 1-month test period and all the solutions were refreshed daily. Surface hardness was measured using a Vickers hardness measuring instrument at baseline, after 1-week and 1-month. Data were statistically analyzed using Multivariate repeated measure ANOVA and Bonferroni's multiple comparison tests (α=0.05). Results: Multivariate repeated measures ANOVA revealed that there were statistically significant differences in the hardness of the restorative materials in different immersion times (p<0.001) in different solutions (p<0.001). The effect of different solutions on the surface hardness values of the restorative materials was tested using Bonferroni's multiple comparison tests, and it was observed that specimens stored in distilled water demonstrated statistically significant lower mean surface hardness reductions when compared to the specimens immersed in sports and energy drinks after a 1-month evaluation period (p<0.001). The compomer was the most affected by an acidic environment, whereas the composite resin materials were the least affected materials. Conclusions: The effect of sports and energy drinks on the surface hardness of a restorative material depends on the duration of exposure time, and the composition of the material. PMID:23739850

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.

qFeature

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

2015-09-14

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

Asymptotic Distribution of the Likelihood Ratio Test Statistic for Sphericity of Complex Multivariate Normal Distribution.

DTIC Science & Technology

1981-08-01

RATIO TEST STATISTIC FOR SPHERICITY OF COMPLEX MULTIVARIATE NORMAL DISTRIBUTION* C. Fang P. R. Krishnaiah B. N. Nagarsenker** August 1981 Technical...and their applications in time sEries, the reader is referred to Krishnaiah (1976). Motivated by the applications in the area of inference on multiple...for practical purposes. Here, we note that Krishnaiah , Lee and Chang (1976) approxi- mated the null distribution of certain power of the likeli

Applications of Multivariate Statistical Techniques for Computer Performance Evaluation.

DTIC Science & Technology

1983-12-01

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

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.

GAISE 2016 Promotes Statistical Literacy

ERIC Educational Resources Information Center

Schield, Milo

2017-01-01

In the 2005 Guidelines for Assessment and Instruction in Statistics Education (GAISE), statistical literacy featured as a primary goal. The 2016 revision eliminated statistical literacy as a stated goal. Although this looks like a rejection, this paper argues that by including multivariate thinking and--more importantly--confounding as recommended…

Southeast Atlantic Cloud Properties in a Multivariate Statistical Model - How Relevant is Air Mass History for Local Cloud Properties?

NASA Astrophysics Data System (ADS)

Fuchs, Julia; Cermak, Jan; Andersen, Hendrik

2017-04-01

This study aims at untangling the impacts of external dynamics and local conditions on cloud properties in the Southeast Atlantic (SEA) by combining satellite and reanalysis data using multivariate statistics. The understanding of clouds and their determinants at different scales is important for constraining the Earth's radiative budget, and thus prominent in climate-system research. In this study, SEA stratocumulus cloud properties are observed not only as the result of local environmental conditions but also as affected by external dynamics and spatial origins of air masses entering the study area. In order to assess to what extent cloud properties are impacted by aerosol concentration, air mass history, and meteorology, a multivariate approach is conducted using satellite observations of aerosol and cloud properties (MODIS, SEVIRI), information on aerosol species composition (MACC) and meteorological context (ERA-Interim reanalysis). To account for the often-neglected but important role of air mass origin, information on air mass history based on HYSPLIT modeling is included in the statistical model. This multivariate approach is intended to lead to a better understanding of the physical processes behind observed stratocumulus cloud properties in the SEA.

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.

Contribution of artificial intelligence to the knowledge of prognostic factors in Hodgkin's lymphoma.

PubMed

Buciński, Adam; Marszałł, Michał Piotr; Krysiński, Jerzy; Lemieszek, Andrzej; Załuski, Jerzy

2010-07-01

Hodgkin's lymphoma is one of the most curable malignancies and most patients achieve a lasting complete remission. In this study, artificial neural network (ANN) analysis was shown to provide significant factors with regard to 5-year recurrence after lymphoma treatment. Data from 114 patients treated for Hodgkin's disease were available for evaluation and comparison. A total of 31 variables were subjected to ANN analysis. The ANN approach as an advanced multivariate data processing method was shown to provide objective prognostic data. Some of these prognostic factors are consistent or even identical to the factors evaluated earlier by other statistical methods.

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

PubMed

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

2017-04-01

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

Learning investment indicators through data extension

NASA Astrophysics Data System (ADS)

Dvořák, Marek

2017-07-01

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

1H NMR-based metabolic profiling for evaluating poppy seed rancidity and brewing.

PubMed

Jawień, Ewa; Ząbek, Adam; Deja, Stanisław; Łukaszewicz, Marcin; Młynarz, Piotr

2015-12-01

Poppy seeds are widely used in household and commercial confectionery. The aim of this study was to demonstrate the application of metabolic profiling for industrial monitoring of the molecular changes which occur during minced poppy seed rancidity and brewing processes performed on raw seeds. Both forms of poppy seeds were obtained from a confectionery company. Proton nuclear magnetic resonance (1H NMR) was applied as the analytical method of choice together with multivariate statistical data analysis. Metabolic fingerprinting was applied as a bioprocess control tool to monitor rancidity with the trajectory of change and brewing progressions. Low molecular weight compounds were found to be statistically significant biomarkers of these bioprocesses. Changes in concentrations of chemical compounds were explained relative to the biochemical processes and external conditions. The obtained results provide valuable and comprehensive information to gain a better understanding of the biology of rancidity and brewing processes, while demonstrating the potential for applying NMR spectroscopy combined with multivariate data analysis tools for quality control in food industries involved in the processing of oilseeds. This precious and versatile information gives a better understanding of the biology of these processes.

A standards-based method for compositional analysis by energy dispersive X-ray spectrometry using multivariate statistical analysis: application to multicomponent alloys.

PubMed

Rathi, Monika; Ahrenkiel, S P; Carapella, J J; Wanlass, M W

2013-02-01

Given an unknown multicomponent alloy, and a set of standard compounds or alloys of known composition, can one improve upon popular standards-based methods for energy dispersive X-ray (EDX) spectrometry to quantify the elemental composition of the unknown specimen? A method is presented here for determining elemental composition of alloys using transmission electron microscopy-based EDX with appropriate standards. The method begins with a discrete set of related reference standards of known composition, applies multivariate statistical analysis to those spectra, and evaluates the compositions with a linear matrix algebra method to relate the spectra to elemental composition. By using associated standards, only limited assumptions about the physical origins of the EDX spectra are needed. Spectral absorption corrections can be performed by providing an estimate of the foil thickness of one or more reference standards. The technique was applied to III-V multicomponent alloy thin films: composition and foil thickness were determined for various III-V alloys. The results were then validated by comparing with X-ray diffraction and photoluminescence analysis, demonstrating accuracy of approximately 1% in atomic fraction.

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.

Quick Overview Scout 2008 Version 1.0

EPA Science Inventory

The Scout 2008 version 1.0 statistical software package has been updated from past DOS and Windows versions to provide classical and robust univariate and multivariate graphical and statistical methods that are not typically available in commercial or freeware statistical softwar...

Statistical Learning Analysis in Neuroscience: Aiming for Transparency

PubMed Central

Hanke, Michael; Halchenko, Yaroslav O.; Haxby, James V.; Pollmann, Stefan

2009-01-01

Encouraged by a rise of reciprocal interest between the machine learning and neuroscience communities, several recent studies have demonstrated the explanatory power of statistical learning techniques for the analysis of neural data. In order to facilitate a wider adoption of these methods, neuroscientific research needs to ensure a maximum of transparency to allow for comprehensive evaluation of the employed procedures. We argue that such transparency requires “neuroscience-aware” technology for the performance of multivariate pattern analyses of neural data that can be documented in a comprehensive, yet comprehensible way. Recently, we introduced PyMVPA, a specialized Python framework for machine learning based data analysis that addresses this demand. Here, we review its features and applicability to various neural data modalities. PMID:20582270

A robust bayesian estimate of the concordance correlation coefficient.

PubMed

Feng, Dai; Baumgartner, Richard; Svetnik, Vladimir

2015-01-01

A need for assessment of agreement arises in many situations including statistical biomarker qualification or assay or method validation. Concordance correlation coefficient (CCC) is one of the most popular scaled indices reported in evaluation of agreement. Robust methods for CCC estimation currently present an important statistical challenge. Here, we propose a novel Bayesian method of robust estimation of CCC based on multivariate Student's t-distribution and compare it with its alternatives. Furthermore, we extend the method to practically relevant settings, enabling incorporation of confounding covariates and replications. The superiority of the new approach is demonstrated using simulation as well as real datasets from biomarker application in electroencephalography (EEG). This biomarker is relevant in neuroscience for development of treatments for insomnia.

A multivariate test of disease risk reveals conditions leading to disease amplification.

PubMed

Halliday, Fletcher W; Heckman, Robert W; Wilfahrt, Peter A; Mitchell, Charles E

2017-10-25

Theory predicts that increasing biodiversity will dilute the risk of infectious diseases under certain conditions and will amplify disease risk under others. Yet, few empirical studies demonstrate amplification. This contrast may occur because few studies have considered the multivariate nature of disease risk, which includes richness and abundance of parasites with different transmission modes. By combining a multivariate statistical model developed for biodiversity-ecosystem-multifunctionality with an extensive field manipulation of host (plant) richness, composition and resource supply to hosts, we reveal that (i) host richness alone could not explain most changes in disease risk, and (ii) shifting host composition allowed disease amplification, depending on parasite transmission mode. Specifically, as predicted from theory, the effect of host diversity on parasite abundance differed for microbes (more density-dependent transmission) and insects (more frequency-dependent transmission). Host diversity did not influence microbial parasite abundance, but nearly doubled insect parasite abundance, and this amplification effect was attributable to variation in host composition. Parasite richness was reduced by resource addition, but only in species-rich host communities. Overall, this study demonstrates that multiple drivers, related to both host community and parasite characteristics, can influence disease risk. Furthermore, it provides a framework for evaluating multivariate disease risk in other systems. © 2017 The Author(s).

Robust tests for multivariate factorial designs under heteroscedasticity.

PubMed

Vallejo, Guillermo; Ato, Manuel

2012-06-01

The question of how to analyze several multivariate normal mean vectors when normality and covariance homogeneity assumptions are violated is considered in this article. For the two-way MANOVA layout, we address this problem adapting results presented by Brunner, Dette, and Munk (BDM; 1997) and Vallejo and Ato (modified Brown-Forsythe [MBF]; 2006) in the context of univariate factorial and split-plot designs and a multivariate version of the linear model (MLM) to accommodate heterogeneous data. Furthermore, we compare these procedures with the Welch-James (WJ) approximate degrees of freedom multivariate statistics based on ordinary least squares via Monte Carlo simulation. Our numerical studies show that of the methods evaluated, only the modified versions of the BDM and MBF procedures were robust to violations of underlying assumptions. The MLM approach was only occasionally liberal, and then by only a small amount, whereas the WJ procedure was often liberal if the interactive effects were involved in the design, particularly when the number of dependent variables increased and total sample size was small. On the other hand, it was also found that the MLM procedure was uniformly more powerful than its most direct competitors. The overall success rate was 22.4% for the BDM, 36.3% for the MBF, and 45.0% for the MLM.

Multivariate evaluation of Thyroid Imaging Reporting and Data System (TI-RADS) in diagnosis malignant thyroid nodule: application to PCA and PLS-DA analysis.

PubMed

Zhang, Tan; Li, Fangxuan; Mu, Jiali; Liu, Juntian; Zhang, Sheng

2017-06-01

To explore the significance of ultrasonic features in differential diagnosis of thyroid nodules via combining the thyroid imaging reporting and data system (TI-RADS) and multivariate statistical analysis. Patients who received surgical treatment and was diagnosed with single thyroid nodule by postoperative pathology and preoperative ultrasound were enrolled in this study. Multivariate analysis was applied to assess the significant ultrasonic features which correlated with identifying benign or malignance and grading the TI-RADS classification of thyroid nodule. There were significant differences in the nodule size, aspect ratio, internal, echogenicity, boundary, presence or absence of calcifications, calcification type and CDFI between benign and malignant thyroid nodules. Multivariate analysis showed clear-cut distinction both between benign and malignance and among different TI-RADS categories of malignancy nodules. The shape and calcification of the nodule were important factors for distinguish the benign and malignance. Height of the nodule, aspect and calcification was important factors for grading TI-RADS categories of malignancy thyroid nodules. Ill-defined boundary, irregular shape and presence of calcification related with highly malignant risk for thyroid nodule. The larger height and aspect and presence of calcification related with higher TI-RADS classification of malignancy thyroid nodule.

The Statistical Consulting Center for Astronomy (SCCA)

NASA Technical Reports Server (NTRS)

Akritas, Michael

2001-01-01

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

The use of multivariate statistics in studies of wildlife habitat

Treesearch

David E. Capen

1981-01-01

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

Rejection of Multivariate Outliers.

DTIC Science & Technology

1983-05-01

available in Gnanadesikan (1977). 2 The motivation for the present investigation lies in a recent paper of Schvager and Margolin (1982) who derive a... Gnanadesikan , R. (1977). Methods for Statistical Data Analysis of Multivariate Observations. Wiley, New York. [7] Hawkins, D.M. (1980). Identification of

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 ...

Using Logistic Regression to Predict the Probability of Debris Flows in Areas Burned by Wildfires, Southern California, 2003-2006

USGS Publications Warehouse

Rupert, Michael G.; Cannon, Susan H.; Gartner, Joseph E.; Michael, John A.; Helsel, Dennis R.

2008-01-01

Logistic regression was used to develop statistical models that can be used to predict the probability of debris flows in areas recently burned by wildfires by using data from 14 wildfires that burned in southern California during 2003-2006. Twenty-eight independent variables describing the basin morphology, burn severity, rainfall, and soil properties of 306 drainage basins located within those burned areas were evaluated. The models were developed as follows: (1) Basins that did and did not produce debris flows soon after the 2003 to 2006 fires were delineated from data in the National Elevation Dataset using a geographic information system; (2) Data describing the basin morphology, burn severity, rainfall, and soil properties were compiled for each basin. These data were then input to a statistics software package for analysis using logistic regression; and (3) Relations between the occurrence or absence of debris flows and the basin morphology, burn severity, rainfall, and soil properties were evaluated, and five multivariate logistic regression models were constructed. All possible combinations of independent variables were evaluated to determine which combinations produced the most effective models, and the multivariate models that best predicted the occurrence of debris flows were identified. Percentage of high burn severity and 3-hour peak rainfall intensity were significant variables in all models. Soil organic matter content and soil clay content were significant variables in all models except Model 5. Soil slope was a significant variable in all models except Model 4. The most suitable model can be selected from these five models on the basis of the availability of independent variables in the particular area of interest and field checking of probability maps. The multivariate logistic regression models can be entered into a geographic information system, and maps showing the probability of debris flows can be constructed in recently burned areas of southern California. This study demonstrates that logistic regression is a valuable tool for developing models that predict the probability of debris flows occurring in recently burned landscapes.

Development of a quantitative multivariable radiographic method to evaluate anatomic changes associated with laminitis in the forefeet of donkeys.

PubMed

Collins, Simon N; Dyson, Sue J; Murray, Rachel C; Newton, J Richard; Burden, Faith; Trawford, Andrew F

2012-08-01

To establish and validate an objective method of radiographic diagnosis of anatomic changes in laminitic forefeet of donkeys on the basis of data from a comprehensive series of radiographic measurements. 85 donkeys with and 85 without forelimb laminitis for baseline data determination; a cohort of 44 donkeys with and 18 without forelimb laminitis was used for validation analyses. For each donkey, lateromedial radiographic views of 1 weight-bearing forelimb were obtained; images from 11 laminitic and 2 nonlaminitic donkeys were excluded (motion artifact) from baseline data determination. Data from an a priori selection of 19 measurements of anatomic features of laminitic and nonlaminitic donkey feet were analyzed by use of a novel application of multivariate statistical techniques. The resultant diagnostic models were validated in a blinded manner with data from the separate cohort of laminitic and nonlaminitic donkeys. Data were modeled, and robust statistical rules were established for the diagnosis of anatomic changes within laminitic donkey forefeet. Component 1 scores ≤ -3.5 were indicative of extreme anatomic change, and scores from -2.0 to 0.0 denoted modest change. Nonlaminitic donkeys with a score from 0.5 to 1.0 should be considered as at risk for laminitis. Results indicated that the radiographic procedures evaluated can be used for the identification, assessment, and monitoring of anatomic changes associated with laminitis. Screening assessments by use of this method may enable early detection of mild anatomic change and identification of at-risk donkeys.

Adaptive plasticity of Laguncularia racemosa in response to different environmental conditions: integrating chemical and biological data by chemometrics.

PubMed

da Souza, Iara; Bonomo, Marina Marques; Morozesk, Mariana; Rocha, Lívia Dorsch; Duarte, Ian Drumond; Furlan, Larissa Maria; Arrivabene, Hiulana Pereira; Monferrán, Magdalena Victoria; Matsumoto, Silvia Tamie; Milanez, Camilla Rozindo Dias; Wunderlin, Daniel Alberto; Fernandes, Marisa Narciso

2014-04-01

Mangroves are dynamic environments under constant influence of anthropic contaminants. The correlation between environmental contamination levels and possible changes in the morphology of plants, evaluated by multivariate statistics helps to highlight matching between these variables. This study aimed to evaluate the uptake and translocation of metals and metalloids in roots and leaves as well as the changes induced in both anatomy and histochemistry of roots of Laguncularia racemosa inhabiting two estuaries of Espírito Santo (Brazil) with different pollution degrees. The analysis of 14 elements in interstitial water, sediments and plants followed by multivariate statistics, allowed the differentiation of studied sites, showing good match between levels of elements in the environment with the corresponding in plants. L. racemosa showed variations in their root anatomy in different collection areas, with highest values of cortex/vascular cylinder ratio, periderm thickness and air gap area in Vitória Bay, the most polluted sampling area. These three parameters were also important to differentiate the mangrove areas by linear discriminant analysis. The development stage of aerenchyma in roots reflected the oxygen availability in the water, being found a negative correlation between these variables. The combined use of chemical and biological analyses responded quite well to different pollution scenarios, matching morphological responses to physical and chemical parameters, measured at different partitions within the estuary. Thus, L. racemosa can be confirmed as a reliable sentinel plant for biomonitoring of estuaries impacted by anthropic pollution.

Prognostic implications of adhesion molecule expression in colorectal cancer.

PubMed

Seo, Kyung-Jin; Kim, Maru; Kim, Jeana

2015-01-01

Research on the expression of adhesion molecules, E-cadherin (ECAD), CD24, CD44 and osteopontin (OPN) in colorectal cancer (CRC) has been limited, even though CRC is one of the leading causes of cancer-related deaths. This study was conducted to evaluate the expression of adhesion molecules in CRC and to determine their relationships with clinicopathologic variables, and the prognostic significance. The expression of ECAD, CD24, CD44 and OPN was examined in 174 stage II and III CRC specimens by immunohistochemistry of TMA. Negative ECAD expression was significantly correlated with advanced nodal stage and poor tumor differentiation. Multivariate analysis showed that both negative expression of ECAD and positive expression of CD24 were independent prognostic factors for disease-free survival (DFS) in CRC patients (P<0.001, relative risk [RR] = 5.596, 95% CI = 2.712-11.549; P = 0.038, RR = 3.768, 95% CI = 1.077-13.185, respectively). However, for overall survival (OS), only ECAD negativity showed statistically significant results in multivariate analysis (P<0.001, RR = 4.819, 95% CI = 2.515-9.234). Positive expression of CD24 was associated with poor OS in univariate analysis but was of no prognostic value in multivariate analysis. In conclusion, our study suggests that among these four adhesion molecules, ECAD and CD24 expression can be considered independent prognostic factors. The role of CD44 and OPN may need further evaluation.

Prognostic implications of adhesion molecule expression in colorectal cancer

PubMed Central

Seo, Kyung-Jin; Kim, Maru; Kim, Jeana

2015-01-01

Research on the expression of adhesion molecules, E-cadherin (ECAD), CD24, CD44 and osteopontin (OPN) in colorectal cancer (CRC) has been limited, even though CRC is one of the leading causes of cancer-related deaths. This study was conducted to evaluate the expression of adhesion molecules in CRC and to determine their relationships with clinicopathologic variables, and the prognostic significance. The expression of ECAD, CD24, CD44 and OPN was examined in 174 stage II and III CRC specimens by immunohistochemistry of TMA. Negative ECAD expression was significantly correlated with advanced nodal stage and poor tumor differentiation. Multivariate analysis showed that both negative expression of ECAD and positive expression of CD24 were independent prognostic factors for disease-free survival (DFS) in CRC patients (P<0.001, relative risk [RR] = 5.596, 95% CI = 2.712-11.549; P = 0.038, RR = 3.768, 95% CI = 1.077-13.185, respectively). However, for overall survival (OS), only ECAD negativity showed statistically significant results in multivariate analysis (P<0.001, RR = 4.819, 95% CI = 2.515-9.234). Positive expression of CD24 was associated with poor OS in univariate analysis but was of no prognostic value in multivariate analysis. In conclusion, our study suggests that among these four adhesion molecules, ECAD and CD24 expression can be considered independent prognostic factors. The role of CD44 and OPN may need further evaluation. PMID:26097606

F100 multivariable control synthesis program: Evaluation of a multivariable control using a real-time engine simulation

NASA Technical Reports Server (NTRS)

Szuch, J. R.; Soeder, J. F.; Seldner, K.; Cwynar, D. S.

1977-01-01

The design, evaluation, and testing of a practical, multivariable, linear quadratic regulator control for the F100 turbofan engine were accomplished. NASA evaluation of the multivariable control logic and implementation are covered. The evaluation utilized a real time, hybrid computer simulation of the engine. Results of the evaluation are presented, and recommendations concerning future engine testing of the control are made. Results indicated that the engine testing of the control should be conducted as planned.

Evaluating effects of methylphenidate on brain activity in cocaine addiction: a machine-learning approach

NASA Astrophysics Data System (ADS)

Rish, Irina; Bashivan, Pouya; Cecchi, Guillermo A.; Goldstein, Rita Z.

2016-03-01

The objective of this study is to investigate effects of methylphenidate on brain activity in individuals with cocaine use disorder (CUD) using functional MRI (fMRI). Methylphenidate hydrochloride (MPH) is an indirect dopamine agonist commonly used for treating attention deficit/hyperactivity disorders; it was also shown to have some positive effects on CUD subjects, such as improved stop signal reaction times associated with better control/inhibition,1 as well as normalized task-related brain activity2 and resting-state functional connectivity in specific areas.3 While prior fMRI studies of MPH in CUDs have focused on mass-univariate statistical hypothesis testing, this paper evaluates multivariate, whole-brain effects of MPH as captured by the generalization (prediction) accuracy of different classification techniques applied to features extracted from resting-state functional networks (e.g., node degrees). Our multivariate predictive results based on resting-state data from3 suggest that MPH tends to normalize network properties such as voxel degrees in CUD subjects, thus providing additional evidence for potential benefits of MPH in treating cocaine addiction.

Multivariate-Statistical Assessment of Heavy Metals for Agricultural Soils in Northern China

PubMed Central

Yang, Pingguo; Yang, Miao; Mao, Renzhao; Shao, Hongbo

2014-01-01

The study evaluated eight heavy metals content and soil pollution from agricultural soils in northern China. Multivariate and geostatistical analysis approaches were used to determine the anthropogenic and natural contribution of soil heavy metal concentrations. Single pollution index and integrated pollution index could be used to evaluate soil heavy metal risk. The results show that the first factor explains 27.3% of the eight soil heavy metals with strong positive loadings on Cu, Zn, and Cd, which indicates that Cu, Zn, and Cd are associated with and controlled by anthropic activities. The average value of heavy metal is lower than the second grade standard values of soil environmental quality standards in China. Single pollution index is lower than 1, and the Nemerow integrated pollution index is 0.305, which means that study area has not been polluted. The semivariograms of soil heavy metal single pollution index fitted spherical and exponential models. The variable ratio of single pollution index showed moderately spatial dependence. Heavy metal contents showed relative safety in the study area. PMID:24892058

Early PET/CT scan is more effective than RECIST in predicting outcome of patients with liver metastases from colorectal cancer treated with preoperative chemotherapy plus bevacizumab.

PubMed

Lastoria, Secondo; Piccirillo, Maria Carmela; Caracò, Corradina; Nasti, Guglielmo; Aloj, Luigi; Arrichiello, Cecilia; de Lutio di Castelguidone, Elisabetta; Tatangelo, Fabiana; Ottaiano, Alessandro; Iaffaioli, Rosario Vincenzo; Izzo, Francesco; Romano, Giovanni; Giordano, Pasqualina; Signoriello, Simona; Gallo, Ciro; Perrone, Francesco

2013-12-01

Markers predictive of treatment effect might be useful to improve the treatment of patients with metastatic solid tumors. Particularly, early changes in tumor metabolism measured by PET/CT with (18)F-FDG could predict the efficacy of treatment better than standard dimensional Response Evaluation Criteria In Solid Tumors (RECIST) response. We performed PET/CT evaluation before and after 1 cycle of treatment in patients with resectable liver metastases from colorectal cancer, within a phase 2 trial of preoperative FOLFIRI plus bevacizumab. For each lesion, the maximum standardized uptake value (SUV) and the total lesion glycolysis (TLG) were determined. On the basis of previous studies, a ≤ -50% change from baseline was used as a threshold for significant metabolic response for maximum SUV and, exploratively, for TLG. Standard RECIST response was assessed with CT after 3 mo of treatment. Pathologic response was assessed in patients undergoing resection. The association between metabolic and CT/RECIST and pathologic response was tested with the McNemar test; the ability to predict progression-free survival (PFS) and overall survival (OS) was tested with the Log-rank test and a multivariable Cox model. Thirty-three patients were analyzed. After treatment, there was a notable decrease of all the parameters measured by PET/CT. Early metabolic PET/CT response (either SUV- or TLG-based) had a stronger, independent and statistically significant predictive value for PFS and OS than both CT/RECIST and pathologic response at multivariate analysis, although with different degrees of statistical significance. The predictive value of CT/RECIST response was not significant at multivariate analysis. PET/CT response was significantly predictive of long-term outcomes during preoperative treatment of patients with liver metastases from colorectal cancer, and its predictive ability was higher than that of CT/RECIST response after 3 mo of treatment. Such findings need to be confirmed by larger prospective trials.

Calibration transfer of a Raman spectroscopic quantification method for the assessment of liquid detergent compositions from at-line laboratory to in-line industrial scale.

PubMed

Brouckaert, D; Uyttersprot, J-S; Broeckx, W; De Beer, T

2018-03-01

Calibration transfer or standardisation aims at creating a uniform spectral response on different spectroscopic instruments or under varying conditions, without requiring a full recalibration for each situation. In the current study, this strategy is applied to construct at-line multivariate calibration models and consequently employ them in-line in a continuous industrial production line, using the same spectrometer. Firstly, quantitative multivariate models are constructed at-line at laboratory scale for predicting the concentration of two main ingredients in hard surface cleaners. By regressing the Raman spectra of a set of small-scale calibration samples against their reference concentration values, partial least squares (PLS) models are developed to quantify the surfactant levels in the liquid detergent compositions under investigation. After evaluating the models performance with a set of independent validation samples, a univariate slope/bias correction is applied in view of transporting these at-line calibration models to an in-line manufacturing set-up. This standardisation technique allows a fast and easy transfer of the PLS regression models, by simply correcting the model predictions on the in-line set-up, without adjusting anything to the original multivariate calibration models. An extensive statistical analysis is performed in order to assess the predictive quality of the transferred regression models. Before and after transfer, the R 2 and RMSEP of both models is compared for evaluating if their magnitude is similar. T-tests are then performed to investigate whether the slope and intercept of the transferred regression line are not statistically different from 1 and 0, respectively. Furthermore, it is inspected whether no significant bias can be noted. F-tests are executed as well, for assessing the linearity of the transfer regression line and for investigating the statistical coincidence of the transfer and validation regression line. Finally, a paired t-test is performed to compare the original at-line model to the slope/bias corrected in-line model, using interval hypotheses. It is shown that the calibration models of Surfactant 1 and Surfactant 2 yield satisfactory in-line predictions after slope/bias correction. While Surfactant 1 passes seven out of eight statistical tests, the recommended validation parameters are 100% successful for Surfactant 2. It is hence concluded that the proposed strategy for transferring at-line calibration models to an in-line industrial environment via a univariate slope/bias correction of the predicted values offers a successful standardisation approach. Copyright © 2017 Elsevier B.V. All rights reserved.

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

PubMed

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

2016-01-01

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

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

Multi-criteria evaluation of CMIP5 GCMs for climate change impact analysis

NASA Astrophysics Data System (ADS)

Ahmadalipour, Ali; Rana, Arun; Moradkhani, Hamid; Sharma, Ashish

2017-04-01

Climate change is expected to have severe impacts on global hydrological cycle along with food-water-energy nexus. Currently, there are many climate models used in predicting important climatic variables. Though there have been advances in the field, there are still many problems to be resolved related to reliability, uncertainty, and computing needs, among many others. In the present work, we have analyzed performance of 20 different global climate models (GCMs) from Climate Model Intercomparison Project Phase 5 (CMIP5) dataset over the Columbia River Basin (CRB) in the Pacific Northwest USA. We demonstrate a statistical multicriteria approach, using univariate and multivariate techniques, for selecting suitable GCMs to be used for climate change impact analysis in the region. Univariate methods includes mean, standard deviation, coefficient of variation, relative change (variability), Mann-Kendall test, and Kolmogorov-Smirnov test (KS-test); whereas multivariate methods used were principal component analysis (PCA), singular value decomposition (SVD), canonical correlation analysis (CCA), and cluster analysis. The analysis is performed on raw GCM data, i.e., before bias correction, for precipitation and temperature climatic variables for all the 20 models to capture the reliability and nature of the particular model at regional scale. The analysis is based on spatially averaged datasets of GCMs and observation for the period of 1970 to 2000. Ranking is provided to each of the GCMs based on the performance evaluated against gridded observational data on various temporal scales (daily, monthly, and seasonal). Results have provided insight into each of the methods and various statistical properties addressed by them employed in ranking GCMs. Further; evaluation was also performed for raw GCM simulations against different sets of gridded observational dataset in the area.

Tracking problem solving by multivariate pattern analysis and Hidden Markov Model algorithms.

PubMed

Anderson, John R

2012-03-01

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 involves using fMRI activity to track what students are doing as they solve a sequence of algebra problems. The methodology achieves considerable accuracy at determining both what problem-solving step the students are taking and whether they are performing that step correctly. The second "model discovery" application involves using statistical model evaluation to determine how many substates are involved in performing a step of algebraic problem solving. This research indicates that different steps involve different numbers of substates and these substates are associated with different fluency in algebra problem solving. Copyright © 2011 Elsevier Ltd. All rights reserved.

Multivariate Analysis and Prediction of Dioxin-Furan ...

EPA Pesticide Factsheets

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

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

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

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

PubMed

Friedman, David B

2012-01-01

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

Multivariate statistical assessment of predictors of firefighters' muscular and aerobic work capacity.

PubMed

Lindberg, Ann-Sofie; Oksa, Juha; Antti, Henrik; Malm, Christer

2015-01-01

Physical capacity has previously been deemed important for firefighters physical work capacity, and aerobic fitness, muscular strength, and muscular endurance are the most frequently investigated parameters of importance. Traditionally, bivariate and multivariate linear regression statistics have been used to study relationships between physical capacities and work capacities among firefighters. An alternative way to handle datasets consisting of numerous correlated variables is to use multivariate projection analyses, such as Orthogonal Projection to Latent Structures. The first aim of the present study was to evaluate the prediction and predictive power of field and laboratory tests, respectively, on firefighters' physical work capacity on selected work tasks. Also, to study if valid predictions could be achieved without anthropometric data. The second aim was to externally validate selected models. The third aim was to validate selected models on firefighters' and on civilians'. A total of 38 (26 men and 12 women) + 90 (38 men and 52 women) subjects were included in the models and the external validation, respectively. The best prediction (R2) and predictive power (Q2) of Stairs, Pulling, Demolition, Terrain, and Rescue work capacities included field tests (R2 = 0.73 to 0.84, Q2 = 0.68 to 0.82). The best external validation was for Stairs work capacity (R2 = 0.80) and worst for Demolition work capacity (R2 = 0.40). In conclusion, field and laboratory tests could equally well predict physical work capacities for firefighting work tasks, and models excluding anthropometric data were valid. The predictive power was satisfactory for all included work tasks except Demolition.

When Does Neoadjuvant Chemotherapy Really Avoid Radiotherapy? Clinical Predictors of Adjuvant Radiotherapy in Cervical Cancer.

PubMed

Papadia, Andrea; Bellati, Filippo; Bogani, Giorgio; Ditto, Antonino; Martinelli, Fabio; Lorusso, Domenica; Donfrancesco, Cristina; Gasparri, Maria Luisa; Raspagliesi, Francesco

2015-12-01

The aim of this study was to identify clinical variables that may predict the need for adjuvant radiotherapy after neoadjuvant chemotherapy (NACT) and radical surgery in locally advanced cervical cancer patients. A retrospective series of cervical cancer patients with International Federation of Gynecology and Obstetrics (FIGO) stages IB2-IIB treated with NACT followed by radical surgery was analyzed. Clinical predictors of persistence of intermediate- and/or high-risk factors at final pathological analysis were investigated. Statistical analysis was performed using univariate and multivariate analysis and using a model based on artificial intelligence known as artificial neuronal network (ANN) analysis. Overall, 101 patients were available for the analyses. Fifty-two (51 %) patients were considered at high risk secondary to parametrial, resection margin and/or lymph node involvement. When disease was confined to the cervix, four (4 %) patients were considered at intermediate risk. At univariate analysis, FIGO grade 3, stage IIB disease at diagnosis and the presence of enlarged nodes before NACT predicted the presence of intermediate- and/or high-risk factors at final pathological analysis. At multivariate analysis, only FIGO grade 3 and tumor diameter maintained statistical significance. The specificity of ANN models in evaluating predictive variables was slightly superior to conventional multivariable models. FIGO grade, stage, tumor diameter, and histology are associated with persistence of pathological intermediate- and/or high-risk factors after NACT and radical surgery. This information is useful in counseling patients at the time of treatment planning with regard to the probability of being subjected to pelvic radiotherapy after completion of the initially planned treatment.

Applying Sociocultural Theory to Teaching Statistics for Doctoral Social Work Students

ERIC Educational Resources Information Center

Mogro-Wilson, Cristina; Reeves, Michael G.; Charter, Mollie Lazar

2015-01-01

This article describes the development of two doctoral-level multivariate statistics courses utilizing sociocultural theory, an integrative pedagogical framework. In the first course, the implementation of sociocultural theory helps to support the students through a rigorous introduction to statistics. The second course involves students…

A review on the multivariate statistical methods for dimensional reduction studies

NASA Astrophysics Data System (ADS)

Aik, Lim Eng; Kiang, Lam Chee; Mohamed, Zulkifley Bin; Hong, Tan Wei

2017-05-01

In this research study we have discussed multivariate statistical methods for dimensional reduction, which has been done by various researchers. The reduction of dimensionality is valuable to accelerate algorithm progression, as well as really may offer assistance with the last grouping/clustering precision. A lot of boisterous or even flawed info information regularly prompts a not exactly alluring algorithm progression. Expelling un-useful or dis-instructive information segments may for sure help the algorithm discover more broad grouping locales and principles and generally speaking accomplish better exhibitions on new data set.

Generating an Empirical Probability Distribution for the Andrews-Pregibon Statistic.

ERIC Educational Resources Information Center

Jarrell, Michele G.

A probability distribution was developed for the Andrews-Pregibon (AP) statistic. The statistic, developed by D. F. Andrews and D. Pregibon (1978), identifies multivariate outliers. It is a ratio of the determinant of the data matrix with an observation deleted to the determinant of the entire data matrix. Although the AP statistic has been used…

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

PubMed Central

Jia, Erik; Chen, Tianlu

2018-01-01

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

Multivariate statistical analysis software technologies for astrophysical research involving large data bases

NASA Technical Reports Server (NTRS)

Djorgovski, George

1993-01-01

The existing and forthcoming data bases from NASA missions contain an abundance of information whose complexity cannot be efficiently tapped with simple statistical techniques. Powerful multivariate statistical methods already exist which can be used to harness much of the richness of these data. Automatic classification techniques have been developed to solve the problem of identifying known types of objects in multiparameter data sets, in addition to leading to the discovery of new physical phenomena and classes of objects. We propose an exploratory study and integration of promising techniques in the development of a general and modular classification/analysis system for very large data bases, which would enhance and optimize data management and the use of human research resource.

Multivariate statistical analysis software technologies for astrophysical research involving large data bases

NASA Technical Reports Server (NTRS)

Djorgovski, Stanislav

1992-01-01

The existing and forthcoming data bases from NASA missions contain an abundance of information whose complexity cannot be efficiently tapped with simple statistical techniques. Powerful multivariate statistical methods already exist which can be used to harness much of the richness of these data. Automatic classification techniques have been developed to solve the problem of identifying known types of objects in multi parameter data sets, in addition to leading to the discovery of new physical phenomena and classes of objects. We propose an exploratory study and integration of promising techniques in the development of a general and modular classification/analysis system for very large data bases, which would enhance and optimize data management and the use of human research resources.

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…

A Multivariate Solution of the Multivariate Ranking and Selection Problem

DTIC Science & Technology

1980-02-01

Taneja (1972)), a ’a for a vector of constants c (Krishnaiah and Rizvi (1966)), the generalized variance ( Gnanadesikan and Gupta (1970)), iegier (1976...Olk-in, I. and Sobel, M. (1977). Selecting and Ordering Populations: A New Statistical Methodology, John Wiley & Sons, Inc., New York. Gnanadesikan

Multivariate meta-analysis of individual participant data helped externally validate the performance and implementation of a prediction model.

PubMed

Snell, Kym I E; Hua, Harry; Debray, Thomas P A; Ensor, Joie; Look, Maxime P; Moons, Karel G M; Riley, Richard D

2016-01-01

Our aim was to improve meta-analysis methods for summarizing a prediction model's performance when individual participant data are available from multiple studies for external validation. We suggest multivariate meta-analysis for jointly synthesizing calibration and discrimination performance, while accounting for their correlation. The approach estimates a prediction model's average performance, the heterogeneity in performance across populations, and the probability of "good" performance in new populations. This allows different implementation strategies (e.g., recalibration) to be compared. Application is made to a diagnostic model for deep vein thrombosis (DVT) and a prognostic model for breast cancer mortality. In both examples, multivariate meta-analysis reveals that calibration performance is excellent on average but highly heterogeneous across populations unless the model's intercept (baseline hazard) is recalibrated. For the cancer model, the probability of "good" performance (defined by C statistic ≥0.7 and calibration slope between 0.9 and 1.1) in a new population was 0.67 with recalibration but 0.22 without recalibration. For the DVT model, even with recalibration, there was only a 0.03 probability of "good" performance. Multivariate meta-analysis can be used to externally validate a prediction model's calibration and discrimination performance across multiple populations and to evaluate different implementation strategies. Crown Copyright © 2016. Published by Elsevier Inc. All rights reserved.

Layer-by-Layer Polyelectrolyte Encapsulation of Mycoplasma pneumoniae for Enhanced Raman Detection

PubMed Central

Rivera-Betancourt, Omar E.; Sheppard, Edward S.; Krause, Duncan C.; Dluhy, Richard A.

2014-01-01

Mycoplasma pneumoniae is a major cause of respiratory disease in humans and accounts for as much as 20% of all community-acquired pneumonia. Existing mycoplasma diagnosis is primarily limited by the poor success rate at culturing the bacteria from clinical samples. There is a critical need to develop a new platform for mycoplasma detection that has high sensitivity, specificity, and expediency. Here we report the layer-by-layer (LBL) encapsulation of M. pneumoniae cells with Ag nanoparticles in a matrix of the polyelectrolytes poly(allylamine hydrochloride) (PAH) and poly(styrene sulfonate) (PSS). We evaluated nanoparticle encapsulated mycoplasma cells as a platform for the differentiation of M. pneumoniae strains using surface enhanced Raman scattering (SERS) combined with multivariate statistical analysis. Three separate M. pneumoniae strains (M129, FH and II-3) were studied. Scanning electron microscopy and fluorescence imaging showed that the Ag nanoparticles were incorporated between the oppositely charged polyelectrolyte layers. SERS spectra showed that LBL encapsulation provides excellent spectral reproducibility. Multivariate statistical analysis of the Raman spectra differentiated the three M. pneumoniae strains with 97 – 100% specificity and sensitivity, and low (0.1 – 0.4) root mean square error. These results indicated that nanoparticle and polyelectrolyte encapsulation of M. pneumoniae is a potentially powerful platform for rapid and sensitive SERS-based bacterial identification. PMID:25017005

Adjustment of geochemical background by robust multivariate statistics

USGS Publications Warehouse

Zhou, D.

