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.

Bayesian transformation cure frailty models with multivariate failure time data.

PubMed

Yin, Guosheng

2008-12-10

We propose a class of transformation cure frailty models to accommodate a survival fraction in multivariate failure time data. Established through a general power transformation, this family of cure frailty models includes the proportional hazards and the proportional odds modeling structures as two special cases. Within the Bayesian paradigm, we obtain the joint posterior distribution and the corresponding full conditional distributions of the model parameters for the implementation of Gibbs sampling. Model selection is based on the conditional predictive ordinate statistic and deviance information criterion. As an illustration, we apply the proposed method to a real data set from dentistry.

Copula Multivariate analysis of Gross primary production and its hydro-environmental driver; A BIOME-BGC model applied to the Antisana páramos

NASA Astrophysics Data System (ADS)

Minaya, Veronica; Corzo, Gerald; van der Kwast, Johannes; Galarraga, Remigio; Mynett, Arthur

2014-05-01

Simulations of carbon cycling are prone to uncertainties from different sources, which in general are related to input data, parameters and the model representation capacities itself. The gross carbon uptake in the cycle is represented by the gross primary production (GPP), which deals with the spatio-temporal variability of the precipitation and the soil moisture dynamics. This variability associated with uncertainty of the parameters can be modelled by multivariate probabilistic distributions. Our study presents a novel methodology that uses multivariate Copulas analysis to assess the GPP. Multi-species and elevations variables are included in a first scenario of the analysis. Hydro-meteorological conditions that might generate a change in the next 50 or more years are included in a second scenario of this analysis. The biogeochemical model BIOME-BGC was applied in the Ecuadorian Andean region in elevations greater than 4000 masl with the presence of typical vegetation of páramo. The change of GPP over time is crucial for climate scenarios of the carbon cycling in this type of ecosystem. The results help to improve our understanding of the ecosystem function and clarify the dynamics and the relationship with the change of climate variables. Keywords: multivariate analysis, Copula, BIOME-BGC, NPP, páramos

Up-scaling of multi-variable flood loss models from objects to land use units at the meso-scale

NASA Astrophysics Data System (ADS)

Kreibich, Heidi; Schröter, Kai; Merz, Bruno

2016-05-01

Flood risk management increasingly relies on risk analyses, including loss modelling. Most of the flood loss models usually applied in standard practice have in common that complex damaging processes are described by simple approaches like stage-damage functions. Novel multi-variable models significantly improve loss estimation on the micro-scale and may also be advantageous for large-scale applications. However, more input parameters also reveal additional uncertainty, even more in upscaling procedures for meso-scale applications, where the parameters need to be estimated on a regional area-wide basis. To gain more knowledge about challenges associated with the up-scaling of multi-variable flood loss models the following approach is applied: Single- and multi-variable micro-scale flood loss models are up-scaled and applied on the meso-scale, namely on basis of ATKIS land-use units. Application and validation is undertaken in 19 municipalities, which were affected during the 2002 flood by the River Mulde in Saxony, Germany by comparison to official loss data provided by the Saxon Relief Bank (SAB).In the meso-scale case study based model validation, most multi-variable models show smaller errors than the uni-variable stage-damage functions. The results show the suitability of the up-scaling approach, and, in accordance with micro-scale validation studies, that multi-variable models are an improvement in flood loss modelling also on the meso-scale. However, uncertainties remain high, stressing the importance of uncertainty quantification. Thus, the development of probabilistic loss models, like BT-FLEMO used in this study, which inherently provide uncertainty information are the way forward.

Item Response Modeling of Multivariate Count Data with Zero Inflation, Maximum Inflation, and Heaping

ERIC Educational Resources Information Center

Magnus, Brooke E.; Thissen, David

2017-01-01

Questionnaires that include items eliciting count responses are becoming increasingly common in psychology. This study proposes methodological techniques to overcome some of the challenges associated with analyzing multivariate item response data that exhibit zero inflation, maximum inflation, and heaping at preferred digits. The modeling…

Combining Frequency Doubling Technology Perimetry and Scanning Laser Polarimetry for Glaucoma Detection.

PubMed

Mwanza, Jean-Claude; Warren, Joshua L; Hochberg, Jessica T; Budenz, Donald L; Chang, Robert T; Ramulu, Pradeep Y

2015-01-01

To determine the ability of frequency doubling technology (FDT) and scanning laser polarimetry with variable corneal compensation (GDx-VCC) to detect glaucoma when used individually and in combination. One hundred ten normal and 114 glaucomatous subjects were tested with FDT C-20-5 screening protocol and the GDx-VCC. The discriminating ability was tested for each device individually and for both devices combined using GDx-NFI, GDx-TSNIT, number of missed points of FDT, and normal or abnormal FDT. Measures of discrimination included sensitivity, specificity, area under the curve (AUC), Akaike's information criterion (AIC), and prediction confidence interval lengths. For detecting glaucoma regardless of severity, the multivariable model resulting from the combination of GDx-TSNIT, number of abnormal points on FDT (NAP-FDT), and the interaction GDx-TSNIT×NAP-FDT (AIC: 88.28, AUC: 0.959, sensitivity: 94.6%, specificity: 89.5%) outperformed the best single-variable model provided by GDx-NFI (AIC: 120.88, AUC: 0.914, sensitivity: 87.8%, specificity: 84.2%). The multivariable model combining GDx-TSNIT, NAP-FDT, and interaction GDx-TSNIT×NAP-FDT consistently provided better discriminating abilities for detecting early, moderate, and severe glaucoma than the best single-variable models. The multivariable model including GDx-TSNIT, NAP-FDT, and the interaction GDx-TSNIT×NAP-FDT provides the best glaucoma prediction compared with all other multivariable and univariable models. Combining the FDT C-20-5 screening protocol and GDx-VCC improves glaucoma detection compared with using GDx or FDT alone.

Sensory imbalance as mechanism of orientation disruption in the leafminer, Phyllocnistis citrella: Elucidation by multivariate geometric designs and response surface models

USDA-ARS?s Scientific Manuscript database

Experimental designs developed to address mixtures are ideally suited for many areas of experimental biology including pheromone blend studies because they address the confounding of proportionality and concentration intrinsic to factorial and one-factor-at-a-time designs. Geometric multivariate des...

Load compensation in a lean burn natural gas vehicle

NASA Astrophysics Data System (ADS)

Gangopadhyay, Anupam

A new multivariable PI tuning technique is developed in this research that is primarily developed for regulation purposes. Design guidelines are developed based on closed-loop stability. The new multivariable design is applied in a natural gas vehicle to combine idle and A/F ratio control loops. This results in better recovery during low idle operation of a vehicle under external step torques. A powertrain model of a natural gas engine is developed and validated for steady-state and transient operation. The nonlinear model has three states: engine speed, intake manifold pressure and fuel fraction in the intake manifold. The model includes the effect of fuel partial pressure in the intake manifold filling and emptying dynamics. Due to the inclusion of fuel fraction as a state, fuel flow rate into the cylinders is also accurately modeled. A linear system identification is performed on the nonlinear model. The linear model structure is predicted analytically from the nonlinear model and the coefficients of the predicted transfer function are shown to be functions of key physical parameters in the plant. Simulations of linear system and model parameter identification is shown to converge to the predicted values of the model coefficients. The multivariable controller developed in this research could be designed in an algebraic fashion once the plant model is known. It is thus possible to implement the multivariable PI design in an adaptive fashion combining the controller with identified plant model on-line. This will result in a self-tuning regulator (STR) type controller where the underlying design criteria is the multivariable tuning technique designed in this research.

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.

Space-time variation of respiratory cancers in South Carolina: a flexible multivariate mixture modeling approach to risk estimation.

PubMed

Carroll, Rachel; Lawson, Andrew B; Kirby, Russell S; Faes, Christel; Aregay, Mehreteab; Watjou, Kevin

2017-01-01

Many types of cancer have an underlying spatiotemporal distribution. Spatiotemporal mixture modeling can offer a flexible approach to risk estimation via the inclusion of latent variables. In this article, we examine the application and benefits of using four different spatiotemporal mixture modeling methods in the modeling of cancer of the lung and bronchus as well as "other" respiratory cancer incidences in the state of South Carolina. Of the methods tested, no single method outperforms the other methods; which method is best depends on the cancer under consideration. The lung and bronchus cancer incidence outcome is best described by the univariate modeling formulation, whereas the "other" respiratory cancer incidence outcome is best described by the multivariate modeling formulation. Spatiotemporal multivariate mixture methods can aid in the modeling of cancers with small and sparse incidences when including information from a related, more common type of cancer. Copyright © 2016 Elsevier Inc. All rights reserved.

Combining Frequency Doubling Technology Perimetry and Scanning Laser Polarimetry for Glaucoma Detection

PubMed Central

Mwanza, Jean-Claude; Warren, Joshua L.; Hochberg, Jessica T.; Budenz, Donald L.; Chang, Robert T.; Ramulu, Pradeep Y.

2014-01-01

Purpose To determine the ability of frequency doubling technology (FDT) and scanning laser polarimetry with variable corneal compensation (GDx-VCC) to detect glaucoma when used individually and in combination. Methods One hundred and ten normal and 114 glaucomatous subjects were tested with FDT C-20-5 screening protocol and the GDx-VCC. The discriminating ability was tested for each device individually and for both devices combined using GDx-NFI, GDx-TSNIT, number of missed points of FDT, and normal or abnormal FDT. Measures of discrimination included sensitivity, specificity, area under the curve (AUC), Akaike’s information criterion (AIC), and prediction confidence interval lengths (PIL). Results For detecting glaucoma regardless of severity, the multivariable model resulting from the combination of GDX-TSNIT, number of abnormal points on FDT (NAP-FDT), and the interaction GDx-TSNIT * NAP-FDT (AIC: 88.28, AUC: 0.959, sensitivity: 94.6%, specificity: 89.5%) outperformed the best single variable model provided by GDx-NFI (AIC: 120.88, AUC: 0.914, sensitivity: 87.8%, specificity: 84.2%). The multivariable model combining GDx-TSNIT, NAPFDT, and interaction GDx-TSNIT*NAP-FDT consistently provided better discriminating abilities for detecting early, moderate and severe glaucoma than the best single variable models. Conclusions The multivariable model including GDx-TSNIT, NAP-FDT, and the interaction GDX-TSNIT * NAP-FDT provides the best glaucoma prediction compared to all other multivariable and univariable models. Combining the FDT C-20-5 screening protocol and GDx-VCC improves glaucoma detection compared to using GDx or FDT alone. PMID:24777046

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

PubMed

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

2014-07-21

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

Improved estimation of PM2.5 using Lagrangian satellite-measured aerosol optical depth

NASA Astrophysics Data System (ADS)

Olivas Saunders, Rolando

Suspended particulate matter (aerosols) with aerodynamic diameters less than 2.5 mum (PM2.5) has negative effects on human health, plays an important role in climate change and also causes the corrosion of structures by acid deposition. Accurate estimates of PM2.5 concentrations are thus relevant in air quality, epidemiology, cloud microphysics and climate forcing studies. Aerosol optical depth (AOD) retrieved by the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument has been used as an empirical predictor to estimate ground-level concentrations of PM2.5 . These estimates usually have large uncertainties and errors. The main objective of this work is to assess the value of using upwind (Lagrangian) MODIS-AOD as predictors in empirical models of PM2.5. The upwind locations of the Lagrangian AOD were estimated using modeled backward air trajectories. Since the specification of an arrival elevation is somewhat arbitrary, trajectories were calculated to arrive at four different elevations at ten measurement sites within the continental United States. A systematic examination revealed trajectory model calculations to be sensitive to starting elevation. With a 500 m difference in starting elevation, the 48-hr mean horizontal separation of trajectory endpoints was 326 km. When the difference in starting elevation was doubled and tripled to 1000 m and 1500m, the mean horizontal separation of trajectory endpoints approximately doubled and tripled to 627 km and 886 km, respectively. A seasonal dependence of this sensitivity was also found: the smallest mean horizontal separation of trajectory endpoints was exhibited during the summer and the largest separations during the winter. A daily average AOD product was generated and coupled to the trajectory model in order to determine AOD values upwind of the measurement sites during the period 2003-2007. Empirical models that included in situ AOD and upwind AOD as predictors of PM2.5 were generated by multivariate linear regressions using the least squares method. The multivariate models showed improved performance over the single variable regression (PM2.5 and in situ AOD) models. The statistical significance of the improvement of the multivariate models over the single variable regression models was tested using the extra sum of squares principle. In many cases, even when the R-squared was high for the multivariate models, the improvement over the single models was not statistically significant. The R-squared of these multivariate models varied with respect to seasons, with the best performance occurring during the summer months. A set of seasonal categorical variables was included in the regressions to exploit this variability. The multivariate regression models that included these categorical seasonal variables performed better than the models that didn't account for seasonal variability. Furthermore, 71% of these regressions exhibited improvement over the single variable models that was statistically significant at a 95% confidence level.

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

PubMed

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

2015-12-30

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

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

PubMed Central

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

2013-01-01

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

Analyzing Multiple Outcomes in Clinical Research Using Multivariate Multilevel Models

PubMed Central

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

2014-01-01

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

Multivariate missing data in hydrology - Review and applications

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Prediction of overall survival in stage II and III colon cancer beyond TNM system: a retrospective, pooled biomarker study.

PubMed

Dienstmann, R; Mason, M J; Sinicrope, F A; Phipps, A I; Tejpar, S; Nesbakken, A; Danielsen, S A; Sveen, A; Buchanan, D D; Clendenning, M; Rosty, C; Bot, B; Alberts, S R; Milburn Jessup, J; Lothe, R A; Delorenzi, M; Newcomb, P A; Sargent, D; Guinney, J

2017-05-01

TNM staging alone does not accurately predict outcome in colon cancer (CC) patients who may be eligible for adjuvant chemotherapy. It is unknown to what extent the molecular markers microsatellite instability (MSI) and mutations in BRAF or KRAS improve prognostic estimation in multivariable models that include detailed clinicopathological annotation. After imputation of missing at random data, a subset of patients accrued in phase 3 trials with adjuvant chemotherapy (n = 3016)-N0147 (NCT00079274) and PETACC3 (NCT00026273)-was aggregated to construct multivariable Cox models for 5-year overall survival that were subsequently validated internally in the remaining clinical trial samples (n = 1499), and also externally in different population cohorts of chemotherapy-treated (n = 949) or -untreated (n = 1080) CC patients, and an additional series without treatment annotation (n = 782). TNM staging, MSI and BRAFV600E mutation status remained independent prognostic factors in multivariable models across clinical trials cohorts and observational studies. Concordance indices increased from 0.61-0.68 in the TNM alone model to 0.63-0.71 in models with added molecular markers, 0.65-0.73 with clinicopathological features and 0.66-0.74 with all covariates. In validation cohorts with complete annotation, the integrated time-dependent AUC rose from 0.64 for the TNM alone model to 0.67 for models that included clinicopathological features, with or without molecular markers. In patient cohorts that received adjuvant chemotherapy, the relative proportion of variance explained (R2) by TNM, clinicopathological features and molecular markers was on an average 65%, 25% and 10%, respectively. Incorporation of MSI, BRAFV600E and KRAS mutation status to overall survival models with TNM staging improves the ability to precisely prognosticate in stage II and III CC patients, but only modestly increases prediction accuracy in multivariable models that include clinicopathological features, particularly in chemotherapy-treated patients. © The Author 2017. Published by Oxford University Press on behalf of the European Society for Medical Oncology.

Transient multivariable sensor evaluation

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

Vilim, Richard B.; Heifetz, Alexander

A method and system for performing transient multivariable sensor evaluation. The method and system includes a computer system for identifying a model form, providing training measurement data, generating a basis vector, monitoring system data from sensor, loading the system data in a non-transient memory, performing an estimation to provide desired data and comparing the system data to the desired data and outputting an alarm for a defective sensor.

Sample size calculations for case-control studies

Cancer.gov

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

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

PubMed

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

2016-01-01

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

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

PubMed Central

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

2016-01-01

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

Multitrait, Random Regression, or Simple Repeatability Model in High-Throughput Phenotyping Data Improve Genomic Prediction for Wheat Grain Yield.

PubMed

Sun, Jin; Rutkoski, Jessica E; Poland, Jesse A; Crossa, José; Jannink, Jean-Luc; Sorrells, Mark E

2017-07-01

High-throughput phenotyping (HTP) platforms can be used to measure traits that are genetically correlated with wheat ( L.) grain yield across time. Incorporating such secondary traits in the multivariate pedigree and genomic prediction models would be desirable to improve indirect selection for grain yield. In this study, we evaluated three statistical models, simple repeatability (SR), multitrait (MT), and random regression (RR), for the longitudinal data of secondary traits and compared the impact of the proposed models for secondary traits on their predictive abilities for grain yield. Grain yield and secondary traits, canopy temperature (CT) and normalized difference vegetation index (NDVI), were collected in five diverse environments for 557 wheat lines with available pedigree and genomic information. A two-stage analysis was applied for pedigree and genomic selection (GS). First, secondary traits were fitted by SR, MT, or RR models, separately, within each environment. Then, best linear unbiased predictions (BLUPs) of secondary traits from the above models were used in the multivariate prediction models to compare predictive abilities for grain yield. Predictive ability was substantially improved by 70%, on average, from multivariate pedigree and genomic models when including secondary traits in both training and test populations. Additionally, (i) predictive abilities slightly varied for MT, RR, or SR models in this data set, (ii) results indicated that including BLUPs of secondary traits from the MT model was the best in severe drought, and (iii) the RR model was slightly better than SR and MT models under drought environment. Copyright © 2017 Crop Science Society of America.

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

PubMed Central

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

2003-01-01

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

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

PubMed

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

2016-01-01

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

A 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.

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.

A Multivariate Model of Physics Problem Solving

ERIC Educational Resources Information Center

Taasoobshirazi, Gita; Farley, John

2013-01-01

A model of expertise in physics problem solving was tested on undergraduate science, physics, and engineering majors enrolled in an introductory-level physics course. Structural equation modeling was used to test hypothesized relationships among variables linked to expertise in physics problem solving including motivation, metacognitive planning,…

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.

Univariate and multivariate spatial models of health facility utilisation for childhood fevers in an area on the coast of Kenya.

PubMed

Ouma, Paul O; Agutu, Nathan O; Snow, Robert W; Noor, Abdisalan M

2017-09-18

Precise quantification of health service utilisation is important for the estimation of disease burden and allocation of health resources. Current approaches to mapping health facility utilisation rely on spatial accessibility alone as the predictor. However, other spatially varying social, demographic and economic factors may affect the use of health services. The exclusion of these factors can lead to the inaccurate estimation of health facility utilisation. Here, we compare the accuracy of a univariate spatial model, developed only from estimated travel time, to a multivariate model that also includes relevant social, demographic and economic factors. A theoretical surface of travel time to the nearest public health facility was developed. These were assigned to each child reported to have had fever in the Kenya demographic and health survey of 2014 (KDHS 2014). The relationship of child treatment seeking for fever with travel time, household and individual factors from the KDHS2014 were determined using multilevel mixed modelling. Bayesian information criterion (BIC) and likelihood ratio test (LRT) tests were carried out to measure how selected factors improve parsimony and goodness of fit of the time model. Using the mixed model, a univariate spatial model of health facility utilisation was fitted using travel time as the predictor. The mixed model was also used to compute a multivariate spatial model of utilisation, using travel time and modelled surfaces of selected household and individual factors as predictors. The univariate and multivariate spatial models were then compared using the receiver operating area under the curve (AUC) and a percent correct prediction (PCP) test. The best fitting multivariate model had travel time, household wealth index and number of children in household as the predictors. These factors reduced BIC of the time model from 4008 to 2959, a change which was confirmed by the LRT test. Although there was a high correlation of the two modelled probability surfaces (Adj R 2  = 88%), the multivariate model had better AUC compared to the univariate model; 0.83 versus 0.73 and PCP 0.61 versus 0.45 values. Our study shows that a model that uses travel time, as well as household and individual-level socio-demographic factors, results in a more accurate estimation of use of health facilities for the treatment of childhood fever, compared to one that relies on only travel time.

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

PubMed Central

2014-01-01

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

Ground-Based Telescope Parametric Cost Model

NASA Technical Reports Server (NTRS)

Stahl, H. Philip; Rowell, Ginger Holmes

2004-01-01

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

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.

Multivariable normal-tissue complication modeling of acute esophageal toxicity in advanced stage non-small cell lung cancer patients treated with intensity-modulated (chemo-)radiotherapy.

PubMed

Wijsman, Robin; Dankers, Frank; Troost, Esther G C; Hoffmann, Aswin L; van der Heijden, Erik H F M; de Geus-Oei, Lioe-Fee; Bussink, Johan

2015-10-01

The majority of normal-tissue complication probability (NTCP) models for acute esophageal toxicity (AET) in advanced stage non-small cell lung cancer (AS-NSCLC) patients treated with (chemo-)radiotherapy are based on three-dimensional conformal radiotherapy (3D-CRT). Due to distinct dosimetric characteristics of intensity-modulated radiation therapy (IMRT), 3D-CRT based models need revision. We established a multivariable NTCP model for AET in 149 AS-NSCLC patients undergoing IMRT. An established model selection procedure was used to develop an NTCP model for Grade ⩾2 AET (53 patients) including clinical and esophageal dose-volume histogram parameters. The NTCP model predicted an increased risk of Grade ⩾2 AET in case of: concurrent chemoradiotherapy (CCR) [adjusted odds ratio (OR) 14.08, 95% confidence interval (CI) 4.70-42.19; p<0.001], increasing mean esophageal dose [Dmean; OR 1.12 per Gy increase, 95% CI 1.06-1.19; p<0.001], female patients (OR 3.33, 95% CI 1.36-8.17; p=0.008), and ⩾cT3 (OR 2.7, 95% CI 1.12-6.50; p=0.026). The AUC was 0.82 and the model showed good calibration. A multivariable NTCP model including CCR, Dmean, clinical tumor stage and gender predicts Grade ⩾2 AET after IMRT for AS-NSCLC. Prior to clinical introduction, the model needs validation in an independent patient cohort. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Investigating College and Graduate Students' Multivariable Reasoning in Computational Modeling

ERIC Educational Resources Information Center

Wu, Hsin-Kai; Wu, Pai-Hsing; Zhang, Wen-Xin; Hsu, Ying-Shao

2013-01-01

Drawing upon the literature in computational modeling, multivariable reasoning, and causal attribution, this study aims at characterizing multivariable reasoning practices in computational modeling and revealing the nature of understanding about multivariable causality. We recruited two freshmen, two sophomores, two juniors, two seniors, four…

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.

Prediction of the birch pollen season characteristics in Cracow, Poland using an 18-year data series.

PubMed

Dorota, Myszkowska

2013-03-01

The aim of the study was to construct the model forecasting the birch pollen season characteristics in Cracow on the basis of an 18-year data series. The study was performed using the volumetric method (Lanzoni/Burkard trap). The 98/95 % method was used to calculate the pollen season. The Spearman's correlation test was applied to find the relationship between the meteorological parameters and pollen season characteristics. To construct the predictive model, the backward stepwise multiple regression analysis was used including the multi-collinearity of variables. The predictive models best fitted the pollen season start and end, especially models containing two independent variables. The peak concentration value was predicted with the higher prediction error. Also the accuracy of the models predicting the pollen season characteristics in 2009 was higher in comparison with 2010. Both, the multi-variable model and one-variable model for the beginning of the pollen season included air temperature during the last 10 days of February, while the multi-variable model also included humidity at the beginning of April. The models forecasting the end of the pollen season were based on temperature in March-April, while the peak day was predicted using the temperature during the last 10 days of March.

A Multivariate Model for the Study of Parental Acceptance-Rejection and Child Abuse.

ERIC Educational Resources Information Center

Rohner, Ronald P.; Rohner, Evelyn C.

This paper proposes a multivariate strategy for the study of parental acceptance-rejection and child abuse and describes a research study on parental rejection and child abuse which illustrates the advantages of using a multivariate, (rather than a simple-model) approach. The multivariate model is a combination of three simple models used to study…

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.

POWERLIB: SAS/IML Software for Computing Power in Multivariate Linear Models

PubMed Central

Johnson, Jacqueline L.; Muller, Keith E.; Slaughter, James C.; Gurka, Matthew J.; Gribbin, Matthew J.; Simpson, Sean L.

2014-01-01

The POWERLIB SAS/IML software provides convenient power calculations for a wide range of multivariate linear models with Gaussian errors. The software includes the Box, Geisser-Greenhouse, Huynh-Feldt, and uncorrected tests in the “univariate” approach to repeated measures (UNIREP), the Hotelling Lawley Trace, Pillai-Bartlett Trace, and Wilks Lambda tests in “multivariate” approach (MULTIREP), as well as a limited but useful range of mixed models. The familiar univariate linear model with Gaussian errors is an important special case. For estimated covariance, the software provides confidence limits for the resulting estimated power. All power and confidence limits values can be output to a SAS dataset, which can be used to easily produce plots and tables for manuscripts. PMID:25400516

A multivariate model of parent-adolescent relationship variables in early adolescence.

PubMed

McKinney, Cliff; Renk, Kimberly

2011-08-01

Given the importance of predicting outcomes for early adolescents, this study examines a multivariate model of parent-adolescent relationship variables, including parenting, family environment, and conflict. Participants, who completed measures assessing these variables, included 710 culturally diverse 11-14-year-olds who were attending a middle school in a Southeastern state. The parents of a subset of these adolescents (i.e., 487 mother-father pairs) participated in this study as well. Correlational analyses indicate that authoritative and authoritarian parenting, family cohesion and adaptability, and conflict are significant predictors of early adolescents' internalizing and externalizing problems. Structural equation modeling analyses indicate that fathers' parenting may not predict directly externalizing problems in male and female adolescents but instead may act through conflict. More direct relationships exist when examining mothers' parenting. The impact of parenting, family environment, and conflict on early adolescents' internalizing and externalizing problems and the importance of both gender and cross-informant ratings are emphasized.

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

NASA Astrophysics Data System (ADS)

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

2018-03-01

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

Multi-application controls: Robust nonlinear multivariable aerospace controls applications

NASA Technical Reports Server (NTRS)

Enns, Dale F.; Bugajski, Daniel J.; Carter, John; Antoniewicz, Bob

1994-01-01

This viewgraph presentation describes the general methodology used to apply Honywell's Multi-Application Control (MACH) and the specific application to the F-18 High Angle-of-Attack Research Vehicle (HARV) including piloted simulation handling qualities evaluation. The general steps include insertion of modeling data for geometry and mass properties, aerodynamics, propulsion data and assumptions, requirements and specifications, e.g. definition of control variables, handling qualities, stability margins and statements for bandwidth, control power, priorities, position and rate limits. The specific steps include choice of independent variables for least squares fits to aerodynamic and propulsion data, modifications to the management of the controls with regard to integrator windup and actuation limiting and priorities, e.g. pitch priority over roll, and command limiting to prevent departures and/or undesirable inertial coupling or inability to recover to a stable trim condition. The HARV control problem is characterized by significant nonlinearities and multivariable interactions in the low speed, high angle-of-attack, high angular rate flight regime. Systematic approaches to the control of vehicle motions modeled with coupled nonlinear equations of motion have been developed. This paper will discuss the dynamic inversion approach which explicity accounts for nonlinearities in the control design. Multiple control effectors (including aerodynamic control surfaces and thrust vectoring control) and sensors are used to control the motions of the vehicles in several degrees-of-freedom. Several maneuvers will be used to illustrate performance of MACH in the high angle-of-attack flight regime. Analytical methods for assessing the robust performance of the multivariable control system in the presence of math modeling uncertainty, disturbances, and commands have reached a high level of maturity. The structured singular value (mu) frequency response methodology is presented as a method for analyzing robust performance and the mu-synthesis method will be presented as a method for synthesizing a robust control system. The paper concludes with the author's expectations regarding future applications of robust nonlinear multivariable controls.

Extensions to Multivariate Space Time Mixture Modeling of Small Area Cancer Data.

PubMed

Carroll, Rachel; Lawson, Andrew B; Faes, Christel; Kirby, Russell S; Aregay, Mehreteab; Watjou, Kevin

2017-05-09

Oral cavity and pharynx cancer, even when considered together, is a fairly rare disease. Implementation of multivariate modeling with lung and bronchus cancer, as well as melanoma cancer of the skin, could lead to better inference for oral cavity and pharynx cancer. The multivariate structure of these models is accomplished via the use of shared random effects, as well as other multivariate prior distributions. The results in this paper indicate that care should be taken when executing these types of models, and that multivariate mixture models may not always be the ideal option, depending on the data of interest.

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…

Divergences and estimating tight bounds on Bayes error with applications to multivariate Gaussian copula and latent Gaussian copula

NASA Astrophysics Data System (ADS)

Thelen, Brian J.; Xique, Ismael J.; Burns, Joseph W.; Goley, G. Steven; Nolan, Adam R.; Benson, Jonathan W.

2017-04-01

In Bayesian decision theory, there has been a great amount of research into theoretical frameworks and information- theoretic quantities that can be used to provide lower and upper bounds for the Bayes error. These include well-known bounds such as Chernoff, Battacharrya, and J-divergence. Part of the challenge of utilizing these various metrics in practice is (i) whether they are "loose" or "tight" bounds, (ii) how they might be estimated via either parametric or non-parametric methods, and (iii) how accurate the estimates are for limited amounts of data. In general what is desired is a methodology for generating relatively tight lower and upper bounds, and then an approach to estimate these bounds efficiently from data. In this paper, we explore the so-called triangle divergence which has been around for a while, but was recently made more prominent in some recent research on non-parametric estimation of information metrics. Part of this work is motivated by applications for quantifying fundamental information content in SAR/LIDAR data, and to help in this, we have developed a flexible multivariate modeling framework based on multivariate Gaussian copula models which can be combined with the triangle divergence framework to quantify this information, and provide approximate bounds on Bayes error. In this paper we present an overview of the bounds, including those based on triangle divergence and verify that under a number of multivariate models, the upper and lower bounds derived from triangle divergence are significantly tighter than the other common bounds, and often times, dramatically so. We also propose some simple but effective means for computing the triangle divergence using Monte Carlo methods, and then discuss estimation of the triangle divergence from empirical data based on Gaussian Copula models.

Using Time Series Analysis to Predict Cardiac Arrest in a PICU.

PubMed

Kennedy, Curtis E; Aoki, Noriaki; Mariscalco, Michele; Turley, James P

2015-11-01

To build and test cardiac arrest prediction models in a PICU, using time series analysis as input, and to measure changes in prediction accuracy attributable to different classes of time series data. Retrospective cohort study. Thirty-one bed academic PICU that provides care for medical and general surgical (not congenital heart surgery) patients. Patients experiencing a cardiac arrest in the PICU and requiring external cardiac massage for at least 2 minutes. None. One hundred three cases of cardiac arrest and 109 control cases were used to prepare a baseline dataset that consisted of 1,025 variables in four data classes: multivariate, raw time series, clinical calculations, and time series trend analysis. We trained 20 arrest prediction models using a matrix of five feature sets (combinations of data classes) with four modeling algorithms: linear regression, decision tree, neural network, and support vector machine. The reference model (multivariate data with regression algorithm) had an accuracy of 78% and 87% area under the receiver operating characteristic curve. The best model (multivariate + trend analysis data with support vector machine algorithm) had an accuracy of 94% and 98% area under the receiver operating characteristic curve. Cardiac arrest predictions based on a traditional model built with multivariate data and a regression algorithm misclassified cases 3.7 times more frequently than predictions that included time series trend analysis and built with a support vector machine algorithm. Although the final model lacks the specificity necessary for clinical application, we have demonstrated how information from time series data can be used to increase the accuracy of clinical prediction models.

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.

Identification of multivariable nonlinear systems in the presence of colored noises using iterative hierarchical least squares algorithm.

PubMed

Jafari, Masoumeh; Salimifard, Maryam; Dehghani, Maryam

2014-07-01

This paper presents an efficient method for identification of nonlinear Multi-Input Multi-Output (MIMO) systems in the presence of colored noises. The method studies the multivariable nonlinear Hammerstein and Wiener models, in which, the nonlinear memory-less block is approximated based on arbitrary vector-based basis functions. The linear time-invariant (LTI) block is modeled by an autoregressive moving average with exogenous (ARMAX) model which can effectively describe the moving average noises as well as the autoregressive and the exogenous dynamics. According to the multivariable nature of the system, a pseudo-linear-in-the-parameter model is obtained which includes two different kinds of unknown parameters, a vector and a matrix. Therefore, the standard least squares algorithm cannot be applied directly. To overcome this problem, a Hierarchical Least Squares Iterative (HLSI) algorithm is used to simultaneously estimate the vector and the matrix of unknown parameters as well as the noises. The efficiency of the proposed identification approaches are investigated through three nonlinear MIMO case studies. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

Multivariate Normal Tissue Complication Probability Modeling of Heart Valve Dysfunction in Hodgkin Lymphoma Survivors

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

Cella, Laura, E-mail: laura.cella@cnr.it; Department of Advanced Biomedical Sciences, Federico II University School of Medicine, Naples; Liuzzi, Raffaele

Purpose: To establish a multivariate normal tissue complication probability (NTCP) model for radiation-induced asymptomatic heart valvular defects (RVD). Methods and Materials: Fifty-six patients treated with sequential chemoradiation therapy for Hodgkin lymphoma (HL) were retrospectively reviewed for RVD events. Clinical information along with whole heart, cardiac chambers, and lung dose distribution parameters was collected, and the correlations to RVD were analyzed by means of Spearman's rank correlation coefficient (Rs). For the selection of the model order and parameters for NTCP modeling, a multivariate logistic regression method using resampling techniques (bootstrapping) was applied. Model performance was evaluated using the area under themore » receiver operating characteristic curve (AUC). Results: When we analyzed the whole heart, a 3-variable NTCP model including the maximum dose, whole heart volume, and lung volume was shown to be the optimal predictive model for RVD (Rs = 0.573, P<.001, AUC = 0.83). When we analyzed the cardiac chambers individually, for the left atrium and for the left ventricle, an NTCP model based on 3 variables including the percentage volume exceeding 30 Gy (V30), cardiac chamber volume, and lung volume was selected as the most predictive model (Rs = 0.539, P<.001, AUC = 0.83; and Rs = 0.557, P<.001, AUC = 0.82, respectively). The NTCP values increase as heart maximum dose or cardiac chambers V30 increase. They also increase with larger volumes of the heart or cardiac chambers and decrease when lung volume is larger. Conclusions: We propose logistic NTCP models for RVD considering not only heart irradiation dose but also the combined effects of lung and heart volumes. Our study establishes the statistical evidence of the indirect effect of lung size on radio-induced heart toxicity.« less

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

PubMed

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

2017-11-01

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

Linear Multivariable Regression Models for Prediction of Eddy Dissipation Rate from Available Meteorological Data

NASA Technical Reports Server (NTRS)

MCKissick, Burnell T. (Technical Monitor); Plassman, Gerald E.; Mall, Gerald H.; Quagliano, John R.

2005-01-01

Linear multivariable regression models for predicting day and night Eddy Dissipation Rate (EDR) from available meteorological data sources are defined and validated. Model definition is based on a combination of 1997-2000 Dallas/Fort Worth (DFW) data sources, EDR from Aircraft Vortex Spacing System (AVOSS) deployment data, and regression variables primarily from corresponding Automated Surface Observation System (ASOS) data. Model validation is accomplished through EDR predictions on a similar combination of 1994-1995 Memphis (MEM) AVOSS and ASOS data. Model forms include an intercept plus a single term of fixed optimal power for each of these regression variables; 30-minute forward averaged mean and variance of near-surface wind speed and temperature, variance of wind direction, and a discrete cloud cover metric. Distinct day and night models, regressing on EDR and the natural log of EDR respectively, yield best performance and avoid model discontinuity over day/night data boundaries.

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.

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.

Use of collateral information to improve LANDSAT classification accuracies

NASA Technical Reports Server (NTRS)

Strahler, A. H. (Principal Investigator)

1981-01-01

Methods to improve LANDSAT classification accuracies were investigated including: (1) the use of prior probabilities in maximum likelihood classification as a methodology to integrate discrete collateral data with continuously measured image density variables; (2) the use of the logit classifier as an alternative to multivariate normal classification that permits mixing both continuous and categorical variables in a single model and fits empirical distributions of observations more closely than the multivariate normal density function; and (3) the use of collateral data in a geographic information system as exercised to model a desired output information layer as a function of input layers of raster format collateral and image data base layers.

Multivariable control altitude demonstration on the F100 turbofan engine

NASA Technical Reports Server (NTRS)

Lehtinen, B.; Dehoff, R. L.; Hackney, R. D.

1979-01-01

The F100 Multivariable control synthesis (MVCS) program, was aimed at demonstrating the benefits of LGR synthesis theory in the design of a multivariable engine control system for operation throughout the flight envelope. The advantages of such procedures include: (1) enhanced performance from cross-coupled controls, (2) maximum use of engine variable geometry, and (3) a systematic design procedure that can be applied efficiently to new engine systems. The control system designed, under the MVCS program, for the Pratt & Whitney F100 turbofan engine is described. Basic components of the control include: (1) a reference value generator for deriving a desired equilibrium state and an approximate control vector, (2) a transition model to produce compatible reference point trajectories during gross transients, (3) gain schedules for producing feedback terms appropriate to the flight condition, and (4) integral switching logic to produce acceptable steady-state performance without engine operating limit exceedance.

Locating the Seventh Cervical Spinous Process: Development and Validation of a Multivariate Model Using Palpation and Personal Information.

PubMed

Ferreira, Ana Paula A; Póvoa, Luciana C; Zanier, José F C; Ferreira, Arthur S

2017-02-01

The aim of this study was to develop and validate a multivariate prediction model, guided by palpation and personal information, for locating the seventh cervical spinous process (C7SP). A single-blinded, cross-sectional study at a primary to tertiary health care center was conducted for model development and temporal validation. One-hundred sixty participants were prospectively included for model development (n = 80) and time-split validation stages (n = 80). The C7SP was located using the thorax-rib static method (TRSM). Participants underwent chest radiography for assessment of the inner body structure located with TRSM and using radio-opaque markers placed over the skin. Age, sex, height, body mass, body mass index, and vertex-marker distance (D V-M ) were used to predict the distance from the C7SP to the vertex (D V-C7 ). Multivariate linear regression modeling, limits of agreement plot, histogram of residues, receiver operating characteristic curves, and confusion tables were analyzed. The multivariate linear prediction model for D V-C7 (in centimeters) was D V-C7 = 0.986D V-M + 0.018(mass) + 0.014(age) - 1.008. Receiver operating characteristic curves had better discrimination of D V-C7 (area under the curve = 0.661; 95% confidence interval = 0.541-0.782; P = .015) than D V-M (area under the curve = 0.480; 95% confidence interval = 0.345-0.614; P = .761), with respective cutoff points at 23.40 cm (sensitivity = 41%, specificity = 63%) and 24.75 cm (sensitivity = 69%, specificity = 52%). The C7SP was correctly located more often when using predicted D V-C7 in the validation sample than when using the TRSM in the development sample: n = 53 (66%) vs n = 32 (40%), P < .001. Better accuracy was obtained when locating the C7SP by use of a multivariate model that incorporates palpation and personal information. Copyright © 2016. Published by Elsevier Inc.

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

Flexible mixture modeling via the multivariate t distribution with the Box-Cox transformation: an alternative to the skew-t distribution

PubMed Central

Lo, Kenneth

2011-01-01

Cluster analysis is the automated search for groups of homogeneous observations in a data set. A popular modeling approach for clustering is based on finite normal mixture models, which assume that each cluster is modeled as a multivariate normal distribution. However, the normality assumption that each component is symmetric is often unrealistic. Furthermore, normal mixture models are not robust against outliers; they often require extra components for modeling outliers and/or give a poor representation of the data. To address these issues, we propose a new class of distributions, multivariate t distributions with the Box-Cox transformation, for mixture modeling. This class of distributions generalizes the normal distribution with the more heavy-tailed t distribution, and introduces skewness via the Box-Cox transformation. As a result, this provides a unified framework to simultaneously handle outlier identification and data transformation, two interrelated issues. We describe an Expectation-Maximization algorithm for parameter estimation along with transformation selection. We demonstrate the proposed methodology with three real data sets and simulation studies. Compared with a wealth of approaches including the skew-t mixture model, the proposed t mixture model with the Box-Cox transformation performs favorably in terms of accuracy in the assignment of observations, robustness against model misspecification, and selection of the number of components. PMID:22125375

Flexible mixture modeling via the multivariate t distribution with the Box-Cox transformation: an alternative to the skew-t distribution.

PubMed

Lo, Kenneth; Gottardo, Raphael

2012-01-01

Cluster analysis is the automated search for groups of homogeneous observations in a data set. A popular modeling approach for clustering is based on finite normal mixture models, which assume that each cluster is modeled as a multivariate normal distribution. However, the normality assumption that each component is symmetric is often unrealistic. Furthermore, normal mixture models are not robust against outliers; they often require extra components for modeling outliers and/or give a poor representation of the data. To address these issues, we propose a new class of distributions, multivariate t distributions with the Box-Cox transformation, for mixture modeling. This class of distributions generalizes the normal distribution with the more heavy-tailed t distribution, and introduces skewness via the Box-Cox transformation. As a result, this provides a unified framework to simultaneously handle outlier identification and data transformation, two interrelated issues. We describe an Expectation-Maximization algorithm for parameter estimation along with transformation selection. We demonstrate the proposed methodology with three real data sets and simulation studies. Compared with a wealth of approaches including the skew-t mixture model, the proposed t mixture model with the Box-Cox transformation performs favorably in terms of accuracy in the assignment of observations, robustness against model misspecification, and selection of the number of components.

Comparison of Optimum Interpolation and Cressman Analyses

NASA Technical Reports Server (NTRS)

Baker, W. E.; Bloom, S. C.; Nestler, M. S.

1984-01-01

The objective of this investigation is to develop a state-of-the-art optimum interpolation (O/I) objective analysis procedure for use in numerical weather prediction studies. A three-dimensional multivariate O/I analysis scheme has been developed. Some characteristics of the GLAS O/I compared with those of the NMC and ECMWF systems are summarized. Some recent enhancements of the GLAS scheme include a univariate analysis of water vapor mixing ratio, a geographically dependent model prediction error correlation function and a multivariate oceanic surface analysis.

Comparison of Optimum Interpolation and Cressman Analyses

NASA Technical Reports Server (NTRS)

Baker, W. E.; Bloom, S. C.; Nestler, M. S.

1985-01-01

The development of a state of the art optimum interpolation (O/I) objective analysis procedure for use in numerical weather prediction studies was investigated. A three dimensional multivariate O/I analysis scheme was developed. Some characteristics of the GLAS O/I compared with those of the NMC and ECMWF systems are summarized. Some recent enhancements of the GLAS scheme include a univariate analysis of water vapor mixing ratio, a geographically dependent model prediction error correlation function and a multivariate oceanic surface analysis.

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

PubMed

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

2009-02-01

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

Comparison of Multidimensional Item Response Models: Multivariate Normal Ability Distributions versus Multivariate Polytomous Ability Distributions. Research Report. ETS RR-08-45

ERIC Educational Resources Information Center

Haberman, Shelby J.; von Davier, Matthias; Lee, Yi-Hsuan

2008-01-01

Multidimensional item response models can be based on multivariate normal ability distributions or on multivariate polytomous ability distributions. For the case of simple structure in which each item corresponds to a unique dimension of the ability vector, some applications of the two-parameter logistic model to empirical data are employed to…

Measurement bias detection with Kronecker product restricted models for multivariate longitudinal data: an illustration with health-related quality of life data from thirteen measurement occasions

PubMed Central

Verdam, Mathilde G. E.; Oort, Frans J.

2014-01-01

Highlights Application of Kronecker product to construct parsimonious structural equation models for multivariate longitudinal data. A method for the investigation of measurement bias with Kronecker product restricted models. Application of these methods to health-related quality of life data from bone metastasis patients, collected at 13 consecutive measurement occasions. The use of curves to facilitate substantive interpretation of apparent measurement bias. Assessment of change in common factor means, after accounting for apparent measurement bias. Longitudinal measurement invariance is usually investigated with a longitudinal factor model (LFM). However, with multiple measurement occasions, the number of parameters to be estimated increases with a multiple of the number of measurement occasions. To guard against too low ratios of numbers of subjects and numbers of parameters, we can use Kronecker product restrictions to model the multivariate longitudinal structure of the data. These restrictions can be imposed on all parameter matrices, including measurement invariance restrictions on factor loadings and intercepts. The resulting models are parsimonious and have attractive interpretation, but require different methods for the investigation of measurement bias. Specifically, additional parameter matrices are introduced to accommodate possible violations of measurement invariance. These additional matrices consist of measurement bias parameters that are either fixed at zero or free to be estimated. In cases of measurement bias, it is also possible to model the bias over time, e.g., with linear or non-linear curves. Measurement bias detection with Kronecker product restricted models will be illustrated with multivariate longitudinal data from 682 bone metastasis patients whose health-related quality of life (HRQL) was measured at 13 consecutive weeks. PMID:25295016

Measurement bias detection with Kronecker product restricted models for multivariate longitudinal data: an illustration with health-related quality of life data from thirteen measurement occasions.

PubMed

Verdam, Mathilde G E; Oort, Frans J

2014-01-01

Application of Kronecker product to construct parsimonious structural equation models for multivariate longitudinal data.A method for the investigation of measurement bias with Kronecker product restricted models.Application of these methods to health-related quality of life data from bone metastasis patients, collected at 13 consecutive measurement occasions.The use of curves to facilitate substantive interpretation of apparent measurement bias.Assessment of change in common factor means, after accounting for apparent measurement bias.Longitudinal measurement invariance is usually investigated with a longitudinal factor model (LFM). However, with multiple measurement occasions, the number of parameters to be estimated increases with a multiple of the number of measurement occasions. To guard against too low ratios of numbers of subjects and numbers of parameters, we can use Kronecker product restrictions to model the multivariate longitudinal structure of the data. These restrictions can be imposed on all parameter matrices, including measurement invariance restrictions on factor loadings and intercepts. The resulting models are parsimonious and have attractive interpretation, but require different methods for the investigation of measurement bias. Specifically, additional parameter matrices are introduced to accommodate possible violations of measurement invariance. These additional matrices consist of measurement bias parameters that are either fixed at zero or free to be estimated. In cases of measurement bias, it is also possible to model the bias over time, e.g., with linear or non-linear curves. Measurement bias detection with Kronecker product restricted models will be illustrated with multivariate longitudinal data from 682 bone metastasis patients whose health-related quality of life (HRQL) was measured at 13 consecutive weeks.

Development of multivariate NTCP models for radiation-induced hypothyroidism: a comparative analysis.

PubMed

Cella, Laura; Liuzzi, Raffaele; Conson, Manuel; D'Avino, Vittoria; Salvatore, Marco; Pacelli, Roberto

2012-12-27

Hypothyroidism is a frequent late side effect of radiation therapy of the cervical region. Purpose of this work is to develop multivariate normal tissue complication probability (NTCP) models for radiation-induced hypothyroidism (RHT) and to compare them with already existing NTCP models for RHT. Fifty-three patients treated with sequential chemo-radiotherapy for Hodgkin's lymphoma (HL) were retrospectively reviewed for RHT events. Clinical information along with thyroid gland dose distribution parameters were collected and their correlation to RHT was analyzed by Spearman's rank correlation coefficient (Rs). Multivariate logistic regression method using resampling methods (bootstrapping) was applied to select model order and parameters for NTCP modeling. Model performance was evaluated through the area under the receiver operating characteristic curve (AUC). Models were tested against external published data on RHT and compared with other published NTCP models. If we express the thyroid volume exceeding X Gy as a percentage (Vx(%)), a two-variable NTCP model including V30(%) and gender resulted to be the optimal predictive model for RHT (Rs = 0.615, p < 0.001. AUC = 0.87). Conversely, if absolute thyroid volume exceeding X Gy (Vx(cc)) was analyzed, an NTCP model based on 3 variables including V30(cc), thyroid gland volume and gender was selected as the most predictive model (Rs = 0.630, p < 0.001. AUC = 0.85). The three-variable model performs better when tested on an external cohort characterized by large inter-individuals variation in thyroid volumes (AUC = 0.914, 95% CI 0.760-0.984). A comparable performance was found between our model and that proposed in the literature based on thyroid gland mean dose and volume (p = 0.264). The absolute volume of thyroid gland exceeding 30 Gy in combination with thyroid gland volume and gender provide an NTCP model for RHT with improved prediction capability not only within our patient population but also in an external cohort.

A Unified Framework for Association Analysis with Multiple Related Phenotypes

PubMed Central

Stephens, Matthew

2013-01-01

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

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

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

Loveday, D.L.; Craggs, C.

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

Augmented classical least squares multivariate spectral analysis

DOEpatents

Haaland, David M.; Melgaard, David K.

2004-02-03

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

Augmented Classical Least Squares Multivariate Spectral Analysis

DOEpatents

Haaland, David M.; Melgaard, David K.

2005-07-26

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

Augmented Classical Least Squares Multivariate Spectral Analysis

DOEpatents

Haaland, David M.; Melgaard, David K.

2005-01-11

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

A Study of Effects of MultiCollinearity in the Multivariable Analysis

PubMed Central

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

2015-01-01

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

A Study of Effects of MultiCollinearity in the Multivariable Analysis.

PubMed

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

2014-10-01

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

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

A Comparison of Conventional Linear Regression Methods and Neural Networks for Forecasting Educational Spending.

ERIC Educational Resources Information Center

Baker, Bruce D.; Richards, Craig E.

1999-01-01

Applies neural network methods for forecasting 1991-95 per-pupil expenditures in U.S. public elementary and secondary schools. Forecasting models included the National Center for Education Statistics' multivariate regression model and three neural architectures. Regarding prediction accuracy, neural network results were comparable or superior to…

Stability of Teacher Value-Added Rankings across Measurement Model and Scaling Conditions

ERIC Educational Resources Information Center

Hawley, Leslie R.; Bovaird, James A.; Wu, ChaoRong

2017-01-01

Value-added assessment methods have been criticized by researchers and policy makers for a number of reasons. One issue includes the sensitivity of model results across different outcome measures. This study examined the utility of incorporating multivariate latent variable approaches within a traditional value-added framework. We evaluated the…

Exploring Sex Differences in Worry with a Cognitive Vulnerability Model

ERIC Educational Resources Information Center

Zalta, Alyson K.; Chambless, Dianne L.

2008-01-01

A multivariate model was developed to examine the relative contributions of mastery, stress, interpretive bias, and coping to sex differences in worry. Rumination was incorporated as a second outcome variable to test the specificity of these associations. Participants included two samples of undergraduates totaling 302 men and 379 women. A path…

Multivariate Non-Symmetric Stochastic Models for Spatial Dependence Models

NASA Astrophysics Data System (ADS)

Haslauer, C. P.; Bárdossy, A.

2017-12-01

A copula based multivariate framework allows more flexibility to describe different kind of dependences than what is possible using models relying on the confining assumption of symmetric Gaussian models: different quantiles can be modelled with a different degree of dependence; it will be demonstrated how this can be expected given process understanding. maximum likelihood based multivariate quantitative parameter estimation yields stable and reliable results; not only improved results in cross-validation based measures of uncertainty are obtained but also a more realistic spatial structure of uncertainty compared to second order models of dependence; as much information as is available is included in the parameter estimation: incorporation of censored measurements (e.g., below detection limit, or ones that are above the sensitive range of the measurement device) yield to more realistic spatial models; the proportion of true zeros can be jointly estimated with and distinguished from censored measurements which allow estimates about the age of a contaminant in the system; secondary information (categorical and on the rational scale) has been used to improve the estimation of the primary variable; These copula based multivariate statistical techniques are demonstrated based on hydraulic conductivity observations at the Borden (Canada) site, the MADE site (USA), and a large regional groundwater quality data-set in south-west Germany. Fields of spatially distributed K were simulated with identical marginal simulation, identical second order spatial moments, yet substantially differing solute transport characteristics when numerical tracer tests were performed. A statistical methodology is shown that allows the delineation of a boundary layer separating homogenous parts of a spatial data-set. The effects of this boundary layer (macro structure) and the spatial dependence of K (micro structure) on solute transport behaviour is shown.

