External model validation of binary clinical risk prediction models in cardiovascular and thoracic surgery.

PubMed

Hickey, Graeme L; Blackstone, Eugene H

2016-08-01

Clinical risk-prediction models serve an important role in healthcare. They are used for clinical decision-making and measuring the performance of healthcare providers. To establish confidence in a model, external model validation is imperative. When designing such an external model validation study, thought must be given to patient selection, risk factor and outcome definitions, missing data, and the transparent reporting of the analysis. In addition, there are a number of statistical methods available for external model validation. Execution of a rigorous external validation study rests in proper study design, application of suitable statistical methods, and transparent reporting. Copyright © 2016 The American Association for Thoracic Surgery. Published by Elsevier Inc. 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.

Statistical evaluation of forecasts

NASA Astrophysics Data System (ADS)

Mader, Malenka; Mader, Wolfgang; Gluckman, Bruce J.; Timmer, Jens; Schelter, Björn

2014-08-01

Reliable forecasts of extreme but rare events, such as earthquakes, financial crashes, and epileptic seizures, would render interventions and precautions possible. Therefore, forecasting methods have been developed which intend to raise an alarm if an extreme event is about to occur. In order to statistically validate the performance of a prediction system, it must be compared to the performance of a random predictor, which raises alarms independent of the events. Such a random predictor can be obtained by bootstrapping or analytically. We propose an analytic statistical framework which, in contrast to conventional methods, allows for validating independently the sensitivity and specificity of a forecasting method. Moreover, our method accounts for the periods during which an event has to remain absent or occur after a respective forecast.

STATISTICAL VALIDATION OF SULFATE QUANTIFICATION METHODS USED FOR ANALYSIS OF ACID MINE DRAINAGE

EPA Science Inventory

Turbidimetric method (TM), ion chromatography (IC) and inductively coupled plasma atomic emission spectrometry (ICP-AES) with and without acid digestion have been compared and validated for the determination of sulfate in mining wastewater. Analytical methods were chosen to compa...

Improving validation methods for molecular diagnostics: application of Bland-Altman, Deming and simple linear regression analyses in assay comparison and evaluation for next-generation sequencing.

PubMed

Misyura, Maksym; Sukhai, Mahadeo A; Kulasignam, Vathany; Zhang, Tong; Kamel-Reid, Suzanne; Stockley, Tracy L

2018-02-01

A standard approach in test evaluation is to compare results of the assay in validation to results from previously validated methods. For quantitative molecular diagnostic assays, comparison of test values is often performed using simple linear regression and the coefficient of determination (R 2 ), using R 2 as the primary metric of assay agreement. However, the use of R 2 alone does not adequately quantify constant or proportional errors required for optimal test evaluation. More extensive statistical approaches, such as Bland-Altman and expanded interpretation of linear regression methods, can be used to more thoroughly compare data from quantitative molecular assays. We present the application of Bland-Altman and linear regression statistical methods to evaluate quantitative outputs from next-generation sequencing assays (NGS). NGS-derived data sets from assay validation experiments were used to demonstrate the utility of the statistical methods. Both Bland-Altman and linear regression were able to detect the presence and magnitude of constant and proportional error in quantitative values of NGS data. Deming linear regression was used in the context of assay comparison studies, while simple linear regression was used to analyse serial dilution data. Bland-Altman statistical approach was also adapted to quantify assay accuracy, including constant and proportional errors, and precision where theoretical and empirical values were known. The complementary application of the statistical methods described in this manuscript enables more extensive evaluation of performance characteristics of quantitative molecular assays, prior to implementation in the clinical molecular laboratory. © 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.

Assessing Discriminative Performance at External Validation of Clinical Prediction Models

PubMed Central

Nieboer, Daan; van der Ploeg, Tjeerd; Steyerberg, Ewout W.

2016-01-01

Introduction External validation studies are essential to study the generalizability of prediction models. Recently a permutation test, focusing on discrimination as quantified by the c-statistic, was proposed to judge whether a prediction model is transportable to a new setting. We aimed to evaluate this test and compare it to previously proposed procedures to judge any changes in c-statistic from development to external validation setting. Methods We compared the use of the permutation test to the use of benchmark values of the c-statistic following from a previously proposed framework to judge transportability of a prediction model. In a simulation study we developed a prediction model with logistic regression on a development set and validated them in the validation set. We concentrated on two scenarios: 1) the case-mix was more heterogeneous and predictor effects were weaker in the validation set compared to the development set, and 2) the case-mix was less heterogeneous in the validation set and predictor effects were identical in the validation and development set. Furthermore we illustrated the methods in a case study using 15 datasets of patients suffering from traumatic brain injury. Results The permutation test indicated that the validation and development set were homogenous in scenario 1 (in almost all simulated samples) and heterogeneous in scenario 2 (in 17%-39% of simulated samples). Previously proposed benchmark values of the c-statistic and the standard deviation of the linear predictors correctly pointed at the more heterogeneous case-mix in scenario 1 and the less heterogeneous case-mix in scenario 2. Conclusion The recently proposed permutation test may provide misleading results when externally validating prediction models in the presence of case-mix differences between the development and validation population. To correctly interpret the c-statistic found at external validation it is crucial to disentangle case-mix differences from incorrect regression coefficients. PMID:26881753