1985-01-01

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

Rapid differentiation of Chinese hop varieties (Humulus lupulus) using volatile fingerprinting by HS-SPME-GC-MS combined with multivariate statistical analysis.

PubMed

Liu, Zechang; Wang, Liping; Liu, Yumei

2018-01-18

Hops impart flavor to beer, with the volatile components characterizing the various hop varieties and qualities. Fingerprinting, especially flavor fingerprinting, is often used to identify 'flavor products' because inconsistencies in the description of flavor may lead to an incorrect definition of beer quality. Compared to flavor fingerprinting, volatile fingerprinting is simpler and easier. We performed volatile fingerprinting using head space-solid phase micro-extraction gas chromatography-mass spectrometry combined with similarity analysis and principal component analysis (PCA) for evaluating and distinguishing between three major Chinese hops. Eighty-four volatiles were identified, which were classified into seven categories. Volatile fingerprinting based on similarity analysis did not yield any obvious result. By contrast, hop varieties and qualities were identified using volatile fingerprinting based on PCA. The potential variables explained the variance in the three hop varieties. In addition, the dendrogram and principal component score plot described the differences and classifications of hops. Volatile fingerprinting plus multivariate statistical analysis can rapidly differentiate between the different varieties and qualities of the three major Chinese hops. Furthermore, this method can be used as a reference in other fields. © 2018 Society of Chemical Industry. © 2018 Society of Chemical Industry.

Integrated Application of Multivariate Statistical Methods to Source Apportionment of Watercourses in the Liao River Basin, Northeast China

PubMed Central

Chen, Jiabo; Li, Fayun; Fan, Zhiping; Wang, Yanjie

2016-01-01

Source apportionment of river water pollution is critical in water resource management and aquatic conservation. Comprehensive application of various GIS-based multivariate statistical methods was performed to analyze datasets (2009–2011) on water quality in the Liao River system (China). Cluster analysis (CA) classified the 12 months of the year into three groups (May–October, February–April and November–January) and the 66 sampling sites into three groups (groups A, B and C) based on similarities in water quality characteristics. Discriminant analysis (DA) determined that temperature, dissolved oxygen (DO), pH, chemical oxygen demand (CODMn), 5-day biochemical oxygen demand (BOD5), NH4+–N, total phosphorus (TP) and volatile phenols were significant variables affecting temporal variations, with 81.2% correct assignments. Principal component analysis (PCA) and positive matrix factorization (PMF) identified eight potential pollution factors for each part of the data structure, explaining more than 61% of the total variance. Oxygen-consuming organics from cropland and woodland runoff were the main latent pollution factor for group A. For group B, the main pollutants were oxygen-consuming organics, oil, nutrients and fecal matter. For group C, the evaluated pollutants primarily included oxygen-consuming organics, oil and toxic organics. PMID:27775679

How to compare cross-lagged associations in a multilevel autoregressive model.

PubMed

Schuurman, Noémi K; Ferrer, Emilio; de Boer-Sonnenschein, Mieke; Hamaker, Ellen L

2016-06-01

By modeling variables over time it is possible to investigate the Granger-causal cross-lagged associations between variables. By comparing the standardized cross-lagged coefficients, the relative strength of these associations can be evaluated in order to determine important driving forces in the dynamic system. The aim of this study was twofold: first, to illustrate the added value of a multilevel multivariate autoregressive modeling approach for investigating these associations over more traditional techniques; and second, to discuss how the coefficients of the multilevel autoregressive model should be standardized for comparing the strength of the cross-lagged associations. The hierarchical structure of multilevel multivariate autoregressive models complicates standardization, because subject-based statistics or group-based statistics can be used to standardize the coefficients, and each method may result in different conclusions. We argue that in order to make a meaningful comparison of the strength of the cross-lagged associations, the coefficients should be standardized within persons. We further illustrate the bivariate multilevel autoregressive model and the standardization of the coefficients, and we show that disregarding individual differences in dynamics can prove misleading, by means of an empirical example on experienced competence and exhaustion in persons diagnosed with burnout. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

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

PubMed

Buttigieg, Pier Luigi; Ramette, Alban

2014-12-01

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

Ensembles of radial basis function networks for spectroscopic detection of cervical precancer

NASA Technical Reports Server (NTRS)

Tumer, K.; Ramanujam, N.; Ghosh, J.; Richards-Kortum, R.

1998-01-01

The mortality related to cervical cancer can be substantially reduced through early detection and treatment. However, current detection techniques, such as Pap smear and colposcopy, fail to achieve a concurrently high sensitivity and specificity. In vivo fluorescence spectroscopy is a technique which quickly, noninvasively and quantitatively probes the biochemical and morphological changes that occur in precancerous tissue. A multivariate statistical algorithm was used to extract clinically useful information from tissue spectra acquired from 361 cervical sites from 95 patients at 337-, 380-, and 460-nm excitation wavelengths. The multivariate statistical analysis was also employed to reduce the number of fluorescence excitation-emission wavelength pairs required to discriminate healthy tissue samples from precancerous tissue samples. The use of connectionist methods such as multilayered perceptrons, radial basis function (RBF) networks, and ensembles of such networks was investigated. RBF ensemble algorithms based on fluorescence spectra potentially provide automated and near real-time implementation of precancer detection in the hands of nonexperts. The results are more reliable, direct, and accurate than those achieved by either human experts or multivariate statistical algorithms.

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

PubMed Central

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

2014-01-01

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

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

PubMed

Angthong, Wirana; Jiarakoop, Kran; Tangtiang, Kaan

2018-05-21

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

Chromatography methods and chemometrics for determination of milk fat adulterants

NASA Astrophysics Data System (ADS)

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

2017-09-01

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

A statistical approach for segregating cognitive task stages from multivariate fMRI BOLD time series.

PubMed

Demanuele, Charmaine; Bähner, Florian; Plichta, Michael M; Kirsch, Peter; Tost, Heike; Meyer-Lindenberg, Andreas; Durstewitz, Daniel

2015-01-01

Multivariate pattern analysis can reveal new information from neuroimaging data to illuminate human cognition and its disturbances. Here, we develop a methodological approach, based on multivariate statistical/machine learning and time series analysis, to discern cognitive processing stages from functional magnetic resonance imaging (fMRI) blood oxygenation level dependent (BOLD) time series. We apply this method to data recorded from a group of healthy adults whilst performing a virtual reality version of the delayed win-shift radial arm maze (RAM) task. This task has been frequently used to study working memory and decision making in rodents. Using linear classifiers and multivariate test statistics in conjunction with time series bootstraps, we show that different cognitive stages of the task, as defined by the experimenter, namely, the encoding/retrieval, choice, reward and delay stages, can be statistically discriminated from the BOLD time series in brain areas relevant for decision making and working memory. Discrimination of these task stages was significantly reduced during poor behavioral performance in dorsolateral prefrontal cortex (DLPFC), but not in the primary visual cortex (V1). Experimenter-defined dissection of time series into class labels based on task structure was confirmed by an unsupervised, bottom-up approach based on Hidden Markov Models. Furthermore, we show that different groupings of recorded time points into cognitive event classes can be used to test hypotheses about the specific cognitive role of a given brain region during task execution. We found that whilst the DLPFC strongly differentiated between task stages associated with different memory loads, but not between different visual-spatial aspects, the reverse was true for V1. Our methodology illustrates how different aspects of cognitive information processing during one and the same task can be separated and attributed to specific brain regions based on information contained in multivariate patterns of voxel activity.

Calypso: a user-friendly web-server for mining and visualizing microbiome-environment interactions.

PubMed

Zakrzewski, Martha; Proietti, Carla; Ellis, Jonathan J; Hasan, Shihab; Brion, Marie-Jo; Berger, Bernard; Krause, Lutz

2017-03-01

Calypso is an easy-to-use online software suite that allows non-expert users to mine, interpret and compare taxonomic information from metagenomic or 16S rDNA datasets. Calypso has a focus on multivariate statistical approaches that can identify complex environment-microbiome associations. The software enables quantitative visualizations, statistical testing, multivariate analysis, supervised learning, factor analysis, multivariable regression, network analysis and diversity estimates. Comprehensive help pages, tutorials and videos are provided via a wiki page. The web-interface is accessible via http://cgenome.net/calypso/ . The software is programmed in Java, PERL and R and the source code is available from Zenodo ( https://zenodo.org/record/50931 ). The software is freely available for non-commercial users. l.krause@uq.edu.au. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

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.

Detecting subtle hydrochemical anomalies with multivariate statistics: an example from homogeneous groundwaters in the Great Artesian Basin, Australia

NASA Astrophysics Data System (ADS)

O'Shea, Bethany; Jankowski, Jerzy

2006-12-01

The major ion composition of Great Artesian Basin groundwater in the lower Namoi River valley is relatively homogeneous in chemical composition. Traditional graphical techniques have been combined with multivariate statistical methods to determine whether subtle differences in the chemical composition of these waters can be delineated. Hierarchical cluster analysis and principal components analysis were successful in delineating minor variations within the groundwaters of the study area that were not visually identified in the graphical techniques applied. Hydrochemical interpretation allowed geochemical processes to be identified in each statistically defined water type and illustrated how these groundwaters differ from one another. Three main geochemical processes were identified in the groundwaters: ion exchange, precipitation, and mixing between waters from different sources. Both statistical methods delineated an anomalous sample suspected of being influenced by magmatic CO2 input. The use of statistical methods to complement traditional graphical techniques for waters appearing homogeneous is emphasized for all investigations of this type. Copyright

Quality of reporting of multivariable logistic regression models in Chinese clinical medical journals.

PubMed

Zhang, Ying-Ying; Zhou, Xiao-Bin; Wang, Qiu-Zhen; Zhu, Xiao-Yan

2017-05-01

Multivariable logistic regression (MLR) has been increasingly used in Chinese clinical medical research during the past few years. However, few evaluations of the quality of the reporting strategies in these studies are available.To evaluate the reporting quality and model accuracy of MLR used in published work, and related advice for authors, readers, reviewers, and editors.A total of 316 articles published in 5 leading Chinese clinical medical journals with high impact factor from January 2010 to July 2015 were selected for evaluation. Articles were evaluated according 12 established criteria for proper use and reporting of MLR models.Among the articles, the highest quality score was 9, the lowest 1, and the median 5 (4-5). A total of 85.1% of the articles scored below 6. No significant differences were found among these journals with respect to quality score (χ = 6.706, P = .15). More than 50% of the articles met the following 5 criteria: complete identification of the statistical software application that was used (97.2%), calculation of the odds ratio and its confidence interval (86.4%), description of sufficient events (>10) per variable, selection of variables, and fitting procedure (78.2%, 69.3%, and 58.5%, respectively). Less than 35% of the articles reported the coding of variables (18.7%). The remaining 5 criteria were not satisfied by a sufficient number of articles: goodness-of-fit (10.1%), interactions (3.8%), checking for outliers (3.2%), collinearity (1.9%), and participation of statisticians and epidemiologists (0.3%). The criterion of conformity with linear gradients was applicable to 186 articles; however, only 7 (3.8%) mentioned or tested it.The reporting quality and model accuracy of MLR in selected articles were not satisfactory. In fact, severe deficiencies were noted. Only 1 article scored 9. We recommend authors, readers, reviewers, and editors to consider MLR models more carefully and cooperate more closely with statisticians and epidemiologists. Journals should develop statistical reporting guidelines concerning MLR.

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

PubMed Central

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

2014-01-01

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

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

NASA Astrophysics Data System (ADS)

Theodorakou, Chrysoula; Farquharson, Michael J.

2009-08-01

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

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

PubMed Central

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

2011-01-01

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

A Descriptive Study of Individual and Cross-Cultural Differences in Statistics Anxiety

ERIC Educational Resources Information Center

Baloglu, Mustafa; Deniz, M. Engin; Kesici, Sahin

2011-01-01

The present study investigated individual and cross-cultural differences in statistics anxiety among 223 Turkish and 237 American college students. A 2 x 2 between-subjects factorial multivariate analysis of covariance (MANCOVA) was performed on the six dependent variables which are the six subscales of the Statistical Anxiety Rating Scale.…

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

NASA Astrophysics Data System (ADS)

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

2017-02-01

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

Comparison of Logistic Regression and Random Forests techniques for shallow landslide susceptibility assessment in Giampilieri (NE Sicily, Italy)

NASA Astrophysics Data System (ADS)

Trigila, Alessandro; Iadanza, Carla; Esposito, Carlo; Scarascia-Mugnozza, Gabriele

2015-11-01

The aim of this work is to define reliable susceptibility models for shallow landslides using Logistic Regression and Random Forests multivariate statistical techniques. The study area, located in North-East Sicily, was hit on October 1st 2009 by a severe rainstorm (225 mm of cumulative rainfall in 7 h) which caused flash floods and more than 1000 landslides. Several small villages, such as Giampilieri, were hit with 31 fatalities, 6 missing persons and damage to buildings and transportation infrastructures. Landslides, mainly types such as earth and debris translational slides evolving into debris flows, were triggered on steep slopes and involved colluvium and regolith materials which cover the underlying metamorphic bedrock. The work has been carried out with the following steps: i) realization of a detailed event landslide inventory map through field surveys coupled with observation of high resolution aerial colour orthophoto; ii) identification of landslide source areas; iii) data preparation of landslide controlling factors and descriptive statistics based on a bivariate method (Frequency Ratio) to get an initial overview on existing relationships between causative factors and shallow landslide source areas; iv) choice of criteria for the selection and sizing of the mapping unit; v) implementation of 5 multivariate statistical susceptibility models based on Logistic Regression and Random Forests techniques and focused on landslide source areas; vi) evaluation of the influence of sample size and type of sampling on results and performance of the models; vii) evaluation of the predictive capabilities of the models using ROC curve, AUC and contingency tables; viii) comparison of model results and obtained susceptibility maps; and ix) analysis of temporal variation of landslide susceptibility related to input parameter changes. Models based on Logistic Regression and Random Forests have demonstrated excellent predictive capabilities. Land use and wildfire variables were found to have a strong control on the occurrence of very rapid shallow landslides.

Evaluation of an F100 multivariable control using a real-time engine simulation

NASA Technical Reports Server (NTRS)

Szuch, J. R.; Skira, C.; Soeder, J. F.

1977-01-01

A multivariable control design for the F100 turbofan engine was evaluated, as part of the F100 multivariable control synthesis (MVCS) program. The evaluation utilized a real-time, hybrid computer simulation of the engine and a digital computer implementation of the control. Significant results of the evaluation are presented and recommendations concerning future engine testing of the control are made.

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...

Parametric Cost Models for Space Telescopes

NASA Technical Reports Server (NTRS)

Stahl, H. Philip

2010-01-01

A study is in-process to develop a multivariable parametric cost model for space telescopes. Cost and engineering parametric data has been collected on 30 different space telescopes. Statistical correlations have been developed between 19 variables of 59 variables sampled. Single Variable and Multi-Variable Cost Estimating Relationships have been developed. Results are being published.

Preliminary Multi-Variable Parametric Cost Model for Space Telescopes

NASA Technical Reports Server (NTRS)

Stahl, H. Philip; Hendrichs, Todd

2010-01-01

This slide presentation reviews creating a preliminary multi-variable cost model for the contract costs of making a space telescope. There is discussion of the methodology for collecting the data, definition of the statistical analysis methodology, single variable model results, testing of historical models and an introduction of the multi variable models.

Statistical Model of Dynamic Markers of the Alzheimer's Pathological Cascade.

PubMed

Balsis, Steve; Geraci, Lisa; Benge, Jared; Lowe, Deborah A; Choudhury, Tabina K; Tirso, Robert; Doody, Rachelle S

2018-05-05

Alzheimer's disease (AD) is a progressive disease reflected in markers across assessment modalities, including neuroimaging, cognitive testing, and evaluation of adaptive function. Identifying a single continuum of decline across assessment modalities in a single sample is statistically challenging because of the multivariate nature of the data. To address this challenge, we implemented advanced statistical analyses designed specifically to model complex data across a single continuum. We analyzed data from the Alzheimer's Disease Neuroimaging Initiative (ADNI; N = 1,056), focusing on indicators from the assessments of magnetic resonance imaging (MRI) volume, fluorodeoxyglucose positron emission tomography (FDG-PET) metabolic activity, cognitive performance, and adaptive function. Item response theory was used to identify the continuum of decline. Then, through a process of statistical scaling, indicators across all modalities were linked to that continuum and analyzed. Findings revealed that measures of MRI volume, FDG-PET metabolic activity, and adaptive function added measurement precision beyond that provided by cognitive measures, particularly in the relatively mild range of disease severity. More specifically, MRI volume, and FDG-PET metabolic activity become compromised in the very mild range of severity, followed by cognitive performance and finally adaptive function. Our statistically derived models of the AD pathological cascade are consistent with existing theoretical models.

Mapping Quantitative Traits in Unselected Families: Algorithms and Examples

PubMed Central

Dupuis, Josée; Shi, Jianxin; Manning, Alisa K.; Benjamin, Emelia J.; Meigs, James B.; Cupples, L. Adrienne; Siegmund, David

2009-01-01

Linkage analysis has been widely used to identify from family data genetic variants influencing quantitative traits. Common approaches have both strengths and limitations. Likelihood ratio tests typically computed in variance component analysis can accommodate large families but are highly sensitive to departure from normality assumptions. Regression-based approaches are more robust but their use has primarily been restricted to nuclear families. In this paper, we develop methods for mapping quantitative traits in moderately large pedigrees. Our methods are based on the score statistic which in contrast to the likelihood ratio statistic, can use nonparametric estimators of variability to achieve robustness of the false positive rate against departures from the hypothesized phenotypic model. Because the score statistic is easier to calculate than the likelihood ratio statistic, our basic mapping methods utilize relatively simple computer code that performs statistical analysis on output from any program that computes estimates of identity-by-descent. This simplicity also permits development and evaluation of methods to deal with multivariate and ordinal phenotypes, and with gene-gene and gene-environment interaction. We demonstrate our methods on simulated data and on fasting insulin, a quantitative trait measured in the Framingham Heart Study. PMID:19278016

Facilitating the Transition from Bright to Dim Environments

DTIC Science & Technology

2016-03-04

For the parametric data, a multivariate ANOVA was used in determining the systematic presence of any statistically significant performance differences...performed. All significance levels were p < 0.05, and statistical analyses were performed with the Statistical Package for Social Sciences ( SPSS ...1950. Age changes in rate and level of visual dark adaptation. Journal of Applied Physiology, 2, 407–411. Field, A. 2009. Discovering statistics

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

PubMed Central

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

2016-01-01

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

Using Meteorological Analogues for Reordering Postprocessed Precipitation Ensembles in Hydrological Forecasting

NASA Astrophysics Data System (ADS)

Bellier, Joseph; Bontron, Guillaume; Zin, Isabella

2017-12-01

Meteorological ensemble forecasts are nowadays widely used as input of hydrological models for probabilistic streamflow forecasting. These forcings are frequently biased and have to be statistically postprocessed, using most of the time univariate techniques that apply independently to individual locations, lead times and weather variables. Postprocessed ensemble forecasts therefore need to be reordered so as to reconstruct suitable multivariate dependence structures. The Schaake shuffle and ensemble copula coupling are the two most popular methods for this purpose. This paper proposes two adaptations of them that make use of meteorological analogues for reconstructing spatiotemporal dependence structures of precipitation forecasts. Performances of the original and adapted techniques are compared through a multistep verification experiment using real forecasts from the European Centre for Medium-Range Weather Forecasts. This experiment evaluates not only multivariate precipitation forecasts but also the corresponding streamflow forecasts that derive from hydrological modeling. Results show that the relative performances of the different reordering methods vary depending on the verification step. In particular, the standard Schaake shuffle is found to perform poorly when evaluated on streamflow. This emphasizes the crucial role of the precipitation spatiotemporal dependence structure in hydrological ensemble forecasting.

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.

Evaluation of multivariate calibration models with different pre-processing and processing algorithms for a novel resolution and quantitation of spectrally overlapped quaternary mixture in syrup

NASA Astrophysics Data System (ADS)

Moustafa, Azza A.; Hegazy, Maha A.; Mohamed, Dalia; Ali, Omnia

2016-02-01

A novel approach for the resolution and quantitation of severely overlapped quaternary mixture of carbinoxamine maleate (CAR), pholcodine (PHL), ephedrine hydrochloride (EPH) and sunset yellow (SUN) in syrup was demonstrated utilizing different spectrophotometric assisted multivariate calibration methods. The applied methods have used different processing and pre-processing algorithms. The proposed methods were partial least squares (PLS), concentration residuals augmented classical least squares (CRACLS), and a novel method; continuous wavelet transforms coupled with partial least squares (CWT-PLS). These methods were applied to a training set in the concentration ranges of 40-100 μg/mL, 40-160 μg/mL, 100-500 μg/mL and 8-24 μg/mL for the four components, respectively. The utilized methods have not required any preliminary separation step or chemical pretreatment. The validity of the methods was evaluated by an external validation set. The selectivity of the developed methods was demonstrated by analyzing the drugs in their combined pharmaceutical formulation without any interference from additives. The obtained results were statistically compared with the official and reported methods where no significant difference was observed regarding both accuracy and precision.

Statistical inferences for data from studies conducted with an aggregated multivariate outcome-dependent sample design

PubMed Central

Lu, Tsui-Shan; Longnecker, Matthew P.; Zhou, Haibo

2016-01-01

Outcome-dependent sampling (ODS) scheme is a cost-effective sampling scheme where one observes the exposure with a probability that depends on the outcome. The well-known such design is the case-control design for binary response, the case-cohort design for the failure time data and the general ODS design for a continuous response. While substantial work has been done for the univariate response case, statistical inference and design for the ODS with multivariate cases remain under-developed. Motivated by the need in biological studies for taking the advantage of the available responses for subjects in a cluster, we propose a multivariate outcome dependent sampling (Multivariate-ODS) design that is based on a general selection of the continuous responses within a cluster. The proposed inference procedure for the Multivariate-ODS design is semiparametric where all the underlying distributions of covariates are modeled nonparametrically using the empirical likelihood methods. We show that the proposed estimator is consistent and developed the asymptotically normality properties. Simulation studies show that the proposed estimator is more efficient than the estimator obtained using only the simple-random-sample portion of the Multivariate-ODS or the estimator from a simple random sample with the same sample size. The Multivariate-ODS design together with the proposed estimator provides an approach to further improve study efficiency for a given fixed study budget. We illustrate the proposed design and estimator with an analysis of association of PCB exposure to hearing loss in children born to the Collaborative Perinatal Study. PMID:27966260

Trace elements of concern affecting urban agriculture in industrialized areas: A multivariate approach.

PubMed

Boente, C; Matanzas, N; García-González, N; Rodríguez-Valdés, E; Gallego, J R

2017-09-01

The urban and peri-urban soils used for agriculture could be contaminated by atmospheric deposition or industrial releases, thus raising concerns about the potential risk to public health. Here we propose a method to evaluate potential soil pollution based on multivariate statistics, geostatistics (kriging), a novel soil pollution index, and bioavailability assessments. This approach was tested in two districts of a highly populated and industrialized city (Gijón, Spain). The soils showed anomalous content of several trace elements, such as As and Pb (up to 80 and 585 mg kg -1 respectively). In addition, factor analyses associated these elements with anthropogenic activity, whereas other elements were attributed to natural sources. Subsequent clustering also facilitated the differentiation between the northern area studied (only limited Pb pollution found) and the southern area (pattern of coal combustion, including simultaneous anomalies of trace elements and benzo(a)pyrene). A normalized soil pollution index (SPI) was calculated by kriging, using only the elements falling above threshold levels; therefore point-source polluted zones in the northern area and diffuse contamination in the south were identified. In addition, in the six mapping units with the highest SPIs of the fifty studied, we observed low bioavailability for most of the elements that surpassed the threshold levels. However, some anomalies of Pb contents and the pollution fingerprint in the central area of the southern grid call for further site-specific studies. On the whole, the combination of a multivariate (geo) statistic approach and a bioavailability assessment allowed us to efficiently identify sources of contamination and potential risks. Copyright © 2017 Elsevier Ltd. All rights reserved.

Initial Experience with Balloon-Occluded Trans-catheter Arterial Chemoembolization (B-TACE) for Hepatocellular Carcinoma

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

Maruyama, Mitsunari, E-mail: mitunari@med-shimane.u.ac.jp; Yoshizako, Takeshi, E-mail: yosizako@med.shimane-u.ac.jp; Nakamura, Tomonori, E-mail: t-naka@med.shimane-u.ac.jp

2016-03-15

PurposeThis study was performed to evaluate the accumulation of lipiodol emulsion (LE) and adverse events during our initial experience of balloon-occluded trans-catheter arterial chemoembolization (B-TACE) for hepatocellular carcinoma (HCC) compared with conventional TACE (C-TACE).MethodsB-TACE group (50 cases) was compared with C-TACE group (50 cases). The ratio of the LE concentration in the tumor to that in the surrounding embolized liver parenchyma (LE ratio) was calculated after each treatment. Adverse events were evaluated according to the Common Terminology Criteria for Adverse Effects (CTCAE) version 4.0.ResultsThe LE ratio at the level of subsegmental showed a statistically significant difference between the groups (tmore » test: P < 0.05). Only elevation of alanine aminotransferase was more frequent in the B-TACE group, showing a statistically significant difference (Mann–Whitney test: P < 0.05). While B-TACE caused severe adverse events (liver abscess and infarction) in patients with bile duct dilatation, there was no statistically significant difference in incidence between the groups. Multivariate logistic regression analysis suggested that the significant risk factor for liver abscess/infarction was bile duct dilatation (P < 0.05).ConclusionThe LE ratio at the level of subsegmental showed a statistically significant difference between the groups (t test: P < 0.05). B-TACE caused severe adverse events (liver abscess and infarction) in patients with bile duct dilatation.« less

Neural-genetic synthesis for state-space controllers based on linear quadratic regulator design for eigenstructure assignment.

PubMed

da Fonseca Neto, João Viana; Abreu, Ivanildo Silva; da Silva, Fábio Nogueira

2010-04-01

Toward the synthesis of state-space controllers, a neural-genetic model based on the linear quadratic regulator design for the eigenstructure assignment of multivariable dynamic systems is presented. The neural-genetic model represents a fusion of a genetic algorithm and a recurrent neural network (RNN) to perform the selection of the weighting matrices and the algebraic Riccati equation solution, respectively. A fourth-order electric circuit model is used to evaluate the convergence of the computational intelligence paradigms and the control design method performance. The genetic search convergence evaluation is performed in terms of the fitness function statistics and the RNN convergence, which is evaluated by landscapes of the energy and norm, as a function of the parameter deviations. The control problem solution is evaluated in the time and frequency domains by the impulse response, singular values, and modal analysis.

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.

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.

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

PubMed Central

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

2017-01-01

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

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

PubMed

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

2015-01-01

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

Fruit and vegetable intake and risk of breast cancer by hormone receptor status.

PubMed

Jung, Seungyoun; Spiegelman, Donna; Baglietto, Laura; Bernstein, Leslie; Boggs, Deborah A; van den Brandt, Piet A; Buring, Julie E; Cerhan, James R; Gaudet, Mia M; Giles, Graham G; Goodman, Gary; Hakansson, Niclas; Hankinson, Susan E; Helzlsouer, Kathy; Horn-Ross, Pamela L; Inoue, Manami; Krogh, Vittorio; Lof, Marie; McCullough, Marjorie L; Miller, Anthony B; Neuhouser, Marian L; Palmer, Julie R; Park, Yikyung; Robien, Kim; Rohan, Thomas E; Scarmo, Stephanie; Schairer, Catherine; Schouten, Leo J; Shikany, James M; Sieri, Sabina; Tsugane, Schoichiro; Visvanathan, Kala; Weiderpass, Elisabete; Willett, Walter C; Wolk, Alicja; Zeleniuch-Jacquotte, Anne; Zhang, Shumin M; Zhang, Xuehong; Ziegler, Regina G; Smith-Warner, Stephanie A

2013-02-06

Estrogen receptor-negative (ER(-)) breast cancer has few known or modifiable risk factors. Because ER(-) tumors account for only 15% to 20% of breast cancers, large pooled analyses are necessary to evaluate precisely the suspected inverse association between fruit and vegetable intake and risk of ER(-) breast cancer. Among 993 466 women followed for 11 to 20 years in 20 cohort studies, we documented 19 869 estrogen receptor positive (ER(+)) and 4821 ER(-) breast cancers. We calculated study-specific multivariable relative risks (RRs) and 95% confidence intervals (CIs) using Cox proportional hazards regression analyses and then combined them using a random-effects model. All statistical tests were two-sided. Total fruit and vegetable intake was statistically significantly inversely associated with risk of ER(-) breast cancer but not with risk of breast cancer overall or of ER(+) tumors. The inverse association for ER(-) tumors was observed primarily for vegetable consumption. The pooled relative risks comparing the highest vs lowest quintile of total vegetable consumption were 0.82 (95% CI = 0.74 to 0.90) for ER(-) breast cancer and 1.04 (95% CI = 0.97 to 1.11) for ER(+) breast cancer (P (common-effects) by ER status < .001). Total fruit consumption was non-statistically significantly associated with risk of ER(-) breast cancer (pooled multivariable RR comparing the highest vs lowest quintile = 0.94, 95% CI = 0.85 to 1.04). We observed no association between total fruit and vegetable intake and risk of overall breast cancer. However, vegetable consumption was inversely associated with risk of ER(-) breast cancer in our large pooled analyses.

Preoperative Carcinoembryonic Antigen and Prognosis of Colorectal Cancer. An Independent Prognostic Factor Still Reliable

PubMed Central

Li Destri, Giovanni; Rubino, Antonio Salvatore; Latino, Rosalia; Giannone, Fabio; Lanteri, Raffaele; Scilletta, Beniamino; Di Cataldo, Antonio

2015-01-01

To evaluate whether, in a sample of patients radically treated for colorectal carcinoma, the preoperative determination of the carcinoembryonic antigen (p-CEA) may have a prognostic value and constitute an independent risk factor in relation to disease-free survival. The preoperative CEA seems to be related both to the staging of colorectal neoplasia and to the patient's prognosis, although this—to date—has not been conclusively demonstrated and is still a matter of intense debate in the scientific community. This is a retrospective analysis of prospectively collected data. A total of 395 patients were radically treated for colorectal carcinoma. The preoperative CEA was statistically compared with the 2010 American Joint Committee on Cancer (AJCC) staging, the T and N parameters, and grading. All parameters recorded in our database were tested for an association with disease-free survival (DFS). Only factors significantly associated (P < 0.05) with the DFS were used to build multivariate stepwise forward logistic regression models to establish their independent predictors. A statistically significant relationship was found between p-CEA and tumor staging (P < 0.001), T (P < 0.001) and N parameters (P = 0.006). In a multivariate analysis, the independent prognostic factors found were: p-CEA, stages N1 and N2 according to AJCC, and G3 grading (grade). A statistically significant difference (P < 0.001) was evident between the DFS of patients with normal and high p-CEA levels. Preoperative CEA makes a pre-operative selection possible of those patients for whom it is likely to be able to predict a more advanced staging. PMID:25875542

Preoperative carcinoembryonic antigen and prognosis of colorectal cancer. An independent prognostic factor still reliable.

PubMed

Li Destri, Giovanni; Rubino, Antonio Salvatore; Latino, Rosalia; Giannone, Fabio; Lanteri, Raffaele; Scilletta, Beniamino; Di Cataldo, Antonio

2015-04-01

To evaluate whether, in a sample of patients radically treated for colorectal carcinoma, the preoperative determination of the carcinoembryonic antigen (p-CEA) may have a prognostic value and constitute an independent risk factor in relation to disease-free survival. The preoperative CEA seems to be related both to the staging of colorectal neoplasia and to the patient's prognosis, although this-to date-has not been conclusively demonstrated and is still a matter of intense debate in the scientific community. This is a retrospective analysis of prospectively collected data. A total of 395 patients were radically treated for colorectal carcinoma. The preoperative CEA was statistically compared with the 2010 American Joint Committee on Cancer (AJCC) staging, the T and N parameters, and grading. All parameters recorded in our database were tested for an association with disease-free survival (DFS). Only factors significantly associated (P < 0.05) with the DFS were used to build multivariate stepwise forward logistic regression models to establish their independent predictors. A statistically significant relationship was found between p-CEA and tumor staging (P < 0.001), T (P < 0.001) and N parameters (P = 0.006). In a multivariate analysis, the independent prognostic factors found were: p-CEA, stages N1 and N2 according to AJCC, and G3 grading (grade). A statistically significant difference (P < 0.001) was evident between the DFS of patients with normal and high p-CEA levels. Preoperative CEA makes a pre-operative selection possible of those patients for whom it is likely to be able to predict a more advanced staging.

Attitudes toward Advanced and Multivariate Statistics When Using Computers.

ERIC Educational Resources Information Center

Kennedy, Robert L.; McCallister, Corliss Jean

This study investigated the attitudes toward statistics of graduate students who studied advanced statistics in a course in which the focus of instruction was the use of a computer program in class. The use of the program made it possible to provide an individualized, self-paced, student-centered, and activity-based course. The three sections…

Statistics Anxiety and Worry: The Roles of Worry Beliefs, Negative Problem Orientation, and Cognitive Avoidance

ERIC Educational Resources Information Center

Williams, Amanda S.

2015-01-01

Statistics anxiety is a common problem for graduate students. This study explores the multivariate relationship between a set of worry-related variables and six types of statistics anxiety. Canonical correlation analysis indicates a significant relationship between the two sets of variables. Findings suggest that students who are more intolerant…

Basic principles of Hasse diagram technique in chemistry.

PubMed

Brüggemann, Rainer; Voigt, Kristina

2008-11-01

Principles of partial order applied to ranking are explained. The Hasse diagram technique (HDT) is the application of partial order theory based on a data matrix. In this paper, HDT is introduced in a stepwise procedure, and some elementary theorems are exemplified. The focus is to show how the multivariate character of a data matrix is realized by HDT and in which cases one should apply other mathematical or statistical methods. Many simple examples illustrate the basic theoretical ideas. Finally, it is shown that HDT is a useful alternative for the evaluation of antifouling agents, which was originally performed by amoeba diagrams.

Statistical methods and neural network approaches for classification of data from multiple sources

NASA Technical Reports Server (NTRS)

Benediktsson, Jon Atli; Swain, Philip H.

1990-01-01

Statistical methods for classification of data from multiple data sources are investigated and compared to neural network models. A problem with using conventional multivariate statistical approaches for classification of data of multiple types is in general that a multivariate distribution cannot be assumed for the classes in the data sources. Another common problem with statistical classification methods is that the data sources are not equally reliable. This means that the data sources need to be weighted according to their reliability but most statistical classification methods do not have a mechanism for this. This research focuses on statistical methods which can overcome these problems: a method of statistical multisource analysis and consensus theory. Reliability measures for weighting the data sources in these methods are suggested and investigated. Secondly, this research focuses on neural network models. The neural networks are distribution free since no prior knowledge of the statistical distribution of the data is needed. This is an obvious advantage over most statistical classification methods. The neural networks also automatically take care of the problem involving how much weight each data source should have. On the other hand, their training process is iterative and can take a very long time. Methods to speed up the training procedure are introduced and investigated. Experimental results of classification using both neural network models and statistical methods are given, and the approaches are compared based on these results.

Interfaces between statistical analysis packages and the ESRI geographic information system

NASA Technical Reports Server (NTRS)

Masuoka, E.

1980-01-01

Interfaces between ESRI's geographic information system (GIS) data files and real valued data files written to facilitate statistical analysis and display of spatially referenced multivariable data are described. An example of data analysis which utilized the GIS and the statistical analysis system is presented to illustrate the utility of combining the analytic capability of a statistical package with the data management and display features of the GIS.

Decomposing biodiversity data using the Latent Dirichlet Allocation model, a probabilistic multivariate statistical method

Treesearch

Denis Valle; Benjamin Baiser; Christopher W. Woodall; Robin Chazdon; Jerome Chave

2014-01-01

We propose a novel multivariate method to analyse biodiversity data based on the Latent Dirichlet Allocation (LDA) model. LDA, a probabilistic model, reduces assemblages to sets of distinct component communities. It produces easily interpretable results, can represent abrupt and gradual changes in composition, accommodates missing data and allows for coherent estimates...

Departure from Normality in Multivariate Normative Comparison: The Cramer Alternative for Hotelling's "T[squared]"

ERIC Educational Resources Information Center

Grasman, Raoul P. P. P.; Huizenga, Hilde M.; Geurts, Hilde M.

2010-01-01

Crawford and Howell (1998) have pointed out that the common practice of z-score inference on cognitive disability is inappropriate if a patient's performance on a task is compared with relatively few typical control individuals. Appropriate univariate and multivariate statistical tests have been proposed for these studies, but these are only valid…

Applied statistics in agricultural, biological, and environmental sciences.

USDA-ARS?s Scientific Manuscript database

Agronomic research often involves measurement and collection of multiple response variables in an effort to understand the more complex nature of the system being studied. Multivariate statistical methods encompass the simultaneous analysis of all random variables measured on each experimental or s...

A Java-based fMRI processing pipeline evaluation system for assessment of univariate general linear model and multivariate canonical variate analysis-based pipelines.

PubMed

Zhang, Jing; Liang, Lichen; Anderson, Jon R; Gatewood, Lael; Rottenberg, David A; Strother, Stephen C

2008-01-01

As functional magnetic resonance imaging (fMRI) becomes widely used, the demands for evaluation of fMRI processing pipelines and validation of fMRI analysis results is increasing rapidly. The current NPAIRS package, an IDL-based fMRI processing pipeline evaluation framework, lacks system interoperability and the ability to evaluate general linear model (GLM)-based pipelines using prediction metrics. Thus, it can not fully evaluate fMRI analytical software modules such as FSL.FEAT and NPAIRS.GLM. In order to overcome these limitations, a Java-based fMRI processing pipeline evaluation system was developed. It integrated YALE (a machine learning environment) into Fiswidgets (a fMRI software environment) to obtain system interoperability and applied an algorithm to measure GLM prediction accuracy. The results demonstrated that the system can evaluate fMRI processing pipelines with univariate GLM and multivariate canonical variates analysis (CVA)-based models on real fMRI data based on prediction accuracy (classification accuracy) and statistical parametric image (SPI) reproducibility. In addition, a preliminary study was performed where four fMRI processing pipelines with GLM and CVA modules such as FSL.FEAT and NPAIRS.CVA were evaluated with the system. The results indicated that (1) the system can compare different fMRI processing pipelines with heterogeneous models (NPAIRS.GLM, NPAIRS.CVA and FSL.FEAT) and rank their performance by automatic performance scoring, and (2) the rank of pipeline performance is highly dependent on the preprocessing operations. These results suggest that the system will be of value for the comparison, validation, standardization and optimization of functional neuroimaging software packages and fMRI processing pipelines.

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

PubMed

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

2010-10-01

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

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

USGS Publications Warehouse

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

1993-01-01

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

Multivariate analysis of heavy metal contamination using river sediment cores of Nankan River, northern Taiwan

NASA Astrophysics Data System (ADS)

Lee, An-Sheng; Lu, Wei-Li; Huang, Jyh-Jaan; Chang, Queenie; Wei, Kuo-Yen; Lin, Chin-Jung; Liou, Sofia Ya Hsuan

2016-04-01

Through the geology and climate characteristic in Taiwan, generally rivers carry a lot of suspended particles. After these particles settled, they become sediments which are good sorbent for heavy metals in river system. Consequently, sediments can be found recording contamination footprint at low flow energy region, such as estuary. Seven sediment cores were collected along Nankan River, northern Taiwan, which is seriously contaminated by factory, household and agriculture input. Physico-chemical properties of these cores were derived from Itrax-XRF Core Scanner and grain size analysis. In order to interpret these complex data matrices, the multivariate statistical techniques (cluster analysis, factor analysis and discriminant analysis) were introduced to this study. Through the statistical determination, the result indicates four types of sediment. One of them represents contamination event which shows high concentration of Cu, Zn, Pb, Ni and Fe, and low concentration of Si and Zr. Furthermore, three possible contamination sources of this type of sediment were revealed by Factor Analysis. The combination of sediment analysis and multivariate statistical techniques used provides new insights into the contamination depositional history of Nankan River and could be similarly applied to other river systems to determine the scale of anthropogenic contamination.

Hierarchical multivariate covariance analysis of metabolic connectivity.