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

PubMed

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

2017-01-01

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

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

PubMed

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

2015-04-01

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

Determination of Leaf Water Content by Visible and Near-Infrared Spectrometry and Multivariate Calibration in Miscanthus

DOE PAGES

Jin, Xiaoli; Shi, Chunhai; Yu, Chang Yeon; ...

2017-05-19

Leaf water content is one of the most common physiological parameters limiting efficiency of photosynthesis and biomass productivity in plants including Miscanthus. Therefore, it is of great significance to determine or predict the water content quickly and non-destructively. In this study, we explored the relationship between leaf water content and diffuse reflectance spectra in Miscanthus. Three multivariate calibrations including partial least squares (PLS), least squares support vector machine regression (LSSVR), and radial basis function (RBF) neural network (NN) were developed for the models of leaf water content determination. The non-linear models including RBF_LSSVR and RBF_NN showed higher accuracy than themore » PLS and Lin_LSSVR models. Moreover, 75 sensitive wavelengths were identified to be closely associated with the leaf water content in Miscanthus. The RBF_LSSVR and RBF_NN models for predicting leaf water content, based on 75 characteristic wavelengths, obtained the high determination coefficients of 0.9838 and 0.9899, respectively. The results indicated the non-linear models were more accurate than the linear models using both wavelength intervals. These results demonstrated that visible and near-infrared (VIS/NIR) spectroscopy combined with RBF_LSSVR or RBF_NN is a useful, non-destructive tool for determinations of the leaf water content in Miscanthus, and thus very helpful for development of drought-resistant varieties in Miscanthus.« less

Determination of Leaf Water Content by Visible and Near-Infrared Spectrometry and Multivariate Calibration in Miscanthus

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

Jin, Xiaoli; Shi, Chunhai; Yu, Chang Yeon

Leaf water content is one of the most common physiological parameters limiting efficiency of photosynthesis and biomass productivity in plants including Miscanthus. Therefore, it is of great significance to determine or predict the water content quickly and non-destructively. In this study, we explored the relationship between leaf water content and diffuse reflectance spectra in Miscanthus. Three multivariate calibrations including partial least squares (PLS), least squares support vector machine regression (LSSVR), and radial basis function (RBF) neural network (NN) were developed for the models of leaf water content determination. The non-linear models including RBF_LSSVR and RBF_NN showed higher accuracy than themore » PLS and Lin_LSSVR models. Moreover, 75 sensitive wavelengths were identified to be closely associated with the leaf water content in Miscanthus. The RBF_LSSVR and RBF_NN models for predicting leaf water content, based on 75 characteristic wavelengths, obtained the high determination coefficients of 0.9838 and 0.9899, respectively. The results indicated the non-linear models were more accurate than the linear models using both wavelength intervals. These results demonstrated that visible and near-infrared (VIS/NIR) spectroscopy combined with RBF_LSSVR or RBF_NN is a useful, non-destructive tool for determinations of the leaf water content in Miscanthus, and thus very helpful for development of drought-resistant varieties in Miscanthus.« less

Calculating the individual probability of successful ocriplasmin treatment in eyes with VMT syndrome: a multivariable prediction model from the EXPORT study.

PubMed

Paul, Christoph; Heun, Christine; Müller, Hans-Helge; Hoerauf, Hans; Feltgen, Nicolas; Wachtlin, Joachim; Kaymak, Hakan; Mennel, Stefan; Koss, Michael Janusz; Fauser, Sascha; Maier, Mathias M; Schumann, Ricarda G; Mueller, Simone; Chang, Petrus; Schmitz-Valckenberg, Steffen; Kazerounian, Sara; Szurman, Peter; Lommatzsch, Albrecht; Bertelmann, Thomas

2017-10-31

To evaluate predictive factors for the treatment success of ocriplasmin and to use these factors to generate a multivariate model to calculate the individual probability of successful treatment. Data were collected in a retrospective, multicentre cohort study. Patients with vitreomacular traction (VMT) syndrome without a full-thickness macular hole were included if they received an intravitreal injection (IVI) of ocriplasmin. Five factors (age, gender, lens status, presence of epiretinal membrane (ERM) formation and horizontal diameter of VMT) were assessed on their association with VMT resolution. A multivariable logistic regression model was employed to further analyse these factors and calculate the individual probability of successful treatment. 167 eyes of 167 patients were included. Univariate analysis revealed a significant correlation to VMT resolution for all analysed factors: age (years) (OR 0.9208; 95% CI 0.8845 to 0.9586; p<0.0001), gender (male) (OR 0.480; 95% CI 0.241 to 0.957; p=0.0371), lens status (phakic) (OR 2.042; 95% CI 1.054 to 3.958; p=0.0344), ERM formation (present) (OR 0.384; 95% CI 0.179 to 0.821; p=0.0136) and horizontal VMT diameter (µm) (OR 0.99812; 95% CI 0.99684 to 0.99941, p=0.0042). A significant multivariable logistic regression model was established with age and VMT diameter. Known predictive factors for VMT resolution after ocriplasmin IVI were confirmed in our study. We were able to combine them into a formula, ultimately allowing the calculation of an individual probability of treatment success with ocriplasmin in patients with VMT syndrome without FTHM. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Small Sample Properties of Bayesian Multivariate Autoregressive Time Series Models

ERIC Educational Resources Information Center

Price, Larry R.

2012-01-01

The aim of this study was to compare the small sample (N = 1, 3, 5, 10, 15) performance of a Bayesian multivariate vector autoregressive (BVAR-SEM) time series model relative to frequentist power and parameter estimation bias. A multivariate autoregressive model was developed based on correlated autoregressive time series vectors of varying…

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.

Part 2. Development of Enhanced Statistical Methods for Assessing Health Effects Associated with an Unknown Number of Major Sources of Multiple Air Pollutants.

PubMed

Park, Eun Sug; Symanski, Elaine; Han, Daikwon; Spiegelman, Clifford

2015-06-01

A major difficulty with assessing source-specific health effects is that source-specific exposures cannot be measured directly; rather, they need to be estimated by a source-apportionment method such as multivariate receptor modeling. The uncertainty in source apportionment (uncertainty in source-specific exposure estimates and model uncertainty due to the unknown number of sources and identifiability conditions) has been largely ignored in previous studies. Also, spatial dependence of multipollutant data collected from multiple monitoring sites has not yet been incorporated into multivariate receptor modeling. The objectives of this project are (1) to develop a multipollutant approach that incorporates both sources of uncertainty in source-apportionment into the assessment of source-specific health effects and (2) to develop enhanced multivariate receptor models that can account for spatial correlations in the multipollutant data collected from multiple sites. We employed a Bayesian hierarchical modeling framework consisting of multivariate receptor models, health-effects models, and a hierarchical model on latent source contributions. For the health model, we focused on the time-series design in this project. Each combination of number of sources and identifiability conditions (additional constraints on model parameters) defines a different model. We built a set of plausible models with extensive exploratory data analyses and with information from previous studies, and then computed posterior model probability to estimate model uncertainty. Parameter estimation and model uncertainty estimation were implemented simultaneously by Markov chain Monte Carlo (MCMC*) methods. We validated the methods using simulated data. We illustrated the methods using PM2.5 (particulate matter ≤ 2.5 μm in aerodynamic diameter) speciation data and mortality data from Phoenix, Arizona, and Houston, Texas. The Phoenix data included counts of cardiovascular deaths and daily PM2.5 speciation data from 1995-1997. The Houston data included respiratory mortality data and 24-hour PM2.5 speciation data sampled every six days from a region near the Houston Ship Channel in years 2002-2005. We also developed a Bayesian spatial multivariate receptor modeling approach that, while simultaneously dealing with the unknown number of sources and identifiability conditions, incorporated spatial correlations in the multipollutant data collected from multiple sites into the estimation of source profiles and contributions based on the discrete process convolution model for multivariate spatial processes. This new modeling approach was applied to 24-hour ambient air concentrations of 17 volatile organic compounds (VOCs) measured at nine monitoring sites in Harris County, Texas, during years 2000 to 2005. Simulation results indicated that our methods were accurate in identifying the true model and estimated parameters were close to the true values. The results from our methods agreed in general with previous studies on the source apportionment of the Phoenix data in terms of estimated source profiles and contributions. However, we had a greater number of statistically insignificant findings, which was likely a natural consequence of incorporating uncertainty in the estimated source contributions into the health-effects parameter estimation. For the Houston data, a model with five sources (that seemed to be Sulfate-Rich Secondary Aerosol, Motor Vehicles, Industrial Combustion, Soil/Crustal Matter, and Sea Salt) showed the highest posterior model probability among the candidate models considered when fitted simultaneously to the PM2.5 and mortality data. There was a statistically significant positive association between respiratory mortality and same-day PM2.5 concentrations attributed to one of the sources (probably industrial combustion). The Bayesian spatial multivariate receptor modeling approach applied to the VOC data led to a highest posterior model probability for a model with five sources (that seemed to be refinery, petrochemical production, gasoline evaporation, natural gas, and vehicular exhaust) among several candidate models, with the number of sources varying between three and seven and with different identifiability conditions. Our multipollutant approach assessing source-specific health effects is more advantageous than a single-pollutant approach in that it can estimate total health effects from multiple pollutants and can also identify emission sources that are responsible for adverse health effects. Our Bayesian approach can incorporate not only uncertainty in the estimated source contributions, but also model uncertainty that has not been addressed in previous studies on assessing source-specific health effects. The new Bayesian spatial multivariate receptor modeling approach enables predictions of source contributions at unmonitored sites, minimizing exposure misclassification and providing improved exposure estimates along with their uncertainty estimates, as well as accounting for uncertainty in the number of sources and identifiability conditions.

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

ERIC Educational Resources Information Center

McKinney, Cliff; Renk, Kimberly

2008-01-01

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

Probabilistic, meso-scale flood loss modelling

NASA Astrophysics Data System (ADS)

Kreibich, Heidi; Botto, Anna; Schröter, Kai; Merz, Bruno

2016-04-01

Flood risk analyses are an important basis for decisions on flood risk management and adaptation. However, such analyses are associated with significant uncertainty, even more if changes in risk due to global change are expected. Although uncertainty analysis and probabilistic approaches have received increased attention during the last years, they are still not standard practice for flood risk assessments and even more for flood loss modelling. State of the art in flood loss modelling is still the use of simple, deterministic approaches like stage-damage functions. Novel probabilistic, multi-variate flood loss models have been developed and validated on the micro-scale using a data-mining approach, namely bagging decision trees (Merz et al. 2013). In this presentation we demonstrate and evaluate the upscaling of the approach to the meso-scale, namely on the basis of land-use units. The model is applied in 19 municipalities which were affected during the 2002 flood by the River Mulde in Saxony, Germany (Botto et al. submitted). The application of bagging decision tree based loss models provide a probability distribution of estimated loss per municipality. Validation is undertaken on the one hand via a comparison with eight deterministic loss models including stage-damage functions as well as multi-variate models. On the other hand the results are compared with official loss data provided by the Saxon Relief Bank (SAB). The results show, that uncertainties of loss estimation remain high. Thus, the significant advantage of this probabilistic flood loss estimation approach is that it inherently provides quantitative information about the uncertainty of the prediction. References: Merz, B.; Kreibich, H.; Lall, U. (2013): Multi-variate flood damage assessment: a tree-based data-mining approach. NHESS, 13(1), 53-64. Botto A, Kreibich H, Merz B, Schröter K (submitted) Probabilistic, multi-variable flood loss modelling on the meso-scale with BT-FLEMO. Risk Analysis.

Antimicrobial Drug Prescription and Neisseria gonorrhoeae Susceptibility, United States, 2005–2013

PubMed Central

Bartoces, Monina G.; Soge, Olusegun O.; Riedel, Stefan; Kubin, Grace; Del Rio, Carlos; Papp, John R.; Hook, Edward W.; Hicks, Lauri A.

2017-01-01

We investigated whether outpatient antimicrobial drug prescribing is associated with Neisseria gonorrhoeae antimicrobial drug susceptibility in the United States. Using susceptibility data from the Gonococcal Isolate Surveillance Project during 2005–2013 and QuintilesIMS data on outpatient cephalosporin, macrolide, and fluoroquinolone prescribing, we constructed multivariable linear mixed models for each antimicrobial agent with 1-year lagged annual prescribing per 1,000 persons as the exposure and geometric mean MIC as the outcome of interest. Multivariable models did not demonstrate associations between antimicrobial drug prescribing and N. gonorrhoeae susceptibility for any of the studied antimicrobial drugs during 2005–2013. Elucidation of epidemiologic factors contributing to resistance, including further investigation of the potential role of antimicrobial drug use, is needed. PMID:28930001

Predictors of psychiatric readmission among patients with bipolar disorder at an academic safety-net hospital.

PubMed

Hamilton, Jane E; Passos, Ives C; de Azevedo Cardoso, Taiane; Jansen, Karen; Allen, Melissa; Begley, Charles E; Soares, Jair C; Kapczinski, Flavio

2016-06-01

Even with treatment, approximately one-third of patients with bipolar disorder relapse into depression or mania within 1 year. Unfavorable clinical outcomes for patients with bipolar disorder include increased rates of psychiatric hospitalization and functional impairment. However, only a few studies have examined predictors of psychiatric hospital readmission in a sample of patients with bipolar disorder. The purpose of this study was to examine predictors of psychiatric readmission within 30 days, 90 days and 1 year of discharge among patients with bipolar disorder using a conceptual model adapted from Andersen's Behavioral Model of Health Service Use. In this retrospective study, univariate and multivariate logistic regression analyses were conducted in a sample of 2443 adult patients with bipolar disorder who were consecutively admitted to a public psychiatric hospital in the United States from 1 January to 31 December 2013. In the multivariate models, several enabling and need factors were significantly associated with an increased risk of readmission across all time periods examined, including being uninsured, having ⩾3 psychiatric hospitalizations and having a lower Global Assessment of Functioning score. Additional factors associated with psychiatric readmission within 30 and 90 days of discharge included patient homelessness. Patient race/ethnicity, bipolar disorder type or a current manic episode did not significantly predict readmission across all time periods examined; however, patients who were male were more likely to readmit within 1 year. The 30-day and 1-year multivariate models showed the best model fit. Our study found enabling and need factors to be the strongest predictors of psychiatric readmission, suggesting that the prevention of psychiatric readmission for patients with bipolar disorder at safety-net hospitals may be best achieved by developing and implementing innovative transitional care initiatives that address the issues of multiple psychiatric hospitalizations, housing instability, insurance coverage and functional impairment. © The Royal Australian and New Zealand College of Psychiatrists 2015.

A retrospective review of fall risk factors in the bone marrow transplant inpatient service.

PubMed

Vela, Cory M; Grate, Lisa M; McBride, Ali; Devine, Steven; Andritsos, Leslie A

2018-06-01

Purpose The purpose of this study was to compare medications and potential risk factors between patients who experienced a fall during hospitalization compared to those who did not fall while admitted to the Blood and Marrow Transplant inpatient setting at The James Cancer Hospital. Secondary objectives included evaluation of transplant-related disease states and medications in the post-transplant setting that may lead to an increased risk of falls, post-fall variables, and number of tests ordered after a fall. Methods This retrospective, case-control study matched patients in a 2:1 ratio of nonfallers to fallers. Data from The Ohio State University Wexner Medical Center (OSUWMC) reported fall events and patient electronic medical records were utilized. A total of 168 adult Blood and Marrow Transplant inpatients with a hematological malignancy diagnosis were evaluated from 1 January 2010 to 30 September 2012. Results Univariable and multivariable conditional logistic regression models were used to assess the relationship between potential predictor variables of interest and falls. Variables that were found to be significant predictors of falls from the univariable models include age group, incontinence, benzodiazepines, corticosteroids, anticonvulsants and antidepressants, and number of days status-post transplant. When considered for a multivariable model age group, corticosteroids, and a cancer diagnosis of leukemia were significant in the final model. Conclusion Recent medication utilization such as benzodiazepines, anticonvulsants, corticosteroids, and antidepressants placed patients at a higher risk of experiencing a fall. Other significant factors identified from a multivariable analysis found were patients older than age 65, patients with recent corticosteroid administration and a cancer diagnosis of leukemia.

Predicting crash frequency for multi-vehicle collision types using multivariate Poisson-lognormal spatial model: A comparative analysis.

PubMed

Hosseinpour, Mehdi; Sahebi, Sina; Zamzuri, Zamira Hasanah; Yahaya, Ahmad Shukri; Ismail, Noriszura

2018-06-01

According to crash configuration and pre-crash conditions, traffic crashes are classified into different collision types. Based on the literature, multi-vehicle crashes, such as head-on, rear-end, and angle crashes, are more frequent than single-vehicle crashes, and most often result in serious consequences. From a methodological point of view, the majority of prior studies focused on multivehicle collisions have employed univariate count models to estimate crash counts separately by collision type. However, univariate models fail to account for correlations which may exist between different collision types. Among others, multivariate Poisson lognormal (MVPLN) model with spatial correlation is a promising multivariate specification because it not only allows for unobserved heterogeneity (extra-Poisson variation) and dependencies between collision types, but also spatial correlation between adjacent sites. However, the MVPLN spatial model has rarely been applied in previous research for simultaneously modelling crash counts by collision type. Therefore, this study aims at utilizing a MVPLN spatial model to estimate crash counts for four different multi-vehicle collision types, including head-on, rear-end, angle, and sideswipe collisions. To investigate the performance of the MVPLN spatial model, a two-stage model and a univariate Poisson lognormal model (UNPLN) spatial model were also developed in this study. Detailed information on roadway characteristics, traffic volume, and crash history were collected on 407 homogeneous segments from Malaysian federal roads. The results indicate that the MVPLN spatial model outperforms the other comparing models in terms of goodness-of-fit measures. The results also show that the inclusion of spatial heterogeneity in the multivariate model significantly improves the model fit, as indicated by the Deviance Information Criterion (DIC). The correlation between crash types is high and positive, implying that the occurrence of a specific collision type is highly associated with the occurrence of other crash types on the same road segment. These results support the utilization of the MVPLN spatial model when predicting crash counts by collision manner. In terms of contributing factors, the results show that distinct crash types are attributed to different subsets of explanatory variables. Copyright © 2018 Elsevier Ltd. All rights reserved.

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.

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

PubMed Central

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

2015-01-01

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

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

PubMed Central

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

2016-01-01

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

HYTESS 2: A Hypothetical Turbofan Engine Simplified Simulation with multivariable control and sensor analytical redundancy

NASA Technical Reports Server (NTRS)

Merrill, W. C.

1986-01-01

A hypothetical turbofan engine simplified simulation with a multivariable control and sensor failure detection, isolation, and accommodation logic (HYTESS II) is presented. The digital program, written in FORTRAN, is self-contained, efficient, realistic and easily used. Simulated engine dynamics were developed from linearized operating point models. However, essential nonlinear effects are retained. The simulation is representative of the hypothetical, low bypass ratio turbofan engine with an advanced control and failure detection logic. Included is a description of the engine dynamics, the control algorithm, and the sensor failure detection logic. Details of the simulation including block diagrams, variable descriptions, common block definitions, subroutine descriptions, and input requirements are given. Example simulation results are also presented.

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.

Tuning algorithms for fractional order internal model controllers for time delay processes

NASA Astrophysics Data System (ADS)

Muresan, Cristina I.; Dutta, Abhishek; Dulf, Eva H.; Pinar, Zehra; Maxim, Anca; Ionescu, Clara M.

2016-03-01

This paper presents two tuning algorithms for fractional-order internal model control (IMC) controllers for time delay processes. The two tuning algorithms are based on two specific closed-loop control configurations: the IMC control structure and the Smith predictor structure. In the latter, the equivalency between IMC and Smith predictor control structures is used to tune a fractional-order IMC controller as the primary controller of the Smith predictor structure. Fractional-order IMC controllers are designed in both cases in order to enhance the closed-loop performance and robustness of classical integer order IMC controllers. The tuning procedures are exemplified for both single-input-single-output as well as multivariable processes, described by first-order and second-order transfer functions with time delays. Different numerical examples are provided, including a general multivariable time delay process. Integer order IMC controllers are designed in each case, as well as fractional-order IMC controllers. The simulation results show that the proposed fractional-order IMC controller ensures an increased robustness to modelling uncertainties. Experimental results are also provided, for the design of a multivariable fractional-order IMC controller in a Smith predictor structure for a quadruple-tank system.

Influence of professional preparation and class structure on sexuality topics taught in middle and high schools.

PubMed

Rhodes, Darson L; Kirchofer, Gregg; Hammig, Bart J; Ogletree, Roberta J

2013-05-01

This study examined the impact of professional preparation and class structure on sexuality topics taught and use of practice-based instructional strategies in US middle and high school health classes. Data from the classroom-level file of the 2006 School Health Policies and Programs were used. A series of multivariable logistic regression models were employed to determine if sexuality content taught was dependent on professional preparation and /or class structure (HE only versus HE/another subject combined). Additional multivariable logistic regression models were employed to determine if use of practice-based instructional strategies was dependent upon professional preparation and/or class structure. Years of teaching health topics and size of the school district were included as covariates in the multivariable logistic regression models. Findings indicated professionally prepared health educators were significantly more likely to teach 7 of the 13 sexuality topics as compared to nonprofessionally prepared health educators. There was no statistically significant difference in the instructional strategies used by professionally prepared and nonprofessionally prepared health educators. Exclusively health education classes versus combined classes were significantly more likely to have included 6 of the 13 topics and to have incorporated practice-based instructional strategies in the curricula. This study indicated professional preparation and class structure impacted sexuality content taught. Class structure also impacted whether opportunities for students to practice skills were made available. Results support the need for continued advocacy for professionally prepared health educators and health only courses. © 2013, American School Health Association.

Modelling Truck Camper Production

ERIC Educational Resources Information Center

Kramlich, G. R., II; Kobylski, G.; Ahner, D.

2008-01-01

This note describes an interdisciplinary project designed to enhance students' knowledge of the basic techniques taught in a multivariable calculus course. The note discusses the four main requirements of the project and then the solutions for each requirement. Concepts covered include differentials, gradients, Lagrange multipliers, constrained…

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

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

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

2017-01-15

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

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

PubMed

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

2017-01-01

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

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

PubMed Central

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

2017-01-01

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

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

PubMed

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

2017-02-14

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

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

PubMed

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

2015-10-01

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

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

NASA Technical Reports Server (NTRS)

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

1988-01-01

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

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

PubMed

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

2015-01-01

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

Multivariate spatial models of excess crash frequency at area level: case of Costa Rica.

PubMed

Aguero-Valverde, Jonathan

2013-10-01

Recently, areal models of crash frequency have being used in the analysis of various area-wide factors affecting road crashes. On the other hand, disease mapping methods are commonly used in epidemiology to assess the relative risk of the population at different spatial units. A natural next step is to combine these two approaches to estimate the excess crash frequency at area level as a measure of absolute crash risk. Furthermore, multivariate spatial models of crash severity are explored in order to account for both frequency and severity of crashes and control for the spatial correlation frequently found in crash data. This paper aims to extent the concept of safety performance functions to be used in areal models of crash frequency. A multivariate spatial model is used for that purpose and compared to its univariate counterpart. Full Bayes hierarchical approach is used to estimate the models of crash frequency at canton level for Costa Rica. An intrinsic multivariate conditional autoregressive model is used for modeling spatial random effects. The results show that the multivariate spatial model performs better than its univariate counterpart in terms of the penalized goodness-of-fit measure Deviance Information Criteria. Additionally, the effects of the spatial smoothing due to the multivariate spatial random effects are evident in the estimation of excess equivalent property damage only crashes. Copyright © 2013 Elsevier Ltd. All rights reserved.

Lateralization of temporal lobe epilepsy by multimodal multinomial hippocampal response-driven models.

PubMed

Nazem-Zadeh, Mohammad-Reza; Elisevich, Kost V; Schwalb, Jason M; Bagher-Ebadian, Hassan; Mahmoudi, Fariborz; Soltanian-Zadeh, Hamid

2014-12-15

Multiple modalities are used in determining laterality in mesial temporal lobe epilepsy (mTLE). It is unclear how much different imaging modalities should be weighted in decision-making. The purpose of this study is to develop response-driven multimodal multinomial models for lateralization of epileptogenicity in mTLE patients based upon imaging features in order to maximize the accuracy of noninvasive studies. The volumes, means and standard deviations of FLAIR intensity and means of normalized ictal-interictal SPECT intensity of the left and right hippocampi were extracted from preoperative images of a retrospective cohort of 45 mTLE patients with Engel class I surgical outcomes, as well as images of a cohort of 20 control, nonepileptic subjects. Using multinomial logistic function regression, the parameters of various univariate and multivariate models were estimated. Based on the Bayesian model averaging (BMA) theorem, response models were developed as compositions of independent univariate models. A BMA model composed of posterior probabilities of univariate response models of hippocampal volumes, means and standard deviations of FLAIR intensity, and means of SPECT intensity with the estimated weighting coefficients of 0.28, 0.32, 0.09, and 0.31, respectively, as well as a multivariate response model incorporating all mentioned attributes, demonstrated complete reliability by achieving a probability of detection of one with no false alarms to establish proper laterality in all mTLE patients. The proposed multinomial multivariate response-driven model provides a reliable lateralization of mesial temporal epileptogenicity including those patients who require phase II assessment. Copyright © 2014 Elsevier B.V. All rights reserved.

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

PubMed

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

2016-11-01

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

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

PubMed

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

2014-01-01

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

Multivariate matrix model for source identification of inrush water: A case study from Renlou and Tongting coal mine in northern Anhui province, China

NASA Astrophysics Data System (ADS)

Zhang, Jun; Yao, Duoxi; Su, Yue

2018-02-01

Under the current situation of energy demand, coal is still one of the major energy sources in China for a certain period of time, so the task of coal mine safety production remains arduous. In order to identify the water source of the mine accurately, this article takes the example from Renlou and Tongting coal mines in the northern Anhui mining area. A total of 7 conventional water chemical indexes were selected, including Ca2+, Mg2+, Na++K+, Cl-, SO4 2-, HCO3 - and TDS, to establish a multivariate matrix model for the source identifying inrush water. The results show that the model is simple and is rarely limited by the quantity of water samples, and the recognition effect is ideal, which can be applied to the control and treatment for water inrush.

Modelling lifetime data with multivariate Tweedie distribution

NASA Astrophysics Data System (ADS)

Nor, Siti Rohani Mohd; Yusof, Fadhilah; Bahar, Arifah

2017-05-01

This study aims to measure the dependence between individual lifetimes by applying multivariate Tweedie distribution to the lifetime data. Dependence between lifetimes incorporated in the mortality model is a new form of idea that gives significant impact on the risk of the annuity portfolio which is actually against the idea of standard actuarial methods that assumes independent between lifetimes. Hence, this paper applies Tweedie family distribution to the portfolio of lifetimes to induce the dependence between lives. Tweedie distribution is chosen since it contains symmetric and non-symmetric, as well as light-tailed and heavy-tailed distributions. Parameter estimation is modified in order to fit the Tweedie distribution to the data. This procedure is developed by using method of moments. In addition, the comparison stage is made to check for the adequacy between the observed mortality and expected mortality. Finally, the importance of including systematic mortality risk in the model is justified by the Pearson's chi-squared test.

Dose-surface analysis for prediction of severe acute radio-induced skin toxicity in breast cancer patients.

PubMed

Pastore, Francesco; Conson, Manuel; D'Avino, Vittoria; Palma, Giuseppe; Liuzzi, Raffaele; Solla, Raffaele; Farella, Antonio; Salvatore, Marco; Cella, Laura; Pacelli, Roberto

2016-01-01

Severe acute radiation-induced skin toxicity (RIST) after breast irradiation is a side effect impacting the quality of life in breast cancer (BC) patients. The aim of the present study was to develop normal tissue complication probability (NTCP) models of severe acute RIST in BC patients. We evaluated 140 consecutive BC patients undergoing conventional three-dimensional conformal radiotherapy (3D-CRT) after breast conserving surgery in a prospective study assessing acute RIST. The acute RIST was classified according to the RTOG scoring system. Dose-surface histograms (DSHs) of the body structure in the breast region were extracted as representative of skin irradiation. Patient, disease, and treatment-related characteristics were analyzed along with DSHs. NTCP modeling by Lyman-Kutcher-Burman (LKB) and by multivariate logistic regression using bootstrap resampling techniques was performed. Models were evaluated by Spearman's Rs coefficient and ROC area. By the end of radiotherapy, 139 (99%) patients developed any degree of acute RIST. G3 RIST was found in 11 of 140 (8%) patients. Mild-moderate (G1-G2) RIST was still present at 40 days after treatment in six (4%) patients. Using DSHs for LKB modeling of acute RIST severity (RTOG G3 vs. G0-2), parameter estimates were TD50=39 Gy, n=0.38 and m=0.14 [Rs = 0.25, area under the curve (AUC) = 0.77, p = 0.003]. On multivariate analysis, the most predictive model of acute RIST severity was a two-variable model including the skin receiving ≥30 Gy (S30) and psoriasis [Rs = 0.32, AUC = 0.84, p < 0.001]. Using body DSH as representative of skin dose, the LKB n parameter was consistent with a surface effect for the skin. A good prediction performance was obtained using a data-driven multivariate model including S30 and a pre-existing skin disease (psoriasis) as a clinical factor.

Preliminary Cost Model for Space Telescopes

NASA Technical Reports Server (NTRS)

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

2009-01-01

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

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.

PERIODIC AUTOREGRESSIVE-MOVING AVERAGE (PARMA) MODELING WITH APPLICATIONS TO WATER RESOURCES.

USGS Publications Warehouse

Vecchia, A.V.

1985-01-01

Results involving correlation properties and parameter estimation for autogressive-moving average models with periodic parameters are presented. A multivariate representation of the PARMA model is used to derive parameter space restrictions and difference equations for the periodic autocorrelations. Close approximation to the likelihood function for Gaussian PARMA processes results in efficient maximum-likelihood estimation procedures. Terms in the Fourier expansion of the parameters are sequentially included, and a selection criterion is given for determining the optimal number of harmonics to be included. Application of the techniques is demonstrated through analysis of a monthly streamflow time series.

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

PubMed

Mulder, P G

1988-01-01

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

A Comparison of Three Multivariate Models for Estimating Test Battery Reliability.

ERIC Educational Resources Information Center

Wood, Terry M.; Safrit, Margaret J.

1987-01-01

A comparison of three multivariate models (canonical reliability model, maximum generalizability model, canonical correlation model) for estimating test battery reliability indicated that the maximum generalizability model showed the least degree of bias, smallest errors in estimation, and the greatest relative efficiency across all experimental…

Multivariate non-normally distributed random variables in climate research - introduction to the copula approach

NASA Astrophysics Data System (ADS)

Schölzel, C.; Friederichs, P.

2008-10-01

Probability distributions of multivariate random variables are generally more complex compared to their univariate counterparts which is due to a possible nonlinear dependence between the random variables. One approach to this problem is the use of copulas, which have become popular over recent years, especially in fields like econometrics, finance, risk management, or insurance. Since this newly emerging field includes various practices, a controversial discussion, and vast field of literature, it is difficult to get an overview. The aim of this paper is therefore to provide an brief overview of copulas for application in meteorology and climate research. We examine the advantages and disadvantages compared to alternative approaches like e.g. mixture models, summarize the current problem of goodness-of-fit (GOF) tests for copulas, and discuss the connection with multivariate extremes. An application to station data shows the simplicity and the capabilities as well as the limitations of this approach. Observations of daily precipitation and temperature are fitted to a bivariate model and demonstrate, that copulas are valuable complement to the commonly used methods.

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.

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.

Quantifying uncertainty in high-resolution coupled hydrodynamic-ecosystem models

NASA Astrophysics Data System (ADS)

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

2007-01-01

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

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

PubMed

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

2011-06-15

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

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

PubMed Central

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

2013-01-01

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

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

PubMed

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

2013-10-15

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

The rate of country-level improvements of the infant mortality rate is mainly determined by previous history.

PubMed

Bremberg, Sven G

2016-08-01

Studies of country-level determinants of health have produced conflicting results even when the analyses have been restricted to high-income counties. Yet, most of these studies have not taken historical, country-specific developments into account. Thus, it is appropriate to separate the influence of current exposures from historical aspects. Determinants of the infant mortality rate (IMR) were studied in 28 OECD countries over the period 1990-2012. Twelve determinants were selected. They refer to the level of general resources, resources that specifically address child health and characteristics that affect knowledge dissemination, including level of trust, and a health related behaviour: the rate of female smoking. Bivariate analyses with the IMR in year 2000 as outcome and the 12 determinants produced six statistically significant models. In multivariate analyses, the rate of decrease in the IMR was investigated as outcome and a history variable (IMR in 1990) was included in the models. The history variable alone explained 95% of the variation. None of the multivariate models, with the 12 determinants included, explained significantly more variation. Taking into account the historical development of the IMR will critically affect correlations between country-level determinants and the IMR. © The Author 2016. Published by Oxford University Press on behalf of the European Public Health Association. All rights reserved.

Extracting galactic structure parameters from multivariated density estimation

NASA Technical Reports Server (NTRS)

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

1992-01-01

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

Commentary: Academic Enablers and School Learning.

ERIC Educational Resources Information Center

Keith, Timothy Z.

2002-01-01

This commentary presents academic enablers within the broader, overlapping context of school learning theory, including the theories of Carroll, Harnishfeger and Wiley, Walberg, and others. Multivariate models are needed to understand the influences of academic enabler and school learning variables on learning, as well as the influences of these…

Modeling multivariate time series on manifolds with skew radial basis functions.

PubMed

Jamshidi, Arta A; Kirby, Michael J

2011-01-01

We present an approach for constructing nonlinear empirical mappings from high-dimensional domains to multivariate ranges. We employ radial basis functions and skew radial basis functions for constructing a model using data that are potentially scattered or sparse. The algorithm progresses iteratively, adding a new function at each step to refine the model. The placement of the functions is driven by a statistical hypothesis test that accounts for correlation in the multivariate range variables. The test is applied on training and validation data and reveals nonstatistical or geometric structure when it fails. At each step, the added function is fit to data contained in a spatiotemporally defined local region to determine the parameters--in particular, the scale of the local model. The scale of the function is determined by the zero crossings of the autocorrelation function of the residuals. The model parameters and the number of basis functions are determined automatically from the given data, and there is no need to initialize any ad hoc parameters save for the selection of the skew radial basis functions. Compactly supported skew radial basis functions are employed to improve model accuracy, order, and convergence properties. The extension of the algorithm to higher-dimensional ranges produces reduced-order models by exploiting the existence of correlation in the range variable data. Structure is tested not just in a single time series but between all pairs of time series. We illustrate the new methodologies using several illustrative problems, including modeling data on manifolds and the prediction of chaotic time series.

A multivariate model exploring the predictive value of demographic, adolescent, and family factors on glycemic control in adolescents with type 1 diabetes.

PubMed

Agarwal, Shivani; Jawad, Abbas F; Miller, Victoria A

2016-11-01

The current study examined how a comprehensive set of variables from multiple domains, including at the adolescent and family level, were predictive of glycemic control in adolescents with type 1 diabetes (T1D). Participants included 100 adolescents with T1D ages 10-16 yrs and their parents. Participants were enrolled in a longitudinal study about youth decision-making involvement in chronic illness management of which the baseline data were available for analysis. Bivariate associations with glycemic control (HbA1C) were tested. Hierarchical linear regression was implemented to inform the predictive model. In bivariate analyses, race, family structure, household income, insulin regimen, adolescent-reported adherence to diabetes self-management, cognitive development, adolescent responsibility for T1D management, and parent behavior during the illness management discussion were associated with HbA1c. In the multivariate model, the only significant predictors of HbA1c were race and insulin regimen, accounting for 17% of the variance. Caucasians had better glycemic control than other racial groups. Participants using pre-mixed insulin therapy and basal-bolus insulin had worse glycemic control than those on insulin pumps. This study shows that despite associations of adolescent and family-level variables with glycemic control at the bivariate level, only race and insulin regimen are predictive of glycemic control in hierarchical multivariate analyses. This model offers an alternative way to examine the relationship of demographic and psychosocial factors on glycemic control in adolescents with T1D. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Semiparametric Thurstonian Models for Recurrent Choices: A Bayesian Analysis

ERIC Educational Resources Information Center

Ansari, Asim; Iyengar, Raghuram

2006-01-01

We develop semiparametric Bayesian Thurstonian models for analyzing repeated choice decisions involving multinomial, multivariate binary or multivariate ordinal data. Our modeling framework has multiple components that together yield considerable flexibility in modeling preference utilities, cross-sectional heterogeneity and parameter-driven…

Colorectal cancer screening and adverse childhood experiences: Which adversities matter?

PubMed

Alcalá, Héctor E; Keim-Malpass, Jessica; Mitchell, Emma

2017-07-01

Adverse Childhood Experiences (ACEs) have been associated with an increased risk of a variety of diseases, including cancer. However, research has not paid enough attention to the association between ACEs and cancer screening. As such, the present study examined the association between ACEs and ever using colorectal cancer (CRC) screening, among adults age 50 and over. Analyses used the 2011 Behavioral Risk Factor Surveillance System (n=24,938) to model odds of ever engaging in CRC screening from nine different adversities. Bivariate and multivariate models were fit. In bivariate models, physical abuse, having parents that were divorced or separated, and living in a household where adults treated each other violently were associated with lower odds of engaging in CRC. In multivariate models that accounted for potential confounders, emotional and sexual abuse were each associated with higher odds of engaging in CRC. Results suggest potential pathways by which early childhood experiences can impact future health behaviors. Future research should examine this association longitudinally. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

PubMed

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

2017-09-01

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

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

PubMed

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

2016-12-01

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

Systematic wavelength selection for improved multivariate spectral analysis

DOEpatents

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

1995-01-01

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

Multi-country health surveys: are the analyses misleading?

PubMed

Masood, Mohd; Reidpath, Daniel D

2014-05-01

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

Development Of A Multivariate Prognostic Model For Pain And Activity Limitation In People With Low Back Disorders Receiving Physiotherapy.

PubMed

Ford, Jon J; Richards BPhysio, Matt C; Surkitt BPhysio, Luke D; Chan BPhysio, Alexander Yp; Slater, Sarah L; Taylor, Nicholas F; Hahne, Andrew J

2018-05-28

To identify predictors for back pain, leg pain and activity limitation in patients with early persistent low back disorders. Prospective inception cohort study; Setting: primary care private physiotherapy clinics in Melbourne, Australia. 300 adults aged 18-65 years with low back and/or referred leg pain of ≥6-weeks and ≤6-months duration. Not applicable. Numerical rating scales for back pain and leg pain as well as the Oswestry Disability Scale. Prognostic factors included sociodemographics, treatment related factors, subjective/physical examination, subgrouping factors and standardized questionnaires. Univariate analysis followed by generalized estimating equations were used to develop a multivariate prognostic model for back pain, leg pain and activity limitation. Fifty-eight prognostic factors progressed to the multivariate stage where 15 showed significant (p<0.05) associations with at least one of the three outcomes. There were five indicators of positive outcome (two types of low back disorder subgroups, paresthesia below waist, walking as an easing factor and low transversus abdominis tone) and 10 indicators of negative outcome (both parents born overseas, deep leg symptoms, longer sick leave duration, high multifidus tone, clinically determined inflammation, higher back and leg pain severity, lower lifting capacity, lower work capacity and higher pain drawing percentage coverage). The preliminary model identifying predictors of low back disorders explained up to 37% of the variance in outcome. This study evaluated a comprehensive range of prognostic factors reflective of both the biomedical and psychosocial domains of low back disorders. The preliminary multivariate model requires further validation before being considered for clinical use. Copyright © 2018. Published by Elsevier Inc.

Error Covariance Penalized Regression: A novel multivariate model combining penalized regression with multivariate error structure.

PubMed

Allegrini, Franco; Braga, Jez W B; Moreira, Alessandro C O; Olivieri, Alejandro C

2018-06-29

A new multivariate regression model, named Error Covariance Penalized Regression (ECPR) is presented. Following a penalized regression strategy, the proposed model incorporates information about the measurement error structure of the system, using the error covariance matrix (ECM) as a penalization term. Results are reported from both simulations and experimental data based on replicate mid and near infrared (MIR and NIR) spectral measurements. The results for ECPR are better under non-iid conditions when compared with traditional first-order multivariate methods such as ridge regression (RR), principal component regression (PCR) and partial least-squares regression (PLS). Copyright © 2018 Elsevier B.V. All rights reserved.

Effects of Covariance Heterogeneity on Three Procedures for Analyzing Multivariate Repeated Measures Designs.

ERIC Educational Resources Information Center

Vallejo, Guillermo; Fidalgo, Angel; Fernandez, Paula

2001-01-01

Estimated empirical Type I error rate and power rate for three procedures for analyzing multivariate repeated measures designs: (1) the doubly multivariate model; (2) the Welch-James multivariate solution (H. Keselman, M. Carriere, a nd L. Lix, 1993); and (3) the multivariate version of the modified Brown-Forsythe procedure (M. Brown and A.…

Predicting the multi-domain progression of Parkinson's disease: a Bayesian multivariate generalized linear mixed-effect model.

PubMed

Wang, Ming; Li, Zheng; Lee, Eun Young; Lewis, Mechelle M; Zhang, Lijun; Sterling, Nicholas W; Wagner, Daymond; Eslinger, Paul; Du, Guangwei; Huang, Xuemei

2017-09-25

It is challenging for current statistical models to predict clinical progression of Parkinson's disease (PD) because of the involvement of multi-domains and longitudinal data. Past univariate longitudinal or multivariate analyses from cross-sectional trials have limited power to predict individual outcomes or a single moment. The multivariate generalized linear mixed-effect model (GLMM) under the Bayesian framework was proposed to study multi-domain longitudinal outcomes obtained at baseline, 18-, and 36-month. The outcomes included motor, non-motor, and postural instability scores from the MDS-UPDRS, and demographic and standardized clinical data were utilized as covariates. The dynamic prediction was performed for both internal and external subjects using the samples from the posterior distributions of the parameter estimates and random effects, and also the predictive accuracy was evaluated based on the root of mean square error (RMSE), absolute bias (AB) and the area under the receiver operating characteristic (ROC) curve. First, our prediction model identified clinical data that were differentially associated with motor, non-motor, and postural stability scores. Second, the predictive accuracy of our model for the training data was assessed, and improved prediction was gained in particularly for non-motor (RMSE and AB: 2.89 and 2.20) compared to univariate analysis (RMSE and AB: 3.04 and 2.35). Third, the individual-level predictions of longitudinal trajectories for the testing data were performed, with ~80% observed values falling within the 95% credible intervals. Multivariate general mixed models hold promise to predict clinical progression of individual outcomes in PD. The data was obtained from Dr. Xuemei Huang's NIH grant R01 NS060722 , part of NINDS PD Biomarker Program (PDBP). All data was entered within 24 h of collection to the Data Management Repository (DMR), which is publically available ( https://pdbp.ninds.nih.gov/data-management ).

The Association Between Internet Use and Ambulatory Care-Seeking Behaviors in Taiwan: A Cross-Sectional Study

PubMed Central

Chen, Tsung-Fu; Liang, Jyh-Chong; Lin, Tzu-Bin; Tsai, Chin-Chung

2016-01-01

Background Compared with the traditional ways of gaining health-related information from newspapers, magazines, radio, and television, the Internet is inexpensive, accessible, and conveys diverse opinions. Several studies on how increasing Internet use affected outpatient clinic visits were inconclusive. Objective The objective of this study was to examine the role of Internet use on ambulatory care-seeking behaviors as indicated by the number of outpatient clinic visits after adjusting for confounding variables. Methods We conducted this study using a sample randomly selected from the general population in Taiwan. To handle the missing data, we built a multivariate logistic regression model for propensity score matching using age and sex as the independent variables. The questionnaires with no missing data were then included in a multivariate linear regression model for examining the association between Internet use and outpatient clinic visits. Results We included a sample of 293 participants who answered the questionnaire with no missing data in the multivariate linear regression model. We found that Internet use was significantly associated with more outpatient clinic visits (P=.04). The participants with chronic diseases tended to make more outpatient clinic visits (P<.01). Conclusions The inconsistent quality of health-related information obtained from the Internet may be associated with patients’ increasing need for interpreting and discussing the information with health care professionals, thus resulting in an increasing number of outpatient clinic visits. In addition, the media literacy of Web-based health-related information seekers may also affect their ambulatory care-seeking behaviors, such as outpatient clinic visits. PMID:27927606

[Use of multiple regression models in observational studies (1970-2013) and requirements of the STROBE guidelines in Spanish scientific journals].

PubMed

Real, J; Cleries, R; Forné, C; Roso-Llorach, A; Martínez-Sánchez, J M

In medicine and biomedical research, statistical techniques like logistic, linear, Cox and Poisson regression are widely known. The main objective is to describe the evolution of multivariate techniques used in observational studies indexed in PubMed (1970-2013), and to check the requirements of the STROBE guidelines in the author guidelines in Spanish journals indexed in PubMed. A targeted PubMed search was performed to identify papers that used logistic linear Cox and Poisson models. Furthermore, a review was also made of the author guidelines of journals published in Spain and indexed in PubMed and Web of Science. Only 6.1% of the indexed manuscripts included a term related to multivariate analysis, increasing from 0.14% in 1980 to 12.3% in 2013. In 2013, 6.7, 2.5, 3.5, and 0.31% of the manuscripts contained terms related to logistic, linear, Cox and Poisson regression, respectively. On the other hand, 12.8% of journals author guidelines explicitly recommend to follow the STROBE guidelines, and 35.9% recommend the CONSORT guideline. A low percentage of Spanish scientific journals indexed in PubMed include the STROBE statement requirement in the author guidelines. Multivariate regression models in published observational studies such as logistic regression, linear, Cox and Poisson are increasingly used both at international level, as well as in journals published in Spanish. Copyright © 2015 Sociedad Española de Médicos de Atención Primaria (SEMERGEN). Publicado por Elsevier España, S.L.U. All rights reserved.

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.

Factors Associated with the Emergence of Highly Pathogenic Avian Influenza A (H5N1) Poultry Outbreaks in China: Evidence from an Epidemiological Investigation in Ningxia, 2012.

PubMed

Liu, H; Zhou, X; Zhao, Y; Zheng, D; Wang, J; Wang, X; Castellan, D; Huang, B; Wang, Z; Soares Magalhães, R J

2017-06-01

In April 2012, highly pathogenic avian influenza virus of the H5N1 subtype (HPAIV H5N1) emerged in poultry layers in Ningxia. A retrospective case-control study was conducted to identify possible risk factors associated with the emergence of H5N1 infection and describe and quantify the spatial variation in H5N1 infection. A multivariable logistic regression model was used to identify risk factors significantly associated with the presence of infection; residual spatial variation in H5N1 risk unaccounted by the factors included in the multivariable model was investigated using a semivariogram. Our results indicate that HPAIV H5N1-infected farms were three times more likely to improperly dispose farm waste [adjusted OR = 0.37; 95% CI: 0.12-0.82] and five times more likely to have had visitors in their farm within the past month [adjusted OR = 5.47; 95% CI: 1.97-15.64] compared to H5N1-non-infected farms. The variables included in the final multivariable model accounted only 20% for the spatial clustering of H5N1 infection. The average size of a H5N1 cluster was 660 m. Bio-exclusion practices should be strengthened on poultry farms to prevent further emergence of H5N1 infection. For future poultry depopulation, operations should consider H5N1 disease clusters to be as large as 700 m. © 2015 Blackwell Verlag GmbH.

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

PubMed

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

2013-01-01

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

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

NASA Technical Reports Server (NTRS)

Belcastro, Christine M.

1998-01-01

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

Modeling an Outbreak of Anthrax

ERIC Educational Resources Information Center

Sturdivant, Rod; Watts, Krista

2010-01-01

This article presents material that has been used as a classroom activity in a calculus-based probability and statistics course. The application was used in the first few lessons of this course. Students had three previous semesters of math, including calculus (single and multivariable), differential equations, and a course in mathematical…

Racial Variation in Vocational Rehabilitation Outcomes: A Structural Equation Modeling Approach

ERIC Educational Resources Information Center

Martin, Frank H.

2010-01-01

Numerous studies have indicated racial and ethnic disparities in the vocational rehabilitation (VR) system, including differences in acceptance, services provided, closure types, and employment outcomes. Few of these studies, however, have used advanced multivariate techniques or latent constructs to measure quality of employment outcomes (QEO) or…

Parental IQ and cognitive development of malnourished Indonesian children.

PubMed

Webb, K E; Horton, N J; Katz, D L

2005-04-01

A cross-sectional study of children in West Kalimantan, Indonesia, was conducted to examine the relationship between malnutrition history, child IQ, school attendance, socioeconomic status, parental education and parental IQ. In unadjusted analyses, severely stunted children had significantly lower IQ scores than mild-moderately stunted children. This effect was significant when stunting, school attendance and parental education were included in multivariable models but was attenuated when parental IQ was included. Our research underscores the importance of accounting for parental IQ as a critical covariate when modeling the association between childhood stunting and IQ.

Analysis models for the estimation of oceanic fields

NASA Technical Reports Server (NTRS)

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

1987-01-01

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

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

PubMed

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

2015-11-01

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

Gender, ethnicity and smoking affect pain and function in patients with rotator cuff tears.

PubMed

Maher, Anthony; Leigh, Warren; Brick, Matt; Young, Simon; Millar, James; Walker, Cameron; Caughey, Michael

2017-09-01

This study is a collation of baseline demographic characteristics of those presenting for rotator cuff repair in New Zealand, and exploration of associations with preoperative function and pain. Data were obtained from the New Zealand Rotator Cuff Registry; a multicentre, nationwide prospective cohort of rotator cuff repairs undertaken from 1 March 2009 until 31 December 2010. A total of 1383 patients were included in the study. This required complete demographic information, preoperative Flex-SF (functional score) and pain scores. Following univariate analysis, a multivariate model was used. The average age was 58 years (69% males and 11% smokers). New Zealand Europeans made up 90% and Maori 5%. The average preoperative Flex-SF was significantly lower (poorer function) in those over 65 years, females, smokers and Maori, in the non-dominant patients, using a multivariate model. Average preoperative pain scores were significantly worse (higher scores) in females, Maori, Polynesians, smokers, using a multivariate model. This is the largest reported prospective cohort of patients presenting for rotator cuff surgery. Results can be used to understand the effect of rotator cuff tears on the different patients, for example Maori patients who are under-represented, present younger, with more pain and poorer function. © 2017 Royal Australasian College of Surgeons.

Risk factors for lower extremity injuries among half marathon and marathon runners of the Lage Landen Marathon Eindhoven 2012: A prospective cohort study in the Netherlands.

PubMed

van Poppel, D; de Koning, J; Verhagen, A P; Scholten-Peeters, G G M

2016-02-01

To determine risk factors for running injuries during the Lage Landen Marathon Eindhoven 2012. Prospective cohort study. Population-based study. This study included 943 runners. Running injuries after the Lage Landen Marathon. Sociodemographic and training-related factors as well as lifestyle factors were considered as potential risk factors and assessed in a questionnaire 1 month before the running event. The association between potential risk factors and injuries was determined, per running distance separately, using univariate and multivariate logistic regression analysis. In total, 154 respondents sustained a running injury. Among the marathon runners, in the univariate model, body mass index ≥ 26 kg/m(2), ≤ 5 years of running experience, and often performing interval training, were significantly associated with running injuries, whereas in the multivariate model only ≤ 5 years of running experience and not performing interval training on a regular basis were significantly associated with running injuries. Among marathon runners, no multivariate model could be created because of the low number of injuries and participants. This study indicates that interval training on a regular basis may be recommended to marathon runners to reduce the risk of injury. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

MULTIVARIATE RECEPTOR MODELS AND MODEL UNCERTAINTY. (R825173)

EPA Science Inventory

Abstract

Estimation of the number of major pollution sources, the source composition profiles, and the source contributions are the main interests in multivariate receptor modeling. Due to lack of identifiability of the receptor model, however, the estimation cannot be...

A Review of Multivariate Distributions for Count Data Derived from the Poisson Distribution

PubMed Central

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. PMID:28983398

Prostate Health Index improves multivariable risk prediction of aggressive prostate cancer.