Evaluating statistical validity of research reports: a guide for managers, planners, and researchers

Treesearch

Amanda L. Golbeck

1986-01-01

Inappropriate statistical methods, as well as appropriate methods inappropriately used, can lead to incorrect conclusions in any research report. Incorrect conclusions may also be due to the fact that the research problem is just hard to quantify in a satisfactory way. Publication of a research report does not guarantee that appropriate statistical methods have been...

Analyzing the Validity of the Adult-Adolescent Parenting Inventory for Low-Income Populations

ERIC Educational Resources Information Center

Lawson, Michael A.; Alameda-Lawson, Tania; Byrnes, Edward

2017-01-01

Objectives: The purpose of this study was to examine the construct and predictive validity of the Adult-Adolescent Parenting Inventory (AAPI-2). Methods: The validity of the AAPI-2 was evaluated using multiple statistical methods, including exploratory factor analysis, confirmatory factor analysis, and latent class analysis. These analyses were…

An intercomparison of a large ensemble of statistical downscaling methods for Europe: Overall results from the VALUE perfect predictor cross-validation experiment

NASA Astrophysics Data System (ADS)

Gutiérrez, Jose Manuel; Maraun, Douglas; Widmann, Martin; Huth, Radan; Hertig, Elke; Benestad, Rasmus; Roessler, Ole; Wibig, Joanna; Wilcke, Renate; Kotlarski, Sven

2016-04-01

VALUE is an open European network to validate and compare downscaling methods for climate change research (http://www.value-cost.eu). A key deliverable of VALUE is the development of a systematic validation framework to enable the assessment and comparison of both dynamical and statistical downscaling methods. This framework is based on a user-focused validation tree, guiding the selection of relevant validation indices and performance measures for different aspects of the validation (marginal, temporal, spatial, multi-variable). Moreover, several experiments have been designed to isolate specific points in the downscaling procedure where problems may occur (assessment of intrinsic performance, effect of errors inherited from the global models, effect of non-stationarity, etc.). The list of downscaling experiments includes 1) cross-validation with perfect predictors, 2) GCM predictors -aligned with EURO-CORDEX experiment- and 3) pseudo reality predictors (see Maraun et al. 2015, Earth's Future, 3, doi:10.1002/2014EF000259, for more details). The results of these experiments are gathered, validated and publicly distributed through the VALUE validation portal, allowing for a comprehensive community-open downscaling intercomparison study. In this contribution we describe the overall results from Experiment 1), consisting of a European wide 5-fold cross-validation (with consecutive 6-year periods from 1979 to 2008) using predictors from ERA-Interim to downscale precipitation and temperatures (minimum and maximum) over a set of 86 ECA&D stations representative of the main geographical and climatic regions in Europe. As a result of the open call for contribution to this experiment (closed in Dec. 2015), over 40 methods representative of the main approaches (MOS and Perfect Prognosis, PP) and techniques (linear scaling, quantile mapping, analogs, weather typing, linear and generalized regression, weather generators, etc.) were submitted, including information both data (downscaled values) and metadata (characterizing different aspects of the downscaling methods). This constitutes the largest and most comprehensive to date intercomparison of statistical downscaling methods. Here, we present an overall validation, analyzing marginal and temporal aspects to assess the intrinsic performance and added value of statistical downscaling methods at both annual and seasonal levels. This validation takes into account the different properties/limitations of different approaches and techniques (as reported in the provided metadata) in order to perform a fair comparison. It is pointed out that this experiment alone is not sufficient to evaluate the limitations of (MOS) bias correction techniques. Moreover, it also does not fully validate PP since we don't learn whether we have the right predictors and whether the PP assumption is valid. These problems will be analyzed in the subsequent community-open VALUE experiments 2) and 3), which will be open for participation along the present year.

Evaluation Using Sequential Trials Methods.

ERIC Educational Resources Information Center

Cohen, Mark E.; Ralls, Stephen A.

1986-01-01

Although dental school faculty as well as practitioners are interested in evaluating products and procedures used in clinical practice, research design and statistical analysis can sometimes pose problems. Sequential trials methods provide an analytical structure that is both easy to use and statistically valid. (Author/MLW)

Validating Coherence Measurements Using Aligned and Unaligned Coherence Functions

NASA Technical Reports Server (NTRS)

Miles, Jeffrey Hilton

2006-01-01

This paper describes a novel approach based on the use of coherence functions and statistical theory for sensor validation in a harsh environment. By the use of aligned and unaligned coherence functions and statistical theory one can test for sensor degradation, total sensor failure or changes in the signal. This advanced diagnostic approach and the novel data processing methodology discussed provides a single number that conveys this information. This number as calculated with standard statistical procedures for comparing the means of two distributions is compared with results obtained using Yuen's robust statistical method to create confidence intervals. Examination of experimental data from Kulite pressure transducers mounted in a Pratt & Whitney PW4098 combustor using spectrum analysis methods on aligned and unaligned time histories has verified the effectiveness of the proposed method. All the procedures produce good results which demonstrates how robust the technique is.