PubMed

Carbonell, Felix; Charil, Arnaud; Zijdenbos, Alex P; Evans, Alan C; Bedell, Barry J

2014-12-01

Conventional brain connectivity analysis is typically based on the assessment of interregional correlations. Given that correlation coefficients are derived from both covariance and variance, group differences in covariance may be obscured by differences in the variance terms. To facilitate a comprehensive assessment of connectivity, we propose a unified statistical framework that interrogates the individual terms of the correlation coefficient. We have evaluated the utility of this method for metabolic connectivity analysis using [18F]2-fluoro-2-deoxyglucose (FDG) positron emission tomography (PET) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. As an illustrative example of the utility of this approach, we examined metabolic connectivity in angular gyrus and precuneus seed regions of mild cognitive impairment (MCI) subjects with low and high β-amyloid burdens. This new multivariate method allowed us to identify alterations in the metabolic connectome, which would not have been detected using classic seed-based correlation analysis. Ultimately, this novel approach should be extensible to brain network analysis and broadly applicable to other imaging modalities, such as functional magnetic resonance imaging (MRI).

Multivariate analysis of historical data (2004-2013) in assessing the possible environmental impact of the Bellolampo landfill (Palermo).

PubMed

Indelicato, Serena; Bongiorno, David; Tuzzolino, Nicola; Mannino, Maria Rosaria; Muscarella, Rosalia; Fradella, Pasquale; Gargano, Maria Elena; Nicosia, Salvatore; Ceraulo, Leopoldo

2018-03-14

Multivariate analysis was performed on a large data set of groundwater and leachate samples collected during 9 years of operation of the Bellolampo municipal solid waste landfill (located above Palermo, Italy). The aim was to obtain the most likely correlations among the data. The analysis results are presented. Groundwater samples were collected in the period 2004-2013, whereas the leachate analysis refers to the period 2006-2013. For groundwater, statistical data evaluation revealed notable differences among the samples taken from the numerous wells located around the landfill. Characteristic parameters revealed by principal component analysis (PCA) were more deeply investigated, and corresponding thematic maps were drawn. The composition of the leachate was also thoroughly investigated. Several chemical macro-descriptors were calculated, and the results are presented. A comparison of PCA results for the leachate and groundwater data clearly reveals that the groundwater's main components substantially differ from those of the leachate. This outcome strongly suggests excluding leachate permeation through the multiple landfill lining.

Enhancing Multimedia Imbalanced Concept Detection Using VIMP in Random Forests.

PubMed

Sadiq, Saad; Yan, Yilin; Shyu, Mei-Ling; Chen, Shu-Ching; Ishwaran, Hemant

2016-07-01

Recent developments in social media and cloud storage lead to an exponential growth in the amount of multimedia data, which increases the complexity of managing, storing, indexing, and retrieving information from such big data. Many current content-based concept detection approaches lag from successfully bridging the semantic gap. To solve this problem, a multi-stage random forest framework is proposed to generate predictor variables based on multivariate regressions using variable importance (VIMP). By fine tuning the forests and significantly reducing the predictor variables, the concept detection scores are evaluated when the concept of interest is rare and imbalanced, i.e., having little collaboration with other high level concepts. Using classical multivariate statistics, estimating the value of one coordinate using other coordinates standardizes the covariates and it depends upon the variance of the correlations instead of the mean. Thus, conditional dependence on the data being normally distributed is eliminated. Experimental results demonstrate that the proposed framework outperforms those approaches in the comparison in terms of the Mean Average Precision (MAP) values.

Multivariate neural biomarkers of emotional states are categorically distinct

PubMed Central

Kragel, Philip A.

2015-01-01

Understanding how emotions are represented neurally is a central aim of affective neuroscience. Despite decades of neuroimaging efforts addressing this question, it remains unclear whether emotions are represented as distinct entities, as predicted by categorical theories, or are constructed from a smaller set of underlying factors, as predicted by dimensional accounts. Here, we capitalize on multivariate statistical approaches and computational modeling to directly evaluate these theoretical perspectives. We elicited discrete emotional states using music and films during functional magnetic resonance imaging scanning. Distinct patterns of neural activation predicted the emotion category of stimuli and tracked subjective experience. Bayesian model comparison revealed that combining dimensional and categorical models of emotion best characterized the information content of activation patterns. Surprisingly, categorical and dimensional aspects of emotion experience captured unique and opposing sources of neural information. These results indicate that diverse emotional states are poorly differentiated by simple models of valence and arousal, and that activity within separable neural systems can be mapped to unique emotion categories. PMID:25813790

Neuroanatomical morphometric characterization of sex differences in youth using statistical learning.

PubMed

Sepehrband, Farshid; Lynch, Kirsten M; Cabeen, Ryan P; Gonzalez-Zacarias, Clio; Zhao, Lu; D'Arcy, Mike; Kesselman, Carl; Herting, Megan M; Dinov, Ivo D; Toga, Arthur W; Clark, Kristi A

2018-05-15

Exploring neuroanatomical sex differences using a multivariate statistical learning approach can yield insights that cannot be derived with univariate analysis. While gross differences in total brain volume are well-established, uncovering the more subtle, regional sex-related differences in neuroanatomy requires a multivariate approach that can accurately model spatial complexity as well as the interactions between neuroanatomical features. Here, we developed a multivariate statistical learning model using a support vector machine (SVM) classifier to predict sex from MRI-derived regional neuroanatomical features from a single-site study of 967 healthy youth from the Philadelphia Neurodevelopmental Cohort (PNC). Then, we validated the multivariate model on an independent dataset of 682 healthy youth from the multi-site Pediatric Imaging, Neurocognition and Genetics (PING) cohort study. The trained model exhibited an 83% cross-validated prediction accuracy, and correctly predicted the sex of 77% of the subjects from the independent multi-site dataset. Results showed that cortical thickness of the middle occipital lobes and the angular gyri are major predictors of sex. Results also demonstrated the inferential benefits of going beyond classical regression approaches to capture the interactions among brain features in order to better characterize sex differences in male and female youths. We also identified specific cortical morphological measures and parcellation techniques, such as cortical thickness as derived from the Destrieux atlas, that are better able to discriminate between males and females in comparison to other brain atlases (Desikan-Killiany, Brodmann and subcortical atlases). Copyright © 2018 Elsevier Inc. All rights reserved.

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

NASA Astrophysics Data System (ADS)

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

2008-12-01

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

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

PubMed

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

2016-10-01

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

MANCOVA for one way classification with homogeneity of regression coefficient vectors

NASA Astrophysics Data System (ADS)

Mokesh Rayalu, G.; Ravisankar, J.; Mythili, G. Y.

2017-11-01

The MANOVA and MANCOVA are the extensions of the univariate ANOVA and ANCOVA techniques to multidimensional or vector valued observations. The assumption of a Gaussian distribution has been replaced with the Multivariate Gaussian distribution for the vectors data and residual term variables in the statistical models of these techniques. The objective of MANCOVA is to determine if there are statistically reliable mean differences that can be demonstrated between groups later modifying the newly created variable. When randomization assignment of samples or subjects to groups is not possible, multivariate analysis of covariance (MANCOVA) provides statistical matching of groups by adjusting dependent variables as if all subjects scored the same on the covariates. In this research article, an extension has been made to the MANCOVA technique with more number of covariates and homogeneity of regression coefficient vectors is also tested.

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.

Noncentral Chi-Square versus Normal Distributions in Describing the Likelihood Ratio Statistic: The Univariate Case and Its Multivariate Implication

ERIC Educational Resources Information Center

Yuan, Ke-Hai

2008-01-01

In the literature of mean and covariance structure analysis, noncentral chi-square distribution is commonly used to describe the behavior of the likelihood ratio (LR) statistic under alternative hypothesis. Due to the inaccessibility of the rather technical literature for the distribution of the LR statistic, it is widely believed that the…

A scoring system for ascertainment of incident stroke; the Risk Index Score (RISc).

PubMed

Kass-Hout, T A; Moyé, L A; Smith, M A; Morgenstern, L B

2006-01-01

The main objective of this study was to develop and validate a computer-based statistical algorithm that could be translated into a simple scoring system in order to ascertain incident stroke cases using hospital admission medical records data. The Risk Index Score (RISc) algorithm was developed using data collected prospectively by the Brain Attack Surveillance in Corpus Christi (BASIC) project, 2000. The validity of RISc was evaluated by estimating the concordance of scoring system stroke ascertainment to stroke ascertainment by physician and/or abstractor review of hospital admission records. RISc was developed on 1718 randomly selected patients (training set) and then statistically validated on an independent sample of 858 patients (validation set). A multivariable logistic model was used to develop RISc and subsequently evaluated by goodness-of-fit and receiver operating characteristic (ROC) analyses. The higher the value of RISc, the higher the patient's risk of potential stroke. The study showed RISc was well calibrated and discriminated those who had potential stroke from those that did not on initial screening. In this study we developed and validated a rapid, easy, efficient, and accurate method to ascertain incident stroke cases from routine hospital admission records for epidemiologic investigations. Validation of this scoring system was achieved statistically; however, clinical validation in a community hospital setting is warranted.

Acoustic correlates of Japanese expressions associated with voice quality of male adults

NASA Astrophysics Data System (ADS)

Kido, Hiroshi; Kasuya, Hideki

2004-05-01

Japanese expressions associated with the voice quality of male adults were extracted by a series of questionnaire surveys and statistical multivariate analysis. One hundred and thirty-seven Japanese expressions were collected through the first questionnaire and careful investigations of well-established Japanese dictionaries and articles. From the second questionnaire about familiarity with each of the expressions and synonymity that were addressed to 249 subjects, 25 expressions were extracted. The third questionnaire was about an evaluation of their own voice quality. By applying a statistical clustering method and a correlation analysis to the results of the questionnaires, eight bipolar expressions and one unipolar expression were obtained. They constituted high-pitched/low-pitched, masculine/feminine, hoarse/clear, calm/excited, powerful/weak, youthful/elderly, thick/thin, tense/lax, and nasal, respectively. Acoustic correlates of each of the eight bipolar expressions were extracted by means of perceptual evaluation experiments that were made with sentence utterances of 36 males and by a statistical decision tree method. They included an average of the fundamental frequency (F0) of the utterance, speaking rate, spectral tilt, formant frequency parameter, standard deviation of F0 values, and glottal noise, when SPL of each of the stimuli was maintained identical in the perceptual experiments.

Some Tests of Randomness with Applications

DTIC Science & Technology

1981-02-01

freedom. For further details, the reader is referred to Gnanadesikan (1977, p. 169) wherein other relevant tests are also given, Graphical tests, as...sample from a gamma distri- bution. J. Am. Statist. Assoc. 71, 480-7. Gnanadesikan , R. (1977). Methods for Statistical Data Analysis of Multivariate

Statistical polarization in greenhouse gas emissions: Theory and evidence.

PubMed

Remuzgo, Lorena; Trueba, Carmen

2017-11-01

The current debate on climate change is over whether global warming can be limited in order to lessen its impacts. In this sense, evidence of a decrease in the statistical polarization in greenhouse gas (GHG) emissions could encourage countries to establish a stronger multilateral climate change agreement. Based on the interregional and intraregional components of the multivariate generalised entropy measures (Maasoumi, 1986), Gigliarano and Mosler (2009) proposed to study the statistical polarization concept from a multivariate view. In this paper, we apply this approach to study the evolution of such phenomenon in the global distribution of the main GHGs. The empirical analysis has been carried out for the time period 1990-2011, considering an endogenous grouping of countries (Aghevli and Mehran, 1981; Davies and Shorrocks, 1989). Most of the statistical polarization indices showed a slightly increasing pattern that was similar regardless of the number of groups considered. Finally, some policy implications are commented. Copyright © 2017 Elsevier Ltd. All rights reserved.

Statistical inferences for data from studies conducted with an aggregated multivariate outcome-dependent sample design.

PubMed

Lu, Tsui-Shan; Longnecker, Matthew P; Zhou, Haibo

2017-03-15

Outcome-dependent sampling (ODS) scheme is a cost-effective sampling scheme where one observes the exposure with a probability that depends on the outcome. The well-known such design is the case-control design for binary response, the case-cohort design for the failure time data, and the general ODS design for a continuous response. While substantial work has been carried out for the univariate response case, statistical inference and design for the ODS with multivariate cases remain under-developed. Motivated by the need in biological studies for taking the advantage of the available responses for subjects in a cluster, we propose a multivariate outcome-dependent sampling (multivariate-ODS) design that is based on a general selection of the continuous responses within a cluster. The proposed inference procedure for the multivariate-ODS design is semiparametric where all the underlying distributions of covariates are modeled nonparametrically using the empirical likelihood methods. We show that the proposed estimator is consistent and developed the asymptotically normality properties. Simulation studies show that the proposed estimator is more efficient than the estimator obtained using only the simple-random-sample portion of the multivariate-ODS or the estimator from a simple random sample with the same sample size. The multivariate-ODS design together with the proposed estimator provides an approach to further improve study efficiency for a given fixed study budget. We illustrate the proposed design and estimator with an analysis of association of polychlorinated biphenyl exposure to hearing loss in children born to the Collaborative Perinatal Study. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

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

PubMed Central

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

2013-01-01

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

Importance of early nutritional screening in patients with gastric cancer.

PubMed

Gavazzi, Cecilia; Colatruglio, Silvia; Sironi, Alessandro; Mazzaferro, Vincenzo; Miceli, Rosalba

2011-12-01

In the present study, we evaluated the relationship between nutritional status, disease stage and quality of life (QoL) in 100 patients recently diagnosed with gastric carcinoma. The patients' nutritional status was investigated with anthropometric, biochemical, inflammatory and functional variables; and we also evaluated the nutritional risk with the Nutritional Risk Screening 2002. Oncological staging was standard. QoL was evaluated using the Functional Assessment of Anorexia/Cachexia Therapy questionnaire. The statistical correlation between nutritional risk score (NRS) and oncological characteristics or QoL was evaluated using both univariable and multivariable analyses. Weight loss and reduction of food intake were the most frequent pathological nutritional indicators, while biochemical, inflammatory and functional variables were in the normal range. According to NRS, thirty-six patients were malnourished or at risk for malnutrition. Patients with NRS ≥ 3 presented a significantly greater percentage of stage IV gastric cancer and pathological values of C-reactive protein, while no correlation was found with the site of tumour. NRS was negatively associated with QoL (P < 0·001) and this relation was independent from oncological and inflammatory variables as confirmed by multivariable analysis. In the present study, we found that in patients with gastric cancer malnutrition is frequent at diagnosis and this is likely due to reduction in food intake. Moreover, NRS is directly correlated with tumour stage and inversely correlated with QoL, which makes it a useful tool to identify patients in need of an early nutritional intervention during oncological treatments.

Methodology to assess clinical liver safety data.

PubMed

Merz, Michael; Lee, Kwan R; Kullak-Ublick, Gerd A; Brueckner, Andreas; Watkins, Paul B

2014-11-01

Analysis of liver safety data has to be multivariate by nature and needs to take into account time dependency of observations. Current standard tools for liver safety assessment such as summary tables, individual data listings, and narratives address these requirements to a limited extent only. Using graphics in the context of a systematic workflow including predefined graph templates is a valuable addition to standard instruments, helping to ensure completeness of evaluation, and supporting both hypothesis generation and testing. Employing graphical workflows interactively allows analysis in a team-based setting and facilitates identification of the most suitable graphics for publishing and regulatory reporting. Another important tool is statistical outlier detection, accounting for the fact that for assessment of Drug-Induced Liver Injury, identification and thorough evaluation of extreme values has much more relevance than measures of central tendency in the data. Taken together, systematical graphical data exploration and statistical outlier detection may have the potential to significantly improve assessment and interpretation of clinical liver safety data. A workshop was convened to discuss best practices for the assessment of drug-induced liver injury (DILI) in clinical trials.

Live weight, carcass ultrasound images, and visual scores in Angus cattle under feeding regimes in Brazil.

PubMed

Pinto, Luís Fernando Batista; Tarouco, Jaime Urdapilleta; Pedrosa, Victor Breno; de Farias Jucá, Adriana; Leão, André Gustavo; Moita, Antonia Kécya França

2013-08-01

This study aimed to evaluate visual precocity, muscling, conformation, skeletal, and breed scores; live weights at birth, at 205, and at 550 days of age; and, besides, rib eye area and fat thickness between the 12th and 13th ribs obtained by ultrasound. Those traits were evaluated in 1,645 Angus cattle kept in five feeding conditions as follows: supplemented or non-supplemented, grazing native pasture or grazing cultivated pasture, and feedlot. Descriptive statistics, Pearson's correlations, and principal component analysis were carried out. Gender and feeding conditions were fixed effects, while animal's age and mother's weight at weaning were the covariates analyzed. Gender and feeding conditions were very significant for the studied traits, but visual scores were not influenced by gender. Animal's age and mother's weight at weaning influenced many traits and must be appropriately adjusted in the statistical models. An important correlation between visual scores, live weights, and carcass traits obtained by ultrasound was found, which can be analyzed by univariate procedure. However, the multivariate approach revealed some information that cannot be neglected in order to ensure a more detailed assessment.

Kidney transplantation from deceased donors with elevated serum creatinine.

PubMed

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

2016-12-01

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

[Prognostic value of three different staging schemes based on pN, MLR and LODDS in patients with T3 esophageal cancer].

PubMed

Wang, L; Cai, L; Chen, Q; Jiang, Y H

2017-10-23

Objective: To evaluate the prognostic value of three different staging schemes based on positive lymph nodes (pN), metastatic lymph nodes ratio (MLR) and log odds of positive lymph nodes (LODDS) in patients with T3 esophageal cancer. Methods: From 2007 to 2014, clinicopathological characteristics of 905 patients who were pathologically diagnosed as T3 esophageal cancer and underwent radical esophagectomy in Zhejiang Cancer Hospital were retrospectively analyzed. Kaplan-Meier curves and Multivariate Cox proportional hazards models were used to evaluate the independent prognostic factors. The values of three lymph node staging schemes for predicting 5-year survival were analyzed by using receiver operating characteristic (ROC) curves. Results: The 1-, 3- and 5-year overall survival rates of patients with T3 esophageal cancer were 80.9%, 50.0% and 38.4%, respectively. Multivariate analysis showed that MLR stage, LODDS stage and differentiation were independent prognostic survival factors ( P <0.05 for all). ROC curves showed that the area under the curve of pN stage, MLR stage, LODDS stage was 0.607, 0.613 and 0.618, respectively. However, the differences were not statistically significant ( P >0.05). Conclusions: LODDS is an independent prognostic factor for patients with T3 esophageal cancer. The value of LODDS staging system may be superior to pN staging system for evaluating the prognosis of these patients.

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.

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

PubMed

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

2017-03-01

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

Post-processing of multi-hydrologic model simulations for improved streamflow projections

NASA Astrophysics Data System (ADS)

khajehei, sepideh; Ahmadalipour, Ali; Moradkhani, Hamid

2016-04-01

Hydrologic model outputs are prone to bias and uncertainty due to knowledge deficiency in model and data. Uncertainty in hydroclimatic projections arises due to uncertainty in hydrologic model as well as the epistemic or aleatory uncertainties in GCM parameterization and development. This study is conducted to: 1) evaluate the recently developed multi-variate post-processing method for historical simulations and 2) assess the effect of post-processing on uncertainty and reliability of future streamflow projections in both high-flow and low-flow conditions. The first objective is performed for historical period of 1970-1999. Future streamflow projections are generated for 10 statistically downscaled GCMs from two widely used downscaling methods: Bias Corrected Statistically Downscaled (BCSD) and Multivariate Adaptive Constructed Analogs (MACA), over the period of 2010-2099 for two representative concentration pathways of RCP4.5 and RCP8.5. Three semi-distributed hydrologic models were employed and calibrated at 1/16 degree latitude-longitude resolution for over 100 points across the Columbia River Basin (CRB) in the pacific northwest USA. Streamflow outputs are post-processed through a Bayesian framework based on copula functions. The post-processing approach is relying on a transfer function developed based on bivariate joint distribution between the observation and simulation in historical period. Results show that application of post-processing technique leads to considerably higher accuracy in historical simulations and also reducing model uncertainty in future streamflow projections.

Landslide susceptibility modeling applying machine learning methods: A case study from Longju in the Three Gorges Reservoir area, China

NASA Astrophysics Data System (ADS)

Zhou, Chao; Yin, Kunlong; Cao, Ying; Ahmed, Bayes; Li, Yuanyao; Catani, Filippo; Pourghasemi, Hamid Reza

2018-03-01

Landslide is a common natural hazard and responsible for extensive damage and losses in mountainous areas. In this study, Longju in the Three Gorges Reservoir area in China was taken as a case study for landslide susceptibility assessment in order to develop effective risk prevention and mitigation strategies. To begin, 202 landslides were identified, including 95 colluvial landslides and 107 rockfalls. Twelve landslide causal factor maps were prepared initially, and the relationship between these factors and each landslide type was analyzed using the information value model. Later, the unimportant factors were selected and eliminated using the information gain ratio technique. The landslide locations were randomly divided into two groups: 70% for training and 30% for verifying. Two machine learning models: the support vector machine (SVM) and artificial neural network (ANN), and a multivariate statistical model: the logistic regression (LR), were applied for landslide susceptibility modeling (LSM) for each type. The LSM index maps, obtained from combining the assessment results of the two landslide types, were classified into five levels. The performance of the LSMs was evaluated using the receiver operating characteristics curve and Friedman test. Results show that the elimination of noise-generating factors and the separated modeling of each landslide type have significantly increased the prediction accuracy. The machine learning models outperformed the multivariate statistical model and SVM model was found ideal for the case study area.

Multivariate analysis of chromatographic retention data as a supplementary means for grouping structurally related compounds.

PubMed

Fasoula, S; Zisi, Ch; Sampsonidis, I; Virgiliou, Ch; Theodoridis, G; Gika, H; Nikitas, P; Pappa-Louisi, A

2015-03-27

In the present study a series of 45 metabolite standards belonging to four chemically similar metabolite classes (sugars, amino acids, nucleosides and nucleobases, and amines) was subjected to LC analysis on three HILIC columns under 21 different gradient conditions with the aim to explore whether the retention properties of these analytes are determined from the chemical group they belong. Two multivariate techniques, principal component analysis (PCA) and discriminant analysis (DA), were used for statistical evaluation of the chromatographic data and extraction similarities between chemically related compounds. The total variance explained by the first two principal components of PCA was found to be about 98%, whereas both statistical analyses indicated that all analytes are successfully grouped in four clusters of chemical structure based on the retention obtained in four or at least three chromatographic runs, which, however should be performed on two different HILIC columns. Moreover, leave-one-out cross-validation of the above retention data set showed that the chemical group in which an analyte belongs can be 95.6% correctly predicted when the analyte is subjected to LC analysis under the same four or three experimental conditions as the all set of analytes was run beforehand. That, in turn, may assist with disambiguation of analyte identification in complex biological extracts. Copyright © 2015 Elsevier B.V. All rights reserved.

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

PubMed

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

2015-12-01

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

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

An extended data mining method for identifying differentially expressed assay-specific signatures in functional genomic studies.

PubMed

Rollins, Derrick K; Teh, Ailing

2010-12-17

Microarray data sets provide relative expression levels for thousands of genes for a small number, in comparison, of different experimental conditions called assays. Data mining techniques are used to extract specific information of genes as they relate to the assays. The multivariate statistical technique of principal component analysis (PCA) has proven useful in providing effective data mining methods. This article extends the PCA approach of Rollins et al. to the development of ranking genes of microarray data sets that express most differently between two biologically different grouping of assays. This method is evaluated on real and simulated data and compared to a current approach on the basis of false discovery rate (FDR) and statistical power (SP) which is the ability to correctly identify important genes. This work developed and evaluated two new test statistics based on PCA and compared them to a popular method that is not PCA based. Both test statistics were found to be effective as evaluated in three case studies: (i) exposing E. coli cells to two different ethanol levels; (ii) application of myostatin to two groups of mice; and (iii) a simulated data study derived from the properties of (ii). The proposed method (PM) effectively identified critical genes in these studies based on comparison with the current method (CM). The simulation study supports higher identification accuracy for PM over CM for both proposed test statistics when the gene variance is constant and for one of the test statistics when the gene variance is non-constant. PM compares quite favorably to CM in terms of lower FDR and much higher SP. Thus, PM can be quite effective in producing accurate signatures from large microarray data sets for differential expression between assays groups identified in a preliminary step of the PCA procedure and is, therefore, recommended for use in these applications.

Empirical performance of the self-controlled case series design: lessons for developing a risk identification and analysis system.

PubMed

Suchard, Marc A; Zorych, Ivan; Simpson, Shawn E; Schuemie, Martijn J; Ryan, Patrick B; Madigan, David

2013-10-01

The self-controlled case series (SCCS) offers potential as an statistical method for risk identification involving medical products from large-scale observational healthcare data. However, analytic design choices remain in encoding the longitudinal health records into the SCCS framework and its risk identification performance across real-world databases is unknown. To evaluate the performance of SCCS and its design choices as a tool for risk identification in observational healthcare data. We examined the risk identification performance of SCCS across five design choices using 399 drug-health outcome pairs in five real observational databases (four administrative claims and one electronic health records). In these databases, the pairs involve 165 positive controls and 234 negative controls. We also consider several synthetic databases with known relative risks between drug-outcome pairs. We evaluate risk identification performance through estimating the area under the receiver-operator characteristics curve (AUC) and bias and coverage probability in the synthetic examples. The SCCS achieves strong predictive performance. Twelve of the twenty health outcome-database scenarios return AUCs >0.75 across all drugs. Including all adverse events instead of just the first per patient and applying a multivariate adjustment for concomitant drug use are the most important design choices. However, the SCCS as applied here returns relative risk point-estimates biased towards the null value of 1 with low coverage probability. The SCCS recently extended to apply a multivariate adjustment for concomitant drug use offers promise as a statistical tool for risk identification in large-scale observational healthcare databases. Poor estimator calibration dampens enthusiasm, but on-going work should correct this short-coming.

Evaluation of the Risk of Grade 3 Oral and Pharyngeal Dysphagia Using Atlas-Based Method and Multivariate Analyses of Individual Patient Dose Distributions

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

Otter, Sophie; Schick, Ulrike; Gulliford, Sarah

Purpose: The study aimed to apply the atlas of complication incidence (ACI) method to patients receiving radical treatment for head and neck squamous cell carcinomas (HNSCC), to generate constraints based on dose-volume histograms (DVHs), and to identify clinical and dosimetric parameters that predict the risk of grade 3 oral mucositis (g3OM) and pharyngeal dysphagia (g3PD). Methods and Materials: Oral and pharyngeal mucosal DVHs were generated for 253 patients who received radiation (RT) or chemoradiation (CRT). They were used to produce ACI for g3OM and g3PD. Multivariate analysis (MVA) of the effect of dosimetry, clinical, and patient-related variables was performed usingmore » logistic regression and bootstrapping. Receiver operating curve (ROC) analysis was also performed, and the Youden index was used to find volume constraints that discriminated between volumes that predicted for toxicity. Results: We derived statistically significant dose-volume constraints for g3OM over the range v28 to v70. Only 3 statistically significant constraints were derived for g3PD v67, v68, and v69. On MVA, mean dose to the oral mucosa predicted for g3OM and concomitant chemotherapy and mean dose to the inferior constrictor (IC) predicted for g3PD. Conclusions: We have used the ACI method to evaluate incidences of g3OM and g3PD and ROC analysis to generate constraints to predict g3OM and g3PD derived from entire individual patient DVHs. On MVA, the strongest predictors were radiation dose (for g3OM) and concomitant chemotherapy (for g3PD).« less

Interval between surgery and radiotherapy: effect on local control of soft tissue sarcoma.

PubMed

Ballo, Matthew T; Zagars, Gunar K; Cormier, Janice N; Hunt, Kelly K; Feig, Barry W; Patel, Shreyaskumar R; Pisters, Peter W T

2004-04-01

To evaluate the clinical significance of the interval between surgery and postoperative radiotherapy (RT) for patients with soft tissue sarcoma. The records of 799 patients who underwent postoperative RT for soft tissue sarcoma between 1960 and 2000 were retrospectively reviewed. Univariate and multivariate analyses were used to evaluate the potential impact of the timing of postoperative RT on the rate of local control (LC). The actuarial overall LC rate was 79% at 10 years and 78% at 15 years. Univariate analysis indicated that the factors associated with an inferior 10-year LC rate were positive resection margins (p <0.0001); treatment for recurrent disease (p <0.0001); primary location in the head and neck or deep trunk (p <0.0001); age >64 years (p <0.0001); histopathologic subtype of malignant fibrous histiocytoma, neurogenic sarcoma, or epithelioid sarcoma (p = 0.01); tumor size >10 cm (p = 0.02); postoperative radiation dose <64 Gy (p = 0.03); and high histologic grade (p = 0.05). On multivariate analysis, all these factors remained statistically significant, except for high histologic grade and large size. A delay between surgery and the start of RT of >30 days was associated with a decreased 10-year LC rate, but this association was not statistically significant (76% vs. 83%, p = 0.07). The potential association between RT delay and inferior LC could be explained by an imbalance in the distribution of other prognostic factors. The interval between surgery and RT did not significantly impact the 10-year LC rate. These findings indicate that an RT delay should not be viewed as an independent adverse factor for LC and that treatment intensification may not be necessary for patients in whom a treatment delay has already occurred.

Determination of dominant biogeochemical processes in a contaminated aquifer-wetland system using multivariate statistical analysis

USGS Publications Warehouse

Baez-Cazull, S. E.; McGuire, J.T.; Cozzarelli, I.M.; Voytek, M.A.

2008-01-01

Determining the processes governing aqueous biogeochemistry in a wetland hydrologically linked to an underlying contaminated aquifer is challenging due to the complex exchange between the systems and their distinct responses to changes in precipitation, recharge, and biological activities. To evaluate temporal and spatial processes in the wetland-aquifer system, water samples were collected using cm-scale multichambered passive diffusion samplers (peepers) to span the wetland-aquifer interface over a period of 3 yr. Samples were analyzed for major cations and anions, methane, and a suite of organic acids resulting in a large dataset of over 8000 points, which was evaluated using multivariate statistics. Principal component analysis (PCA) was chosen with the purpose of exploring the sources of variation in the dataset to expose related variables and provide insight into the biogeochemical processes that control the water chemistry of the system. Factor scores computed from PCA were mapped by date and depth. Patterns observed suggest that (i) fermentation is the process controlling the greatest variability in the dataset and it peaks in May; (ii) iron and sulfate reduction were the dominant terminal electron-accepting processes in the system and were associated with fermentation but had more complex seasonal variability than fermentation; (iii) methanogenesis was also important and associated with bacterial utilization of minerals as a source of electron acceptors (e.g., barite BaSO4); and (iv) seasonal hydrological patterns (wet and dry periods) control the availability of electron acceptors through the reoxidation of reduced iron-sulfur species enhancing iron and sulfate reduction. Copyright ?? 2008 by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America. All rights reserved.

Predictors of Mortality in the Critically Ill Cirrhotic Patient: Is the Model for End-Stage Liver Disease Enough?

PubMed

Annamalai, Alagappan; Harada, Megan Y; Chen, Melissa; Tran, Tram; Ko, Ara; Ley, Eric J; Nuno, Miriam; Klein, Andrew; Nissen, Nicholas; Noureddin, Mazen

2017-03-01

Critically ill cirrhotics require liver transplantation urgently, but are at high risk for perioperative mortality. The Model for End-stage Liver Disease (MELD) score, recently updated to incorporate serum sodium, estimates survival probability in patients with cirrhosis, but needs additional evaluation in the critically ill. The purpose of this study was to evaluate the predictive power of ICU admission MELD scores and identify clinical risk factors associated with increased mortality. This was a retrospective review of cirrhotic patients admitted to the ICU between January 2011 and December 2014. Patients who were discharged or underwent transplantation (survivors) were compared with those who died (nonsurvivors). Demographic characteristics, admission MELD scores, and clinical risk factors were recorded. Multivariate regression was used to identify independent predictors of mortality, and measures of model performance were assessed to determine predictive accuracy. Of 276 patients who met inclusion criteria, 153 were considered survivors and 123 were nonsurvivors. Survivor and nonsurvivor cohorts had similar demographic characteristics. Nonsurvivors had increased MELD, gastrointestinal bleeding, infection, mechanical ventilation, encephalopathy, vasopressors, dialysis, renal replacement therapy, requirement of blood products, and ICU length of stay. The MELD demonstrated low predictive power (c-statistic 0.73). Multivariate analysis identified MELD score (adjusted odds ratio [AOR] = 1.05), mechanical ventilation (AOR = 4.55), vasopressors (AOR = 3.87), and continuous renal replacement therapy (AOR = 2.43) as independent predictors of mortality, with stronger predictive accuracy (c-statistic 0.87). The MELD demonstrated relatively poor predictive accuracy in critically ill patients with cirrhosis and might not be the best indicator for prognosis in the ICU population. Prognostic accuracy is significantly improved when variables indicating organ support (mechanical ventilation, vasopressors, and continuous renal replacement therapy) are included in the model. Copyright © 2016. Published by Elsevier Inc.

Risk factors for hydrocephalus and neurological deficit in children born with an encephalocele.

PubMed

Da Silva, Stephanie L; Jeelani, Yasser; Dang, Ha; Krieger, Mark D; McComb, J Gordon

2015-04-01

There is a known association of hydrocephalus with encephaloceles. Risk factors for hydrocephalus and neurological deficit were ascertained in a series of patients born with an encephalocele. A retrospective analysis was undertaken of patients treated for encephaloceles at Children's Hospital Los Angeles between 1994 and 2012. The following factors were evaluated for their prognostic value: age at presentation, sex, location of encephalocele, size, contents, microcephaly, presence of hydrocephalus, CSF leak, associated cranial anomalies, and neurological outcome. Seventy children were identified, including 38 girls and 32 boys. The median age at presentation was 2 months. The mean follow-up duration was 3.7 years. Encephalocele location was classified as anterior (n = 14) or posterior (n = 56) to the coronal suture. The average maximum encephalocele diameter was 4 cm (range 0.5-23 cm). Forty-seven encephaloceles contained neural tissue. Eight infants presented at birth with CSF leaking from the encephalocele, with 1 being infected. Six patients presented with hydrocephalus, while 11 developed progressive hydrocephalus postoperatively. On univariate analysis, the presence of neural tissue, cranial anomalies, encephalocele size of at least 2 cm, seizure disorder, and microcephaly were each positively associated with hydrocephalus. On multivariate logistic regression modeling, the single prognostic factor for hydrocephalus of borderline statistical significance was the presence of neural tissue (odds ratio [OR] = 5.8, 95% confidence interval [CI] = 0.8-74.0). Fourteen patients had severe developmental delay, 28 had mild/moderate delay, and 28 were neurologically normal. On univariate analysis, the presence of cranial anomalies, larger size of encephalocele, hydrocephalus, and microcephaly were positively associated with neurological deficit. In the multivariable model, the only statistically significant prognostic factor for neurological deficit was presence of hydrocephalus (OR 17.2, 95% CI 1.7-infinity). In multivariate models, the presence of neural tissue was borderline significantly associated with hydrocephalus and the presence of hydrocephalus was significantly associated with neurological deficit. The location of the encephalocele did not have a statistically significant association with incidence of hydrocephalus or neurological deficit. In contrast to modestly good/fair neurological outcome in children with an encephalocele without hydrocephalus, the presence of hydrocephalus resulted in a far worse neurological outcome.

Analysis of river pollution data from low-flow period by means of multivariate techniques: a case study from the oil-shale industry region, northeastern Estonia.

PubMed

Truu, Jaak; Heinaru, Eeva; Talpsep, Ene; Heinaru, Ain

2002-01-01

The oil-shale industry has created serious pollution problems in northeastern Estonia. Untreated, phenol-rich leachate from semi-coke mounds formed as a by-product of oil-shale processing is discharged into the Baltic Sea via channels and rivers. An exploratory analysis of water chemical and microbiological data sets from the low-flow period was carried out using different multivariate analysis techniques. Principal component analysis allowed us to distinguish different locations in the river system. The riverine microbial community response to water chemical parameters was assessed by co-inertia analysis. Water pH, COD and total nitrogen were negatively related to the number of biodegradative bacteria, while oxygen concentration promoted the abundance of these bacteria. The results demonstrate the utility of multivariate statistical techniques as tools for estimating the magnitude and extent of pollution based on river water chemical and microbiological parameters. An evaluation of river chemical and microbiological data suggests that the ambient natural attenuation mechanisms only partly eliminate pollutants from river water, and that a sufficient reduction of more recalcitrant compounds could be achieved through the reduction of wastewater discharge from the oil-shale chemical industry into the rivers.

Multivariate Analysis of Effects of Asthmatic Patient Respiratory Profiles on the In Vitro Performance of a Reservoir Multidose and a Capsule-Based Dry Powder Inhaler.

PubMed

Buttini, Francesca; Pasquali, Irene; Brambilla, Gaetano; Copelli, Diego; Alberi, Massimiliano Dagli; Balducci, Anna Giulia; Bettini, Ruggero; Sisti, Viviana

2016-03-01

The aim of this work was to evaluate the effect of two different dry powder inhalers, of the NGI induction port and Alberta throat and of the actual inspiratory profiles of asthmatic patients on in-vitro drug inhalation performances. The two devices considered were a reservoir multidose and a capsule-based inhaler. The formulation used to test the inhalers was a combination of formoterol fumarate and beclomethasone dipropionate. A breath simulator was used to mimic inhalatory patterns previously determined in vivo. A multivariate approach was adopted to estimate the significance of the effect of the investigated variables in the explored domain. Breath simulator was a useful tool to mimic in vitro the in vivo inspiratory profiles of asthmatic patients. The type of throat coupled with the impactor did not affect the aerodynamic distribution of the investigated formulation. However, the type of inhaler and inspiratory profiles affected the respirable dose of drugs. The multivariate statistical approach demonstrated that the multidose inhaler, released efficiently a high fine particle mass independently from the inspiratory profiles adopted. Differently, the single dose capsule inhaler, showed a significant decrease of fine particle mass of both drugs when the device was activated using the minimum inspiratory volume (592 mL).

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…

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

ERIC Educational Resources Information Center

Magis, David; De Boeck, Paul

2011-01-01

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

Uses of Multivariate Analytical Techniques in Online and Blended Business Education: An Assessment of Current Practice and Recommendations for Future Research

ERIC Educational Resources Information Center

Arbaugh, J. B.; Hwang, Alvin

2013-01-01

Seeking to assess the analytical rigor of empirical research in management education, this article reviews the use of multivariate statistical techniques in 85 studies of online and blended management education over the past decade and compares them with prescriptions offered by both the organization studies and educational research communities.…

On Some Multiple Decision Problems

DTIC Science & Technology

1976-08-01

parameter space. Some recent results in the area of subset selection formulation are Gnanadesikan and Gupta [28], Gupta and Studden [43], Gupta and...York, pp. 363-376. [27) Gnanadesikan , M. (1966). Some Selection and Ranking Procedures for Multivariate Normal Populations. Ph.D. Thesis. Dept. of...Statist., Purdue Univ., West Lafayette, Indiana 47907. [28) Gnanadesikan , M. and Gupta, S. S. (1970). Selection procedures for multivariate normal

Identifying pleiotropic genes in genome-wide association studies from related subjects using the linear mixed model and Fisher combination function.

PubMed

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

2017-08-24

A multivariate genome-wide association test is proposed for analyzing data on multivariate quantitative phenotypes collected from related subjects. The proposed method is a two-step approach. The first step models the association between the genotype and marginal phenotype using a linear mixed model. The second step uses the correlation between residuals of the linear mixed model to estimate the null distribution of the Fisher combination test statistic. The simulation results show that the proposed method controls the type I error rate and is more powerful than the marginal tests across different population structures (admixed or non-admixed) and relatedness (related or independent). The statistical analysis on the database of the Study of Addiction: Genetics and Environment (SAGE) demonstrates that applying the multivariate association test may facilitate identification of the pleiotropic genes contributing to the risk for alcohol dependence commonly expressed by four correlated phenotypes. This study proposes a multivariate method for identifying pleiotropic genes while adjusting for cryptic relatedness and population structure between subjects. The two-step approach is not only powerful but also computationally efficient even when the number of subjects and the number of phenotypes are both very large.

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.