PubMed

Loeb, Stacy; Shin, Sanghyuk S; Broyles, Dennis L; Wei, John T; Sanda, Martin; Klee, George; Partin, Alan W; Sokoll, Lori; Chan, Daniel W; Bangma, Chris H; van Schaik, Ron H N; Slawin, Kevin M; Marks, Leonard S; Catalona, William J

2017-07-01

To examine the use of the Prostate Health Index (PHI) as a continuous variable in multivariable risk assessment for aggressive prostate cancer in a large multicentre US study. The study population included 728 men, with prostate-specific antigen (PSA) levels of 2-10 ng/mL and a negative digital rectal examination, enrolled in a prospective, multi-site early detection trial. The primary endpoint was aggressive prostate cancer, defined as biopsy Gleason score ≥7. First, we evaluated whether the addition of PHI improves the performance of currently available risk calculators (the Prostate Cancer Prevention Trial [PCPT] and European Randomised Study of Screening for Prostate Cancer [ERSPC] risk calculators). We also designed and internally validated a new PHI-based multivariable predictive model, and created a nomogram. Of 728 men undergoing biopsy, 118 (16.2%) had aggressive prostate cancer. The PHI predicted the risk of aggressive prostate cancer across the spectrum of values. Adding PHI significantly improved the predictive accuracy of the PCPT and ERSPC risk calculators for aggressive disease. A new model was created using age, previous biopsy, prostate volume, PSA and PHI, with an area under the curve of 0.746. The bootstrap-corrected model showed good calibration with observed risk for aggressive prostate cancer and had net benefit on decision-curve analysis. Using PHI as part of multivariable risk assessment leads to a significant improvement in the detection of aggressive prostate cancer, potentially reducing harms from unnecessary prostate biopsy and overdiagnosis. © 2016 The Authors BJU International © 2016 BJU International Published by John Wiley & Sons Ltd.

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.

Descriptive Epidemiology of Factors Associated with HIV Infections Among Men and Transgender Women Who Have Sex with Men in South India.

PubMed

Shaw, Souradet Y; Lorway, Robert; Bhattacharjee, Parinita; Reza-Paul, Sushena; du Plessis, Elsabé; McKinnon, Lyle; Thompson, Laura H; Isac, Shajy; Ramesh, Banadakoppa M; Washington, Reynold; Moses, Stephen; Blanchard, James F

2016-08-01

Men and transgender women who have sex with men (MTWSM) continue to be an at-risk population for human immunodeficiency virus (HIV) infection in India. Identification of risk factors and determinants of HIV infection is urgently needed to inform prevention and intervention programming. Data were collected from cross-sectional biological and behavioral surveys from four districts in Karnataka, India. Multivariable logistic regression models were constructed to examine factors related to HIV infection. Sociodemographic, sexual history, sex work history, condom practices, and substance use covariates were included in regression models. A total of 456 participants were included; HIV prevalence was 12.4%, with the highest prevalence (26%) among MTWSM from Bellary District. In bivariate analyses, district (P = 0.002), lack of a current regular female partner (P = 0.022), and reported consumption of an alcoholic drink in the last month (P = 0.004) were associated with HIV infection. In multivariable models, only alcohol use remained statistically significant (adjusted odds ratios: 2.6, 95% confidence intervals: 1.2-5.8; P = 0.02). The prevalence of HIV continues to be high among MTWSM, with the highest prevalence found in Bellary district.

Probabilistic flood damage modelling at the meso-scale

NASA Astrophysics Data System (ADS)

Kreibich, Heidi; Botto, Anna; Schröter, Kai; Merz, Bruno

2014-05-01

Decisions on flood risk management and adaptation are usually based on risk analyses. Such analyses are associated with significant uncertainty, even more if changes in risk due to global change are expected. Although uncertainty analysis and probabilistic approaches have received increased attention during the last years, they are still not standard practice for flood risk assessments. Most damage models have in common that complex damaging processes are described by simple, deterministic approaches like stage-damage functions. Novel probabilistic, multi-variate flood damage models have been developed and validated on the micro-scale using a data-mining approach, namely bagging decision trees (Merz et al. 2013). In this presentation we show how the model BT-FLEMO (Bagging decision Tree based Flood Loss Estimation MOdel) can be applied on the meso-scale, namely on the basis of ATKIS land-use units. The model is applied in 19 municipalities which were affected during the 2002 flood by the River Mulde in Saxony, Germany. The application of BT-FLEMO provides a probability distribution of estimated damage to residential buildings per municipality. Validation is undertaken on the one hand via a comparison with eight other damage models including stage-damage functions as well as multi-variate models. On the other hand the results are compared with official damage data provided by the Saxon Relief Bank (SAB). The results show, that uncertainties of damage estimation remain high. Thus, the significant advantage of this probabilistic flood loss estimation model BT-FLEMO is that it inherently provides quantitative information about the uncertainty of the prediction. Reference: Merz, B.; Kreibich, H.; Lall, U. (2013): Multi-variate flood damage assessment: a tree-based data-mining approach. NHESS, 13(1), 53-64.

Generating functions and stability study of multivariate self-excited epidemic processes

NASA Astrophysics Data System (ADS)

Saichev, A. I.; Sornette, D.

2011-09-01

We present a stability study of the class of multivariate self-excited Hawkes point processes, that can model natural and social systems, including earthquakes, epileptic seizures and the dynamics of neuron assemblies, bursts of exchanges in social communities, interactions between Internet bloggers, bank network fragility and cascading of failures, national sovereign default contagion, and so on. We present the general theory of multivariate generating functions to derive the number of events over all generations of various types that are triggered by a mother event of a given type. We obtain the stability domains of various systems, as a function of the topological structure of the mutual excitations across different event types. We find that mutual triggering tends to provide a significant extension of the stability (or subcritical) domain compared with the case where event types are decoupled, that is, when an event of a given type can only trigger events of the same type.

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

PubMed

Tan, Chao; Zhao, Jia; Dong, Feng

2015-03-01

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

Perceived Risks and Normative Beliefs as Explanatory Models for College Student Alcohol Involvement: An Assessment of a Campus with Conventional Alcohol Control Policies and Enforcement Practices

ERIC Educational Resources Information Center

Lewis, Todd F.; Thombs, Dennis L.

2005-01-01

The aim of this study was to conduct a multivariate assessment of college student drinking motivations at a campus with conventional alcohol control policies and enforcement practices, including the establishment and dissemination of alcohol policies and the use of warnings to arouse fear of sanctions. Two explanatory models were compared:…

Multivariate Radiological-Based Models for the Prediction of Future Knee Pain: Data from the OAI

PubMed Central

Galván-Tejada, Jorge I.; Celaya-Padilla, José M.; Treviño, Victor; Tamez-Peña, José G.

2015-01-01

In this work, the potential of X-ray based multivariate prognostic models to predict the onset of chronic knee pain is presented. Using X-rays quantitative image assessments of joint-space-width (JSW) and paired semiquantitative central X-ray scores from the Osteoarthritis Initiative (OAI), a case-control study is presented. The pain assessments of the right knee at the baseline and the 60-month visits were used to screen for case/control subjects. Scores were analyzed at the time of pain incidence (T-0), the year prior incidence (T-1), and two years before pain incidence (T-2). Multivariate models were created by a cross validated elastic-net regularized generalized linear models feature selection tool. Univariate differences between cases and controls were reported by AUC, C-statistics, and ODDs ratios. Univariate analysis indicated that the medial osteophytes were significantly more prevalent in cases than controls: C-stat 0.62, 0.62, and 0.61, at T-0, T-1, and T-2, respectively. The multivariate JSW models significantly predicted pain: AUC = 0.695, 0.623, and 0.620, at T-0, T-1, and T-2, respectively. Semiquantitative multivariate models predicted paint with C-stat = 0.671, 0.648, and 0.645 at T-0, T-1, and T-2, respectively. Multivariate models derived from plain X-ray radiography assessments may be used to predict subjects that are at risk of developing knee pain. PMID:26504490

Wind Tunnel Database Development using Modern Experiment Design and Multivariate Orthogonal Functions

NASA Technical Reports Server (NTRS)

Morelli, Eugene A.; DeLoach, Richard

2003-01-01

A wind tunnel experiment for characterizing the aerodynamic and propulsion forces and moments acting on a research model airplane is described. The model airplane called the Free-flying Airplane for Sub-scale Experimental Research (FASER), is a modified off-the-shelf radio-controlled model airplane, with 7 ft wingspan, a tractor propeller driven by an electric motor, and aerobatic capability. FASER was tested in the NASA Langley 12-foot Low-Speed Wind Tunnel, using a combination of traditional sweeps and modern experiment design. Power level was included as an independent variable in the wind tunnel test, to allow characterization of power effects on aerodynamic forces and moments. A modeling technique that employs multivariate orthogonal functions was used to develop accurate analytic models for the aerodynamic and propulsion force and moment coefficient dependencies from the wind tunnel data. Efficient methods for generating orthogonal modeling functions, expanding the orthogonal modeling functions in terms of ordinary polynomial functions, and analytical orthogonal blocking were developed and discussed. The resulting models comprise a set of smooth, differentiable functions for the non-dimensional aerodynamic force and moment coefficients in terms of ordinary polynomials in the independent variables, suitable for nonlinear aircraft simulation.

A symmetric multivariate leakage correction for MEG connectomes

PubMed Central

Colclough, G.L.; Brookes, M.J.; Smith, S.M.; Woolrich, M.W.

2015-01-01

Ambiguities in the source reconstruction of magnetoencephalographic (MEG) measurements can cause spurious correlations between estimated source time-courses. In this paper, we propose a symmetric orthogonalisation method to correct for these artificial correlations between a set of multiple regions of interest (ROIs). This process enables the straightforward application of network modelling methods, including partial correlation or multivariate autoregressive modelling, to infer connectomes, or functional networks, from the corrected ROIs. Here, we apply the correction to simulated MEG recordings of simple networks and to a resting-state dataset collected from eight subjects, before computing the partial correlations between power envelopes of the corrected ROItime-courses. We show accurate reconstruction of our simulated networks, and in the analysis of real MEGresting-state connectivity, we find dense bilateral connections within the motor and visual networks, together with longer-range direct fronto-parietal connections. PMID:25862259

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.

Early predictors of lumbar spine surgery after occupational back injury: results from a prospective study of workers in Washington State.

PubMed

Keeney, Benjamin J; Fulton-Kehoe, Deborah; Turner, Judith A; Wickizer, Thomas M; Chan, Kwun Chuen Gary; Franklin, Gary M

2013-05-15

Prospective population-based cohort study. To identify early predictors of lumbar spine surgery within 3 years after occupational back injury. Back injuries are the most prevalent occupational injury in the United States. Few prospective studies have examined early predictors of spine surgery after work-related back injury. Using Disability Risk Identification Study Cohort (D-RISC) data, we examined the early predictors of lumbar spine surgery within 3 years among Washington State workers, with new workers compensation temporary total disability claims for back injuries. Baseline measures included worker-reported measures obtained approximately 3 weeks after claim submission. We used medical bill data to determine whether participants underwent surgery, covered by the claim, within 3 years. Baseline predictors (P < 0.10) of surgery in bivariate analyses were included in a multivariate logistic regression model predicting lumbar spine surgery. The area under the receiver operating characteristic curve of the model was used to determine the model's ability to identify correctly workers who underwent surgery. In the D-RISC sample of 1885 workers, 174 (9.2%) had a lumbar spine surgery within 3 years. Baseline variables associated with surgery (P < 0.05) in the multivariate model included higher Roland-Morris Disability Questionnaire scores, greater injury severity, and surgeon as first provider seen for the injury. Reduced odds of surgery were observed for those younger than 35 years, females, Hispanics, and those whose first provider was a chiropractor. Approximately 42.7% of workers who first saw a surgeon had surgery, in contrast to only 1.5% of those who saw a chiropractor. The area under the receiver operating characteristic curve of the multivariate model was 0.93 (95% confidence interval, 0.92-0.95), indicating excellent ability to discriminate between workers who would versus would not have surgery. Baseline variables in multiple domains predicted lumbar spine surgery. There was a very strong association between surgery and first provider seen for the injury even after adjustment for other important variables.

Multivariate Models for Normal and Binary Responses in Intervention Studies

ERIC Educational Resources Information Center

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

2016-01-01

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

[The influence of perceived discrimination on health in migrants].

PubMed

Igel, Ulrike; Brähler, Elmar; Grande, Gesine

2010-05-01

The aim of the study was to investigate the influence of racial discrimination on subjective health in migrants. The sample included 1.844 migrants from the SOEP. Discrimination was assessed by two items. Socioeconomic status, country of origin, and health behavior were included in multivariate regression models to control for effects on health. Differential models with regard to gender and origin were analysed. Migrants who experienced discrimination report a worse health status. Discrimination determines mental and physical health of migrants. There are differences in models due to gender and origin. In addition to socioeconomic factors experienced discrimination should be taken into account as a psycho-social stressor of migrants.

Cider fermentation process monitoring by Vis-NIR sensor system and chemometrics.

PubMed

Villar, Alberto; Vadillo, Julen; Santos, Jose I; Gorritxategi, Eneko; Mabe, Jon; Arnaiz, Aitor; Fernández, Luis A

2017-04-15

Optimization of a multivariate calibration process has been undertaken for a Visible-Near Infrared (400-1100nm) sensor system, applied in the monitoring of the fermentation process of the cider produced in the Basque Country (Spain). The main parameters that were monitored included alcoholic proof, l-lactic acid content, glucose+fructose and acetic acid content. The multivariate calibration was carried out using a combination of different variable selection techniques and the most suitable pre-processing strategies were selected based on the spectra characteristics obtained by the sensor system. The variable selection techniques studied in this work include Martens Uncertainty test, interval Partial Least Square Regression (iPLS) and Genetic Algorithm (GA). This procedure arises from the need to improve the calibration models prediction ability for cider monitoring. Copyright © 2016 Elsevier Ltd. All rights reserved.

Pattern of Utilisation of Dental Health Care Among HIV-positive Adult Nigerians.

PubMed

Adedigba, Michael A; Adekanmbi, Victor T; Asa, Sola; Fakande, Ibiyemi

2016-01-01

To determine the pattern of dental care utilisation of people living with HIV (PLHIV). A cross-sectional questionnaire survey of 239 PLHIV patients in three care centres was done. Information on sociodemographics, dental visit, risk groups, living arrangement, medical insurance and need of dental care was recorded. The EC Clearinghouse and WHO clinical staging was used to determine the stage of HIV/AIDS infection following routine oral examinations under natural daylight. Multivariate logistic regression models were created after adjusting for all the covariates that were statistically significant at univariate/bivariate levels. The majority of subjects were younger than 50 years, about 93% had not seen a dentist before being diagnosed HIV positive and 92% reported no dental visit after contracting HIV. Among nonusers of dental care, 14.3% reported that they wanted care but were afraid to seek it. Other reasons included poor awareness, lack of money and stigmatisation. Multivariate analysis showed that lack of dental care was associated with employment status, living arrangements, educational status, income per annum and presenting with oral symptoms. The area under the receiver operating curve was 84% for multivariate logistic regression model 1, 70% for model 2, 67% for model 3 and 71% for model 4, which means that the predictive power of the models were good. Contrary to our expectations, dental utilisation among PLHIV was generally poor among this group of patients. There is serious and immediate need to improve the awareness of PLHIVs in African settings and barriers to dental care utilisation should also be removed or reduced.

Modeling in the quality by design environment: Regulatory requirements and recommendations for design space and control strategy appointment.

PubMed

Djuris, Jelena; Djuric, Zorica

2017-11-30

Mathematical models can be used as an integral part of the quality by design (QbD) concept throughout the product lifecycle for variety of purposes, including appointment of the design space and control strategy, continual improvement and risk assessment. Examples of different mathematical modeling techniques (mechanistic, empirical and hybrid) in the pharmaceutical development and process monitoring or control are provided in the presented review. In the QbD context, mathematical models are predominantly used to support design space and/or control strategies. Considering their impact to the final product quality, models can be divided into the following categories: high, medium and low impact models. Although there are regulatory guidelines on the topic of modeling applications, review of QbD-based submission containing modeling elements revealed concerns regarding the scale-dependency of design spaces and verification of models predictions at commercial scale of manufacturing, especially regarding real-time release (RTR) models. Authors provide critical overview on the good modeling practices and introduce concepts of multiple-unit, adaptive and dynamic design space, multivariate specifications and methods for process uncertainty analysis. RTR specification with mathematical model and different approaches to multivariate statistical process control supporting process analytical technologies are also presented. Copyright © 2017 Elsevier B.V. All rights reserved.

A New Approach to Identifying the Drivers of Regulation Compliance Using Multivariate Behavioural Models

PubMed Central

Thomas, Alyssa S.; Milfont, Taciano L.; Gavin, Michael C.

2016-01-01

Non-compliance with fishing regulations can undermine management effectiveness. Previous bivariate approaches were unable to untangle the complex mix of factors that may influence fishers’ compliance decisions, including enforcement, moral norms, perceived legitimacy of regulations and the behaviour of others. We compared seven multivariate behavioural models of fisher compliance decisions using structural equation modeling. An online survey of over 300 recreational fishers tested the ability of each model to best predict their compliance with two fishing regulations (daily and size limits). The best fitting model for both regulations was composed solely of psycho-social factors, with social norms having the greatest influence on fishers’ compliance behaviour. Fishers’ attitude also directly affected compliance with size limit, but to a lesser extent. On the basis of these findings, we suggest behavioural interventions to target social norms instead of increasing enforcement for the focal regulations in the recreational blue cod fishery in the Marlborough Sounds, New Zealand. These interventions could include articles in local newspapers and fishing magazines highlighting the extent of regulation compliance as well as using respected local fishers to emphasize the benefits of compliance through public meetings or letters to the editor. Our methodological approach can be broadly applied by natural resource managers as an effective tool to identify drivers of compliance that can then guide the design of interventions to decrease illegal resource use. PMID:27727292

Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables

NASA Astrophysics Data System (ADS)

Cannon, Alex J.

2018-01-01

Most bias correction algorithms used in climatology, for example quantile mapping, are applied to univariate time series. They neglect the dependence between different variables. Those that are multivariate often correct only limited measures of joint dependence, such as Pearson or Spearman rank correlation. Here, an image processing technique designed to transfer colour information from one image to another—the N-dimensional probability density function transform—is adapted for use as a multivariate bias correction algorithm (MBCn) for climate model projections/predictions of multiple climate variables. MBCn is a multivariate generalization of quantile mapping that transfers all aspects of an observed continuous multivariate distribution to the corresponding multivariate distribution of variables from a climate model. When applied to climate model projections, changes in quantiles of each variable between the historical and projection period are also preserved. The MBCn algorithm is demonstrated on three case studies. First, the method is applied to an image processing example with characteristics that mimic a climate projection problem. Second, MBCn is used to correct a suite of 3-hourly surface meteorological variables from the Canadian Centre for Climate Modelling and Analysis Regional Climate Model (CanRCM4) across a North American domain. Components of the Canadian Forest Fire Weather Index (FWI) System, a complicated set of multivariate indices that characterizes the risk of wildfire, are then calculated and verified against observed values. Third, MBCn is used to correct biases in the spatial dependence structure of CanRCM4 precipitation fields. Results are compared against a univariate quantile mapping algorithm, which neglects the dependence between variables, and two multivariate bias correction algorithms, each of which corrects a different form of inter-variable correlation structure. MBCn outperforms these alternatives, often by a large margin, particularly for annual maxima of the FWI distribution and spatiotemporal autocorrelation of precipitation fields.

Application and validation of Cox regression models in a single-center series of double kidney transplantation.

PubMed

Santori, G; Fontana, I; Bertocchi, M; Gasloli, G; Magoni Rossi, A; Tagliamacco, A; Barocci, S; Nocera, A; Valente, U

2010-05-01

A useful approach to reduce the number of discarded marginal kidneys and to increase the nephron mass is double kidney transplantation (DKT). In this study, we retrospectively evaluated the potential predictors for patient and graft survival in a single-center series of 59 DKT procedures performed between April 21, 1999, and September 21, 2008. The kidney recipients of mean age 63.27 +/- 5.17 years included 16 women (27%) and 43 men (73%). The donors of mean age 69.54 +/- 7.48 years included 32 women (54%) and 27 men (46%). The mean posttransplant dialysis time was 2.37 +/- 3.61 days. The mean hospitalization was 20.12 +/- 13.65 days. Average serum creatinine (SCr) at discharge was 1.5 +/- 0.59 mg/dL. In view of the limited numbers of recipient deaths (n = 4) and graft losses (n = 8) that occurred in our series, the proportional hazards assumption for each Cox regression model with P < .05 was tested by using correlation coefficients between transformed survival times and scaled Schoenfeld residuals, and checked with smoothed plots of Schoenfeld residuals. For patient survival, the variables that reached statistical significance were donor SCr (P = .007), donor creatinine cleararance (P = .023), and recipient age (P = .047). Each significant model passed the Schoenfeld test. By entering these variables into a multivariate Cox model for patient survival, no further significance was observed. In the univariate Cox models performed for graft survival, statistical significance was noted for donor SCr (P = .027), SCr 3 months post-DKT (P = .043), and SCr 6 months post-DKT (P = .017). All significant univariate models for graft survival passed the Schoenfeld test. A final multivariate model retained SCr at 6 months (beta = 1.746, P = .042) and donor SCr (beta = .767, P = .090). In our analysis, SCr at 6 months seemed to emerge from both univariate and multivariate Cox models as a potential predictor of graft survival among DKT. Multicenter studies with larger recipient populations and more graft losses should be performed to confirm our findings. Copyright (c) 2010 Elsevier Inc. All rights reserved.

Retention of community college students in online courses

NASA Astrophysics Data System (ADS)

Krajewski, Sarah

The issue of attrition in online courses at higher learning institutions remains a high priority in the United States. A recent rapid growth of online courses at community colleges has been instigated by student demand, as they meet the time constraints many nontraditional community college students have as a result of the need to work and care for dependents. Failure in an online course can cause students to become frustrated with the college experience, financially burdened, or to even give up and leave college. Attrition could be avoided by proper guidance of who is best suited for online courses. This study examined factors related to retention (i.e., course completion) and success (i.e., receiving a C or better) in an online biology course at a community college in the Midwest by operationalizing student characteristics (age, race, gender), student skills (whether or not the student met the criteria to be placed in an AFP course), and external factors (Pell recipient, full/part time status, first term) from the persistence model developed by Rovai. Internal factors from this model were not included in this study. Both univariate analyses and multivariate logistic regression were used to analyze the variables. Results suggest that race and Pell recipient were both predictive of course completion on univariate analyses. However, multivariate analyses showed that age, race, academic load and first term were predictive of completion and Pell recipient was no longer predictive. The univariate results for the C or better showed that age, race, Pell recipient, academic load, and meeting AFP criteria were predictive of success. Multivariate analyses showed that only age, race, and Pell recipient were significant predictors of success. Both regression models explained very little (<15%) of the variability within the outcome variables of retention and success. Therefore, although significant predictors were identified for course completion and retention, there are still many factors that remain unaccounted for in both regression models. Further research into the operationalization of Rovai's model, including internal factors, to predict completion and success is necessary.

Creation of mortality risk charts using 123I meta-iodobenzylguanidine heart-to-mediastinum ratio in patients with heart failure: 2- and 5-year risk models.

PubMed

Nakajima, Kenichi; Nakata, Tomoaki; Matsuo, Shinro; Jacobson, Arnold F

2016-10-01

(123)I meta-iodobenzylguanidine (MIBG) imaging has been extensively used for prognostication in patients with chronic heart failure (CHF). The purpose of this study was to create mortality risk charts for short-term (2 years) and long-term (5 years) prediction of cardiac mortality. Using a pooled database of 1322 CHF patients, multivariate analysis, including (123)I-MIBG late heart-to-mediastinum ratio (HMR), left ventricular ejection fraction (LVEF), and clinical factors, was performed to determine optimal variables for the prediction of 2- and 5-year mortality risk using subsets of the patients (n = 1280 and 933, respectively). Multivariate logistic regression analysis was performed to create risk charts. Cardiac mortality was 10 and 22% for the sub-population of 2- and 5-year analyses. A four-parameter multivariate logistic regression model including age, New York Heart Association (NYHA) functional class, LVEF, and HMR was used. Annualized mortality rate was <1% in patients with NYHA Class I-II and HMR ≥ 2.0, irrespective of age and LVEF. In patients with NYHA Class III-IV, mortality rate was 4-6 times higher for HMR < 1.40 compared with HMR ≥ 2.0 in all LVEF classes. Among the subset of patients with b-type natriuretic peptide (BNP) results (n = 491 and 359 for 2- and 5-year models, respectively), the 5-year model showed incremental value of HMR in addition to BNP. Both 2- and 5-year risk prediction models with (123)I-MIBG HMR can be used to identify low-risk as well as high-risk patients, which can be effective for further risk stratification of CHF patients even when BNP is available. © The Author 2015. Published by Oxford University Press on behalf of the European Society of Cardiology.

Multivariate methods for evaluating the efficiency of electrodialytic removal of heavy metals from polluted harbour sediments.

PubMed

Pedersen, Kristine Bondo; Kirkelund, Gunvor M; Ottosen, Lisbeth M; Jensen, Pernille E; Lejon, Tore

2015-01-01

Chemometrics was used to develop a multivariate model based on 46 previously reported electrodialytic remediation experiments (EDR) of five different harbour sediments. The model predicted final concentrations of Cd, Cu, Pb and Zn as a function of current density, remediation time, stirring rate, dry/wet sediment, cell set-up as well as sediment properties. Evaluation of the model showed that remediation time and current density had the highest comparative influence on the clean-up levels. Individual models for each heavy metal showed variance in the variable importance, indicating that the targeted heavy metals were bound to different sediment fractions. Based on the results, a PLS model was used to design five new EDR experiments of a sixth sediment to achieve specified clean-up levels of Cu and Pb. The removal efficiencies were up to 82% for Cu and 87% for Pb and the targeted clean-up levels were met in four out of five experiments. The clean-up levels were better than predicted by the model, which could hence be used for predicting an approximate remediation strategy; the modelling power will however improve with more data included. Copyright © 2014 Elsevier B.V. All rights reserved.

Multiple imputation for handling missing outcome data when estimating the relative risk.

PubMed

Sullivan, Thomas R; Lee, Katherine J; Ryan, Philip; Salter, Amy B

2017-09-06

Multiple imputation is a popular approach to handling missing data in medical research, yet little is known about its applicability for estimating the relative risk. Standard methods for imputing incomplete binary outcomes involve logistic regression or an assumption of multivariate normality, whereas relative risks are typically estimated using log binomial models. It is unclear whether misspecification of the imputation model in this setting could lead to biased parameter estimates. Using simulated data, we evaluated the performance of multiple imputation for handling missing data prior to estimating adjusted relative risks from a correctly specified multivariable log binomial model. We considered an arbitrary pattern of missing data in both outcome and exposure variables, with missing data induced under missing at random mechanisms. Focusing on standard model-based methods of multiple imputation, missing data were imputed using multivariate normal imputation or fully conditional specification with a logistic imputation model for the outcome. Multivariate normal imputation performed poorly in the simulation study, consistently producing estimates of the relative risk that were biased towards the null. Despite outperforming multivariate normal imputation, fully conditional specification also produced somewhat biased estimates, with greater bias observed for higher outcome prevalences and larger relative risks. Deleting imputed outcomes from analysis datasets did not improve the performance of fully conditional specification. Both multivariate normal imputation and fully conditional specification produced biased estimates of the relative risk, presumably since both use a misspecified imputation model. Based on simulation results, we recommend researchers use fully conditional specification rather than multivariate normal imputation and retain imputed outcomes in the analysis when estimating relative risks. However fully conditional specification is not without its shortcomings, and so further research is needed to identify optimal approaches for relative risk estimation within the multiple imputation framework.

A simplified parsimonious higher order multivariate Markov chain model

NASA Astrophysics Data System (ADS)

Wang, Chao; Yang, Chuan-sheng

2017-09-01

In this paper, a simplified parsimonious higher-order multivariate Markov chain model (SPHOMMCM) is presented. Moreover, parameter estimation method of TPHOMMCM is give. Numerical experiments shows the effectiveness of TPHOMMCM.

["Who profits?" - patient characteristics as outcome predictors in psychosomatic rehabilitation].

PubMed

Oster, J; Müller, G; Wietersheim, J von

2009-04-01

The study was to examine how far treatment success in psychosomatic rehabilitation can be predicted from patients' characteristics. The aim of this study included the development of outcome criteria, the analysis of bivariate correlations, as well as development and examination of multivariate models. The motivation for dealing with job-related problems was evaluated separately. Data were available from admission, discharge and three-months follow-up. The data of 463 patients were included. Generated were success criteria concerning sociomedical development, health as well as the ability to work. All success criteria were dichotomized. In the criteria defined, successful outcomes were found in 40 to 60% of the patients. In the bivariate analyses, it was shown that many sick days before rehabilitation, applications for pension, severe disability, high impairment, and suggestion for rehabilitation by the insurance agency, have basically negative effects on success. Correlations with the variables concerning motivation for dealing with job-related problems were rather weak. In multivariate model development, models of different quality were found. For prediction of working ability at discharge, there was an explained variance of nearly 60%. In the other success criteria as well, explained variance amounted to over 20%. The models consist of different constellations of variables, the number of sick days before rehabilitation, variables of application for pension and severity of the impairment frequently included. In case of a current sick leave, rehabilitation should be started early, sociomedical problems have to be dealt with explicitly, and rehabilitation should be accompanied by preparatory and aftercare measures.

A panel of kallikrein markers can reduce unnecessary biopsy for prostate cancer: data from the European Randomized Study of Prostate Cancer Screening in Göteborg, Sweden

PubMed Central

Vickers, Andrew J; Cronin, Angel M; Aus, Gunnar; Pihl, Carl-Gustav; Becker, Charlotte; Pettersson, Kim; Scardino, Peter T; Hugosson, Jonas; Lilja, Hans

2008-01-01

Background Prostate-specific antigen (PSA) is widely used to detect prostate cancer. The low positive predictive value of elevated PSA results in large numbers of unnecessary prostate biopsies. We set out to determine whether a multivariable model including four kallikrein forms (total, free, and intact PSA, and human kallikrein 2 (hK2)) could predict prostate biopsy outcome in previously unscreened men with elevated total PSA. Methods The study cohort comprised 740 men in Göteborg, Sweden, undergoing biopsy during the first round of the European Randomized study of Screening for Prostate Cancer. We calculated the area-under-the-curve (AUC) for predicting prostate cancer at biopsy. AUCs for a model including age and PSA (the 'laboratory' model) and age, PSA and digital rectal exam (the 'clinical' model) were compared with those for models that also included additional kallikreins. Results Addition of free and intact PSA and hK2 improved AUC from 0.68 to 0.83 and from 0.72 to 0.84, for the laboratory and clinical models respectively. Using a 20% risk of prostate cancer as the threshold for biopsy would have reduced the number of biopsies by 424 (57%) and missed only 31 out of 152 low-grade and 3 out of 40 high-grade cancers. Conclusion Multiple kallikrein forms measured in blood can predict the result of biopsy in previously unscreened men with elevated PSA. A multivariable model can determine which men should be advised to undergo biopsy and which might be advised to continue screening, but defer biopsy until there was stronger evidence of malignancy. PMID:18611265

Identifying Pedophiles "Eligible" for Community Notification under Megan's Law: A Multivariate Model for Actuarially Anchored Decisions.

ERIC Educational Resources Information Center

Pallone, Nathaniel J.; Hennessy, James J.; Voelbel, Gerald T.

1998-01-01

A scientifically sound methodology for identifying offenders about whose presence the community should be notified is demonstrated. A stepwise multiple regression was calculated among incarcerated pedophiles (N=52) including both psychological and legal data; a precision-weighted equation produced 90.4% "true positives." This methodology can be…

Piecewise multivariate modelling of sequential metabolic profiling data.

PubMed

Rantalainen, Mattias; Cloarec, Olivier; Ebbels, Timothy M D; Lundstedt, Torbjörn; Nicholson, Jeremy K; Holmes, Elaine; Trygg, Johan

2008-02-19

Modelling the time-related behaviour of biological systems is essential for understanding their dynamic responses to perturbations. In metabolic profiling studies, the sampling rate and number of sampling points are often restricted due to experimental and biological constraints. A supervised multivariate modelling approach with the objective to model the time-related variation in the data for short and sparsely sampled time-series is described. A set of piecewise Orthogonal Projections to Latent Structures (OPLS) models are estimated, describing changes between successive time points. The individual OPLS models are linear, but the piecewise combination of several models accommodates modelling and prediction of changes which are non-linear with respect to the time course. We demonstrate the method on both simulated and metabolic profiling data, illustrating how time related changes are successfully modelled and predicted. The proposed method is effective for modelling and prediction of short and multivariate time series data. A key advantage of the method is model transparency, allowing easy interpretation of time-related variation in the data. The method provides a competitive complement to commonly applied multivariate methods such as OPLS and Principal Component Analysis (PCA) for modelling and analysis of short time-series data.

A tensor approach to modeling of nonhomogeneous nonlinear systems

NASA Technical Reports Server (NTRS)

Yurkovich, S.; Sain, M.

1980-01-01

Model following control methodology plays a key role in numerous application areas. Cases in point include flight control systems and gas turbine engine control systems. Typical uses of such a design strategy involve the determination of nonlinear models which generate requested control and response trajectories for various commands. Linear multivariable techniques provide trim about these motions; and protection logic is added to secure the hardware from excursions beyond the specification range. This paper reports upon experience in developing a general class of such nonlinear models based upon the idea of the algebraic tensor product.

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

PubMed Central

Oliver, Julianne; Pandya, Anand

2012-01-01

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

Affective temperaments and suicidal ideation and behavior in mood and anxiety disorder patients.

PubMed

Baldessarini, Ross J; Vázquez, Gustavo H; Tondo, Leonardo

2016-07-01

Clinical characteristics proposed to be associated with suicidal risk include affective temperament types. We tested this proposal with two methods in a large sample of subjects with mood and anxiety disorders. We assessed consecutive, consenting subjects clinically for affective temperament types and by TEMPS-A self-ratings for associations of temperament with suicidal ideation and acts, using standard bivariate methods, and multivariate logistic regression models. Among 2561 subjects (major depressive, 1171; bipolar, 919, anxiety disorders, 471), temperament-types and TEMPS-A (39-item Italian version) subscale scores differed by risk of suicidal acts or ideation. Suicidal acts and ideation were most associated with cyclothymic and dysthymic, and less with hyperthymic temperaments. These associations were sustained by multivariate modeling that included diagnosis, age, sex, and diagnosis. Not all subjects completed TEMPS-A self-ratings; clinical assessments of temperaments were not standardized, and long-term stability of temperament assessments was not tested. The findings support and extend associations of cyclothymic-dysthymic temperaments with suicidal acts and ideation, whereas hyperthymic temperament may be protective. Copyright © 2016 Elsevier B.V. All rights reserved.

A tridiagonal parsimonious higher order multivariate Markov chain model

NASA Astrophysics Data System (ADS)

Wang, Chao; Yang, Chuan-sheng

2017-09-01

In this paper, we present a tridiagonal parsimonious higher-order multivariate Markov chain model (TPHOMMCM). Moreover, estimation method of the parameters in TPHOMMCM is give. Numerical experiments illustrate the effectiveness of TPHOMMCM.

A Comparison of Multivariable Control Design Techniques for a Turbofan Engine Control

NASA Technical Reports Server (NTRS)

Garg, Sanjay; Watts, Stephen R.

1995-01-01

This paper compares two previously published design procedures for two different multivariable control design techniques for application to a linear engine model of a jet engine. The two multivariable control design techniques compared were the Linear Quadratic Gaussian with Loop Transfer Recovery (LQG/LTR) and the H-Infinity synthesis. The two control design techniques were used with specific previously published design procedures to synthesize controls which would provide equivalent closed loop frequency response for the primary control loops while assuring adequate loop decoupling. The resulting controllers were then reduced in order to minimize the programming and data storage requirements for a typical implementation. The reduced order linear controllers designed by each method were combined with the linear model of an advanced turbofan engine and the system performance was evaluated for the continuous linear system. Included in the performance analysis are the resulting frequency and transient responses as well as actuator usage and rate capability for each design method. The controls were also analyzed for robustness with respect to structured uncertainties in the unmodeled system dynamics. The two controls were then compared for performance capability and hardware implementation issues.

MULTIVARIATE LINEAR MIXED MODELS FOR MULTIPLE OUTCOMES. (R824757)

EPA Science Inventory

We propose a multivariate linear mixed (MLMM) for the analysis of multiple outcomes, which generalizes the latent variable model of Sammel and Ryan. The proposed model assumes a flexible correlation structure among the multiple outcomes, and allows a global test of the impact of ...

Electricity Consumption in the Industrial Sector of Jordan: Application of Multivariate Linear Regression and Adaptive Neuro-Fuzzy Techniques

NASA Astrophysics Data System (ADS)

Samhouri, M.; Al-Ghandoor, A.; Fouad, R. H.

2009-08-01

In this study two techniques, for modeling electricity consumption of the Jordanian industrial sector, are presented: (i) multivariate linear regression and (ii) neuro-fuzzy models. Electricity consumption is modeled as function of different variables such as number of establishments, number of employees, electricity tariff, prevailing fuel prices, production outputs, capacity utilizations, and structural effects. It was found that industrial production and capacity utilization are the most important variables that have significant effect on future electrical power demand. The results showed that both the multivariate linear regression and neuro-fuzzy models are generally comparable and can be used adequately to simulate industrial electricity consumption. However, comparison that is based on the square root average squared error of data suggests that the neuro-fuzzy model performs slightly better for future prediction of electricity consumption than the multivariate linear regression model. Such results are in full agreement with similar work, using different methods, for other countries.

Comparing Within-Person Effects from Multivariate Longitudinal Models

ERIC Educational Resources Information Center

Bainter, Sierra A.; Howard, Andrea L.

2016-01-01

Several multivariate models are motivated to answer similar developmental questions regarding within-person (intraindividual) effects between 2 or more constructs over time, yet the within-person effects tested by each model are distinct. In this article, the authors clarify the types of within-person inferences that can be made from each model.…

Applying the multivariate time-rescaling theorem to neural population models

PubMed Central

Gerhard, Felipe; Haslinger, Robert; Pipa, Gordon

2011-01-01

Statistical models of neural activity are integral to modern neuroscience. Recently, interest has grown in modeling the spiking activity of populations of simultaneously recorded neurons to study the effects of correlations and functional connectivity on neural information processing. However any statistical model must be validated by an appropriate goodness-of-fit test. Kolmogorov-Smirnov tests based upon the time-rescaling theorem have proven to be useful for evaluating point-process-based statistical models of single-neuron spike trains. Here we discuss the extension of the time-rescaling theorem to the multivariate (neural population) case. We show that even in the presence of strong correlations between spike trains, models which neglect couplings between neurons can be erroneously passed by the univariate time-rescaling test. We present the multivariate version of the time-rescaling theorem, and provide a practical step-by-step procedure for applying it towards testing the sufficiency of neural population models. Using several simple analytically tractable models and also more complex simulated and real data sets, we demonstrate that important features of the population activity can only be detected using the multivariate extension of the test. PMID:21395436

Microcomputer-based classification of environmental data in municipal areas

NASA Astrophysics Data System (ADS)

Thiergärtner, H.

1995-10-01

Multivariate data-processing methods used in mineral resource identification can be used to classify urban regions. Using elements of expert systems, geographical information systems, as well as known classification and prognosis systems, it is possible to outline a single model that consists of resistant and of temporary parts of a knowledge base including graphical input and output treatment and of resistant and temporary elements of a bank of methods and algorithms. Whereas decision rules created by experts will be stored in expert systems directly, powerful classification rules in form of resistant but latent (implicit) decision algorithms may be implemented in the suggested model. The latent functions will be transformed into temporary explicit decision rules by learning processes depending on the actual task(s), parameter set(s), pixels selection(s), and expert control(s). This takes place both at supervised and nonsupervised classification of multivariately described pixel sets representing municipal subareas. The model is outlined briefly and illustrated by results obtained in a target area covering a part of the city of Berlin (Germany).

Assessment of Anthropometric Trends and the Effects on Thermal Regulatory Models: Females Versus Males

DTIC Science & Technology

2007-08-01

primary somatotypes , which were identified by multivariate analysis, had no significant effect on the simulated thermo-physiological responses ...population. Anthropometric values for each somatotype applied to a thermal regulatory model resulted into physiological response comparisons of Figure 2 and...Public report ing burden for this collect ion of information is est imated to average 1 hour per response , including the time for review ing instruct ions

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.

What matters? Assessing and developing inquiry and multivariable reasoning skills in high school chemistry

NASA Astrophysics Data System (ADS)

Daftedar Abdelhadi, Raghda Mohamed

Although the Next Generation Science Standards (NGSS) present a detailed set of Science and Engineering Practices, a finer grained representation of the underlying skills is lacking in the standards document. Therefore, it has been reported that teachers are facing challenges deciphering and effectively implementing the standards, especially with regards to the Practices. This analytical study assessed the development of high school chemistry students' (N = 41) inquiry, multivariable causal reasoning skills, and metacognition as a mediator for their development. Inquiry tasks based on concepts of element properties of the periodic table as well as reaction kinetics required students to conduct controlled thought experiments, make inferences, and declare predictions of the level of the outcome variable by coordinating the effects of multiple variables. An embedded mixed methods design was utilized for depth and breadth of understanding. Various sources of data were collected including students' written artifacts, audio recordings of in-depth observational groups and interviews. Data analysis was informed by a conceptual framework formulated around the concepts of coordinating theory and evidence, metacognition, and mental models of multivariable causal reasoning. Results of the study indicated positive change towards conducting controlled experimentation, making valid inferences and justifications. Additionally, significant positive correlation between metastrategic and metacognitive competencies, and sophistication of experimental strategies, signified the central role metacognition played. Finally, lack of consistency in indicating effective variables during the multivariable prediction task pointed towards the fragile mental models of multivariable causal reasoning the students had. Implications for teacher education, science education policy as well as classroom research methods are discussed. Finally, recommendations for developing reform-based chemistry curricula based on the Practices are presented.

A Bayesian joint probability modeling approach for seasonal forecasting of streamflows at multiple sites

NASA Astrophysics Data System (ADS)

Wang, Q. J.; Robertson, D. E.; Chiew, F. H. S.

2009-05-01

Seasonal forecasting of streamflows can be highly valuable for water resources management. In this paper, a Bayesian joint probability (BJP) modeling approach for seasonal forecasting of streamflows at multiple sites is presented. A Box-Cox transformed multivariate normal distribution is proposed to model the joint distribution of future streamflows and their predictors such as antecedent streamflows and El Niño-Southern Oscillation indices and other climate indicators. Bayesian inference of model parameters and uncertainties is implemented using Markov chain Monte Carlo sampling, leading to joint probabilistic forecasts of streamflows at multiple sites. The model provides a parametric structure for quantifying relationships between variables, including intersite correlations. The Box-Cox transformed multivariate normal distribution has considerable flexibility for modeling a wide range of predictors and predictands. The Bayesian inference formulated allows the use of data that contain nonconcurrent and missing records. The model flexibility and data-handling ability means that the BJP modeling approach is potentially of wide practical application. The paper also presents a number of statistical measures and graphical methods for verification of probabilistic forecasts of continuous variables. Results for streamflows at three river gauges in the Murrumbidgee River catchment in southeast Australia show that the BJP modeling approach has good forecast quality and that the fitted model is consistent with observed data.

Remote-sensing data processing with the multivariate regression analysis method for iron mineral resource potential mapping: a case study in the Sarvian area, central Iran

NASA Astrophysics Data System (ADS)

Mansouri, Edris; Feizi, Faranak; Jafari Rad, Alireza; Arian, Mehran

2018-03-01

This paper uses multivariate regression to create a mathematical model for iron skarn exploration in the Sarvian area, central Iran, using multivariate regression for mineral prospectivity mapping (MPM). The main target of this paper is to apply multivariate regression analysis (as an MPM method) to map iron outcrops in the northeastern part of the study area in order to discover new iron deposits in other parts of the study area. Two types of multivariate regression models using two linear equations were employed to discover new mineral deposits. This method is one of the reliable methods for processing satellite images. ASTER satellite images (14 bands) were used as unique independent variables (UIVs), and iron outcrops were mapped as dependent variables for MPM. According to the results of the probability value (p value), coefficient of determination value (R2) and adjusted determination coefficient (Radj2), the second regression model (which consistent of multiple UIVs) fitted better than other models. The accuracy of the model was confirmed by iron outcrops map and geological observation. Based on field observation, iron mineralization occurs at the contact of limestone and intrusive rocks (skarn type).

Advanced multivariable control of a turboexpander plant

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

Altena, D.; Howard, M.; Bullin, K.

1998-12-31

This paper describes an application of advanced multivariable control on a natural gas plant and compares its performance to the previous conventional feed-back control. This control algorithm utilizes simple models from existing plant data and/or plant tests to hold the process at the desired operating point in the presence of disturbances and changes in operating conditions. The control software is able to accomplish this due to effective handling of process variable interaction, constraint avoidance and feed-forward of measured disturbances. The economic benefit of improved control lies in operating closer to the process constraints while avoiding significant violations. The South Texasmore » facility where this controller was implemented experienced reduced variability in process conditions which increased liquids recovery because the plant was able to operate much closer to the customer specified impurity constraint. An additional benefit of this implementation of multivariable control is the ability to set performance criteria beyond simple setpoints, including process variable constraints, relative variable merit and optimizing use of manipulated variables. The paper also details the control scheme applied to the complex turboexpander process and some of the safety features included to improve reliability.« less

Generating level-dependent models of cervical and thoracic spinal cord injury: Exploring the interplay of neuroanatomy, physiology, and function.

PubMed

Wilcox, Jared T; Satkunendrarajah, Kajana; Nasirzadeh, Yasmin; Laliberte, Alex M; Lip, Alyssa; Cadotte, David W; Foltz, Warren D; Fehlings, Michael G

2017-09-01

The majority of spinal cord injuries (SCI) occur at the cervical level, which results in significant impairment. Neurologic level and severity of injury are primary endpoints in clinical trials; however, how level-specific damages relate to behavioural performance in cervical injury is incompletely understood. We hypothesized that ascending level of injury leads to worsening forelimb performance, and correlates with loss of neural tissue and muscle-specific neuron pools. A direct comparison of multiple models was made with injury realized at the C5, C6, C7 and T7 vertebral levels using clip compression with sham-operated controls. Animals were assessed for 10weeks post-injury with numerous (40) outcome measures, including: classic behavioural tests, CatWalk, non-invasive MRI, electrophysiology, histologic lesion morphometry, neuron counts, and motor compartment quantification, and multivariate statistics on the total dataset. Histologic staining and T1-weighted MR imaging revealed similar structural changes and distinct tissue loss with cystic cavitation across all injuries. Forelimb tests, including grip strength, F-WARP motor scale, Inclined Plane, and forelimb ladder walk, exhibited stratification between all groups and marked impairment with C5 and C6 injuries. Classic hindlimb tests including BBB, hindlimb ladder walk, bladder recovery, and mortality were not different between cervical and thoracic injuries. CatWalk multivariate gait analysis showed reciprocal and progressive changes forelimb and hindlimb function with ascending level of injury. Electrophysiology revealed poor forelimb axonal conduction in cervical C5 and C6 groups alone. The cervical enlargement (C5-T2) showed progressive ventral horn atrophy and loss of specific motor neuron populations with ascending injury. Multivariate statistics revealed a robust dataset, rank-order contribution of outcomes, and allowed prediction of injury level with single-level discrimination using forelimb performance and neuron counts. Level-dependent models were generated using clip-compression SCI, with marked and reliable differences in forelimb performance and specific neuron pool loss. Copyright © 2017 Elsevier Inc. All rights reserved.

Determinants of calcium and oxalate excretion in subjects with calcium nephrolithiasis: the role of metabolic syndrome traits.

PubMed

Ticinesi, Andrea; Guerra, Angela; Allegri, Franca; Nouvenne, Antonio; Cervellin, Gianfranco; Maggio, Marcello; Lauretani, Fulvio; Borghi, Loris; Meschi, Tiziana

2018-06-01

The association of metabolic syndrome (MetS) traits with urinary calcium (UCE) or oxalate excretion (UOE) is uncertain in calcium stone formers (CSFs). Our aim was to investigate this association in a large group of Caucasian CSFs. We retrospectively reviewed data of CSFs evaluated at our Kidney Stone Clinic from 1984 to 2015. Data on body mass index (BMI), MetS traits defined according to international consensus, family history of urolithiasis, anti-hypertensive treatments, calcemia, renal function, and 24-h urinary profile of lithogenic risk were collected. The association between MetS traits and UCE or UOE was tested with multivariate linear regression models accounting for a long list of potential confounders. We included 3003 CSFs, aged 44 ± 14 years. The prevalence of hypertension, diabetes, overweight (BMI ≥ 25 kg/m 2 ) and dyslipidemia was 17, 2, 42 and 38%, respectively. Median values of UCE and UOE were 211 mg/24 h (IQR 143-296) and 28 mg/24 h (IQR 22-34), respectively. At a multivariate model, including age, sex, date of examination, drug treatments, family history, renal function, blood calcium and urinary factors as covariates, hypertension was a significant positive determinant of UCE (β ± SE 0.23 ± 0.07, p = 0.003), but overweight, dyslipidemia and diabetes were not. No MetS trait was significantly associated with UOE in multivariate models. In a large group of Caucasian CSFs, hypertension was the only MetS trait significantly associated with UCE, while no MetS trait was associated with oxalate excretion.

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

PubMed

Montes, Alejandro; Pazos, Gustavo

2016-02-01

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

Relation of Pericardial Fat, Intrathoracic Fat, and Abdominal Visceral Fat with Incident Atrial Fibrillation (From the Framingham Heart Study)

PubMed Central

Lee, Jane J.; Yin, Xiaoyan; Hoffmann, Udo; Fox, Caroline S.; Benjamin, Emelia J.

2016-01-01

Obesity is associated with increased risk of developing atrial fibrillation (AF). Different fat depots may have differential associations with cardiac pathology. We examined the longitudinal associations between pericardial, intrathoracic, and visceral fat with incident AF. We studied Framingham Heart Study Offspring and Third Generation Cohorts who participated in the multi-detector computed tomography sub-study examination 1. We constructed multivariable-adjusted Cox proportional hazard models for risk of incident AF. Body mass index (BMI) was included in the multivariable-adjusted model as a secondary adjustment. We included 2,135 participants (53.3% women; mean age 58.8 years). During a median follow-up of 9.7 years, we identified 162 cases of incident AF. Across the increasing tertiles of pericardial fat volume, age- and sex-adjusted incident AF rate per 1000 person-years of follow-up were 8.4, 7.5, and 10.2. Based on an age- and sex-adjusted model, greater pericardial fat [hazard ratio (HR) 1.17, 95% confidence interval (CI) 1.03-1.34] and intrathoracic fat (HR 1.24, 95% CI 1.06-1.45) were associated with increased risk of incident AF. The HRs (95% CI) for incident AF were 1.13 (0.99-1.30) for pericardial fat, 1.19 (1.01-1.40) for intrathoracic fat, and 1.09 (0.93-1.28) for abdominal visceral fat after multivariable adjustment. After additional adjustment of BMI, none of the associations remained significant (all p>0.05). Our findings suggest that cardiac ectopic fat depots may share common risk factors with AF, which may have led to a lack of independence in the association between pericardial fat with incident AF. PMID:27666172

Pregnancy outcome of patients following bariatric surgery as compared with obese women: a population-based study.

PubMed

Shai, Daniel; Shoham-Vardi, Ilana; Amsalem, Doron; Silverberg, Daniel; Levi, Isaac; Sheiner, Eyal

2014-02-01

To evaluate pregnancy outcome and rates of anemia in patients following bariatric operation in comparison with obese pregnant women. A retrospective population-based study comparing pregnancy outcome of patients following bariatric with the obese population was conducted. Multivariate logistic regression models were constructed to control for confounders. To evaluate the change in hemoglobin levels, we included women who had one pregnancy before the bariatric surgery and one following the surgery or two pregnancies for women with obesity. This study included 326 women who had one pregnancy before and after a bariatric surgery and 1612 obese women who had at least two consecutive deliveries. Using a multivariable logistic regression model, controlling for confounders such as maternal age, patients following bariatric surgery had lower rates of gestational diabetes mellitus (OR 0.7; 95% CI 0.5-0.9; p = 0.49) and macrosomia (OR 0.3; 95% CI 0.2-0.5; p < 0.001) as compared with obese parturients. Women post bariatric surgery were more likely to be anemic (hemoglobin <10 g/dL) as compared to obese parturients (48% versus 37%; OR, 1.5; 95% CI, 1.2-1.9; p < 0.001). A significant decline in hemoglobin level was noted in patients following bariatric surgery (a decline of 0.33 g/dL versus 0.18 g/dL between two consecutive pregnancies of obese women). Using another multivariable model with anemia as the outcome variable, bariatric was noted as a risk factor for anemia (adjusted OR = 1.45, 95%CI 1.13-1.86, p = 0.004). Women following bariatric surgery have lower risk for gestational diabetes mellitus and fetal macrosomia as compared with obese parturients. Nevertheless, bariatric surgery is a risk factor for anemia.