Measuring the statistical validity of summary meta-analysis and meta-regression results for use in clinical practice.

PubMed

Willis, Brian H; Riley, Richard D

2017-09-20

An important question for clinicians appraising a meta-analysis is: are the findings likely to be valid in their own practice-does the reported effect accurately represent the effect that would occur in their own clinical population? To this end we advance the concept of statistical validity-where the parameter being estimated equals the corresponding parameter for a new independent study. Using a simple ('leave-one-out') cross-validation technique, we demonstrate how we may test meta-analysis estimates for statistical validity using a new validation statistic, Vn, and derive its distribution. We compare this with the usual approach of investigating heterogeneity in meta-analyses and demonstrate the link between statistical validity and homogeneity. Using a simulation study, the properties of Vn and the Q statistic are compared for univariate random effects meta-analysis and a tailored meta-regression model, where information from the setting (included as model covariates) is used to calibrate the summary estimate to the setting of application. Their properties are found to be similar when there are 50 studies or more, but for fewer studies Vn has greater power but a higher type 1 error rate than Q. The power and type 1 error rate of Vn are also shown to depend on the within-study variance, between-study variance, study sample size, and the number of studies in the meta-analysis. Finally, we apply Vn to two published meta-analyses and conclude that it usefully augments standard methods when deciding upon the likely validity of summary meta-analysis estimates in clinical practice. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.

Validity and reliability of a method for assessment of cervical vertebral maturation.

PubMed

Zhao, Xiao-Guang; Lin, Jiuxiang; Jiang, Jiu-Hui; Wang, Qingzhu; Ng, Sut Hong

2012-03-01

To evaluate the validity and reliability of the cervical vertebral maturation (CVM) method with a longitudinal sample. Eighty-six cephalograms from 18 subjects (5 males and 13 females) were selected from the longitudinal database. Total mandibular length was measured on each film; an increased rate served as the gold standard in examination of the validity of the CVM method. Eleven orthodontists, after receiving intensive training in the CVM method, evaluated all films twice. Kendall's W and the weighted kappa statistic were employed. Kendall's W values were higher than 0.8 at both times, indicating strong interobserver reproducibility, but interobserver agreement was documented twice at less than 50%. A wide range of intraobserver agreement was noted (40.7%-79.1%), and substantial intraobserver reproducibility was proved by kappa values (0.53-0.86). With regard to validity, moderate agreement was reported between the gold standard and observer staging at the initial time (kappa values 0.44-0.61). However, agreement seemed to be unacceptable for clinical use, especially in cervical stage 3 (26.8%). Even though the validity and reliability of the CVM method proved statistically acceptable, we suggest that many other growth indicators should be taken into consideration in evaluating adolescent skeletal maturation.

Validation of surrogate endpoints in advanced solid tumors: systematic review of statistical methods, results, and implications for policy makers.

PubMed

Ciani, Oriana; Davis, Sarah; Tappenden, Paul; Garside, Ruth; Stein, Ken; Cantrell, Anna; Saad, Everardo D; Buyse, Marc; Taylor, Rod S

2014-07-01

Licensing of, and coverage decisions on, new therapies should rely on evidence from patient-relevant endpoints such as overall survival (OS). Nevertheless, evidence from surrogate endpoints may also be useful, as it may not only expedite the regulatory approval of new therapies but also inform coverage decisions. It is, therefore, essential that candidate surrogate endpoints be properly validated. However, there is no consensus on statistical methods for such validation and on how the evidence thus derived should be applied by policy makers. We review current statistical approaches to surrogate-endpoint validation based on meta-analysis in various advanced-tumor settings. We assessed the suitability of two surrogates (progression-free survival [PFS] and time-to-progression [TTP]) using three current validation frameworks: Elston and Taylor's framework, the German Institute of Quality and Efficiency in Health Care's (IQWiG) framework and the Biomarker-Surrogacy Evaluation Schema (BSES3). A wide variety of statistical methods have been used to assess surrogacy. The strength of the association between the two surrogates and OS was generally low. The level of evidence (observation-level versus treatment-level) available varied considerably by cancer type, by evaluation tools and was not always consistent even within one specific cancer type. Not in all solid tumors the treatment-level association between PFS or TTP and OS has been investigated. According to IQWiG's framework, only PFS achieved acceptable evidence of surrogacy in metastatic colorectal and ovarian cancer treated with cytotoxic agents. Our study emphasizes the challenges of surrogate-endpoint validation and the importance of building consensus on the development of evaluation frameworks.