Multiple Hypothesis Testing for Experimental Gingivitis Based on Wilcoxon Signed Rank Statistics

PubMed Central

Preisser, John S.; Sen, Pranab K.; Offenbacher, Steven

2011-01-01

Dental research often involves repeated multivariate outcomes on a small number of subjects for which there is interest in identifying outcomes that exhibit change in their levels over time as well as to characterize the nature of that change. In particular, periodontal research often involves the analysis of molecular mediators of inflammation for which multivariate parametric methods are highly sensitive to outliers and deviations from Gaussian assumptions. In such settings, nonparametric methods may be favored over parametric ones. Additionally, there is a need for statistical methods that control an overall error rate for multiple hypothesis testing. We review univariate and multivariate nonparametric hypothesis tests and apply them to longitudinal data to assess changes over time in 31 biomarkers measured from the gingival crevicular fluid in 22 subjects whereby gingivitis was induced by temporarily withholding tooth brushing. To identify biomarkers that can be induced to change, multivariate Wilcoxon signed rank tests for a set of four summary measures based upon area under the curve are applied for each biomarker and compared to their univariate counterparts. Multiple hypothesis testing methods with choice of control of the false discovery rate or strong control of the family-wise error rate are examined. PMID:21984957

An Evaluation of the Euroncap Crash Test Safety Ratings in the Real World

PubMed Central

Segui-Gomez, Maria; Lopez-Valdes, Francisco J.; Frampton, Richard

2007-01-01

We investigated whether the rating obtained in the EuroNCAP test procedures correlates with injury protection to vehicle occupants in real crashes using data in the UK Cooperative Crash Injury Study (CCIS) database from 1996 to 2005. Multivariate Poisson regression models were developed, using the Abbreviated Injury Scale (AIS) score by body region as the dependent variable and the EuroNCAP score for that particular body region, seat belt use, mass ratio and Equivalent Test Speed (ETS) as independent variables. Our models identified statistically significant relationships between injury severity and safety belt use, mass ratio and ETS. We could not identify any statistically significant relationships between the EuroNCAP body region scores and real injury outcome except for the protection to pelvis-femur-knee in frontal impacts where scoring “green” is significantly better than scoring “yellow” or “red”.

Optimization of Dissolution Compartments in a Biorelevant Dissolution Apparatus Golem v2, Supported by Multivariate Analysis.

PubMed

Stupák, Ivan; Pavloková, Sylvie; Vysloužil, Jakub; Dohnal, Jiří; Čulen, Martin

2017-11-23

Biorelevant dissolution instruments represent an important tool for pharmaceutical research and development. These instruments are designed to simulate the dissolution of drug formulations in conditions most closely mimicking the gastrointestinal tract. In this work, we focused on the optimization of dissolution compartments/vessels for an updated version of the biorelevant dissolution apparatus-Golem v2. We designed eight compartments of uniform size but different inner geometry. The dissolution performance of the compartments was tested using immediate release caffeine tablets and evaluated by standard statistical methods and principal component analysis. Based on two phases of dissolution testing (using 250 and 100 mL of dissolution medium), we selected two compartment types yielding the highest measurement reproducibility. We also confirmed a statistically ssignificant effect of agitation rate and dissolution volume on the extent of drug dissolved and measurement reproducibility.

Mortality of aircraft maintenance workers exposed to trichloroethylene and other hydrocarbons and chemicals: extended follow up

PubMed Central

Radican, Larry; Blair, Aaron; Stewart, Patricia; Wartenberg, Daniel

2009-01-01

Objective To extend follow-up of 14,455 workers from 1990 to 2000, and evaluate mortality risk from exposure to trichloroethylene (TCE) and other chemicals. Methods Multivariable Cox models were used to estimate relative risk for exposed vs. unexposed workers based on previously developed exposure surrogates. Results Among TCE exposed workers, there was no statistically significant increased risk of all-cause mortality (RR=1.04) or death from all cancers (RR=1.03). Exposure-response gradients for TCE were relatively flat and did not materially change since 1990. Statistically significant excesses were found for several chemical exposure subgroups and causes, and were generally consistent with the previous follow up. Conclusions Patterns of mortality have not changed substantially since 1990. While positive associations with several cancers were observed, and are consistent with the published literature, interpretation is limited due to the small numbers of events for specific exposures. PMID:19001957

Evaluating physical habitat and water chemistry data from statewide stream monitoring programs to establish least-impacted conditions in Washington State

USGS Publications Warehouse

Wilmoth, Siri K.; Irvine, Kathryn M.; Larson, Chad

2015-01-01

Various GIS-generated land-use predictor variables, physical habitat metrics, and water chemistry variables from 75 reference streams and 351 randomly sampled sites throughout Washington State were evaluated for effectiveness at discriminating reference from random sites within level III ecoregions. A combination of multivariate clustering and ordination techniques were used. We describe average observed conditions for a subset of predictor variables as well as proposing statistical criteria for establishing reference conditions for stream habitat in Washington. Using these criteria, we determined whether any of the random sites met expectations for reference condition and whether any of the established reference sites failed to meet expectations for reference condition. Establishing these criteria will set a benchmark from which future data will be compared.

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.

Randomized clinical trial of encapsulated and hand-mixed glass-ionomer ART restorations: one-year follow-up.

PubMed

Freitas, Maria Cristina Carvalho de Almendra; Fagundes, Ticiane Cestari; Modena, Karin Cristina da Silva; Cardia, Guilherme Saintive; Navarro, Maria Fidela de Lima

2018-01-18

This prospective, randomized, split-mouth clinical trial evaluated the clinical performance of conventional glass ionomer cement (GIC; Riva Self-Cure, SDI), supplied in capsules or in powder/liquid kits and placed in Class I cavities in permanent molars by the Atraumatic Restorative Treatment (ART) approach. A total of 80 restorations were randomly placed in 40 patients aged 11-15 years. Each patient received one restoration with each type of GIC. The restorations were evaluated after periods of 15 days (baseline), 6 months, and 1 year, according to ART criteria. Wilcoxon matched pairs, multivariate logistic regression, and Gehan-Wilcoxon tests were used for statistical analysis. Patients were evaluated after 15 days (n=40), 6 months (n=34), and 1 year (n=29). Encapsulated GICs showed significantly superior clinical performance compared with hand-mixed GICs at baseline (p=0.017), 6 months (p=0.001), and 1 year (p=0.026). For hand-mixed GIC, a statistically significant difference was only observed over the period of baseline to 1 year (p=0.001). Encapsulated GIC presented statistically significant differences for the following periods: 6 months to 1 year (p=0.028) and baseline to 1 year (p=0.002). Encapsulated GIC presented superior cumulative survival rate than hand-mixed GIC over one year. Importantly, both GICs exhibited decreased survival over time. Encapsulated GIC promoted better ART performance, with an annual failure rate of 24%; in contrast, hand-mixed GIC demonstrated a failure rate of 42%.

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

NASA Astrophysics Data System (ADS)

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

2014-12-01

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

Multivariate statistical analysis to investigate the subduction zone parameters favoring the occurrence of giant megathrust earthquakes

NASA Astrophysics Data System (ADS)

Brizzi, S.; Sandri, L.; Funiciello, F.; Corbi, F.; Piromallo, C.; Heuret, A.

2018-03-01

The observed maximum magnitude of subduction megathrust earthquakes is highly variable worldwide. One key question is which conditions, if any, favor the occurrence of giant earthquakes (Mw ≥ 8.5). Here we carry out a multivariate statistical study in order to investigate the factors affecting the maximum magnitude of subduction megathrust earthquakes. We find that the trench-parallel extent of subduction zones and the thickness of trench sediments provide the largest discriminating capability between subduction zones that have experienced giant earthquakes and those having significantly lower maximum magnitude. Monte Carlo simulations show that the observed spatial distribution of giant earthquakes cannot be explained by pure chance to a statistically significant level. We suggest that the combination of a long subduction zone with thick trench sediments likely promotes a great lateral rupture propagation, characteristic of almost all giant earthquakes.

Comparative Research of Navy Voluntary Education at Operational Commands

DTIC Science & Technology

2017-03-01

return on investment, ROI, logistic regression, multivariate analysis, descriptive statistics, Markov, time-series, linear programming 15. NUMBER...21  B.  DESCRIPTIVE STATISTICS TABLES ...............................................25  C.  PRIVACY CONSIDERATIONS...THIS PAGE INTENTIONALLY LEFT BLANK xi LIST OF TABLES Table 1.  Variables and Descriptions . Adapted from NETC (2016). .......................21

Spatial Dynamics and Determinants of County-Level Education Expenditure in China

ERIC Educational Resources Information Center

Gu, Jiafeng

2012-01-01

In this paper, a multivariate spatial autoregressive model of local public education expenditure determination with autoregressive disturbance is developed and estimated. The existence of spatial interdependence is tested using Moran's I statistic and Lagrange multiplier test statistics for both the spatial error and spatial lag models. The full…

Establishing Benchmarks for Outcome Indicators: A Statistical Approach to Developing Performance Standards.

ERIC Educational Resources Information Center

Henry, Gary T.; And Others

1992-01-01

A statistical technique is presented for developing performance standards based on benchmark groups. The benchmark groups are selected using a multivariate technique that relies on a squared Euclidean distance method. For each observation unit (a school district in the example), a unique comparison group is selected. (SLD)

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

EPA Science Inventory

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

Challenging Conventional Wisdom for Multivariate Statistical Models with Small Samples

ERIC Educational Resources Information Center

McNeish, Daniel

2017-01-01

In education research, small samples are common because of financial limitations, logistical challenges, or exploratory studies. With small samples, statistical principles on which researchers rely do not hold, leading to trust issues with model estimates and possible replication issues when scaling up. Researchers are generally aware of such…

[Monitoring method of extraction process for Schisandrae Chinensis Fructus based on near infrared spectroscopy and multivariate statistical process control].

PubMed

Xu, Min; Zhang, Lei; Yue, Hong-Shui; Pang, Hong-Wei; Ye, Zheng-Liang; Ding, Li

2017-10-01

To establish an on-line monitoring method for extraction process of Schisandrae Chinensis Fructus, the formula medicinal material of Yiqi Fumai lyophilized injection by combining near infrared spectroscopy with multi-variable data analysis technology. The multivariate statistical process control (MSPC) model was established based on 5 normal batches in production and 2 test batches were monitored by PC scores, DModX and Hotelling T2 control charts. The results showed that MSPC model had a good monitoring ability for the extraction process. The application of the MSPC model to actual production process could effectively achieve on-line monitoring for extraction process of Schisandrae Chinensis Fructus, and can reflect the change of material properties in the production process in real time. This established process monitoring method could provide reference for the application of process analysis technology in the process quality control of traditional Chinese medicine injections. Copyright© by the Chinese Pharmaceutical Association.

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.

Construct validity of the Groningen Frailty Indicator established in a large sample of home-dwelling elderly persons: Evidence of stability across age and gender.

PubMed

Peters, L L; Boter, H; Burgerhof, J G M; Slaets, J P J; Buskens, E

2015-09-01

The primary objective of the present study was to evaluate the validity of the Groningen Frailty Indicator (GFI) in a sample of Dutch elderly persons participating in LifeLines, a large population-based cohort study. Additional aims were to assess differences between frail and non-frail elderly and examine which individual characteristics were associated with frailty. By December 2012, 5712 elderly persons were enrolled in LifeLines and complied with the inclusion criteria of the present study. Mann-Whitney U or Kruskal-Wallis tests were used to assess the variability of GFI-scores among elderly subgroups that differed in demographic characteristics, morbidity, obesity, and healthcare utilization. Within subgroups Kruskal-Wallis tests were also used to examine differences in GFI-scores across age groups. Multivariate logistic regression analyses were performed to assess associations between individual characteristics and frailty. The GFI discriminated between subgroups: statistically significantly higher GFI-median scores (interquartile range) were found in e.g. males (1 [0-2]), the oldest old (2 [1-3]), in elderly who were single (1 [0-2]), with lower socio economic status (1 [0-3]), with increasing co-morbidity (2 [1-3]), who were obese (2 [1-3]), and used more healthcare (2 [1-4]). Overall age had an independent and statistically significant association with GFI scores. Compared with the non-frail, frail elderly persons experienced statistically significantly more chronic stress and more social/psychological related problems. In the multivariate logistic regression model, psychological morbidity had the strongest association with frailty. The present study supports the construct validity of the GFI and provides an insight in the characteristics of (non)frail community-dwelling elderly persons participating in LifeLines. Copyright © 2015 Elsevier Inc. All rights reserved.

MUC4: a novel prognostic factor of oral squamous cell carcinoma.

PubMed

Hamada, Tomofumi; Wakamatsu, Tsunenobu; Miyahara, Mayumi; Nagata, Satoshi; Nomura, Masahiro; Kamikawa, Yoshiaki; Yamada, Norishige; Batra, Surinder K; Yonezawa, Suguru; Sugihara, Kazumasa

2012-04-15

MUC4 mucin is now known to be expressed in various normal and cancer tissues. We have previously reported that MUC4 expression is a novel prognostic factor in several malignant tumors; however, it has not been investigated in oral squamous cell carcinoma (OSCC). The aim of our study is to evaluate the prognostic significance of MUC4 expression in OSCC. We examined the expression profile of MUC4 in OSCC tissues from 150 patients using immunohistochemistry. Its prognostic significance in OSCC was statistically analyzed. MUC4 was expressed in 61 of the 150 patients with OSCC. MUC4 expression was significantly correlated with higher T classification (p = 0.0004), positive nodal metastasis (p = 0.049), advanced tumor stage (p = 0.002), diffuse invasion of cancer cells (p = 0.004) and patient's death (p = 0.004) in OSCC. Multivariate analysis showed that MUC4 expression (p = 0.011), tumor location (p = 0.032) and diffuse invasion (p = 0.009) were statistically significant risk factors. Backward stepwise multivariate analysis demonstrated MUC4 expression (p = 0.0015) and diffuse invasion (p = 0.018) to be statistically significant independent risk factors of poor survival in OSCC. The disease-free and overall survival of patients with MUC4 expression was significantly worse than those without MUC4 expression (p < 0.0001 and p = 0.0001). In addition, the MUC4 expression was a significant risk factor for local recurrence and subsequent nodal metastasis in OSCC (p = 0.017 and p = 0.0001). We first report MUC4 overexpression is an independent factor for poor prognosis of patients with OSCC; therefore, patients with OSCC showing positive MUC4 expression should be followed up carefully. Copyright © 2011 UICC.

Quality of reporting of modern randomized controlled trials in medical oncology: a systematic review.

PubMed

Péron, Julien; Pond, Gregory R; Gan, Hui K; Chen, Eric X; Almufti, Roula; Maillet, Denis; You, Benoit

2012-07-03

The Consolidated Standards of Reporting Trials (CONSORT) guidelines were developed in the mid-1990s for the explicit purpose of improving clinical trial reporting. However, there is little information regarding the adherence to CONSORT guidelines of recent publications of randomized controlled trials (RCTs) in oncology. All phase III RCTs published between 2005 and 2009 were reviewed using an 18-point overall quality score for reporting based on the 2001 CONSORT statement. Multivariable linear regression was used to identify features associated with improved reporting quality. To provide baseline data for future evaluations of reporting quality, RCTs were also assessed according to the 2010 revised CONSORT statement. All statistical tests were two-sided. A total of 357 RCTs were reviewed. The mean 2001 overall quality score was 13.4 on a scale of 0-18, whereas the mean 2010 overall quality score was 19.3 on a scale of 0-27. The overall RCT reporting quality score improved by 0.21 points per year from 2005 to 2009. Poorly reported items included method used to generate the random allocation (adequately reported in 29% of trials), whether and how blinding was applied (41%), method of allocation concealment (51%), and participant flow (59%). High impact factor (IF, P = .003), recent publication date (P = .008), and geographic origin of RCTs (P = .003) were independent factors statistically significantly associated with higher reporting quality in a multivariable regression model. Sample size, tumor type, and positivity of trial results were not associated with higher reporting quality, whereas funding source and treatment type had a borderline statistically significant impact. The results show that numerous items remained unreported for many trials. Thus, given the potential impact of poorly reported trials, oncology journals should require even stricter adherence to the CONSORT guidelines.

Effect of Flexible Duty Hour Policies on Length of Stay for Complex Intra-Abdominal Operations: A Flexibility in Duty Hour Requirements for Surgical Trainees (FIRST) Trial Analysis.

PubMed

Stulberg, Jonah J; Pavey, Emily S; Cohen, Mark E; Ko, Clifford Y; Hoyt, David B; Bilimoria, Karl Y

2017-02-01

Changes to resident duty hour policies in the Flexibility in Duty Hour Requirements for Surgical Trainees (FIRST) trial could impact hospitalized patients' length of stay (LOS) by altering care coordination. Length of stay can also serve as a reflection of all complications, particularly those not captured in the FIRST trial (eg pneumothorax from central line). Programs were randomized to either maintaining current ACGME duty hour policies (Standard arm) or more flexible policies waiving rules on maximum shift lengths and time off between shifts (Flexible arm). Our objective was to determine whether flexibility in resident duty hours affected LOS in patients undergoing high-risk surgical operations. Patients were identified who underwent hepatectomy, pancreatectomy, laparoscopic colectomy, open colectomy, or ventral hernia repair (2014-2015 academic year) at 154 hospitals participating in the FIRST trial. Two procedure-stratified evaluations of LOS were undertaken: multivariable negative binomial regression analysis on LOS and a multivariable logistic regression analysis on the likelihood of a prolonged LOS (>75 th percentile). Before any adjustments, there was no statistically significant difference in overall mean LOS between study arms (Flexible Policy: mean [SD] LOS 6.03 [5.78] days vs Standard Policy: mean LOS 6.21 [5.82] days; p = 0.74). In adjusted analyses, there was no statistically significant difference in LOS between study arms overall (incidence rate ratio for Flexible vs Standard: 0.982; 95% CI, 0.939-1.026; p = 0.41) or for any individual procedures. In addition, there was no statistically significant difference in the proportion of patients with prolonged LOS between study arms overall (Flexible vs Standard: odds ratio = 1.028; 95% CI, 0.871-1.212) or for any individual procedures. Duty hour flexibility had no statistically significant effect on LOS in patients undergoing complex intra-abdominal operations. Copyright © 2016 American College of Surgeons. Published by Elsevier Inc. All rights reserved.

EMMPRIN/CD147 is an independent prognostic biomarker in cutaneous melanoma.

PubMed

Caudron, Anne; Battistella, Maxime; Feugeas, Jean-Paul; Pages, Cécile; Basset-Seguin, Nicole; Mazouz Dorval, Sarra; Funck Brentano, Elisa; Sadoux, Aurélie; Podgorniak, Marie-Pierre; Menashi, Suzanne; Janin, Anne; Lebbé, Céleste; Mourah, Samia

2016-08-01

CD147 has been implicated in melanoma invasion and metastasis mainly through increasing metalloproteinase synthesis and regulating VEGF/VEGFR signalling. In this study, the prognostic value of CD147 expression was investigated in a cohort of 196 cutaneous melanomas including 136 consecutive primary malignant melanomas, 30 lymph nodes, 16 in-transit and 14 visceral metastases. A series of 10 normal skin, 10 blue nevi and 10 dermal nevi was used as control. CD147 expression was assessed by immunohistochemistry, and the association of its expression with the clinicopathological characteristics of patients and survival was evaluated using univariate and multivariate statistical analyses. Univariate analysis showed that high CD147 expression was significantly associated with metastatic potential and with a reduced overall survival (P < 0.05 for both) in primary melanoma patients. CD147 expression level was correlated with histological factors which were associated with prognosis: Clark level, ulceration status and more particularly with Breslow index (r = 0.7, P < 10(-8) ). Multivariate analysis retained CD147 expression level and ulceration status as predicting factors for metastasis and overall survival (P < 0.05 for both). CD147 emerges as an important factor in the aggressive behaviour of melanoma and deserves further evaluation as an independent prognostic biomarker. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Prognostic significance of MRI findings in patients with myxoid-round cell liposarcoma.

PubMed

Tateishi, Ukihide; Hasegawa, Tadashi; Beppu, Yasuo; Kawai, Akira; Satake, Mitsuo; Moriyama, Noriyuki

2004-03-01

The aims of this study were to determine the prognostic significance of MRI findings in patients with myxoid-round cell liposarcomas and to clarify which MRI features best indicate tumors with adverse clinical behavior. The initial MRI studies of 36 pathologically confirmed myxoid-round cell liposarcomas were retrospectively reviewed, and observations from this review were correlated with the histopathologic features. MR images were evaluated by two radiologists with agreement by consensus, and both univariate and multivariate analyses were conducted to evaluate survival with a median clinical follow-up of 33 months (range, 9-276 months). Statistically significant MRI findings that favored a diagnosis of intermediate- or high-grade tumor were large tumor size (> 10 cm), deeply situated tumor, tumor possessing irregular contours, absence of lobulation, absence of thin septa, presence of thick septa, absence of tumor capsule, high-intensity signal pattern, pronounced enhancement, and globular or nodular enhancement. Of these MRI findings, thin septa (p < 0.05), a tumor capsule (p < 0.01), and pronounced enhancement (p < 0.01) were associated significantly, according to univariate analysis, with overall survival. Multivariate analysis indicated that pronounced enhancement was associated significantly with overall survival (p < 0.05). Contrast-enhanced MRI findings can indicate a good or adverse prognosis in patients with myxoid-round cell liposarcomas.

Exploring laser-induced breakdown spectroscopy for nuclear materials analysis and in-situ applications

NASA Astrophysics Data System (ADS)

Martin, Madhavi Z.; Allman, Steve; Brice, Deanne J.; Martin, Rodger C.; Andre, Nicolas O.

2012-08-01

Laser-induced breakdown spectroscopy (LIBS) has been used to determine the limits of detection of strontium (Sr) and cesium (Cs), common nuclear fission products. Additionally, detection limits were determined for cerium (Ce), often used as a surrogate for radioactive plutonium in laboratory studies. Results were obtained using a laboratory instrument with a Nd:YAG laser at fundamental wavelength of 1064 nm, frequency doubled to 532 nm with energy of 50 mJ/pulse. The data was compared for different concentrations of Sr and Ce dispersed in a CaCO3 (white) and carbon (black) matrix. We have addressed the sampling errors, limits of detection, reproducibility, and accuracy of measurements as they relate to multivariate analysis in pellets that were doped with the different elements at various concentrations. These results demonstrate that LIBS technique is inherently well suited for in situ analysis of nuclear materials in hot cells. Three key advantages are evident: (1) small samples (mg) can be evaluated; (2) nuclear materials can be analyzed with minimal sample preparation; and (3) samples can be remotely analyzed very rapidly (ms-seconds). Our studies also show that the methods can be made quantitative. Very robust multivariate models have been used to provide quantitative measurement and statistical evaluation of complex materials derived from our previous research on wood and soil samples.

Investigations of interference between electromagnetic transponders and wireless MOSFET dosimeters: a phantom study.

PubMed

Su, Zhong; Zhang, Lisha; Ramakrishnan, V; Hagan, Michael; Anscher, Mitchell

2011-05-01

To evaluate both the Calypso Systems' (Calypso Medical Technologies, Inc., Seattle, WA) localization accuracy in the presence of wireless metal-oxide-semiconductor field-effect transistor (MOSFET) dosimeters of dose verification system (DVS, Sicel Technologies, Inc., Morrisville, NC) and the dosimeters' reading accuracy in the presence of wireless electromagnetic transponders inside a phantom. A custom-made, solid-water phantom was fabricated with space for transponders and dosimeters. Two inserts were machined with positioning grooves precisely matching the dimensions of the transponders and dosimeters and were arranged in orthogonal and parallel orientations, respectively. To test the transponder localization accuracy with/without presence of dosimeters (hypothesis 1), multivariate analyses were performed on transponder-derived localization data with and without dosimeters at each preset distance to detect statistically significant localization differences between the control and test sets. To test dosimeter dose-reading accuracy with/without presence of transponders (hypothesis 2), an approach of alternating the transponder presence in seven identical fraction dose (100 cGy) deliveries and measurements was implemented. Two-way analysis of variance was performed to examine statistically significant dose-reading differences between the two groups and the different fractions. A relative-dose analysis method was also used to evaluate transponder impact on dose-reading accuracy after dose-fading effect was removed by a second-order polynomial fit. Multivariate analysis indicated that hypothesis 1 was false; there was a statistically significant difference between the localization data from the control and test sets. However, the upper and lower bounds of the 95% confidence intervals of the localized positional differences between the control and test sets were less than 0.1 mm, which was significantly smaller than the minimum clinical localization resolution of 0.5 mm. For hypothesis 2, analysis of variance indicated that there was no statistically significant difference between the dosimeter readings with and without the presence of transponders. Both orthogonal and parallel configurations had difference of polynomial-fit dose to measured dose values within 1.75%. The phantom study indicated that the Calypso System's localization accuracy was not affected clinically due to the presence of DVS wireless MOSFET dosimeters and the dosimeter-measured doses were not affected by the presence of transponders. Thus, the same patients could be implanted with both transponders and dosimeters to benefit from improved accuracy of radiotherapy treatments offered by conjunctional use of the two systems.

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.

[Low consumption of fruit, vegetables and greens: associated factors among the elderly in a Midwest Brazilian city].

PubMed

Silveira, Erika Aparecida; Martins, Bruna Bittar; de Abreu, Laísa Ribeiro Silva; Cardoso, Camila Kellen de Souza

2015-12-01

The scope of the study was to evaluate the prevalence of daily consumption of fruit, vegetables and greens by the elderly and its association with sociodemographic, lifestyle, morbidity and hospitalization variables. The study was part of the multiple-stage sampling cross-sectional research entitled the Goiânia Elderly Project (Projeto Idosos Goiânia). 416 elderly people were interviewed in their homes. Multivariate analysis was conducted using Poisson regression to analyze statistical associations. P values of <.05 were considered statistically significant. Daily consumption of fruit, vegetables and greens was 16.6%: fruit accounted for 44%, vegetables 39.7% and greens 32.5%. Factors statistically associated with daily consumption of fruits and vegetables were female sex, age between 70 and 79, higher education level, social class A/B and C, alcohol consumption, use of sweeteners, regular physical activity during leisure time, abdominal obesity and hospitalization. Public policies to promote health should develop strategies that encourage adequate intake of fruit, vegetables and greens among the elderly, since regular consumption of same can improve quality of life and prevent/control diseases.

Defining the ecological hydrology of Taiwan Rivers using multivariate statistical methods

NASA Astrophysics Data System (ADS)

Chang, Fi-John; Wu, Tzu-Ching; Tsai, Wen-Ping; Herricks, Edwin E.

2009-09-01

SummaryThe identification and verification of ecohydrologic flow indicators has found new support as the importance of ecological flow regimes is recognized in modern water resources management, particularly in river restoration and reservoir management. An ecohydrologic indicator system reflecting the unique characteristics of Taiwan's water resources and hydrology has been developed, the Taiwan ecohydrological indicator system (TEIS). A major challenge for the water resources community is using the TEIS to provide environmental flow rules that improve existing water resources management. This paper examines data from the extensive network of flow monitoring stations in Taiwan using TEIS statistics to define and refine environmental flow options in Taiwan. Multivariate statistical methods were used to examine TEIS statistics for 102 stations representing the geographic and land use diversity of Taiwan. The Pearson correlation coefficient showed high multicollinearity between the TEIS statistics. Watersheds were separated into upper and lower-watershed locations. An analysis of variance indicated significant differences between upstream, more natural, and downstream, more developed, locations in the same basin with hydrologic indicator redundancy in flow change and magnitude statistics. Issues of multicollinearity were examined using a Principal Component Analysis (PCA) with the first three components related to general flow and high/low flow statistics, frequency and time statistics, and quantity statistics. These principle components would explain about 85% of the total variation. A major conclusion is that managers must be aware of differences among basins, as well as differences within basins that will require careful selection of management procedures to achieve needed flow regimes.

A Bayesian approach to multivariate measurement system assessment

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

Hamada, Michael Scott

This article considers system assessment for multivariate measurements and presents a Bayesian approach to analyzing gauge R&R study data. The evaluation of variances for univariate measurement becomes the evaluation of covariance matrices for multivariate measurements. The Bayesian approach ensures positive definite estimates of the covariance matrices and easily provides their uncertainty. Furthermore, various measurement system assessment criteria are easily evaluated. The approach is illustrated with data from a real gauge R&R study as well as simulated data.

A Bayesian approach to multivariate measurement system assessment

DOE PAGES

Hamada, Michael Scott

2016-07-01

This article considers system assessment for multivariate measurements and presents a Bayesian approach to analyzing gauge R&R study data. The evaluation of variances for univariate measurement becomes the evaluation of covariance matrices for multivariate measurements. The Bayesian approach ensures positive definite estimates of the covariance matrices and easily provides their uncertainty. Furthermore, various measurement system assessment criteria are easily evaluated. The approach is illustrated with data from a real gauge R&R study as well as simulated data.

[PROGNOSTIC MODELS IN MODERN MANAGEMENT OF VULVAR CANCER].

PubMed

Tsvetkov, Ch; Gorchev, G; Tomov, S; Nikolova, M; Genchev, G

2016-01-01

The aim of the research was to evaluate and analyse prognosis and prognostic factors in patients with squamous cell vulvar carcinoma after primary surgery with individual approach applied during the course of treatment. In the period between January 2000 and July 2010, 113 patients with squamous cell carcinoma of the vulva were diagnosed and operated on at Gynecologic Oncology Clinic of Medical University, Pleven. All the patients were monitored at the same clinic. Individual approach was applied to each patient and whenever it was possible, more conservative operative techniques were applied. The probable clinicopathological characteristics influencing the overall survival and recurrence free survival were analyzed. Univariate statistical analysis and Cox regression analysis were made in order to evaluate the characteristics, which were statistically significant for overall survival and survival without recurrence. A multivariate logistic regression analysis (Forward Wald procedure) was applied to evaluate the combined influence of the significant factors. While performing the multivariate analysis, the synergic effect of the independent prognostic factors of both kinds of survivals was also evaluated. Approaching individually each patient, we applied the following operative techniques: 1. Deep total radical vulvectomy with separate incisions for lymph dissection (LD) or without dissection--68 (60.18 %) patients. 2. En-bloc vulvectomy with bilateral LD without vulva reconstruction--10 (8.85%) 3. Modified radical vulvactomy (hemivulvectomy, patial vulvactomy)--25 (22.02%). 4. wide-local excision--3 (2.65%). 5. Simple (total /partial) vulvectomy--5 (4.43%) patients. 6. En-bloc resection with reconstruction--2 (1.77%) After a thorough analysis of the overall survival and recurrence free survival, we made the conclusion that the relapse occurrence and clinical stage of FIGO were independent prognostic factors for overall survival and the independent prognostic factors for recurrence free survival were: metastatic inguinal nodes (unilateral or bilateral), tumor size (above or below 3 cm) and lymphovascular space invasion. On the basis of these results we created two prognostic models: 1. A prognostic model of overall survival 2. A prognostic model for survival without recurrence. Following the surgical staging of the disease, were able to gather and analyse important clinicopathological indexes, which gave us the opportunity to form prognostic groups for overall survival and recurrence-free survival.

Mathematical background and attitudes toward statistics in a sample of Spanish college students.

PubMed

Carmona, José; Martínez, Rafael J; Sánchez, Manuel

2005-08-01

To examine the relation of mathematical background and initial attitudes toward statistics of Spanish college students in social sciences the Survey of Attitudes Toward Statistics was given to 827 students. Multivariate analyses tested the effects of two indicators of mathematical background (amount of exposure and achievement in previous courses) on the four subscales. Analysis suggested grades in previous courses are more related to initial attitudes toward statistics than the number of mathematics courses taken. Mathematical background was related with students' affective responses to statistics but not with their valuing of statistics. Implications of possible research are discussed.

Characterizations of linear sufficient statistics

NASA Technical Reports Server (NTRS)

Peters, B. C., Jr.; Reoner, R.; Decell, H. P., Jr.

1977-01-01

A surjective bounded linear operator T from a Banach space X to a Banach space Y must be a sufficient statistic for a dominated family of probability measures defined on the Borel sets of X. These results were applied, so that they characterize linear sufficient statistics for families of the exponential type, including as special cases the Wishart and multivariate normal distributions. The latter result was used to establish precisely which procedures for sampling from a normal population had the property that the sample mean was a sufficient statistic.

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

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.

Evaluation of systemic markers of inflammation in atomic-bomb survivors with special reference to radiation and age effects.

PubMed

Hayashi, Tomonori; Morishita, Yukari; Khattree, Ravindra; Misumi, Munechika; Sasaki, Keiko; Hayashi, Ikue; Yoshida, Kengo; Kajimura, Junko; Kyoizumi, Seishi; Imai, Kazue; Kusunoki, Yoichiro; Nakachi, Kei

2012-11-01

Past exposure to atomic bomb (A-bomb) radiation has exerted various long-lasting deleterious effects on the health of survivors. Some of these effects are seen even after >60 yr. In this study, we evaluated the subclinical inflammatory status of 442 A-bomb survivors, in terms of 8 inflammation-related cytokines or markers, comprised of plasma levels of reactive oxygen species (ROS), interleukin (IL)-6, tumor necrosis factor α (TNF-α), C-reactive protein (CRP), IL-4, IL-10, and immunoglobulins, and erythrocyte sedimentation rate (ESR). The effects of past radiation exposure and natural aging on these markers were individually assessed and compared. Next, to assess the biologically significant relationship between inflammation and radiation exposure or aging, which was masked by the interrelationship of those cytokines/markers, we used multivariate statistical analyses and evaluated the systemic markers of inflammation as scores being calculated by linear combinations of selected cytokines and markers. Our results indicate that a linear combination of ROS, IL-6, CRP, and ESR generated a score that was the most indicative of inflammation and revealed clear dependences on radiation dose and aging that were found to be statistically significant. The results suggest that collectively, radiation exposure, in conjunction with natural aging, may enhance the persistent inflammatory status of A-bomb survivors.

Quantitative Evaluation of Stereo Visual Odometry for Autonomous Vessel Localisation in Inland Waterway Sensing Applications

PubMed Central

Kriechbaumer, Thomas; Blackburn, Kim; Breckon, Toby P.; Hamilton, Oliver; Rivas Casado, Monica

2015-01-01

Autonomous survey vessels can increase the efficiency and availability of wide-area river environment surveying as a tool for environment protection and conservation. A key challenge is the accurate localisation of the vessel, where bank-side vegetation or urban settlement preclude the conventional use of line-of-sight global navigation satellite systems (GNSS). In this paper, we evaluate unaided visual odometry, via an on-board stereo camera rig attached to the survey vessel, as a novel, low-cost localisation strategy. Feature-based and appearance-based visual odometry algorithms are implemented on a six degrees of freedom platform operating under guided motion, but stochastic variation in yaw, pitch and roll. Evaluation is based on a 663 m-long trajectory (>15,000 image frames) and statistical error analysis against ground truth position from a target tracking tachymeter integrating electronic distance and angular measurements. The position error of the feature-based technique (mean of ±0.067 m) is three times smaller than that of the appearance-based algorithm. From multi-variable statistical regression, we are able to attribute this error to the depth of tracked features from the camera in the scene and variations in platform yaw. Our findings inform effective strategies to enhance stereo visual localisation for the specific application of river monitoring. PMID:26694411

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

PubMed

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

2017-01-01

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

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

PubMed Central

Thibaut, Loïc; Wang, Yi Alice

2017-01-01

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

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

Statistical Knowledge for Teaching: Exploring it in the Classroom

ERIC Educational Resources Information Center

Burgess, Tim

2009-01-01

This paper first reports on the methodology of a study of teacher knowledge for statistics, conducted in a classroom at the primary school level. The methodology included videotaping of a sequence of lessons that involved students in investigating multivariate data sets, followed up by audiotaped interviews with each teacher. These stimulated…

Performance of the S - [chi][squared] Statistic for Full-Information Bifactor Models

ERIC Educational Resources Information Center

Li, Ying; Rupp, Andre A.

2011-01-01

This study investigated the Type I error rate and power of the multivariate extension of the S - [chi][squared] statistic using unidimensional and multidimensional item response theory (UIRT and MIRT, respectively) models as well as full-information bifactor (FI-bifactor) models through simulation. Manipulated factors included test length, sample…

Spatial Statistical Model and Optimal Survey Design for Rapid Geophysical Characterization of UXO Sites

DTIC Science & Technology

2003-07-01

4, Gnanadesikan , 1977). An entity whose measured features fall into one of the regions is classified accordingly. For the approaches we discuss here... Gnanadesikan , R. 1977. Methods for Statistical Data Analysis of Multivariate Observations. John Wiley & Sons, New York. Hassig, N. L., O’Brien, R. F

History and physical examination findings predictive of testicular torsion: an attempt to promote clinical diagnosis by house staff.

PubMed

Srinivasan, Arun; Cinman, Nadya; Feber, Kevin M; Gitlin, Jordan; Palmer, Lane S

2011-08-01

To standardize the history and physical examination of boys who present with acute scrotum and identify parameters that best predict testicular torsion. Over a 5-month period, a standardized history and physical examination form with 22 items was used for all boys presenting with scrotal pain. Management decisions for radiological evaluation and surgical intervention were based on the results. Data were statistically analyzed in correlation with the eventual diagnosis. Of the 79 boys evaluated, 8 (10.1%) had testicular torsion. On univariate analysis, age, worsening pain, nausea/vomiting, severe pain at rest, absence of ipsilateral cremaster reflex, abnormal testicular position and scrotal skin changes were statistically predictive of torsion. After multivariate analysis and adjusting for confounding effect of other co-existing variables, absence of ipsilateral cremaster reflex (P < 0.001), nausea/vomiting (P < 0.05) and scrotal skin changes (P < 0.001) were the only consistent predictive factors of testicular torsion. An accurate history and physical examination of boys with acute scrotum should be primary in deciding upon further radiographic or surgical evaluation. While several forces have led to less consistent overnight resident staffing, consistent and reliable clinical evaluation of the acute scrotum using a standardized approach should reduce error, improve patient care and potentially reduce health care costs. Copyright © 2011 Journal of Pediatric Urology Company. Published by Elsevier Ltd. All rights reserved.

Meat Quality Assessment by Electronic Nose (Machine Olfaction Technology)

PubMed Central

Ghasemi-Varnamkhasti, Mahdi; Mohtasebi, Seyed Saeid; Siadat, Maryam; Balasubramanian, Sundar

2009-01-01

Over the last twenty years, newly developed chemical sensor systems (so called “electronic noses”) have made odor analyses possible. These systems involve various types of electronic chemical gas sensors with partial specificity, as well as suitable statistical methods enabling the recognition of complex odors. As commercial instruments have become available, a substantial increase in research into the application of electronic noses in the evaluation of volatile compounds in food, cosmetic and other items of everyday life is observed. At present, the commercial gas sensor technologies comprise metal oxide semiconductors, metal oxide semiconductor field effect transistors, organic conducting polymers, and piezoelectric crystal sensors. Further sensors based on fibreoptic, electrochemical and bi-metal principles are still in the developmental stage. Statistical analysis techniques range from simple graphical evaluation to multivariate analysis such as artificial neural network and radial basis function. The introduction of electronic noses into the area of food is envisaged for quality control, process monitoring, freshness evaluation, shelf-life investigation and authenticity assessment. Considerable work has already been carried out on meat, grains, coffee, mushrooms, cheese, sugar, fish, beer and other beverages, as well as on the odor quality evaluation of food packaging material. This paper describes the applications of these systems for meat quality assessment, where fast detection methods are essential for appropriate product management. The results suggest the possibility of using this new technology in meat handling. PMID:22454572

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

PubMed

Barrantes, Gilbert; Sandoval, Luis

2009-09-01

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

Texture as a basis for acoustic classification of substrate in the nearshore region

NASA Astrophysics Data System (ADS)

Dennison, A.; Wattrus, N. J.

2016-12-01

Segmentation and classification of substrate type from two locations in Lake Superior, are predicted using multivariate statistical processing of textural measures derived from shallow-water, high-resolution multibeam bathymetric data. During a multibeam sonar survey, both bathymetric and backscatter data are collected. It is well documented that the statistical characteristic of a sonar backscatter mosaic is dependent on substrate type. While classifying the bottom-type on the basis on backscatter alone can accurately predict and map bottom-type, it lacks the ability to resolve and capture fine textural details, an important factor in many habitat mapping studies. Statistical processing can capture the pertinent details about the bottom-type that are rich in textural information. Further multivariate statistical processing can then isolate characteristic features, and provide the basis for an accurate classification scheme. Preliminary results from an analysis of bathymetric data and ground-truth samples collected from the Amnicon River, Superior, Wisconsin, and the Lester River, Duluth, Minnesota, demonstrate the ability to process and develop a novel classification scheme of the bottom type in two geomorphologically distinct areas.

Exploratory Multivariate Analysis. A Graphical Approach.

DTIC Science & Technology

1981-01-01

Gnanadesikan , 1977) but we feel that these should be used with great caution unless one really has good reason to believe that the data came from such a...are referred to Gnanadesikan (1977). The present author hopes that the convenience of a single summary or significance level will not deter his readers...fit of a harmonic model to meteorological data. (In preparation). Gnanadesikan , R. (1977). Methods for Statistical Data Analysis of Multivariate

The intervals method: a new approach to analyse finite element outputs using multivariate statistics

PubMed Central

De Esteban-Trivigno, Soledad; Püschel, Thomas A.; Fortuny, Josep

2017-01-01

Background In this paper, we propose a new method, named the intervals’ method, to analyse data from finite element models in a comparative multivariate framework. As a case study, several armadillo mandibles are analysed, showing that the proposed method is useful to distinguish and characterise biomechanical differences related to diet/ecomorphology. Methods The intervals’ method consists of generating a set of variables, each one defined by an interval of stress values. Each variable is expressed as a percentage of the area of the mandible occupied by those stress values. Afterwards these newly generated variables can be analysed using multivariate methods. Results Applying this novel method to the biological case study of whether armadillo mandibles differ according to dietary groups, we show that the intervals’ method is a powerful tool to characterize biomechanical performance and how this relates to different diets. This allows us to positively discriminate between specialist and generalist species. Discussion We show that the proposed approach is a useful methodology not affected by the characteristics of the finite element mesh. Additionally, the positive discriminating results obtained when analysing a difficult case study suggest that the proposed method could be a very useful tool for comparative studies in finite element analysis using multivariate statistical approaches. PMID:29043107

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.