The Association Between Internet Use and Ambulatory Care-Seeking Behaviors in Taiwan: A Cross-Sectional Study.

PubMed

Hsieh, Ronan Wenhan; Chen, Likwang; Chen, Tsung-Fu; Liang, Jyh-Chong; Lin, Tzu-Bin; Chen, Yen-Yuan; Tsai, Chin-Chung

2016-12-07

Compared with the traditional ways of gaining health-related information from newspapers, magazines, radio, and television, the Internet is inexpensive, accessible, and conveys diverse opinions. Several studies on how increasing Internet use affected outpatient clinic visits were inconclusive. The objective of this study was to examine the role of Internet use on ambulatory care-seeking behaviors as indicated by the number of outpatient clinic visits after adjusting for confounding variables. We conducted this study using a sample randomly selected from the general population in Taiwan. To handle the missing data, we built a multivariate logistic regression model for propensity score matching using age and sex as the independent variables. The questionnaires with no missing data were then included in a multivariate linear regression model for examining the association between Internet use and outpatient clinic visits. We included a sample of 293 participants who answered the questionnaire with no missing data in the multivariate linear regression model. We found that Internet use was significantly associated with more outpatient clinic visits (P=.04). The participants with chronic diseases tended to make more outpatient clinic visits (P<.01). The inconsistent quality of health-related information obtained from the Internet may be associated with patients' increasing need for interpreting and discussing the information with health care professionals, thus resulting in an increasing number of outpatient clinic visits. In addition, the media literacy of Web-based health-related information seekers may also affect their ambulatory care-seeking behaviors, such as outpatient clinic visits. ©Ronan Wenhan Hsieh, Likwang Chen, Tsung-Fu Chen, Jyh-Chong Liang, Tzu-Bin Lin, Yen-Yuan Chen, Chin-Chung Tsai. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 07.12.2016.

Quantifying the Value of Downscaled Climate Model Information for Adaptation Decisions: When is Downscaling a Smart Decision?

NASA Astrophysics Data System (ADS)

Terando, A. J.; Wootten, A.; Eaton, M. J.; Runge, M. C.; Littell, J. S.; Bryan, A. M.; Carter, S. L.

2015-12-01

Two types of decisions face society with respect to anthropogenic climate change: (1) whether to enact a global greenhouse gas abatement policy, and (2) how to adapt to the local consequences of current and future climatic changes. The practice of downscaling global climate models (GCMs) is often used to address (2) because GCMs do not resolve key features that will mediate global climate change at the local scale. In response, the development of downscaling techniques and models has accelerated to aid decision makers seeking adaptation guidance. However, quantifiable estimates of the value of information are difficult to obtain, particularly in decision contexts characterized by deep uncertainty and low system-controllability. Here we demonstrate a method to quantify the additional value that decision makers could expect if research investments are directed towards developing new downscaled climate projections. As a proof of concept we focus on a real-world management problem: whether to undertake assisted migration for an endangered tropical avian species. We also take advantage of recently published multivariate methods that account for three vexing issues in climate impacts modeling: maximizing climate model quality information, accounting for model dependence in ensembles of opportunity, and deriving probabilistic projections. We expand on these global methods by including regional (Caribbean Basin) and local (Puerto Rico) domains. In the local domain, we test whether a high resolution (2km) dynamically downscaled GCM reduces the multivariate error estimate compared to the original coarse-scale GCM. Initial tests show little difference between the downscaled and original GCM multivariate error. When propagated through to a species population model, the Value of Information analysis indicates that the expected utility that would accrue to the manager (and species) if this downscaling were completed may not justify the cost compared to alternative actions.

Meta-Analytic Structural Equation Modeling (MASEM): Comparison of the Multivariate Methods

ERIC Educational Resources Information Center

Zhang, Ying

2011-01-01

Meta-analytic Structural Equation Modeling (MASEM) has drawn interest from many researchers recently. In doing MASEM, researchers usually first synthesize correlation matrices across studies using meta-analysis techniques and then analyze the pooled correlation matrix using structural equation modeling techniques. Several multivariate methods of…

MULTIVARIATE RECEPTOR MODELS-CURRENT PRACTICE AND FUTURE TRENDS. (R826238)

EPA Science Inventory

Multivariate receptor models have been applied to the analysis of air quality data for sometime. However, solving the general mixture problem is important in several other fields. This paper looks at the panoply of these models with a view of identifying common challenges and ...

Regression Models For Multivariate Count Data

PubMed Central

Zhang, Yiwen; Zhou, Hua; Zhou, Jin; Sun, Wei

2016-01-01

Data with multivariate count responses frequently occur in modern applications. The commonly used multinomial-logit model is limiting due to its restrictive mean-variance structure. For instance, analyzing count data from the recent RNA-seq technology by the multinomial-logit model leads to serious errors in hypothesis testing. The ubiquity of over-dispersion and complicated correlation structures among multivariate counts calls for more flexible regression models. In this article, we study some generalized linear models that incorporate various correlation structures among the counts. Current literature lacks a treatment of these models, partly due to the fact that they do not belong to the natural exponential family. We study the estimation, testing, and variable selection for these models in a unifying framework. The regression models are compared on both synthetic and real RNA-seq data. PMID:28348500

Regression Models For Multivariate Count Data.

PubMed

Zhang, Yiwen; Zhou, Hua; Zhou, Jin; Sun, Wei

2017-01-01

Data with multivariate count responses frequently occur in modern applications. The commonly used multinomial-logit model is limiting due to its restrictive mean-variance structure. For instance, analyzing count data from the recent RNA-seq technology by the multinomial-logit model leads to serious errors in hypothesis testing. The ubiquity of over-dispersion and complicated correlation structures among multivariate counts calls for more flexible regression models. In this article, we study some generalized linear models that incorporate various correlation structures among the counts. Current literature lacks a treatment of these models, partly due to the fact that they do not belong to the natural exponential family. We study the estimation, testing, and variable selection for these models in a unifying framework. The regression models are compared on both synthetic and real RNA-seq data.

Inference of reactive transport model parameters using a Bayesian multivariate approach

NASA Astrophysics Data System (ADS)

Carniato, Luca; Schoups, Gerrit; van de Giesen, Nick

2014-08-01

Parameter estimation of subsurface transport models from multispecies data requires the definition of an objective function that includes different types of measurements. Common approaches are weighted least squares (WLS), where weights are specified a priori for each measurement, and weighted least squares with weight estimation (WLS(we)) where weights are estimated from the data together with the parameters. In this study, we formulate the parameter estimation task as a multivariate Bayesian inference problem. The WLS and WLS(we) methods are special cases in this framework, corresponding to specific prior assumptions about the residual covariance matrix. The Bayesian perspective allows for generalizations to cases where residual correlation is important and for efficient inference by analytically integrating out the variances (weights) and selected covariances from the joint posterior. Specifically, the WLS and WLS(we) methods are compared to a multivariate (MV) approach that accounts for specific residual correlations without the need for explicit estimation of the error parameters. When applied to inference of reactive transport model parameters from column-scale data on dissolved species concentrations, the following results were obtained: (1) accounting for residual correlation between species provides more accurate parameter estimation for high residual correlation levels whereas its influence for predictive uncertainty is negligible, (2) integrating out the (co)variances leads to an efficient estimation of the full joint posterior with a reduced computational effort compared to the WLS(we) method, and (3) in the presence of model structural errors, none of the methods is able to identify the correct parameter values.

Non-parametric directionality analysis - Extension for removal of a single common predictor and application to time series.

PubMed

Halliday, David M; Senik, Mohd Harizal; Stevenson, Carl W; Mason, Rob

2016-08-01

The ability to infer network structure from multivariate neuronal signals is central to computational neuroscience. Directed network analyses typically use parametric approaches based on auto-regressive (AR) models, where networks are constructed from estimates of AR model parameters. However, the validity of using low order AR models for neurophysiological signals has been questioned. A recent article introduced a non-parametric approach to estimate directionality in bivariate data, non-parametric approaches are free from concerns over model validity. We extend the non-parametric framework to include measures of directed conditional independence, using scalar measures that decompose the overall partial correlation coefficient summatively by direction, and a set of functions that decompose the partial coherence summatively by direction. A time domain partial correlation function allows both time and frequency views of the data to be constructed. The conditional independence estimates are conditioned on a single predictor. The framework is applied to simulated cortical neuron networks and mixtures of Gaussian time series data with known interactions. It is applied to experimental data consisting of local field potential recordings from bilateral hippocampus in anaesthetised rats. The framework offers a non-parametric approach to estimation of directed interactions in multivariate neuronal recordings, and increased flexibility in dealing with both spike train and time series data. The framework offers a novel alternative non-parametric approach to estimate directed interactions in multivariate neuronal recordings, and is applicable to spike train and time series data. Copyright © 2016 Elsevier B.V. All rights reserved.

A prospective cohort study on radiation-induced hypothyroidism: development of an NTCP model.

PubMed

Boomsma, Marjolein J; Bijl, Hendrik P; Christianen, Miranda E M C; Beetz, Ivo; Chouvalova, Olga; Steenbakkers, Roel J H M; van der Laan, Bernard F A M; Wolffenbuttel, Bruce H R; Oosting, Sjoukje F; Schilstra, Cornelis; Langendijk, Johannes A

2012-11-01

To establish a multivariate normal tissue complication probability (NTCP) model for radiation-induced hypothyroidism. The thyroid-stimulating hormone (TSH) level of 105 patients treated with (chemo-) radiation therapy for head-and-neck cancer was prospectively measured during a median follow-up of 2.5 years. Hypothyroidism was defined as elevated serum TSH with decreased or normal free thyroxin (T4). A multivariate logistic regression model with bootstrapping was used to determine the most important prognostic variables for radiation-induced hypothyroidism. Thirty-five patients (33%) developed primary hypothyroidism within 2 years after radiation therapy. An NTCP model based on 2 variables, including the mean thyroid gland dose and the thyroid gland volume, was most predictive for radiation-induced hypothyroidism. NTCP values increased with higher mean thyroid gland dose (odds ratio [OR]: 1.064/Gy) and decreased with higher thyroid gland volume (OR: 0.826/cm(3)). Model performance was good with an area under the curve (AUC) of 0.85. This is the first prospective study resulting in an NTCP model for radiation-induced hypothyroidism. The probability of hypothyroidism rises with increasing dose to the thyroid gland, whereas it reduces with increasing thyroid gland volume. Copyright © 2012 Elsevier Inc. All rights reserved.

Assessing Multivariate Constraints to Evolution across Ten Long-Term Avian Studies

PubMed Central

Teplitsky, Celine; Tarka, Maja; Møller, Anders P.; Nakagawa, Shinichi; Balbontín, Javier; Burke, Terry A.; Doutrelant, Claire; Gregoire, Arnaud; Hansson, Bengt; Hasselquist, Dennis; Gustafsson, Lars; de Lope, Florentino; Marzal, Alfonso; Mills, James A.; Wheelwright, Nathaniel T.; Yarrall, John W.; Charmantier, Anne

2014-01-01

Background In a rapidly changing world, it is of fundamental importance to understand processes constraining or facilitating adaptation through microevolution. As different traits of an organism covary, genetic correlations are expected to affect evolutionary trajectories. However, only limited empirical data are available. Methodology/Principal Findings We investigate the extent to which multivariate constraints affect the rate of adaptation, focusing on four morphological traits often shown to harbour large amounts of genetic variance and considered to be subject to limited evolutionary constraints. Our data set includes unique long-term data for seven bird species and a total of 10 populations. We estimate population-specific matrices of genetic correlations and multivariate selection coefficients to predict evolutionary responses to selection. Using Bayesian methods that facilitate the propagation of errors in estimates, we compare (1) the rate of adaptation based on predicted response to selection when including genetic correlations with predictions from models where these genetic correlations were set to zero and (2) the multivariate evolvability in the direction of current selection to the average evolvability in random directions of the phenotypic space. We show that genetic correlations on average decrease the predicted rate of adaptation by 28%. Multivariate evolvability in the direction of current selection was systematically lower than average evolvability in random directions of space. These significant reductions in the rate of adaptation and reduced evolvability were due to a general nonalignment of selection and genetic variance, notably orthogonality of directional selection with the size axis along which most (60%) of the genetic variance is found. Conclusions These results suggest that genetic correlations can impose significant constraints on the evolution of avian morphology in wild populations. This could have important impacts on evolutionary dynamics and hence population persistence in the face of rapid environmental change. PMID:24608111

An AD100 implementation of a real-time STOVL aircraft propulsion system

NASA Technical Reports Server (NTRS)

Ouzts, Peter J.; Drummond, Colin K.

1990-01-01

A real-time dynamic model of the propulsion system for a Short Take-Off and Vertical Landing (STOVL) aircraft was developed for the AD100 simulation environment. The dynamic model was adapted from a FORTRAN based simulation using the dynamic programming capabilities of the AD100 ADSIM simulation language. The dynamic model includes an aerothermal representation of a turbofan jet engine, actuator and sensor models, and a multivariable control system. The AD100 model was tested for agreement with the FORTRAN model and real-time execution performance. The propulsion system model was also linked to an airframe dynamic model to provide an overall STOVL aircraft simulation for the purposes of integrated flight and propulsion control studies. An evaluation of the AD100 system for use as an aircraft simulation environment is included.

Sexual Assault Disclosure: The Effect of Victim Race and Perpetrator Type on Empathy, Culpability, and Service Referral for Survivors in a Hypothetical Scenario.

PubMed

Franklin, Cortney A; Garza, Alondra D

2018-03-01

The aftermath of sexual assault warrants further attention surrounding the responses provided by those to whom survivors disclose, especially when perpetrator type or victim race may affect whether the bystander response is supportive or attributes culpability to the victim. Disclosure responses have significant consequences for survivors' posttrauma mental health and formal help-seeking behavior. The current study used a sample of 348 self-report, paper-and-pencil surveys administered during the fall 2015 semester to a purposive sample of undergraduate students with a mean age of 20.94 years old at a midsized, Southern public university. Survey design included a randomly assigned 2 × 2 hypothetical sexual assault disclosure vignette. The objective of the study was to assess the effect of perpetrator type (stranger vs. acquaintance) and victim race (White vs. Black) on empathic concern, culpability attributions, and resource referral. Between-subjects factorial ANOVA and multivariate ordinary least squares (OLS) regression models were estimated to identify the role of vignette manipulations, participant-sexual victimization history, and rape myth acceptance on empathy, culpability, and resource referral for the sexual assault survivor portrayed in the vignette. Multivariate analyses included main effects and moderation models. Findings revealed increased culpability and decreased resource referral for victims of acquaintance rape as compared with stranger rape, independent of victim race. Although no direct victim race effects emerged in the multivariate analyses, race moderated the effect of culpability on resource referral indicating culpability attributions decreased resource referral, but only when the victim was Black . Implications from the results presented here include a continued focus on bystander intervention strategies, empathy-building techniques, and educational programming targeting potential sexual assault disclosees and race stereotypes that disadvantage victims of color.

Preoperative nomogram to predict the likelihood of complications after radical nephroureterectomy.

PubMed

Raman, Jay D; Lin, Yu-Kuan; Shariat, Shahrokh F; Krabbe, Laura-Maria; Margulis, Vitaly; Arnouk, Alex; Lallas, Costas D; Trabulsi, Edouard J; Drouin, Sarah J; Rouprêt, Morgan; Bozzini, Gregory; Colin, Pierre; Peyronnet, Benoit; Bensalah, Karim; Bailey, Kari; Canes, David; Klatte, Tobias

2017-02-01

To construct a nomogram based on preoperative variables to better predict the likelihood of complications occurring within 30 days of radical nephroureterectomy (RNU). The charts of 731 patients undergoing RNU at eight academic medical centres between 2002 and 2014 were reviewed. Preoperative clinical, demographic and comorbidity indices were collected. Complications occurring within 30 days of surgery were graded using the modified Clavien-Dindo scale. Multivariate logistic regression determined the association between preoperative variables and post-RNU complications. A nomogram was created from the reduced multivariate model with internal validation using the bootstrapping technique with 200 repetitions. A total of 408 men and 323 women with a median age of 70 years and a body mass index of 27 kg/m 2 were included. A total of 75% of the cohort was white, 18% had an Eastern Cooperative Oncology Group (ECOG) performance status ≥2, 20% had a Charlson comorbidity index (CCI) score >5 and 50% had baseline chronic kidney disease (CKD) ≥ stage III. Overall, 279 patients (38%) experienced a complication, including 61 events (22%) with Clavien grade ≥ III. A multivariate model identified five variables associated with complications, including patient age, race, ECOG performance status, CKD stage and CCI score. A preoperative nomogram incorporating these risk factors was constructed with an area under curve of 72.2%. Using standard preoperative variables from this multi-institutional RNU experience, we constructed and validated a nomogram for predicting peri-operative complications after RNU. Such information may permit more accurate risk stratification on an individual cases basis before major surgery. © 2016 The Authors BJU International © 2016 BJU International Published by John Wiley & Sons Ltd.

Multivariate normal maximum likelihood with both ordinal and continuous variables, and data missing at random.

PubMed

Pritikin, Joshua N; Brick, Timothy R; Neale, Michael C

2018-04-01

A novel method for the maximum likelihood estimation of structural equation models (SEM) with both ordinal and continuous indicators is introduced using a flexible multivariate probit model for the ordinal indicators. A full information approach ensures unbiased estimates for data missing at random. Exceeding the capability of prior methods, up to 13 ordinal variables can be included before integration time increases beyond 1 s per row. The method relies on the axiom of conditional probability to split apart the distribution of continuous and ordinal variables. Due to the symmetry of the axiom, two similar methods are available. A simulation study provides evidence that the two similar approaches offer equal accuracy. A further simulation is used to develop a heuristic to automatically select the most computationally efficient approach. Joint ordinal continuous SEM is implemented in OpenMx, free and open-source software.

Parenting Style and Behavior as Longitudinal Predictors of Adolescent Alcohol Use.

PubMed

Minaie, Matin Ghayour; Hui, Ka Kit; Leung, Rachel K; Toumbourou, John W; King, Ross M

2015-09-01

Adolescent alcohol use is a serious problem in Australia and other nations. Longitudinal data on family predictors are valuable to guide parental education efforts. The present study tested Baumrind's proposal that parenting styles are direct predictors of adolescent alcohol use. Latent class modeling was used to investigate adolescent perceptions of parenting styles and multivariate regression to examine their predictive effect on the development of adolescent alcohol use. The data set comprised 2,081 secondary school students (55.9% female) from metropolitan Melbourne, Australia, who completed three waves of annual longitudinal data starting in 2004. Baumrind's parenting styles were significant predictors in unadjusted analyses, but these effects were not maintained in multivariate models that also included parenting behavior dimensions. Family influences on the development of adolescent alcohol use appear to operate more directly through specific family management behaviors rather than through more global parenting styles.

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.

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.

High-Dimensional Sparse Factor Modeling: Applications in Gene Expression Genomics

PubMed Central

Carvalho, Carlos M.; Chang, Jeffrey; Lucas, Joseph E.; Nevins, Joseph R.; Wang, Quanli; West, Mike

2010-01-01

We describe studies in molecular profiling and biological pathway analysis that use sparse latent factor and regression models for microarray gene expression data. We discuss breast cancer applications and key aspects of the modeling and computational methodology. Our case studies aim to investigate and characterize heterogeneity of structure related to specific oncogenic pathways, as well as links between aggregate patterns in gene expression profiles and clinical biomarkers. Based on the metaphor of statistically derived “factors” as representing biological “subpathway” structure, we explore the decomposition of fitted sparse factor models into pathway subcomponents and investigate how these components overlay multiple aspects of known biological activity. Our methodology is based on sparsity modeling of multivariate regression, ANOVA, and latent factor models, as well as a class of models that combines all components. Hierarchical sparsity priors address questions of dimension reduction and multiple comparisons, as well as scalability of the methodology. The models include practically relevant non-Gaussian/nonparametric components for latent structure, underlying often quite complex non-Gaussianity in multivariate expression patterns. Model search and fitting are addressed through stochastic simulation and evolutionary stochastic search methods that are exemplified in the oncogenic pathway studies. Supplementary supporting material provides more details of the applications, as well as examples of the use of freely available software tools for implementing the methodology. PMID:21218139

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

PubMed Central

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

2015-01-01

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

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

PubMed

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

2016-08-01

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

A "Model" Multivariable Calculus Course.

ERIC Educational Resources Information Center

Beckmann, Charlene E.; Schlicker, Steven J.

1999-01-01

Describes a rich, investigative approach to multivariable calculus. Introduces a project in which students construct physical models of surfaces that represent real-life applications of their choice. The models, along with student-selected datasets, serve as vehicles to study most of the concepts of the course from both continuous and discrete…

Bayesian Estimation of Multivariate Latent Regression Models: Gauss versus Laplace

ERIC Educational Resources Information Center

Culpepper, Steven Andrew; Park, Trevor

2017-01-01

A latent multivariate regression model is developed that employs a generalized asymmetric Laplace (GAL) prior distribution for regression coefficients. The model is designed for high-dimensional applications where an approximate sparsity condition is satisfied, such that many regression coefficients are near zero after accounting for all the model…

A Sandwich-Type Standard Error Estimator of SEM Models with Multivariate Time Series

ERIC Educational Resources Information Center

Zhang, Guangjian; Chow, Sy-Miin; Ong, Anthony D.

2011-01-01

Structural equation models are increasingly used as a modeling tool for multivariate time series data in the social and behavioral sciences. Standard error estimators of SEM models, originally developed for independent data, require modifications to accommodate the fact that time series data are inherently dependent. In this article, we extend a…

Multivariate Autoregressive Modeling and Granger Causality Analysis of Multiple Spike Trains

PubMed Central

Krumin, Michael; Shoham, Shy

2010-01-01

Recent years have seen the emergence of microelectrode arrays and optical methods allowing simultaneous recording of spiking activity from populations of neurons in various parts of the nervous system. The analysis of multiple neural spike train data could benefit significantly from existing methods for multivariate time-series analysis which have proven to be very powerful in the modeling and analysis of continuous neural signals like EEG signals. However, those methods have not generally been well adapted to point processes. Here, we use our recent results on correlation distortions in multivariate Linear-Nonlinear-Poisson spiking neuron models to derive generalized Yule-Walker-type equations for fitting ‘‘hidden” Multivariate Autoregressive models. We use this new framework to perform Granger causality analysis in order to extract the directed information flow pattern in networks of simulated spiking neurons. We discuss the relative merits and limitations of the new method. PMID:20454705

A joint modeling and estimation method for multivariate longitudinal data with mixed types of responses to analyze physical activity data generated by accelerometers.

PubMed

Li, Haocheng; Zhang, Yukun; Carroll, Raymond J; Keadle, Sarah Kozey; Sampson, Joshua N; Matthews, Charles E

2017-11-10

A mixed effect model is proposed to jointly analyze multivariate longitudinal data with continuous, proportion, count, and binary responses. The association of the variables is modeled through the correlation of random effects. We use a quasi-likelihood type approximation for nonlinear variables and transform the proposed model into a multivariate linear mixed model framework for estimation and inference. Via an extension to the EM approach, an efficient algorithm is developed to fit the model. The method is applied to physical activity data, which uses a wearable accelerometer device to measure daily movement and energy expenditure information. Our approach is also evaluated by a simulation study. Copyright © 2017 John Wiley & Sons, Ltd.

Factors Associated with Sexual Violence against Men Who Have Sex with Men and Transgendered Individuals in Karnataka, India

PubMed Central

Shaw, Souradet Y.; Lorway, Robert R.; Deering, Kathleen N.; Avery, Lisa; Mohan, H. L.; Bhattacharjee, Parinita; Reza-Paul, Sushena; Isac, Shajy; Ramesh, Banadakoppa M.; Washington, Reynold; Moses, Stephen; Blanchard, James F.

2012-01-01

Objectives There is a lack of information on sexual violence (SV) among men who have sex with men and transgendered individuals (MSM-T) in southern India. As SV has been associated with HIV vulnerability, this study examined health related behaviours and practices associated with SV among MSM-T. Design Data were from cross-sectional surveys from four districts in Karnataka, India. Methods Multivariable logistic regression models were constructed to examine factors related to SV. Multivariable negative binomial regression models examined the association between physician visits and SV. Results A total of 543 MSM-T were included in the study. Prevalence of SV was 18% in the past year. HIV prevalence among those reporting SV was 20%, compared to 12% among those not reporting SV (p = .104). In multivariable models, and among sex workers, those reporting SV were more likely to report anal sex with 5+ casual sex partners in the past week (AOR: 4.1; 95%CI: 1.2–14.3, p = .029). Increased physician visits among those reporting SV was reported only for those involved in sex work (ARR: 1.7; 95%CI: 1.1–2.7, p = .012). Conclusions These results demonstrate high levels of SV among MSM-T populations, highlighting the importance of integrating interventions to reduce violence as part of HIV prevention programs and health services. PMID:22448214

Practical robustness measures in multivariable control system analysis. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Lehtomaki, N. A.

1981-01-01

The robustness of the stability of multivariable linear time invariant feedback control systems with respect to model uncertainty is considered using frequency domain criteria. Available robustness tests are unified under a common framework based on the nature and structure of model errors. These results are derived using a multivariable version of Nyquist's stability theorem in which the minimum singular value of the return difference transfer matrix is shown to be the multivariable generalization of the distance to the critical point on a single input, single output Nyquist diagram. Using the return difference transfer matrix, a very general robustness theorem is presented from which all of the robustness tests dealing with specific model errors may be derived. The robustness tests that explicitly utilized model error structure are able to guarantee feedback system stability in the face of model errors of larger magnitude than those robustness tests that do not. The robustness of linear quadratic Gaussian control systems are analyzed.

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.

Prevalence and Determinants of Suicide Ideation among Lebanese Adolescents: Results of the GSHS Lebanon 2005

ERIC Educational Resources Information Center

Mahfoud, Ziyad R.; Afifi, Rema A.; Haddad, Pascale H.; DeJong, Jocelyn

2011-01-01

The current study examined prevalence and risk factors for suicide ideation in 5038 Lebanese adolescents using Global School Health Survey data. Around 16% of Lebanese adolescents thought of suicide. Multivariate logistic regression models showed that risk factors for suicide ideation included poor mental health (felt lonely, felt worried, felt…

Follow-Up Care for Older Women With Breast Cancer

DTIC Science & Technology

1998-08-01

and node status (positive/negative); and breast cancer treatments received. For the breast cancer treatments variables , we used two different ...interview. Independent Variables . We constructed five different measures of comorbidity. The first was a self-reported measure of cardiopulmonary...Candidate variables for our multivariate models included: baseline measures of the relevant outcome, age, stage, comorbidity, primary tumor therapy

Causal diagrams and multivariate analysis II: precision work.

PubMed

Jupiter, Daniel C

2014-01-01

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

Describing the Elephant: Structure and Function in Multivariate Data.

ERIC Educational Resources Information Center

McDonald, Roderick P.

1986-01-01

There is a unity underlying the diversity of models for the analysis of multivariate data. Essentially, they constitute a family of models, most generally nonlinear, for structural/functional relations between variables drawn from a behavior domain. (Author)

Social-ecological factors and preventive actions decrease the risk of dengue infection at the household-level: Results from a prospective dengue surveillance study in Machala, Ecuador

PubMed Central

Kenneson, Aileen; Beltrán-Ayala, Efraín; Borbor-Cordova, Mercy J.; Polhemus, Mark E.; Ryan, Sadie J.; Endy, Timothy P.

2017-01-01

Background In Ecuador, dengue virus (DENV) infections transmitted by the Aedes aegypti mosquito are among the greatest public health concerns in urban coastal communities. Community- and household-level vector control is the principal means of controlling disease outbreaks. This study aimed to assess the impact of knowledge, attitudes, and practices (KAPs) and social-ecological factors on the presence or absence of DENV infections in the household. Methods In 2014 and 2015, individuals with DENV infections from sentinel clinics in Machala, Ecuador, were invited to participate in the study, as well as members of their household and members of four neighboring households located within 200 meters. We conducted diagnostic testing for DENV on all study participants; we surveyed heads of households (HOHs) regarding demographics, housing conditions and KAPs. We compared KAPs and social-ecological factors between households with (n = 139) versus without (n = 80) DENV infections, using bivariate analyses and multivariate logistic regression models with and without interactions. Results Significant risk factors in multivariate models included proximity to abandoned properties, interruptions in piped water, and shaded patios (p<0.05). Significant protective factors included the use of mosquito bed nets, fumigation inside the home, and piped water inside the home (p<0.05). In bivariate analyses (but not multivariate modeling), DENV infections were positively associated with HOHs who were male, employed, and of younger age than households without infections (p<0.05). DENV infections were not associated with knowledge, attitude, or reported barriers to prevention activities. Discussion Specific actions that can be considered to decrease the risk of DENV infections in the household include targeting vector control in highly shaded properties, fumigating inside the home, and use of mosquito bed nets. Community-level interventions include cleanup of abandoned properties, daily garbage collection, and reliable piped water inside houses. These findings can inform interventions to reduce the risk of other diseases transmitted by the Ae. aegypti mosquito, such as chikungunya and Zika fever. PMID:29253873

Universal Pressure Ulcer Prevention Bundle With WOC Nurse Support.

PubMed

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

2015-01-01

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

Psychosocial Characteristics and Social Networks of Suicidal Prisoners: Towards a Model of Suicidal Behaviour in Detention

PubMed Central

Rivlin, Adrienne; Hawton, Keith; Marzano, Lisa; Fazel, Seena

2013-01-01

Prisoners are at increased risk of suicide. Investigation of both individual and environmental risk factors may assist in developing suicide prevention policies for prisoners and other high-risk populations. We conducted a matched case-control interview study with 60 male prisoners who had made near-lethal suicide attempts in prison (cases) and 60 male prisoners who had not (controls). We compared levels of depression, hopelessness, self-esteem, impulsivity, aggression, hostility, childhood abuse, life events (including events occurring in prison), social support, and social networks in univariate and multivariate models. A range of psychosocial factors was associated with near-lethal self-harm in prisoners. Compared with controls, cases reported higher levels of depression, hopelessness, impulsivity, and aggression, and lower levels of self-esteem and social support (all p values <0.001). Adverse life events and criminal history factors were also associated with near-lethal self-harm, especially having a prior prison spell and having been bullied in prison, both of which remained significant in multivariate analyses. The findings support a model of suicidal behaviour in prisoners that incorporates imported vulnerability factors, clinical factors, and prison experiences, and underscores their interaction. Strategies to reduce self-harm and suicide in prisoners should include attention to such factors. PMID:23922671

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.

Clinical risk assessment of patients with chronic kidney disease by using clinical data and multivariate models.

PubMed

Chen, Zewei; Zhang, Xin; Zhang, Zhuoyong

2016-12-01

Timely risk assessment of chronic kidney disease (CKD) and proper community-based CKD monitoring are important to prevent patients with potential risk from further kidney injuries. As many symptoms are associated with the progressive development of CKD, evaluating risk of CKD through a set of clinical data of symptoms coupled with multivariate models can be considered as an available method for prevention of CKD and would be useful for community-based CKD monitoring. Three common used multivariate models, i.e., K-nearest neighbor (KNN), support vector machine (SVM), and soft independent modeling of class analogy (SIMCA), were used to evaluate risk of 386 patients based on a series of clinical data taken from UCI machine learning repository. Different types of composite data, in which proportional disturbances were added to simulate measurement deviations caused by environment and instrument noises, were also utilized to evaluate the feasibility and robustness of these models in risk assessment of CKD. For the original data set, three mentioned multivariate models can differentiate patients with CKD and non-CKD with the overall accuracies over 93 %. KNN and SVM have better performances than SIMCA has in this study. For the composite data set, SVM model has the best ability to tolerate noise disturbance and thus are more robust than the other two models. Using clinical data set on symptoms coupled with multivariate models has been proved to be feasible approach for assessment of patient with potential CKD risk. SVM model can be used as useful and robust tool in this study.

Cole-Cole, linear and multivariate modeling of capacitance data for on-line monitoring of biomass.

PubMed

Dabros, Michal; Dennewald, Danielle; Currie, David J; Lee, Mark H; Todd, Robert W; Marison, Ian W; von Stockar, Urs

2009-02-01

This work evaluates three techniques of calibrating capacitance (dielectric) spectrometers used for on-line monitoring of biomass: modeling of cell properties using the theoretical Cole-Cole equation, linear regression of dual-frequency capacitance measurements on biomass concentration, and multivariate (PLS) modeling of scanning dielectric spectra. The performance and robustness of each technique is assessed during a sequence of validation batches in two experimental settings of differing signal noise. In more noisy conditions, the Cole-Cole model had significantly higher biomass concentration prediction errors than the linear and multivariate models. The PLS model was the most robust in handling signal noise. In less noisy conditions, the three models performed similarly. Estimates of the mean cell size were done additionally using the Cole-Cole and PLS models, the latter technique giving more satisfactory results.

Fruit and vegetable consumption - the influence of aspects associated with trust in food and safety and quality of food.

PubMed

Taylor, Anne W; Coveney, John; Ward, Paul R; Henderson, Julie; Meyer, Samantha B; Pilkington, Rhiannon; Gill, Tiffany K

2012-02-01

To profile adults who eat less than the recommended servings of fruit and vegetables per day. Australia-wide population telephone survey on a random sample of the Australian population, with results analysed by univariate and multivariate models. Australia. One thousand one hundred and eight interviews, respondents' (49·3 % males) mean age was 45·12 (sd 17·63) years. Overall 54·8 % and 10·7 % were eating the recommended number of servings of fruit and vegetables. Variables included in the multivariate model indicating low fruit consumption included gender, age, employment, education and those who were less likely to consider the safety and quality of food as important. In regard to low vegetable consumption, people who were more likely to do the food shopping only 'some of the time' and have a high level of trust in groups of people such as immediate family, neighbours, doctors and different levels of government were included in the final model. They were also less likely to neither consider the safety and quality of food as important nor trust organisations/institutions such as the press, television and politicians. In the final model depicting both low fruit and low vegetable servings, sex, age and a low level of importance with regard to safety and quality of food were included. To increase fruit and vegetable consumption, research into a broad range of determinants associated with behaviours should be coupled with a deeper understanding of the process associated with changing behaviours. While levels of trust are related to behaviour change, knowledge and attitudes about aspects associated with safety and quality of food are also of importance.

Antipyretic Therapy in Critically Ill Patients with Sepsis: An Interaction with Body Temperature

PubMed Central

Zhang, Zhongheng; Chen, Lin; Ni, Hongying

2015-01-01

Background and Objective The effect of antipyretic therapy on mortality in patients with sepsis remains undetermined. The present study aimed to investigate the role of antipyretic therapy in ICU patients with sepsis by using a large clinical database. Methods The multiparameter intelligent monitoring in intensive care II (MIMIC- II) database was employed for the study. Adult patients with sepsis were included for analysis. Antipyretic therapy included antipyretic medication and external cooling. Multivariable model with interaction terms were employed to explore the association of antipyretic therapy and mortality risk. Main Results A total of 15,268 patients fulfilled inclusion criteria and were included in the study. In multivariable model by treating temperature as a continuous variable, there was significant interaction between antipyretic therapy and the maximum temperature (Tmax). While antipyretic therapy had no significant effect on mortality in low temperature quintiles, antipyretic therapy was associated with increased risk of death in the quintile with body temperature >39°C (OR: 1.29, 95% CI: 1.04–1.61). Conclusion Our study shows that there is no beneficial effect on reducing mortality risk with the use of antipyretic therapy in ICU patients with sepsis. External cooling may even be harmful in patients with sepsis. PMID:25822614

Multivariate regression model for predicting lumber grade volumes of northern red oak sawlogs

Treesearch

Daniel A. Yaussy; Robert L. Brisbin

1983-01-01

A multivariate regression model was developed to predict green board-foot yields for the seven common factory lumber grades processed from northern red oak (Quercus rubra L.) factory grade logs. The model uses the standard log measurements of grade, scaling diameter, length, and percent defect. It was validated with an independent data set. The model...

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

Predictive and mechanistic multivariate linear regression models for reaction development

PubMed Central

Santiago, Celine B.; Guo, Jing-Yao

2018-01-01

Multivariate Linear Regression (MLR) models utilizing computationally-derived and empirically-derived physical organic molecular descriptors are described in this review. Several reports demonstrating the effectiveness of this methodological approach towards reaction optimization and mechanistic interrogation are discussed. A detailed protocol to access quantitative and predictive MLR models is provided as a guide for model development and parameter analysis. PMID:29719711

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

PubMed

Dinç, Erdal; Ozdemir, Abdil

2005-01-01

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

Power of Models in Longitudinal Study: Findings from a Full-Crossed Simulation Design

ERIC Educational Resources Information Center

Fang, Hua; Brooks, Gordon P.; Rizzo, Maria L.; Espy, Kimberly Andrews; Barcikowski, Robert S.

2009-01-01

Because the power properties of traditional repeated measures and hierarchical multivariate linear models have not been clearly determined in the balanced design for longitudinal studies in the literature, the authors present a power comparison study of traditional repeated measures and hierarchical multivariate linear models under 3…

Species distribution modelling for plant communities: Stacked single species or multivariate modelling approaches?

Treesearch

Emilie B. Henderson; Janet L. Ohmann; Matthew J. Gregory; Heather M. Roberts; Harold S.J. Zald

2014-01-01

Landscape management and conservation planning require maps of vegetation composition and structure over large regions. Species distribution models (SDMs) are often used for individual species, but projects mapping multiple species are rarer. We compare maps of plant community composition assembled by stacking results from many SDMs with multivariate maps constructed...

IRT-ZIP Modeling for Multivariate Zero-Inflated Count Data

ERIC Educational Resources Information Center

Wang, Lijuan

2010-01-01

This study introduces an item response theory-zero-inflated Poisson (IRT-ZIP) model to investigate psychometric properties of multiple items and predict individuals' latent trait scores for multivariate zero-inflated count data. In the model, two link functions are used to capture two processes of the zero-inflated count data. Item parameters are…

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.

Multiscale climate emulator of multimodal wave spectra: MUSCLE-spectra

NASA Astrophysics Data System (ADS)

Rueda, Ana; Hegermiller, Christie A.; Antolinez, Jose A. A.; Camus, Paula; Vitousek, Sean; Ruggiero, Peter; Barnard, Patrick L.; Erikson, Li H.; Tomás, Antonio; Mendez, Fernando J.

2017-02-01

Characterization of multimodal directional wave spectra is important for many offshore and coastal applications, such as marine forecasting, coastal hazard assessment, and design of offshore wave energy farms and coastal structures. However, the multivariate and multiscale nature of wave climate variability makes this complex problem tractable using computationally expensive numerical models. So far, the skill of statistical-downscaling model-based parametric (unimodal) wave conditions is limited in large ocean basins such as the Pacific. The recent availability of long-term directional spectral data from buoys and wave hindcast models allows for development of stochastic models that include multimodal sea-state parameters. This work introduces a statistical downscaling framework based on weather types to predict multimodal wave spectra (e.g., significant wave height, mean wave period, and mean wave direction from different storm systems, including sea and swells) from large-scale atmospheric pressure fields. For each weather type, variables of interest are modeled using the categorical distribution for the sea-state type, the Generalized Extreme Value (GEV) distribution for wave height and wave period, a multivariate Gaussian copula for the interdependence between variables, and a Markov chain model for the chronology of daily weather types. We apply the model to the southern California coast, where local seas and swells from both the Northern and Southern Hemispheres contribute to the multimodal wave spectrum. This work allows attribution of particular extreme multimodal wave events to specific atmospheric conditions, expanding knowledge of time-dependent, climate-driven offshore and coastal sea-state conditions that have a significant influence on local nearshore processes, coastal morphology, and flood hazards.

Multiscale Climate Emulator of Multimodal Wave Spectra: MUSCLE-spectra

NASA Astrophysics Data System (ADS)

Rueda, A.; Hegermiller, C.; Alvarez Antolinez, J. A.; Camus, P.; Vitousek, S.; Ruggiero, P.; Barnard, P.; Erikson, L. H.; Tomas, A.; Mendez, F. J.

2016-12-01

Characterization of multimodal directional wave spectra is important for many offshore and coastal applications, such as marine forecasting, coastal hazard assessment, and design of offshore wave energy farms and coastal structures. However, the multivariate and multiscale nature of wave climate variability makes this problem complex yet tractable using computationally-expensive numerical models. So far, the skill of statistical-downscaling models based parametric (unimodal) wave conditions is limited in large ocean basins such as the Pacific. The recent availability of long-term directional spectral data from buoys and wave hindcast models allows for development of stochastic models that include multimodal sea-state parameters. This work introduces a statistical-downscaling framework based on weather types to predict multimodal wave spectra (e.g., significant wave height, mean wave period, and mean wave direction from different storm systems, including sea and swells) from large-scale atmospheric pressure fields. For each weather type, variables of interest are modeled using the categorical distribution for the sea-state type, the Generalized Extreme Value (GEV) distribution for wave height and wave period, a multivariate Gaussian copula for the interdependence between variables, and a Markov chain model for the chronology of daily weather types. We apply the model to the Southern California coast, where local seas and swells from both the Northern and Southern Hemispheres contribute to the multimodal wave spectrum. This work allows attribution of particular extreme multimodal wave events to specific atmospheric conditions, expanding knowledge of time-dependent, climate-driven offshore and coastal sea-state conditions that have a significant influence on local nearshore processes, coastal morphology, and flood hazards.

A flexible model for multivariate interval-censored survival times with complex correlation structure.

PubMed

Falcaro, Milena; Pickles, Andrew

2007-02-10

We focus on the analysis of multivariate survival times with highly structured interdependency and subject to interval censoring. Such data are common in developmental genetics and genetic epidemiology. We propose a flexible mixed probit model that deals naturally with complex but uninformative censoring. The recorded ages of onset are treated as possibly censored ordinal outcomes with the interval censoring mechanism seen as arising from a coarsened measurement of a continuous variable observed as falling between subject-specific thresholds. This bypasses the requirement for the failure times to be observed as falling into non-overlapping intervals. The assumption of a normal age-of-onset distribution of the standard probit model is relaxed by embedding within it a multivariate Box-Cox transformation whose parameters are jointly estimated with the other parameters of the model. Complex decompositions of the underlying multivariate normal covariance matrix of the transformed ages of onset become possible. The new methodology is here applied to a multivariate study of the ages of first use of tobacco and first consumption of alcohol without parental permission in twins. The proposed model allows estimation of the genetic and environmental effects that are shared by both of these risk behaviours as well as those that are specific. 2006 John Wiley & Sons, Ltd.

Beyond a bigger brain: Multivariable structural brain imaging and intelligence

PubMed Central

Ritchie, Stuart J.; Booth, Tom; Valdés Hernández, Maria del C.; Corley, Janie; Maniega, Susana Muñoz; Gow, Alan J.; Royle, Natalie A.; Pattie, Alison; Karama, Sherif; Starr, John M.; Bastin, Mark E.; Wardlaw, Joanna M.; Deary, Ian J.

2015-01-01

People with larger brains tend to score higher on tests of general intelligence (g). It is unclear, however, how much variance in intelligence other brain measurements would account for if included together with brain volume in a multivariable model. We examined a large sample of individuals in their seventies (n = 672) who were administered a comprehensive cognitive test battery. Using structural equation modelling, we related six common magnetic resonance imaging-derived brain variables that represent normal and abnormal features—brain volume, cortical thickness, white matter structure, white matter hyperintensity load, iron deposits, and microbleeds—to g and to fluid intelligence. As expected, brain volume accounted for the largest portion of variance (~ 12%, depending on modelling choices). Adding the additional variables, especially cortical thickness (+~ 5%) and white matter hyperintensity load (+~ 2%), increased the predictive value of the model. Depending on modelling choices, all neuroimaging variables together accounted for 18–21% of the variance in intelligence. These results reveal which structural brain imaging measures relate to g over and above the largest contributor, total brain volume. They raise questions regarding which other neuroimaging measures might account for even more of the variance in intelligence. PMID:26240470

Modeling longitudinal data, I: principles of multivariate analysis.

PubMed

Ravani, Pietro; Barrett, Brendan; Parfrey, Patrick

2009-01-01

Statistical models are used to study the relationship between exposure and disease while accounting for the potential role of other factors' impact on outcomes. This adjustment is useful to obtain unbiased estimates of true effects or to predict future outcomes. Statistical models include a systematic component and an error component. The systematic component explains the variability of the response variable as a function of the predictors and is summarized in the effect estimates (model coefficients). The error element of the model represents the variability in the data unexplained by the model and is used to build measures of precision around the point estimates (confidence intervals).

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.

Linear, multivariable robust control with a mu perspective

NASA Technical Reports Server (NTRS)

Packard, Andy; Doyle, John; Balas, Gary

1993-01-01

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

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

NASA Astrophysics Data System (ADS)

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

2016-05-01

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

Multivariate-$t$ nonlinear mixed models with application to censored multi-outcome AIDS studies.

PubMed

Lin, Tsung-I; Wang, Wan-Lun

2017-10-01

In multivariate longitudinal HIV/AIDS studies, multi-outcome repeated measures on each patient over time may contain outliers, and the viral loads are often subject to a upper or lower limit of detection depending on the quantification assays. In this article, we consider an extension of the multivariate nonlinear mixed-effects model by adopting a joint multivariate-$t$ distribution for random effects and within-subject errors and taking the censoring information of multiple responses into account. The proposed model is called the multivariate-$t$ nonlinear mixed-effects model with censored responses (MtNLMMC), allowing for analyzing multi-outcome longitudinal data exhibiting nonlinear growth patterns with censorship and fat-tailed behavior. Utilizing the Taylor-series linearization method, a pseudo-data version of expectation conditional maximization either (ECME) algorithm is developed for iteratively carrying out maximum likelihood estimation. We illustrate our techniques with two data examples from HIV/AIDS studies. Experimental results signify that the MtNLMMC performs favorably compared to its Gaussian analogue and some existing approaches. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Multivariate analysis of longitudinal rates of change.

PubMed

Bryan, Matthew; Heagerty, Patrick J

2016-12-10

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

Voxelwise multivariate analysis of multimodality magnetic resonance imaging

PubMed Central

Naylor, Melissa G.; Cardenas, Valerie A.; Tosun, Duygu; Schuff, Norbert; Weiner, Michael; Schwartzman, Armin

2015-01-01

Most brain magnetic resonance imaging (MRI) studies concentrate on a single MRI contrast or modality, frequently structural MRI. By performing an integrated analysis of several modalities, such as structural, perfusion-weighted, and diffusion-weighted MRI, new insights may be attained to better understand the underlying processes of brain diseases. We compare two voxelwise approaches: (1) fitting multiple univariate models, one for each outcome and then adjusting for multiple comparisons among the outcomes and (2) fitting a multivariate model. In both cases, adjustment for multiple comparisons is performed over all voxels jointly to account for the search over the brain. The multivariate model is able to account for the multiple comparisons over outcomes without assuming independence because the covariance structure between modalities is estimated. Simulations show that the multivariate approach is more powerful when the outcomes are correlated and, even when the outcomes are independent, the multivariate approach is just as powerful or more powerful when at least two outcomes are dependent on predictors in the model. However, multiple univariate regressions with Bonferroni correction remains a desirable alternative in some circumstances. To illustrate the power of each approach, we analyze a case control study of Alzheimer's disease, in which data from three MRI modalities are available. PMID:23408378

Preliminary Multivariable Cost Model for Space Telescopes

NASA Technical Reports Server (NTRS)

Stahl, H. Philip

2010-01-01

Parametric cost models are routinely used to plan missions, compare concepts and justify technology investments. Previously, the authors published two single variable cost models based on 19 flight missions. The current paper presents the development of a multi-variable space telescopes cost model. The validity of previously published models are tested. Cost estimating relationships which are and are not significant cost drivers are identified. And, interrelationships between variables are explored

Scalable Joint Models for Reliable Uncertainty-Aware Event Prediction.

PubMed

Soleimani, Hossein; Hensman, James; Saria, Suchi

2017-08-21

Missing data and noisy observations pose significant challenges for reliably predicting events from irregularly sampled multivariate time series (longitudinal) data. Imputation methods, which are typically used for completing the data prior to event prediction, lack a principled mechanism to account for the uncertainty due to missingness. Alternatively, state-of-the-art joint modeling techniques can be used for jointly modeling the longitudinal and event data and compute event probabilities conditioned on the longitudinal observations. These approaches, however, make strong parametric assumptions and do not easily scale to multivariate signals with many observations. Our proposed approach consists of several key innovations. First, we develop a flexible and scalable joint model based upon sparse multiple-output Gaussian processes. Unlike state-of-the-art joint models, the proposed model can explain highly challenging structure including non-Gaussian noise while scaling to large data. Second, we derive an optimal policy for predicting events using the distribution of the event occurrence estimated by the joint model. The derived policy trades-off the cost of a delayed detection versus incorrect assessments and abstains from making decisions when the estimated event probability does not satisfy the derived confidence criteria. Experiments on a large dataset show that the proposed framework significantly outperforms state-of-the-art techniques in event prediction.

Exploratory Long-Range Models to Estimate Summer Climate Variability over Southern Africa.

NASA Astrophysics Data System (ADS)

Jury, Mark R.; Mulenga, Henry M.; Mason, Simon J.

1999-07-01

Teleconnection predictors are explored using multivariate regression models in an effort to estimate southern African summer rainfall and climate impacts one season in advance. The preliminary statistical formulations include many variables influenced by the El Niño-Southern Oscillation (ENSO) such as tropical sea surface temperatures (SST) in the Indian and Atlantic Oceans. Atmospheric circulation responses to ENSO include the alternation of tropical zonal winds over Africa and changes in convective activity within oceanic monsoon troughs. Numerous hemispheric-scale datasets are employed to extract predictors and include global indexes (Southern Oscillation index and quasi-biennial oscillation), SST principal component scores for the global oceans, indexes of tropical convection (outgoing longwave radiation), air pressure, and surface and upper winds over the Indian and Atlantic Oceans. Climatic targets include subseasonal, area-averaged rainfall over South Africa and the Zambezi river basin, and South Africa's annual maize yield. Predictors and targets overlap in the years 1971-93, the defined training period. Each target time series is fitted by an optimum group of predictors from the preceding spring, in a linear multivariate formulation. To limit artificial skill, predictors are restricted to three, providing 17 degrees of freedom. Models with colinear predictors are screened out, and persistence of the target time series is considered. The late summer rainfall models achieve a mean r2 fit of 72%, contributed largely through ENSO modulation. Early summer rainfall cross validation correlations are lower (61%). A conceptual understanding of the climate dynamics and ocean-atmosphere coupling processes inherent in the exploratory models is outlined.Seasonal outlooks based on the exploratory models could help mitigate the impacts of southern Africa's fluctuating climate. It is believed that an advance warning of drought risk and seasonal rainfall prospects will improve the economic growth potential of southern Africa and provide additional security for food and water supplies.

Does information available at admission for delivery improve prediction of vaginal birth after cesarean?

PubMed Central

Grobman, William A.; Lai, Yinglei; Landon, Mark B.; Spong, Catherine Y.; Leveno, Kenneth J.; Rouse, Dwight J.; Varner, Michael W.; Moawad, Atef H.; Simhan, Hyagriv N.; Harper, Margaret; Wapner, Ronald J.; Sorokin, Yoram; Miodovnik, Menachem; Carpenter, Marshall; O'sullivan, Mary J.; Sibai, Baha M.; Langer, Oded; Thorp, John M.; Ramin, Susan M.; Mercer, Brian M.