An investigation of new toxicity test method performance in validation studies: 1. Toxicity test methods that have predictive capacity no greater than chance.

PubMed

Bruner, L H; Carr, G J; Harbell, J W; Curren, R D

2002-06-01

An approach commonly used to measure new toxicity test method (NTM) performance in validation studies is to divide toxicity results into positive and negative classifications, and the identify true positive (TP), true negative (TN), false positive (FP) and false negative (FN) results. After this step is completed, the contingent probability statistics (CPS), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) are calculated. Although these statistics are widely used and often the only statistics used to assess the performance of toxicity test methods, there is little specific guidance in the validation literature on what values for these statistics indicate adequate performance. The purpose of this study was to begin developing data-based answers to this question by characterizing the CPS obtained from an NTM whose data have a completely random association with a reference test method (RTM). Determining the CPS of this worst-case scenario is useful because it provides a lower baseline from which the performance of an NTM can be judged in future validation studies. It also provides an indication of relationships in the CPS that help identify random or near-random relationships in the data. The results from this study of randomly associated tests show that the values obtained for the statistics vary significantly depending on the cut-offs chosen, that high values can be obtained for individual statistics, and that the different measures cannot be considered independently when evaluating the performance of an NTM. When the association between results of an NTM and RTM is random the sum of the complementary pairs of statistics (sensitivity + specificity, NPV + PPV) is approximately 1, and the prevalence (i.e., the proportion of toxic chemicals in the population of chemicals) and PPV are equal. Given that combinations of high sensitivity-low specificity or low specificity-high sensitivity (i.e., the sum of the sensitivity and specificity equal to approximately 1) indicate lack of predictive capacity, an NTM having these performance characteristics should be considered no better for predicting toxicity than by chance alone.

Approach for Input Uncertainty Propagation and Robust Design in CFD Using Sensitivity Derivatives

NASA Technical Reports Server (NTRS)

Putko, Michele M.; Taylor, Arthur C., III; Newman, Perry A.; Green, Lawrence L.

2002-01-01

An implementation of the approximate statistical moment method for uncertainty propagation and robust optimization for quasi 3-D Euler CFD code is presented. Given uncertainties in statistically independent, random, normally distributed input variables, first- and second-order statistical moment procedures are performed to approximate the uncertainty in the CFD output. Efficient calculation of both first- and second-order sensitivity derivatives is required. In order to assess the validity of the approximations, these moments are compared with statistical moments generated through Monte Carlo simulations. The uncertainties in the CFD input variables are also incorporated into a robust optimization procedure. For this optimization, statistical moments involving first-order sensitivity derivatives appear in the objective function and system constraints. Second-order sensitivity derivatives are used in a gradient-based search to successfully execute a robust optimization. The approximate methods used throughout the analyses are found to be valid when considering robustness about input parameter mean values.

Beware of external validation! - A Comparative Study of Several Validation Techniques used in QSAR Modelling.

PubMed

Majumdar, Subhabrata; Basak, Subhash C

2018-04-26

Proper validation is an important aspect of QSAR modelling. External validation is one of the widely used validation methods in QSAR where the model is built on a subset of the data and validated on the rest of the samples. However, its effectiveness for datasets with a small number of samples but large number of predictors remains suspect. Calculating hundreds or thousands of molecular descriptors using currently available software has become the norm in QSAR research, owing to computational advances in the past few decades. Thus, for n chemical compounds and p descriptors calculated for each molecule, the typical chemometric dataset today has high value of p but small n (i.e. n < p). Motivated by the evidence of inadequacies of external validation in estimating the true predictive capability of a statistical model in recent literature, this paper performs an extensive and comparative study of this method with several other validation techniques. We compared four validation methods: leave-one-out, K-fold, external and multi-split validation, using statistical models built using the LASSO regression, which simultaneously performs variable selection and modelling. We used 300 simulated datasets and one real dataset of 95 congeneric amine mutagens for this evaluation. External validation metrics have high variation among different random splits of the data, hence are not recommended for predictive QSAR models. LOO has the overall best performance among all validation methods applied in our scenario. Results from external validation are too unstable for the datasets we analyzed. Based on our findings, we recommend using the LOO procedure for validating QSAR predictive models built on high-dimensional small-sample data. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Implementing statistical equating for MRCP(UK) Parts 1 and 2.