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.

Factors affecting post-pubertal penile size in patients with hypospadias.

PubMed

Moriya, Kimihiko; Nakamura, Michiko; Nishimura, Yoko; Kitta, Takeya; Kanno, Yukiko; Chiba, Hiroki; Kon, Masafumi; Shinohara, Nobuo

2016-09-01

To evaluate actual post-pubertal penile size and factors affecting it in hypospadias patients, we retrospectively reviewed medical charts. Hypospadias patients whose external genitalia were categorized into Tanner stage 5, and whose stretched penile length was evaluated at 15 years old or older from April 2008 to April 2015, were enrolled in the present study. Stretched penile length was measured by a single examiner. Actual post-pubertal stretched penile length and factors affecting the post-pubertal stretched penile length were estimated. Statistical analysis was performed using Mann-Whitney U test and univariate and multivariate linear regression models for the determination of independent factors. Thirty patients met the inclusion criteria. Median age at evaluation was 17.2 years. Thirteen and 17 had mild and severe hypospadias, respectively. Endocrinological abnormality was identified in 5. Multivariate analysis showed that the severity of hypospadias and endocrinological abnormality were significant factors affecting stretched penile length. Stretched penile length in 25 patients without endocrinological abnormality was significantly longer than that in those with endocrinological abnormality (p = 0.036). Among patients without endocrinological abnormality, stretched penile length in 13 with severe hypospadias was significantly shorter than that in 12 with mild hypospadias (p = 0.004). While the severity of hypospadias and endocrinological abnormality at post-pubertal evaluation were factors affecting post-pubertal penile size, stretched penile length in patients with severe hypospadias was shorter even in cases without endocrinological abnormality. These results suggest that severe hypospadias is not only a disorder of urethral development, but also a disorder of penile development.

Computed Tomographic Evaluation of Posttreatment Soft-Tissue Changes by Using a Lymphedema Scoring System in Patients with Oral Cancer.

PubMed

Akashi, Masaya; Teraoka, Shun; Kakei, Yasumasa; Kusumoto, Junya; Hasegawa, Takumi; Minamikawa, Tsutomu; Hashikawa, Kazunobu; Komori, Takahide

2018-04-01

This study aimed to evaluate posttreatment soft-tissue changes in patients with oral cancer with computed tomography (CT). To accomplish that purpose, a scoring system was established, referring to the criteria of lower leg lymphedema (LE). One hundred and six necks in 95 patients who underwent oral oncologic surgery with neck dissection (ND) were analyzed retrospectively using routine follow-up CT images. A two-point scoring system to evaluate soft-tissue changes (so-called "LE score") was established as follows: Necks with a "honeycombing" appearance were assigned 1 point. Necks with "taller than wide" fat lobules were assigned 1 point. Necks with neither appearance were assigned 0 points. Comparisons between patients with LE score ≥1 and LE score = 0 at 6 months postoperatively were performed using the Fisher exact test for discrete variables and the Mann-Whitney U test for continuous variables. Univariate predictors associated with posttreatment changes (i.e., LE score ≥1 at 6 months postoperatively) were entered into a multivariate logistic regression analysis. Values of p < 0.05 were considered to indicate statistical significance. The occurrence of the posttreatment soft-tissue changes was 32%. Multivariate logistic regression analysis showed that postoperative radiation therapy (RT) and bilateral ND were potential risk factors of posttreatment soft-tissue changes on CT images. Sequential evaluation of "honeycombing" and the "taller than wide" appearances on routine follow-up CT revealed the persistence of posttreatment soft-tissue changes in patients who underwent oral cancer treatment, and those potential risk factors were postoperative RT and bilateral ND.

Identifying prognostic intratumor heterogeneity using pre- and post-radiotherapy 18F-FDG PET images for pancreatic cancer patients.

PubMed

Yue, Yong; Osipov, Arsen; Fraass, Benedick; Sandler, Howard; Zhang, Xiao; Nissen, Nicholas; Hendifar, Andrew; Tuli, Richard

2017-02-01

To stratify risks of pancreatic adenocarcinoma (PA) patients using pre- and post-radiotherapy (RT) PET/CT images, and to assess the prognostic value of texture variations in predicting therapy response of patients. Twenty-six PA patients treated with RT from 2011-2013 with pre- and post-treatment 18F-FDG-PET/CT scans were identified. Tumor locoregional texture was calculated using 3D kernel-based approach, and texture variations were identified by fitting discrepancies of texture maps of pre- and post-treatment images. A total of 48 texture and clinical variables were identified and evaluated for association with overall survival (OS). The prognostic heterogeneity features were selected using lasso/elastic net regression, and further were evaluated by multivariate Cox analysis. Median age was 69 y (range, 46-86 y). The texture map and temporal variations between pre- and post-treatment were well characterized by histograms and statistical fitting. The lasso analysis identified seven predictors (age, node stage, post-RT SUVmax, variations of homogeneity, variance, sum mean, and cluster tendency). The multivariate Cox analysis identified five significant variables: age, node stage, variations of homogeneity, variance, and cluster tendency (with P=0.020, 0.040, 0.065, 0.078, and 0.081, respectively). The patients were stratified into two groups based on the risk score of multivariate analysis with log-rank P=0.001: a low risk group (n=11) with a longer mean OS (29.3 months) and higher texture variation (>30%), and a high risk group (n=15) with a shorter mean OS (17.7 months) and lower texture variation (<15%). Locoregional metabolic texture response provides a feasible approach for evaluating and predicting clinical outcomes following treatment of PA with RT. The proposed method can be used to stratify patient risk and help select appropriate treatment strategies for individual patients toward implementing response-driven adaptive RT.

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

PubMed

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

2016-04-01

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

Multivariate analysis of the immune response to a vaccine as an alternative to the repetition of animal challenge studies for vaccines with demonstrated efficacy.

PubMed

Chapat, Ludivine; Hilaire, Florence; Bouvet, Jérome; Pialot, Daniel; Philippe-Reversat, Corinne; Guiot, Anne-Laure; Remolue, Lydie; Lechenet, Jacques; Andreoni, Christine; Poulet, Hervé; Day, Michael J; De Luca, Karelle; Cariou, Carine; Cupillard, Lionel

2017-07-01

The assessment of vaccine combinations, or the evaluation of the impact of minor modifications of one component in well-established vaccines, requires animal challenges in the absence of previously validated correlates of protection. As an alternative, we propose conducting a multivariate analysis of the specific immune response to the vaccine. This approach is consistent with the principles of the 3Rs (Refinement, Reduction and Replacement) and avoids repeating efficacy studies based on infectious challenges in vivo. To validate this approach, a set of nine immunological parameters was selected in order to characterize B and T lymphocyte responses against canine rabies virus and to evaluate the compatibility between two canine vaccines, an inactivated rabies vaccine (RABISIN ® ) and a combined vaccine (EURICAN ® DAPPi-Lmulti) injected at two different sites in the same animals. The analysis was focused on the magnitude and quality of the immune response. The multi-dimensional picture given by this 'immune fingerprint' was used to assess the impact of the concomitant injection of the combined vaccine on the immunogenicity of the rabies vaccine. A principal component analysis fully discriminated the control group from the groups vaccinated with RABISIN ® alone or RABISIN ® +EURICAN ® DAPPi-Lmulti and confirmed the compatibility between the rabies vaccines. This study suggests that determining the immune fingerprint, combined with a multivariate statistical analysis, is a promising approach to characterizing the immunogenicity of a vaccine with an established record of efficacy. It may also avoid the need to repeat efficacy studies involving challenge infection in case of minor modifications of the vaccine or for compatibility studies. Copyright © 2017 Elsevier B.V. All rights reserved.

Green Chemistry Metrics with Special Reference to Green Analytical Chemistry.

PubMed

Tobiszewski, Marek; Marć, Mariusz; Gałuszka, Agnieszka; Namieśnik, Jacek

2015-06-12

The concept of green chemistry is widely recognized in chemical laboratories. To properly measure an environmental impact of chemical processes, dedicated assessment tools are required. This paper summarizes the current state of knowledge in the field of development of green chemistry and green analytical chemistry metrics. The diverse methods used for evaluation of the greenness of organic synthesis, such as eco-footprint, E-Factor, EATOS, and Eco-Scale are described. Both the well-established and recently developed green analytical chemistry metrics, including NEMI labeling and analytical Eco-scale, are presented. Additionally, this paper focuses on the possibility of the use of multivariate statistics in evaluation of environmental impact of analytical procedures. All the above metrics are compared and discussed in terms of their advantages and disadvantages. The current needs and future perspectives in green chemistry metrics are also discussed.

Two models for evaluating landslide hazards

USGS Publications Warehouse

Davis, J.C.; Chung, C.-J.; Ohlmacher, G.C.

2006-01-01

Two alternative procedures for estimating landslide hazards were evaluated using data on topographic digital elevation models (DEMs) and bedrock lithologies in an area adjacent to the Missouri River in Atchison County, Kansas, USA. The two procedures are based on the likelihood ratio model but utilize different assumptions. The empirical likelihood ratio model is based on non-parametric empirical univariate frequency distribution functions under an assumption of conditional independence while the multivariate logistic discriminant model assumes that likelihood ratios can be expressed in terms of logistic functions. The relative hazards of occurrence of landslides were estimated by an empirical likelihood ratio model and by multivariate logistic discriminant analysis. Predictor variables consisted of grids containing topographic elevations, slope angles, and slope aspects calculated from a 30-m DEM. An integer grid of coded bedrock lithologies taken from digitized geologic maps was also used as a predictor variable. Both statistical models yield relative estimates in the form of the proportion of total map area predicted to already contain or to be the site of future landslides. The stabilities of estimates were checked by cross-validation of results from random subsamples, using each of the two procedures. Cell-by-cell comparisons of hazard maps made by the two models show that the two sets of estimates are virtually identical. This suggests that the empirical likelihood ratio and the logistic discriminant analysis models are robust with respect to the conditional independent assumption and the logistic function assumption, respectively, and that either model can be used successfully to evaluate landslide hazards. ?? 2006.

Study design and statistical analysis of data in human population studies with the micronucleus assay.

PubMed

Ceppi, Marcello; Gallo, Fabio; Bonassi, Stefano

2011-01-01

The most common study design performed in population studies based on the micronucleus (MN) assay, is the cross-sectional study, which is largely performed to evaluate the DNA damaging effects of exposure to genotoxic agents in the workplace, in the environment, as well as from diet or lifestyle factors. Sample size is still a critical issue in the design of MN studies since most recent studies considering gene-environment interaction, often require a sample size of several hundred subjects, which is in many cases difficult to achieve. The control of confounding is another major threat to the validity of causal inference. The most popular confounders considered in population studies using MN are age, gender and smoking habit. Extensive attention is given to the assessment of effect modification, given the increasing inclusion of biomarkers of genetic susceptibility in the study design. Selected issues concerning the statistical treatment of data have been addressed in this mini-review, starting from data description, which is a critical step of statistical analysis, since it allows to detect possible errors in the dataset to be analysed and to check the validity of assumptions required for more complex analyses. Basic issues dealing with statistical analysis of biomarkers are extensively evaluated, including methods to explore the dose-response relationship among two continuous variables and inferential analysis. A critical approach to the use of parametric and non-parametric methods is presented, before addressing the issue of most suitable multivariate models to fit MN data. In the last decade, the quality of statistical analysis of MN data has certainly evolved, although even nowadays only a small number of studies apply the Poisson model, which is the most suitable method for the analysis of MN data.

Multivariate Statistical Modelling of Drought and Heat Wave Events

NASA Astrophysics Data System (ADS)

Manning, Colin; Widmann, Martin; Vrac, Mathieu; Maraun, Douglas; Bevaqua, Emanuele

2016-04-01

Multivariate Statistical Modelling of Drought and Heat Wave Events C. Manning1,2, M. Widmann1, M. Vrac2, D. Maraun3, E. Bevaqua2,3 1. School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham, UK 2. Laboratoire des Sciences du Climat et de l'Environnement, (LSCE-IPSL), Centre d'Etudes de Saclay, Gif-sur-Yvette, France 3. Wegener Center for Climate and Global Change, University of Graz, Brandhofgasse 5, 8010 Graz, Austria Compound extreme events are a combination of two or more contributing events which in themselves may not be extreme but through their joint occurrence produce an extreme impact. Compound events are noted in the latest IPCC report as an important type of extreme event that have been given little attention so far. As part of the CE:LLO project (Compound Events: muLtivariate statisticaL mOdelling) we are developing a multivariate statistical model to gain an understanding of the dependence structure of certain compound events. One focus of this project is on the interaction between drought and heat wave events. Soil moisture has both a local and non-local effect on the occurrence of heat waves where it strongly controls the latent heat flux affecting the transfer of sensible heat to the atmosphere. These processes can create a feedback whereby a heat wave maybe amplified or suppressed by the soil moisture preconditioning, and vice versa, the heat wave may in turn have an effect on soil conditions. An aim of this project is to capture this dependence in order to correctly describe the joint probabilities of these conditions and the resulting probability of their compound impact. We will show an application of Pair Copula Constructions (PCCs) to study the aforementioned compound event. PCCs allow in theory for the formulation of multivariate dependence structures in any dimension where the PCC is a decomposition of a multivariate distribution into a product of bivariate components modelled using copulas. A copula is a multivariate distribution function which allows one to model the dependence structure of given variables separately from the marginal behaviour. We firstly look at the structure of soil moisture drought over the entire of France using the SAFRAN dataset between 1959 and 2009. Soil moisture is represented using the Standardised Precipitation Evapotranspiration Index (SPEI). Drought characteristics are computed at grid point scale where drought conditions are identified as those with an SPEI value below -1.0. We model the multivariate dependence structure of drought events defined by certain characteristics and compute return levels of these events. We initially find that drought characteristics such as duration, mean SPEI and the maximum contiguous area to a grid point all have positive correlations, though the degree to which they are correlated can vary considerably spatially. A spatial representation of return levels then may provide insight into the areas most prone to drought conditions. As a next step, we analyse the dependence structure between soil moisture conditions preceding the onset of a heat wave and the heat wave itself.

Psychological well-being and social support in chronic myeloid leukemia patients receiving lifelong targeted therapies.

PubMed

Efficace, Fabio; Breccia, Massimo; Cottone, Francesco; Okumura, Iris; Doro, Maribel; Riccardi, Francesca; Rosti, Gianantonio; Baccarani, Michele

2016-12-01

The main objective of this study was to investigate whether social support is independently associated with psychological well-being in chronic myeloid leukemia (CML) patients. Secondary objectives were to compare the psychological well-being profile of CML patients with that of their peers in general population and to examine possible age- and sex-related differences. Analysis was performed on 417 patients in treatment with lifelong molecularly targeted therapies. Mean age of patients analyzed was 56 years (range 19-87 years) and 247 (59 %) were male and 170 (41 %) were female. Social support was assessed with the Multidimensional Scale of Perceived Social Support and psychological well-being was evaluated with the short version of the Psychological General Well-Being Index. Descriptive statistics and multivariate logistic regression analyses were used. Multivariate logistic regression analysis revealed that a greater social support was independently associated with lower anxiety and depression, as well as with higher positive well-being, self-control, and vitality (p < 0.001). Female patients reported statistically significant worse outcomes in all dimensions of psychological well-being. Age- and sex-adjusted comparisons with population norms revealed that depression (ES = -0.42, p < 0.001) and self-control (ES = -0.48, p < 0.001) were the two main impaired psychological dimensions. This study indicates that social support is a critical factor associated with psychological well-being of CML patients treated with modern lifelong targeted therapies.

Identifying the hydrochemical characteristics of rivers and groundwater by multivariate statistical analysis in the Sanjiang Plain, China

NASA Astrophysics Data System (ADS)

Cao, Yingjie; Tang, Changyuan; Song, Xianfang; Liu, Changming; Zhang, Yinghua

2016-06-01

Two multivariate statistical technologies, factor analysis (FA) and discriminant analysis (DA), are applied to study the river and groundwater hydrochemistry and its controlling processes in the Sanjiang Plain of the northeast China. Factor analysis identifies five factors which account for 79.65 % of the total variance in the dataset. Four factors bearing specific meanings as the river and groundwater hydrochemistry controlling processes are divided into two groups, the "natural hydrochemistry evolution" group and the "pollution" group. The "natural hydrochemistry evolution" group includes the salinity factor (factor 1) caused by rock weathering and the residence time factor (factor 2) reflecting the groundwater traveling time. The "pollution" group represents the groundwater quality deterioration due to geogenic pollution caused by elevated Fe and Mn (factor 3) and elevated nitrate (NO3 -) introduced by human activities such as agriculture exploitations (factor 5). The hydrochemical difference and hydraulic connection among rivers (surface water, SW), shallow groundwater (SG) and deep groundwater (DG) group are evaluated by the factor scores obtained from FA and DA (Fisher's method). It is showed that the river water is characterized as low salinity and slight pollution, and the shallow groundwater has the highest salinity and severe pollution. The SW is well separated from SG and DG by Fisher's discriminant function, but the SG and DG can not be well separated showing their hydrochemical similarities, and emphasize hydraulic connections between SG and DG.

Prognostic value of cell cycle regulatory proteins in muscle-infiltrating bladder cancer.

PubMed

Galmozzi, Fabia; Rubagotti, Alessandra; Romagnoli, Andrea; Carmignani, Giorgio; Perdelli, Luisa; Gatteschi, Beatrice; Boccardo, Francesco

2006-12-01

The aims of this study were to investigate the expression levels of proteins involved in cell cycle regulation in specimens of bladder cancer and to correlate them with the clinicopathological characteristics, proliferative activity and survival. Eighty-two specimens obtained from patients affected by muscle-invasive bladder cancer were evaluated immunohistochemically for p53, p21 and cyclin D1 expression, as well as for the tumour proliferation index, Ki-67. The statistical analysis included Kaplan-Meier curves with log-rank test and Cox proportional hazards models. In univariate analyses, low Ki-67 proliferation index (P = 0.045) and negative p21 immunoreactivity (P = 0.04) were associated to patient's overall survival (OS), but in multivariate models p21 did not reach statistical significance. When the combinations of the variables were assessed in two separate multivariate models that included tumour stage, grading, lymph node status, vascular invasion and perineural invasion, the combined variables p21/Ki-67 or p21/cyclin D1 expression were independent predictors for OS; in particular, patients with positive p21/high Ki-67 (P = 0.015) or positive p21/negative cyclin D1 (P = 0.04) showed the worst survival outcome. Important alterations in the cell cycle regulatory pathways occur in muscle-invasive bladder cancer and the combined use of cell cycle regulators appears to provide significant prognostic information that could be used to select the patients most suitable for multimodal therapeutic approaches.

Profiling and multivariate statistical analysis of Panax ginseng based on ultra-high-performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry.

PubMed

Wu, Wei; Sun, Le; Zhang, Zhe; Guo, Yingying; Liu, Shuying

2015-03-25

An ultra-high-performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry (UHPLC-Q-TOF-MS) method was developed for the detection and structural analysis of ginsenosides in white ginseng and related processed products (red ginseng). Original neutral, malonyl, and chemically transformed ginsenosides were identified in white and red ginseng samples. The aglycone types of ginsenosides were determined by MS/MS as PPD (m/z 459), PPT (m/z 475), C-24, -25 hydrated-PPD or PPT (m/z 477 or m/z 493), and Δ20(21)-or Δ20(22)-dehydrated-PPD or PPT (m/z 441 or m/z 457). Following the structural determination, the UHPLC-Q-TOF-MS-based chemical profiling coupled with multivariate statistical analysis method was applied for global analysis of white and processed ginseng samples. The chemical markers present between the processed products red ginseng and white ginseng could be assigned. Process-mediated chemical changes were recognized as the hydrolysis of ginsenosides with large molecular weight, chemical transformations of ginsenosides, changes in malonyl-ginsenosides, and generation of 20-(R)-ginsenoside enantiomers. The relative contents of compounds classified as PPD, PPT, malonyl, and transformed ginsenosides were calculated based on peak areas in ginseng before and after processing. This study provides possibility to monitor multiple components for the quality control and global evaluation of ginseng products during processing. Copyright © 2014 Elsevier B.V. All rights reserved.

Comparative statistical analysis of chrome and vegetable tanning effluents and their effects on related soil.

PubMed

Tariq, Saadia R; Shah, Munir H; Shaheen, Nazia

2009-09-30

Two tanning units of Pakistan, namely, Kasur and Mian Channun were investigated with respect to the tanning processes (chrome and vegetable, respectively) and the effects of the tanning agents on the quality of soil in vicinity of tanneries were evaluated. The effluent and soil samples from 16 tanneries each of Kasur and Mian Channun were collected. The levels of selected metals (Na, K, Ca, Mg, Fe, Cr, Mn, Co, Cd, Ni, Pb and Zn) were determined by using flame atomic absorption spectrophotometer under optimum analytical conditions. The data thus obtained were subjected to univariate and multivariate statistical analyses. Most of the metals exhibited considerably higher concentrations in the effluents and soils of Kasur compared with those of Mian Channun. It was observed that the soil of Kasur was highly contaminated by Na, K, Ca and Mg emanating from various processes of leather manufacture. Furthermore, the levels of Cr were also present at much enhanced levels than its background concentration due to the adoption of chrome tanning. The levels of Cr determined in soil samples collected from the vicinity of Mian Channun tanneries were almost comparable to the background levels. The soil of this city was found to have contaminated only by the metals originating from pre-tanning processes. The apportionment of selected metals in the effluent and soil samples was determined by a multivariate cluster analysis, which revealed significant differences in chrome and vegetable tanning processes.

Evaluation of probabilistic forecasts with the scoringRules package

NASA Astrophysics Data System (ADS)

Jordan, Alexander; Krüger, Fabian; Lerch, Sebastian

2017-04-01

Over the last decades probabilistic forecasts in the form of predictive distributions have become popular in many scientific disciplines. With the proliferation of probabilistic models arises the need for decision-theoretically principled tools to evaluate the appropriateness of models and forecasts in a generalized way in order to better understand sources of prediction errors and to improve the models. Proper scoring rules are functions S(F,y) which evaluate the accuracy of a forecast distribution F , given that an outcome y was observed. In coherence with decision-theoretical principles they allow to compare alternative models, a crucial ability given the variety of theories, data sources and statistical specifications that is available in many situations. This contribution presents the software package scoringRules for the statistical programming language R, which provides functions to compute popular scoring rules such as the continuous ranked probability score for a variety of distributions F that come up in applied work. For univariate variables, two main classes are parametric distributions like normal, t, or gamma distributions, and distributions that are not known analytically, but are indirectly described through a sample of simulation draws. For example, ensemble weather forecasts take this form. The scoringRules package aims to be a convenient dictionary-like reference for computing scoring rules. We offer state of the art implementations of several known (but not routinely applied) formulas, and implement closed-form expressions that were previously unavailable. Whenever more than one implementation variant exists, we offer statistically principled default choices. Recent developments include the addition of scoring rules to evaluate multivariate forecast distributions. The use of the scoringRules package is illustrated in an example on post-processing ensemble forecasts of temperature.

Use of proxy measures in estimating socioeconomic inequalities in malaria prevalence.

PubMed

Somi, Masha F; Butler, James R; Vahid, Farshid; Njau, Joseph D; Kachur, S P; Abdulla, Salim

2008-03-01

To present and compare socioeconomic status (SES) rankings of households using consumption and an asset-based index as two alternative measures of SES; and to compare and evaluate the performance of these two measures in multivariate analyses of the socioeconomic gradient in malaria prevalence. Data for the study come from a survey of 557 households in 25 study villages in Tanzania in 2004. Household SES was determined using consumption and an asset-based index calculated using Principal Components Analysis on a set of household variables. In multivariate analyses of malaria prevalence, we also used two other measures of disease prevalence: parasitaemia and self-report of malaria or fever in the 2 weeks before interview. Household rankings based on the two measures of SES differ substantially. In multivariate analyses, there was a statistically significant negative association between both measures of SES and parasitaemia but not between either measure of SES and self-reported malaria. Age of individual, use of a mosquito net, and wall construction were negatively and significantly associated with parasitaemia, whilst roof construction was positively associated with parasitaemia. Only age remained significant when malaria self-report was used as the measure of disease prevalence. An asset index is an effective alternative to consumption in measuring the socioeconomic gradient in malaria parasitaemia, but self-report may be an unreliable measure of malaria prevalence for this purpose.

Differences in passenger car and large truck involved crash frequencies at urban signalized intersections: an exploratory analysis.

PubMed

Dong, Chunjiao; Clarke, David B; Richards, Stephen H; Huang, Baoshan

2014-01-01

The influence of intersection features on safety has been examined extensively because intersections experience a relatively large proportion of motor vehicle conflicts and crashes. Although there are distinct differences between passenger cars and large trucks-size, operating characteristics, dimensions, and weight-modeling crash counts across vehicle types is rarely addressed. This paper develops and presents a multivariate regression model of crash frequencies by collision vehicle type using crash data for urban signalized intersections in Tennessee. In addition, the performance of univariate Poisson-lognormal (UVPLN), multivariate Poisson (MVP), and multivariate Poisson-lognormal (MVPLN) regression models in establishing the relationship between crashes, traffic factors, and geometric design of roadway intersections is investigated. Bayesian methods are used to estimate the unknown parameters of these models. The evaluation results suggest that the MVPLN model possesses most of the desirable statistical properties in developing the relationships. Compared to the UVPLN and MVP models, the MVPLN model better identifies significant factors and predicts crash frequencies. The findings suggest that traffic volume, truck percentage, lighting condition, and intersection angle significantly affect intersection safety. Important differences in car, car-truck, and truck crash frequencies with respect to various risk factors were found to exist between models. The paper provides some new or more comprehensive observations that have not been covered in previous studies. Copyright © 2013 Elsevier Ltd. All rights reserved.

Risk prediction for myocardial infarction via generalized functional regression models.

PubMed

Ieva, Francesca; Paganoni, Anna M

2016-08-01

In this paper, we propose a generalized functional linear regression model for a binary outcome indicating the presence/absence of a cardiac disease with multivariate functional data among the relevant predictors. In particular, the motivating aim is the analysis of electrocardiographic traces of patients whose pre-hospital electrocardiogram (ECG) has been sent to 118 Dispatch Center of Milan (the Italian free-toll number for emergencies) by life support personnel of the basic rescue units. The statistical analysis starts with a preprocessing of ECGs treated as multivariate functional data. The signals are reconstructed from noisy observations. The biological variability is then removed by a nonlinear registration procedure based on landmarks. Thus, in order to perform a data-driven dimensional reduction, a multivariate functional principal component analysis is carried out on the variance-covariance matrix of the reconstructed and registered ECGs and their first derivatives. We use the scores of the Principal Components decomposition as covariates in a generalized linear model to predict the presence of the disease in a new patient. Hence, a new semi-automatic diagnostic procedure is proposed to estimate the risk of infarction (in the case of interest, the probability of being affected by Left Bundle Brunch Block). The performance of this classification method is evaluated and compared with other methods proposed in literature. Finally, the robustness of the procedure is checked via leave-j-out techniques. © The Author(s) 2013.

Hierarchical multivariate covariance analysis of metabolic connectivity

PubMed Central

Carbonell, Felix; Charil, Arnaud; Zijdenbos, Alex P; Evans, Alan C; Bedell, Barry J

2014-01-01

Conventional brain connectivity analysis is typically based on the assessment of interregional correlations. Given that correlation coefficients are derived from both covariance and variance, group differences in covariance may be obscured by differences in the variance terms. To facilitate a comprehensive assessment of connectivity, we propose a unified statistical framework that interrogates the individual terms of the correlation coefficient. We have evaluated the utility of this method for metabolic connectivity analysis using [18F]2-fluoro-2-deoxyglucose (FDG) positron emission tomography (PET) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. As an illustrative example of the utility of this approach, we examined metabolic connectivity in angular gyrus and precuneus seed regions of mild cognitive impairment (MCI) subjects with low and high β-amyloid burdens. This new multivariate method allowed us to identify alterations in the metabolic connectome, which would not have been detected using classic seed-based correlation analysis. Ultimately, this novel approach should be extensible to brain network analysis and broadly applicable to other imaging modalities, such as functional magnetic resonance imaging (MRI). PMID:25294129

Evaluating the efficiency of spectral resolution of univariate methods manipulating ratio spectra and comparing to multivariate methods: An application to ternary mixture in common cold preparation

NASA Astrophysics Data System (ADS)

Moustafa, Azza Aziz; Salem, Hesham; Hegazy, Maha; Ali, Omnia

2015-02-01

Simple, accurate, and selective methods have been developed and validated for simultaneous determination of a ternary mixture of Chlorpheniramine maleate (CPM), Pseudoephedrine HCl (PSE) and Ibuprofen (IBF), in tablet dosage form. Four univariate methods manipulating ratio spectra were applied, method A is the double divisor-ratio difference spectrophotometric method (DD-RD). Method B is double divisor-derivative ratio spectrophotometric method (DD-RD). Method C is derivative ratio spectrum-zero crossing method (DRZC), while method D is mean centering of ratio spectra (MCR). Two multivariate methods were also developed and validated, methods E and F are Principal Component Regression (PCR) and Partial Least Squares (PLSs). The proposed methods have the advantage of simultaneous determination of the mentioned drugs without prior separation steps. They were successfully applied to laboratory-prepared mixtures and to commercial pharmaceutical preparation without any interference from additives. The proposed methods were validated according to the ICH guidelines. The obtained results were statistically compared with the official methods where no significant difference was observed regarding both accuracy and precision.

Multivariate neural biomarkers of emotional states are categorically distinct.

PubMed

Kragel, Philip A; LaBar, Kevin S

2015-11-01

Understanding how emotions are represented neurally is a central aim of affective neuroscience. Despite decades of neuroimaging efforts addressing this question, it remains unclear whether emotions are represented as distinct entities, as predicted by categorical theories, or are constructed from a smaller set of underlying factors, as predicted by dimensional accounts. Here, we capitalize on multivariate statistical approaches and computational modeling to directly evaluate these theoretical perspectives. We elicited discrete emotional states using music and films during functional magnetic resonance imaging scanning. Distinct patterns of neural activation predicted the emotion category of stimuli and tracked subjective experience. Bayesian model comparison revealed that combining dimensional and categorical models of emotion best characterized the information content of activation patterns. Surprisingly, categorical and dimensional aspects of emotion experience captured unique and opposing sources of neural information. These results indicate that diverse emotional states are poorly differentiated by simple models of valence and arousal, and that activity within separable neural systems can be mapped to unique emotion categories. © The Author (2015). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Characterization of monofloral honeys with multivariate analysis of their chemical profile and antioxidant activity.

PubMed

Sant'Ana, Luiza D'O; Sousa, Juliana P L M; Salgueiro, Fernanda B; Lorenzon, Maria Cristina Affonso; Castro, Rosane N

2012-01-01

Various bioactive chemical constituents were quantified for 21 honey samples obtained at Rio de Janeiro and Minas Gerais, Brazil. To evaluate their antioxidant activity, 3 different methods were used: the ferric reducing antioxidant power, the 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical-scavenging activity, and the 2,2'-azinobis (3-ethylbenzothiazolin)-6-sulfonate (ABTS) assays. Correlations between the parameters were statistically significant (-0.6684 ≤ r ≤-0.8410, P < 0.05). Principal component analysis showed that honey samples from the same floral origins had more similar profiles, which made it possible to group the eucalyptus, morrão de candeia, and cambara honey samples in 3 distinct areas, while cluster analysis could separate the artificial honey from the floral honeys. This research might aid in the discrimination of honey floral origin, by using simple analytical methods in association with multivariate analysis, which could also show a great difference among floral honeys and artificial honey, indicating a possible way to help with the identification of artificial honeys. © 2011 Institute of Food Technologists®

Descriptor selection for banana accessions based on univariate and multivariate analysis.

PubMed

Brandão, L P; Souza, C P F; Pereira, V M; Silva, S O; Santos-Serejo, J A; Ledo, C A S; Amorim, E P

2013-05-14

Our objective was to establish a minimum number of morphological descriptors for the characterization of banana germplasm and evaluate the efficiency of removal of redundant characters, based on univariate and multivariate statistical analyses. Phenotypic characterization was made of 77 accessions from Bahia, Brazil, using 92 descriptors. The selection of the descriptors was carried out by principal components analysis (quantitative) and by entropy (multi-category). Efficiency of elimination was analyzed by a comparative study between the clusters formed, taking into consideration all 92 descriptors and smaller groups. The selected descriptors were analyzed with the Ward-MLM procedure and a combined matrix formed by the Gower algorithm. We were able to reduce the number of descriptors used for characterizing the banana germplasm (42%). The correlation between the matrices considering the 92 descriptors and the selected ones was 0.82, showing that the reduction in the number of descriptors did not influence estimation of genetic variability between the banana accessions. We conclude that removing these descriptors caused no loss of information, considering the groups formed from pre-established criteria, including subgroup/subspecies.

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

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

Carver, A; Rowbottom, C

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

Biomarkers of cardiovascular stress and incident chronic kidney disease.

PubMed

Ho, Jennifer E; Hwang, Shih-Jen; Wollert, Kai C; Larson, Martin G; Cheng, Susan; Kempf, Tibor; Vasan, Ramachandran S; Januzzi, James L; Wang, Thomas J; Fox, Caroline S

2013-11-01

Growth differentiation factor-15 (GDF-15), soluble ST2 (sST2), and high-sensitivity troponin I (hsTnI) are emerging predictors of adverse clinical outcomes. We examined whether circulating concentrations are related to the development of kidney disease in the community. Plasma GDF-15, sST2, and hsTnI concentrations were measured in 2614 Framingham Offspring cohort participants (mean age 57 years, 54% women) at the sixth examination cycle (1995-1998). Associations of biomarkers with incident chronic kidney disease [CKD, eGFR <60 mL · min(-1) · (1.73 m(2)) (-1), n = 276], microalbuminuria (urinary albumin to creatinine ratio ≥25 mg/g in women and 17 mg/g in men, n = 191), and rapid decline in renal function [decline in eGFR ≥3 mL · min(-1) · (1.73 m(2)) (-1) per year, n = 237], were evaluated using multivariable logistic regression; P < 0.006 was considered statistically significant in primary analyses. Participants were followed over a mean of 9.5 years. Higher plasma GDF-15 was associated with incident CKD [multivariable-adjusted odds ratio (OR) 1.9 per 1-U increase in log-GDF-15, 95% CI 1.6-2.3, P < 0.0001] and rapid decline in renal function (OR, 1.6; 95% CI, 1.3-1.8; P < 0.0001). GDF-15, sST2, and hsTnI had suggestive associations with incident microalbuminuria but did not meet the prespecified P-value threshold after multivariable adjustment. Adding plasma GDF-15 to clinical covariates improved risk prediction of incident CKD: the c-statistic increased from 0.826 to 0.845 (P = 0.0007), and categorical net reclassification was 6.3% (95% CI, 2.7-9.9%). Higher circulating GDF-15 is associated with incident renal outcomes and improves risk prediction of incident CKD. These findings may provide insights into the mechanisms of renal injury.

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

PubMed

Cleophas, Ton J

2016-01-01

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

A simple rapid approach using coupled multivariate statistical methods, GIS and trajectory models to delineate areas of common oil spill risk

NASA Astrophysics Data System (ADS)

Guillen, George; Rainey, Gail; Morin, Michelle

2004-04-01

Currently, the Minerals Management Service uses the Oil Spill Risk Analysis model (OSRAM) to predict the movement of potential oil spills greater than 1000 bbl originating from offshore oil and gas facilities. OSRAM generates oil spill trajectories using meteorological and hydrological data input from either actual physical measurements or estimates generated from other hydrological models. OSRAM and many other models produce output matrices of average, maximum and minimum contact probabilities to specific landfall or target segments (columns) from oil spills at specific points (rows). Analysts and managers are often interested in identifying geographic areas or groups of facilities that pose similar risks to specific targets or groups of targets if a spill occurred. Unfortunately, due to the potentially large matrix generated by many spill models, this question is difficult to answer without the use of data reduction and visualization methods. In our study we utilized a multivariate statistical method called cluster analysis to group areas of similar risk based on potential distribution of landfall target trajectory probabilities. We also utilized ArcView™ GIS to display spill launch point groupings. The combination of GIS and multivariate statistical techniques in the post-processing of trajectory model output is a powerful tool for identifying and delineating areas of similar risk from multiple spill sources. We strongly encourage modelers, statistical and GIS software programmers to closely collaborate to produce a more seamless integration of these technologies and approaches to analyzing data. They are complimentary methods that strengthen the overall assessment of spill risks.

Socio-Demographic and Clinical Characteristics are Not Clinically Useful Predictors of Refill Adherence in Patients with Hypertension

PubMed Central

Steiner, John F.; Ho, P. Michael; Beaty, Brenda L.; Dickinson, L. Miriam; Hanratty, Rebecca; Zeng, Chan; Tavel, Heather M.; Havranek, Edward P.; Davidson, Arthur J.; Magid, David J.; Estacio, Raymond O.

2009-01-01

Background Although many studies have identified patient characteristics or chronic diseases associated with medication adherence, the clinical utility of such predictors has rarely been assessed. We attempted to develop clinical prediction rules for adherence with antihypertensive medications in two health care delivery systems. Methods and Results Retrospective cohort studies of hypertension registries in an inner-city health care delivery system (N = 17176) and a health maintenance organization (N = 94297) in Denver, Colorado. Adherence was defined by acquisition of 80% or more of antihypertensive medications. A multivariable model in the inner-city system found that adherent patients (36.3% of the total) were more likely than non-adherent patients to be older, white, married, and acculturated in US society, to have diabetes or cerebrovascular disease, not to abuse alcohol or controlled substances, and to be prescribed less than three antihypertensive medications. Although statistically significant, all multivariate odds ratios were 1.7 or less, and the model did not accurately discriminate adherent from non-adherent patients (C-statistic = 0.606). In the health maintenance organization, where 72.1% of patients were adherent, significant but weak associations existed between adherence and older age, white race, the lack of alcohol abuse, and fewer antihypertensive medications. The multivariate model again failed to accurately discriminate adherent from non-adherent individuals (C-statistic = 0.576). Conclusions Although certain socio-demographic characteristics or clinical diagnoses are statistically associated with adherence to refills of antihypertensive medications, a combination of these characteristics is not sufficiently accurate to allow clinicians to predict whether their patients will be adherent with treatment. PMID:20031876

Publication of statistically significant research findings in prosthodontics & implant dentistry in the context of other dental specialties.

PubMed

Papageorgiou, Spyridon N; Kloukos, Dimitrios; Petridis, Haralampos; Pandis, Nikolaos

2015-10-01

To assess the hypothesis that there is excessive reporting of statistically significant studies published in prosthodontic and implantology journals, which could indicate selective publication. The last 30 issues of 9 journals in prosthodontics and implant dentistry were hand-searched for articles with statistical analyses. The percentages of significant and non-significant results were tabulated by parameter of interest. Univariable/multivariable logistic regression analyses were applied to identify possible predictors of reporting statistically significance findings. The results of this study were compared with similar studies in dentistry with random-effects meta-analyses. From the 2323 included studies 71% of them reported statistically significant results, with the significant results ranging from 47% to 86%. Multivariable modeling identified that geographical area and involvement of statistician were predictors of statistically significant results. Compared to interventional studies, the odds that in vitro and observational studies would report statistically significant results was increased by 1.20 times (OR: 2.20, 95% CI: 1.66-2.92) and 0.35 times (OR: 1.35, 95% CI: 1.05-1.73), respectively. The probability of statistically significant results from randomized controlled trials was significantly lower compared to various study designs (difference: 30%, 95% CI: 11-49%). Likewise the probability of statistically significant results in prosthodontics and implant dentistry was lower compared to other dental specialties, but this result did not reach statistical significant (P>0.05). The majority of studies identified in the fields of prosthodontics and implant dentistry presented statistically significant results. The same trend existed in publications of other specialties in dentistry. Copyright © 2015 Elsevier Ltd. All rights reserved.

Estimation and model selection of semiparametric multivariate survival functions under general censorship.