2010-01-01

Objective To construct a predictive model for vaginal birth after cesarean (VBAC) that combines factors that can be ascertained only as the pregnancy progresses with those known at initiation of prenatal care. Study design Using multivariable modeling, we constructed a predictive model for VBAC that included patient factors known at the initial prenatal visit as well as those that only became evident as the pregancy progressed to the admission for delivery. Results 9616 women were analyzed. The regression equation for VBAC success included multiple factors that could not be known at the first prenatal visit. The area under the curve for this model was significantly greater (P < .001) than that of a model that included only factors available at the first prenatal visit. Conclusion A prediction model for VBAC success that incorporates factors that can be ascertained only as the pregnancy progresses adds to the predictive accuracy of a model that uses only factors available at a first prenatal visit. PMID:19813165

DUALITY IN MULTIVARIATE RECEPTOR MODEL. (R831078)

EPA Science Inventory

Multivariate receptor models are used for source apportionment of multiple observations of compositional data of air pollutants that obey mass conservation. Singular value decomposition of the data leads to two sets of eigenvectors. One set of eigenvectors spans a space in whi...

On a Family of Multivariate Modified Humbert Polynomials

PubMed Central

Aktaş, Rabia; Erkuş-Duman, Esra

2013-01-01

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

Methodological challenges to multivariate syndromic surveillance: a case study using Swiss animal health data.

PubMed

Vial, Flavie; Wei, Wei; Held, Leonhard

2016-12-20

In an era of ubiquitous electronic collection of animal health data, multivariate surveillance systems (which concurrently monitor several data streams) should have a greater probability of detecting disease events than univariate systems. However, despite their limitations, univariate aberration detection algorithms are used in most active syndromic surveillance (SyS) systems because of their ease of application and interpretation. On the other hand, a stochastic modelling-based approach to multivariate surveillance offers more flexibility, allowing for the retention of historical outbreaks, for overdispersion and for non-stationarity. While such methods are not new, they are yet to be applied to animal health surveillance data. We applied an example of such stochastic model, Held and colleagues' two-component model, to two multivariate animal health datasets from Switzerland. In our first application, multivariate time series of the number of laboratories test requests were derived from Swiss animal diagnostic laboratories. We compare the performance of the two-component model to parallel monitoring using an improved Farrington algorithm and found both methods yield a satisfactorily low false alarm rate. However, the calibration test of the two-component model on the one-step ahead predictions proved satisfactory, making such an approach suitable for outbreak prediction. In our second application, the two-component model was applied to the multivariate time series of the number of cattle abortions and the number of test requests for bovine viral diarrhea (a disease that often results in abortions). We found that there is a two days lagged effect from the number of abortions to the number of test requests. We further compared the joint modelling and univariate modelling of the number of laboratory test requests time series. The joint modelling approach showed evidence of superiority in terms of forecasting abilities. Stochastic modelling approaches offer the potential to address more realistic surveillance scenarios through, for example, the inclusion of times series specific parameters, or of covariates known to have an impact on syndrome counts. Nevertheless, many methodological challenges to multivariate surveillance of animal SyS data still remain. Deciding on the amount of corroboration among data streams that is required to escalate into an alert is not a trivial task given the sparse data on the events under consideration (e.g. disease outbreaks).

Higher-order Multivariable Polynomial Regression to Estimate Human Affective States

NASA Astrophysics Data System (ADS)

Wei, Jie; Chen, Tong; Liu, Guangyuan; Yang, Jiemin

2016-03-01

From direct observations, facial, vocal, gestural, physiological, and central nervous signals, estimating human affective states through computational models such as multivariate linear-regression analysis, support vector regression, and artificial neural network, have been proposed in the past decade. In these models, linear models are generally lack of precision because of ignoring intrinsic nonlinearities of complex psychophysiological processes; and nonlinear models commonly adopt complicated algorithms. To improve accuracy and simplify model, we introduce a new computational modeling method named as higher-order multivariable polynomial regression to estimate human affective states. The study employs standardized pictures in the International Affective Picture System to induce thirty subjects’ affective states, and obtains pure affective patterns of skin conductance as input variables to the higher-order multivariable polynomial model for predicting affective valence and arousal. Experimental results show that our method is able to obtain efficient correlation coefficients of 0.98 and 0.96 for estimation of affective valence and arousal, respectively. Moreover, the method may provide certain indirect evidences that valence and arousal have their brain’s motivational circuit origins. Thus, the proposed method can serve as a novel one for efficiently estimating human affective states.

Higher-order Multivariable Polynomial Regression to Estimate Human Affective States

PubMed Central

Wei, Jie; Chen, Tong; Liu, Guangyuan; Yang, Jiemin

2016-01-01

From direct observations, facial, vocal, gestural, physiological, and central nervous signals, estimating human affective states through computational models such as multivariate linear-regression analysis, support vector regression, and artificial neural network, have been proposed in the past decade. In these models, linear models are generally lack of precision because of ignoring intrinsic nonlinearities of complex psychophysiological processes; and nonlinear models commonly adopt complicated algorithms. To improve accuracy and simplify model, we introduce a new computational modeling method named as higher-order multivariable polynomial regression to estimate human affective states. The study employs standardized pictures in the International Affective Picture System to induce thirty subjects’ affective states, and obtains pure affective patterns of skin conductance as input variables to the higher-order multivariable polynomial model for predicting affective valence and arousal. Experimental results show that our method is able to obtain efficient correlation coefficients of 0.98 and 0.96 for estimation of affective valence and arousal, respectively. Moreover, the method may provide certain indirect evidences that valence and arousal have their brain’s motivational circuit origins. Thus, the proposed method can serve as a novel one for efficiently estimating human affective states. PMID:26996254

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

PubMed

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

2017-05-07

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

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

NASA Astrophysics Data System (ADS)

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

2017-05-01

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

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

Allogeneic transplantation provides durable remission in a subset of DLBCL patients relapsing after autologous transplantation.

PubMed

Fenske, Timothy S; Ahn, Kwang W; Graff, Tara M; DiGilio, Alyssa; Bashir, Qaiser; Kamble, Rammurti T; Ayala, Ernesto; Bacher, Ulrike; Brammer, Jonathan E; Cairo, Mitchell; Chen, Andy; Chen, Yi-Bin; Chhabra, Saurabh; D'Souza, Anita; Farooq, Umar; Freytes, Cesar; Ganguly, Siddhartha; Hertzberg, Mark; Inwards, David; Jaglowski, Samantha; Kharfan-Dabaja, Mohamed A; Lazarus, Hillard M; Nathan, Sunita; Pawarode, Attaphol; Perales, Miguel-Angel; Reddy, Nishitha; Seo, Sachiko; Sureda, Anna; Smith, Sonali M; Hamadani, Mehdi

2016-07-01

For diffuse large B-cell lymphoma (DLBCL) patients progressing after autologous haematopoietic cell transplantation (autoHCT), allogeneic HCT (alloHCT) is often considered, although limited information is available to guide patient selection. Using the Center for International Blood and Marrow Transplant Research (CIBMTR) database, we identified 503 patients who underwent alloHCT after disease progression/relapse following a prior autoHCT. The 3-year probabilities of non-relapse mortality, progression/relapse, progression-free survival (PFS) and overall survival (OS) were 30, 38, 31 and 37% respectively. Factors associated with inferior PFS on multivariate analysis included Karnofsky performance status (KPS) <80, chemoresistance, autoHCT to alloHCT interval <1-year and myeloablative conditioning. Factors associated with worse OS on multivariate analysis included KPS<80, chemoresistance and myeloablative conditioning. Three adverse prognostic factors were used to construct a prognostic model for PFS, including KPS<80 (4 points), autoHCT to alloHCT interval <1-year (2 points) and chemoresistant disease at alloHCT (5 points). This CIBMTR prognostic model classified patients into four groups: low-risk (0 points), intermediate-risk (2-5 points), high-risk (6-9 points) or very high-risk (11 points), predicting 3-year PFS of 40, 32, 11 and 6%, respectively, with 3-year OS probabilities of 43, 39, 19 and 11% respectively. In conclusion, the CIBMTR prognostic model identifies a subgroup of DLBCL patients experiencing long-term survival with alloHCT after a failed prior autoHCT. © 2016 John Wiley & Sons Ltd.

Diagnosing perforated appendicitis in pediatric patients: a new model.

PubMed

van den Bogaard, Veerle A B; Euser, Sjoerd M; van der Ploeg, Tjeerd; de Korte, Niels; Sanders, Dave G M; de Winter, Derek; Vergroesen, Diederik; van Groningen, Krijn; de Winter, Peter

2016-03-01

Studies have investigated sensitivity and specificity of symptoms and tests for diagnosing appendicitis in children. Less is known with regard to the predictive value of these symptoms and tests with respect to the severity of appendicitis. The aim of this study was to determine the predictive value of patient's characteristics and tests for discriminating between perforated and nonperforated appendicitis in children. Pediatric patients who underwent an appendectomy at Spaarne Hospital Hoofddorp, the Netherlands, between January 1, 2009 and December 31, 2013, were included. Baseline patient's characteristics, history, physical examination, laboratory data and results of ultrasounds were collected. Univariate and multivariate logistic regressions were used to determine predictors of perforation. In total, 375 patients were included in this study of which 97 children (25.9%) had significant signs of perforation. Univariate analysis showed that age, duration of complaints, temperature, vomiting, CRP, WBC, different findings on ultrasound and the diameter of the appendix were good predictors of a perforated appendicitis. The final multivariate prediction model included temperature, CRP, clearly visible appendix and free fluids on ultrasound and diameter of the appendix and resulted in an area under the curve (AUC) of 0.91 showing sensitivity and specificity of respectively 85.2% and 81.2%. This prediction model can be used for identification of 'high-risk' children for a perforated appendicitis and might be helpful to prevent complications and longer hospitalization by bringing these children to theater earlier. Copyright © 2016 Elsevier Inc. All rights reserved.

Stress and Personal Resource as Predictors of the Adjustment of Parents to Autistic Children: A Multivariate Model

ERIC Educational Resources Information Center

Siman-Tov, Ayelet; Kaniel, Shlomo

2011-01-01

The research validates a multivariate model that predicts parental adjustment to coping successfully with an autistic child. The model comprises four elements: parental stress, parental resources, parental adjustment and the child's autism symptoms. 176 parents of children aged between 6 to 16 diagnosed with PDD answered several questionnaires…

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...

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...

Landscape controls on total and methyl Hg in the Upper Hudson River basin, New York, USA

USGS Publications Warehouse

Burns, Douglas A.; Riva-Murray, K.; Bradley, P.M.; Aiken, G.R.; Brigham, M.E.

2012-01-01

Approaches are needed to better predict spatial variation in riverine Hg concentrations across heterogeneous landscapes that include mountains, wetlands, and open waters. We applied multivariate linear regression to determine the landscape factors and chemical variables that best account for the spatial variation of total Hg (THg) and methyl Hg (MeHg) concentrations in 27 sub-basins across the 493 km2 upper Hudson River basin in the Adirondack Mountains of New York. THg concentrations varied by sixfold, and those of MeHg by 40-fold in synoptic samples collected at low-to-moderate flow, during spring and summer of 2006 and 2008. Bivariate linear regression relations of THg and MeHg concentrations with either percent wetland area or DOC concentrations were significant but could account for only about 1/3 of the variation in these Hg forms in summer. In contrast, multivariate linear regression relations that included metrics of (1) hydrogeomorphology, (2) riparian/wetland area, and (3) open water, explained about 66% to >90% of spatial variation in each Hg form in spring and summer samples. These metrics reflect the influence of basin morphometry and riparian soils on Hg source and transport, and the role of open water as a Hg sink. Multivariate models based solely on these landscape metrics generally accounted for as much or more of the variation in Hg concentrations than models based on chemical and physical metrics, and show great promise for identifying waters with expected high Hg concentrations in the Adirondack region and similar glaciated riverine ecosystems.

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

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

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

2013-01-01

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

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

Adjustment of automatic control systems of production facilities at coal processing plants using multivariant physico- mathematical models

NASA Astrophysics Data System (ADS)

Evtushenko, V. F.; Myshlyaev, L. P.; Makarov, G. V.; Ivushkin, K. A.; Burkova, E. V.

2016-10-01

The structure of multi-variant physical and mathematical models of control system is offered as well as its application for adjustment of automatic control system (ACS) of production facilities on the example of coal processing plant.

A simplified parsimonious higher order multivariate Markov chain model with new convergence condition

NASA Astrophysics Data System (ADS)

Wang, Chao; Yang, Chuan-sheng

2017-09-01

In this paper, we present a simplified parsimonious higher-order multivariate Markov chain model with new convergence condition. (TPHOMMCM-NCC). Moreover, estimation method of the parameters in TPHOMMCM-NCC is give. Numerical experiments illustrate the effectiveness of TPHOMMCM-NCC.

Various forms of indexing HDMR for modelling multivariate classification problems

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

Aksu, Çağrı; Tunga, M. Alper

2014-12-10

The Indexing HDMR method was recently developed for modelling multivariate interpolation problems. The method uses the Plain HDMR philosophy in partitioning the given multivariate data set into less variate data sets and then constructing an analytical structure through these partitioned data sets to represent the given multidimensional problem. Indexing HDMR makes HDMR be applicable to classification problems having real world data. Mostly, we do not know all possible class values in the domain of the given problem, that is, we have a non-orthogonal data structure. However, Plain HDMR needs an orthogonal data structure in the given problem to be modelled.more » In this sense, the main idea of this work is to offer various forms of Indexing HDMR to successfully model these real life classification problems. To test these different forms, several well-known multivariate classification problems given in UCI Machine Learning Repository were used and it was observed that the accuracy results lie between 80% and 95% which are very satisfactory.« less

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.

Insights on multivariate updates of physical and biogeochemical ocean variables using an Ensemble Kalman Filter and an idealized model of upwelling

NASA Astrophysics Data System (ADS)

Yu, Liuqian; Fennel, Katja; Bertino, Laurent; Gharamti, Mohamad El; Thompson, Keith R.

2018-06-01

Effective data assimilation methods for incorporating observations into marine biogeochemical models are required to improve hindcasts, nowcasts and forecasts of the ocean's biogeochemical state. Recent assimilation efforts have shown that updating model physics alone can degrade biogeochemical fields while only updating biogeochemical variables may not improve a model's predictive skill when the physical fields are inaccurate. Here we systematically investigate whether multivariate updates of physical and biogeochemical model states are superior to only updating either physical or biogeochemical variables. We conducted a series of twin experiments in an idealized ocean channel that experiences wind-driven upwelling. The forecast model was forced with biased wind stress and perturbed biogeochemical model parameters compared to the model run representing the "truth". Taking advantage of the multivariate nature of the deterministic Ensemble Kalman Filter (DEnKF), we assimilated different combinations of synthetic physical (sea surface height, sea surface temperature and temperature profiles) and biogeochemical (surface chlorophyll and nitrate profiles) observations. We show that when biogeochemical and physical properties are highly correlated (e.g., thermocline and nutricline), multivariate updates of both are essential for improving model skill and can be accomplished by assimilating either physical (e.g., temperature profiles) or biogeochemical (e.g., nutrient profiles) observations. In our idealized domain, the improvement is largely due to a better representation of nutrient upwelling, which results in a more accurate nutrient input into the euphotic zone. In contrast, assimilating surface chlorophyll improves the model state only slightly, because surface chlorophyll contains little information about the vertical density structure. We also show that a degradation of the correlation between observed subsurface temperature and nutrient fields, which has been an issue in several previous assimilation studies, can be reduced by multivariate updates of physical and biogeochemical fields.

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

NASA Astrophysics Data System (ADS)

Sikora, Roman; Markiewicz, Przemysław; Pabjańczyk, Wiesława

2018-04-01

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

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

Treesearch

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

2004-01-01

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

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

PubMed

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

2016-02-01

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

Disparate molecular, histopathology, and clinical factors in HNSCC racial groups

PubMed Central

Worsham, Maria J.; Stephen, Josena K.; Lu, Mei; Chen, Kang Mei; Havard, Shaleta; Shah, Veena; Schweitzer, Vanessa P.

2013-01-01

Objective The causes of the differences in the higher incidence of and the mortality from head and neck squamous cell carcinoma (HNSCC) in African American (AA) versus Caucasian Americans (CA) lack a consensus. We examined a comprehensive array of risk factors influencing health and disease in an access to care, racially diverse, primary HNSCC cohort. Study Design Cross-sectional study. Setting Primary care academic health care system. Subjects and Methods The cohort of 673 comprised 391 CA and 282 AA (42%). Risk variables included demographic, histopathology, and clinical/epidemiologic factors. Tumor DNA was interrogated for loss and gain of 113 genes with known involvement in HNSCC/cancer. Logistic regression for univariate analysis was followed by multivariate modeling with determination of model predictability (c-index). Results Of the 39 univariate differences between AA and CA, multivariate modeling (c-index=0.81) retained seven (p<0.05). AA were less likely to be married, more likely to have tumor lymphocytic response, undergo radiation treatment, and smoke. Insurance type was a significant predictor of race. AA were more likely to have Medicaid, Medicare, and other HMO types. AA tumors were more likely to have loss of CDKN2A and gain of SCYA3 versus CA. Conclusions Multivariate modeling indicated significant differences between AA and CA HNSCC for histopathology, treatment, smoking, marital status, type of insurance, as well as tumor gene copy number alterations. Our data reiterate that for HNSCC as in the case of other complex diseases, tumor genetics or biology is only one of many potential contributors to differences among racial groups. PMID:22412179

Mapping information exposure on social media to explain differences in HPV vaccine coverage in the United States.

PubMed

Dunn, Adam G; Surian, Didi; Leask, Julie; Dey, Aditi; Mandl, Kenneth D; Coiera, Enrico

2017-05-25

Together with access, acceptance of vaccines affects human papillomavirus (HPV) vaccine coverage, yet little is known about media's role. Our aim was to determine whether measures of information exposure derived from Twitter could be used to explain differences in coverage in the United States. We conducted an analysis of exposure to information about HPV vaccines on Twitter, derived from 273.8 million exposures to 258,418 tweets posted between 1 October 2013 and 30 October 2015. Tweets were classified by topic using machine learning methods. Proportional exposure to each topic was used to construct multivariable models for predicting state-level HPV vaccine coverage, and compared to multivariable models constructed using socioeconomic factors: poverty, education, and insurance. Outcome measures included correlations between coverage and the individual topics and socioeconomic factors; and differences in the predictive performance of the multivariable models. Topics corresponding to media controversies were most closely correlated with coverage (both positively and negatively); education and insurance were highest among socioeconomic indicators. Measures of information exposure explained 68% of the variance in one dose 2015 HPV vaccine coverage in females (males: 63%). In comparison, models based on socioeconomic factors explained 42% of the variance in females (males: 40%). Measures of information exposure derived from Twitter explained differences in coverage that were not explained by socioeconomic factors. Vaccine coverage was lower in states where safety concerns, misinformation, and conspiracies made up higher proportions of exposures, suggesting that negative representations of vaccines in the media may reflect or influence vaccine acceptance. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Multivariate generalized multifactor dimensionality reduction to detect gene-gene interactions

PubMed Central

2013-01-01

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

Development and validation of a risk calculator predicting exercise-induced ventricular arrhythmia in patients with cardiovascular disease.

PubMed

Hermes, Ilarraza-Lomelí; Marianna, García-Saldivia; Jessica, Rojano-Castillo; Carlos, Barrera-Ramírez; Rafael, Chávez-Domínguez; María Dolores, Rius-Suárez; Pedro, Iturralde

2016-10-01

Mortality due to cardiovascular disease is often associated with ventricular arrhythmias. Nowadays, patients with cardiovascular disease are more encouraged to take part in physical training programs. Nevertheless, high-intensity exercise is associated to a higher risk for sudden death, even in apparently healthy people. During an exercise testing (ET), health care professionals provide patients, in a controlled scenario, an intense physiological stimulus that could precipitate cardiac arrhythmia in high risk individuals. There is still no clinical or statistical tool to predict this incidence. The aim of this study was to develop a statistical model to predict the incidence of exercise-induced potentially life-threatening ventricular arrhythmia (PLVA) during high intensity exercise. 6415 patients underwent a symptom-limited ET with a Balke ramp protocol. A multivariate logistic regression model where the primary outcome was PLVA was performed. Incidence of PLVA was 548 cases (8.5%). After a bivariate model, thirty one clinical or ergometric variables were statistically associated with PLVA and were included in the regression model. In the multivariate model, 13 of these variables were found to be statistically significant. A regression model (G) with a X(2) of 283.987 and a p<0.001, was constructed. Significant variables included: heart failure, antiarrhythmic drugs, myocardial lower-VD, age and use of digoxin, nitrates, among others. This study allows clinicians to identify patients at risk of ventricular tachycardia or couplets during exercise, and to take preventive measures or appropriate supervision. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Identifying Nonprovider Factors Affecting Pediatric Emergency Medicine Provider Efficiency.

PubMed

Saleh, Fareed; Breslin, Kristen; Mullan, Paul C; Tillett, Zachary; Chamberlain, James M

2017-10-31

The aim of this study was to create a multivariable model of standardized relative value units per hour by adjusting for nonprovider factors that influence efficiency. We obtained productivity data based on billing records measured in emergency relative value units for (1) both evaluation and management of visits and (2) procedures for 16 pediatric emergency medicine providers with more than 750 hours worked per year. Eligible shifts were in an urban, academic pediatric emergency department (ED) with 2 sites: a tertiary care main campus and a satellite community site. We used multivariable linear regression to adjust for the impact of shift and pediatric ED characteristics on individual-provider efficiency and then removed variables from the model with minimal effect on productivity. There were 2998 eligible shifts for the 16 providers during a 3-year period. The resulting model included 4 variables when looking at both ED sites combined. These variables include the following: (1) number of procedures billed by provider, (2) season of the year, (3) shift start time, and (4) day of week. Results were improved when we separately modeled each ED location. A 3-variable model using procedures billed by provider, shift start time, and season explained 23% of the variation in provider efficiency at the academic ED site. A 3-variable model using procedures billed by provider, patient arrivals per hour, and shift start time explained 45% of the variation in provider efficiency at the satellite ED site. Several nonprovider factors affect provider efficiency. These factors should be considered when designing productivity-based incentives.

Fasting Glucose, Obesity, and Coronary Artery Calcification in Community-Based People Without Diabetes

PubMed Central

Rutter, Martin K.; Massaro, Joseph M.; Hoffmann, Udo; O’Donnell, Christopher J.; Fox, Caroline S.

2012-01-01

OBJECTIVE Our objective was to assess whether impaired fasting glucose (IFG) and obesity are independently related to coronary artery calcification (CAC) in a community-based population. RESEARCH DESIGN AND METHODS We assessed CAC using multidetector computed tomography in 3,054 Framingham Heart Study participants (mean [SD] age was 50 [10] years, 49% were women, 29% had IFG, and 25% were obese) free from known vascular disease or diabetes. We tested the hypothesis that IFG (5.6–6.9 mmol/L) and obesity (BMI ≥30 kg/m2) were independently associated with high CAC (>90th percentile for age and sex) after adjusting for hypertension, lipids, smoking, and medication. RESULTS High CAC was significantly related to IFG in an age- and sex-adjusted model (odds ratio 1.4 [95% CI 1.1–1.7], P = 0.002; referent: normal fasting glucose) and after further adjustment for obesity (1.3 [1.0–1.6], P = 0.045). However, IFG was not associated with high CAC in multivariable-adjusted models before (1.2 [0.9–1.4], P = 0.20) or after adjustment for obesity. Obesity was associated with high CAC in age- and sex-adjusted models (1.6 [1.3–2.0], P < 0.001) and in multivariable models that included IFG (1.4 [1.1–1.7], P = 0.005). Multivariable-adjusted spline regression models suggested nonlinear relationships linking high CAC with BMI (J-shaped), waist circumference (J-shaped), and fasting glucose. CONCLUSIONS In this community-based cohort, CAC was associated with obesity, but not IFG, after adjusting for important confounders. With the increasing worldwide prevalence of obesity and nondiabetic hyperglycemia, these data underscore the importance of obesity in the pathogenesis of CAC. PMID:22773705

Fasting glucose, obesity, and coronary artery calcification in community-based people without diabetes.

PubMed

Rutter, Martin K; Massaro, Joseph M; Hoffmann, Udo; O'Donnell, Christopher J; Fox, Caroline S

2012-09-01

Our objective was to assess whether impaired fasting glucose (IFG) and obesity are independently related to coronary artery calcification (CAC) in a community-based population. We assessed CAC using multidetector computed tomography in 3,054 Framingham Heart Study participants (mean [SD] age was 50 [10] years, 49% were women, 29% had IFG, and 25% were obese) free from known vascular disease or diabetes. We tested the hypothesis that IFG (5.6-6.9 mmol/L) and obesity (BMI ≥30 kg/m(2)) were independently associated with high CAC (>90th percentile for age and sex) after adjusting for hypertension, lipids, smoking, and medication. High CAC was significantly related to IFG in an age- and sex-adjusted model (odds ratio 1.4 [95% CI 1.1-1.7], P = 0.002; referent: normal fasting glucose) and after further adjustment for obesity (1.3 [1.0-1.6], P = 0.045). However, IFG was not associated with high CAC in multivariable-adjusted models before (1.2 [0.9-1.4], P = 0.20) or after adjustment for obesity. Obesity was associated with high CAC in age- and sex-adjusted models (1.6 [1.3-2.0], P < 0.001) and in multivariable models that included IFG (1.4 [1.1-1.7], P = 0.005). Multivariable-adjusted spline regression models suggested nonlinear relationships linking high CAC with BMI (J-shaped), waist circumference (J-shaped), and fasting glucose. In this community-based cohort, CAC was associated with obesity, but not IFG, after adjusting for important confounders. With the increasing worldwide prevalence of obesity and nondiabetic hyperglycemia, these data underscore the importance of obesity in the pathogenesis of CAC.

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

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

Comparative Robustness of Recent Methods for Analyzing Multivariate Repeated Measures Designs

ERIC Educational Resources Information Center

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

2007-01-01

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

Effects of climatological parameters in modeling and forecasting seasonal influenza transmission in Abidjan, Cote d'Ivoire.

PubMed

N'gattia, A K; Coulibaly, D; Nzussouo, N Talla; Kadjo, H A; Chérif, D; Traoré, Y; Kouakou, B K; Kouassi, P D; Ekra, K D; Dagnan, N S; Williams, T; Tiembré, I

2016-09-13

In temperate regions, influenza epidemics occur in the winter and correlate with certain climatological parameters. In African tropical regions, the effects of climatological parameters on influenza epidemics are not well defined. This study aims to identify and model the effects of climatological parameters on seasonal influenza activity in Abidjan, Cote d'Ivoire. We studied the effects of weekly rainfall, humidity, and temperature on laboratory-confirmed influenza cases in Abidjan from 2007 to 2010. We used the Box-Jenkins method with the autoregressive integrated moving average (ARIMA) process to create models using data from 2007-2010 and to assess the predictive value of best model on data from 2011 to 2012. The weekly number of influenza cases showed significant cross-correlation with certain prior weeks for both rainfall, and relative humidity. The best fitting multivariate model (ARIMAX (2,0,0) _RF) included the number of influenza cases during 1-week and 2-weeks prior, and the rainfall during the current week and 5-weeks prior. The performance of this model showed an increase of >3 % for Akaike Information Criterion (AIC) and 2.5 % for Bayesian Information Criterion (BIC) compared to the reference univariate ARIMA (2,0,0). The prediction of the weekly number of influenza cases during 2011-2012 with the best fitting multivariate model (ARIMAX (2,0,0) _RF), showed that the observed values were within the 95 % confidence interval of the predicted values during 97 of 104 weeks. Including rainfall increases the performances of fitted and predicted models. The timing of influenza in Abidjan can be partially explained by rainfall influence, in a setting with little change in temperature throughout the year. These findings can help clinicians to anticipate influenza cases during the rainy season by implementing preventive measures.

Critical elements on fitting the Bayesian multivariate Poisson Lognormal model

NASA Astrophysics Data System (ADS)

Zamzuri, Zamira Hasanah binti

2015-10-01

Motivated by a problem on fitting multivariate models to traffic accident data, a detailed discussion of the Multivariate Poisson Lognormal (MPL) model is presented. This paper reveals three critical elements on fitting the MPL model: the setting of initial estimates, hyperparameters and tuning parameters. These issues have not been highlighted in the literature. Based on simulation studies conducted, we have shown that to use the Univariate Poisson Model (UPM) estimates as starting values, at least 20,000 iterations are needed to obtain reliable final estimates. We also illustrated the sensitivity of the specific hyperparameter, which if it is not given extra attention, may affect the final estimates. The last issue is regarding the tuning parameters where they depend on the acceptance rate. Finally, a heuristic algorithm to fit the MPL model is presented. This acts as a guide to ensure that the model works satisfactorily given any data set.

Multivariate Bayesian modeling of known and unknown causes of events--an application to biosurveillance.

PubMed

Shen, Yanna; Cooper, Gregory F

2012-09-01

This paper investigates Bayesian modeling of known and unknown causes of events in the context of disease-outbreak detection. We introduce a multivariate Bayesian approach that models multiple evidential features of every person in the population. This approach models and detects (1) known diseases (e.g., influenza and anthrax) by using informative prior probabilities and (2) unknown diseases (e.g., a new, highly contagious respiratory virus that has never been seen before) by using relatively non-informative prior probabilities. We report the results of simulation experiments which support that this modeling method can improve the detection of new disease outbreaks in a population. A contribution of this paper is that it introduces a multivariate Bayesian approach for jointly modeling both known and unknown causes of events. Such modeling has general applicability in domains where the space of known causes is incomplete. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Fitting Nonlinear Curves by use of Optimization Techniques

NASA Technical Reports Server (NTRS)

Hill, Scott A.

2005-01-01

MULTIVAR is a FORTRAN 77 computer program that fits one of the members of a set of six multivariable mathematical models (five of which are nonlinear) to a multivariable set of data. The inputs to MULTIVAR include the data for the independent and dependent variables plus the user s choice of one of the models, one of the three optimization engines, and convergence criteria. By use of the chosen optimization engine, MULTIVAR finds values for the parameters of the chosen model so as to minimize the sum of squares of the residuals. One of the optimization engines implements a routine, developed in 1982, that utilizes the Broydon-Fletcher-Goldfarb-Shanno (BFGS) variable-metric method for unconstrained minimization in conjunction with a one-dimensional search technique that finds the minimum of an unconstrained function by polynomial interpolation and extrapolation without first finding bounds on the solution. The second optimization engine is a faster and more robust commercially available code, denoted Design Optimization Tool, that also uses the BFGS method. The third optimization engine is a robust and relatively fast routine that implements the Levenberg-Marquardt algorithm.

Functional inverted Wishart for Bayesian multivariate spatial modeling with application to regional climatology model data.

PubMed

Duan, L L; Szczesniak, R D; Wang, X

2017-11-01

Modern environmental and climatological studies produce multiple outcomes at high spatial resolutions. Multivariate spatial modeling is an established means to quantify cross-correlation among outcomes. However, existing models typically suffer from poor computational efficiency and lack the flexibility to simultaneously estimate auto- and cross-covariance structures. In this article, we undertake a novel construction of covariance by utilizing spectral convolution and by imposing an inverted Wishart prior on the cross-correlation structure. The cross-correlation structure with this functional inverted Wishart prior flexibly accommodates not only positive but also weak or negative associations among outcomes while preserving spatial resolution. Furthermore, the proposed model is computationally efficient and produces easily interpretable results, including the individual autocovariances and full cross-correlation matrices, as well as a partial cross-correlation matrix reflecting the outcome correlation after excluding the effects caused by spatial convolution. The model is examined using simulated data sets under different scenarios. It is also applied to the data from the North American Regional Climate Change Assessment Program, examining long-term associations between surface outcomes for air temperature, pressure, humidity, and radiation, on the land area of the North American West Coast. Results and predictive performance are compared with findings from approaches using convolution only or coregionalization.

A novel strategy for forensic age prediction by DNA methylation and support vector regression model

PubMed Central

Xu, Cheng; Qu, Hongzhu; Wang, Guangyu; Xie, Bingbing; Shi, Yi; Yang, Yaran; Zhao, Zhao; Hu, Lan; Fang, Xiangdong; Yan, Jiangwei; Feng, Lei

2015-01-01

High deviations resulting from prediction model, gender and population difference have limited age estimation application of DNA methylation markers. Here we identified 2,957 novel age-associated DNA methylation sites (P < 0.01 and R2 > 0.5) in blood of eight pairs of Chinese Han female monozygotic twins. Among them, nine novel sites (false discovery rate < 0.01), along with three other reported sites, were further validated in 49 unrelated female volunteers with ages of 20–80 years by Sequenom Massarray. A total of 95 CpGs were covered in the PCR products and 11 of them were built the age prediction models. After comparing four different models including, multivariate linear regression, multivariate nonlinear regression, back propagation neural network and support vector regression, SVR was identified as the most robust model with the least mean absolute deviation from real chronological age (2.8 years) and an average accuracy of 4.7 years predicted by only six loci from the 11 loci, as well as an less cross-validated error compared with linear regression model. Our novel strategy provides an accurate measurement that is highly useful in estimating the individual age in forensic practice as well as in tracking the aging process in other related applications. PMID:26635134

A Prospective Cohort Study on Radiation-induced Hypothyroidism: Development of an NTCP Model

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

Boomsma, Marjolein J.; Bijl, Hendrik P.; Christianen, Miranda E.M.C.

Purpose: To establish a multivariate normal tissue complication probability (NTCP) model for radiation-induced hypothyroidism. Methods and Materials: The thyroid-stimulating hormone (TSH) level of 105 patients treated with (chemo-) radiation therapy for head-and-neck cancer was prospectively measured during a median follow-up of 2.5 years. Hypothyroidism was defined as elevated serum TSH with decreased or normal free thyroxin (T4). A multivariate logistic regression model with bootstrapping was used to determine the most important prognostic variables for radiation-induced hypothyroidism. Results: Thirty-five patients (33%) developed primary hypothyroidism within 2 years after radiation therapy. An NTCP model based on 2 variables, including the mean thyroidmore » gland dose and the thyroid gland volume, was most predictive for radiation-induced hypothyroidism. NTCP values increased with higher mean thyroid gland dose (odds ratio [OR]: 1.064/Gy) and decreased with higher thyroid gland volume (OR: 0.826/cm{sup 3}). Model performance was good with an area under the curve (AUC) of 0.85. Conclusions: This is the first prospective study resulting in an NTCP model for radiation-induced hypothyroidism. The probability of hypothyroidism rises with increasing dose to the thyroid gland, whereas it reduces with increasing thyroid gland volume.« less

Functional inverted Wishart for Bayesian multivariate spatial modeling with application to regional climatology model data

PubMed Central

Duan, L. L.; Szczesniak, R. D.; Wang, X.

2018-01-01

Modern environmental and climatological studies produce multiple outcomes at high spatial resolutions. Multivariate spatial modeling is an established means to quantify cross-correlation among outcomes. However, existing models typically suffer from poor computational efficiency and lack the flexibility to simultaneously estimate auto- and cross-covariance structures. In this article, we undertake a novel construction of covariance by utilizing spectral convolution and by imposing an inverted Wishart prior on the cross-correlation structure. The cross-correlation structure with this functional inverted Wishart prior flexibly accommodates not only positive but also weak or negative associations among outcomes while preserving spatial resolution. Furthermore, the proposed model is computationally efficient and produces easily interpretable results, including the individual autocovariances and full cross-correlation matrices, as well as a partial cross-correlation matrix reflecting the outcome correlation after excluding the effects caused by spatial convolution. The model is examined using simulated data sets under different scenarios. It is also applied to the data from the North American Regional Climate Change Assessment Program, examining long-term associations between surface outcomes for air temperature, pressure, humidity, and radiation, on the land area of the North American West Coast. Results and predictive performance are compared with findings from approaches using convolution only or coregionalization. PMID:29576735

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.

FACTOR ANALYTIC MODELS OF CLUSTERED MULTIVARIATE DATA WITH INFORMATIVE CENSORING

EPA Science Inventory

This paper describes a general class of factor analytic models for the analysis of clustered multivariate data in the presence of informative missingness. We assume that there are distinct sets of cluster-level latent variables related to the primary outcomes and to the censorin...

Predictive value of sperm morphology and progressively motile sperm count for pregnancy outcomes in intrauterine insemination.

PubMed

Lemmens, Louise; Kos, Snjezana; Beijer, Cornelis; Brinkman, Jacoline W; van der Horst, Frans A L; van den Hoven, Leonie; Kieslinger, Dorit C; van Trooyen-van Vrouwerff, Netty J; Wolthuis, Albert; Hendriks, Jan C M; Wetzels, Alex M M

2016-06-01

To investigate the value of sperm parameters to predict an ongoing pregnancy outcome in couples treated with intrauterine insemination (IUI), during a methodologically stable period of time. Retrospective, observational study with logistic regression analyses. University hospital. A total of 1,166 couples visiting the fertility laboratory for their first IUI episode, including 4,251 IUI cycles. None. Sperm morphology, total progressively motile sperm count (TPMSC), and number of inseminated progressively motile spermatozoa (NIPMS); odds ratios (ORs) of the sperm parameters after the first IUI cycle and the first finished IUI episode; discriminatory accuracy of the multivariable model. None of the sperm parameters was of predictive value for pregnancy after the first IUI cycle. In the first finished IUI episode, a positive relationship was found for ≤4% of morphologically normal spermatozoa (OR 1.39) and a moderate NIPMS (5-10 million; OR 1.73). Low NIPMS showed a negative relation (≤1 million; OR 0.42). The TPMSC had no predictive value. The multivariable model (i.e., sperm morphology, NIPMS, female age, male age, and the number of cycles in the episode) had a moderate discriminatory accuracy (area under the curve 0.73). Intrauterine insemination is especially relevant for couples with moderate male factor infertility (sperm morphology ≤4%, NIPMS 5-10 million). In the multivariable model, however, the predictive power of these sperm parameters is rather low. Copyright © 2016 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.

Multivariate Analyses of Rotator Cuff Pathologies in Shoulder Disability

PubMed Central

Henseler, Jan F.; Raz, Yotam; Nagels, Jochem; van Zwet, Erik W.; Raz, Vered; Nelissen, Rob G. H. H.

2015-01-01

Background Disability of the shoulder joint is often caused by a tear in the rotator cuff (RC) muscles. Four RC muscles coordinate shoulder movement and stability, among them the supraspinatus and infraspinatus muscle which are predominantly torn. The contribution of each RC muscle to tear pathology is not fully understood. We hypothesized that muscle atrophy and fatty infiltration, features of RC muscle degeneration, are predictive of superior humeral head translation and shoulder functional disability. Methods Shoulder features, including RC muscle surface area and fatty infiltration, superior humeral translation and RC tear size were obtained from a consecutive series of Magnetic Resonance Imaging with arthrography (MRA). We investigated patients with superior (supraspinatus, n = 39) and posterosuperior (supraspinatus and infraspinatus, n = 30) RC tears, and patients with an intact RC (n = 52) as controls. The individual or combinatorial contribution of RC measures to superior humeral translation, as a sign of RC dysfunction, was investigated with univariate or multivariate models, respectively. Results Using the univariate model the infraspinatus surface area and fatty infiltration in both the supraspinatus and infraspinatus had a significant contribution to RC dysfunction. With the multivariate model, however, the infraspinatus surface area only affected superior humeral translation (p<0.001) and discriminated between superior and posterosuperior tears. In contrast neither tear size nor fatty infiltration of the supraspinatus or infraspinatus contributed to superior humeral translation. Conclusion Our study reveals that infraspinatus atrophy has the strongest contribution to RC tear pathologies. This suggests a pivotal role for the infraspinatus in preventing shoulder disability. PMID:25710703

Novel high-resolution computed tomography-based radiomic classifier for screen-identified pulmonary nodules in the National Lung Screening Trial.

PubMed

Peikert, Tobias; Duan, Fenghai; Rajagopalan, Srinivasan; Karwoski, Ronald A; Clay, Ryan; Robb, Richard A; Qin, Ziling; Sicks, JoRean; Bartholmai, Brian J; Maldonado, Fabien

2018-01-01

Optimization of the clinical management of screen-detected lung nodules is needed to avoid unnecessary diagnostic interventions. Herein we demonstrate the potential value of a novel radiomics-based approach for the classification of screen-detected indeterminate nodules. Independent quantitative variables assessing various radiologic nodule features such as sphericity, flatness, elongation, spiculation, lobulation and curvature were developed from the NLST dataset using 726 indeterminate nodules (all ≥ 7 mm, benign, n = 318 and malignant, n = 408). Multivariate analysis was performed using least absolute shrinkage and selection operator (LASSO) method for variable selection and regularization in order to enhance the prediction accuracy and interpretability of the multivariate model. The bootstrapping method was then applied for the internal validation and the optimism-corrected AUC was reported for the final model. Eight of the originally considered 57 quantitative radiologic features were selected by LASSO multivariate modeling. These 8 features include variables capturing Location: vertical location (Offset carina centroid z), Size: volume estimate (Minimum enclosing brick), Shape: flatness, Density: texture analysis (Score Indicative of Lesion/Lung Aggression/Abnormality (SILA) texture), and surface characteristics: surface complexity (Maximum shape index and Average shape index), and estimates of surface curvature (Average positive mean curvature and Minimum mean curvature), all with P<0.01. The optimism-corrected AUC for these 8 features is 0.939. Our novel radiomic LDCT-based approach for indeterminate screen-detected nodule characterization appears extremely promising however independent external validation is needed.

An Examination of the Domain of Multivariable Functions Using the Pirie-Kieren Model

ERIC Educational Resources Information Center

Sengul, Sare; Yildiz, Sevda Goktepe

2016-01-01

The aim of this study is to employ the Pirie-Kieren model so as to examine the understandings relating to the domain of multivariable functions held by primary school mathematics preservice teachers. The data obtained was categorized according to Pirie-Kieren model and demonstrated visually in tables and bar charts. The study group consisted of…

Multivariate regression model for predicting yields of grade lumber from yellow birch sawlogs

Treesearch

Andrew F. Howard; Daniel A. Yaussy

1986-01-01

A multivariate regression model was developed to predict green board-foot yields for the common grades of factory lumber processed from yellow birch factory-grade logs. The model incorporates the standard log measurements of scaling diameter, length, proportion of scalable defects, and the assigned USDA Forest Service log grade. Differences in yields between band and...

A Multivariate Model for the Meta-Analysis of Study Level Survival Data at Multiple Times

ERIC Educational Resources Information Center

Jackson, Dan; Rollins, Katie; Coughlin, Patrick

2014-01-01

Motivated by our meta-analytic dataset involving survival rates after treatment for critical leg ischemia, we develop and apply a new multivariate model for the meta-analysis of study level survival data at multiple times. Our data set involves 50 studies that provide mortality rates at up to seven time points, which we model simultaneously, and…

Analytical framework for reconstructing heterogeneous environmental variables from mammal community structure.

PubMed

Louys, Julien; Meloro, Carlo; Elton, Sarah; Ditchfield, Peter; Bishop, Laura C

2015-01-01

We test the performance of two models that use mammalian communities to reconstruct multivariate palaeoenvironments. While both models exploit the correlation between mammal communities (defined in terms of functional groups) and arboreal heterogeneity, the first uses a multiple multivariate regression of community structure and arboreal heterogeneity, while the second uses a linear regression of the principal components of each ecospace. The success of these methods means the palaeoenvironment of a particular locality can be reconstructed in terms of the proportions of heavy, moderate, light, and absent tree canopy cover. The linear regression is less biased, and more precisely and accurately reconstructs heavy tree canopy cover than the multiple multivariate model. However, the multiple multivariate model performs better than the linear regression for all other canopy cover categories. Both models consistently perform better than randomly generated reconstructions. We apply both models to the palaeocommunity of the Upper Laetolil Beds, Tanzania. Our reconstructions indicate that there was very little heavy tree cover at this site (likely less than 10%), with the palaeo-landscape instead comprising a mixture of light and absent tree cover. These reconstructions help resolve the previous conflicting palaeoecological reconstructions made for this site. Copyright © 2014 Elsevier Ltd. All rights reserved.

Physical Function in Older Men With Hyperkyphosis

PubMed Central

Harrison, Stephanie L.; Fink, Howard A.; Marshall, Lynn M.; Orwoll, Eric; Barrett-Connor, Elizabeth; Cawthon, Peggy M.; Kado, Deborah M.

2015-01-01

Background. Age-related hyperkyphosis has been associated with poor physical function and is a well-established predictor of adverse health outcomes in older women, but its impact on health in older men is less well understood. Methods. We conducted a cross-sectional study to evaluate the association of hyperkyphosis and physical function in 2,363 men, aged 71–98 (M = 79) from the Osteoporotic Fractures in Men Study. Kyphosis was measured using the Rancho Bernardo Study block method. Measurements of grip strength and lower extremity function, including gait speed over 6 m, narrow walk (measure of dynamic balance), repeated chair stands ability and time, and lower extremity power (Nottingham Power Rig) were included separately as primary outcomes. We investigated associations of kyphosis and each outcome in age-adjusted and multivariable linear or logistic regression models, controlling for age, clinic, education, race, bone mineral density, height, weight, diabetes, and physical activity. Results. In multivariate linear regression, we observed a dose-related response of worse scores on each lower extremity physical function test as number of blocks increased, p for trend ≤.001. Using a cutoff of ≥4 blocks, 20% (N = 469) of men were characterized with hyperkyphosis. In multivariate logistic regression, men with hyperkyphosis had increased odds (range 1.5–1.8) of being in the worst quartile of performing lower extremity physical function tasks (p < .001 for each outcome). Kyphosis was not associated with grip strength in any multivariate analysis. Conclusions. Hyperkyphosis is associated with impaired lower extremity physical function in older men. Further studies are needed to determine the direction of causality. PMID:25431353

Early identification of patients requiring massive transfusion, embolization, or hemostatic surgery for traumatic hemorrhage: a systematic review protocol.

PubMed

Tran, Alexandre; Matar, Maher; Steyerberg, Ewout W; Lampron, Jacinthe; Taljaard, Monica; Vaillancourt, Christian

2017-04-13

Hemorrhage is a major cause of early mortality following a traumatic injury. The progression and consequences of significant blood loss occur quickly as death from hemorrhagic shock or exsanguination often occurs within the first few hours. The mainstay of treatment therefore involves early identification of patients at risk for hemorrhagic shock in order to provide blood products and control of the bleeding source if necessary. The intended scope of this review is to identify and assess combinations of predictors informing therapeutic decision-making for clinicians during the initial trauma assessment. The primary objective of this systematic review is to identify and critically assess any existing multivariable models predicting significant traumatic hemorrhage that requires intervention, defined as a composite outcome comprising massive transfusion, surgery for hemostasis, or angiography with embolization for the purpose of external validation or updating in other study populations. If no suitable existing multivariable models are identified, the secondary objective is to identify candidate predictors to inform the development of a new prediction rule. We will search the EMBASE and MEDLINE databases for all randomized controlled trials and prospective and retrospective cohort studies developing or validating predictors of intervention for traumatic hemorrhage in adult patients 16 years of age or older. Eligible predictors must be available to the clinician during the first hour of trauma resuscitation and may be clinical, lab-based, or imaging-based. Outcomes of interest include the need for surgical intervention, angiographic embolization, or massive transfusion within the first 24 h. Data extraction will be performed independently by two reviewers. Items for extraction will be based on the CHARMS checklist. We will evaluate any existing models for relevance, quality, and the potential for external validation and updating in other populations. Relevance will be described in terms of appropriateness of outcomes and predictors. Quality criteria will include variable selection strategies, adequacy of sample size, handling of missing data, validation techniques, and measures of model performance. This systematic review will describe the availability of multivariable prediction models and summarize evidence regarding predictors that can be used to identify the need for intervention in patients with traumatic hemorrhage. PROSPERO CRD42017054589.

The NLS-Based Nonlinear Grey Multivariate Model for Forecasting Pollutant Emissions in China.

PubMed

Pei, Ling-Ling; Li, Qin; Wang, Zheng-Xin

2018-03-08

The relationship between pollutant discharge and economic growth has been a major research focus in environmental economics. To accurately estimate the nonlinear change law of China's pollutant discharge with economic growth, this study establishes a transformed nonlinear grey multivariable (TNGM (1, N )) model based on the nonlinear least square (NLS) method. The Gauss-Seidel iterative algorithm was used to solve the parameters of the TNGM (1, N ) model based on the NLS basic principle. This algorithm improves the precision of the model by continuous iteration and constantly approximating the optimal regression coefficient of the nonlinear model. In our empirical analysis, the traditional grey multivariate model GM (1, N ) and the NLS-based TNGM (1, N ) models were respectively adopted to forecast and analyze the relationship among wastewater discharge per capita (WDPC), and per capita emissions of SO₂ and dust, alongside GDP per capita in China during the period 1996-2015. Results indicated that the NLS algorithm is able to effectively help the grey multivariable model identify the nonlinear relationship between pollutant discharge and economic growth. The results show that the NLS-based TNGM (1, N ) model presents greater precision when forecasting WDPC, SO₂ emissions and dust emissions per capita, compared to the traditional GM (1, N ) model; WDPC indicates a growing tendency aligned with the growth of GDP, while the per capita emissions of SO₂ and dust reduce accordingly.

On the use of spectra from portable Raman and ATR-IR instruments in synthesis route attribution of a chemical warfare agent by multivariate modeling.

PubMed

Wiktelius, Daniel; Ahlinder, Linnea; Larsson, Andreas; Höjer Holmgren, Karin; Norlin, Rikard; Andersson, Per Ola

2018-08-15

Collecting data under field conditions for forensic investigations of chemical warfare agents calls for the use of portable instruments. In this study, a set of aged, crude preparations of sulfur mustard were characterized spectroscopically without any sample preparation using handheld Raman and portable IR instruments. The spectral data was used to construct Random Forest multivariate models for the attribution of test set samples to the synthetic method used for their production. Colored and fluorescent samples were included in the study, which made Raman spectroscopy challenging although fluorescence was diminished by using an excitation wavelength of 1064 nm. The predictive power of models constructed with IR or Raman data alone, as well as with combined data was investigated. Both techniques gave useful data for attribution. Model performance was enhanced when Raman and IR spectra were combined, allowing correct classification of 19/23 (83%) of test set spectra. The results demonstrate that data obtained with spectroscopy instruments amenable for field deployment can be useful in forensic studies of chemical warfare agents. Copyright © 2018 Elsevier B.V. All rights reserved.

Multivariate normality

NASA Technical Reports Server (NTRS)

Crutcher, H. L.; Falls, L. W.

1976-01-01

Sets of experimentally determined or routinely observed data provide information about the past, present and, hopefully, future sets of similarly produced data. An infinite set of statistical models exists which may be used to describe the data sets. The normal distribution is one model. If it serves at all, it serves well. If a data set, or a transformation of the set, representative of a larger population can be described by the normal distribution, then valid statistical inferences can be drawn. There are several tests which may be applied to a data set to determine whether the univariate normal model adequately describes the set. The chi-square test based on Pearson's work in the late nineteenth and early twentieth centuries is often used. Like all tests, it has some weaknesses which are discussed in elementary texts. Extension of the chi-square test to the multivariate normal model is provided. Tables and graphs permit easier application of the test in the higher dimensions. Several examples, using recorded data, illustrate the procedures. Tests of maximum absolute differences, mean sum of squares of residuals, runs and changes of sign are included in these tests. Dimensions one through five with selected sample sizes 11 to 101 are used to illustrate the statistical tests developed.

Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): The TRIPOD Statement.

PubMed

Collins, Gary S; Reitsma, Johannes B; Altman, Douglas G; Moons, Karel G M

2015-06-01

Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web-based survey and revised during a 3-day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org). The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.

Voxelwise multivariate analysis of multimodality magnetic resonance imaging.

PubMed

Naylor, Melissa G; Cardenas, Valerie A; Tosun, Duygu; Schuff, Norbert; Weiner, Michael; Schwartzman, Armin

2014-03-01

Most brain magnetic resonance imaging (MRI) studies concentrate on a single MRI contrast or modality, frequently structural MRI. By performing an integrated analysis of several modalities, such as structural, perfusion-weighted, and diffusion-weighted MRI, new insights may be attained to better understand the underlying processes of brain diseases. We compare two voxelwise approaches: (1) fitting multiple univariate models, one for each outcome and then adjusting for multiple comparisons among the outcomes and (2) fitting a multivariate model. In both cases, adjustment for multiple comparisons is performed over all voxels jointly to account for the search over the brain. The multivariate model is able to account for the multiple comparisons over outcomes without assuming independence because the covariance structure between modalities is estimated. Simulations show that the multivariate approach is more powerful when the outcomes are correlated and, even when the outcomes are independent, the multivariate approach is just as powerful or more powerful when at least two outcomes are dependent on predictors in the model. However, multiple univariate regressions with Bonferroni correction remain a desirable alternative in some circumstances. To illustrate the power of each approach, we analyze a case control study of Alzheimer's disease, in which data from three MRI modalities are available. Copyright © 2013 Wiley Periodicals, Inc.