PubMed

McManus, I C; Chis, Liliana; Fox, Ray; Waller, Derek; Tang, Peter

2014-09-26

The MRCP(UK) exam, in 2008 and 2010, changed the standard-setting of its Part 1 and Part 2 examinations from a hybrid Angoff/Hofstee method to statistical equating using Item Response Theory, the reference group being UK graduates. The present paper considers the implementation of the change, the question of whether the pass rate increased amongst non-UK candidates, any possible role of Differential Item Functioning (DIF), and changes in examination predictive validity after the change. Analysis of data of MRCP(UK) Part 1 exam from 2003 to 2013 and Part 2 exam from 2005 to 2013. Inspection suggested that Part 1 pass rates were stable after the introduction of statistical equating, but showed greater annual variation probably due to stronger candidates taking the examination earlier. Pass rates seemed to have increased in non-UK graduates after equating was introduced, but was not associated with any changes in DIF after statistical equating. Statistical modelling of the pass rates for non-UK graduates found that pass rates, in both Part 1 and Part 2, were increasing year on year, with the changes probably beginning before the introduction of equating. The predictive validity of Part 1 for Part 2 was higher with statistical equating than with the previous hybrid Angoff/Hofstee method, confirming the utility of IRT-based statistical equating. Statistical equating was successfully introduced into the MRCP(UK) Part 1 and Part 2 written examinations, resulting in higher predictive validity than the previous Angoff/Hofstee standard setting. Concerns about an artefactual increase in pass rates for non-UK candidates after equating were shown not to be well-founded. Most likely the changes resulted from a genuine increase in candidate ability, albeit for reasons which remain unclear, coupled with a cognitive illusion giving the impression of a step-change immediately after equating began. Statistical equating provides a robust standard-setting method, with a better theoretical foundation than judgemental techniques such as Angoff, and is more straightforward and requires far less examiner time to provide a more valid result. The present study provides a detailed case study of introducing statistical equating, and issues which may need to be considered with its introduction.

Teaching method validation in the clinical laboratory science curriculum.

PubMed

Moon, Tara C; Legrys, Vicky A

2008-01-01

With the Clinical Laboratory Improvement Amendment's (CLIA) final rule, the ability of the Clinical Laboratory Scientist (CLS) to perform method validation has become increasingly important. Knowledge of the statistical methods and procedures used in method validation is imperative for clinical laboratory scientists. However, incorporating these concepts in a CLS curriculum can be challenging, especially at a time of limited resources. This paper provides an outline of one approach to addressing these topics in lecture courses and integrating them in the student laboratory and the clinical practicum for direct application.

Semiquantitative determination of mesophilic, aerobic microorganisms in cocoa products using the Soleris NF-TVC method.

PubMed

Montei, Carolyn; McDougal, Susan; Mozola, Mark; Rice, Jennifer

2014-01-01

The Soleris Non-fermenting Total Viable Count method was previously validated for a wide variety of food products, including cocoa powder. A matrix extension study was conducted to validate the method for use with cocoa butter and cocoa liquor. Test samples included naturally contaminated cocoa liquor and cocoa butter inoculated with natural microbial flora derived from cocoa liquor. A probability of detection statistical model was used to compare Soleris results at multiple test thresholds (dilutions) with aerobic plate counts determined using the AOAC Official Method 966.23 dilution plating method. Results of the two methods were not statistically different at any dilution level in any of the three trials conducted. The Soleris method offers the advantage of results within 24 h, compared to the 48 h required by standard dilution plating methods.

Statistical Selection of Biological Models for Genome-Wide Association Analyses.

PubMed

Bi, Wenjian; Kang, Guolian; Pounds, Stanley B

2018-05-24

Genome-wide association studies have discovered many biologically important associations of genes with phenotypes. Typically, genome-wide association analyses formally test the association of each genetic feature (SNP, CNV, etc) with the phenotype of interest and summarize the results with multiplicity-adjusted p-values. However, very small p-values only provide evidence against the null hypothesis of no association without indicating which biological model best explains the observed data. Correctly identifying a specific biological model may improve the scientific interpretation and can be used to more effectively select and design a follow-up validation study. Thus, statistical methodology to identify the correct biological model for a particular genotype-phenotype association can be very useful to investigators. Here, we propose a general statistical method to summarize how accurately each of five biological models (null, additive, dominant, recessive, co-dominant) represents the data observed for each variant in a GWAS study. We show that the new method stringently controls the false discovery rate and asymptotically selects the correct biological model. Simulations of two-stage discovery-validation studies show that the new method has these properties and that its validation power is similar to or exceeds that of simple methods that use the same statistical model for all SNPs. Example analyses of three data sets also highlight these advantages of the new method. An R package is freely available at www.stjuderesearch.org/site/depts/biostats/maew. Copyright © 2018. Published by Elsevier Inc.

Measuring the statistical validity of summary meta‐analysis and meta‐regression results for use in clinical practice

PubMed Central

Riley, Richard D.