PubMed

Chen, Xiaohong; Fan, Yanqin; Pouzo, Demian; Ying, Zhiliang

2010-07-01

We study estimation and model selection of semiparametric models of multivariate survival functions for censored data, which are characterized by possibly misspecified parametric copulas and nonparametric marginal survivals. We obtain the consistency and root- n asymptotic normality of a two-step copula estimator to the pseudo-true copula parameter value according to KLIC, and provide a simple consistent estimator of its asymptotic variance, allowing for a first-step nonparametric estimation of the marginal survivals. We establish the asymptotic distribution of the penalized pseudo-likelihood ratio statistic for comparing multiple semiparametric multivariate survival functions subject to copula misspecification and general censorship. An empirical application is provided.

Estimation and model selection of semiparametric multivariate survival functions under general censorship

PubMed Central

Chen, Xiaohong; Fan, Yanqin; Pouzo, Demian; Ying, Zhiliang

2013-01-01

We study estimation and model selection of semiparametric models of multivariate survival functions for censored data, which are characterized by possibly misspecified parametric copulas and nonparametric marginal survivals. We obtain the consistency and root-n asymptotic normality of a two-step copula estimator to the pseudo-true copula parameter value according to KLIC, and provide a simple consistent estimator of its asymptotic variance, allowing for a first-step nonparametric estimation of the marginal survivals. We establish the asymptotic distribution of the penalized pseudo-likelihood ratio statistic for comparing multiple semiparametric multivariate survival functions subject to copula misspecification and general censorship. An empirical application is provided. PMID:24790286

Effect of oxygen on volatile and sensory characteristics of Cabernet Sauvignon during secondary shelf life.

PubMed

Lee, Dong-Hyun; Kang, Bo-Sik; Park, Hyun-Jin

2011-11-09

The oxidation of Cabernet Sauvignon wines during secondary shelf life was studied by headspace solid-phase microextraction (HS-SPME) coupled to gas chromatography-quadrupole mass spectrometry (GC-qMS) and sensory tests, with the support of multivariate statistical analyses such as OPLS-DA loading plot and PCA score plot. Four different oxidation conditions were established during a 1-week secondary shelf life. Samples collected on a regular basis were analyzed to determine the changes of volatile chemicals, with sensory characteristics evaluated through pattern recognition models. During secondary shelf life the separation among collected samples depended on the degree of oxidation in wine. Isoamyl acetate, ethyl decanoate, nonanoic acid, n-decanoic acid, undecanoic acid, 2-furancarboxylic acid, dodecanoic acid, and phenylacetaldehyde were determined to be associated with the oxidation of the wine. PCA sensory evaluation revealed that least oxidized wine and fresh wine was well-separated from more oxidized wines, demonstrating that sensory characteristics of less oxidized wines tend toward "fruity", "citrous", and "sweetness", while those of more oxidized wines are positively correlated with "animal", "bitterness", and "dairy". The study also demonstrates that OPLS-DA and PCA are very useful statistical tools for the understanding of wine oxidation.

A Novel Genome-Information Content-Based Statistic for Genome-Wide Association Analysis Designed for Next-Generation Sequencing Data

PubMed Central

Luo, Li; Zhu, Yun

2012-01-01

Abstract The genome-wide association studies (GWAS) designed for next-generation sequencing data involve testing association of genomic variants, including common, low frequency, and rare variants. The current strategies for association studies are well developed for identifying association of common variants with the common diseases, but may be ill-suited when large amounts of allelic heterogeneity are present in sequence data. Recently, group tests that analyze their collective frequency differences between cases and controls shift the current variant-by-variant analysis paradigm for GWAS of common variants to the collective test of multiple variants in the association analysis of rare variants. However, group tests ignore differences in genetic effects among SNPs at different genomic locations. As an alternative to group tests, we developed a novel genome-information content-based statistics for testing association of the entire allele frequency spectrum of genomic variation with the diseases. To evaluate the performance of the proposed statistics, we use large-scale simulations based on whole genome low coverage pilot data in the 1000 Genomes Project to calculate the type 1 error rates and power of seven alternative statistics: a genome-information content-based statistic, the generalized T2, collapsing method, multivariate and collapsing (CMC) method, individual χ2 test, weighted-sum statistic, and variable threshold statistic. Finally, we apply the seven statistics to published resequencing dataset from ANGPTL3, ANGPTL4, ANGPTL5, and ANGPTL6 genes in the Dallas Heart Study. We report that the genome-information content-based statistic has significantly improved type 1 error rates and higher power than the other six statistics in both simulated and empirical datasets. PMID:22651812

A novel genome-information content-based statistic for genome-wide association analysis designed for next-generation sequencing data.

PubMed

Luo, Li; Zhu, Yun; Xiong, Momiao

2012-06-01

The genome-wide association studies (GWAS) designed for next-generation sequencing data involve testing association of genomic variants, including common, low frequency, and rare variants. The current strategies for association studies are well developed for identifying association of common variants with the common diseases, but may be ill-suited when large amounts of allelic heterogeneity are present in sequence data. Recently, group tests that analyze their collective frequency differences between cases and controls shift the current variant-by-variant analysis paradigm for GWAS of common variants to the collective test of multiple variants in the association analysis of rare variants. However, group tests ignore differences in genetic effects among SNPs at different genomic locations. As an alternative to group tests, we developed a novel genome-information content-based statistics for testing association of the entire allele frequency spectrum of genomic variation with the diseases. To evaluate the performance of the proposed statistics, we use large-scale simulations based on whole genome low coverage pilot data in the 1000 Genomes Project to calculate the type 1 error rates and power of seven alternative statistics: a genome-information content-based statistic, the generalized T(2), collapsing method, multivariate and collapsing (CMC) method, individual χ(2) test, weighted-sum statistic, and variable threshold statistic. Finally, we apply the seven statistics to published resequencing dataset from ANGPTL3, ANGPTL4, ANGPTL5, and ANGPTL6 genes in the Dallas Heart Study. We report that the genome-information content-based statistic has significantly improved type 1 error rates and higher power than the other six statistics in both simulated and empirical datasets.

Fragility of Results in Ophthalmology Randomized Controlled Trials: A Systematic Review.

PubMed

Shen, Carl; Shamsudeen, Isabel; Farrokhyar, Forough; Sabri, Kourosh

2018-05-01

Evidence-based medicine is guided by our interpretation of randomized controlled trials (RCTs) that address important clinical questions. Evaluation of the robustness of statistically significant outcomes adds a crucial element to the global assessment of trial findings. The purpose of this systematic review was to determine the robustness of ophthalmology RCTs through application of the Fragility Index (FI), a novel metric of the robustness of statistically significant outcomes. Systematic review. A literature search (MEDLINE) was performed for all RCTs published in top ophthalmology journals and ophthalmology-related RCTs published in high-impact journals in the past 10 years. Two reviewers independently screened 1811 identified articles for inclusion if they (1) were a human ophthalmology-related trial, (2) had a 1:1 prospective study design, and (3) reported a statistically significant dichotomous outcome in the abstract. All relevant data, including outcome, P value, number of patients in each group, number of events in each group, number of patients lost to follow-up, and trial characteristics, were extracted. The FI of each RCT was calculated and multivariate regression applied to determine predictive factors. The 156 trials had a median sample size of 91.5 (range, 13-2593) patients/eyes, and a median of 28 (range, 4-2217) events. The median FI of the included trials was 2 (range, 0-48), meaning that if 2 non-events were switched to events in the treatment group, the result would lose its statistical significance. A quarter of all trials had an FI of 1 or less, and 75% of trials had an FI of 6 or less. The FI was less than the number of missing data points in 52.6% of trials. Predictive factors for FI by multivariate regression included smaller P value (P < 0.001), larger sample size (P = 0.001), larger number of events (P = 0.011), and journal impact factor (P = 0.029). In ophthalmology trials, statistically significant dichotomous results are often fragile, meaning that a difference of only a couple of events can change the statistical significance. An application of the FI in RCTs may aid in the interpretation of results and assessment of quality of evidence. Copyright © 2017 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.

Edward J. Wolfrum | NREL

Science.gov Websites

. Another project used multivariate statistics to develop a novel device to non-invasively measure hydrogen Cellulosic Ethanol Production due to Experimental Measurement Uncertainty," Biotechnology for Biofuels

Application of Maxent Multivariate Analysis to Define Climate-Change Effects on Species Distributions and Changes

DTIC Science & Technology

2014-09-01

approaches. Ecological Modelling Volume 200, Issues 1–2, 10, pp 1–19. Buhlmann, Kurt A ., Thomas S.B. Akre , John B. Iverson, Deno Karapatakis, Russell A ...statistical multivariate analysis to define the current and projected future range probability for species of interest to Army land managers. A software...15 Figure 4. RCW omission rate and predicted area as a function of the cumulative threshold

Deterministic annealing for density estimation by multivariate normal mixtures

NASA Astrophysics Data System (ADS)

Kloppenburg, Martin; Tavan, Paul

1997-03-01

An approach to maximum-likelihood density estimation by mixtures of multivariate normal distributions for large high-dimensional data sets is presented. Conventionally that problem is tackled by notoriously unstable expectation-maximization (EM) algorithms. We remove these instabilities by the introduction of soft constraints, enabling deterministic annealing. Our developments are motivated by the proof that algorithmically stable fuzzy clustering methods that are derived from statistical physics analogs are special cases of EM procedures.

A Note on Asymptotic Joint Distribution of the Eigenvalues of a Noncentral Multivariate F Matrix.

DTIC Science & Technology

1984-11-01

Krishnaiah (1982). Now, let us consider the samples drawn from the k multivariate normal popuiejons. Let (Xlt....Xpt) denote the mean vector of the t...to maltivariate problems. Sankh-ya, 4, 381-39(s. (71 KRISHNAIAH , P. R. (1982). Selection of variables in discrimlnant analysis. In Handbook of...Statistics, Volume 2 (P. R. Krishnaiah , editor), 805-820. North-Holland Publishing Company. 6. Unclassifie INSTRUCTIONS REPORT DOCUMENTATION PAGE

Determining the Number of Component Clusters in the Standard Multivariate Normal Mixture Model Using Model-Selection Criteria.

DTIC Science & Technology

1983-06-16

has been advocated by Gnanadesikan and 𔃾ilk (1969), and others in the literature. This suggests that, if we use the formal signficance test type...American Statistical Asso., 62, 1159-1178. Gnanadesikan , R., and Wilk, M..B. (1969). Data Analytic Methods in Multi- variate Statistical Analysis. In

What is a good index? Problems with statistically based indicators and the Malmquist index as alternative

USDA-ARS?s Scientific Manuscript database

Conventional multivariate statistical methods have been used for decades to calculate environmental indicators. These methods generally work fine if they are used in a situation where the method can be tailored to the data. But there is some skepticism that the methods might fail in the context of s...

Randomized clinical trial of encapsulated and hand-mixed glass-ionomer ART restorations: one-year follow-up

PubMed Central

Freitas, Maria Cristina Carvalho de Almendra; Fagundes, Ticiane Cestari; Modena, Karin Cristina da Silva; Cardia, Guilherme Saintive; Navarro, Maria Fidela de Lima

2018-01-01

Abstract Objective This prospective, randomized, split-mouth clinical trial evaluated the clinical performance of conventional glass ionomer cement (GIC; Riva Self-Cure, SDI), supplied in capsules or in powder/liquid kits and placed in Class I cavities in permanent molars by the Atraumatic Restorative Treatment (ART) approach. Material and Methods A total of 80 restorations were randomly placed in 40 patients aged 11-15 years. Each patient received one restoration with each type of GIC. The restorations were evaluated after periods of 15 days (baseline), 6 months, and 1 year, according to ART criteria. Wilcoxon matched pairs, multivariate logistic regression, and Gehan-Wilcoxon tests were used for statistical analysis. Results Patients were evaluated after 15 days (n=40), 6 months (n=34), and 1 year (n=29). Encapsulated GICs showed significantly superior clinical performance compared with hand-mixed GICs at baseline (p=0.017), 6 months (p=0.001), and 1 year (p=0.026). For hand-mixed GIC, a statistically significant difference was only observed over the period of baseline to 1 year (p=0.001). Encapsulated GIC presented statistically significant differences for the following periods: 6 months to 1 year (p=0.028) and baseline to 1 year (p=0.002). Encapsulated GIC presented superior cumulative survival rate than hand-mixed GIC over one year. Importantly, both GICs exhibited decreased survival over time. Conclusions Encapsulated GIC promoted better ART performance, with an annual failure rate of 24%; in contrast, hand-mixed GIC demonstrated a failure rate of 42%. PMID:29364343

Is there a relationship between periodontal disease and causes of death? A cross sectional study.

PubMed

Natto, Zuhair S; Aladmawy, Majdi; Alasqah, Mohammed; Papas, Athena

2015-01-01

The aim of this study was to evaluate whether there is any correlation between periodontal disease and mortality contributing factors, such as cardiovascular disease and diabetes mellitus in the elderly population. A dental evaluation was performed by a single examiner at Tufts University dental clinics for 284 patients. Periodontal assessments were performed by probing with a manual UNC-15 periodontal probe to measure pocket depth and clinical attachment level (CAL) at 6 sites. Causes of death abstracted from death certificate. Statistical analysis involved ANOVA, chi-square and multivariate logistic regression analysis. The demographics of the population sample indicated that, most were females (except for diabetes mellitus), white, married, completed 13 years of education and were 83 years old on average. CAL (continuous or dichotomous) and marital status attained statistical significance (p<0.05) in contingency table analysis (Chi-square for independence). Individuals with increased CAL were 2.16 times more likely (OR=2.16, 95% CI=1.47-3.17) to die due to CVD and this effect persisted even after control for age, marital status, gender, race, years of education (OR=2.03, 95% CI=1.35-3.03). CAL (continuous or dichotomous) was much higher among those who died due to diabetes mellitus or out of state of Massachusetts. However, these results were not statistically significant. The same pattern was observed with pocket depth (continuous or dichotomous), but these results were not statistically significant either. CAL seems to be more sensitive to chronic diseases than pocket depth. Among those conditions, cardiovascular disease has the strongest effect.

Statistically Correct Methodology for Compositional Data in New Discriminant Function Tectonomagmatic Diagrams and Application to Ophiolite Origin

NASA Astrophysics Data System (ADS)

Verma, Surendra P.; Pandarinath, Kailasa; Verma, Sanjeet K.

2011-07-01

In the lead presentation (invited talk) of Session SE05 (Frontiers in Geochemistry with Reference to Lithospheric Evolution and Metallogeny) of AOGS2010, we have highlighted the requirement of correct statistical treatment of geochemical data. In most diagrams used for interpreting compositional data, the basic statistical assumption of open space for all variables is violated. Among these graphic tools, discrimination diagrams have been in use for nearly 40 years to decipher tectonic setting. The newer set of five tectonomagmatic discrimination diagrams published in 2006 (based on major-elements) and two sets made available in 2008 and 2011 (both based on immobile elements) fulfill all statistical requirements for correct handling of compositional data, including the multivariate nature of compositional variables, representative sampling, and probability-based tectonic field boundaries. Additionally in the most recent proposal of 2011, samples having normally distributed, discordant-outlier free, log-ratio variables were used in linear discriminant analysis. In these three sets of five diagrams each, discrimination was successfully documented for four tectonic settings (island arc, continental rift, ocean-island, and mid-ocean ridge). The discrimination diagrams have been extensively evaluated for their performance by different workers. We exemplify these two sets of new diagrams (one set based on major-elements and the other on immobile elements) using ophiolites from Boso Peninsula, Japan. This example is included for illustration purposes only and is not meant for testing of these newer diagrams. Their evaluation and comparison with older, conventional bivariate or ternary diagrams have been reported in other papers.

The association of 83 plasma proteins with CHD mortality, BMI, HDL-, and total-cholesterol in men: applying multivariate statistics to identify proteins with prognostic value and biological relevance.

PubMed

Heidema, A Geert; Thissen, Uwe; Boer, Jolanda M A; Bouwman, Freek G; Feskens, Edith J M; Mariman, Edwin C M

2009-06-01

In this study, we applied the multivariate statistical tool Partial Least Squares (PLS) to analyze the relative importance of 83 plasma proteins in relation to coronary heart disease (CHD) mortality and the intermediate end points body mass index, HDL-cholesterol and total cholesterol. From a Dutch monitoring project for cardiovascular disease risk factors, men who died of CHD between initial participation (1987-1991) and end of follow-up (January 1, 2000) (N = 44) and matched controls (N = 44) were selected. Baseline plasma concentrations of proteins were measured by a multiplex immunoassay. With the use of PLS, we identified 15 proteins with prognostic value for CHD mortality and sets of proteins associated with the intermediate end points. Subsequently, sets of proteins and intermediate end points were analyzed together by Principal Components Analysis, indicating that proteins involved in inflammation explained most of the variance, followed by proteins involved in metabolism and proteins associated with total-C. This study is one of the first in which the association of a large number of plasma proteins with CHD mortality and intermediate end points is investigated by applying multivariate statistics, providing insight in the relationships among proteins, intermediate end points and CHD mortality, and a set of proteins with prognostic value.

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.

Extracting chemical information from high-resolution Kβ X-ray emission spectroscopy

NASA Astrophysics Data System (ADS)

Limandri, S.; Robledo, J.; Tirao, G.

2018-06-01

High-resolution X-ray emission spectroscopy allows studying the chemical environment of a wide variety of materials. Chemical information can be obtained by fitting the X-ray spectra and observing the behavior of some spectral features. Spectral changes can also be quantified by means of statistical parameters calculated by considering the spectrum as a probability distribution. Another possibility is to perform statistical multivariate analysis, such as principal component analysis. In this work the performance of these procedures for extracting chemical information in X-ray emission spectroscopy spectra for mixtures of Mn2+ and Mn4+ oxides are studied. A detail analysis of the parameters obtained, as well as the associated uncertainties is shown. The methodologies are also applied for Mn oxidation state characterization of double perovskite oxides Ba1+xLa1-xMnSbO6 (with 0 ≤ x ≤ 0.7). The results show that statistical parameters and multivariate analysis are the most suitable for the analysis of this kind of spectra.

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

DOE PAGES

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

2014-12-02

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

Multivariate statistical model for 3D image segmentation with application to medical images.

PubMed

John, Nigel M; Kabuka, Mansur R; Ibrahim, Mohamed O

2003-12-01

In this article we describe a statistical model that was developed to segment brain magnetic resonance images. The statistical segmentation algorithm was applied after a pre-processing stage involving the use of a 3D anisotropic filter along with histogram equalization techniques. The segmentation algorithm makes use of prior knowledge and a probability-based multivariate model designed to semi-automate the process of segmentation. The algorithm was applied to images obtained from the Center for Morphometric Analysis at Massachusetts General Hospital as part of the Internet Brain Segmentation Repository (IBSR). The developed algorithm showed improved accuracy over the k-means, adaptive Maximum Apriori Probability (MAP), biased MAP, and other algorithms. Experimental results showing the segmentation and the results of comparisons with other algorithms are provided. Results are based on an overlap criterion against expertly segmented images from the IBSR. The algorithm produced average results of approximately 80% overlap with the expertly segmented images (compared with 85% for manual segmentation and 55% for other algorithms).

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

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

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

2014-12-01

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

Harnessing Multivariate Statistics for Ellipsoidal Data in Structural Geology

NASA Astrophysics Data System (ADS)

Roberts, N.; Davis, J. R.; Titus, S.; Tikoff, B.

2015-12-01

Most structural geology articles do not state significance levels, report confidence intervals, or perform regressions to find trends. This is, in part, because structural data tend to include directions, orientations, ellipsoids, and tensors, which are not treatable by elementary statistics. We describe a full procedural methodology for the statistical treatment of ellipsoidal data. We use a reconstructed dataset of deformed ooids in Maryland from Cloos (1947) to illustrate the process. Normalized ellipsoids have five degrees of freedom and can be represented by a second order tensor. This tensor can be permuted into a five dimensional vector that belongs to a vector space and can be treated with standard multivariate statistics. Cloos made several claims about the distribution of deformation in the South Mountain fold, Maryland, and we reexamine two particular claims using hypothesis testing: 1) octahedral shear strain increases towards the axial plane of the fold; 2) finite strain orientation varies systematically along the trend of the axial trace as it bends with the Appalachian orogen. We then test the null hypothesis that the southern segment of South Mountain is the same as the northern segment. This test illustrates the application of ellipsoidal statistics, which combine both orientation and shape. We report confidence intervals for each test, and graphically display our results with novel plots. This poster illustrates the importance of statistics in structural geology, especially when working with noisy or small datasets.

A Non-parametric Cutout Index for Robust Evaluation of Identified Proteins*

PubMed Central

Serang, Oliver; Paulo, Joao; Steen, Hanno; Steen, Judith A.

2013-01-01

This paper proposes a novel, automated method for evaluating sets of proteins identified using mass spectrometry. The remaining peptide-spectrum match score distributions of protein sets are compared to an empirical absent peptide-spectrum match score distribution, and a Bayesian non-parametric method reminiscent of the Dirichlet process is presented to accurately perform this comparison. Thus, for a given protein set, the process computes the likelihood that the proteins identified are correctly identified. First, the method is used to evaluate protein sets chosen using different protein-level false discovery rate (FDR) thresholds, assigning each protein set a likelihood. The protein set assigned the highest likelihood is used to choose a non-arbitrary protein-level FDR threshold. Because the method can be used to evaluate any protein identification strategy (and is not limited to mere comparisons of different FDR thresholds), we subsequently use the method to compare and evaluate multiple simple methods for merging peptide evidence over replicate experiments. The general statistical approach can be applied to other types of data (e.g. RNA sequencing) and generalizes to multivariate problems. PMID:23292186

Informing Federal Policy on Firearm Restrictions for Veterans with Fiduciaries: Risk Indicators in the Post-Deployment Mental Health Study.

PubMed

Swanson, Jeffrey; Easter, Michele; Brancu, Mira; Fairbank, John A

2018-05-24

This article examines the public safety rationale for a federal policy of prohibiting gun sales to veterans with psychiatric disabilities who are assigned a fiduciary to manage their benefits from the Department of Veterans Affairs. The policy was evaluated using data on 3200 post-deployment veterans from the Iraq and Afghanistan war era. Three proxy measures of fiduciary need-based on intellectual disability, drug abuse, or acute psychopathology-were associated in bivariate analysis with interpersonal violence and suicidality. In multivariate analysis, statistical significance remained only for the measure based on acute psychopathology. Implications for reforms to the fiduciary firearm restriction policy are discussed.

The Raman spectrum character of skin tumor induced by UVB

NASA Astrophysics Data System (ADS)

Wu, Shulian; Hu, Liangjun; Wang, Yunxia; Li, Yongzeng

2016-03-01

In our study, the skin canceration processes induced by UVB were analyzed from the perspective of tissue spectrum. A home-made Raman spectral system with a millimeter order excitation laser spot size combined with a multivariate statistical analysis for monitoring the skin changed irradiated by UVB was studied and the discrimination were evaluated. Raman scattering signals of the SCC and normal skin were acquired. Spectral differences in Raman spectra were revealed. Linear discriminant analysis (LDA) based on principal component analysis (PCA) were employed to generate diagnostic algorithms for the classification of skin SCC and normal. The results indicated that Raman spectroscopy combined with PCA-LDA demonstrated good potential for improving the diagnosis of skin cancers.

Longitudinal flying qualities criteria for single-pilot instrument flight operations

NASA Technical Reports Server (NTRS)

Stengel, R. F.; Bar-Gill, A.

1983-01-01

Modern estimation and control theory, flight testing, and statistical analysis were used to deduce flying qualities criteria for General Aviation Single Pilot Instrument Flight Rule (SPIFR) operations. The principal concern is that unsatisfactory aircraft dynamic response combined with high navigation/communication workload can produce problems of safety and efficiency. To alleviate these problems. The relative importance of these factors must be determined. This objective was achieved by flying SPIFR tasks with different aircraft dynamic configurations and assessing the effects of such variations under these conditions. The experimental results yielded quantitative indicators of pilot's performance and workload, and for each of them, multivariate regression was applied to evaluate several candidate flying qualities criteria.

Multivariate test power approximations for balanced linear mixed models in studies with missing data.

PubMed

Ringham, Brandy M; Kreidler, Sarah M; Muller, Keith E; Glueck, Deborah H

2016-07-30

Multilevel and longitudinal studies are frequently subject to missing data. For example, biomarker studies for oral cancer may involve multiple assays for each participant. Assays may fail, resulting in missing data values that can be assumed to be missing completely at random. Catellier and Muller proposed a data analytic technique to account for data missing at random in multilevel and longitudinal studies. They suggested modifying the degrees of freedom for both the Hotelling-Lawley trace F statistic and its null case reference distribution. We propose parallel adjustments to approximate power for this multivariate test in studies with missing data. The power approximations use a modified non-central F statistic, which is a function of (i) the expected number of complete cases, (ii) the expected number of non-missing pairs of responses, or (iii) the trimmed sample size, which is the planned sample size reduced by the anticipated proportion of missing data. The accuracy of the method is assessed by comparing the theoretical results to the Monte Carlo simulated power for the Catellier and Muller multivariate test. Over all experimental conditions, the closest approximation to the empirical power of the Catellier and Muller multivariate test is obtained by adjusting power calculations with the expected number of complete cases. The utility of the method is demonstrated with a multivariate power analysis for a hypothetical oral cancer biomarkers study. We describe how to implement the method using standard, commercially available software products and give example code. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

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.

Seasonal rationalization of river water quality sampling locations: a comparative study of the modified Sanders and multivariate statistical approaches.

PubMed

Varekar, Vikas; Karmakar, Subhankar; Jha, Ramakar

2016-02-01

The design of surface water quality sampling location is a crucial decision-making process for rationalization of monitoring network. The quantity, quality, and types of available dataset (watershed characteristics and water quality data) may affect the selection of appropriate design methodology. The modified Sanders approach and multivariate statistical techniques [particularly factor analysis (FA)/principal component analysis (PCA)] are well-accepted and widely used techniques for design of sampling locations. However, their performance may vary significantly with quantity, quality, and types of available dataset. In this paper, an attempt has been made to evaluate performance of these techniques by accounting the effect of seasonal variation, under a situation of limited water quality data but extensive watershed characteristics information, as continuous and consistent river water quality data is usually difficult to obtain, whereas watershed information may be made available through application of geospatial techniques. A case study of Kali River, Western Uttar Pradesh, India, is selected for the analysis. The monitoring was carried out at 16 sampling locations. The discrete and diffuse pollution loads at different sampling sites were estimated and accounted using modified Sanders approach, whereas the monitored physical and chemical water quality parameters were utilized as inputs for FA/PCA. The designed optimum number of sampling locations for monsoon and non-monsoon seasons by modified Sanders approach are eight and seven while that for FA/PCA are eleven and nine, respectively. Less variation in the number and locations of designed sampling sites were obtained by both techniques, which shows stability of results. A geospatial analysis has also been carried out to check the significance of designed sampling location with respect to river basin characteristics and land use of the study area. Both methods are equally efficient; however, modified Sanders approach outperforms FA/PCA when limited water quality and extensive watershed information is available. The available water quality dataset is limited and FA/PCA-based approach fails to identify monitoring locations with higher variation, as these multivariate statistical approaches are data-driven. The priority/hierarchy and number of sampling sites designed by modified Sanders approach are well justified by the land use practices and observed river basin characteristics of the study area.

Testing for significance of phase synchronisation dynamics in the EEG.

PubMed

Daly, Ian; Sweeney-Reed, Catherine M; Nasuto, Slawomir J

2013-06-01

A number of tests exist to check for statistical significance of phase synchronisation within the Electroencephalogram (EEG); however, the majority suffer from a lack of generality and applicability. They may also fail to account for temporal dynamics in the phase synchronisation, regarding synchronisation as a constant state instead of a dynamical process. Therefore, a novel test is developed for identifying the statistical significance of phase synchronisation based upon a combination of work characterising temporal dynamics of multivariate time-series and Markov modelling. We show how this method is better able to assess the significance of phase synchronisation than a range of commonly used significance tests. We also show how the method may be applied to identify and classify significantly different phase synchronisation dynamics in both univariate and multivariate datasets.

Processes and subdivisions in diogenites, a multivariate statistical analysis

NASA Technical Reports Server (NTRS)

Harriott, T. A.; Hewins, R. H.

1984-01-01

Multivariate statistical techniques used on diogenite orthopyroxene analyses show the relationships that occur within diogenites and the two orthopyroxenite components (class I and II) in the polymict diogenite Garland. Cluster analysis shows that only Peckelsheim is similar to Garland class I (Fe-rich) and the other diogenites resemble Garland class II. The unique diogenite Y 75032 may be related to type I by fractionation. Factor analysis confirms the subdivision and shows that Fe does not correlate with the weakly incompatible elements across the entire pyroxene composition range, indicating that igneous fractionation is not the process controlling total diogenite composition variation. The occurrence of two groups of diogenites is interpreted as the result of sampling or mixing of two main sequences of orthopyroxene cumulates with slightly different compositions.

Predictors of clinical outcome in pediatric oligodendroglioma: meta-analysis of individual patient data and multiple imputation.

PubMed

Wang, Kevin Yuqi; Vankov, Emilian R; Lin, Doris Da May

2018-02-01

OBJECTIVE Oligodendroglioma is a rare primary CNS neoplasm in the pediatric population, and only a limited number of studies in the literature have characterized this entity. Existing studies are limited by small sample sizes and discrepant interstudy findings in identified prognostic factors. In the present study, the authors aimed to increase the statistical power in evaluating for potential prognostic factors of pediatric oligodendrogliomas and sought to reconcile the discrepant findings present among existing studies by performing an individual-patient-data (IPD) meta-analysis and using multiple imputation to address data not directly available from existing studies. METHODS A systematic search was performed, and all studies found to be related to pediatric oligodendrogliomas and associated outcomes were screened for inclusion. Each study was searched for specific demographic and clinical characteristics of each patient and the duration of event-free survival (EFS) and overall survival (OS). Given that certain demographic and clinical information of each patient was not available within all studies, a multivariable imputation via chained equations model was used to impute missing data after the mechanism of missing data was determined. The primary end points of interest were hazard ratios for EFS and OS, as calculated by the Cox proportional-hazards model. Both univariate and multivariate analyses were performed. The multivariate model was adjusted for age, sex, tumor grade, mixed pathologies, extent of resection, chemotherapy, radiation therapy, tumor location, and initial presentation. A p value of less than 0.05 was considered statistically significant. RESULTS A systematic search identified 24 studies with both time-to-event and IPD characteristics available, and a total of 237 individual cases were available for analysis. A median of 19.4% of the values among clinical, demographic, and outcome variables in the compiled 237 cases were missing. Multivariate Cox regression analysis revealed subtotal resection (p = 0.007 [EFS] and 0.043 [OS]), initial presentation of headache (p = 0.006 [EFS] and 0.004 [OS]), mixed pathologies (p = 0.005 [EFS] and 0.049 [OS]), and location of the tumor in the parietal lobe (p = 0.044 [EFS] and 0.030 [OS]) to be significant predictors of tumor progression or recurrence and death. CONCLUSIONS The use of IPD meta-analysis provides a valuable means for increasing statistical power in investigations of disease entities with a very low incidence. Missing data are common in research, and multiple imputation is a flexible and valid approach for addressing this issue, when it is used conscientiously. Undergoing subtotal resection, having a parietal tumor, having tumors with mixed pathologies, and suffering headaches at the time of diagnosis portended a poorer prognosis in pediatric patients with oligodendroglioma.

Multivariable Parametric Cost Model for Ground Optical Telescope Assembly

NASA Technical Reports Server (NTRS)

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

2005-01-01

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

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.

MTHFR, TS and XRCC1 genetic variants may affect survival in patients with myelodysplastic syndromes treated with supportive care or azacitidine.

PubMed

Visani, G; Loscocco, F; Ruzzo, A; Galimberti, S; Graziano, F; Voso, M T; Giacomini, E; Finelli, C; Ciabatti, E; Fabiani, E; Barulli, S; Volpe, A; Magro, D; Piccaluga, P; Fuligni, F; Vignetti, M; Fazi, P; Piciocchi, A; Gabucci, E; Rocchi, M; Magnani, M; Isidori, A

2017-12-05

We evaluated the impact of genomic polymorphisms in folate-metabolizing, DNA synthesis and DNA repair enzymes on the clinical outcome of 108 patients with myelodysplastic syndromes (MDS) receiving best supportive care (BSC) or azacitidine. A statistically significant association between methylenetetrahydrofolate reductase (MTHFR) 677T/T, thymidylate synthase (TS) 5'-untranslated region (UTR) 3RG, TS 3'-UTR -6 bp/-6 bp, XRCC1 399G/G genotypes and short survival was found in patients receiving BSC by multivariate analysis (P<0.001; P=0.026; P=0.058; P=0.024). MTHFR 677T/T, TS 3'-UTR -6 bp/-6 bp and XRCC1 399G/G genotypes were associated with short survival in patients receiving azacitidine by multivariate analysis (P<0.001; P=0.004; P=0.002). We then performed an exploratory analysis to evaluate the effect of the simultaneous presence of multiple adverse variant genotypes. Interestingly, patients with ⩾1 adverse genetic variants had a short survival, independently from their International Prognostic Scoring System (IPSS) and therapy received. To our knowledge, this is the first study showing that polymorphisms in folate-metabolizing pathway, DNA synthesis and DNA repair genes could influence survival of MDS patients.The Pharmacogenomics Journal advance online publication, 5 December 2017; doi:10.1038/tpj.2017.48.

Use of metabolomics and lipidomics to evaluate the hypocholestreolemic effect of Proanthocyanidins from grape seed in a pig model.

PubMed

Quifer-Rada, Paola; Choy, Ying Yng; Calvert, Christopher C; Waterhouse, Andrew L; Lamuela-Raventos, Rosa M

2016-10-01

This work aims to evaluate changes in the fecal metabolomic profile due to grape seed extract (GSE) intake by untargeted and targeted analysis using high resolution mass spectrometry in conjunction with multivariate statistics. An intervention study with six crossbred female pigs was performed. The pigs followed a standard diet for 3 days, then they were fed with a supplemented diet containing 1% (w/w) of MegaNatural® Gold grape seed extract for 6 days. Fresh pig fecal samples were collected daily. A combination of untargeted high resolution mass spectrometry, multivariate analysis (PLS-DA), data-dependent MS/MS scan, and accurate mass database matching was used to measure the effect of the treatment on fecal composition. The resultant PLS-DA models showed a good discrimination among classes with great robustness and predictability. A total of 14 metabolites related to the GSE consumption were identified including biliary acid, dicarboxylic fatty acid, cholesterol metabolites, purine metabolites, and eicosanoid metabolites among others. Moreover, targeted metabolomics using GC-MS showed that cholesterol and its metabolites fecal excretion was increased due to the proanthocyanidins from grape seed extract. The results show that oligomeric procyanidins from GSE modifies bile acid and steroid excretion, which could exert a hypocholesterolemic effect. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Anger expression, violent behavior, and symptoms of depression among male college students in Ethiopia.

PubMed

Terasaki, Dale J; Gelaye, Bizu; Berhane, Yemane; Williams, Michelle A

2009-01-12

Depression is an important global public health problem. Given the scarcity of studies involving African youths, this study was conducted to evaluate the associations of anger expression and violent behavior with symptoms of depression among male college students. A self-administered questionnaire was used to collect information on socio-demographic and lifestyle characteristics and violent behavior among 1,176 college students in Awassa, Ethiopia in June, 2006. The questionnaire incorporated the Spielberger Anger-Out Expression (SAOE) scale and symptoms of depression were evaluated using the Patient Health Questionnaire (PHQ-9). Multivariable logistic regression procedures were used to calculate adjusted odds ratios (OR) and 95% confidence intervals (95%CI). Symptoms of depression were evident in 23.6% of participants. Some 54.3% of students reported committing at least one act of violence in the current academic year; and 29.3% of students reported high (SAOE score > or = 15) levels of anger-expression. In multivariate analysis, moderate (OR = 1.97; 95%CI 1.33-2.93) and high (OR = 3.23; 95%CI 2.14-4.88) outward anger were statistically significantly associated with increased risks of depressive symptoms. Violent behavior was noted to be associated with depressive symptoms (OR = 1.82; 95%CI 1.37-2.40). Further research should be conducted to better characterize community and individual level determinants of anger-expression, violent behavior and depression among youths.

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

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

PubMed

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

2017-05-15

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

[Quality assurance program for pain management after obstetrical perineal injury].

PubMed

Urion, L; Bayoumeu, F; Jandard, C; Fontaine, B; Bouaziz, H

2004-11-01

A quality insurance program has been set up in order to improve the relief of pain in patients with perineal injury after childbirth. The program has been developed according to the French standards of accreditation. After elaboration of a referential, a first study (103 patients) allowed to evaluate the ongoing practices and to appreciate the pain intensities. After analysis of the results, an action strategy has been elaborated, with a brand new therapeutic standard and a pain-monitoring program for nurses. Six months later, a second study (105 patients) measured the efficiency of the accomplished actions. The statistic analysis used chi2 and Kruskal-Wallis tests and a multivariate analyse (p <0.05). Several indicators led to conclude to the success of this program: analgesics prescribed systematically and earlier, best observance, larger utilisation of the NSAI, decrease of the analgesics requests, improvement of the satisfaction referred to the relief of pain. The multivariate analyse showed a risk twice as little as in the second study to have a 36th hour VAS score superior to four (p =0.03). The apply of this quality insurance program allowed to improve the analgesia after obstetric perineal injuries. A few adaptations are needed, and also more formations of the medical and paramedical staff. The durability of the accomplished actions shall be evaluated in the future.

Optimal moment determination in POME-copula based hydrometeorological dependence modelling

NASA Astrophysics Data System (ADS)

Liu, Dengfeng; Wang, Dong; Singh, Vijay P.; Wang, Yuankun; Wu, Jichun; Wang, Lachun; Zou, Xinqing; Chen, Yuanfang; Chen, Xi

2017-07-01

Copula has been commonly applied in multivariate modelling in various fields where marginal distribution inference is a key element. To develop a flexible, unbiased mathematical inference framework in hydrometeorological multivariate applications, the principle of maximum entropy (POME) is being increasingly coupled with copula. However, in previous POME-based studies, determination of optimal moment constraints has generally not been considered. The main contribution of this study is the determination of optimal moments for POME for developing a coupled optimal moment-POME-copula framework to model hydrometeorological multivariate events. In this framework, margins (marginals, or marginal distributions) are derived with the use of POME, subject to optimal moment constraints. Then, various candidate copulas are constructed according to the derived margins, and finally the most probable one is determined, based on goodness-of-fit statistics. This optimal moment-POME-copula framework is applied to model the dependence patterns of three types of hydrometeorological events: (i) single-site streamflow-water level; (ii) multi-site streamflow; and (iii) multi-site precipitation, with data collected from Yichang and Hankou in the Yangtze River basin, China. Results indicate that the optimal-moment POME is more accurate in margin fitting and the corresponding copulas reflect a good statistical performance in correlation simulation. Also, the derived copulas, capturing more patterns which traditional correlation coefficients cannot reflect, provide an efficient way in other applied scenarios concerning hydrometeorological multivariate modelling.

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

PubMed Central

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

2010-01-01

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

Formalized classification of moss litters in swampy spruce forests of intermontane depressions of Kuznetsk Alatau

NASA Astrophysics Data System (ADS)

Efremova, T. T.; Avrova, A. F.; Efremov, S. P.

2016-09-01

The approaches of multivariate statistics have been used for the numerical classification of morphogenetic types of moss litters in swampy spruce forests according to their physicochemical properties (the ash content, decomposition degree, bulk density, pH, mass, and thickness). Three clusters of moss litters— peat, peaty, and high-ash peaty—have been specified. The functions of classification for identification of new objects have been calculated and evaluated. The degree of decomposition and the ash content are the main classification parameters of litters, though all other characteristics are also statistically significant. The final prediction accuracy of the assignment of a litter to a particular cluster is 86%. Two leading factors participating in the clustering of litters have been determined. The first factor—the degree of transformation of plant remains (quality)—specifies 49% of the total variance, and the second factor—the accumulation rate (quantity)— specifies 26% of the total variance. The morphogenetic structure and physicochemical properties of the clusters of moss litters are characterized.

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

PubMed

Campos-Filho, N; Franco, E L

1989-02-01

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

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.

On measures of association among genetic variables

PubMed Central

Gianola, Daniel; Manfredi, Eduardo; Simianer, Henner

2012-01-01

Summary Systems involving many variables are important in population and quantitative genetics, for example, in multi-trait prediction of breeding values and in exploration of multi-locus associations. We studied departures of the joint distribution of sets of genetic variables from independence. New measures of association based on notions of statistical distance between distributions are presented. These are more general than correlations, which are pairwise measures, and lack a clear interpretation beyond the bivariate normal distribution. Our measures are based on logarithmic (Kullback-Leibler) and on relative ‘distances’ between distributions. Indexes of association are developed and illustrated for quantitative genetics settings in which the joint distribution of the variables is either multivariate normal or multivariate-t, and we show how the indexes can be used to study linkage disequilibrium in a two-locus system with multiple alleles and present applications to systems of correlated beta distributions. Two multivariate beta and multivariate beta-binomial processes are examined, and new distributions are introduced: the GMS-Sarmanov multivariate beta and its beta-binomial counterpart. PMID:22742500

Implementation of the Iterative Proportion Fitting Algorithm for Geostatistical Facies Modeling

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

Li Yupeng, E-mail: yupeng@ualberta.ca; Deutsch, Clayton V.