Multivariate Analysis of Longitudinal Rates of Change

PubMed Central

Bryan, Matthew; Heagerty, Patrick J.

2016-01-01

Longitudinal data allow direct comparison of the change in patient outcomes associated with treatment or exposure. Frequently, several longitudinal measures are collected that either reflect a common underlying health status, or characterize processes that are influenced in a similar way by covariates such as exposure or demographic characteristics. Statistical methods that can combine multivariate response variables into common measures of covariate effects have been proposed by Roy and Lin [1]; Proust-Lima, Letenneur and Jacqmin-Gadda [2]; and Gray and Brookmeyer [3] among others. Current methods for characterizing the relationship between covariates and the rate of change in multivariate outcomes are limited to select models. For example, Gray and Brookmeyer [3] introduce an “accelerated time” method which assumes that covariates rescale time in longitudinal models for disease progression. In this manuscript we detail an alternative multivariate model formulation that directly structures longitudinal rates of change, and that permits a common covariate effect across multiple outcomes. We detail maximum likelihood estimation for a multivariate longitudinal mixed model. We show via asymptotic calculations the potential gain in power that may be achieved with a common analysis of multiple outcomes. We apply the proposed methods to the analysis of a trivariate outcome for infant growth and compare rates of change for HIV infected and uninfected infants. PMID:27417129

A Multivariate Descriptive Model of Motivation for Orthodontic Treatment.

ERIC Educational Resources Information Center

Hackett, Paul M. W.; And Others

1993-01-01

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

Mathematical Formulation of Multivariate Euclidean Models for Discrimination Methods.

ERIC Educational Resources Information Center

Mullen, Kenneth; Ennis, Daniel M.

1987-01-01

Multivariate models for the triangular and duo-trio methods are described, and theoretical methods are compared to a Monte Carlo simulation. Implications are discussed for a new theory of multidimensional scaling which challenges the traditional assumption that proximity measures and perceptual distances are monotonically related. (Author/GDC)

Hierarchical Bayesian spatial models for predicting multiple forest variables using waveform LiDAR, hyperspectral imagery, and large inventory datasets

USGS Publications Warehouse

Finley, Andrew O.; Banerjee, Sudipto; Cook, Bruce D.; Bradford, John B.

2013-01-01

In this paper we detail a multivariate spatial regression model that couples LiDAR, hyperspectral and forest inventory data to predict forest outcome variables at a high spatial resolution. The proposed model is used to analyze forest inventory data collected on the US Forest Service Penobscot Experimental Forest (PEF), ME, USA. In addition to helping meet the regression model's assumptions, results from the PEF analysis suggest that the addition of multivariate spatial random effects improves model fit and predictive ability, compared with two commonly applied modeling approaches. This improvement results from explicitly modeling the covariation among forest outcome variables and spatial dependence among observations through the random effects. Direct application of such multivariate models to even moderately large datasets is often computationally infeasible because of cubic order matrix algorithms involved in estimation. We apply a spatial dimension reduction technique to help overcome this computational hurdle without sacrificing richness in modeling.

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).

Total anthocyanin content determination in intact açaí (Euterpe oleracea Mart.) and palmitero-juçara (Euterpe edulis Mart.) fruit using near infrared spectroscopy (NIR) and multivariate calibration.

PubMed

Inácio, Maria Raquel Cavalcanti; de Lima, Kássio Michell Gomes; Lopes, Valquiria Garcia; Pessoa, José Dalton Cruz; de Almeida Teixeira, Gustavo Henrique

2013-02-15

The aim of this study was to evaluate near-infrared reflectance spectroscopy (NIR), and multivariate calibration potential as a rapid method to determinate anthocyanin content in intact fruit (açaí and palmitero-juçara). Several multivariate calibration techniques, including partial least squares (PLS), interval partial least squares, genetic algorithm, successive projections algorithm, and net analyte signal were compared and validated by establishing figures of merit. Suitable results were obtained with the PLS model (four latent variables and 5-point smoothing) with a detection limit of 6.2 g kg(-1), limit of quantification of 20.7 g kg(-1), accuracy estimated as root mean square error of prediction of 4.8 g kg(-1), mean selectivity of 0.79 g kg(-1), sensitivity of 5.04×10(-3) g kg(-1), precision of 27.8 g kg(-1), and signal-to-noise ratio of 1.04×10(-3) g kg(-1). These results suggest NIR spectroscopy and multivariate calibration can be effectively used to determine anthocyanin content in intact açaí and palmitero-juçara fruit. Copyright © 2012 Elsevier Ltd. All rights reserved.

A mixed-effects regression model for longitudinal multivariate ordinal data.

PubMed

Liu, Li C; Hedeker, Donald

2006-03-01

A mixed-effects item response theory model that allows for three-level multivariate ordinal outcomes and accommodates multiple random subject effects is proposed for analysis of multivariate ordinal outcomes in longitudinal studies. This model allows for the estimation of different item factor loadings (item discrimination parameters) for the multiple outcomes. The covariates in the model do not have to follow the proportional odds assumption and can be at any level. Assuming either a probit or logistic response function, maximum marginal likelihood estimation is proposed utilizing multidimensional Gauss-Hermite quadrature for integration of the random effects. An iterative Fisher scoring solution, which provides standard errors for all model parameters, is used. An analysis of a longitudinal substance use data set, where four items of substance use behavior (cigarette use, alcohol use, marijuana use, and getting drunk or high) are repeatedly measured over time, is used to illustrate application of the proposed model.

Bayesian meta-analytical methods to incorporate multiple surrogate endpoints in drug development process.

PubMed

Bujkiewicz, Sylwia; Thompson, John R; Riley, Richard D; Abrams, Keith R

2016-03-30

A number of meta-analytical methods have been proposed that aim to evaluate surrogate endpoints. Bivariate meta-analytical methods can be used to predict the treatment effect for the final outcome from the treatment effect estimate measured on the surrogate endpoint while taking into account the uncertainty around the effect estimate for the surrogate endpoint. In this paper, extensions to multivariate models are developed aiming to include multiple surrogate endpoints with the potential benefit of reducing the uncertainty when making predictions. In this Bayesian multivariate meta-analytic framework, the between-study variability is modelled in a formulation of a product of normal univariate distributions. This formulation is particularly convenient for including multiple surrogate endpoints and flexible for modelling the outcomes which can be surrogate endpoints to the final outcome and potentially to one another. Two models are proposed, first, using an unstructured between-study covariance matrix by assuming the treatment effects on all outcomes are correlated and second, using a structured between-study covariance matrix by assuming treatment effects on some of the outcomes are conditionally independent. While the two models are developed for the summary data on a study level, the individual-level association is taken into account by the use of the Prentice's criteria (obtained from individual patient data) to inform the within study correlations in the models. The modelling techniques are investigated using an example in relapsing remitting multiple sclerosis where the disability worsening is the final outcome, while relapse rate and MRI lesions are potential surrogates to the disability progression. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Data driven discrete-time parsimonious identification of a nonlinear state-space model for a weakly nonlinear system with short data record

NASA Astrophysics Data System (ADS)

Relan, Rishi; Tiels, Koen; Marconato, Anna; Dreesen, Philippe; Schoukens, Johan

2018-05-01

Many real world systems exhibit a quasi linear or weakly nonlinear behavior during normal operation, and a hard saturation effect for high peaks of the input signal. In this paper, a methodology to identify a parsimonious discrete-time nonlinear state space model (NLSS) for the nonlinear dynamical system with relatively short data record is proposed. The capability of the NLSS model structure is demonstrated by introducing two different initialisation schemes, one of them using multivariate polynomials. In addition, a method using first-order information of the multivariate polynomials and tensor decomposition is employed to obtain the parsimonious decoupled representation of the set of multivariate real polynomials estimated during the identification of NLSS model. Finally, the experimental verification of the model structure is done on the cascaded water-benchmark identification problem.

Assessing exposure to violence using multiple informants: application of hierarchical linear model.

PubMed

Kuo, M; Mohler, B; Raudenbush, S L; Earls, F J

2000-11-01

The present study assesses the effects of demographic risk factors on children's exposure to violence (ETV) and how these effects vary by informants. Data on exposure to violence of 9-, 12-, and 15-year-olds were collected from both child participants (N = 1880) and parents (N = 1776), as part of the assessment of the Project on Human Development in Chicago Neighborhoods (PHDCN). A two-level hierarchical linear model (HLM) with multivariate outcomes was employed to analyze information obtained from these two different groups of informants. The findings indicate that parents generally report less ETV than do their children and that associations of age, gender, and parent education with ETV are stronger in the self-reports than in the parent reports. The findings support a multivariate approach when information obtained from different sources is being integrated. The application of HLM allows an assessment of interactions between risk factors and informants and uses all available data, including data from one informant when data from the other informant is missing.

Business closure and relocation: a comparative analysis of the Loma Prieta earthquake and Hurricane Andrew.

PubMed

Wasileski, Gabriela; Rodríguez, Havidán; Diaz, Walter

2011-01-01

The occurrence of a number of large-scale disasters or catastrophes in recent years, including the Indian Ocean tsunami (2004), the Kashmir earthquake (2005), Hurricane Katrina (2005) and Hurricane Ike (2008), have raised our awareness regarding the devastating effects of disasters on human populations and the importance of developing mitigation and preparedness strategies to limit the consequences of such events. However, there is still a dearth of social science research focusing on the socio-economic impact of disasters on businesses in the United States. This paper contributes to this research literature by focusing on the impact of disasters on business closure and relocation through the use of multivariate logistic regression models, specifically focusing on the Loma Prieta earthquake (1989) and Hurricane Andrew (1992). Using a multivariate model, we examine how physical damage to the infrastructure, lifeline disruption and business characteristics, among others, impact business closure and relocation following major disasters. © 2011 The Author(s). Disasters © Overseas Development Institute, 2011.

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

PubMed Central

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

2009-01-01

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

Path analysis and multi-criteria decision making: an approach for multivariate model selection and analysis in health.

PubMed

Vasconcelos, A G; Almeida, R M; Nobre, F F

2001-08-01

This paper introduces an approach that includes non-quantitative factors for the selection and assessment of multivariate complex models in health. A goodness-of-fit based methodology combined with fuzzy multi-criteria decision-making approach is proposed for model selection. Models were obtained using the Path Analysis (PA) methodology in order to explain the interrelationship between health determinants and the post-neonatal component of infant mortality in 59 municipalities of Brazil in the year 1991. Socioeconomic and demographic factors were used as exogenous variables, and environmental, health service and agglomeration as endogenous variables. Five PA models were developed and accepted by statistical criteria of goodness-of fit. These models were then submitted to a group of experts, seeking to characterize their preferences, according to predefined criteria that tried to evaluate model relevance and plausibility. Fuzzy set techniques were used to rank the alternative models according to the number of times a model was superior to ("dominated") the others. The best-ranked model explained above 90% of the endogenous variables variation, and showed the favorable influences of income and education levels on post-neonatal mortality. It also showed the unfavorable effect on mortality of fast population growth, through precarious dwelling conditions and decreased access to sanitation. It was possible to aggregate expert opinions in model evaluation. The proposed procedure for model selection allowed the inclusion of subjective information in a clear and systematic manner.

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.

Predictors of Better Self-Care in Patients with Heart Failure after Six Months of Follow-Up Home Visits

PubMed Central

Trojahn, Melina Maria; Ruschel, Karen Brasil; Nogueira de Souza, Emiliane; Mussi, Cláudia Motta; Naomi Hirakata, Vânia; Nogueira Mello Lopes, Alexandra; Rabelo-Silva, Eneida Rejane

2013-01-01

This study aimed to examine the predictors of better self-care behavior in patients with heart failure (HF) in a home visiting program. This is a longitudinal study nested in a randomized controlled trial (ISRCTN01213862) in which the home-based educational intervention consisted of a six-month followup that included four home visits by a nurse, interspersed with four telephone calls. The self-care score was measured at baseline and at six months using the Brazilian version of the European Heart Failure Self-Care Behaviour Scale. The associations included eight variables: age, sex, schooling, having received the intervention, social support, income, comorbidities, and symptom severity. A simple linear regression model was developed using significant variables (P ≤ 0.20), followed by a multivariate model to determine the predictors of better self-care. One hundred eighty-eight patients completed the study. A better self-care behavior was associated with patients who received intervention (P < 0.001), had more years of schooling (P = 0.016), and had more comorbidities (P = 0.008). Having received the intervention (P < 0.001) and having a greater number of comorbidities (P = 0.038) were predictors of better self-care. In the multivariate regression model, being in the intervention group and having more comorbidities were a predictor of better self-care. PMID:24083023

Psychosocial work conditions, unemployment and health locus of control: a population-based study.

PubMed

Sadiq Mohammad Ali; Lindström, Martin

2008-06-01

To investigate the association between psychosocial work conditions, unemployment and lack of belief in the possibility of influencing one's own health. The 2000 public health survey in Scania is a cross-sectional postal questionnaire study with a 59% participation rate. In total, 5180 persons aged 18-64 years who belonged to the workforce and the unemployed were included in this study. Logistic regression models were used to investigate the associations between psychosocial factors at work and unemployment, and lack of belief in the possibility of influencing one's own health (external locus of control). Psychosocial conditions at work were defined according to the Karasek-Theorell demand-control/decision latitudes into relaxed, active, passive, and job strain categories. The multivariate analyses included age, country of birth, education, economic stress, and social participation. In total, 26.6% of all men and 26.9% of all women lack an internal locus of control. The passive, job strain and unemployed categories have significantly higher odds ratios of lack of internal locus of control, as compared to the relaxed reference category. No such significant differences are observed for the active category. These patterns remain in the multivariate models, with the exception of the passive and unemployed categories among men, in which the significant differences disappear. Psychosocial work conditions and unemployment may affect health locus of control. The control dimension in the Karasek-Theorell model seems to be of greatest importance.

Multivariate Formation Pressure Prediction with Seismic-derived Petrophysical Properties from Prestack AVO inversion and Poststack Seismic Motion Inversion

NASA Astrophysics Data System (ADS)

Yu, H.; Gu, H.

2017-12-01

A novel multivariate seismic formation pressure prediction methodology is presented, which incorporates high-resolution seismic velocity data from prestack AVO inversion, and petrophysical data (porosity and shale volume) derived from poststack seismic motion inversion. In contrast to traditional seismic formation prediction methods, the proposed methodology is based on a multivariate pressure prediction model and utilizes a trace-by-trace multivariate regression analysis on seismic-derived petrophysical properties to calibrate model parameters in order to make accurate predictions with higher resolution in both vertical and lateral directions. With prestack time migration velocity as initial velocity model, an AVO inversion was first applied to prestack dataset to obtain high-resolution seismic velocity with higher frequency that is to be used as the velocity input for seismic pressure prediction, and the density dataset to calculate accurate Overburden Pressure (OBP). Seismic Motion Inversion (SMI) is an inversion technique based on Markov Chain Monte Carlo simulation. Both structural variability and similarity of seismic waveform are used to incorporate well log data to characterize the variability of the property to be obtained. In this research, porosity and shale volume are first interpreted on well logs, and then combined with poststack seismic data using SMI to build porosity and shale volume datasets for seismic pressure prediction. A multivariate effective stress model is used to convert velocity, porosity and shale volume datasets to effective stress. After a thorough study of the regional stratigraphic and sedimentary characteristics, a regional normally compacted interval model is built, and then the coefficients in the multivariate prediction model are determined in a trace-by-trace multivariate regression analysis on the petrophysical data. The coefficients are used to convert velocity, porosity and shale volume datasets to effective stress and then to calculate formation pressure with OBP. Application of the proposed methodology to a research area in East China Sea has proved that the method can bridge the gap between seismic and well log pressure prediction and give predicted pressure values close to pressure meassurements from well testing.

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

Determining the Relationship Between Moral Waivers and Marine Corps Unsuitability Attrition

DTIC Science & Technology

2008-03-01

observed characteristics. However, econometric research indicates that the magnitude of interaction effects estimated via probit or logit models may...1997 to 2005. Multivariate probit models were used to analyze the effects of moral waivers on unsatisfactory service separations. 15. NUMBER OF...files from fiscal years 1997 to 2005. Multivariate probit models were used to analyze the effects of moral waivers on unsatisfactory service

Assessment of Platelet Function in Traumatic Brain Injury-A Retrospective Observational Study in the Neuro-Critical Care Setting.

PubMed

Lindblad, Caroline; Thelin, Eric Peter; Nekludov, Michael; Frostell, Arvid; Nelson, David W; Svensson, Mikael; Bellander, Bo-Michael

2018-01-01

Despite seemingly functional coagulation, hemorrhagic lesion progression is a common and devastating condition following traumatic brain injury (TBI), stressing the need for new diagnostic techniques. Multiple electrode aggregometry (MEA) measures platelet function and could aid in coagulopathy assessment following TBI. The aims of this study were to evaluate MEA temporal dynamics, influence of concomitant therapy, and its capabilities to predict lesion progression and clinical outcome in a TBI cohort. Adult TBI patients in a neurointensive care unit that underwent MEA sampling were retrospectively included. MEA was sampled if the patient was treated with antiplatelet therapy, bled heavily during surgery, or had abnormal baseline coagulation values. We assessed platelet activation pathways involving the arachidonic acid receptor (ASPI), P2Y 12 receptor, and thrombin receptor (TRAP). ASPI was the primary focus of analysis. If several samples were obtained, they were included. Retrospective data were extracted from hospital charts. Outcome variables were radiologic hemorrhagic progression and Glasgow Outcome Scale assessed prospectively at 12 months posttrauma. MEA levels were compared between patients on antiplatelet therapy. Linear mixed effect models and uni-/multivariable regression models were used to study longitudinal dynamics, hemorrhagic progression and outcome, respectively. In total, 178 patients were included (48% unfavorable outcome). ASPI levels increased from initially low values in a time-dependent fashion ( p  < 0.001). Patients on cyclooxygenase inhibitors demonstrated low ASPI levels ( p  < 0.001), while platelet transfusion increased them ( p  < 0.001). The first ASPI ( p  = 0.039) and TRAP ( p  = 0.009) were significant predictors of outcome, but not lesion progression, in univariate analyses. In multivariable analysis, MEA values were not independently correlated with outcome. A general longitudinal trend of MEA is identified in this TBI cohort, even in patients without known antiplatelet therapies. Values appear also affected by platelet inhibitory treatment and by platelet transfusions. While significant in univariate models to predict outcome, MEA values did not independently correlate to outcome or lesion progression in multivariable analyses. Further prospective studies to monitor coagulation in TBI patients are warranted, in particular the interpretation of pathological MEA values in patients without antiplatelet therapies.

Early warning indicators for first-line virologic failure independent of adherence measures in a South African urban clinic.

PubMed

Marconi, Vincent C; Wu, Baohua; Hampton, Jane; Ordóñez, Claudia E; Johnson, Brent A; Singh, Dinesh; John, Sally; Gordon, Michelle; Hare, Anna; Murphy, Richard; Nachega, Jean; Kuritzkes, Daniel R; del Rio, Carlos; Sunpath, Henry

2013-12-01

We sought to develop individual-level Early Warning Indicators (EWI) of virologic failure (VF) for clinicians to use during routine care complementing WHO population-level EWI. A case-control study was conducted at a Durban clinic. Patients after ≥ 5 months of first-line antiretroviral therapy (ART) were defined as cases if they had VF [HIV-1 viral load (VL)>1000 copies/mL] and controls (2:1) if they had VL ≤ 1000 copies/mL. Pharmacy refills and pill counts were used as adherence measures. Participants responded to a questionnaire including validated psychosocial and symptom scales. Data were also collected from the medical record. Multivariable logistic regression models of VF included factors associated with VF (p<0.05) in univariable analyses. We enrolled 158 cases and 300 controls. In the final multivariable model, male gender, not having an active religious faith, practicing unsafe sex, having a family member with HIV, not being pleased with the clinic experience, symptoms of depression, fatigue, or rash, low CD4 counts, family recommending HIV care, and using a TV/radio as ART reminders (compared to mobile phones) were associated with VF independent of adherence measures. In this setting, we identified several key individual-level EWI associated with VF including novel psychosocial factors independent of adherence measures.

Medication possession ratio predicts antiretroviral regimens persistence in Peru.

PubMed

Salinas, Jorge L; Alave, Jorge L; Westfall, Andrew O; Paz, Jorge; Moran, Fiorella; Carbajal-Gonzalez, Danny; Callacondo, David; Avalos, Odalie; Rodriguez, Martin; Gotuzzo, Eduardo; Echevarria, Juan; Willig, James H

2013-01-01

In developing nations, the use of operational parameters (OPs) in the prediction of clinical care represents a missed opportunity to enhance the care process. We modeled the impact of multiple measurements of antiretroviral treatment (ART) adherence on antiretroviral treatment outcomes in Peru. Retrospective cohort study including ART naïve, non-pregnant, adults initiating therapy at Hospital Nacional Cayetano Heredia, Lima-Peru (2006-2010). Three OPs were defined: 1) Medication possession ratio (MPR): days with antiretrovirals dispensed/days on first-line therapy; 2) Laboratory monitory constancy (LMC): proportion of 6 months intervals with ≥1 viral load or CD4 reported; 3) Clinic visit constancy (CVC): proportion of 6 months intervals with ≥1 clinic visit. Three multi-variable Cox proportional hazard (PH) models (one per OP) were fit for (1) time of first-line ART persistence and (2) time to second-line virologic failure. All models were adjusted for socio-demographic, clinical and laboratory variables. 856 patients were included in first-line persistence analyses, median age was 35.6 years [29.4-42.9] and most were male (624; 73%). In multivariable PH models, MPR (per 10% increase HR=0.66; 95%CI=0.61-0.71) and LMC (per 10% increase 0.83; 0.71-0.96) were associated with prolonged time on first-line therapies. Among 79 individuals included in time to second-line virologic failure analyses, MPR was the only OP independently associated with prolonged time to second-line virologic failure (per 10% increase 0.88; 0.77-0.99). The capture and utilization of program level parameters such as MPR can provide valuable insight into patient-level treatment outcomes.

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

PubMed

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

2016-01-01

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

Multivariate Curve Resolution Applied to Infrared Reflection Measurements of Soil Contaminated with an Organophosphorus Analyte

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

Gallagher, Neal B.; Blake, Thomas A.; Gassman, Paul L.

2006-07-01

Multivariate curve resolution (MCR) is a powerful technique for extracting chemical information from measured spectra on complex mixtures. The difficulty with applying MCR to soil reflectance measurements is that light scattering artifacts can contribute much more variance to the measurements than the analyte(s) of interest. Two methods were integrated into a MCR decomposition to account for light scattering effects. Firstly, an extended mixture model using pure analyte spectra augmented with scattering ‘spectra’ was used for the measured spectra. And secondly, second derivative preprocessed spectra, which have higher selectivity than the unprocessed spectra, were included in a second block as amore » part of the decomposition. The conventional alternating least squares (ALS) algorithm was modified to simultaneously decompose the measured and second derivative spectra in a two-block decomposition. Equality constraints were also included to incorporate information about sampling conditions. The result was an MCR decomposition that provided interpretable spectra from soil reflectance measurements.« less

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

ERIC Educational Resources Information Center

Grochowalski, Joseph H.

2015-01-01

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

A New Approach in Generating Meteorological Forecasts for Ensemble Streamflow Forecasting using Multivariate Functions

NASA Astrophysics Data System (ADS)

Khajehei, S.; Madadgar, S.; Moradkhani, H.

2014-12-01

The reliability and accuracy of hydrological predictions are subject to various sources of uncertainty, including meteorological forcing, initial conditions, model parameters and model structure. To reduce the total uncertainty in hydrological applications, one approach is to reduce the uncertainty in meteorological forcing by using the statistical methods based on the conditional probability density functions (pdf). However, one of the requirements for current methods is to assume the Gaussian distribution for the marginal distribution of the observed and modeled meteorology. Here we propose a Bayesian approach based on Copula functions to develop the conditional distribution of precipitation forecast needed in deriving a hydrologic model for a sub-basin in the Columbia River Basin. Copula functions are introduced as an alternative approach in capturing the uncertainties related to meteorological forcing. Copulas are multivariate joint distribution of univariate marginal distributions, which are capable to model the joint behavior of variables with any level of correlation and dependency. The method is applied to the monthly forecast of CPC with 0.25x0.25 degree resolution to reproduce the PRISM dataset over 1970-2000. Results are compared with Ensemble Pre-Processor approach as a common procedure used by National Weather Service River forecast centers in reproducing observed climatology during a ten-year verification period (2000-2010).

Probabilistic estimates of drought impacts on agricultural production

NASA Astrophysics Data System (ADS)

Madadgar, Shahrbanou; AghaKouchak, Amir; Farahmand, Alireza; Davis, Steven J.

2017-08-01

Increases in the severity and frequency of drought in a warming climate may negatively impact agricultural production and food security. Unlike previous studies that have estimated agricultural impacts of climate condition using single-crop yield distributions, we develop a multivariate probabilistic model that uses projected climatic conditions (e.g., precipitation amount or soil moisture) throughout a growing season to estimate the probability distribution of crop yields. We demonstrate the model by an analysis of the historical period 1980-2012, including the Millennium Drought in Australia (2001-2009). We find that precipitation and soil moisture deficit in dry growing seasons reduced the average annual yield of the five largest crops in Australia (wheat, broad beans, canola, lupine, and barley) by 25-45% relative to the wet growing seasons. Our model can thus produce region- and crop-specific agricultural sensitivities to climate conditions and variability. Probabilistic estimates of yield may help decision-makers in government and business to quantitatively assess the vulnerability of agriculture to climate variations. We develop a multivariate probabilistic model that uses precipitation to estimate the probability distribution of crop yields. The proposed model shows how the probability distribution of crop yield changes in response to droughts. During Australia's Millennium Drought precipitation and soil moisture deficit reduced the average annual yield of the five largest crops.

Conditional Survival in Anal Carcinoma Using the National Population-Based Survey of Epidemiology and End Results Database (1988-2012).

PubMed

Kim, Ellen; Kim, Jong S; Choi, Mehee; Thomas, Charles R

2016-04-01

Conditional survival can provide valuable information for both patients and healthcare providers about the changing prognosis in surviving patients over time. This study estimated conditional survival for patients with anal cancer in the United States through analysis of a national population-based cancer registry. Log-rank test identified significant covariates of cause-specific survival (defined as time from diagnosis until death from anal cancer). Significant covariates were considered in the multivariable regression of cause-specific survival using Cox proportional hazards models. Covariates included cancer stage and demographic variables. Patients in Surveillance, Epidemiology, and End Results regions diagnosed with anal squamous cell carcinoma as their first and only cancer diagnosis from 1988 to 2012 were selected from this database, and 5145 patients were included in the retrospective cohort study. Five-year conditional survival stratified by each variable in the final Cox models was measured : The final multivariable models of overall and cause-specific survivals included stage, grade, sex, age, race, and relationship status. Over the first 6 years after diagnosis, conditional survival of distant stage increased from 37% to 89%, whereas regional stage increased from 65% to 93% and localized stage increased from 84% to 96%. The other variables had increasing prognosis as well, but the subgroups increased at a more similar rate over time. The data source used does not include information on chemotherapy treatment, patient comorbidities, or socioeconomic status. Conditional survival showed improvement over time. Patients with advanced stage had the greatest improvement in conditional survival. This is the first study to provide specific conditional survival probabilities for patients with anal cancer.

A General Multivariate Latent Growth Model with Applications to Student Achievement

ERIC Educational Resources Information Center

Bianconcini, Silvia; Cagnone, Silvia

2012-01-01

The evaluation of the formative process in the University system has been assuming an ever increasing importance in the European countries. Within this context, the analysis of student performance and capabilities plays a fundamental role. In this work, the authors propose a multivariate latent growth model for studying the performances of a…

Bayesian Estimation of Random Coefficient Dynamic Factor Models

ERIC Educational Resources Information Center

Song, Hairong; Ferrer, Emilio

2012-01-01

Dynamic factor models (DFMs) have typically been applied to multivariate time series data collected from a single unit of study, such as a single individual or dyad. The goal of DFMs application is to capture dynamics of multivariate systems. When multiple units are available, however, DFMs are not suited to capture variations in dynamics across…

Rotation in the Dynamic Factor Modeling of Multivariate Stationary Time Series.

ERIC Educational Resources Information Center

Molenaar, Peter C. M.; Nesselroade, John R.

2001-01-01

Proposes a special rotation procedure for the exploratory dynamic factor model for stationary multivariate time series. The rotation procedure applies separately to each univariate component series of a q-variate latent factor series and transforms such a component, initially represented as white noise, into a univariate moving-average.…

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

ERIC Educational Resources Information Center

Tchumtchoua, Sylvie; Dey, Dipak K.

2012-01-01

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

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.

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

PubMed Central

Avalappampatty Sivasamy, Aneetha; Sundan, Bose

2015-01-01

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

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

PubMed

Sivasamy, Aneetha Avalappampatty; Sundan, Bose

2015-01-01

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

Predictive model for falling in Parkinson disease patients.

PubMed

Custodio, Nilton; Lira, David; Herrera-Perez, Eder; Montesinos, Rosa; Castro-Suarez, Sheila; Cuenca-Alfaro, Jose; Cortijo, Patricia

2016-12-01

Falls are a common complication of advancing Parkinson's disease (PD). Although numerous risk factors are known, reliable predictors of future falls are still lacking. The aim of this study was to develop a multivariate model to predict falling in PD patients. Prospective cohort with forty-nine PD patients. The area under the receiver-operating characteristic curve (AUC) was calculated to evaluate predictive performance of the purposed multivariate model. The median of PD duration and UPDRS-III score in the cohort was 6 years and 24 points, respectively. Falls occurred in 18 PD patients (30%). Predictive factors for falling identified by univariate analysis were age, PD duration, physical activity, and scores of UPDRS motor, FOG, ACE, IFS, PFAQ and GDS ( p -value < 0.001), as well as fear of falling score ( p -value = 0.04). The final multivariate model (PD duration, FOG, ACE, and physical activity) showed an AUC = 0.9282 (correctly classified = 89.83%; sensitivity = 92.68%; specificity = 83.33%). This study showed that our multivariate model have a high performance to predict falling in a sample of PD patients.

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

NASA Astrophysics Data System (ADS)

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

2013-12-01

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

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

Inferring phase equations from multivariate time series.

PubMed

Tokuda, Isao T; Jain, Swati; Kiss, István Z; Hudson, John L

2007-08-10

An approach is presented for extracting phase equations from multivariate time series data recorded from a network of weakly coupled limit cycle oscillators. Our aim is to estimate important properties of the phase equations including natural frequencies and interaction functions between the oscillators. Our approach requires the measurement of an experimental observable of the oscillators; in contrast with previous methods it does not require measurements in isolated single or two-oscillator setups. This noninvasive technique can be advantageous in biological systems, where extraction of few oscillators may be a difficult task. The method is most efficient when data are taken from the nonsynchronized regime. Applicability to experimental systems is demonstrated by using a network of electrochemical oscillators; the obtained phase model is utilized to predict the synchronization diagram of the system.

The added value of percentage of free to total prostate-specific antigen, PCA3, and a kallikrein panel to the ERSPC risk calculator for prostate cancer in prescreened men.

PubMed

Vedder, Moniek M; de Bekker-Grob, Esther W; Lilja, Hans G; Vickers, Andrew J; van Leenders, Geert J L H; Steyerberg, Ewout W; Roobol, Monique J

2014-12-01

Prostate-specific antigen (PSA) testing has limited accuracy for the early detection of prostate cancer (PCa). To assess the value added by percentage of free to total PSA (%fPSA), prostate cancer antigen 3 (PCA3), and a kallikrein panel (4k-panel) to the European Randomised Study of Screening for Prostate Cancer (ERSPC) multivariable prediction models: risk calculator (RC) 4, including transrectal ultrasound, and RC 4 plus digital rectal examination (4+DRE) for prescreened men. Participants were invited for rescreening between October 2007 and February 2009 within the Dutch part of the ERSPC study. Biopsies were taken in men with a PSA level ≥3.0 ng/ml or a PCA3 score ≥10. Additional analyses of the 4k-panel were done on serum samples. Outcome was defined as PCa detectable by sextant biopsy. Receiver operating characteristic curve and decision curve analyses were performed to compare the predictive capabilities of %fPSA, PCA3, 4k-panel, the ERSPC RCs, and their combinations in logistic regression models. PCa was detected in 119 of 708 men. The %fPSA did not perform better univariately or added to the RCs compared with the RCs alone. In 202 men with an elevated PSA, the 4k-panel discriminated better than PCA3 when modelled univariately (area under the curve [AUC]: 0.78 vs. 0.62; p=0.01). The multivariable models with PCA3 or the 4k-panel were equivalent (AUC: 0.80 for RC 4+DRE). In the total population, PCA3 discriminated better than the 4k-panel (univariate AUC: 0.63 vs. 0.56; p=0.05). There was no statistically significant difference between the multivariable model with PCA3 (AUC: 0.73) versus the model with the 4k-panel (AUC: 0.71; p=0.18). The multivariable model with PCA3 performed better than the reference model (0.73 vs. 0.70; p=0.02). Decision curves confirmed these patterns, although numbers were small. Both PCA3 and, to a lesser extent, a 4k-panel have added value to the DRE-based ERSPC RC in detecting PCa in prescreened men. We studied the added value of novel biomarkers to previously developed risk prediction models for prostate cancer. We found that inclusion of these biomarkers resulted in an increase in predictive ability. Copyright © 2014. Published by Elsevier B.V.

Studying Resist Stochastics with the Multivariate Poisson Propagation Model

DOE PAGES

Naulleau, Patrick; Anderson, Christopher; Chao, Weilun; ...

2014-01-01

Progress in the ultimate performance of extreme ultraviolet resist has arguably decelerated in recent years suggesting an approach to stochastic limits both in photon counts and material parameters. Here we report on the performance of a variety of leading extreme ultraviolet resist both with and without chemical amplification. The measured performance is compared to stochastic modeling results using the Multivariate Poisson Propagation Model. The results show that the best materials are indeed nearing modeled performance limits.

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

PubMed

Rosen, Sophia; Davidov, Ori

2012-07-20

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

Modern CACSD using the Robust-Control Toolbox

NASA Technical Reports Server (NTRS)

Chiang, Richard Y.; Safonov, Michael G.

1989-01-01

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

Positron emission tomography–computed tomography predictors of progression after DA-R-EPOCH for PMBCL

PubMed Central

Ng, Andrea K.; Dabaja, Bouthaina S.; Milgrom, Sarah A.; Gunther, Jillian R.; Fuller, C. David; Smith, Grace L.; Abou Yehia, Zeinab; Qiao, Wei; Wogan, Christine F.; Akhtari, Mani; Mawlawi, Osama; Medeiros, L. Jeffrey; Chuang, Hubert H.; Martin-Doyle, William; Armand, Philippe; LaCasce, Ann S.; Oki, Yasuhiro; Fanale, Michelle; Westin, Jason; Neelapu, Sattva; Nastoupil, Loretta

2018-01-01

Dose-adjusted rituximab plus etoposide, prednisone, vincristine, cyclophosphamide, and doxorubicin (DA-R-EPOCH) has produced good outcomes in primary mediastinal B-cell lymphoma (PMBCL), but predictors of resistance to this treatment are unclear. We investigated whether [18F]fluorodeoxyglucose positron emission tomography–computed tomography (PET-CT) findings could identify patients with PMBCL who would not respond completely to DA-R-EPOCH. We performed a retrospective analysis of 65 patients with newly diagnosed stage I to IV PMBCL treated at 2 tertiary cancer centers who had PET-CT scans available before and after frontline therapy with DA-R-EPOCH. Pretreatment variables assessed included metabolic tumor volume (MTV) and total lesion glycolysis (TLG). Optimal cutoff points for progression-free survival (PFS) were determined by a machine learning approach. Univariate and multivariable models were constructed to assess associations between radiographic variables and PFS. At a median follow-up of 36.6 months (95% confidence interval, 28.1-45.1), 2-year PFS and overall survival rates for the 65 patients were 81.4% and 98.4%, respectively. Machine learning–derived thresholds for baseline MTV and TLG were associated with inferior PFS (elevated MTV: hazard ratio [HR], 11.5; P = .019; elevated TLG: HR, 8.99; P = .005); other pretreatment clinical factors, including International Prognostic Index and bulky (>10 cm) disease, were not. On multivariable analysis, only TLG retained statistical significance (P = .049). Univariate analysis of posttreatment variables revealed that residual CT tumor volume, maximum standardized uptake value, and Deauville score were associated with PFS; a Deauville score of 5 remained significant on multivariable analysis (P = .006). A model combining baseline TLG and end-of-therapy Deauville score identified patients at increased risk of progression. PMID:29895624

Loss to follow-up in the Australian HIV Observational Database

PubMed Central

McManus, Hamish; Petoumenos, Kathy; Brown, Katherine; Baker, David; Russell, Darren; Read, Tim; Smith, Don; Wray, Lynne; Giles, Michelle; Hoy, Jennifer; Carr, Andrew; Law, Matthew

2015-01-01

Background Loss to follow-up (LTFU) in HIV-positive cohorts is an important surrogate for interrupted clinical care which can potentially influence the assessment of HIV disease status and outcomes. After preliminary evaluation of LTFU rates and patient characteristics, we evaluated the risk of mortality by LTFU status in a high resource setting. Methods Rates of LTFU were measured in the Australian HIV Observational Database for a range of patient characteristics. Multivariate repeated measures regression methods were used to identify determinants of LTFU. Mortality by LTFU status was ascertained using linkage to the National Death Index. Survival following combination antiretroviral therapy initiation was investigated using the Kaplan-Meier (KM) method and Cox proportional hazards models. Results Of 3,413 patients included in this analysis, 1,632 (47.8%) had at least one episode of LTFU after enrolment. Multivariate predictors of LTFU included viral load (VL)>10,000 copies/ml (Rate ratio (RR) 1.63 (95% confidence interval (CI):1.45–1.84) (ref ≤400)), time under follow-up (per year) (RR 1.03 (95% CI: 1.02–1.04)) and prior LTFU (per episode) (RR 1.15 (95% CI: 1.06–1.24)). KM curves for survival were similar by LTFU status (p=0.484). LTFU was not associated with mortality in Cox proportional hazards models (univariate hazard ratio (HR) 0.93 (95% CI: 0.69–1.26) and multivariate HR 1.04 (95% CI: 0.77–1.43)). Conclusions Increased risk of LTFU was identified amongst patients with potentially higher infectiousness. We did not find significant mortality risk associated with LTFU. This is consistent with timely re-engagement with treatment, possibly via high levels of unreported linkage to other health care providers. PMID:25377928

Serum uric acid levels contribute to new renal damage in systemic lupus erythematosus patients.

PubMed

Reátegui-Sokolova, C; Ugarte-Gil, Manuel F; Gamboa-Cárdenas, Rocío V; Zevallos, Francisco; Cucho-Venegas, Jorge M; Alfaro-Lozano, José L; Medina, Mariela; Rodriguez-Bellido, Zoila; Pastor-Asurza, Cesar A; Alarcón, Graciela S; Perich-Campos, Risto A

2017-04-01

This study aims to determine whether uric acid levels contribute to new renal damage in systemic lupus erythematosus (SLE) patients. This prospective study was conducted in consecutive patients seen since 2012. Patients had a baseline visit and follow-up visits every 6 months. Patients with ≥2 visits were included; those with end-stage renal disease (regardless of dialysis or transplantation) were excluded. Renal damage was ascertained using the SLICC/ACR damage index (SDI). Univariable and multivariable Cox-regression models were performed to determine the risk of new renal damage. Uric acid was included as a continuous and dichotomous (per receiving operating characteristic curve) variable. Multivariable models were adjusted for age at diagnosis, disease duration, socioeconomic status, SLEDAI, SDI, serum creatinine, baseline use of prednisone, antimalarials, and immunosuppressive drugs. One hundred and eighty-six patients were evaluated; their mean (SD) age at diagnosis was 36.8 (13.7) years; nearly all patients were mestizo. Disease duration was 7.7 (6.8) years. Follow-up time was 2.3 (1.1) years. The SLEDAI was 5.2 (4.3) and the SDI 0.8 (1.1). Uric acid levels were 4.5 (1.3) mg/dl. During follow-up, 16 (8.6%) patients developed at least one new point in the renal domain of the SDI. In multivariable analyses, uric acid levels (continuous and dichotomous) at baseline predicted the development of new renal damage (HR 3.21 (1.39-7.42), p 0.006; HR 18.28 (2.80-119.48), p 0.002; respectively). Higher uric acid levels contribute to the development of new renal damage in SLE patients independent of other well-known risk factors for such occurrence.

Positron emission tomography-computed tomography predictors of progression after DA-R-EPOCH for PMBCL.

PubMed

Pinnix, Chelsea C; Ng, Andrea K; Dabaja, Bouthaina S; Milgrom, Sarah A; Gunther, Jillian R; Fuller, C David; Smith, Grace L; Abou Yehia, Zeinab; Qiao, Wei; Wogan, Christine F; Akhtari, Mani; Mawlawi, Osama; Medeiros, L Jeffrey; Chuang, Hubert H; Martin-Doyle, William; Armand, Philippe; LaCasce, Ann S; Oki, Yasuhiro; Fanale, Michelle; Westin, Jason; Neelapu, Sattva; Nastoupil, Loretta

2018-06-12

Dose-adjusted rituximab plus etoposide, prednisone, vincristine, cyclophosphamide, and doxorubicin (DA-R-EPOCH) has produced good outcomes in primary mediastinal B-cell lymphoma (PMBCL), but predictors of resistance to this treatment are unclear. We investigated whether [ 18 F]fluorodeoxyglucose positron emission tomography-computed tomography (PET-CT) findings could identify patients with PMBCL who would not respond completely to DA-R-EPOCH. We performed a retrospective analysis of 65 patients with newly diagnosed stage I to IV PMBCL treated at 2 tertiary cancer centers who had PET-CT scans available before and after frontline therapy with DA-R-EPOCH. Pretreatment variables assessed included metabolic tumor volume (MTV) and total lesion glycolysis (TLG). Optimal cutoff points for progression-free survival (PFS) were determined by a machine learning approach. Univariate and multivariable models were constructed to assess associations between radiographic variables and PFS. At a median follow-up of 36.6 months (95% confidence interval, 28.1-45.1), 2-year PFS and overall survival rates for the 65 patients were 81.4% and 98.4%, respectively. Machine learning-derived thresholds for baseline MTV and TLG were associated with inferior PFS (elevated MTV: hazard ratio [HR], 11.5; P = .019; elevated TLG: HR, 8.99; P = .005); other pretreatment clinical factors, including International Prognostic Index and bulky (>10 cm) disease, were not. On multivariable analysis, only TLG retained statistical significance ( P = .049). Univariate analysis of posttreatment variables revealed that residual CT tumor volume, maximum standardized uptake value, and Deauville score were associated with PFS; a Deauville score of 5 remained significant on multivariable analysis ( P = .006). A model combining baseline TLG and end-of-therapy Deauville score identified patients at increased risk of progression. © 2018 by The American Society of Hematology.

Loss to follow-up in the Australian HIV Observational Database.

PubMed

McManus, Hamish; Petoumenos, Kathy; Brown, Katherine; Baker, David; Russell, Darren; Read, Tim; Smith, Don; Wray, Lynne; Giles, Michelle; Hoy, Jennifer; Carr, Andrew; Law, Matthew G

2015-01-01

Loss to follow-up (LTFU) in HIV-positive cohorts is an important surrogate for interrupted clinical care, which can potentially influence the assessment of HIV disease status and outcomes. After preliminary evaluation of LTFU rates and patient characteristics, we evaluated the risk of mortality by LTFU status in a high-resource setting. Rates of LTFU were measured in the Australian HIV Observational Database for a range of patient characteristics. Multivariate repeated measures regression methods were used to identify determinants of LTFU. Mortality by LTFU status was ascertained using linkage to the National Death Index. Survival following combination antiretroviral therapy initiation was investigated using the Kaplan-Meier (KM) method and Cox proportional hazards models. Of 3,413 patients included in this analysis, 1,632 (47.8%) had at least one episode of LTFU after enrolment. Multivariate predictors of LTFU included viral load (VL)>10,000 copies/ml (rate ratio [RR] 1.63; 95% CI 1.45, 1.84; ref ≤400), time under follow-up (per year; RR 1.03; 95% CI 1.02, 1.04) and prior LTFU (per episode; RR 1.15; 95% CI 1.06, 1.24). KM curves for survival were similar by LTFU status (P=0.484). LTFU was not associated with mortality in Cox proportional hazards models (univariate hazard ratio [HR] 0.93; 95% CI 0.69, 1.26) and multivariate HR 1.04 (95% CI 0.77, 1.43). Increased risk of LTFU was identified amongst patients with potentially higher infectiousness. We did not find significant mortality risk associated with LTFU. This is consistent with timely re-engagement with treatment, possibly via high levels of unreported linkage to other health-care providers.

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.

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

PubMed Central

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

2016-01-01

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

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

PubMed

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

2016-01-01

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

A Comparison of Multivariate and Pre-Processing Methods for Quantitative Laser-Induced Breakdown Spectroscopy of Geologic Samples

NASA Technical Reports Server (NTRS)

Anderson, R. B.; Morris, R. V.; Clegg, S. M.; Bell, J. F., III; Humphries, S. D.; Wiens, R. C.

2011-01-01

The ChemCam instrument selected for the Curiosity rover is capable of remote laser-induced breakdown spectroscopy (LIBS).[1] We used a remote LIBS instrument similar to ChemCam to analyze 197 geologic slab samples and 32 pressed-powder geostandards. The slab samples are well-characterized and have been used to validate the calibration of previous instruments on Mars missions, including CRISM [2], OMEGA [3], the MER Pancam [4], Mini-TES [5], and Moessbauer [6] instruments and the Phoenix SSI [7]. The resulting dataset was used to compare multivariate methods for quantitative LIBS and to determine the effect of grain size on calculations. Three multivariate methods - partial least squares (PLS), multilayer perceptron artificial neural networks (MLP ANNs) and cascade correlation (CC) ANNs - were used to generate models and extract the quantitative composition of unknown samples. PLS can be used to predict one element (PLS1) or multiple elements (PLS2) at a time, as can the neural network methods. Although MLP and CC ANNs were successful in some cases, PLS generally produced the most accurate and precise results.

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

PubMed

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

2015-12-01

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

The NLS-Based Nonlinear Grey Multivariate Model for Forecasting Pollutant Emissions in China

PubMed Central

Pei, Ling-Ling; Li, Qin

2018-01-01

The relationship between pollutant discharge and economic growth has been a major research focus in environmental economics. To accurately estimate the nonlinear change law of China’s pollutant discharge with economic growth, this study establishes a transformed nonlinear grey multivariable (TNGM (1, N)) model based on the nonlinear least square (NLS) method. The Gauss–Seidel iterative algorithm was used to solve the parameters of the TNGM (1, N) model based on the NLS basic principle. This algorithm improves the precision of the model by continuous iteration and constantly approximating the optimal regression coefficient of the nonlinear model. In our empirical analysis, the traditional grey multivariate model GM (1, N) and the NLS-based TNGM (1, N) models were respectively adopted to forecast and analyze the relationship among wastewater discharge per capita (WDPC), and per capita emissions of SO2 and dust, alongside GDP per capita in China during the period 1996–2015. Results indicated that the NLS algorithm is able to effectively help the grey multivariable model identify the nonlinear relationship between pollutant discharge and economic growth. The results show that the NLS-based TNGM (1, N) model presents greater precision when forecasting WDPC, SO2 emissions and dust emissions per capita, compared to the traditional GM (1, N) model; WDPC indicates a growing tendency aligned with the growth of GDP, while the per capita emissions of SO2 and dust reduce accordingly. PMID:29517985

A Diagnostic Calculator for Detecting Glaucoma on the Basis of Retinal Nerve Fiber Layer, Optic Disc, and Retinal Ganglion Cell Analysis by Optical Coherence Tomography.

PubMed

Larrosa, José Manuel; Moreno-Montañés, Javier; Martinez-de-la-Casa, José María; Polo, Vicente; Velázquez-Villoria, Álvaro; Berrozpe, Clara; García-Granero, Marta

2015-10-01

The purpose of this study was to develop and validate a multivariate predictive model to detect glaucoma by using a combination of retinal nerve fiber layer (RNFL), retinal ganglion cell-inner plexiform (GCIPL), and optic disc parameters measured using spectral-domain optical coherence tomography (OCT). Five hundred eyes from 500 participants and 187 eyes of another 187 participants were included in the study and validation groups, respectively. Patients with glaucoma were classified in five groups based on visual field damage. Sensitivity and specificity of all glaucoma OCT parameters were analyzed. Receiver operating characteristic curves (ROC) and areas under the ROC (AUC) were compared. Three predictive multivariate models (quantitative, qualitative, and combined) that used a combination of the best OCT parameters were constructed. A diagnostic calculator was created using the combined multivariate model. The best AUC parameters were: inferior RNFL, average RNFL, vertical cup/disc ratio, minimal GCIPL, and inferior-temporal GCIPL. Comparisons among the parameters did not show that the GCIPL parameters were better than those of the RNFL in early and advanced glaucoma. The highest AUC was in the combined predictive model (0.937; 95% confidence interval, 0.911-0.957) and was significantly (P = 0.0001) higher than the other isolated parameters considered in early and advanced glaucoma. The validation group displayed similar results to those of the study group. Best GCIPL, RNFL, and optic disc parameters showed a similar ability to detect glaucoma. The combined predictive formula improved the glaucoma detection compared to the best isolated parameters evaluated. The diagnostic calculator obtained good classification from participants in both the study and validation groups.

Multivariate Time Series Decomposition into Oscillation Components.

PubMed

Matsuda, Takeru; Komaki, Fumiyasu

2017-08-01

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

On set-valued functionals: Multivariate risk measures and Aumann integrals

NASA Astrophysics Data System (ADS)

Ararat, Cagin

In this dissertation, multivariate risk measures for random vectors and Aumann integrals of set-valued functions are studied. Both are set-valued functionals with values in a complete lattice of subsets of Rm. Multivariate risk measures are considered in a general d-asset financial market with trading opportunities in discrete time. Specifically, the following features of the market are incorporated in the evaluation of multivariate risk: convex transaction costs modeled by solvency regions, intermediate trading constraints modeled by convex random sets, and the requirement of liquidation into the first m ≤ d of the assets. It is assumed that the investor has a "pure" multivariate risk measure R on the space of m-dimensional random vectors which represents her risk attitude towards the assets but does not take into account the frictions of the market. Then, the investor with a d-dimensional position minimizes the set-valued functional R over all m-dimensional positions that she can reach by trading in the market subject to the frictions described above. The resulting functional Rmar on the space of d-dimensional random vectors is another multivariate risk measure, called the market-extension of R. A dual representation for R mar that decomposes the effects of R and the frictions of the market is proved. Next, multivariate risk measures are studied in a utility-based framework. It is assumed that the investor has a complete risk preference towards each individual asset, which can be represented by a von Neumann-Morgenstern utility function. Then, an incomplete preference is considered for multivariate positions which is represented by the vector of the individual utility functions. Under this structure, multivariate shortfall and divergence risk measures are defined as the optimal values of set minimization problems. The dual relationship between the two classes of multivariate risk measures is constructed via a recent Lagrange duality for set optimization. In particular, it is shown that a shortfall risk measure can be written as an intersection over a family of divergence risk measures indexed by a scalarization parameter. Examples include the multivariate versions of the entropic risk measure and the average value at risk. In the second part, Aumann integrals of set-valued functions on a measurable space are viewed as set-valued functionals and a Daniell-Stone type characterization theorem is proved for such functionals. More precisely, it is shown that a functional that maps measurable set-valued functions into a certain complete lattice of subsets of Rm can be written as the Aumann integral with respect to a measure if and only if the functional is (1) additive and (2) positively homogeneous, (3) it preserves decreasing limits, (4) it maps halfspace-valued functions to halfspaces, and (5) it maps shifted cone-valued functions to shifted cones. While the first three properties already exist in the classical Daniell-Stone theorem for the Lebesgue integral, the last two properties are peculiar to the set-valued framework and they suffice to complement the first three properties to identify a set-valued functional as the Aumann integral with respect to a measure.