2017-01-01

An important question for clinicians appraising a meta‐analysis is: are the findings likely to be valid in their own practice—does the reported effect accurately represent the effect that would occur in their own clinical population? To this end we advance the concept of statistical validity—where the parameter being estimated equals the corresponding parameter for a new independent study. Using a simple (‘leave‐one‐out’) cross‐validation technique, we demonstrate how we may test meta‐analysis estimates for statistical validity using a new validation statistic, Vn, and derive its distribution. We compare this with the usual approach of investigating heterogeneity in meta‐analyses and demonstrate the link between statistical validity and homogeneity. Using a simulation study, the properties of Vn and the Q statistic are compared for univariate random effects meta‐analysis and a tailored meta‐regression model, where information from the setting (included as model covariates) is used to calibrate the summary estimate to the setting of application. Their properties are found to be similar when there are 50 studies or more, but for fewer studies Vn has greater power but a higher type 1 error rate than Q. The power and type 1 error rate of Vn are also shown to depend on the within‐study variance, between‐study variance, study sample size, and the number of studies in the meta‐analysis. Finally, we apply Vn to two published meta‐analyses and conclude that it usefully augments standard methods when deciding upon the likely validity of summary meta‐analysis estimates in clinical practice. © 2017 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd. PMID:28620945

Determination of polarimetric parameters of honey by near-infrared transflectance spectroscopy.

PubMed

García-Alvarez, M; Ceresuela, S; Huidobro, J F; Hermida, M; Rodríguez-Otero, J L

2002-01-30

NIR transflectance spectroscopy was used to determine polarimetric parameters (direct polarization, polarization after inversion, specific rotation in dry matter, and polarization due to nonmonosaccharides) and sucrose in honey. In total, 156 honey samples were collected during 1992 (45 samples), 1995 (56 samples), and 1996 (55 samples). Samples were analyzed by NIR spectroscopy and polarimetric methods. Calibration (118 samples) and validation (38 samples) sets were made up; honeys from the three years were included in both sets. Calibrations were performed by modified partial least-squares regression and scatter correction by standard normal variation and detrend methods. For direct polarization, polarization after inversion, specific rotation in dry matter, and polarization due to nonmonosaccharides, good statistics (bias, SEV, and R(2)) were obtained for the validation set, and no statistically (p = 0.05) significant differences were found between instrumental and polarimetric methods for these parameters. Statistical data for sucrose were not as good as those of the other parameters. Therefore, NIR spectroscopy is not an effective method for quantitative analysis of sucrose in these honey samples. However, NIR spectroscopy may be an acceptable method for semiquantitative evaluation of sucrose for honeys, such as those in our study, containing up to 3% of sucrose. Further work is necessary to validate the uncertainty at higher levels.

Cost model validation: a technical and cultural approach

NASA Technical Reports Server (NTRS)

Hihn, J.; Rosenberg, L.; Roust, K.; Warfield, K.

2001-01-01

This paper summarizes how JPL's parametric mission cost model (PMCM) has been validated using both formal statistical methods and a variety of peer and management reviews in order to establish organizational acceptance of the cost model estimates.

A Deep Machine Learning Method for Classifying Cyclic Time Series of Biological Signals Using Time-Growing Neural Network.

PubMed

Gharehbaghi, Arash; Linden, Maria

2017-10-12

This paper presents a novel method for learning the cyclic contents of stochastic time series: the deep time-growing neural network (DTGNN). The DTGNN combines supervised and unsupervised methods in different levels of learning for an enhanced performance. It is employed by a multiscale learning structure to classify cyclic time series (CTS), in which the dynamic contents of the time series are preserved in an efficient manner. This paper suggests a systematic procedure for finding the design parameter of the classification method for a one-versus-multiple class application. A novel validation method is also suggested for evaluating the structural risk, both in a quantitative and a qualitative manner. The effect of the DTGNN on the performance of the classifier is statistically validated through the repeated random subsampling using different sets of CTS, from different medical applications. The validation involves four medical databases, comprised of 108 recordings of the electroencephalogram signal, 90 recordings of the electromyogram signal, 130 recordings of the heart sound signal, and 50 recordings of the respiratory sound signal. Results of the statistical validations show that the DTGNN significantly improves the performance of the classification and also exhibits an optimal structural risk.

A high-fidelity weather time series generator using the Markov Chain process on a piecewise level

NASA Astrophysics Data System (ADS)

Hersvik, K.; Endrerud, O.-E. V.

2017-12-01

A method is developed for generating a set of unique weather time-series based on an existing weather series. The method allows statistically valid weather variations to take place within repeated simulations of offshore operations. The numerous generated time series need to share the same statistical qualities as the original time series. Statistical qualities here refer mainly to the distribution of weather windows available for work, including durations and frequencies of such weather windows, and seasonal characteristics. The method is based on the Markov chain process. The core new development lies in how the Markov Process is used, specifically by joining small pieces of random length time series together rather than joining individual weather states, each from a single time step, which is a common solution found in the literature. This new Markov model shows favorable characteristics with respect to the requirements set forth and all aspects of the validation performed.

Approach for Uncertainty Propagation and Robust Design in CFD Using Sensitivity Derivatives

NASA Technical Reports Server (NTRS)

Putko, Michele M.; Newman, Perry A.; Taylor, Arthur C., III; Green, Lawrence L.