2012-06-15

In geostatistics, most stochastic algorithm for simulation of categorical variables such as facies or rock types require a conditional probability distribution. The multivariate probability distribution of all the grouped locations including the unsampled location permits calculation of the conditional probability directly based on its definition. In this article, the iterative proportion fitting (IPF) algorithm is implemented to infer this multivariate probability. Using the IPF algorithm, the multivariate probability is obtained by iterative modification to an initial estimated multivariate probability using lower order bivariate probabilities as constraints. The imposed bivariate marginal probabilities are inferred from profiles along drill holes or wells.more » In the IPF process, a sparse matrix is used to calculate the marginal probabilities from the multivariate probability, which makes the iterative fitting more tractable and practical. This algorithm can be extended to higher order marginal probability constraints as used in multiple point statistics. The theoretical framework is developed and illustrated with estimation and simulation example.« less

Metabolomic evaluation of ginsenosides distribution in Panax genus (Panax ginseng and Panax quinquefolius) using multivariate statistical analysis.

PubMed

Pace, Roberto; Martinelli, Ernesto Marco; Sardone, Nicola; D E Combarieu, Eric

2015-03-01

Ginseng is any one of the eleven species belonging to the genus Panax of the family Araliaceae and is found in North America and in eastern Asia. Ginseng is characterized by the presence of ginsenosides. Principally Panax ginseng and Panax quinquefolius are the adaptogenic herbs and are commonly distributed as health food markets. In the present study high performance liquid chromatography has been used to identify and quantify ginsenosides in the two subject species and the different parts of the plant (roots, neck, leaves, flowers, fruits). The power of this chromatographic technique to evaluate the identity of botanical material and to distinguishing different part of the plants has been investigated with metabolomic technique such as principal component analysis. Metabolomics provide a good opportunity for mining useful chemical information from the chromatographic data set resulting an important tool for quality evaluation of medicinal plants in the authenticity, consistency and efficacy. Copyright © 2015 Elsevier B.V. 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.

Why the evidence for outpatient commitment is good enough.

PubMed

Swanson, Jeffrey W; Swartz, Marvin S

2014-06-01

After nearly three decades of studies evaluating the legal practice of involuntary outpatient commitment, there is yet little consensus about its effectiveness and only limited implementation. Debate continues over how best to assist adults with serious mental illnesses who are unable or unwilling to participate in prescribed community treatment and as a result experience repeated involuntary hospitalizations or involvement with the criminal justice system. The authors comment on the Oxford Community Treatment Order Evaluation Trial (OCTET), a recently conducted randomized trial of outpatient commitment, and discuss the limitations of the study's design for resolving the persistent question of whether compulsory treatment is more effective than purely voluntary treatment for this difficult-to-reach target population. The authors conclude that the search for a definitive and generalizable randomized trial of outpatient commitment may be a quixotic quest; the field should, rather, welcome the results of well-conducted, large-scale, quasi-experimental and naturalistic studies with rigorous multivariable statistical controls.

Factors associated with inadequate fine motor skills in Brazilian students of different socioeconomic status.

PubMed

Bobbio, Tatiana Godoy; Morcillo, André Moreno; Barros Filho, Antonio de Azevedo; Concalves, Vanda Maria Gimenes

2007-12-01

The objective of this study was to evaluate and compare the motor coordination of Brazilian schoolchildren of different socioeconomic status in their first year of primary education. Factors associated with inadequate fine motor skills were identified. A total of 238 schoolchildren, 118 from a public school and 120 from a private school, were evaluated on fine motor skills using the Evolutional Neurological Examination. Statistical analysis was performed using univariate logistic regression followed by multivariate analysis. Children attending public school had a 5.5-fold greater risk of having inadequate fine motor skills for their age compared to children attending private school, while children who started school after four years of age had a 2.8-fold greater risk of having inadequate motor coordination compared to children who began school earlier. Data for this sample suggest socioeconomic factors and later entry of children to school may be associated with their fine motor skills.

Use of Stereotactic Radiosurgery in Elderly and Very Elderly Patients With Brain Metastases to Limit Toxicity Associated With Whole Brain Radiation Therapy.

PubMed

Chen, Linda; Shen, Colette; Redmond, Kristin J; Page, Brandi R; Kummerlowe, Megan; Mcnutt, Todd; Bettegowda, Chetan; Rigamonti, Daniele; Lim, Michael; Kleinberg, Lawrence

2017-07-15

We evaluated the toxicity associated with stereotactic radiosurgery (SRS) and whole brain radiation therapy (WBRT) in elderly and very elderly patients with brain metastases, as the role of SRS in geriatric patients who would traditionally receive WBRT is unclear. We conducted a retrospective review of elderly patients (aged 70-79 years) and very elderly patients (aged ≥80 years) with brain metastases who underwent RT from 2010 to 2015 at Johns Hopkins Hospital. Patients received either upfront WBRT or SRS for metastatic solid malignancies, excluding small cell lung cancer. Acute central nervous system toxicity within 3 months of RT was graded using the Radiation Therapy Oncology Group acute radiation central nervous system morbidity scale. The toxicity data between age groups and treatment modalities were analyzed using Fisher's exact test and multivariate logistic regression analysis. Kaplan-Meier curves were used to estimate the median overall survival, and the Cox proportion hazard model was used for multivariate analysis. A total of 811 brain metastases received RT in 119 geriatric patients. The median overall survival from the diagnosis of brain metastases was 4.3 months for the patients undergoing WBRT and 14.4 months for the patients undergoing SRS. On multivariate analysis, WBRT was associated with worse overall survival in this cohort of geriatric patients (odds ratio [OR] 3.7, 95% confidence interval [CI] 1.9-7.0, P<.0001) and age ≥80 years was not. WBRT was associated with significantly greater rates of any grade 1 to 4 toxicity (OR 7.5, 95% CI 1.6-33.3, P=.009) and grade 2 to 4 toxicity (OR 2.8, 95% CI 1.0-8.1, P=.047) on multivariate analysis. Elderly and very elderly patients did not have significantly different statistically acute toxicity rates when stratified by age. WBRT was associated with increased toxicity compared with SRS in elderly and very elderly patients with brain metastases. SRS, rather than WBRT, should be prospectively evaluated in geriatric patients with the goal of minimizing treatment-related toxicity. Copyright © 2017. Published by Elsevier Inc.

Probability of detecting atrazine/desethyl-atrazine and elevated concentrations of nitrate in ground water in Colorado

USGS Publications Warehouse

Rupert, Michael G.

2003-01-01

Draft Federal regulations may require that each State develop a State Pesticide Management Plan for the herbicides atrazine, alachlor, metolachlor, and simazine. Maps were developed that the State of Colorado could use to predict the probability of detecting atrazine and desethyl-atrazine (a breakdown product of atrazine) in ground water in Colorado. These maps can be incorporated into the State Pesticide Management Plan and can help provide a sound hydrogeologic basis for atrazine management in Colorado. Maps showing the probability of detecting elevated nitrite plus nitrate as nitrogen (nitrate) concentrations in ground water in Colorado also were developed because nitrate is a contaminant of concern in many areas of Colorado. Maps showing the probability of detecting atrazine and(or) desethyl-atrazine (atrazine/DEA) at or greater than concentrations of 0.1 microgram per liter and nitrate concentrations in ground water greater than 5 milligrams per liter were developed as follows: (1) Ground-water quality data were overlaid with anthropogenic and hydrogeologic data using a geographic information system to produce a data set in which each well had corresponding data on atrazine use, fertilizer use, geology, hydrogeomorphic regions, land cover, precipitation, soils, and well construction. These data then were downloaded to a statistical software package for analysis by logistic regression. (2) Relations were observed between ground-water quality and the percentage of land-cover categories within circular regions (buffers) around wells. Several buffer sizes were evaluated; the buffer size that provided the strongest relation was selected for use in the logistic regression models. (3) Relations between concentrations of atrazine/DEA and nitrate in ground water and atrazine use, fertilizer use, geology, hydrogeomorphic regions, land cover, precipitation, soils, and well-construction data were evaluated, and several preliminary multivariate models with various combinations of independent variables were constructed. (4) The multivariate models that best predicted the presence of atrazine/DEA and elevated concentrations of nitrate in ground water were selected. (5) The accuracy of the multivariate models was confirmed by validating the models with an independent set of ground-water quality data. (6) The multivariate models were entered into a geographic information system and the probability maps were constructed.

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.

An Analytic Solution to the Computation of Power and Sample Size for Genetic Association Studies under a Pleiotropic Mode of Inheritance.

PubMed

Gordon, Derek; Londono, Douglas; Patel, Payal; Kim, Wonkuk; Finch, Stephen J; Heiman, Gary A

2016-01-01

Our motivation here is to calculate the power of 3 statistical tests used when there are genetic traits that operate under a pleiotropic mode of inheritance and when qualitative phenotypes are defined by use of thresholds for the multiple quantitative phenotypes. Specifically, we formulate a multivariate function that provides the probability that an individual has a vector of specific quantitative trait values conditional on having a risk locus genotype, and we apply thresholds to define qualitative phenotypes (affected, unaffected) and compute penetrances and conditional genotype frequencies based on the multivariate function. We extend the analytic power and minimum-sample-size-necessary (MSSN) formulas for 2 categorical data-based tests (genotype, linear trend test [LTT]) of genetic association to the pleiotropic model. We further compare the MSSN of the genotype test and the LTT with that of a multivariate ANOVA (Pillai). We approximate the MSSN for statistics by linear models using a factorial design and ANOVA. With ANOVA decomposition, we determine which factors most significantly change the power/MSSN for all statistics. Finally, we determine which test statistics have the smallest MSSN. In this work, MSSN calculations are for 2 traits (bivariate distributions) only (for illustrative purposes). We note that the calculations may be extended to address any number of traits. Our key findings are that the genotype test usually has lower MSSN requirements than the LTT. More inclusive thresholds (top/bottom 25% vs. top/bottom 10%) have higher sample size requirements. The Pillai test has a much larger MSSN than both the genotype test and the LTT, as a result of sample selection. With these formulas, researchers can specify how many subjects they must collect to localize genes for pleiotropic phenotypes. © 2017 S. Karger AG, Basel.

Assessment of Coastal and Urban Flooding Hazards Applying Extreme Value Analysis and Multivariate Statistical Techniques: A Case Study in Elwood, Australia

NASA Astrophysics Data System (ADS)

Guimarães Nobre, Gabriela; Arnbjerg-Nielsen, Karsten; Rosbjerg, Dan; Madsen, Henrik

2016-04-01

Traditionally, flood risk assessment studies have been carried out from a univariate frequency analysis perspective. However, statistical dependence between hydrological variables, such as extreme rainfall and extreme sea surge, is plausible to exist, since both variables to some extent are driven by common meteorological conditions. Aiming to overcome this limitation, multivariate statistical techniques has the potential to combine different sources of flooding in the investigation. The aim of this study was to apply a range of statistical methodologies for analyzing combined extreme hydrological variables that can lead to coastal and urban flooding. The study area is the Elwood Catchment, which is a highly urbanized catchment located in the city of Port Phillip, Melbourne, Australia. The first part of the investigation dealt with the marginal extreme value distributions. Two approaches to extract extreme value series were applied (Annual Maximum and Partial Duration Series), and different probability distribution functions were fit to the observed sample. Results obtained by using the Generalized Pareto distribution demonstrate the ability of the Pareto family to model the extreme events. Advancing into multivariate extreme value analysis, first an investigation regarding the asymptotic properties of extremal dependence was carried out. As a weak positive asymptotic dependence between the bivariate extreme pairs was found, the Conditional method proposed by Heffernan and Tawn (2004) was chosen. This approach is suitable to model bivariate extreme values, which are relatively unlikely to occur together. The results show that the probability of an extreme sea surge occurring during a one-hour intensity extreme precipitation event (or vice versa) can be twice as great as what would occur when assuming independent events. Therefore, presuming independence between these two variables would result in severe underestimation of the flooding risk in the study area.

MULTIVARIATERESIDUES : A Mathematica package for computing multivariate residues

NASA Astrophysics Data System (ADS)

Larsen, Kasper J.; Rietkerk, Robbert

2018-01-01

Multivariate residues appear in many different contexts in theoretical physics and algebraic geometry. In theoretical physics, they for example give the proper definition of generalized-unitarity cuts, and they play a central role in the Grassmannian formulation of the S-matrix by Arkani-Hamed et al. In realistic cases their evaluation can be non-trivial. In this paper we provide a Mathematica package for efficient evaluation of multivariate residues based on methods from computational algebraic geometry.

Comparison of 21-Gauge and 22-Gauge Aspiration Needle in Endobronchial Ultrasound-Guided Transbronchial Needle Aspiration

PubMed Central

Akulian, Jason; Lechtzin, Noah; Yasin, Faiza; Kamdar, Biren; Ernst, Armin; Ost, David E.; Ray, Cynthia; Greenhill, Sarah R.; Jimenez, Carlos A.; Filner, Joshua; Feller-Kopman, David

2013-01-01

Background: Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is a minimally invasive procedure originally performed using a 22-gauge (22G) needle. A recently introduced 21-gauge (21G) needle may improve the diagnostic yield and sample adequacy of EBUS-TBNA, but prior smaller studies have shown conflicting results. To our knowledge, this is the largest study undertaken to date to determine whether the 21G needle adds diagnostic benefit. Methods: We retrospectively evaluated the results of 1,299 patients from the American College of Chest Physicians Quality Improvement Registry, Education, and Evaluation (AQuIRE) Diagnostic Registry who underwent EBUS-TBNA between February 2009 and September 2010 at six centers throughout the United States. Data collection included patient demographics, sample adequacy, and diagnostic yield. Analysis consisted of univariate and multivariate hierarchical logistic regression comparing diagnostic yield and sample adequacy of EBUS-TBNA specimens by needle gauge. Results: A total of 1,235 patients met inclusion criteria. Sample adequacy was obtained in 94.9% of the 22G needle group and in 94.6% of the 21G needle group (P = .81). A diagnosis was made in 51.4% of the 22G and 51.3% of the 21G groups (P = .98). Multivariate hierarchical logistic regression showed no statistical difference in sample adequacy or diagnostic yield between the two groups. The presence of rapid onsite cytologic evaluation was associated with significantly fewer needle passes per procedure when using the 21G needle (P < .001). Conclusions: There is no difference in specimen adequacy or diagnostic yield between the 21G and 22G needle groups. EBUS-TBNA in conjunction with rapid onsite cytologic evaluation and a 21G needle is associated with fewer needle passes compared with a 22G needle. PMID:23632441

Influence of vision and dental occlusion on body posture in pilots.

PubMed

Baldini, Alberto; Nota, Alessandro; Cravino, Gaia; Cioffi, Clementina; Rinaldi, Antonio; Cozza, Paola

2013-08-01

Air force pilots have great postural control, movement coordination, motor learning, and motor transformation. They undergo abnormal stresses during flight that affect their organs and systems, with consequences such as barodontalgia, bruxism, TMJ dysfunctions, and cervical pain. The aim of this study was to evaluate the influence of dental occlusion and vision on their body posture. In collaboration with the "A. Mosso" Legal Medical Institute (Aeronautica Militare), two groups, consisting of 20 air force and 20 civilian pilots, were selected for the study using a protocol approved by the Italian Air Force. An oral examination and a force platform test were performed in order to evaluate the subjects' postural system efficiency. A MANOVA (Multivariate analysis of variance) analysis was performed by using the Wilkes' criterion, in order to statistically evaluate the influence of each factor. Both the sway area and velocity parameters are very strongly influenced by vision: the sway area increases by approximately 32% and the sway velocity increases by approximately 50% when the pilot closes his eyes. Only the sway area parameter was significantly influenced by the mandibular position: the mandibular position with eyes open changed the sway area by about 51% and with eyes closed by about 40%. No statistically significant differences were found between air force and civilian pilots. The results of this analysis show that occlusion and visual function could influence posture in air force and civilian pilots.

Fractures in women treated with raloxifene or alendronate: a retrospective database analysis

PubMed Central

2013-01-01

Background Raloxifene and alendronate are anti-resorptive therapies approved for the prevention and treatment of postmenopausal osteoporosis. Raloxifene is also indicated to reduce the risk of invasive breast cancer in postmenopausal women with osteoporosis and in postmenopausal women at high risk of invasive breast cancer. A definitive study comparing the fracture effectiveness and rate of breast cancer for raloxifene and alendronate has not been published. The purpose of this retrospective cohort study was to evaluate fracture and breast cancer rates among patients treated with raloxifene or alendronate. Methods Females ≥45 years who initiated raloxifene or alendronate in 1998–2006 Truven Health Analytics MarketScan® Databases, had continuous enrollment 12 months prior to and at least 12 months after the index date, and had a treatment medication possession ratio ≥80% were included in this study. Rates of vertebral and nonvertebral fractures and breast cancer during 1, 3, 5, 6, 7, and 8 years of treatment with raloxifene or alendronate were evaluated. Fracture rates were adjusted for potential treatment bias using inverse probability of treatment weights. Multivariate hazard ratios were estimated for vertebral and nonvertebral fractures. Results Raloxifene patients had statistically significantly lower rates of vertebral fractures in 1, 3, 5, and 7 years and for nonvertebral fractures in 1 and 5 years. There were no statistically significant differences in the adjusted fracture rates between raloxifene and alendronate cohorts, except in the 3-year nonvertebral fracture rates where raloxifene was higher. Multivariate hazard ratios of raloxifene versus alendronate cohorts were not significantly different for vertebral and nonvertebral fracture in 1, 3, 5, 6, 7, and 8 years. Unweighted and weighted breast cancer rates were lower among raloxifene recipients. Conclusions Patients treated with alendronate and raloxifene had similar adjusted fracture rates in up to 8 years of adherent treatment, and raloxifene patients had lower breast cancer rates. PMID:23521803

The effect of single and repeated UVB radiation on rabbit cornea.

PubMed

Fris, Miroslav; Tessem, May-Britt; Cejková, Jitka; Midelfart, Anna

2006-12-01

Cumulative effect of ultraviolet radiation (UVR) is an important aspect of UV corneal damage. The purpose of this study was to apply high resolution magic angle spinning proton nuclear magnetic resonance (HR-MAS 1H NMR) spectroscopy to evaluate the effect of single and repeated UV radiation exposure of the same overall dose on the rabbit cornea. Corneal surfaces of 24 normal rabbit eyes were examined for the effects of UVB exposure (312 nm). In the first group (UVB1), animals were irradiated with a single dose (3.12 J/cm2; 21 min) of UVB radiation. The animals in the second group (UVB2) were irradiated three times for 7 min every other day (dose of 1.04 J/cm2; days 1, 3, 5) to give the same overall dose (3.12 J/cm2). The third group served as an untreated control group. One day after the last irradiation, the animals were sacrificed, and the corneas were removed and frozen. HR-MAS 1H NMR spectra from intact corneas were obtained. Special grouping patterns among the tissue samples and the relative percentage changes in particular metabolite concentrations were evaluated using modern statistical methods (multivariate analysis, one-way ANOVA). The metabolic profile of both groups of UVB-irradiated samples was significantly different from the control corneas. Substantial decreases in taurine, hypo-taurine and choline-derivatives concentrations and substantial elevation in glucose and betaine levels were observed following the UVR exposure. There was no significant difference between the effect of a single and repeated UVB irradiation of the same overall dose. For the first time, the effects of single and repeated UVR doses on the metabolic profile of the rabbit cornea were analysed and compared. The combination of HR-MAS 1H NMR spectroscopy and modern statistical methods (multivariate analysis, one-way ANOVA) proved suitable to assess the overall view of the metabolic alterations in the rabbit corneal tissue following UVB radiation exposure.

Experimental and statistical determination of indicator parameters for the evaluation of fly ash and boiler ash PCDD/PCDF concentration from municipal solid waste incinerators.

PubMed

Streibel, T; Nordsieck, H; Neuer-Etscheidt, K; Schnelle-Kreis, J; Zimmermann, R

2007-04-01

On-line detectable indicator parameters in the flue gas of municipal solid waste incinerators (MSWI) such as chlorinated benzenes (PCBz) are well known surrogate compounds for gas-phase PCDD/PCDF concentration. In the here presented work derivation of indicators is broadened to the detection of fly and boiler ash fractions with increased PCDD/PCDF content. Subsequently these fractions could be subject to further treatment such as recirculation in the combustion chamber to destroy their PCDD/PCDF and other organic pollutants' content. Aim of this work was to detect suitable on-line detectable indicator parameters in the gas phase, which are well correlated to PCDD/PCDF concentration in the solid residues. For this, solid residues and gas-phase samples were taken at three MSWI plants in Bavaria. Analysis of the ash content from different plants yielded a broad variation range of PCDD/PCDF concentrations especially after disturbed combustion conditions. Even during normal operation conditions significantly increased PCDD/PCDF concentrations may occur after unanticipated disturbances. Statistical evaluation of gas phase and ash measurements was carried out by means of principal component analysis, uni- and multivariate correlation analysis. Surprisingly, well known indicators for gas-phase PCDD/PCDF concentration such as polychlorinated benzenes and phenols proved to be insufficiently correlated to PCDD/PCDF content of the solid residues. Moreover, no single parameter alone was found appropriate to describe the PCDD/PCDF content of fly and boiler ashes. On the other hand, multivariate fitting of three or four parameters yielded convenient correlation coefficients of at least r=0.8 for every investigated case. Thereby, comprehension of plant operation parameters such as temperatures and air flow alongside concentrations of inorganic compounds in the gas phase (HCl, CO, SO2, NOx) gave the best results. However, the suitable set of parameters suited best for estimation of PCDD/PCDF concentration in solid residues has to be derived anew for each individual plant and type of ash.

[Statistical prediction methods in violence risk assessment and its application].

PubMed

Liu, Yuan-Yuan; Hu, Jun-Mei; Yang, Min; Li, Xiao-Song

2013-06-01

It is an urgent global problem how to improve the violence risk assessment. As a necessary part of risk assessment, statistical methods have remarkable impacts and effects. In this study, the predicted methods in violence risk assessment from the point of statistics are reviewed. The application of Logistic regression as the sample of multivariate statistical model, decision tree model as the sample of data mining technique, and neural networks model as the sample of artificial intelligence technology are all reviewed. This study provides data in order to contribute the further research of violence risk assessment.

A statistical approach to evaluate the performance of cardiac biomarkers in predicting death due to acute myocardial infarction: time-dependent ROC curve

PubMed

Karaismailoğlu, Eda; Dikmen, Zeliha Günnur; Akbıyık, Filiz; Karaağaoğlu, Ahmet Ergun

2018-04-30

Background/aim: Myoglobin, cardiac troponin T, B-type natriuretic peptide (BNP), and creatine kinase isoenzyme MB (CK-MB) are frequently used biomarkers for evaluating risk of patients admitted to an emergency department with chest pain. Recently, time- dependent receiver operating characteristic (ROC) analysis has been used to evaluate the predictive power of biomarkers where disease status can change over time. We aimed to determine the best set of biomarkers that estimate cardiac death during follow-up time. We also obtained optimal cut-off values of these biomarkers, which differentiates between patients with and without risk of death. A web tool was developed to estimate time intervals in risk. Materials and methods: A total of 410 patients admitted to the emergency department with chest pain and shortness of breath were included. Cox regression analysis was used to determine an optimal set of biomarkers that can be used for estimating cardiac death and to combine the significant biomarkers. Time-dependent ROC analysis was performed for evaluating performances of significant biomarkers and a combined biomarker during 240 h. The bootstrap method was used to compare statistical significance and the Youden index was used to determine optimal cut-off values. Results : Myoglobin and BNP were significant by multivariate Cox regression analysis. Areas under the time-dependent ROC curves of myoglobin and BNP were about 0.80 during 240 h, and that of the combined biomarker (myoglobin + BNP) increased to 0.90 during the first 180 h. Conclusion: Although myoglobin is not clinically specific to a cardiac event, in our study both myoglobin and BNP were found to be statistically significant for estimating cardiac death. Using this combined biomarker may increase the power of prediction. Our web tool can be useful for evaluating the risk status of new patients and helping clinicians in making decisions.

Investigations of interference between electromagnetic transponders and wireless MOSFET dosimeters: A phantom study

PubMed Central

Su, Zhong; Zhang, Lisha; Ramakrishnan, V.; Hagan, Michael; Anscher, Mitchell

2011-01-01

Purpose: To evaluate both the Calypso Systems’ (Calypso Medical Technologies, Inc., Seattle, WA) localization accuracy in the presence of wireless metal–oxide–semiconductor field-effect transistor (MOSFET) dosimeters of dose verification system (DVS, Sicel Technologies, Inc., Morrisville, NC) and the dosimeters’ reading accuracy in the presence of wireless electromagnetic transponders inside a phantom.Methods: A custom-made, solid-water phantom was fabricated with space for transponders and dosimeters. Two inserts were machined with positioning grooves precisely matching the dimensions of the transponders and dosimeters and were arranged in orthogonal and parallel orientations, respectively. To test the transponder localization accuracy with∕without presence of dosimeters (hypothesis 1), multivariate analyses were performed on transponder-derived localization data with and without dosimeters at each preset distance to detect statistically significant localization differences between the control and test sets. To test dosimeter dose-reading accuracy with∕without presence of transponders (hypothesis 2), an approach of alternating the transponder presence in seven identical fraction dose (100 cGy) deliveries and measurements was implemented. Two-way analysis of variance was performed to examine statistically significant dose-reading differences between the two groups and the different fractions. A relative-dose analysis method was also used to evaluate transponder impact on dose-reading accuracy after dose-fading effect was removed by a second-order polynomial fit.Results: Multivariate analysis indicated that hypothesis 1 was false; there was a statistically significant difference between the localization data from the control and test sets. However, the upper and lower bounds of the 95% confidence intervals of the localized positional differences between the control and test sets were less than 0.1 mm, which was significantly smaller than the minimum clinical localization resolution of 0.5 mm. For hypothesis 2, analysis of variance indicated that there was no statistically significant difference between the dosimeter readings with and without the presence of transponders. Both orthogonal and parallel configurations had difference of polynomial-fit dose to measured dose values within 1.75%.Conclusions: The phantom study indicated that the Calypso System’s localization accuracy was not affected clinically due to the presence of DVS wireless MOSFET dosimeters and the dosimeter-measured doses were not affected by the presence of transponders. Thus, the same patients could be implanted with both transponders and dosimeters to benefit from improved accuracy of radiotherapy treatments offered by conjunctional use of the two systems. PMID:21776780

Multivariable Parametric Cost Model for Ground Optical: Telescope Assembly

NASA Technical Reports Server (NTRS)

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

2004-01-01

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

Medicaid Managed Care Structures and Care Coordination.

PubMed

Gilchrist-Scott, Douglas H; Feinstein, James A; Agrawal, Rishi

2017-09-01

Child enrollment in Medicaid managed care (MMC) has expanded dramatically, primarily through state mandates. Care coordination is a key metric in MMC evaluation because it drives much of the proposed cost savings and may be associated with improved health outcomes and utilization. We evaluated the relationships between enrollment in 2 MMC structures, primary care case management (PCCM) and health maintenance organization (HMO) and access to and receipt of care coordination by children. Using data from the 2011/2012 National Survey of Children's Health and the Medicaid Statistical Information System state data mart, we conducted a retrospective, cross-sectional analysis of the relationships between fee-for-service, PCCM or HMO enrollment, and access to and receipt of care coordination. State-level univariate analyses and individual and state multilevel multivariable analyses evaluated correlations between MMC enrollment and care coordination, controlling for demographic characteristics and state financing levels. In univariate and multilevel multivariable analyses, the PCCM penetration rate was significantly associated with increased access to care coordination (adjusted odds ratio: 1.23, P = .034) and receipt of care coordination (adjusted odds ratio: 1.37, P = .02). The HMO penetration rate was significantly associated with lower access to care coordination (adjusted odds ratio: 0.85, P = .05) and receipt of care coordination (adjusted odds ratio: 0.71, P < .001). Fee-for-service served as the referent. State utilization of MMC varied widely. These data suggest that care coordination may be more effective in PCCM than HMO structures. States should consider care coordination outcomes when structuring their Medicaid programs. Copyright © 2017 by the American Academy of Pediatrics.

Ecological equivalency as a tool for endangered species management.

PubMed

Searcy, Christopher A; Rollins, Hilary B; Shaffer, H Bradley

2016-01-01

The use of taxon substitutes for extinct or endangered species is a controversial conservation measure. We use the example of the endangered California tiger salamander (Ambystoma californiense; CTS), which is being replaced by hybrids with the invasive barred tiger salamander (Ambystoma mavortium), to illustrate a strategy for evaluating taxon substitutes based on their position in a multivariate community space. Approximately one-quarter of CTS's range is currently occupied by "full hybrids" with 70% nonnative genes, while another one-quarter is occupied by "superinvasives" where a specific set of 3/68 genes comprising 4% of the surveyed genome is nonnative. Based on previous surveys of natural CTS breeding ponds, we stocked experimental mesocosms with field-verified, realistic densities of tiger salamander larvae and their prey, and used these mesocosms to evaluate ecological equivalency between pure CTS, full hybrids, and superinvasives in experimental pond communities. We also included a fourth treatment with no salamanders present to evaluate the community effects of eliminating Ambystoma larvae altogether. We found that pure CTS and superinvasive larvae were ecologically equivalent, because their positions in the multivariate community space were statistically indistinguishable and they did not differ significantly along any univariate community axes. Full hybrids were ecologically similar, but not equivalent, to the other two genotypes, and the no-Ambystoma treatment was by far the most divergent. We conclude that, at least for the larval stage, superinvasives are adequate taxon substitutes for pure CTS and should probably be afforded protection under the Endangered Species Act. The proper conservation status for full hybrids remains debatable.

Association between split selection instability and predictive error in survival trees.

PubMed

Radespiel-Tröger, M; Gefeller, O; Rabenstein, T; Hothorn, T

2006-01-01

To evaluate split selection instability in six survival tree algorithms and its relationship with predictive error by means of a bootstrap study. We study the following algorithms: logrank statistic with multivariate p-value adjustment without pruning (LR), Kaplan-Meier distance of survival curves (KM), martingale residuals (MR), Poisson regression for censored data (PR), within-node impurity (WI), and exponential log-likelihood loss (XL). With the exception of LR, initial trees are pruned by using split-complexity, and final trees are selected by means of cross-validation. We employ a real dataset from a clinical study of patients with gallbladder stones. The predictive error is evaluated using the integrated Brier score for censored data. The relationship between split selection instability and predictive error is evaluated by means of box-percentile plots, covariate and cutpoint selection entropy, and cutpoint selection coefficients of variation, respectively, in the root node. We found a positive association between covariate selection instability and predictive error in the root node. LR yields the lowest predictive error, while KM and MR yield the highest predictive error. The predictive error of survival trees is related to split selection instability. Based on the low predictive error of LR, we recommend the use of this algorithm for the construction of survival trees. Unpruned survival trees with multivariate p-value adjustment can perform equally well compared to pruned trees. The analysis of split selection instability can be used to communicate the results of tree-based analyses to clinicians and to support the application of survival trees.

Tumor deposits counted as positive lymph nodes in TNM staging for advanced colorectal cancer: a retrospective multicenter study.

PubMed

Li, Jun; Yang, Shengke; Hu, Junjie; Liu, Hao; Du, Feng; Yin, Jie; Liu, Sai; Li, Ci; Xing, Shasha; Yuan, Jiatian; Lv, Bo; Fan, Jun; Leng, Shusheng; Zhang, Xin; Wang, Bing

2016-04-05

We investigated the possibility of counting tumor deposits (TDs) as positive lymph nodes (pLNs) in the pN category and evaluated its prognostic value for colorectal cancer (CRC) patients. A new pN category (npN category) was calculated using the numbers of pLNs plus TDs. The npN category included 4 tiers: npN1a (1 tumor node), npN1b (2-3 tumor nodes), npN2a (4-6 tumor nodes), and npN2b (≥7 tumor nodes). We identified 4,121 locally advanced CRC patients, including 717 (11.02%) cases with TDs. Univariate and multivariate analyses were performed to evaluate the disease-free and overall survival (DFS and OS) for npN and pN categories. Multivariate analysis showed that the npN and pN categories were both independent prognostic factors for DFS (HR 1.614, 95% CI 1.541 to 1.673; HR 1.604, 95% CI 1.533 to 1.679) and OS (HR 1.633, 95% CI 1.550 to 1.720; HR 1.470, 95% CI 1.410 to 1.532). However, the npN category was superior to the pN category by Harrell's C statistic. We conclude that it is thus feasible to consider TDs as positive lymph nodes in the pN category when evaluating the prognoses of CRC patients, and the npN category is potentially superior to the TNM (7th edition) pN category for predicting DFS and OS among advanced CRC patients.

A Bayesian approach for parameter estimation and prediction using a computationally intensive model

DOE PAGES

Higdon, Dave; McDonnell, Jordan D.; Schunck, Nicolas; ...

2015-02-05

Bayesian methods have been successful in quantifying uncertainty in physics-based problems in parameter estimation and prediction. In these cases, physical measurements y are modeled as the best fit of a physics-based modelmore » $$\\eta (\\theta )$$, where θ denotes the uncertain, best input setting. Hence the statistical model is of the form $$y=\\eta (\\theta )+\\epsilon ,$$ where $$\\epsilon $$ accounts for measurement, and possibly other, error sources. When nonlinearity is present in $$\\eta (\\cdot )$$, the resulting posterior distribution for the unknown parameters in the Bayesian formulation is typically complex and nonstandard, requiring computationally demanding computational approaches such as Markov chain Monte Carlo (MCMC) to produce multivariate draws from the posterior. Although generally applicable, MCMC requires thousands (or even millions) of evaluations of the physics model $$\\eta (\\cdot )$$. This requirement is problematic if the model takes hours or days to evaluate. To overcome this computational bottleneck, we present an approach adapted from Bayesian model calibration. This approach combines output from an ensemble of computational model runs with physical measurements, within a statistical formulation, to carry out inference. A key component of this approach is a statistical response surface, or emulator, estimated from the ensemble of model runs. We demonstrate this approach with a case study in estimating parameters for a density functional theory model, using experimental mass/binding energy measurements from a collection of atomic nuclei. Lastly, we also demonstrate how this approach produces uncertainties in predictions for recent mass measurements obtained at Argonne National Laboratory.« less

Randomized trials are frequently fragmented in multiple secondary publications.

PubMed

Ebrahim, Shanil; Montoya, Luis; Kamal El Din, Mostafa; Sohani, Zahra N; Agarwal, Arnav; Bance, Sheena; Saquib, Juliann; Saquib, Nazmus; Ioannidis, John P A

2016-11-01

To assess the frequency and features of secondary publications of randomized controlled trials (RCTs). For 191 RCTs published in high-impact journals in 2009, we searched for secondary publications coauthored by at least one same author of the primary trial publication. We evaluated the probability of having secondary publications, characteristics of the primary trial publication that predict having secondary publications, types of secondary analyses conducted, and statistical significance of those analyses. Of 191 primary trials, 88 (46%) had a total of 475 secondary publications by 2/2014. Eight trials had >10 (up to 51) secondary publications each. In multivariable modeling, the risk of having subsequent secondary publications increased 1.32-fold (95% CI 1.05-1.68) per 10-fold increase in sample size, and 1.71-fold (95% CI 1.19-2.45) in the presence of a design article. In a sample of 197 secondary publications examined in depth, 193 tested different hypotheses than the primary publication. Of the 193, 43 tested differences between subgroups, 85 assessed predictive factors associated with an outcome of interest, 118 evaluated different outcomes than the original article, 71 had differences in eligibility criteria, and 21 assessed different durations of follow-up; 176 (91%) presented at least one analysis with statistically significant results. Approximately half of randomized trials in high-impact journals have secondary publications published with a few trials followed by numerous secondary publications. Almost all of these publications report some statistically significant results. Copyright © 2016 Elsevier Inc. All rights reserved.

Breast cancer lymphoscintigraphy: Factors associated with sentinel lymph node non visualization.

PubMed

Vaz, S C; Silva, Â; Sousa, R; Ferreira, T C; Esteves, S; Carvalho, I P; Ratão, P; Daniel, A; Salgado, L

2015-01-01

To evaluate factors associated with non identification of the sentinel lymph node (SLN) in lymphoscintigraphy of breast cancer patients and analyze the relationship with SLN metastases. A single-center, cross-sectional and retrospective study was performed. Forty patients with lymphoscintigraphy without sentinel lymph node identification (negative lymphoscintigraphy - NL) were enrolled. The control group included 184 patients with SLN identification (positive lymphoscintigraphy - PL). Evaluated factors were age, body mass index (BMI), tumor size, histology, localization, preoperative breast lesion hookwire (harpoon) marking and SLN metastases. The statistical analysis was performed with uni- and multivariate logistic regression models and matched-pairs analysis. Age (p=0.036) or having BMI (p=0.047) were the only factors significantly associated with NL. Being ≥60 years with a BMI ≥30 increased the odds of having a NL 2 and 3.8 times, respectively. Marking with hookwire seems to increase the likelihood of NL, but demonstrated statistical significance is lacking (p=0.087). The other tested variables did not affect the examination result. When controlling for age, BMI and marking with the harpoon, a significant association between lymph node metastization and NL was not found (p=0.565). The most important factors related with non identification of SLN in the patients were age, BMI and marking with hook wire. However, only the first two had statistical importance. When these variables were controlled, no association was found between NL and axillary metastases. Copyright © 2015 Elsevier España, S.L.U. and SEMNIM. All rights reserved.

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.

Peripheral vascular damage in systemic lupus erythematosus: data from LUMINA, a large multi-ethnic U.S. cohort (LXIX).

PubMed

Burgos, P I; Vilá, L M; Reveille, J D; Alarcón, G S

2009-12-01

To determine the factors associated with peripheral vascular damage in systemic lupus erythematosus patients and its impact on survival from Lupus in Minorities, Nature versus Nurture, a longitudinal US multi-ethnic cohort. Peripheral vascular damage was defined by the Systemic Lupus International Collaborating Clinics Damage Index (SDI). Factors associated with peripheral vascular damage were examined by univariable and multi-variable logistic regression models and its impact on survival by a Cox multi-variable regression. Thirty-four (5.3%) of 637 patients (90% women, mean [SD] age 36.5 [12.6] [16-87] years) developed peripheral vascular damage. Age and the SDI (without peripheral vascular damage) were statistically significant (odds ratio [OR] = 1.05, 95% confidence interval [CI] 1.01-1.08; P = 0.0107 and OR = 1.30, 95% CI 0.09-1.56; P = 0.0043, respectively) in multi-variable analyses. Azathioprine, warfarin and statins were also statistically significant, and glucocorticoid use was borderline statistically significant (OR = 1.03, 95% CI 0.10-1.06; P = 0.0975). In the survival analysis, peripheral vascular damage was independently associated with a diminished survival (hazard ratio = 2.36; 95% CI 1.07-5.19; P = 0.0334). In short, age was independently associated with peripheral vascular damage, but so was the presence of damage in other organs (ocular, neuropsychiatric, renal, cardiovascular, pulmonary, musculoskeletal and integument) and some medications (probably reflecting more severe disease). Peripheral vascular damage also negatively affected survival.

Fatty acid methyl ester analysis to identify sources of soil in surface water.

PubMed

Banowetz, Gary M; Whittaker, Gerald W; Dierksen, Karen P; Azevedo, Mark D; Kennedy, Ann C; Griffith, Stephen M; Steiner, Jeffrey J

2006-01-01

Efforts to improve land-use practices to prevent contamination of surface waters with soil are limited by an inability to identify the primary sources of soil present in these waters. We evaluated the utility of fatty acid methyl ester (FAME) profiles of dry reference soils for multivariate statistical classification of soils collected from surface waters adjacent to agricultural production fields and a wooded riparian zone. Trials that compared approaches to concentrate soil from surface water showed that aluminum sulfate precipitation provided comparable yields to that obtained by vacuum filtration and was more suitable for handling large numbers of samples. Fatty acid methyl ester profiles were developed from reference soils collected from contrasting land uses in different seasons to determine whether specific fatty acids would consistently serve as variables in multivariate statistical analyses to permit reliable classification of soils. We used a Bayesian method and an independent iterative process to select appropriate fatty acids and found that variable selection was strongly impacted by the season during which soil was collected. The apparent seasonal variation in the occurrence of marker fatty acids in FAME profiles from reference soils prevented preparation of a standardized set of variables. Nevertheless, accurate classification of soil in surface water was achieved utilizing fatty acid variables identified in seasonally matched reference soils. Correlation analysis of entire chromatograms and subsequent discriminant analyses utilizing a restricted number of fatty acid variables showed that FAME profiles of soils exposed to the aquatic environment still had utility for classification at least 1 wk after submersion.