Prediction of MeV electron fluxes throughout the outer radiation belt using multivariate autoregressive models

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

Sakaguchi, Kaori; Nagatsuma, Tsutomu; Reeves, Geoffrey D.

The Van Allen radiation belts surrounding the Earth are filled with MeV-energy electrons. This region poses ionizing radiation risks for spacecraft that operate within it, including those in geostationary orbit (GEO) and medium Earth orbit. In order to provide alerts of electron flux enhancements, 16 prediction models of the electron log-flux variation throughout the equatorial outer radiation belt as a function of the McIlwain L parameter were developed using the multivariate autoregressive model and Kalman filter. Measurements of omnidirectional 2.3 MeV electron flux from the Van Allen Probes mission as well as >2 MeV electrons from the GOES 15 spacecraftmore » were used as the predictors. Furthermore, we selected model explanatory parameters from solar wind parameters, the electron log-flux at GEO, and geomagnetic indices. For the innermost region of the outer radiation belt, the electron flux is best predicted by using the Dst index as the sole input parameter. For the central to outermost regions, at L≥4.8 and L ≥5.6, the electron flux is predicted most accurately by including also the solar wind velocity and then the dynamic pressure, respectively. The Dst index is the best overall single parameter for predicting at 3 ≤ L ≤ 6, while for the GEO flux prediction, the K P index is better than Dst. Finally, a test calculation demonstrates that the model successfully predicts the timing and location of the flux maximum as much as 2 days in advance and that the electron flux decreases faster with time at higher L values, both model features consistent with the actually observed behavior.« less

Prediction of MeV electron fluxes throughout the outer radiation belt using multivariate autoregressive models

DOE PAGES

Sakaguchi, Kaori; Nagatsuma, Tsutomu; Reeves, Geoffrey D.; ...

2015-12-22

The Van Allen radiation belts surrounding the Earth are filled with MeV-energy electrons. This region poses ionizing radiation risks for spacecraft that operate within it, including those in geostationary orbit (GEO) and medium Earth orbit. In order to provide alerts of electron flux enhancements, 16 prediction models of the electron log-flux variation throughout the equatorial outer radiation belt as a function of the McIlwain L parameter were developed using the multivariate autoregressive model and Kalman filter. Measurements of omnidirectional 2.3 MeV electron flux from the Van Allen Probes mission as well as >2 MeV electrons from the GOES 15 spacecraftmore » were used as the predictors. Furthermore, we selected model explanatory parameters from solar wind parameters, the electron log-flux at GEO, and geomagnetic indices. For the innermost region of the outer radiation belt, the electron flux is best predicted by using the Dst index as the sole input parameter. For the central to outermost regions, at L≥4.8 and L ≥5.6, the electron flux is predicted most accurately by including also the solar wind velocity and then the dynamic pressure, respectively. The Dst index is the best overall single parameter for predicting at 3 ≤ L ≤ 6, while for the GEO flux prediction, the K P index is better than Dst. Finally, a test calculation demonstrates that the model successfully predicts the timing and location of the flux maximum as much as 2 days in advance and that the electron flux decreases faster with time at higher L values, both model features consistent with the actually observed behavior.« less

Prediction of MeV electron fluxes throughout the outer radiation belt using multivariate autoregressive models

NASA Astrophysics Data System (ADS)

Sakaguchi, Kaori; Nagatsuma, Tsutomu; Reeves, Geoffrey D.; Spence, Harlan E.

2015-12-01

The Van Allen radiation belts surrounding the Earth are filled with MeV-energy electrons. This region poses ionizing radiation risks for spacecraft that operate within it, including those in geostationary orbit (GEO) and medium Earth orbit. To provide alerts of electron flux enhancements, 16 prediction models of the electron log-flux variation throughout the equatorial outer radiation belt as a function of the McIlwain L parameter were developed using the multivariate autoregressive model and Kalman filter. Measurements of omnidirectional 2.3 MeV electron flux from the Van Allen Probes mission as well as >2 MeV electrons from the GOES 15 spacecraft were used as the predictors. Model explanatory parameters were selected from solar wind parameters, the electron log-flux at GEO, and geomagnetic indices. For the innermost region of the outer radiation belt, the electron flux is best predicted by using the Dst index as the sole input parameter. For the central to outermost regions, at L ≧ 4.8 and L ≧ 5.6, the electron flux is predicted most accurately by including also the solar wind velocity and then the dynamic pressure, respectively. The Dst index is the best overall single parameter for predicting at 3 ≦ L ≦ 6, while for the GEO flux prediction, the KP index is better than Dst. A test calculation demonstrates that the model successfully predicts the timing and location of the flux maximum as much as 2 days in advance and that the electron flux decreases faster with time at higher L values, both model features consistent with the actually observed behavior.

Multivariate meta-analysis using individual participant data

PubMed Central

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

2016-01-01

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

Using "big data" to optimally model hydrology and water quality across expansive regions

USGS Publications Warehouse

Roehl, E.A.; Cook, J.B.; Conrads, P.A.

2009-01-01

This paper describes a new divide and conquer approach that leverages big environmental data, utilizing all available categorical and time-series data without subjectivity, to empirically model hydrologic and water-quality behaviors across expansive regions. The approach decomposes large, intractable problems into smaller ones that are optimally solved; decomposes complex signals into behavioral components that are easier to model with "sub- models"; and employs a sequence of numerically optimizing algorithms that include time-series clustering, nonlinear, multivariate sensitivity analysis and predictive modeling using multi-layer perceptron artificial neural networks, and classification for selecting the best sub-models to make predictions at new sites. This approach has many advantages over traditional modeling approaches, including being faster and less expensive, more comprehensive in its use of available data, and more accurate in representing a system's physical processes. This paper describes the application of the approach to model groundwater levels in Florida, stream temperatures across Western Oregon and Wisconsin, and water depths in the Florida Everglades. ?? 2009 ASCE.

A Multivariate Multilevel Approach to the Modeling of Accuracy and Speed of Test Takers

ERIC Educational Resources Information Center

Klein Entink, R. H.; Fox, J. P.; van der Linden, W. J.

2009-01-01

Response times on test items are easily collected in modern computerized testing. When collecting both (binary) responses and (continuous) response times on test items, it is possible to measure the accuracy and speed of test takers. To study the relationships between these two constructs, the model is extended with a multivariate multilevel…

Multivariate regression model for partitioning tree volume of white oak into round-product classes

Treesearch

Daniel A. Yaussy; David L. Sonderman

1984-01-01

Describes the development of multivariate equations that predict the expected cubic volume of four round-product classes from independent variables composed of individual tree-quality characteristics. Although the model has limited application at this time, it does demonstrate the feasibility of partitioning total tree cubic volume into round-product classes based on...

The Dirichlet-Multinomial Model for Multivariate Randomized Response Data and Small Samples

ERIC Educational Resources Information Center

Avetisyan, Marianna; Fox, Jean-Paul

2012-01-01

In survey sampling the randomized response (RR) technique can be used to obtain truthful answers to sensitive questions. Although the individual answers are masked due to the RR technique, individual (sensitive) response rates can be estimated when observing multivariate response data. The beta-binomial model for binary RR data will be generalized…

Tracking Problem Solving by Multivariate Pattern Analysis and Hidden Markov Model Algorithms

ERIC Educational Resources Information Center

Anderson, John R.

2012-01-01

Multivariate pattern analysis can be combined with Hidden Markov Model algorithms to track the second-by-second thinking as people solve complex problems. Two applications of this methodology are illustrated with a data set taken from children as they interacted with an intelligent tutoring system for algebra. The first "mind reading" application…

Four Families of Multi-Variant Issues in Graduate-Level Asynchronous Online Courses

ERIC Educational Resources Information Center

Gisburne, Jaclyn M.; Fairchild, Patricia J.

2004-01-01

This is the first of several papers developed from a faculty and student perspective describing a new distance learning (DL) model. Integral to the model are four interrelated families of multi-variant issues, referred to here as (a) the academic divide, (b) student misalignment, (c) administrative influences, and (d) the use of student…

Assessing Reliability of Student Ratings of Advisor: A Comparison of Univariate and Multivariate Generalizability Approaches.

ERIC Educational Resources Information Center

Sun, Anji; Valiga, Michael J.

In this study, the reliability of the American College Testing (ACT) Program's "Survey of Academic Advising" (SAA) was examined using both univariate and multivariate generalizability theory approaches. The primary purpose of the study was to compare the results of three generalizability theory models (a random univariate model, a mixed…

Web-Based Tools for Modelling and Analysis of Multivariate Data: California Ozone Pollution Activity

ERIC Educational Resources Information Center

Dinov, Ivo D.; Christou, Nicolas

2011-01-01

This article presents a hands-on web-based activity motivated by the relation between human health and ozone pollution in California. This case study is based on multivariate data collected monthly at 20 locations in California between 1980 and 2006. Several strategies and tools for data interrogation and exploratory data analysis, model fitting…

Multivariate Generalizations of Student's t-Distribution. ONR Technical Report. [Biometric Lab Report No. 90-3.

ERIC Educational Resources Information Center

Gibbons, Robert D.; And Others

In the process of developing a conditionally-dependent item response theory (IRT) model, the problem arose of modeling an underlying multivariate normal (MVN) response process with general correlation among the items. Without the assumption of conditional independence, for which the underlying MVN cdf takes on comparatively simple forms and can be…

Bias and Precision of Measures of Association for a Fixed-Effect Multivariate Analysis of Variance Model

ERIC Educational Resources Information Center

Kim, Soyoung; Olejnik, Stephen

2005-01-01

The sampling distributions of five popular measures of association with and without two bias adjusting methods were examined for the single factor fixed-effects multivariate analysis of variance model. The number of groups, sample sizes, number of outcomes, and the strength of association were manipulated. The results indicate that all five…

The extension of total gain (TG) statistic in survival models: properties and applications.

PubMed

Choodari-Oskooei, Babak; Royston, Patrick; Parmar, Mahesh K B

2015-07-01

The results of multivariable regression models are usually summarized in the form of parameter estimates for the covariates, goodness-of-fit statistics, and the relevant p-values. These statistics do not inform us about whether covariate information will lead to any substantial improvement in prediction. Predictive ability measures can be used for this purpose since they provide important information about the practical significance of prognostic factors. R (2)-type indices are the most familiar forms of such measures in survival models, but they all have limitations and none is widely used. In this paper, we extend the total gain (TG) measure, proposed for a logistic regression model, to survival models and explore its properties using simulations and real data. TG is based on the binary regression quantile plot, otherwise known as the predictiveness curve. Standardised TG ranges from 0 (no explanatory power) to 1 ('perfect' explanatory power). The results of our simulations show that unlike many of the other R (2)-type predictive ability measures, TG is independent of random censoring. It increases as the effect of a covariate increases and can be applied to different types of survival models, including models with time-dependent covariate effects. We also apply TG to quantify the predictive ability of multivariable prognostic models developed in several disease areas. Overall, TG performs well in our simulation studies and can be recommended as a measure to quantify the predictive ability in survival models.

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

PubMed

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

2014-01-01

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

Influence of stroke subtype on quality of care in the Get With The Guidelines-Stroke Program.

PubMed

Smith, E E; Liang, L; Hernandez, A; Reeves, M J; Cannon, C P; Fonarow, G C; Schwamm, L H

2009-09-01

Little is known about in-hospital care for hemorrhagic stroke. We examined quality of care in intracerebral hemorrhage (ICH) and subarachnoid hemorrhage (SAH) admissions in the national Get With The Guidelines-Stroke (GWTG-Stroke) database, and compared them to ischemic stroke (IS) or TIA admissions. Between April 1, 2003, and December 30, 2007, 905 hospitals contributed 479,284 consecutive stroke and TIA admissions. The proportions receiving each quality of care measure were calculated by dividing the total number of patients receiving the intervention by the total number of patients eligible for the intervention, excluding ineligible patients or those with contraindications to treatment. Logistic regression models were used to determine associations between measure compliance and stroke subtype, controlling for patient and hospital characteristics. Stroke subtypes were 61.7% IS, 23.8% TIA, 11.1% ICH, and 3.5% SAH. Performance on care measures was generally lower in ICH and SAH compared to IS/TIA, including guideline-recommended measures for deep venous thrombosis (DVT) prevention (for ICH) and smoking cessation (for SAH) (multivariable-adjusted p < 0.001 for all comparisons). Exceptions were that ICH patients were more likely than IS/TIA to have door-to-CT times <25 minutes (multivariable-adjusted p < 0.001) and to undergo dysphagia screening (multivariable-adjusted p < 0.001). Time spent in the GWTG-Stroke program was associated with improvements in many measures of care for ICH and SAH patients, including DVT prevention and smoking cessation therapy (multivariable-adjusted p < 0.001). Many hospital-based acute care and prevention measures are underutilized in intracerebral hemorrhage and subarachnoid hemorrhage compared to ischemic stroke /TIA. Duration of Get With The Guidelines-Stroke participation is associated with improving quality of care for hemorrhagic stroke.

Physical function in older men with hyperkyphosis.

PubMed

Katzman, Wendy B; Harrison, Stephanie L; Fink, Howard A; Marshall, Lynn M; Orwoll, Eric; Barrett-Connor, Elizabeth; Cawthon, Peggy M; Kado, Deborah M

2015-05-01

Age-related hyperkyphosis has been associated with poor physical function and is a well-established predictor of adverse health outcomes in older women, but its impact on health in older men is less well understood. We conducted a cross-sectional study to evaluate the association of hyperkyphosis and physical function in 2,363 men, aged 71-98 (M = 79) from the Osteoporotic Fractures in Men Study. Kyphosis was measured using the Rancho Bernardo Study block method. Measurements of grip strength and lower extremity function, including gait speed over 6 m, narrow walk (measure of dynamic balance), repeated chair stands ability and time, and lower extremity power (Nottingham Power Rig) were included separately as primary outcomes. We investigated associations of kyphosis and each outcome in age-adjusted and multivariable linear or logistic regression models, controlling for age, clinic, education, race, bone mineral density, height, weight, diabetes, and physical activity. In multivariate linear regression, we observed a dose-related response of worse scores on each lower extremity physical function test as number of blocks increased, p for trend ≤.001. Using a cutoff of ≥4 blocks, 20% (N = 469) of men were characterized with hyperkyphosis. In multivariate logistic regression, men with hyperkyphosis had increased odds (range 1.5-1.8) of being in the worst quartile of performing lower extremity physical function tasks (p < .001 for each outcome). Kyphosis was not associated with grip strength in any multivariate analysis. Hyperkyphosis is associated with impaired lower extremity physical function in older men. Further studies are needed to determine the direction of causality. © The Author 2014. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

A multivariate model of plant species richness in forested systems: Old-growth montane forests with a long history of fire

USGS Publications Warehouse

Laughlin, D.C.; Grace, J.B.

2006-01-01

Recently, efforts to develop multivariate models of plant species richness have been extended to include systems where trees play important roles as overstory elements mediating the influences of environment and disturbance on understory richness. We used structural equation modeling to examine the relationship of understory vascular plant species richness to understory abundance, forest structure, topographic slope, and surface fire history in lower montane forests on the North Rim of Grand Canyon National Park, USA based on data from eighty-two 0.1 ha plots. The questions of primary interest in this analysis were: (1) to what degree are influences of trees on understory richness mediated by effects on understory abundance? (2) To what degree are influences of fire history on richness mediated by effects on trees and/or understory abundance? (3) Can the influences of fire history on this system be related simply to time-since-fire or are there unique influences associated with long-term fire frequency? The results we obtained are consistent with the following inferences. First, it appears that pine trees had a strong inhibitory effect on the abundance of understory plants, which in turn led to lower understory species richness. Second, richness declined over time since the last fire. This pattern appears to result from several processes, including (1) a post-fire stimulation of germination, (2) a decline in understory abundance, and (3) an increase over time in pine abundance (which indirectly leads to reduced richness). Finally, once time-since-fire was statistically controlled, it was seen that areas with higher fire frequency have lower richness than expected, which appears to result from negative effects on understory abundance, possibly by depletions of soil nutrients from repeated surface fire. Overall, it appears that at large temporal and spatial scales, surface fire plays an important and complex role in structuring understory plant communities in old-growth montane forests. These results show how multivariate models of herbaceous richness can be expanded to apply to forested systems. Copyright ?? Oikos 2006.

MULTIVARIATE ANALYSES (CONONICAL CORRELATION AND PARTIAL LEAST SQUARE, PLS) TO MODEL AND ASSESS THE ASSOCIATION OF LANDSCAPE METRICS TO SURFACE WATER CHEMICAL AND BIOLOGICAL PROPERTIES USING SAVANNAH RIVER BASIN DATA.

EPA Science Inventory

Many multivariate methods are used in describing and predicting relation; each has its unique usage of categorical and non-categorical data. In multivariate analysis of variance (MANOVA), many response variables (y's) are related to many independent variables that are categorical...

The EXCITE Trial: Predicting a Clinically Meaningful Motor Activity Log Outcome

PubMed Central

Park, Si-Woon; Wolf, Steven L.; Blanton, Sarah; Winstein, Carolee; Nichols-Larsen, Deborah S.

2013-01-01

Background and Objective This study determined which baseline clinical measurements best predicted a predefined clinically meaningful outcome on the Motor Activity Log (MAL) and developed a predictive multivariate model to determine outcome after 2 weeks of constraint-induced movement therapy (CIMT) and 12 months later using the database from participants in the Extremity Constraint Induced Therapy Evaluation (EXCITE) Trial. Methods A clinically meaningful CIMT outcome was defined as achieving higher than 3 on the MAL Quality of Movement (QOM) scale. Predictive variables included baseline MAL, Wolf Motor Function Test (WMFT), the sensory and motor portion of the Fugl-Meyer Assessment (FMA), spasticity, visual perception, age, gender, type of stroke, concordance, and time after stroke. Significant predictors identified by univariate analysis were used to develop the multivariate model. Predictive equations were generated and odds ratios for predictors were calculated from the multivariate model. Results Pretreatment motor function measured by MAL QOM, WMFT, and FMA were significantly associated with outcome immediately after CIMT. Pretreatment MAL QOM, WMFT, proprioception, and age were significantly associated with outcome after 12 months. Each unit of higher pretreatment MAL QOM score and each unit of faster pretreatment WMFT log mean time improved the probability of achieving a clinically meaningful outcome by 7 and 3 times at posttreatment, and 5 and 2 times after 12 months, respectively. Patients with impaired proprioception had a 20% probability of achieving a clinically meaningful outcome compared with those with intact proprioception. Conclusions Baseline clinical measures of motor and sensory function can be used to predict a clinically meaningful outcome after CIMT. PMID:18780883

The combination of ovarian volume and outline has better diagnostic accuracy than prostate-specific antigen (PSA) concentrations in women with polycystic ovarian syndrome (PCOs).

PubMed

Bili, Eleni; Bili, Authors Eleni; Dampala, Kaliopi; Iakovou, Ioannis; Tsolakidis, Dimitrios; Giannakou, Anastasia; Tarlatzis, Basil C

2014-08-01

The aim of this study was to determine the performance of prostate specific antigen (PSA) and ultrasound parameters, such as ovarian volume and outline, in the diagnosis of polycystic ovary syndrome (PCOS). This prospective, observational, case-controlled study included 43 women with PCOS, and 40 controls. Between day 3 and 5 of the menstrual cycle, fasting serum samples were collected and transvaginal ultrasound was performed. The diagnostic performance of each parameter [total PSA (tPSA), total-to-free PSA ratio (tPSA:fPSA), ovarian volume, ovarian outline] was estimated by means of receiver operating characteristic (ROC) analysis, along with area under the curve (AUC), threshold, sensitivity, specificity as well as positive (+) and negative (-) likelihood ratios (LRs). Multivariate logistical regression models, using ovarian volume and ovarian outline, were constructed. The tPSA and tPSA:fPSA ratio resulted in AUC of 0.74 and 0.70, respectively, with moderate specificity/sensitivity and insufficient LR+/- values. In the multivariate logistic regression model, the combination of ovarian volume and outline had a sensitivity of 97.7% and a specificity of 97.5% in the diagnosis of PCOS, with +LR and -LR values of 39.1 and 0.02, respectively. In women with PCOS, tPSA and tPSA:fPSA ratio have similar diagnostic performance. The use of a multivariate logistic regression model, incorporating ovarian volume and outline, offers very good diagnostic accuracy in distinguishing women with PCOS patients from controls. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

The EXCITE Trial: Predicting a clinically meaningful motor activity log outcome.

PubMed

Park, Si-Woon; Wolf, Steven L; Blanton, Sarah; Winstein, Carolee; Nichols-Larsen, Deborah S

2008-01-01

This study determined which baseline clinical measurements best predicted a predefined clinically meaningful outcome on the Motor Activity Log (MAL) and developed a predictive multivariate model to determine outcome after 2 weeks of constraint-induced movement therapy (CIMT) and 12 months later using the database from participants in the Extremity Constraint Induced Therapy Evaluation (EXCITE) Trial. A clinically meaningful CIMT outcome was defined as achieving higher than 3 on the MAL Quality of Movement (QOM) scale. Predictive variables included baseline MAL, Wolf Motor Function Test (WMFT), the sensory and motor portion of the Fugl-Meyer Assessment (FMA), spasticity, visual perception, age, gender, type of stroke, concordance, and time after stroke. Significant predictors identified by univariate analysis were used to develop the multivariate model. Predictive equations were generated and odds ratios for predictors were calculated from the multivariate model. Pretreatment motor function measured by MAL QOM, WMFT, and FMA were significantly associated with outcome immediately after CIMT. Pretreatment MAL QOM, WMFT, proprioception, and age were significantly associated with outcome after 12 months. Each unit of higher pretreatment MAL QOM score and each unit of faster pretreatment WMFT log mean time improved the probability of achieving a clinically meaningful outcome by 7 and 3 times at posttreatment, and 5 and 2 times after 12 months, respectively. Patients with impaired proprioception had a 20% probability of achieving a clinically meaningful outcome compared with those with intact proprioception. Baseline clinical measures of motor and sensory function can be used to predict a clinically meaningful outcome after CIMT.

Culture and alcohol use: historical and sociocultural themes from 75 years of alcohol research.

PubMed

Castro, Felipe Gonzalez; Barrera, Manuel; Mena, Laura A; Aguirre, Katherine M

2014-01-01

For the period of almost 75 years, we examined the literature for studies regarding the influences of culture on alcohol use and misuse. This review is a chronology of research articles published from 1940 to 2013. From a structured literature search with select criteria, 38 articles were identified and 34 reviewed. This analysis revealed a progression across this period of research from studies that began as descriptive ethnographic evaluations of one or more indigenous societies or cultural groups, evolving to studies using complex multivariate models to test cross-cultural effects in two or more cultural groups. Major findings across this period include the assertions that (a) a function of alcohol use may be to reduce anxiety, (b) certain cultural groups possess features of alcohol use that are not associated with negative consequences, (c) the disruptive effects of acculturative change and the stressors of new demands are associated with an increase in alcohol consumption, (d) cultural groups shape expectations about the effects of alcohol use and their definition of drunkenness, and (e) the hypothesized relationships of culture with alcohol use and misuse have been demonstrated in multivariate model analyses. Across this 75-year period, the early proposition that culture is an important and prominent correlate of alcohol use and misuse has persisted. Within the current era of alcohol studies, this proposition has been supported by multivariate model analyses. Thus, the proposition that culture might affect alcohol use remains prominent and is as relevant today as it was when it was first proposed nearly 75 years ago.

Non-Gaussian and Multivariate Noise Models for Signal Detection.

DTIC Science & Technology

1982-09-01

follow, some of the basic results of asymptotic "theory are presented. both to make the notation clear. and to give some i ~ background for the...densities are considered within a detection framework. The discussions include specific examples and also some general methods of density generation ...densities generated by a memoryless, nonlinear transformation of a correlated, Gaussian source is discussed in some detail. A member of this class has the

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

PubMed

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

2012-01-01

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

Maternal Language and Adverse Birth Outcomes in a Statewide Analysis

PubMed Central

Sentell, Tetine; Chang, Ann; Jun Ahn, Hyeong; Miyamura, Jill

2016-01-01

Background Limited English proficiency is associated with disparities across diverse health outcomes. However, evidence regarding adverse birth outcomes across languages is limited, particularly among US Asian and Pacific Islander populations. The study goal was to consider the relationship of maternal language to birth outcomes using statewide hospitalization data. Methods Detailed discharge data from Hawai‘i childbirth hospitalizations from 2012 (n=11,419) were compared by maternal language (English language or not) for adverse outcomes using descriptive and multivariable log-binomial regression models, controlling for race/ethnicity, age group, and payer. Results Ten percent of mothers spoke a language other than English; 93% of these spoke an Asian or Pacific Islander language. In multivariable models, compared to English speakers non-English speakers had significantly higher risk (adjusted relative risk [ARR]: 2.02; 95% Confidence Interval [CI]: 1.34–3.04) of obstetric trauma in vaginal deliveries without instrumentation. Some significant variation was seen by language for other birth outcomes, including an increased rate of primary Caesarean sections and vaginal births after Caesarean among non-English speakers. Conclusions Non-English speakers had approximately two times higher risk of having an obstetric trauma during a vaginal birth when other factors, including race/ethnicity, were controlled. Non-English speakers also had higher rates of potentially high-risk deliveries. PMID:26361937

Maternal language and adverse birth outcomes in a statewide analysis.

PubMed

Sentell, Tetine; Chang, Ann; Ahn, Hyeong Jun; Miyamura, Jill

2016-01-01

Limited English proficiency is associated with disparities across diverse health outcomes. However, evidence regarding adverse birth outcomes across languages is limited, particularly among U.S. Asian and Pacific Islander populations. The study goal was to consider the relationship of maternal language to birth outcomes using statewide hospitalization data. Detailed discharge data from Hawaii childbirth hospitalizations from 2012 (n = 11,419) were compared by maternal language (English language or not) for adverse outcomes using descriptive and multivariable log-binomial regression models, controlling for race/ethnicity, age group, and payer. Ten percent of mothers spoke a language other than English; 93% of these spoke an Asian or Pacific Islander language. In multivariable models, compared to English speakers, non-English speakers had significantly higher risk (adjusted relative risk [ARR]: 2.02; 95% confidence interval [CI]: 1.34-3.04) of obstetric trauma in vaginal deliveries without instrumentation. Some significant variation was seen by language for other birth outcomes, including an increased rate of primary Caesarean sections and vaginal births after Caesarean, among non-English speakers. Non-English speakers had approximately two times higher risk of having an obstetric trauma during a vaginal birth when other factors, including race/ethnicity, were controlled. Non-English speakers also had higher rates of potentially high-risk deliveries.

Balance between transmitted HLA preadapted and nonassociated polymorphisms is a major determinant of HIV-1 disease progression.

PubMed

Mónaco, Daniela C; Dilernia, Dario A; Fiore-Gartland, Andrew; Yu, Tianwei; Prince, Jessica L; Dennis, Kristine K; Qin, Kai; Schaefer, Malinda; Claiborne, Daniel T; Kilembe, William; Tang, Jianming; Price, Matt A; Farmer, Paul; Gilmour, Jill; Bansal, Anju; Allen, Susan; Goepfert, Paul; Hunter, Eric

2016-09-19

HIV-1 adapts to a new host through mutations that facilitate immune escape. Here, we evaluate the impact on viral control and disease progression of transmitted polymorphisms that were either preadapted to or nonassociated with the new host's HLA. In a cohort of 169 Zambian heterosexual transmission pairs, we found that almost one-third of possible HLA-linked target sites in the transmitted virus Gag protein are already adapted, and that this transmitted preadaptation significantly reduced early immune recognition of epitopes. Transmitted preadapted and nonassociated polymorphisms showed opposing effects on set-point VL and the balance between the two was significantly associated with higher set-point VLs in a multivariable model including other risk factors. Transmitted preadaptation was also significantly associated with faster CD4 decline (<350 cells/µl) and this association was stronger after accounting for nonassociated polymorphisms, which were linked with slower CD4 decline. Overall, the relative ratio of the two classes of polymorphisms was found to be the major determinant of CD4 decline in a multivariable model including other risk factors. This study reveals that, even before an immune response is mounted in the new host, the balance of these opposing factors can significantly influence the outcome of HIV-1 infection. © 2016 Mónaco et al.

Alternatives for jet engine control

NASA Technical Reports Server (NTRS)

Sain, M. K.

1979-01-01

The research is classified in two categories: (1) the use of modern multivariable frequency domain methods for control of engine models in the neighborhood of a set-point, and (2) the use of nonlinear modelling and optimization techniques for control of engine models over a more extensive part of the flight envelope. Progress in the first category included the extension of CARDIAD (Complex Acceptability Region for Diagonal Dominance) methods developed with the help of the grant to the case of engine models with four inputs and four outputs. A suitable bounding procedure for the dominance function was determined. Progress in the second category had its principal focus on automatic nonlinear model generation. Simulations of models produced satisfactory results where compared with the NASA DYNGEN digital engine deck.

Analysis of pelagic species decline in the upper San Francisco Estuary using multivariate autoregressive modeling (MAR)

USGS Publications Warehouse

Mac Nally, Ralph; Thomson, James R.; Kimmerer, Wim J.; Feyrer, Frederick; Newman, Ken B.; Sih, Andy; Bennett, William A.; Brown, Larry; Fleishman, Erica; Culberson, Steven D.; Castillo, Gonzalo

2010-01-01

Four species of pelagic fish of particular management concern in the upper San Francisco Estuary, California, USA, have declined precipitously since ca. 2002: delta smelt (Hypomesus transpacificus), longfin smelt (Spirinchus thaleichthys), striped bass (Morone saxatilis), and threadfin shad (Dorosoma petenense). The estuary has been monitored since the late 1960s with extensive collection of data on the fishes, their pelagic prey, phytoplankton biomass, invasive species, and physical factors. We used multivariate autoregressive (MAR) modeling to discern the main factors responsible for the declines. An expert-elicited model was built to describe the system. Fifty-four relationships were built into the model, only one of which was of uncertain direction a priori. Twenty-eight of the proposed relationships were strongly supported by or consistent with the data, while 26 were close to zero (not supported by the data but not contrary to expectations). The position of the 2‰ isohaline (a measure of the physical response of the estuary to freshwater flow) and increased water clarity over the period of analyses were two factors affecting multiple declining taxa (including fishes and the fishes' main zooplankton prey). Our results were relatively robust with respect to the form of stock–recruitment model used and to inclusion of subsidiary covariates but may be enhanced by using detailed state–space models that describe more fully the life-history dynamics of the declining species.

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

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

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

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

Analysis of pelagic species decline in the upper San Francisco Estuary using multivariate autoregressive modeling (MAR).

PubMed

Mac Nally, Ralph; Thomson, James R; Kimmerer, Wim J; Feyrer, Frederick; Newman, Ken B; Sih, Andy; Bennett, William A; Brown, Larry; Fleishman, Erica; Culberson, Steven D; Castillo, Gonzalo

2010-07-01

Four species of pelagic fish of particular management concern in the upper San Francisco Estuary, California, USA, have declined precipitously since ca. 2002: delta smelt (Hypomesus transpacificus), longfin smelt (Spirinchus thaleichthys), striped bass (Morone saxatilis), and threadfin shad (Dorosoma petenense). The estuary has been monitored since the late 1960s with extensive collection of data on the fishes, their pelagic prey, phytoplankton biomass, invasive species, and physical factors. We used multivariate autoregressive (MAR) modeling to discern the main factors responsible for the declines. An expert-elicited model was built to describe the system. Fifty-four relationships were built into the model, only one of which was of uncertain direction a priori. Twenty-eight of the proposed relationships were strongly supported by or consistent with the data, while 26 were close to zero (not supported by the data but not contrary to expectations). The position of the 2 per thousand isohaline (a measure of the physical response of the estuary to freshwater flow) and increased water clarity over the period of analyses were two factors affecting multiple declining taxa (including fishes and the fishes' main zooplankton prey): Our results were relatively robust with respect to the form of stock-recruitment model used and to inclusion of subsidiary covariates but may be enhanced by using detailed state-space models that describe more fully the life-history dynamics of the declining species.

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

DOE PAGES

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

2017-12-28

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

Late Cardiac Toxicity After Mediastinal Radiation Therapy for Hodgkin Lymphoma: Contributions of Coronary Artery and Whole Heart Dose-Volume Variables to Risk Prediction.

PubMed

Hahn, Ezra; Jiang, Haiyan; Ng, Angela; Bashir, Shaheena; Ahmed, Sameera; Tsang, Richard; Sun, Alexander; Gospodarowicz, Mary; Hodgson, David

2017-08-01

Mediastinal radiation therapy (RT) for Hodgkin lymphoma (HL) is associated with late cardiotoxicity, but there are limited data to indicate which dosimetric parameters are most valuable for predicting this risk. This study investigated which whole heart dosimetric measurements provide the most information regarding late cardiotoxicity, and whether coronary artery dosimetry was more predictive of this outcome than whole heart dosimetry. A random sample of 125 HL patients treated with mediastinal RT was selected, and 3-dimensional cardiac dose-volume data were generated from historical plans using validated methods. Cardiac events were determined by linking patients to population-based datasets of inpatient and same-day hospitalizations and same-day procedures. Variables collected for the whole heart and 3 coronary arteries included the following: Dmean, Dmax, Dmin, dose homogeneity, V5, V10, V20, and V30. Multivariable competing risk regression models were generated for the whole heart and coronary arteries. There were 44 cardiac events documented, of which 70% were ischemic. The best multivariable model included the following covariates: whole heart Dmean (hazard ratio [HR] 1.09, P=.0083), dose homogeneity (HR 0.94, P=.0034), male sex (HR 2.31, P=.014), and age (HR 1.03, P=.0049). When any adverse cardiac event was the outcome, models using coronary artery variables did not perform better than models using whole heart variables. However, in a subanalysis of ischemic cardiac events only, the model using coronary artery variables was superior to the whole heart model and included the following covariates: age (HR 1.05, P<.001), volume of left anterior descending artery receiving 5 Gy (HR 0.98, P=.003), and volume of left circumflex artery receiving 20 Gy (HR 1.03, P<.001). In addition to higher mean heart dose, increasing inhomogeneity in cardiac dose was associated with a greater risk of late cardiac effects. When all types of cardiotoxicity were evaluated, the whole heart variable model outperformed the coronary artery models. However, when events were limited to ischemic cardiotoxicity, the coronary artery-based model was superior. Copyright © 2017 Elsevier Inc. All rights reserved.

Toward the Multivariate Modeling of Achievement, Aptitude, and Personality.

ERIC Educational Resources Information Center

Foshay, Wellesley R.; Misanchuk, Earl R.

1981-01-01

A multivariate investigation of the dynamics of cumulative achievement studied the influence of course grades, personality traits, environmental variables, and previous performance. The latter was the best single predictor of performance. (CJ)

Negative Events in Childhood Predict Trajectories of Internalizing Symptoms Up to Young Adulthood: An 18-Year Longitudinal Study

PubMed Central

Melchior, Maria; Touchette, Évelyne; Prokofyeva, Elena; Chollet, Aude; Fombonne, Eric; Elidemir, Gulizar; Galéra, Cédric

2014-01-01

Background Common negative events can precipitate the onset of internalizing symptoms. We studied whether their occurrence in childhood is associated with mental health trajectories over the course of development. Methods Using data from the TEMPO study, a French community-based cohort study of youths, we studied the association between negative events in 1991 (when participants were aged 4–16 years) and internalizing symptoms, assessed by the ASEBA family of instruments in 1991, 1999, and 2009 (n = 1503). Participants' trajectories of internalizing symptoms were estimated with semi-parametric regression methods (PROC TRAJ). Data were analyzed using multinomial regression models controlled for participants' sex, age, parental family status, socio-economic position, and parental history of depression. Results Negative childhood events were associated with an increased likelihood of concurrent internalizing symptoms which sometimes persisted into adulthood (multivariate ORs associated with > = 3 negative events respectively: high and decreasing internalizing symptoms: 5.54, 95% CI: 3.20–9.58; persistently high internalizing symptoms: 8.94, 95% CI: 2.82–28.31). Specific negative events most strongly associated with youths' persistent internalizing symptoms included: school difficulties (multivariate OR: 5.31, 95% CI: 2.24–12.59), parental stress (multivariate OR: 4.69, 95% CI: 2.02–10.87), serious illness/health problems (multivariate OR: 4.13, 95% CI: 1.76–9.70), and social isolation (multivariate OR: 2.24, 95% CI: 1.00–5.08). Conclusions Common negative events can contribute to the onset of children's lasting psychological difficulties. PMID:25485875

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

PubMed

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

2016-01-01

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

Genetic parameters for growth characteristics of free-range chickens under univariate random regression models.

PubMed

Rovadoscki, Gregori A; Petrini, Juliana; Ramirez-Diaz, Johanna; Pertile, Simone F N; Pertille, Fábio; Salvian, Mayara; Iung, Laiza H S; Rodriguez, Mary Ana P; Zampar, Aline; Gaya, Leila G; Carvalho, Rachel S B; Coelho, Antonio A D; Savino, Vicente J M; Coutinho, Luiz L; Mourão, Gerson B

2016-09-01

Repeated measures from the same individual have been analyzed by using repeatability and finite dimension models under univariate or multivariate analyses. However, in the last decade, the use of random regression models for genetic studies with longitudinal data have become more common. Thus, the aim of this research was to estimate genetic parameters for body weight of four experimental chicken lines by using univariate random regression models. Body weight data from hatching to 84 days of age (n = 34,730) from four experimental free-range chicken lines (7P, Caipirão da ESALQ, Caipirinha da ESALQ and Carijó Barbado) were used. The analysis model included the fixed effects of contemporary group (gender and rearing system), fixed regression coefficients for age at measurement, and random regression coefficients for permanent environmental effects and additive genetic effects. Heterogeneous variances for residual effects were considered, and one residual variance was assigned for each of six subclasses of age at measurement. Random regression curves were modeled by using Legendre polynomials of the second and third orders, with the best model chosen based on the Akaike Information Criterion, Bayesian Information Criterion, and restricted maximum likelihood. Multivariate analyses under the same animal mixed model were also performed for the validation of the random regression models. The Legendre polynomials of second order were better for describing the growth curves of the lines studied. Moderate to high heritabilities (h(2) = 0.15 to 0.98) were estimated for body weight between one and 84 days of age, suggesting that selection for body weight at all ages can be used as a selection criteria. Genetic correlations among body weight records obtained through multivariate analyses ranged from 0.18 to 0.96, 0.12 to 0.89, 0.06 to 0.96, and 0.28 to 0.96 in 7P, Caipirão da ESALQ, Caipirinha da ESALQ, and Carijó Barbado chicken lines, respectively. Results indicate that genetic gain for body weight can be achieved by selection. Also, selection for body weight at 42 days of age can be maintained as a selection criterion. © 2016 Poultry Science Association Inc.

A review of multivariate methods in brain imaging data fusion

NASA Astrophysics Data System (ADS)

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

2010-03-01

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

Modelling and Optimization of Polycaprolactone Ultrafine-Fibres Electrospinning Process Using Response Surface Methodology

PubMed Central

Ruys, Andrew J.

2018-01-01

Electrospun fibres have gained broad interest in biomedical applications, including tissue engineering scaffolds, due to their potential in mimicking extracellular matrix and producing structures favourable for cell and tissue growth. The development of scaffolds often involves multivariate production parameters and multiple output characteristics to define product quality. In this study on electrospinning of polycaprolactone (PCL), response surface methodology (RSM) was applied to investigate the determining parameters and find optimal settings to achieve the desired properties of fibrous scaffold for acetabular labrum implant. The results showed that solution concentration influenced fibre diameter, while elastic modulus was determined by solution concentration, flow rate, temperature, collector rotation speed, and interaction between concentration and temperature. Relationships between these variables and outputs were modelled, followed by an optimization procedure. Using the optimized setting (solution concentration of 10% w/v, flow rate of 4.5 mL/h, temperature of 45 °C, and collector rotation speed of 1500 RPM), a target elastic modulus of 25 MPa could be achieved at a minimum possible fibre diameter (1.39 ± 0.20 µm). This work demonstrated that multivariate factors of production parameters and multiple responses can be investigated, modelled, and optimized using RSM. PMID:29562614

An open-source software package for multivariate modeling and clustering: applications to air quality management.

PubMed

Wang, Xiuquan; Huang, Guohe; Zhao, Shan; Guo, Junhong

2015-09-01

This paper presents an open-source software package, rSCA, which is developed based upon a stepwise cluster analysis method and serves as a statistical tool for modeling the relationships between multiple dependent and independent variables. The rSCA package is efficient in dealing with both continuous and discrete variables, as well as nonlinear relationships between the variables. It divides the sample sets of dependent variables into different subsets (or subclusters) through a series of cutting and merging operations based upon the theory of multivariate analysis of variance (MANOVA). The modeling results are given by a cluster tree, which includes both intermediate and leaf subclusters as well as the flow paths from the root of the tree to each leaf subcluster specified by a series of cutting and merging actions. The rSCA package is a handy and easy-to-use tool and is freely available at http://cran.r-project.org/package=rSCA . By applying the developed package to air quality management in an urban environment, we demonstrate its effectiveness in dealing with the complicated relationships among multiple variables in real-world problems.

Analysis and control of the METC fluid bed gasifier. Final report (includes technical progress report for October 1994--January 1995), September 1994--September 1996

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

NONE

1996-09-01

This document presents a modeling and control study of the Fluid Bed Gasification (FBG) unit at the Morgantown Energy Technology Center (METC). The work is performed under contract no. DE-FG21-94MC31384. The purpose of this study is to generate a simple FBG model from process data, and then use the model to suggest an improved control scheme which will improve operation of the gasifier. The work first developes a simple linear model of the gasifier, then suggests an improved gasifier pressure and MGCR control configuration, and finally suggests the use of a multivariable control strategy for the gasifier.

Estimating suspended sediment load with multivariate adaptive regression spline, teaching-learning based optimization, and artificial bee colony models.

PubMed

Yilmaz, Banu; Aras, Egemen; Nacar, Sinan; Kankal, Murat

2018-05-23

The functional life of a dam is often determined by the rate of sediment delivery to its reservoir. Therefore, an accurate estimate of the sediment load in rivers with dams is essential for designing and predicting a dam's useful lifespan. The most credible method is direct measurements of sediment input, but this can be very costly and it cannot always be implemented at all gauging stations. In this study, we tested various regression models to estimate suspended sediment load (SSL) at two gauging stations on the Çoruh River in Turkey, including artificial bee colony (ABC), teaching-learning-based optimization algorithm (TLBO), and multivariate adaptive regression splines (MARS). These models were also compared with one another and with classical regression analyses (CRA). Streamflow values and previously collected data of SSL were used as model inputs with predicted SSL data as output. Two different training and testing dataset configurations were used to reinforce the model accuracy. For the MARS method, the root mean square error value was found to range between 35% and 39% for the test two gauging stations, which was lower than errors for other models. Error values were even lower (7% to 15%) using another dataset. Our results indicate that simultaneous measurements of streamflow with SSL provide the most effective parameter for obtaining accurate predictive models and that MARS is the most accurate model for predicting SSL. Copyright © 2017 Elsevier B.V. All rights reserved.

Sparse Multivariate Autoregressive Modeling for Mild Cognitive Impairment Classification

PubMed Central

Li, Yang; Wee, Chong-Yaw; Jie, Biao; Peng, Ziwen

2014-01-01

Brain connectivity network derived from functional magnetic resonance imaging (fMRI) is becoming increasingly prevalent in the researches related to cognitive and perceptual processes. The capability to detect causal or effective connectivity is highly desirable for understanding the cooperative nature of brain network, particularly when the ultimate goal is to obtain good performance of control-patient classification with biological meaningful interpretations. Understanding directed functional interactions between brain regions via brain connectivity network is a challenging task. Since many genetic and biomedical networks are intrinsically sparse, incorporating sparsity property into connectivity modeling can make the derived models more biologically plausible. Accordingly, we propose an effective connectivity modeling of resting-state fMRI data based on the multivariate autoregressive (MAR) modeling technique, which is widely used to characterize temporal information of dynamic systems. This MAR modeling technique allows for the identification of effective connectivity using the Granger causality concept and reducing the spurious causality connectivity in assessment of directed functional interaction from fMRI data. A forward orthogonal least squares (OLS) regression algorithm is further used to construct a sparse MAR model. By applying the proposed modeling to mild cognitive impairment (MCI) classification, we identify several most discriminative regions, including middle cingulate gyrus, posterior cingulate gyrus, lingual gyrus and caudate regions, in line with results reported in previous findings. A relatively high classification accuracy of 91.89 % is also achieved, with an increment of 5.4 % compared to the fully-connected, non-directional Pearson-correlation-based functional connectivity approach. PMID:24595922

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

PubMed

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

2018-07-01

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

Neural network-based nonlinear model predictive control vs. linear quadratic gaussian control

USGS Publications Warehouse

Cho, C.; Vance, R.; Mardi, N.; Qian, Z.; Prisbrey, K.

1997-01-01

One problem with the application of neural networks to the multivariable control of mineral and extractive processes is determining whether and how to use them. The objective of this investigation was to compare neural network control to more conventional strategies and to determine if there are any advantages in using neural network control in terms of set-point tracking, rise time, settling time, disturbance rejection and other criteria. The procedure involved developing neural network controllers using both historical plant data and simulation models. Various control patterns were tried, including both inverse and direct neural network plant models. These were compared to state space controllers that are, by nature, linear. For grinding and leaching circuits, a nonlinear neural network-based model predictive control strategy was superior to a state space-based linear quadratic gaussian controller. The investigation pointed out the importance of incorporating state space into neural networks by making them recurrent, i.e., feeding certain output state variables into input nodes in the neural network. It was concluded that neural network controllers can have better disturbance rejection, set-point tracking, rise time, settling time and lower set-point overshoot, and it was also concluded that neural network controllers can be more reliable and easy to implement in complex, multivariable plants.

A comparison of risk assessment models for term and preterm low birthweight.

PubMed

Michielutte, R; Ernest, J M; Moore, M L; Meis, P J; Sharp, P C; Wells, H B; Buescher, P A

1992-01-01

Most epidemiological research dealing with the assessment of risk for low birthweight has focused on all low birthweight births. Studies that have attempted to distinguish between term and preterm low birthweights have tended to examine preterm low birthweight, since the risk of perinatal mortality and morbidity is greatest for this group of infants. This study uses data from 25,408 singleton births in a 20-county region in North Carolina to identify and compare risk factors for term and preterm low birthweights, and also examines the usefulness of separate multivariate risk assessment systems for term and preterm low birthweights that could be used in the clinical setting. Risk factors that overlap as significant predictors of both types of low birthweight include race, no previous live births, smoking, weight under 100 lb, and previous preterm or low birthweight birth. Age also is a significant predictor of both types of low birthweight, but in opposite directions. Younger age is associated with reduced risk of term low birthweight and increased risk of pattern low birthweight. Comparison of all risk factors indicates that different multivariate models are needed to understand the epidemiology of preterm and term low birthweights. In terms of clinical value, a general risk assessment model that combines all low birthweight births is as effective as the separate models.

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

PubMed

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

2007-01-01

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

Data mining for water resource management part 2 - methods and approaches to solving contemporary problems

USGS Publications Warehouse

Roehl, Edwin A.; Conrads, Paul

2010-01-01

This is the second of two papers that describe how data mining can aid natural-resource managers with the difficult problem of controlling the interactions between hydrologic and man-made systems. Data mining is a new science that assists scientists in converting large databases into knowledge, and is uniquely able to leverage the large amounts of real-time, multivariate data now being collected for hydrologic systems. Part 1 gives a high-level overview of data mining, and describes several applications that have addressed major water resource issues in South Carolina. This Part 2 paper describes how various data mining methods are integrated to produce predictive models for controlling surface- and groundwater hydraulics and quality. The methods include: - signal processing to remove noise and decompose complex signals into simpler components; - time series clustering that optimally groups hundreds of signals into "classes" that behave similarly for data reduction and (or) divide-and-conquer problem solving; - classification which optimally matches new data to behavioral classes; - artificial neural networks which optimally fit multivariate data to create predictive models; - model response surface visualization that greatly aids in understanding data and physical processes; and, - decision support systems that integrate data, models, and graphics into a single package that is easy to use.

Multivariate exploration of non-intrusive load monitoring via spatiotemporal pattern network

DOE PAGES

Liu, Chao; Akintayo, Adedotun; Jiang, Zhanhong; ...

2017-12-18

Non-intrusive load monitoring (NILM) of electrical demand for the purpose of identifying load components has thus far mostly been studied using univariate data, e.g., using only whole building electricity consumption time series to identify a certain type of end-use such as lighting load. However, using additional variables in the form of multivariate time series data may provide more information in terms of extracting distinguishable features in the context of energy disaggregation. In this work, a novel probabilistic graphical modeling approach, namely the spatiotemporal pattern network (STPN) is proposed for energy disaggregation using multivariate time-series data. The STPN framework is shownmore » to be capable of handling diverse types of multivariate time-series to improve the energy disaggregation performance. The technique outperforms the state of the art factorial hidden Markov models (FHMM) and combinatorial optimization (CO) techniques in multiple real-life test cases. Furthermore, based on two homes' aggregate electric consumption data, a similarity metric is defined for the energy disaggregation of one home using a trained model based on the other home (i.e., out-of-sample case). The proposed similarity metric allows us to enhance scalability via learning supervised models for a few homes and deploying such models to many other similar but unmodeled homes with significantly high disaggregation accuracy.« less

Multivariate exploration of non-intrusive load monitoring via spatiotemporal pattern network

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

Liu, Chao; Akintayo, Adedotun; Jiang, Zhanhong

Non-intrusive load monitoring (NILM) of electrical demand for the purpose of identifying load components has thus far mostly been studied using univariate data, e.g., using only whole building electricity consumption time series to identify a certain type of end-use such as lighting load. However, using additional variables in the form of multivariate time series data may provide more information in terms of extracting distinguishable features in the context of energy disaggregation. In this work, a novel probabilistic graphical modeling approach, namely the spatiotemporal pattern network (STPN) is proposed for energy disaggregation using multivariate time-series data. The STPN framework is shownmore » to be capable of handling diverse types of multivariate time-series to improve the energy disaggregation performance. The technique outperforms the state of the art factorial hidden Markov models (FHMM) and combinatorial optimization (CO) techniques in multiple real-life test cases. Furthermore, based on two homes' aggregate electric consumption data, a similarity metric is defined for the energy disaggregation of one home using a trained model based on the other home (i.e., out-of-sample case). The proposed similarity metric allows us to enhance scalability via learning supervised models for a few homes and deploying such models to many other similar but unmodeled homes with significantly high disaggregation accuracy.« less

A High-Dimensional, Multivariate Copula Approach to Modeling Multivariate Agricultural Price Relationships and Tail Dependencies

Treesearch

Xuan Chi; Barry Goodwin

2012-01-01

Spatial and temporal relationships among agricultural prices have been an important topic of applied research for many years. Such research is used to investigate the performance of markets and to examine linkages up and down the marketing chain. This research has empirically evaluated price linkages by using correlation and regression models and, later, linear and...

Validation of cross-sectional time series and multivariate adaptive regression splines models for the prediction of energy expenditure in children and adolescents using doubly labeled water

USDA-ARS?s Scientific Manuscript database

Accurate, nonintrusive, and inexpensive techniques are needed to measure energy expenditure (EE) in free-living populations. Our primary aim in this study was to validate cross-sectional time series (CSTS) and multivariate adaptive regression splines (MARS) models based on observable participant cha...

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.

Copula-based prediction of economic movements

NASA Astrophysics Data System (ADS)

García, J. E.; González-López, V. A.; Hirsh, I. D.

2016-06-01

In this paper we model the discretized returns of two paired time series BM&FBOVESPA Dividend Index and BM&FBOVESPA Public Utilities Index using multivariate Markov models. The discretization corresponds to three categories, high losses, high profits and the complementary periods of the series. In technical terms, the maximal memory that can be considered for a Markov model, can be derived from the size of the alphabet and dataset. The number of parameters needed to specify a discrete multivariate Markov chain grows exponentially with the order and dimension of the chain. In this case the size of the database is not large enough for a consistent estimation of the model. We apply a strategy to estimate a multivariate process with an order greater than the order achieved using standard procedures. The new strategy consist on obtaining a partition of the state space which is constructed from a combination, of the partitions corresponding to the two marginal processes and the partition corresponding to the multivariate Markov chain. In order to estimate the transition probabilities, all the partitions are linked using a copula. In our application this strategy provides a significant improvement in the movement predictions.