2001-01-01

This paper presents an implementation of the approximate statistical moment method for uncertainty propagation and robust optimization for a quasi 1-D Euler CFD (computational fluid dynamics) code. Given uncertainties in statistically independent, random, normally distributed input variables, a first- and second-order statistical moment matching procedure is performed to approximate the uncertainty in the CFD output. Efficient calculation of both first- and second-order sensitivity derivatives is required. In order to assess the validity of the approximations, the moments are compared with statistical moments generated through Monte Carlo simulations. The uncertainties in the CFD input variables are also incorporated into a robust optimization procedure. For this optimization, statistical moments involving first-order sensitivity derivatives appear in the objective function and system constraints. Second-order sensitivity derivatives are used in a gradient-based search to successfully execute a robust optimization. The approximate methods used throughout the analyses are found to be valid when considering robustness about input parameter mean values.

Does rational selection of training and test sets improve the outcome of QSAR modeling?

PubMed

Martin, Todd M; Harten, Paul; Young, Douglas M; Muratov, Eugene N; Golbraikh, Alexander; Zhu, Hao; Tropsha, Alexander

2012-10-22

Prior to using a quantitative structure activity relationship (QSAR) model for external predictions, its predictive power should be established and validated. In the absence of a true external data set, the best way to validate the predictive ability of a model is to perform its statistical external validation. In statistical external validation, the overall data set is divided into training and test sets. Commonly, this splitting is performed using random division. Rational splitting methods can divide data sets into training and test sets in an intelligent fashion. The purpose of this study was to determine whether rational division methods lead to more predictive models compared to random division. A special data splitting procedure was used to facilitate the comparison between random and rational division methods. For each toxicity end point, the overall data set was divided into a modeling set (80% of the overall set) and an external evaluation set (20% of the overall set) using random division. The modeling set was then subdivided into a training set (80% of the modeling set) and a test set (20% of the modeling set) using rational division methods and by using random division. The Kennard-Stone, minimal test set dissimilarity, and sphere exclusion algorithms were used as the rational division methods. The hierarchical clustering, random forest, and k-nearest neighbor (kNN) methods were used to develop QSAR models based on the training sets. For kNN QSAR, multiple training and test sets were generated, and multiple QSAR models were built. The results of this study indicate that models based on rational division methods generate better statistical results for the test sets than models based on random division, but the predictive power of both types of models are comparable.

VALUE - Validating and Integrating Downscaling Methods for Climate Change Research

NASA Astrophysics Data System (ADS)

Maraun, Douglas; Widmann, Martin; Benestad, Rasmus; Kotlarski, Sven; Huth, Radan; Hertig, Elke; Wibig, Joanna; Gutierrez, Jose

2013-04-01

Our understanding of global climate change is mainly based on General Circulation Models (GCMs) with a relatively coarse resolution. Since climate change impacts are mainly experienced on regional scales, high-resolution climate change scenarios need to be derived from GCM simulations by downscaling. Several projects have been carried out over the last years to validate the performance of statistical and dynamical downscaling, yet several aspects have not been systematically addressed: variability on sub-daily, decadal and longer time-scales, extreme events, spatial variability and inter-variable relationships. Different downscaling approaches such as dynamical downscaling, statistical downscaling and bias correction approaches have not been systematically compared. Furthermore, collaboration between different communities, in particular regional climate modellers, statistical downscalers and statisticians has been limited. To address these gaps, the EU Cooperation in Science and Technology (COST) action VALUE (www.value-cost.eu) has been brought into life. VALUE is a research network with participants from currently 23 European countries running from 2012 to 2015. Its main aim is to systematically validate and develop downscaling methods for climate change research in order to improve regional climate change scenarios for use in climate impact studies. Inspired by the co-design idea of the international research initiative "future earth", stakeholders of climate change information have been involved in the definition of research questions to be addressed and are actively participating in the network. The key idea of VALUE is to identify the relevant weather and climate characteristics required as input for a wide range of impact models and to define an open framework to systematically validate these characteristics. Based on a range of benchmark data sets, in principle every downscaling method can be validated and compared with competing methods. The results of this exercise will directly provide end users with important information about the uncertainty of regional climate scenarios, and will furthermore provide the basis for further developing downscaling methods. This presentation will provide background information on VALUE and discuss the identified characteristics and the validation framework.

IMPLEMENTATION AND VALIDATION OF STATISTICAL TESTS IN RESEARCH'S SOFTWARE HELPING DATA COLLECTION AND PROTOCOLS ANALYSIS IN SURGERY.