Use of the Wii Gaming System for Balance Rehabilitation: Establishing Parameters for Healthy Individuals.

PubMed

Burns, Melissa K; Andeway, Kathleen; Eppenstein, Paula; Ruroede, Kathleen

2014-06-01

This study was designed to establish balance parameters for the Nintendo(®) (Redmond, WA) "Wii Fit™" Balance Board system with three common games, in a sample of healthy adults, and to evaluate the balance measurement reproducibility with separation by age. This was a prospective, multivariate analysis of variance, cohort study design. Seventy-five participants who satisfied all inclusion criteria and completed an informed consent were enrolled. Participants were grouped into age ranges: 21-35 years (n=24), 36-50 years (n=24), and 51-65 years (n=27). Each participant completed the following games three consecutive times, in a randomized order, during one session: "Balance Bubble" (BB) for distance and duration, "Tight Rope" (TR) for distance and duration, and "Center of Balance" (COB) on the left and right sides. COB distributed weight was fairly symmetrical across all subjects and trials; therefore, no influence was assumed on or interaction with other "Wii Fit" measurements. Homogeneity of variance statistics indicated the assumption of distribution normality of the dependent variables (rates) were tenable. The multivariate analysis of variance included dependent variables BB and TR rates (distance divided by duration to complete) with age group and trials as the independent variables. The BB rate was statistically significant (F=4.725, P<0.005), but not the TR rate. The youngest group's BB rate was significantly larger than those of the other two groups. "Wii Fit" can discriminate among age groups across trials. The results show promise as a viable tool to measure balance and distance across time (speed) and center of balance distribution.

An exploratory factor analysis of nutritional biomarkers associated with major depression in pregnancy.

PubMed

Bodnar, Lisa M; Wisner, Katherine L; Luther, James F; Powers, Robert W; Evans, Rhobert W; Gallaher, Marcia J; Newby, P K

2012-06-01

Major depressive disorder (MDD) during pregnancy increases the risk of adverse maternal and infant outcomes. Maternal nutritional status may be a modifiable risk factor for antenatal depression. We evaluated the association between patterns in mid-pregnancy nutritional biomarkers and MDD. Prospective cohort study. Pittsburgh, Pennsylvania, USA. Women who enrolled at ≤20 weeks' gestation and had a diagnosis of MDD made with the Structured Clinical Interview for DSM-IV (Diagnostic and Statistical Manual of Mental Disorders, 4th edition) at 20-, 30- and 36-week study visits. A total of 135 women contributed 345 person-visits. Non-fasting blood drawn at enrolment was assayed for red cell essential fatty acids, plasma folate, homocysteine and ascorbic acid; serum 25-hydroxyvitamin D, retinol, vitamin E, carotenoids, ferritin and soluble transferrin receptors. Nutritional biomarkers were entered into principal components analysis. Three factors emerged: Factor 1, Essential Fatty Acids; Factor 2, Micronutrients; and Factor 3, Carotenoids. MDD was prevalent in 21·5 % of women. In longitudinal multivariable logistic models, there was no association between the Essential Fatty Acids or Micronutrients pattern and MDD either before or after adjustment for employment, education or pre-pregnancy BMI. In unadjusted analysis, women with factor scores for Carotenoids in the middle and upper tertiles were 60 % less likely than women in the bottom tertile to have MDD during pregnancy, but after adjustment for confounders the associations were no longer statistically significant. While meaningful patterns were derived using nutritional biomarkers, significant associations with MDD were not observed in multivariable adjusted analyses. Larger, more diverse samples are needed to understand nutrition-depression relationships during pregnancy.

Nitrate contamination of groundwater in two areas of the Cameroon Volcanic Line (Banana Plain and Mount Cameroon area)

NASA Astrophysics Data System (ADS)

Ako, Andrew Ako; Eyong, Gloria Eneke Takem; Shimada, Jun; Koike, Katsuaki; Hosono, Takahiro; Ichiyanagi, Kimpei; Richard, Akoachere; Tandia, Beatrice Ketchemen; Nkeng, George Elambo; Roger, Ntankouo Njila

2014-06-01

Water containing high concentrations of nitrate is unfit for human consumption and, if discharging to freshwater or marine habitats, can contribute to algal blooms and eutrophication. The level of nitrate contamination in groundwater of two densely populated, agro-industrial areas of the Cameroon Volcanic Line (CVL) (Banana Plain and Mount Cameroon area) was evaluated. A total of 100 samples from boreholes, open wells and springs (67 from the Banana Plain; 33 from springs only, in the Mount Cameroon area) were collected in April 2009 and January 2010 and analyzed for chemical constituents, including nitrates. The average groundwater nitrate concentrations for the studied areas are: 17.28 mg/l for the Banana Plain and 2.90 mg/l for the Mount Cameroon area. Overall, groundwaters are relatively free from excessive nitrate contamination, with nitrate concentrations in only 6 % of groundwater resources in the Banana Plain exceeding the maximum admissible concentration for drinking water (50 mg/l). Sources of NO3 - in groundwater of this region may be mainly anthropogenic (N-fertilizers, sewerage, animal waste, organic manure, pit latrines, etc.). Multivariate statistical analyses of the hydrochemical data revealed that three factors were responsible for the groundwater chemistry (especially, degree of nitrate contamination): (1) a geogenic factor; (2) nitrate contamination factor; (3) ionic enrichment factor. The impact of anthropogenic activities, especially groundwater nitrate contamination, is more accentuated in the Banana Plain than in the Mount Cameroon area. This study also demonstrates the usefulness of multivariate statistical analysis in groundwater study as a supplementary tool for interpretation of complex hydrochemical data sets.

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.

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.

Short-term Outcomes After Open and Laparoscopic Colostomy Creation.

PubMed

Ivatury, Srinivas Joga; Bostock Rosenzweig, Ian C; Holubar, Stefan D

2016-06-01

Colostomy creation is a common procedure performed in colon and rectal surgery. Outcomes by technique have not been well studied. This study evaluated outcomes related to open versus laparoscopic colostomy creation. This was a retrospective review of patients undergoing colostomy creation using univariate and multivariate propensity score analyses. Hospitals participating in the American College of Surgeons National Surgical Quality Improvement Program database were included. Data on patients were obtained from the American College of Surgeons National Surgical Quality Improvement Program 2005-2011 Participant Use Data Files. We measured 30-day mortality, 30-day complications, and predictors of 30-day mortality. A total of 2179 subjects were in the open group and 1132 in the laparoscopic group. The open group had increased age (open, 64 years vs laparoscopic, 60 years), admission from facility (17.0% vs 14.9%), and disseminated cancer (26.1% vs 21.4%). All were statistically significant. The open group had a significantly higher percentage of emergency operations (24.9% vs 7.9%). Operative time was statistically different (81 vs 86 minutes). Thirty-day mortality was significantly higher in the open group (8.7% vs 3.5%), as was any 30-day complication (25.4% vs 17.0%). Propensity-matching analysis on elective patients only revealed that postoperative length of stay and rate of any wound complication were statistically higher in the open group. Multivariate analysis for mortality was performed on the full, elective, and propensity-matched cohorts; age >65 years and dependent functional status were associated with an increased risk of mortality in all of the models. This study has the potential for selection bias and limited generalizability. Colostomy creation at American College of Surgeons National Surgical Quality Improvement Program hospitals is more commonly performed open rather than laparoscopically. Patient age >65 years and dependent functional status are associated with an increased risk of 30-day mortality.

Meta-analysis of haplotype-association studies: comparison of methods and empirical evaluation of the literature

PubMed Central

2011-01-01

Background Meta-analysis is a popular methodology in several fields of medical research, including genetic association studies. However, the methods used for meta-analysis of association studies that report haplotypes have not been studied in detail. In this work, methods for performing meta-analysis of haplotype association studies are summarized, compared and presented in a unified framework along with an empirical evaluation of the literature. Results We present multivariate methods that use summary-based data as well as methods that use binary and count data in a generalized linear mixed model framework (logistic regression, multinomial regression and Poisson regression). The methods presented here avoid the inflation of the type I error rate that could be the result of the traditional approach of comparing a haplotype against the remaining ones, whereas, they can be fitted using standard software. Moreover, formal global tests are presented for assessing the statistical significance of the overall association. Although the methods presented here assume that the haplotypes are directly observed, they can be easily extended to allow for such an uncertainty by weighting the haplotypes by their probability. Conclusions An empirical evaluation of the published literature and a comparison against the meta-analyses that use single nucleotide polymorphisms, suggests that the studies reporting meta-analysis of haplotypes contain approximately half of the included studies and produce significant results twice more often. We show that this excess of statistically significant results, stems from the sub-optimal method of analysis used and, in approximately half of the cases, the statistical significance is refuted if the data are properly re-analyzed. Illustrative examples of code are given in Stata and it is anticipated that the methods developed in this work will be widely applied in the meta-analysis of haplotype association studies. PMID:21247440

Understanding characteristics in multivariate traffic flow time series from complex network structure

NASA Astrophysics Data System (ADS)

Yan, Ying; Zhang, Shen; Tang, Jinjun; Wang, Xiaofei

2017-07-01

Discovering dynamic characteristics in traffic flow is the significant step to design effective traffic managing and controlling strategy for relieving traffic congestion in urban cities. A new method based on complex network theory is proposed to study multivariate traffic flow time series. The data were collected from loop detectors on freeway during a year. In order to construct complex network from original traffic flow, a weighted Froenius norm is adopt to estimate similarity between multivariate time series, and Principal Component Analysis is implemented to determine the weights. We discuss how to select optimal critical threshold for networks at different hour in term of cumulative probability distribution of degree. Furthermore, two statistical properties of networks: normalized network structure entropy and cumulative probability of degree, are utilized to explore hourly variation in traffic flow. The results demonstrate these two statistical quantities express similar pattern to traffic flow parameters with morning and evening peak hours. Accordingly, we detect three traffic states: trough, peak and transitional hours, according to the correlation between two aforementioned properties. The classifying results of states can actually represent hourly fluctuation in traffic flow by analyzing annual average hourly values of traffic volume, occupancy and speed in corresponding hours.

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

USGS Publications Warehouse

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

2017-01-01

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

Advancing a Model-Validated Statistical Method for Decomposing the Key Oceanic Drivers of Regional Climate: Focus on Northern and Tropical African Climate Variability in the Community Earth System Model (CESM)

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

Wang, Fuyao; Yu, Yan; Notaro, Michael

This study advances the practicality and stability of the traditional multivariate statistical method, generalized equilibrium feedback assessment (GEFA), for decomposing the key oceanic drivers of regional atmospheric variability, especially when available data records are short. An advanced stepwise GEFA methodology is introduced, in which unimportant forcings within the forcing matrix are eliminated through stepwise selection. Method validation of stepwise GEFA is performed using the CESM, with a focused application to northern and tropical Africa (NTA). First, a statistical assessment of the atmospheric response to each primary oceanic forcing is carried out by applying stepwise GEFA to a fully coupled controlmore » run. Then, a dynamical assessment of the atmospheric response to individual oceanic forcings is performed through ensemble experiments by imposing sea surface temperature anomalies over focal ocean basins. Finally, to quantify the reliability of stepwise GEFA, the statistical assessment is evaluated against the dynamical assessment in terms of four metrics: the percentage of grid cells with consistent response sign, the spatial correlation of atmospheric response patterns, the area-averaged seasonal cycle of response magnitude, and consistency in associated mechanisms between assessments. In CESM, tropical modes, namely El Niño–Southern Oscillation and the tropical Indian Ocean Basin, tropical Indian Ocean dipole, and tropical Atlantic Niño modes, are the dominant oceanic controls of NTA climate. In complementary studies, stepwise GEFA is validated in terms of isolating terrestrial forcings on the atmosphere, and observed oceanic and terrestrial drivers of NTA climate are extracted to establish an observational benchmark for subsequent coupled model evaluation and development of process-based weights for regional climate projections.« less

Advancing a Model-Validated Statistical Method for Decomposing the Key Oceanic Drivers of Regional Climate: Focus on Northern and Tropical African Climate Variability in the Community Earth System Model (CESM)

DOE PAGES

Wang, Fuyao; Yu, Yan; Notaro, Michael; ...

2017-09-27

This study advances the practicality and stability of the traditional multivariate statistical method, generalized equilibrium feedback assessment (GEFA), for decomposing the key oceanic drivers of regional atmospheric variability, especially when available data records are short. An advanced stepwise GEFA methodology is introduced, in which unimportant forcings within the forcing matrix are eliminated through stepwise selection. Method validation of stepwise GEFA is performed using the CESM, with a focused application to northern and tropical Africa (NTA). First, a statistical assessment of the atmospheric response to each primary oceanic forcing is carried out by applying stepwise GEFA to a fully coupled controlmore » run. Then, a dynamical assessment of the atmospheric response to individual oceanic forcings is performed through ensemble experiments by imposing sea surface temperature anomalies over focal ocean basins. Finally, to quantify the reliability of stepwise GEFA, the statistical assessment is evaluated against the dynamical assessment in terms of four metrics: the percentage of grid cells with consistent response sign, the spatial correlation of atmospheric response patterns, the area-averaged seasonal cycle of response magnitude, and consistency in associated mechanisms between assessments. In CESM, tropical modes, namely El Niño–Southern Oscillation and the tropical Indian Ocean Basin, tropical Indian Ocean dipole, and tropical Atlantic Niño modes, are the dominant oceanic controls of NTA climate. In complementary studies, stepwise GEFA is validated in terms of isolating terrestrial forcings on the atmosphere, and observed oceanic and terrestrial drivers of NTA climate are extracted to establish an observational benchmark for subsequent coupled model evaluation and development of process-based weights for regional climate projections.« less

A Review of Multivariate Distributions for Count Data Derived from the Poisson Distribution.

PubMed

Inouye, David; Yang, Eunho; Allen, Genevera; Ravikumar, Pradeep

2017-01-01

The Poisson distribution has been widely studied and used for modeling univariate count-valued data. Multivariate generalizations of the Poisson distribution that permit dependencies, however, have been far less popular. Yet, real-world high-dimensional count-valued data found in word counts, genomics, and crime statistics, for example, exhibit rich dependencies, and motivate the need for multivariate distributions that can appropriately model this data. We review multivariate distributions derived from the univariate Poisson, categorizing these models into three main classes: 1) where the marginal distributions are Poisson, 2) where the joint distribution is a mixture of independent multivariate Poisson distributions, and 3) where the node-conditional distributions are derived from the Poisson. We discuss the development of multiple instances of these classes and compare the models in terms of interpretability and theory. Then, we empirically compare multiple models from each class on three real-world datasets that have varying data characteristics from different domains, namely traffic accident data, biological next generation sequencing data, and text data. These empirical experiments develop intuition about the comparative advantages and disadvantages of each class of multivariate distribution that was derived from the Poisson. Finally, we suggest new research directions as explored in the subsequent discussion section.

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

NASA Astrophysics Data System (ADS)

Panagopoulos, George P.

2014-10-01

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

Very low-dose (0.15 mGy) chest CT protocols using the COPDGene 2 test object and a third-generation dual-source CT scanner with corresponding third-generation iterative reconstruction software.

PubMed

Newell, John D; Fuld, Matthew K; Allmendinger, Thomas; Sieren, Jered P; Chan, Kung-Sik; Guo, Junfeng; Hoffman, Eric A

2015-01-01

The purpose of this study was to evaluate the impact of ultralow radiation dose single-energy computed tomographic (CT) acquisitions with Sn prefiltration and third-generation iterative reconstruction on density-based quantitative measures of growing interest in phenotyping pulmonary disease. The effects of both decreasing dose and different body habitus on the accuracy of the mean CT attenuation measurements and the level of image noise (SD) were evaluated using the COPDGene 2 test object, containing 8 different materials of interest ranging from air to acrylic and including various density foams. A third-generation dual-source multidetector CT scanner (Siemens SOMATOM FORCE; Siemens Healthcare AG, Erlangen, Germany) running advanced modeled iterative reconstruction (ADMIRE) software (Siemens Healthcare AG) was used.We used normal and very large body habitus rings at dose levels varying from 1.5 to 0.15 mGy using a spectral-shaped (0.6-mm Sn) tube output of 100 kV(p). Three CT scans were obtained at each dose level using both rings. Regions of interest for each material in the test object scans were automatically extracted. The Hounsfield unit values of each material using weighted filtered back projection (WFBP) at 1.5 mGy was used as the reference value to evaluate shifts in CT attenuation at lower dose levels using either WFBP or ADMIRE. Statistical analysis included basic statistics, Welch t tests, multivariable covariant model using the F test to assess the significance of the explanatory (independent) variables on the response (dependent) variable, and CT mean attenuation, in the multivariable covariant model including reconstruction method. Multivariable regression analysis of the mean CT attenuation values showed a significant difference with decreasing dose between ADMIRE and WFBP. The ADMIRE has reduced noise and more stable CT attenuation compared with WFBP. There was a strong effect on the mean CT attenuation values of the scanned materials for ring size (P < 0.0001) and dose level (P < 0.0001). The number of voxels in the region of interest for the particular material studied did not demonstrate a significant effect (P > 0.05). The SD was lower with ADMIRE compared with WFBP at all dose levels and ring sizes (P < 0.05). The third-generation dual-source CT scanners using third-generation iterative reconstruction methods can acquire accurate quantitative CT images with acceptable image noise at very low-dose levels (0.15 mGy). This opens up new diagnostic and research opportunities in CT phenotyping of the lung for developing new treatments and increased understanding of pulmonary disease.

Nuclear magnetic resonance (NMR) study of the effect of cisplatin on the metabolic profile of MG-63 osteosarcoma cells.

PubMed

Duarte, Iola F; Lamego, Ines; Marques, Joana; Marques, M Paula M; Blaise, Benjamin J; Gil, Ana M

2010-11-05

In the present study, (1)H HRMAS NMR spectroscopy was used to assess the changes in the intracellular metabolic profile of MG-63 human osteosarcoma (OS) cells induced by the chemotherapy agent cisplatin (CDDP) at different times of exposure. Multivariate analysis was applied to the cells spectra, enabling consistent variation patterns to be detected and drug-specific metabolic effects to be identified. Statistical recoupling of variables (SRV) analysis and spectral integration enabled the most relevant spectral changes to be evaluated, revealing significant time-dependent alterations in lipids, choline-containing compounds, some amino acids, polyalcohols, and nitrogenated bases. The metabolic relevance of these compounds in the response of MG-63 cells to CDDP treatment is discussed.

Blood lead levels and risk factors in pregnant women from Durango, Mexico.

PubMed

La-Llave-León, Osmel; Estrada-Martínez, Sergio; Manuel Salas-Pacheco, José; Peña-Elósegui, Rocío; Duarte-Sustaita, Jaime; Candelas Rangel, Jorge-Luís; García Vargas, Gonzalo

2011-01-01

In this cross-sectional study the authors determined blood lead levels (BLLs) and some risk factors for lead exposure in pregnant women. Two hundred ninety-nine pregnant women receiving medical attention by the Secretary of Health, State of Durango, Mexico, participated in this study between 2007 and 2008. BLLs were evaluated with graphite furnace atomic absorption spectrometry. The authors used Student t test, 1-way analysis of variance (ANOVA), and linear regression as statistical treatments. BLLs ranged from 0.36 to 23.6 μg/dL (mean = 2.79 μg/dL, standard deviation = 2.14). Multivariate analysis showed that the main predictors of BLLs were working in a place where lead is used, using lead glazed pottery, and eating soil.

Statistical methods in personality assessment research.

PubMed

Schinka, J A; LaLone, L; Broeckel, J A

1997-06-01

Emerging models of personality structure and advances in the measurement of personality and psychopathology suggest that research in personality and personality assessment has entered a stage of advanced development, in this article we examine whether researchers in these areas have taken advantage of new and evolving statistical procedures. We conducted a review of articles published in the Journal of Personality, Assessment during the past 5 years. Of the 449 articles that included some form of data analysis, 12.7% used only descriptive statistics, most employed only univariate statistics, and fewer than 10% used multivariate methods of data analysis. We discuss the cost of using limited statistical methods, the possible reasons for the apparent reluctance to employ advanced statistical procedures, and potential solutions to this technical shortcoming.

Using Logistic Regression and Random Forests multivariate statistical methods for landslide spatial probability assessment in North-Est Sicily, Italy

NASA Astrophysics Data System (ADS)

Trigila, Alessandro; Iadanza, Carla; Esposito, Carlo; Scarascia-Mugnozza, Gabriele

2015-04-01

North-East Sicily is strongly exposed to shallow landslide events. On October, 1st 2009 a severe rainstorm (225.5 mm of cumulative rainfall in 9 hours) caused flash floods and more than 1000 landslides, which struck several small villages as Giampilieri, Altolia, Molino, Pezzolo, Scaletta Zanclea, Itala, with 31 fatalities, 6 missing persons and damage to buildings and transportation infrastructures. Landslides, mainly consisting in earth and debris translational slides evolving into debris flows, triggered on steep slopes involving colluvium and regolith materials which cover the underlying metamorphic bedrock of Peloritani Mountains. In this area catchments are small (about 10 square kilometres), elongated, with steep slopes, low order streams, short time of concentration, and discharge directly into the sea. In the past, landslides occurred at Altolia in 1613 and 2000, at Molino in 1750, 1805 and 2000, at Giampilieri in 1791, 1918, 1929, 1932, 2000 and on October 25, 2007. The aim of this work is to define susceptibility models for shallow landslides using multivariate statistical analyses in the Giampilieri area (25 square kilometres). A detailed landslide inventory map has been produced, as the first step, through field surveys coupled with the observation of high resolution aerial colour orthophoto taken immediately after the event. 1,490 initiation zones have been identified; most of them have planimetric dimensions ranging between tens to few hundreds of square metres. The spatial hazard assessment has been focused on the detachment areas. Susceptibility models, performed in a GIS environment, took into account several parameters. The morphometric and hydrologic parameters has been derived from a detailed LiDAR 1×1 m. Square grid cells of 4×4 m were adopted as mapping units, on the basis of the area-frequency distribution of the detachment zones, and the optimal representation of the local morphometric conditions (e.g. slope angle, plan curvature). A first phase of the work addressed to identify the spatial relationships between the landslides location and the 13 related factors by using the Frequency Ratio bivariate statistical method. The analysis was then carried out by adopting a multivariate statistical approach, according to the Logistic Regression technique and Random Forests technique that gave best results in terms of AUC. The models were performed and evaluated with different sample sizes and also taking into account the temporal variation of input variables such as burned areas by wildfire. The most significant outcome of this work are: the relevant influence of the sample size on the model results and the strong importance of some environmental factors (e.g. land use and wildfires) for the identification of the depletion zones of extremely rapid shallow landslides.

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.

Can multivariate models based on MOAKS predict OA knee pain? Data from the Osteoarthritis Initiative

NASA Astrophysics Data System (ADS)

Luna-Gómez, Carlos D.; Zanella-Calzada, Laura A.; Galván-Tejada, Jorge I.; Galván-Tejada, Carlos E.; Celaya-Padilla, José M.

2017-03-01

Osteoarthritis is the most common rheumatic disease in the world. Knee pain is the most disabling symptom in the disease, the prediction of pain is one of the targets in preventive medicine, this can be applied to new therapies or treatments. Using the magnetic resonance imaging and the grading scales, a multivariate model based on genetic algorithms is presented. Using a predictive model can be useful to associate minor structure changes in the joint with the future knee pain. Results suggest that multivariate models can be predictive with future knee chronic pain. All models; T0, T1 and T2, were statistically significant, all p values were < 0.05 and all AUC > 0.60.

Why are we regressing?

PubMed

Jupiter, Daniel C

2012-01-01

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

Characterizing Sub-Daily Flow Regimes: Implications of Hydrologic Resolution on Ecohydrology Studies

DOE PAGES

Bevelhimer, Mark S.; McManamay, Ryan A.; O'Connor, B.

2014-05-26

Natural variability in flow is a primary factor controlling geomorphic and ecological processes in riverine ecosystems. Within the hydropower industry, there is growing pressure from environmental groups and natural resource managers to change reservoir releases from daily peaking to run-of-river operations on the basis of the assumption that downstream biological communities will improve under a more natural flow regime. In this paper, we discuss the importance of assessing sub-daily flows for understanding the physical and ecological dynamics within river systems. We present a variety of metrics for characterizing sub-daily flow variation and use these metrics to evaluate general trends amongmore » streams affected by peaking hydroelectric projects, run-of-river projects and streams that are largely unaffected by flow altering activities. Univariate and multivariate techniques were used to assess similarity among different stream types on the basis of these sub-daily metrics. For comparison, similar analyses were performed using analogous metrics calculated with mean daily flow values. Our results confirm that sub-daily flow metrics reveal variation among and within streams that are not captured by daily flow statistics. Using sub-daily flow statistics, we were able to quantify the degree of difference between unaltered and peaking streams and the amount of similarity between unaltered and run-of-river streams. The sub-daily statistics were largely uncorrelated with daily statistics of similar scope. Furthermore, on short temporal scales, sub-daily statistics reveal the relatively constant nature of unaltered streamreaches and the highly variable nature of hydropower-affected streams, whereas daily statistics show just the opposite over longer temporal scales.« less

Histologic prognosticators in feline osteosarcoma: a comparison with phenotypically similar canine osteosarcoma.

PubMed

Dimopoulou, Maria; Kirpensteijn, Jolle; Moens, Hester; Kik, Marja

2008-07-01

To investigate the histologic characteristics of feline osteosarcoma (OS) and compare the histologic data with phenotypically comparable canine OS. The effects of histologic and clinical variables on survival statistics were evaluated. Retrospective study. Cats (n=62) and dogs (22). Medical records of 62 cats with OS were reviewed for clinically relevant data. Clinical outcome was obtained by telephone interview. Histologic characteristics of OS were classified using a standardized grading system. Histologic characteristics in 22 feline skeletal OS were compared with 22 canine skeletal OS of identical location and subtype. Prognostic variables for clinical outcome were determined using multivariate analysis. Feline OS was characterized by moderate to abundant cellular pleomorphism, low mitotic index, small to moderate amounts of matrix, high cellularity, and a moderate amount of necrosis. There was no significant difference between histologic variables in feline and canine OS. Histologic grade, surgery, and mitotic index significantly influenced clinical outcome as determined by multivariate analysis. Tumor invasion into vessels was not identified as a significant prognosticator. Feline and canine skeletal OS have similar histologic but different prognostic characteristics. Prognosis for cats with OS is related to histologic grade and mitotic index of the tumor.

Learning multivariate distributions by competitive assembly of marginals.

PubMed

Sánchez-Vega, Francisco; Younes, Laurent; Geman, Donald

2013-02-01

We present a new framework for learning high-dimensional multivariate probability distributions from estimated marginals. The approach is motivated by compositional models and Bayesian networks, and designed to adapt to small sample sizes. We start with a large, overlapping set of elementary statistical building blocks, or "primitives," which are low-dimensional marginal distributions learned from data. Each variable may appear in many primitives. Subsets of primitives are combined in a Lego-like fashion to construct a probabilistic graphical model; only a small fraction of the primitives will participate in any valid construction. Since primitives can be precomputed, parameter estimation and structure search are separated. Model complexity is controlled by strong biases; we adapt the primitives to the amount of training data and impose rules which restrict the merging of them into allowable compositions. The likelihood of the data decomposes into a sum of local gains, one for each primitive in the final structure. We focus on a specific subclass of networks which are binary forests. Structure optimization corresponds to an integer linear program and the maximizing composition can be computed for reasonably large numbers of variables. Performance is evaluated using both synthetic data and real datasets from natural language processing and computational biology.

Multi-scale pixel-based image fusion using multivariate empirical mode decomposition.

PubMed

Rehman, Naveed ur; Ehsan, Shoaib; Abdullah, Syed Muhammad Umer; Akhtar, Muhammad Jehanzaib; Mandic, Danilo P; McDonald-Maier, Klaus D

2015-05-08

A novel scheme to perform the fusion of multiple images using the multivariate empirical mode decomposition (MEMD) algorithm is proposed. Standard multi-scale fusion techniques make a priori assumptions regarding input data, whereas standard univariate empirical mode decomposition (EMD)-based fusion techniques suffer from inherent mode mixing and mode misalignment issues, characterized respectively by either a single intrinsic mode function (IMF) containing multiple scales or the same indexed IMFs corresponding to multiple input images carrying different frequency information. We show that MEMD overcomes these problems by being fully data adaptive and by aligning common frequency scales from multiple channels, thus enabling their comparison at a pixel level and subsequent fusion at multiple data scales. We then demonstrate the potential of the proposed scheme on a large dataset of real-world multi-exposure and multi-focus images and compare the results against those obtained from standard fusion algorithms, including the principal component analysis (PCA), discrete wavelet transform (DWT) and non-subsampled contourlet transform (NCT). A variety of image fusion quality measures are employed for the objective evaluation of the proposed method. We also report the results of a hypothesis testing approach on our large image dataset to identify statistically-significant performance differences.

Multi-Scale Pixel-Based Image Fusion Using Multivariate Empirical Mode Decomposition

PubMed Central

Rehman, Naveed ur; Ehsan, Shoaib; Abdullah, Syed Muhammad Umer; Akhtar, Muhammad Jehanzaib; Mandic, Danilo P.; McDonald-Maier, Klaus D.

2015-01-01

A novel scheme to perform the fusion of multiple images using the multivariate empirical mode decomposition (MEMD) algorithm is proposed. Standard multi-scale fusion techniques make a priori assumptions regarding input data, whereas standard univariate empirical mode decomposition (EMD)-based fusion techniques suffer from inherent mode mixing and mode misalignment issues, characterized respectively by either a single intrinsic mode function (IMF) containing multiple scales or the same indexed IMFs corresponding to multiple input images carrying different frequency information. We show that MEMD overcomes these problems by being fully data adaptive and by aligning common frequency scales from multiple channels, thus enabling their comparison at a pixel level and subsequent fusion at multiple data scales. We then demonstrate the potential of the proposed scheme on a large dataset of real-world multi-exposure and multi-focus images and compare the results against those obtained from standard fusion algorithms, including the principal component analysis (PCA), discrete wavelet transform (DWT) and non-subsampled contourlet transform (NCT). A variety of image fusion quality measures are employed for the objective evaluation of the proposed method. We also report the results of a hypothesis testing approach on our large image dataset to identify statistically-significant performance differences. PMID:26007714

Multivariate inference of pathway activity in host immunity and response to therapeutics

PubMed Central

Goel, Gautam; Conway, Kara L.; Jaeger, Martin; Netea, Mihai G.; Xavier, Ramnik J.

2014-01-01

Developing a quantitative view of how biological pathways are regulated in response to environmental factors is central for understanding of disease phenotypes. We present a computational framework, named Multivariate Inference of Pathway Activity (MIPA), which quantifies degree of activity induced in a biological pathway by computing five distinct measures from transcriptomic profiles of its member genes. Statistical significance of inferred activity is examined using multiple independent self-contained tests followed by a competitive analysis. The method incorporates a new algorithm to identify a subset of genes that may regulate the extent of activity induced in a pathway. We present an in-depth evaluation of specificity, robustness, and reproducibility of our method. We benchmarked MIPA's false positive rate at less than 1%. Using transcriptomic profiles representing distinct physiological and disease states, we illustrate applicability of our method in (i) identifying gene–gene interactions in autophagy-dependent response to Salmonella infection, (ii) uncovering gene–environment interactions in host response to bacterial and viral pathogens and (iii) identifying driver genes and processes that contribute to wound healing and response to anti-TNFα therapy. We provide relevant experimental validation that corroborates the accuracy and advantage of our method. PMID:25147207

Reagent-free bacterial identification using multivariate analysis of transmission spectra

NASA Astrophysics Data System (ADS)

Smith, Jennifer M.; Huffman, Debra E.; Acosta, Dayanis; Serebrennikova, Yulia; García-Rubio, Luis; Leparc, German F.

2012-10-01

The identification of bacterial pathogens from culture is critical to the proper administration of antibiotics and patient treatment. Many of the tests currently used in the clinical microbiology laboratory for bacterial identification today can be highly sensitive and specific; however, they have the additional burdens of complexity, cost, and the need for specialized reagents. We present an innovative, reagent-free method for the identification of pathogens from culture. A clinical study has been initiated to evaluate the sensitivity and specificity of this approach. Multiwavelength transmission spectra were generated from a set of clinical isolates including Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Staphylococcus aureus. Spectra of an initial training set of these target organisms were used to create identification models representing the spectral variability of each species using multivariate statistical techniques. Next, the spectra of the blinded isolates of targeted species were identified using the model achieving >94% sensitivity and >98% specificity, with 100% accuracy for P. aeruginosa and S. aureus. The results from this on-going clinical study indicate this approach is a powerful and exciting technique for identification of pathogens. The menu of models is being expanded to include other bacterial genera and species of clinical significance.

Agro-forest landscape and the 'fringe' city: a multivariate assessment of land-use changes in a sprawling region and implications for planning.

PubMed

Salvati, Luca

2014-08-15

The present study evaluates the impact of urban expansion on landscape transformations in Rome's metropolitan area (1500 km(2)) during the last sixty years. Landscape composition, structure and dynamics were assessed for 1949 and 2008 by analyzing the distribution of 26 metrics for nine land-use classes. Changes in landscape structure are analysed by way of a multivariate statistical approach providing a summary measure of rapidity-to-change for each metric and class. Land fragmentation increased during the study period due to urban expansion. Poorly protected or medium-low value added classes (vineyards, arable land, olive groves and pastures) experienced fragmentation processes compared with protected or high-value added classes (e.g. forests, olive groves) showing larger 'core' areas and lower fragmentation. The relationship observed between class area and mean patch size indicates increased fragmentation for all uses of land (both expanding and declining) except for urban areas and forests. Reducing the impact of urban expansion for specific land-use classes is an effective planning strategy to contrast the simplification of Mediterranean landscape in peri-urban areas. Copyright © 2014 Elsevier B.V. All rights reserved.

Relation between serum creatinine and postoperative results of open-heart surgery.

PubMed

Ezeldin, Tamer H

2013-10-01

To determine the impact of preoperative serum creatinine level in non-dialyzable patients on postoperative morbidity and mortality. This is a prospective study, where serum creatinine was used to give primary assessment on renal function status preoperatively. This study includes 1,033 patients, who underwent coronary artery bypass grafting, or valve(s) operations. The study took place at Al-Hada Military Hospital, Taif, Kingdom of Saudi between May 2008 and January 2012. Data were statistically analyzed using Chi square (x2) test and multivariable logistic regression, to evaluate the postoperative morbidity and mortality risks associated with low serum creatinine levels. Postoperative mortality increased with high serum creatinine level >1.8 mg/dL (p

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.

Discriminant analysis of fused positive and negative ion mobility spectra using multivariate self-modeling mixture analysis and neural networks.

PubMed

Chen, Ping; Harrington, Peter B

2008-02-01

A new method coupling multivariate self-modeling mixture analysis and pattern recognition has been developed to identify toxic industrial chemicals using fused positive and negative ion mobility spectra (dual scan spectra). A Smiths lightweight chemical detector (LCD), which can measure positive and negative ion mobility spectra simultaneously, was used to acquire the data. Simple-to-use interactive self-modeling mixture analysis (SIMPLISMA) was used to separate the analytical peaks in the ion mobility spectra from the background reactant ion peaks (RIP). The SIMPLSIMA analytical components of the positive and negative ion peaks were combined together in a butterfly representation (i.e., negative spectra are reported with negative drift times and reflected with respect to the ordinate and juxtaposed with the positive ion mobility spectra). Temperature constrained cascade-correlation neural network (TCCCN) models were built to classify the toxic industrial chemicals. Seven common toxic industrial chemicals were used in this project to evaluate the performance of the algorithm. Ten bootstrapped Latin partitions demonstrated that the classification of neural networks using the SIMPLISMA components was statistically better than neural network models trained with fused ion mobility spectra (IMS).

Ellipsoids for anomaly detection in remote sensing imagery

NASA Astrophysics Data System (ADS)

Grosklos, Guenchik; Theiler, James

2015-05-01

For many target and anomaly detection algorithms, a key step is the estimation of a centroid (relatively easy) and a covariance matrix (somewhat harder) that characterize the background clutter. For a background that can be modeled as a multivariate Gaussian, the centroid and covariance lead to an explicit probability density function that can be used in likelihood ratio tests for optimal detection statistics. But ellipsoidal contours can characterize a much larger class of multivariate density function, and the ellipsoids that characterize the outer periphery of the distribution are most appropriate for detection in the low false alarm rate regime. Traditionally the sample mean and sample covariance are used to estimate ellipsoid location and shape, but these quantities are confounded both by large lever-arm outliers and non-Gaussian distributions within the ellipsoid of interest. This paper compares a variety of centroid and covariance estimation schemes with the aim of characterizing the periphery of the background distribution. In particular, we will consider a robust variant of the Khachiyan algorithm for minimum-volume enclosing ellipsoid. The performance of these different approaches is evaluated on multispectral and hyperspectral remote sensing imagery using coverage plots of ellipsoid volume versus false alarm rate.

Misspecification of Cox regression models with composite endpoints

PubMed Central

Wu, Longyang; Cook, Richard J

2012-01-01

Researchers routinely adopt composite endpoints in multicenter randomized trials designed to evaluate the effect of experimental interventions in cardiovascular disease, diabetes, and cancer. Despite their widespread use, relatively little attention has been paid to the statistical properties of estimators of treatment effect based on composite endpoints. We consider this here in the context of multivariate models for time to event data in which copula functions link marginal distributions with a proportional hazards structure. We then examine the asymptotic and empirical properties of the estimator of treatment effect arising from a Cox regression model for the time to the first event. We point out that even when the treatment effect is the same for the component events, the limiting value of the estimator based on the composite endpoint is usually inconsistent for this common value. We find that in this context the limiting value is determined by the degree of association between the events, the stochastic ordering of events, and the censoring distribution. Within the framework adopted, marginal methods for the analysis of multivariate failure time data yield consistent estimators of treatment effect and are therefore preferred. We illustrate the methods by application to a recent asthma study. Copyright © 2012 John Wiley & Sons, Ltd. PMID:22736519

Diagnostic tools for mixing models of stream water chemistry

USGS Publications Warehouse

Hooper, Richard P.

2003-01-01

Mixing models provide a useful null hypothesis against which to evaluate processes controlling stream water chemical data. Because conservative mixing of end‐members with constant concentration is a linear process, a number of simple mathematical and multivariate statistical methods can be applied to this problem. Although mixing models have been most typically used in the context of mixing soil and groundwater end‐members, an extension of the mathematics of mixing models is presented that assesses the “fit” of a multivariate data set to a lower dimensional mixing subspace without the need for explicitly identified end‐members. Diagnostic tools are developed to determine the approximate rank of the data set and to assess lack of fit of the data. This permits identification of processes that violate the assumptions of the mixing model and can suggest the dominant processes controlling stream water chemical variation. These same diagnostic tools can be used to assess the fit of the chemistry of one site into the mixing subspace of a different site, thereby permitting an assessment of the consistency of controlling end‐members across sites. This technique is applied to a number of sites at the Panola Mountain Research Watershed located near Atlanta, Georgia.

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

PubMed Central

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

2015-01-01

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

Simultaneous calibration of ensemble river flow predictions over an entire range of lead times

NASA Astrophysics Data System (ADS)

Hemri, S.; Fundel, F.; Zappa, M.

2013-10-01

Probabilistic estimates of future water levels and river discharge are usually simulated with hydrologic models using ensemble weather forecasts as main inputs. As hydrologic models are imperfect and the meteorological ensembles tend to be biased and underdispersed, the ensemble forecasts for river runoff typically are biased and underdispersed, too. Thus, in order to achieve both reliable and sharp predictions statistical postprocessing is required. In this work Bayesian model averaging (BMA) is applied to statistically postprocess ensemble runoff raw forecasts for a catchment in Switzerland, at lead times ranging from 1 to 240 h. The raw forecasts have been obtained using deterministic and ensemble forcing meteorological models with different forecast lead time ranges. First, BMA is applied based on mixtures of univariate normal distributions, subject to the assumption of independence between distinct lead times. Then, the independence assumption is relaxed in order to estimate multivariate runoff forecasts over the entire range of lead times simultaneously, based on a BMA version that uses multivariate normal distributions. Since river runoff is a highly skewed variable, Box-Cox transformations are applied in order to achieve approximate normality. Both univariate and multivariate BMA approaches are able to generate well calibrated probabilistic forecasts that are considerably sharper than climatological forecasts. Additionally, multivariate BMA provides a promising approach for incorporating temporal dependencies into the postprocessed forecasts. Its major advantage against univariate BMA is an increase in reliability when the forecast system is changing due to model availability.

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

PubMed

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

2017-01-01

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

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

PubMed Central

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

2017-01-01

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