Cross-country transferability of multi-variable damage models

NASA Astrophysics Data System (ADS)

Wagenaar, Dennis; Lüdtke, Stefan; Kreibich, Heidi; Bouwer, Laurens

2017-04-01

Flood damage assessment is often done with simple damage curves based only on flood water depth. Additionally, damage models are often transferred in space and time, e.g. from region to region or from one flood event to another. Validation has shown that depth-damage curve estimates are associated with high uncertainties, particularly when applied in regions outside the area where the data for curve development was collected. Recently, progress has been made with multi-variable damage models created with data-mining techniques, i.e. Bayesian Networks and random forest. However, it is still unknown to what extent and under which conditions model transfers are possible and reliable. Model validations in different countries will provide valuable insights into the transferability of multi-variable damage models. In this study we compare multi-variable models developed on basis of flood damage datasets from Germany as well as from The Netherlands. Data from several German floods was collected using computer aided telephone interviews. Data from the 1993 Meuse flood in the Netherlands is available, based on compensations paid by the government. The Bayesian network and random forest based models are applied and validated in both countries on basis of the individual datasets. A major challenge was the harmonization of the variables between both datasets due to factors like differences in variable definitions, and regional and temporal differences in flood hazard and exposure characteristics. Results of model validations and comparisons in both countries are discussed, particularly in respect to encountered challenges and possible solutions for an improvement of model transferability.

Multi-state succession in wetlands: a novel use of state and transition models

USGS Publications Warehouse

Zweig, Christa L.; Kitchens, Wiley M.

2009-01-01

The complexity of ecosystems and mechanisms of succession are often simplified by linear and mathematical models used to understand and predict system behavior. Such models often do not incorporate multivariate, nonlinear feedbacks in pattern and process that include multiple scales of organization inherent within real-world systems. Wetlands are ecosystems with unique, nonlinear patterns of succession due to the regular, but often inconstant, presence of water on the landscape. We develop a general, nonspatial state and transition (S and T) succession conceptual model for wetlands and apply the general framework by creating annotated succession/management models and hypotheses for use in impact analysis on a portion of an imperiled wetland. The S and T models for our study area, Water Conservation Area 3A South (WCA3), Florida, USA, included hydrologic and peat depth values from multivariate analyses and classification and regression trees. We used the freeware Vegetation Dynamics Development Tool as an exploratory application to evaluate our S and T models with different management actions (equal chance [a control condition], deeper conditions, dry conditions, and increased hydrologic range) for three communities: slough, sawgrass (Cladium jamaicense), and wet prairie. Deeper conditions and increased hydrologic range behaved similarly, with the transition of community states to deeper states, particularly for sawgrass and slough. Hydrology is the primary mechanism for multi-state transitions within our study period, and we show both an immediate and lagged effect on vegetation, depending on community state. We consider these S and T succession models as a fraction of the framework for the Everglades. They are hypotheses for use in adaptive management, represent the community response to hydrology, and illustrate which aspects of hydrologic variability are important to community structure. We intend for these models to act as a foundation for further restoration management and experimentation which will refine transition and threshold concepts. 

Multivariate Prediction Equations for HbA1c Lowering, Weight Change, and Hypoglycemic Events Associated with Insulin Rescue Medication in Type 2 Diabetes Mellitus: Informing Economic Modeling.

PubMed

Willis, Michael; Asseburg, Christian; Nilsson, Andreas; Johnsson, Kristina; Kartman, Bernt

2017-03-01

Type 2 diabetes mellitus (T2DM) is chronic and progressive and the cost-effectiveness of new treatment interventions must be established over long time horizons. Given the limited durability of drugs, assumptions regarding downstream rescue medication can drive results. Especially for insulin, for which treatment effects and adverse events are known to depend on patient characteristics, this can be problematic for health economic evaluation involving modeling. To estimate parsimonious multivariate equations of treatment effects and hypoglycemic event risks for use in parameterizing insulin rescue therapy in model-based cost-effectiveness analysis. Clinical evidence for insulin use in T2DM was identified in PubMed and from published reviews and meta-analyses. Study and patient characteristics and treatment effects and adverse event rates were extracted and the data used to estimate parsimonious treatment effect and hypoglycemic event risk equations using multivariate regression analysis. Data from 91 studies featuring 171 usable study arms were identified, mostly for premix and basal insulin types. Multivariate prediction equations for glycated hemoglobin A 1c lowering and weight change were estimated separately for insulin-naive and insulin-experienced patients. Goodness of fit (R 2 ) for both outcomes were generally good, ranging from 0.44 to 0.84. Multivariate prediction equations for symptomatic, nocturnal, and severe hypoglycemic events were also estimated, though considerable heterogeneity in definitions limits their usefulness. Parsimonious and robust multivariate prediction equations were estimated for glycated hemoglobin A 1c and weight change, separately for insulin-naive and insulin-experienced patients. Using these in economic simulation modeling in T2DM can improve realism and flexibility in modeling insulin rescue medication. Copyright © 2017 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

A multivariate mixed model system for wood specific gravity and moisture content of planted loblolly pine stands in the southern United States

Treesearch

Finto Antony; Laurence R. Schimleck; Alex Clark; Richard F. Daniels

2012-01-01

Specific gravity (SG) and moisture content (MC) both have a strong influence on the quantity and quality of wood fiber. We proposed a multivariate mixed model system to model the two properties simultaneously. Disk SG and MC at different height levels were measured from 3 trees in 135 stands across the natural range of loblolly pine and the stand level values were used...

A multivariate fall risk assessment model for VHA nursing homes using the minimum data set.

PubMed

French, Dustin D; Werner, Dennis C; Campbell, Robert R; Powell-Cope, Gail M; Nelson, Audrey L; Rubenstein, Laurence Z; Bulat, Tatjana; Spehar, Andrea M

2007-02-01

The purpose of this study was to develop a multivariate fall risk assessment model beyond the current fall Resident Assessment Protocol (RAP) triggers for nursing home residents using the Minimum Data Set (MDS). Retrospective, clustered secondary data analysis. National Veterans Health Administration (VHA) long-term care nursing homes (N = 136). The study population consisted of 6577 national VHA nursing home residents who had an annual assessment during FY 2005, identified from the MDS, as well as an earlier annual or admission assessment within a 1-year look-back period. A dichotomous multivariate model of nursing home residents coded with a fall on selected fall risk characteristics from the MDS, estimated with general estimation equations (GEE). There were 17 170 assessments corresponding to 6577 long-term care nursing home residents. The increased odds ratio (OR) of being classified as a faller relative to the omitted "dependent" category of activities of daily living (ADL) ranged from OR = 1.35 for "limited" ADL category up to OR = 1.57 for "extensive-2" ADL (P < .0001). Unsteady gait more than doubles the odds of being a faller (OR = 2.63, P < .0001). The use of assistive devices such as canes, walkers, or crutches, or the use of wheelchairs increases the odds of being a faller (OR = 1.17, P < .0005) or (OR = 1.19, P < .0002), respectively. Foot problems may also increase the odds of being a faller (OR = 1.26, P < .0016). Alzheimer's or other dementias also increase the odds of being classified as a faller (OR = 1.18, P < .0219) or (OR=1.22, P < .0001), respectively. In addition, anger (OR = 1.19, P < .0065); wandering (OR = 1.53, P < .0001); or use of antipsychotic medications (OR = 1.15, P < .0039), antianxiety medications (OR = 1.13, P < .0323), or antidepressant medications (OR = 1.39, P < .0001) was also associated with the odds of being a faller. This national study in one of the largest managed healthcare systems in the United States has empirically confirmed the relative importance of certain risk factors for falls in long-term care settings. The model incorporated an ADL index and adjusted for case mix by including only long-term care nursing home residents. The study offers clinicians practical estimates by combining multiple univariate MDS elements in an empirically based, multivariate fall risk assessment model.

Development and validation of a Partial Least Squares-Discriminant Analysis (PLS-DA) model based on the determination of ethyl glucuronide (EtG) and fatty acid ethyl esters (FAEEs) in hair for the diagnosis of chronic alcohol abuse.

PubMed

Alladio, E; Giacomelli, L; Biosa, G; Corcia, D Di; Gerace, E; Salomone, A; Vincenti, M

2018-01-01

The chronic intake of an excessive amount of alcohol is currently ascertained by determining the concentration of direct alcohol metabolites in the hair samples of the alleged abusers, including ethyl glucuronide (EtG) and, less frequently, fatty acid ethyl esters (FAEEs). Indirect blood biomarkers of alcohol abuse are still determined to support hair EtG results and diagnose a consequent liver impairment. In the present study, the supporting role of hair FAEEs is compared with indirect blood biomarkers with respect to the contexts in which hair EtG interpretation is uncertain. Receiver Operating Characteristics (ROC) curves and multivariate Principal Component Analysis (PCA) demonstrated much stronger correlation of EtG results with FAEEs than with any single indirect biomarker or their combinations. Partial Least Squares Discriminant Analysis (PLS-DA) models based on hair EtG and FAEEs were developed to maximize the biomarkers information content on a multivariate background. The final PLS-DA model yielded 100% correct classification on a training/evaluation dataset of 155 subjects, including both chronic alcohol abusers and social drinkers. Then, the PLS-DA model was validated on an external dataset of 81 individual providing optimal discrimination ability between chronic alcohol abusers and social drinkers, in terms of specificity and sensitivity. The PLS-DA scores obtained for each subject, with respect to the PLS-DA model threshold that separates the probabilistic distributions for the two classes, furnished a likelihood ratio value, which in turn conveys the strength of the experimental data support to the classification decision, within a Bayesian logic. Typical boundary real cases from daily work are discussed, too. Copyright © 2017 Elsevier B.V. All rights reserved.

Inherited genetic variants associated with occurrence of multiple primary melanoma.

PubMed

Gibbs, David C; Orlow, Irene; Kanetsky, Peter A; Luo, Li; Kricker, Anne; Armstrong, Bruce K; Anton-Culver, Hoda; Gruber, Stephen B; Marrett, Loraine D; Gallagher, Richard P; Zanetti, Roberto; Rosso, Stefano; Dwyer, Terence; Sharma, Ajay; La Pilla, Emily; From, Lynn; Busam, Klaus J; Cust, Anne E; Ollila, David W; Begg, Colin B; Berwick, Marianne; Thomas, Nancy E

2015-06-01

Recent studies, including genome-wide association studies, have identified several putative low-penetrance susceptibility loci for melanoma. We sought to determine their generalizability to genetic predisposition for multiple primary melanoma in the international population-based Genes, Environment, and Melanoma (GEM) Study. GEM is a case-control study of 1,206 incident cases of multiple primary melanoma and 2,469 incident first primary melanoma participants as the control group. We investigated the odds of developing multiple primary melanoma for 47 SNPs from 21 distinct genetic regions previously reported to be associated with melanoma. ORs and 95% confidence intervals were determined using logistic regression models adjusted for baseline features (age, sex, age by sex interaction, and study center). We investigated univariable models and built multivariable models to assess independent effects of SNPs. Eleven SNPs in 6 gene neighborhoods (TERT/CLPTM1L, TYRP1, MTAP, TYR, NCOA6, and MX2) and a PARP1 haplotype were associated with multiple primary melanoma. In a multivariable model that included only the most statistically significant findings from univariable modeling and adjusted for pigmentary phenotype, back nevi, and baseline features, we found TERT/CLPTM1L rs401681 (P = 0.004), TYRP1 rs2733832 (P = 0.006), MTAP rs1335510 (P = 0.0005), TYR rs10830253 (P = 0.003), and MX2 rs45430 (P = 0.008) to be significantly associated with multiple primary melanoma, while NCOA6 rs4911442 approached significance (P = 0.06). The GEM Study provides additional evidence for the relevance of these genetic regions to melanoma risk and estimates the magnitude of the observed genetic effect on development of subsequent primary melanoma. ©2015 American Association for Cancer Research.

Inherited genetic variants associated with occurrence of multiple primary melanoma

PubMed Central

Gibbs, David C.; Orlow, Irene; Kanetsky, Peter A.; Luo, Li; Kricker, Anne; Armstrong, Bruce K.; Anton-Culver, Hoda; Gruber, Stephen B.; Marrett, Loraine D.; Gallagher, Richard P.; Zanetti, Roberto; Rosso, Stefano; Dwyer, Terence; Sharma, Ajay; La Pilla, Emily; From, Lynn; Busam, Klaus J.; Cust, Anne E.; Ollila, David W.; Begg, Colin B.; Berwick, Marianne; Thomas, Nancy E.

2015-01-01

Recent studies including genome-wide association studies have identified several putative low-penetrance susceptibility loci for melanoma. We sought to determine their generalizability to genetic predisposition for multiple primary melanoma in the international population-based Genes, Environment, and Melanoma (GEM) Study. GEM is a case-control study of 1,206 incident cases of multiple primary melanoma and 2,469 incident first primary melanoma participants as the control group. We investigated the odds of developing multiple primary melanoma for 47 single nucleotide polymorphisms (SNP) from 21 distinct genetic regions previously reported to be associated with melanoma. ORs and 95% CIs were determined using logistic regression models adjusted for baseline features (age, sex, age by sex interaction, and study center). We investigated univariable models and built multivariable models to assess independent effects of SNPs. Eleven SNPs in 6 gene neighborhoods (TERT/CLPTM1L, TYRP1, MTAP, TYR, NCOA6, and MX2) and a PARP1 haplotype were associated with multiple primary melanoma. In a multivariable model that included only the most statistically significant findings from univariable modeling and adjusted for pigmentary phenotype, back nevi, and baseline features, we found TERT/CLPTM1L rs401681 (P = 0.004), TYRP1 rs2733832 (P = 0.006), MTAP rs1335510 (P = 0.0005), TYR rs10830253 (P = 0.003), and MX2 rs45430 (P = 0.008) to be significantly associated with multiple primary melanoma while NCOA6 rs4911442 approached significance (P = 0.06). The GEM study provides additional evidence for the relevance of these genetic regions to melanoma risk and estimates the magnitude of the observed genetic effect on development of subsequent primary melanoma. PMID:25837821

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

PubMed

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

2013-01-01

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

Multivariate curve resolution-alternating least squares and kinetic modeling applied to near-infrared data from curing reactions of epoxy resins: mechanistic approach and estimation of kinetic rate constants.

PubMed

Garrido, M; Larrechi, M S; Rius, F X

2006-02-01

This study describes the combination of multivariate curve resolution-alternating least squares with a kinetic modeling strategy for obtaining the kinetic rate constants of a curing reaction of epoxy resins. The reaction between phenyl glycidyl ether and aniline is monitored by near-infrared spectroscopy under isothermal conditions for several initial molar ratios of the reagents. The data for all experiments, arranged in a column-wise augmented data matrix, are analyzed using multivariate curve resolution-alternating least squares. The concentration profiles recovered are fitted to a chemical model proposed for the reaction. The selection of the kinetic model is assisted by the information contained in the recovered concentration profiles. The nonlinear fitting provides the kinetic rate constants. The optimized rate constants are in agreement with values reported in the literature.

Herd-level risk factors for lameness in freestall farms in the northeastern United States and California.

PubMed

Chapinal, N; Barrientos, A K; von Keyserlingk, M A G; Galo, E; Weary, D M

2013-01-01

The objective was to investigate the association between herd-level management and facility design factors and the prevalence of lameness in high-producing dairy cows in freestall herds in the northeastern United States (NE; Vermont, New York, Pennsylvania) and California (CA). Housing and management measures such as pen space, stall design, bedding type, and milking routine were collected for the high-producing pen in 40 farms in NE and 39 farms in CA. All cows in the pen were gait scored using a 1-to-5 scale and classified as clinically lame (score ≥3) or severely lame (score ≥4). Measures associated with the (logit-transformed) proportion of clinically or severely lame cows at the univariable level were submitted to multivariable general linear models. In NE, lameness increased on farms that used sawdust bedding [odds ratio (OR)=1.71; 95% confidence interval (CI)=1.06-2.76] and decreased with herd size (OR=0.94; CI=0.90-0.97, for a 100-cow increase), use of deep bedding (OR=0.48; CI=0.29-0.79), and access to pasture (OR=0.52; CI=0.32-0.85). The multivariable model included herd size, access to pasture, and provision of deep bedding, and explained 50% of the variation in clinical lameness. Severe lameness increased with the percentage of stalls with fecal contamination (OR=1.15; CI=1.06-1.25, for a 10% increase) and with use of sawdust bedding (OR=2.13; CI=1.31-3.47), and decreased with use of deep bedding (OR=0.31; CI=0.19-0.50), sand bedding (OR=0.32; CI=0.19-0.53), herd size (OR=0.93; CI=-0.89-0.97, for a 100-cow increase), and rearing replacement heifers on site (OR=0.57; CI=0.32-0.99). The multivariable model included deep bedding and herd size, and explained 59% of the variation of severe lameness. In CA, clinical lameness increased with the percentage of stalls containing fecal contamination (OR=1.15; CI=1.05-1.26, for a 10% increase), and decreased with herd size (OR=0.96; CI=0.94-0.99, for a 100-cow increase), presence of rubber in the alley to the milking parlor (OR=0.46; CI=0.28-0.76), distance of the neck rail from the rear curb (OR=0.97; CI=0.95-0.99, for a 1-cm increase), water space per cow (OR=0.92; CI=0.85-0.99, for a 1-cm increase), and increased frequency of footbaths per week (OR=0.90; CI=081-0.99, for a 1-unit increase). The multivariable model included herd size, percentage of stalls containing fecal contamination, and presence of rubber in the alley to the milking parlor, and explained 44% of the variation of clinical lameness. Severe lameness increased with the percentage of stalls containing fecal contamination (OR=1.23; CI=1.06-1.42, for a 10% increase) and decreased with frequency of manure removal in the pen per day (OR=0.72; CI=0.53-0.97, for a 1-unit increase). The final model included both variables and explained 28% of the variation in severe lameness. In conclusion, changes in housing and management may help decrease the prevalence of lameness on dairy farms, but key risk factors vary across regions. Copyright © 2013 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Prostate Health Index (PHI) Predicts High-stage Pathology in African American Men.

PubMed

Schwen, Zeyad R; Tosoian, Jeffrey J; Sokoll, Lori J; Mangold, Leslie; Humphreys, Elizabeth; Schaeffer, Edward M; Partin, Alan W; Ross, Ashley E

2016-04-01

To evaluate the association between the Prostate Health Index (PHI) and adverse pathology in a cohort of African American (AA) men undergoing radical prostatectomy. Eighty AA men with prostate-specific antigen (PSA) of 2-10 ng/mL underwent measurement of PSA, free PSA (fPSA), and p2PSA prior to radical prostatectomy. PHI was calculated as [(p2PSA/fPSA) × (PSA)(½)]. Biomarker association with pT3 disease was assessed using logistic regression, and covariates were added to a baseline multivariable model including digital rectal examination. Biomarker ability to predict pT3 disease was measured using the area under the receiver operator characteristic curve. Sixteen men (20%) demonstrated pT3 disease on final pathology. Mean age, PSA, and %fPSA were similar in men with and without pT3 disease (all P  >  .05), whereas PHI was significantly greater in men with pT3 disease (mean 57.2 vs 46.6, P  =  .04). Addition of PHI to the baseline multivariable model improved discriminative ability by 12.9% (P  =. .04) and yielded greater diagnostic accuracy than models, including other individual biomarkers. In AA men with PSA of 2-10 ng/mL, PHI was predictive of pT3 prostate cancer and may help to identify men at increased risk of adverse pathology. Additional studies are needed to substantiate these findings and identify appropriate thresholds for clinical use. Copyright © 2016 Elsevier Inc. All rights reserved.

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.

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

NASA Astrophysics Data System (ADS)

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

2017-10-01

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

Realizing Women Living with HIV's Reproductive Rights in the Era of ART: The Negative Impact of Non-consensual HIV Disclosure on Pregnancy Decisions Amongst Women Living with HIV in a Canadian Setting.

PubMed

Duff, Putu; Kestler, Mary; Chamboko, Patience; Braschel, Melissa; Ogilvie, Gina; Krüsi, Andrea; Montaner, Julio; Money, Deborah; Shannon, Kate

2018-04-07

To better understand the structural drivers of women living with HIV's (WLWH's) reproductive rights and choices, this study examined the structural correlates, including non-consensual HIV disclosure, on WLWH's pregnancy decisions and describes access to preconception care. Analyses drew on data (2014-present) from SHAWNA, a longitudinal community-based cohort with WLWH across Metro-Vancouver, Canada. Multivariable logistic regression was used to model the effect of non-consensual HIV disclosure on WLWH's pregnancy decisions. Of the 218 WLWH included in our analysis, 24.8% had ever felt discouraged from becoming pregnant and 11.5% reported accessing preconception counseling. In multivariable analyses, non-consensual HIV disclosure was positively associated with feeling discouraged from wanting to become pregnant (AOR 3.76; 95% CI 1.82-7.80). Non-consensual HIV disclosure adversely affects WLWH's pregnancy decisions. Supporting the reproductive rights of WLWH will require further training among general practitioners on the reproductive health of WLWH and improved access to women-centred, trauma-informed care, including non-judgmental preconception counseling.

Synthesis of a control model for a liquid nitrogen cooled, closed circuit, cryogenic nitrogen wind tunnel and its validation

NASA Technical Reports Server (NTRS)

Balakrishna, S.; Goglia, G. L.

1979-01-01

The details of the efforts to synthesize a control-compatible multivariable model of a liquid nitrogen cooled, gaseous nitrogen operated, closed circuit, cryogenic pressure tunnel are presented. The synthesized model was transformed into a real-time cryogenic tunnel simulator, and this model is validated by comparing the model responses to the actual tunnel responses of the 0.3 m transonic cryogenic tunnel, using the quasi-steady-state and the transient responses of the model and the tunnel. The global nature of the simple, explicit, lumped multivariable model of a closed circuit cryogenic tunnel is demonstrated.

Empirical Modeling of Plant Gas Fluxes in Controlled Environments

NASA Technical Reports Server (NTRS)

Cornett, Jessie David

1994-01-01

As humans extend their reach beyond the earth, bioregenerative life support systems must replace the resupply and physical/chemical systems now used. The Controlled Ecological Life Support System (CELSS) will utilize plants to recycle the carbon dioxide (CO2) and excrement produced by humans and return oxygen (O2), purified water and food. CELSS design requires knowledge of gas flux levels for net photosynthesis (PS(sub n)), dark respiration (R(sub d)) and evapotranspiration (ET). Full season gas flux data regarding these processes for wheat (Triticum aestivum), soybean (Glycine max) and rice (Oryza sativa) from published sources were used to develop empirical models. Univariate models relating crop age (days after planting) and gas flux were fit by simple regression. Models are either high order (5th to 8th) or more complex polynomials whose curves describe crop development characteristics. The models provide good estimates of gas flux maxima, but are of limited utility. To broaden the applicability, data were transformed to dimensionless or correlation formats and, again, fit by regression. Polynomials, similar to those in the initial effort, were selected as the most appropriate models. These models indicate that, within a cultivar, gas flux patterns appear remarkably similar prior to maximum flux, but exhibit considerable variation beyond this point. This suggests that more broadly applicable models of plant gas flux are feasible, but univariate models defining gas flux as a function of crop age are too simplistic. Multivariate models using CO2 and crop age were fit for PS(sub n), and R(sub d) by multiple regression. In each case, the selected model is a subset of a full third order model with all possible interactions. These models are improvements over the univariate models because they incorporate more than the single factor, crop age, as the primary variable governing gas flux. They are still limited, however, by their reliance on the other environmental conditions under which the original data were collected. Three-dimensional plots representing the response surface of each model are included. Suitability of using empirical models to generate engineering design estimates is discussed. Recommendations for the use of more complex multivariate models to increase versatility are included.

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

Bhattacherjee, Biplob; Mukhopadhyay, Satyanarayan; Nojiri, Mihoko M.

Here, we study the impact of including quark- and gluon-initiated jet discrimination in the search for strongly interacting supersymmetric particles at the LHC. Taking the example of gluino pair production, considerable improvement is observed in the LHC search reach on including the jet substructure observables to the standard kinematic variables within a multivariate analysis. In particular, quark and gluon jet separation has higher impact in the region of intermediate mass-gap between the gluino and the lightest neutralino, as the difference between the signal and the standard model background kinematic distributions is reduced in this region. We also compare the predictionsmore » from different Monte Carlo event generators to estimate the uncertainty originating from the modelling of the parton shower and hadronization processes.« less

Uncertainty Modeling for Robustness Analysis of Control Upset Prevention and Recovery Systems

NASA Technical Reports Server (NTRS)

Belcastro, Christine M.; Khong, Thuan H.; Shin, Jong-Yeob; Kwatny, Harry; Chang, Bor-Chin; Balas, Gary J.

2005-01-01

Formal robustness analysis of aircraft control upset prevention and recovery systems could play an important role in their validation and ultimate certification. Such systems (developed for failure detection, identification, and reconfiguration, as well as upset recovery) need to be evaluated over broad regions of the flight envelope and under extreme flight conditions, and should include various sources of uncertainty. However, formulation of linear fractional transformation (LFT) models for representing system uncertainty can be very difficult for complex parameter-dependent systems. This paper describes a preliminary LFT modeling software tool which uses a matrix-based computational approach that can be directly applied to parametric uncertainty problems involving multivariate matrix polynomial dependencies. Several examples are presented (including an F-16 at an extreme flight condition, a missile model, and a generic example with numerous crossproduct terms), and comparisons are given with other LFT modeling tools that are currently available. The LFT modeling method and preliminary software tool presented in this paper are shown to compare favorably with these methods.

Presence of Ureaplasma diversum in the genital tracts of female dairy cattle in Mato Grosso State, Brazil.

PubMed

Azevedo, Jaqueline B; Silva, Gustavo S; Rocha, Priscylla S; Pitchenin, Letícia C; Dutra, Valéria; Nakazato, Luciano; de Oliveira, Anderson Castro Soares; Pescador, Caroline A

2017-02-01

Ureaplasma diversum infection in bovine females may result in various reproductive problems, including granular vulvovaginitis, abortion, weak calves, salpingitis, and spontaneous abortion. The presence of U. diversum in a dairy bovine population from midwestern Brazil has not been established. The aim of this study was to determine whether U. diversum was present in dairy cattle from midwestern Brazil using polymerase chain reaction (PCR). Vulvovaginal mucus was analyzed from 203 cows located in six municipalities in the north region of Mato Grosso State, Brazil. A total of 25% of dairy cows with vulvovaginitis were positive for U. diversum. The factors evaluated were included in a multivariable logistic regression model with the presence of at least one positive cow in the herd serving as the dependent variable. Three variables were significantly associated with a U. diversum-positive PCR and were included in the final multivariable model: number of parities, vulvar lesions, and reproductive problems. For each new parity, the chance of U. diversum infection decreased 0.03-fold, indicating that cows with the highest number of parities were more protected. The presence of vulvar lesions was increased 17.6-fold in females positive for U. diversum, suggesting that this bacterium could be related to the red granular lesions in the vulvar mucosa, whereas reproductive problems were increased 7.6-fold. However, further investigations should be conducted to ascertain the effects of U. diversum in association with other mycoplasma species in the herds studied.

The integrated manual and automatic control of complex flight systems

NASA Technical Reports Server (NTRS)

Schmidt, David K.

1991-01-01

Research dealt with the general area of optimal flight control synthesis for manned flight vehicles. The work was generic; no specific vehicle was the focus of study. However, the class of vehicles generally considered were those for which high authority, multivariable control systems might be considered, for the purpose of stabilization and the achievement of optimal handling characteristics. Within this scope, the topics of study included several optimal control synthesis techniques, control-theoretic modeling of the human operator in flight control tasks, and the development of possible handling qualities metrics and/or measures of merit. Basic contributions were made in all these topics, including human operator (pilot) models for multi-loop tasks, optimal output feedback flight control synthesis techniques; experimental validations of the methods developed, and fundamental modeling studies of the air-to-air tracking and flared landing tasks.

Derivation and External Validation of Prediction Models for Advanced Chronic Kidney Disease Following Acute Kidney Injury

PubMed Central

Pannu, Neesh; Hemmelgarn, Brenda R.; Austin, Peter C.; Tan, Zhi; McArthur, Eric; Manns, Braden J.; Tonelli, Marcello; Wald, Ron; Quinn, Robert R.; Ravani, Pietro; Garg, Amit X.

2017-01-01

Importance Some patients will develop chronic kidney disease after a hospitalization with acute kidney injury; however, no risk-prediction tools have been developed to identify high-risk patients requiring follow-up. Objective To derive and validate predictive models for progression of acute kidney injury to advanced chronic kidney disease. Design, Setting, and Participants Data from 2 population-based cohorts of patients with a prehospitalization estimated glomerular filtration rate (eGFR) of more than 45 mL/min/1.73 m2 and who had survived hospitalization with acute kidney injury (defined by a serum creatinine increase during hospitalization > 0.3 mg/dL or > 50% of their prehospitalization baseline), were used to derive and validate multivariable prediction models. The risk models were derived from 9973 patients hospitalized in Alberta, Canada (April 2004-March 2014, with follow-up to March 2015). The risk models were externally validated with data from a cohort of 2761 patients hospitalized in Ontario, Canada (June 2004-March 2012, with follow-up to March 2013). Exposures Demographic, laboratory, and comorbidity variables measured prior to discharge. Main Outcomes and Measures Advanced chronic kidney disease was defined by a sustained reduction in eGFR less than 30 mL/min/1.73 m2 for at least 3 months during the year after discharge. All participants were followed up for up to 1 year. Results The participants (mean [SD] age, 66 [15] years in the derivation and internal validation cohorts and 69 [11] years in the external validation cohort; 40%-43% women per cohort) had a mean (SD) baseline serum creatinine level of 1.0 (0.2) mg/dL and more than 20% had stage 2 or 3 acute kidney injury. Advanced chronic kidney disease developed in 408 (2.7%) of 9973 patients in the derivation cohort and 62 (2.2%) of 2761 patients in the external validation cohort. In the derivation cohort, 6 variables were independently associated with the outcome: older age, female sex, higher baseline serum creatinine value, albuminuria, greater severity of acute kidney injury, and higher serum creatinine value at discharge. In the external validation cohort, a multivariable model including these 6 variables had a C statistic of 0.81 (95% CI, 0.75-0.86) and improved discrimination and reclassification compared with reduced models that included age, sex, and discharge serum creatinine value alone (integrated discrimination improvement, 2.6%; 95% CI, 1.1%-4.0%; categorical net reclassification index, 13.5%; 95% CI, 1.9%-25.1%) or included age, sex, and acute kidney injury stage alone (integrated discrimination improvement, 8.0%; 95% CI, 5.1%-11.0%; categorical net reclassification index, 79.9%; 95% CI, 60.9%-98.9%). Conclusions and Relevance A multivariable model using routine laboratory data was able to predict advanced chronic kidney disease following hospitalization with acute kidney injury. The utility of this model in clinical care requires further research. PMID:29136443

Derivation and External Validation of Prediction Models for Advanced Chronic Kidney Disease Following Acute Kidney Injury.

PubMed

James, Matthew T; Pannu, Neesh; Hemmelgarn, Brenda R; Austin, Peter C; Tan, Zhi; McArthur, Eric; Manns, Braden J; Tonelli, Marcello; Wald, Ron; Quinn, Robert R; Ravani, Pietro; Garg, Amit X

2017-11-14

Some patients will develop chronic kidney disease after a hospitalization with acute kidney injury; however, no risk-prediction tools have been developed to identify high-risk patients requiring follow-up. To derive and validate predictive models for progression of acute kidney injury to advanced chronic kidney disease. Data from 2 population-based cohorts of patients with a prehospitalization estimated glomerular filtration rate (eGFR) of more than 45 mL/min/1.73 m2 and who had survived hospitalization with acute kidney injury (defined by a serum creatinine increase during hospitalization > 0.3 mg/dL or > 50% of their prehospitalization baseline), were used to derive and validate multivariable prediction models. The risk models were derived from 9973 patients hospitalized in Alberta, Canada (April 2004-March 2014, with follow-up to March 2015). The risk models were externally validated with data from a cohort of 2761 patients hospitalized in Ontario, Canada (June 2004-March 2012, with follow-up to March 2013). Demographic, laboratory, and comorbidity variables measured prior to discharge. Advanced chronic kidney disease was defined by a sustained reduction in eGFR less than 30 mL/min/1.73 m2 for at least 3 months during the year after discharge. All participants were followed up for up to 1 year. The participants (mean [SD] age, 66 [15] years in the derivation and internal validation cohorts and 69 [11] years in the external validation cohort; 40%-43% women per cohort) had a mean (SD) baseline serum creatinine level of 1.0 (0.2) mg/dL and more than 20% had stage 2 or 3 acute kidney injury. Advanced chronic kidney disease developed in 408 (2.7%) of 9973 patients in the derivation cohort and 62 (2.2%) of 2761 patients in the external validation cohort. In the derivation cohort, 6 variables were independently associated with the outcome: older age, female sex, higher baseline serum creatinine value, albuminuria, greater severity of acute kidney injury, and higher serum creatinine value at discharge. In the external validation cohort, a multivariable model including these 6 variables had a C statistic of 0.81 (95% CI, 0.75-0.86) and improved discrimination and reclassification compared with reduced models that included age, sex, and discharge serum creatinine value alone (integrated discrimination improvement, 2.6%; 95% CI, 1.1%-4.0%; categorical net reclassification index, 13.5%; 95% CI, 1.9%-25.1%) or included age, sex, and acute kidney injury stage alone (integrated discrimination improvement, 8.0%; 95% CI, 5.1%-11.0%; categorical net reclassification index, 79.9%; 95% CI, 60.9%-98.9%). A multivariable model using routine laboratory data was able to predict advanced chronic kidney disease following hospitalization with acute kidney injury. The utility of this model in clinical care requires further research.

Maternal dietary intake during pregnancy and offspring body composition: The Healthy Start Study.

PubMed

Crume, Tessa L; Brinton, John T; Shapiro, Allison; Kaar, Jill; Glueck, Deborah H; Siega-Riz, Anna Maria; Dabelea, Dana

2016-11-01

Consistent evidence of an influence of maternal dietary intake during pregnancy on infant body size and composition in human populations is lacking, despite robust evidence in animal models. We sought to evaluate the influence of maternal macronutrient intake and balance during pregnancy on neonatal body size and composition, including fat mass and fat-free mass. The analysis was conducted among 1040 mother-offspring pairs enrolled in the prospective prebirth observational cohort: the Healthy Start Study. Diet during pregnancy was collected using repeated 24-hour dietary recalls (up to 8). Direct measures of body composition were obtained using air displacement plethysmography. The National Cancer Institute measurement error model was used to estimate usual dietary intake during pregnancy. Multivariable partition (nonisocaloric) and nutrient density (isocaloric) linear regression models were used to test the associations between maternal dietary intake and neonatal body composition. The median macronutrient composition during pregnancy was 32.2% from fat, 15.0% from protein, and 47.8% from carbohydrates. In the partition multivariate regression model, individual macronutrient intake values were not associated with birthweight or fat-free mass, but were associated with fat mass. Respectively, 418 kJ increases in total fat, saturated fat, unsaturated fat, and total carbohydrates were associated with 4.2-g (P = .03), 11.1-g (P = .003), 5.9-g (P = .04), and 2.9-g (P = .02) increases in neonatal fat mass, independent of prepregnancy body mass index. In the nutrient density multivariate regression model, macronutrient balance was not associated with fat mass, fat-free mass, or birthweight after adjustment for prepregnancy body mass index. Neonatal adiposity, but not birthweight, is independently associated with increased maternal intake of total fat, saturated fat, unsaturated fat, and total carbohydrates, but not protein, suggesting that most forms of increased caloric intake contribute to fetal fat accretion. Copyright © 2016 Elsevier Inc. All rights reserved.

The genetic basis for cognitive ability, memory, and depression symptomatology in middle-aged and elderly chinese twins.

PubMed

Xu, Chunsheng; Sun, Jianping; Ji, Fuling; Tian, Xiaocao; Duan, Haiping; Zhai, Yaoming; Wang, Shaojie; Pang, Zengchang; Zhang, Dongfeng; Zhao, Zhongtang; Li, Shuxia; Hjelmborg, Jacob V B; Christensen, Kaare; Tan, Qihua

2015-02-01

The genetic influences on aging-related phenotypes, including cognition and depression, have been well confirmed in the Western populations. We performed the first twin-based analysis on cognitive performance, memory and depression status in middle-aged and elderly Chinese twins, representing the world's largest and most rapidly aging population. The sample consisted of 384 twin pairs with a median age of 50 years. Cognitive function was measured using the Montreal Cognitive Assessment (MoCA) scale; memory was assessed using the revised Wechsler Adult Intelligence scale; depression symptomatology was evaluated by the self-reported 30-item Geriatric Depression (GDS-30)scale. Both univariate and multivariate twin models were fitted to the three phenotypes with full and nested models and compared to select the best fitting models. Univariate analysis showed moderate-to-high genetic influences with heritability 0.44 for cognition and 0.56 for memory. Multivariate analysis by the reduced Cholesky model estimated significant genetic (rG = 0.69) and unique environmental (rE = 0.25) correlation between cognitive ability and memory. The model also estimated weak but significant inverse genetic correlation for depression with cognition (-0.31) and memory (-0.28). No significant unique environmental correlation was found for depression with other two phenotypes. In conclusion, there can be a common genetic architecture for cognitive ability and memory that weakly correlates with depression symptomatology, but in the opposite direction.

Spatial land-use inventory, modeling, and projection/Denver metropolitan area, with inputs from existing maps, airphotos, and LANDSAT imagery

NASA Technical Reports Server (NTRS)

Tom, C.; Miller, L. D.; Christenson, J. W.

1978-01-01

A landscape model was constructed with 34 land-use, physiographic, socioeconomic, and transportation maps. A simple Markov land-use trend model was constructed from observed rates of change and nonchange from photointerpreted 1963 and 1970 airphotos. Seven multivariate land-use projection models predicting 1970 spatial land-use changes achieved accuracies from 42 to 57 percent. A final modeling strategy was designed, which combines both Markov trend and multivariate spatial projection processes. Landsat-1 image preprocessing included geometric rectification/resampling, spectral-band, and band/insolation ratioing operations. A new, systematic grid-sampled point training-set approach proved to be useful when tested on the four orginal MSS bands, ten image bands and ratios, and all 48 image and map variables (less land use). Ten variable accuracy was raised over 15 percentage points from 38.4 to 53.9 percent, with the use of the 31 ancillary variables. A land-use classification map was produced with an optimal ten-channel subset of four image bands and six ancillary map variables. Point-by-point verification of 331,776 points against a 1972/1973 U.S. Geological Survey (UGSG) land-use map prepared with airphotos and the same classification scheme showed average first-, second-, and third-order accuracies of 76.3, 58.4, and 33.0 percent, respectively.

Advanced statistics: linear regression, part II: multiple linear regression.

PubMed

Marill, Keith A

2004-01-01

The applications of simple linear regression in medical research are limited, because in most situations, there are multiple relevant predictor variables. Univariate statistical techniques such as simple linear regression use a single predictor variable, and they often may be mathematically correct but clinically misleading. Multiple linear regression is a mathematical technique used to model the relationship between multiple independent predictor variables and a single dependent outcome variable. It is used in medical research to model observational data, as well as in diagnostic and therapeutic studies in which the outcome is dependent on more than one factor. Although the technique generally is limited to data that can be expressed with a linear function, it benefits from a well-developed mathematical framework that yields unique solutions and exact confidence intervals for regression coefficients. Building on Part I of this series, this article acquaints the reader with some of the important concepts in multiple regression analysis. These include multicollinearity, interaction effects, and an expansion of the discussion of inference testing, leverage, and variable transformations to multivariate models. Examples from the first article in this series are expanded on using a primarily graphic, rather than mathematical, approach. The importance of the relationships among the predictor variables and the dependence of the multivariate model coefficients on the choice of these variables are stressed. Finally, concepts in regression model building are discussed.

The impact of covariance misspecification in multivariate Gaussian mixtures on estimation and inference: an application to longitudinal modeling.

PubMed

Heggeseth, Brianna C; Jewell, Nicholas P

2013-07-20

Multivariate Gaussian mixtures are a class of models that provide a flexible parametric approach for the representation of heterogeneous multivariate outcomes. When the outcome is a vector of repeated measurements taken on the same subject, there is often inherent dependence between observations. However, a common covariance assumption is conditional independence-that is, given the mixture component label, the outcomes for subjects are independent. In this paper, we study, through asymptotic bias calculations and simulation, the impact of covariance misspecification in multivariate Gaussian mixtures. Although maximum likelihood estimators of regression and mixing probability parameters are not consistent under misspecification, they have little asymptotic bias when mixture components are well separated or if the assumed correlation is close to the truth even when the covariance is misspecified. We also present a robust standard error estimator and show that it outperforms conventional estimators in simulations and can indicate that the model is misspecified. Body mass index data from a national longitudinal study are used to demonstrate the effects of misspecification on potential inferences made in practice. Copyright © 2013 John Wiley & Sons, Ltd.

Multivariate meta-analysis using individual participant data.

PubMed

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

2015-06-01

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

Determination of rice syrup adulterant concentration in honey using three-dimensional fluorescence spectra and multivariate calibrations

NASA Astrophysics Data System (ADS)

Chen, Quansheng; Qi, Shuai; Li, Huanhuan; Han, Xiaoyan; Ouyang, Qin; Zhao, Jiewen

2014-10-01

To rapidly and efficiently detect the presence of adulterants in honey, three-dimensional fluorescence spectroscopy (3DFS) technique was employed with the help of multivariate calibration. The data of 3D fluorescence spectra were compressed using characteristic extraction and the principal component analysis (PCA). Then, partial least squares (PLS) and back propagation neural network (BP-ANN) algorithms were used for modeling. The model was optimized by cross validation, and its performance was evaluated according to root mean square error of prediction (RMSEP) and correlation coefficient (R) in prediction set. The results showed that BP-ANN model was superior to PLS models, and the optimum prediction results of the mixed group (sunflower ± longan ± buckwheat ± rape) model were achieved as follow: RMSEP = 0.0235 and R = 0.9787 in the prediction set. The study demonstrated that the 3D fluorescence spectroscopy technique combined with multivariate calibration has high potential in rapid, nondestructive, and accurate quantitative analysis of honey adulteration.

Multivariable model predictive control design of reactive distillation column for Dimethyl Ether production

NASA Astrophysics Data System (ADS)

Wahid, A.; Putra, I. G. E. P.

2018-03-01

Dimethyl ether (DME) as an alternative clean energy has attracted a growing attention in the recent years. DME production via reactive distillation has potential for capital cost and energy requirement savings. However, combination of reaction and distillation on a single column makes reactive distillation process a very complex multivariable system with high non-linearity of process and strong interaction between process variables. This study investigates a multivariable model predictive control (MPC) based on two-point temperature control strategy for the DME reactive distillation column to maintain the purities of both product streams. The process model is estimated by a first order plus dead time model. The DME and water purity is maintained by controlling a stage temperature in rectifying and stripping section, respectively. The result shows that the model predictive controller performed faster responses compared to conventional PI controller that are showed by the smaller ISE values. In addition, the MPC controller is able to handle the loop interactions well.

Possible Protective Effect of Hydroxychloroquine on Retarding the Occurrence of Integument Damage in Lupus: Data from LUMINA, a Multiethnic Cohort

PubMed Central

Pons-Estel, Guillermo J.; Alarcón, Graciela S.; González, Luis A.; Zhang, Jie; Vilá, Luis M.; Reveille, John D.; McGwin, Gerald

2010-01-01

Objective To determine the features predictive of time-to-integument damage in patients with systemic lupus erythematosus (SLE) from a multiethnic cohort (LUMINA). Methods SLE LUMINA patients (n=580), age ≥16 years, disease duration ≤5 years at baseline (T0), of African American, Hispanic and Caucasian ethnicity were studied. Integument damage was defined per the SLICC damage index (scarring alopecia, extensive skin scarring and skin ulcers lasting at least six months); factors associated with time-to-its occurrence were examined by Cox proportional univariable and multivariable (main model) hazards regression analyses. Two alternative models were also examined; in model 1 all patients, regardless of when integument damage occurred (n=94), were included; in model 2 a time-varying approach (GEE) was employed. Results Thirty-nine (6.7%) of 580 patients developed integument damage over a mean (SD) total disease duration of 5.9 (3.7) years and were included in the main multivariable regression model. After adjusting for discoid rash, nailfold infarcts, photosensitivity and Raynaud’s phenomenon (significant in the univariable analyses), disease activity over time [Hazard ratio (HR)=1.17; 95% Confidence interval (CI) 1.09–1.26)] was associated with a shorter time-to-integument damage whereas hydroxychloroquine use (HR=0.23, 95% CI 0.12–0.47) and Texan-Hispanic (HR=0.35; 95% CI 0.14–0.87) and Caucasian ethnicities (HR=0.37; 95% CI 0.14–0.99) were associated with a longer time. Results of the alternative models were consistent with those of the main model albeit in model 2 the association with hydroxychloroquine was not significant. Conclusions Our data indicate that hydroxychloroquine use is possibly associated with a delay in integument damage development in patients with SLE. PMID:20391486

Multivariate time series modeling of short-term system scale irrigation demand

NASA Astrophysics Data System (ADS)

Perera, Kushan C.; Western, Andrew W.; George, Biju; Nawarathna, Bandara

2015-12-01

Travel time limits the ability of irrigation system operators to react to short-term irrigation demand fluctuations that result from variations in weather, including very hot periods and rainfall events, as well as the various other pressures and opportunities that farmers face. Short-term system-wide irrigation demand forecasts can assist in system operation. Here we developed a multivariate time series (ARMAX) model to forecast irrigation demands with respect to aggregated service points flows (IDCGi, ASP) and off take regulator flows (IDCGi, OTR) based across 5 command areas, which included area covered under four irrigation channels and the study area. These command area specific ARMAX models forecast 1-5 days ahead daily IDCGi, ASP and IDCGi, OTR using the real time flow data recorded at the service points and the uppermost regulators and observed meteorological data collected from automatic weather stations. The model efficiency and the predictive performance were quantified using the root mean squared error (RMSE), Nash-Sutcliffe model efficiency coefficient (NSE), anomaly correlation coefficient (ACC) and mean square skill score (MSSS). During the evaluation period, NSE for IDCGi, ASP and IDCGi, OTR across 5 command areas were ranged 0.98-0.78. These models were capable of generating skillful forecasts (MSSS ⩾ 0.5 and ACC ⩾ 0.6) of IDCGi, ASP and IDCGi, OTR for all 5 lead days and IDCGi, ASP and IDCGi, OTR forecasts were better than using the long term monthly mean irrigation demand. Overall these predictive performance from the ARMAX time series models were higher than almost all the previous studies we are aware. Further, IDCGi, ASP and IDCGi, OTR forecasts have improved the operators' ability to react for near future irrigation demand fluctuations as the developed ARMAX time series models were self-adaptive to reflect the short-term changes in the irrigation demand with respect to various pressures and opportunities that farmers' face, such as changing water policy, continued development of water markets, drought and changing technology.

Ability of preoperative 3.0-Tesla magnetic resonance imaging to predict the absence of side-specific extracapsular extension of prostate cancer.

PubMed

Hara, Tomohiko; Nakanishi, Hiroyuki; Nakagawa, Tohru; Komiyama, Motokiyo; Kawahara, Takashi; Manabe, Tomoko; Miyake, Mototaka; Arai, Eri; Kanai, Yae; Fujimoto, Hiroyuki

2013-10-01

Recent studies have shown an improvement in prostate cancer diagnosis with the use of 3.0-Tesla magnetic resonance imaging. We retrospectively assessed the ability of this imaging technique to predict side-specific extracapsular extension of prostate cancer. From October 2007 to August 2011, prostatectomy was carried out in 396 patients after preoperative 3.0-Tesla magnetic resonance imaging. Among these, 132 (primary sample) and 134 patients (validation sample) underwent 12-core prostate biopsy at the National Cancer Center Hospital of Tokyo, Japan, and at other institutions, respectively. In the primary dataset, univariate and multivariate analyses were carried out to predict side-specific extracapsular extension using variables determined preoperatively, including 3.0-Tesla magnetic resonance imaging findings (T2-weighted and diffusion-weighted imaging). A prediction model was then constructed and applied to the validation study sample. Multivariate analysis identified four significant independent predictors (P < 0.05), including a biopsy Gleason score of ≥8, positive 3.0-Tesla diffusion-weighted magnetic resonance imaging findings, ≥2 positive biopsy cores on each side and a maximum percentage of positive cores ≥31% on each side. The negative predictive value was 93.9% in the combination model with these four predictors, meanwhile the positive predictive value was 33.8%. Good reproducibility of these four significant predictors and the combination model was observed in the validation study sample. The side-specific extracapsular extension prediction by the biopsy Gleason score and factors associated with tumor location, including a positive 3.0-Tesla diffusion-weighted magnetic resonance imaging finding, have a high negative predictive value, but a low positive predictive value. © 2013 The Japanese Urological Association.

Correlational analysis of neck/shoulder pain and low back pain with the use of digital products, physical activity and psychological status among adolescents in Shanghai.

PubMed

Shan, Zhi; Deng, Guoying; Li, Jipeng; Li, Yangyang; Zhang, Yongxing; Zhao, Qinghua

2013-01-01

This study investigates the neck/shoulder pain (NSP) and low back pain (LBP) among current high school students in Shanghai and explores the relationship between these pains and their possible influences, including digital products, physical activity, and psychological status. An anonymous self-assessment was administered to 3,600 students across 30 high schools in Shanghai. This questionnaire examined the prevalence of NSP and LBP and the level of physical activity as well as the use of mobile phones, personal computers (PC) and tablet computers (Tablet). The CES-D (Center for Epidemiological Studies Depression) scale was also included in the survey. The survey data were analyzed using the chi-square test, univariate logistic analyses and a multivariate logistic regression model. Three thousand sixteen valid questionnaires were received including 1,460 (48.41%) from male respondents and 1,556 (51.59%) from female respondents. The high school students in this study showed NSP and LBP rates of 40.8% and 33.1%, respectively, and the prevalence of both influenced by the student's grade, use of digital products, and mental status; these factors affected the rates of NSP and LBP to varying degrees. The multivariate logistic regression analysis revealed that Gender, grade, soreness after exercise, PC using habits, tablet use, sitting time after school and academic stress entered the final model of NSP, while the final model of LBP consisted of gender, grade, soreness after exercise, PC using habits, mobile phone use, sitting time after school, academic stress and CES-D score. High school students in Shanghai showed high prevalence of NSP and LBP that were closely related to multiple factors. Appropriate interventions should be implemented to reduce the occurrences of NSP and LBP.

Potential for wind extraction from 4D-Var assimilation of aerosols and moisture

NASA Astrophysics Data System (ADS)

Zaplotnik, Žiga; Žagar, Nedjeljka

2017-04-01

We discuss the potential of the four-dimensional variational data assimilation (4D-Var) to retrieve the unobserved wind field from observations of atmospheric tracers and the mass field through internal model dynamics and the multivariate relationships in the background-error term for 4D-Var. The presence of non-linear moist dynamics makes the wind retrieval from tracers very difficult. On the other hand, it has been shown that moisture observations strongly influence both tropical and mid-latitude wind field in 4D-Var. We present an intermediate complexity model that describes nonlinear interactions between the wind, temperature, aerosols and moisture including their sinks and sources in the framework of the so-called first baroclinic mode atmosphere envisaged by A. Gill. Aerosol physical processes, which are included in the model, are the non-linear advection, diffusion and sources and sinks that exist as dry and wet deposition and diffusion. Precipitation is parametrized according to the Betts-Miller scheme. The control vector for 4D-Var includes aerosols, moisture and the three dynamical variables. The former is analysed univariately whereas wind field and mass field are analysed in a multivariate fashion taking into account quasi-geostrophic and unbalanced dynamics. The OSSE type of studies are performed for the tropical region to assess the ability of 4D-Var to extract wind-field information from the time series of observations of tracers as a function of the flow nonlinearity, the observations density and the length of the assimilation window (12 hours and 24 hours), in dry and moist environment. Results show that the 4D-Var assimilation of aerosols and temperature data is beneficial for the wind analysis with analysis errors strongly dependent on the moist processes and reliable background-error covariances.