PubMed

Kuretzki, Carlos Henrique; Campos, Antônio Carlos Ligocki; Malafaia, Osvaldo; Soares, Sandramara Scandelari Kusano de Paula; Tenório, Sérgio Bernardo; Timi, Jorge Rufino Ribas

2016-03-01

The use of information technology is often applied in healthcare. With regard to scientific research, the SINPE(c) - Integrated Electronic Protocols was created as a tool to support researchers, offering clinical data standardization. By the time, SINPE(c) lacked statistical tests obtained by automatic analysis. Add to SINPE(c) features for automatic realization of the main statistical methods used in medicine . The study was divided into four topics: check the interest of users towards the implementation of the tests; search the frequency of their use in health care; carry out the implementation; and validate the results with researchers and their protocols. It was applied in a group of users of this software in their thesis in the strict sensu master and doctorate degrees in one postgraduate program in surgery. To assess the reliability of the statistics was compared the data obtained both automatically by SINPE(c) as manually held by a professional in statistics with experience with this type of study. There was concern for the use of automatic statistical tests, with good acceptance. The chi-square, Mann-Whitney, Fisher and t-Student were considered as tests frequently used by participants in medical studies. These methods have been implemented and thereafter approved as expected. The incorporation of the automatic SINPE (c) Statistical Analysis was shown to be reliable and equal to the manually done, validating its use as a research tool for medical research.

Method development and validation of potent pyrimidine derivative by UV-VIS spectrophotometer.

PubMed

Chaudhary, Anshu; Singh, Anoop; Verma, Prabhakar Kumar

2014-12-01

A rapid and sensitive ultraviolet-visible (UV-VIS) spectroscopic method was developed for the estimation of pyrimidine derivative 6-Bromo-3-(6-(2,6-dichlorophenyl)-2-(morpolinomethylamino) pyrimidine4-yl) -2H-chromen-2-one (BT10M) in bulk form. Pyrimidine derivative was monitored at 275 nm with UV detection, and there is no interference of diluents at 275 nm. The method was found to be linear in the range of 50 to 150 μg/ml. The accuracy and precision were determined and validated statistically. The method was validated as a guideline. The results showed that the proposed method is suitable for the accurate, precise, and rapid determination of pyrimidine derivative. Graphical Abstract Method development and validation of potent pyrimidine derivative by UV spectroscopy.

Improving the Validity of Activity of Daily Living Dependency Risk Assessment

PubMed Central

Clark, Daniel O.; Stump, Timothy E.; Tu, Wanzhu; Miller, Douglas K.

2015-01-01

Objectives Efforts to prevent activity of daily living (ADL) dependency may be improved through models that assess older adults’ dependency risk. We evaluated whether cognition and gait speed measures improve the predictive validity of interview-based models. Method Participants were 8,095 self-respondents in the 2006 Health and Retirement Survey who were aged 65 years or over and independent in five ADLs. Incident ADL dependency was determined from the 2008 interview. Models were developed using random 2/3rd cohorts and validated in the remaining 1/3rd. Results Compared to a c-statistic of 0.79 in the best interview model, the model including cognitive measures had c-statistics of 0.82 and 0.80 while the best fitting gait speed model had c-statistics of 0.83 and 0.79 in the development and validation cohorts, respectively. Conclusion Two relatively brief models, one that requires an in-person assessment and one that does not, had excellent validity for predicting incident ADL dependency but did not significantly improve the predictive validity of the best fitting interview-based models. PMID:24652867

Comparison of statistical methods for detection of serum lipid biomarkers for mesothelioma and asbestos exposure.

PubMed

Xu, Rengyi; Mesaros, Clementina; Weng, Liwei; Snyder, Nathaniel W; Vachani, Anil; Blair, Ian A; Hwang, Wei-Ting

2017-07-01

We compared three statistical methods in selecting a panel of serum lipid biomarkers for mesothelioma and asbestos exposure. Serum samples from mesothelioma, asbestos-exposed subjects and controls (40 per group) were analyzed. Three variable selection methods were considered: top-ranked predictors from univariate model, stepwise and least absolute shrinkage and selection operator. Crossed-validated area under the receiver operating characteristic curve was used to compare the prediction performance. Lipids with high crossed-validated area under the curve were identified. Lipid with mass-to-charge ratio of 372.31 was selected by all three methods comparing mesothelioma versus control. Lipids with mass-to-charge ratio of 1464.80 and 329.21 were selected by two models for asbestos exposure versus control. Different methods selected a similar set of serum lipids. Combining candidate biomarkers can improve prediction.

Statistical considerations on prognostic models for glioma

PubMed Central

Molinaro, Annette M.; Wrensch, Margaret R.; Jenkins, Robert B.; Eckel-Passow, Jeanette E.

2016-01-01

Given the lack of beneficial treatments in glioma, there is a need for prognostic models for therapeutic decision making and life planning. Recently several studies defining subtypes of glioma have been published. Here, we review the statistical considerations of how to build and validate prognostic models, explain the models presented in the current glioma literature, and discuss advantages and disadvantages of each model. The 3 statistical considerations to establishing clinically useful prognostic models are: study design, model building, and validation. Careful study design helps to ensure that the model is unbiased and generalizable to the population of interest. During model building, a discovery cohort of patients can be used to choose variables, construct models, and estimate prediction performance via internal validation. Via external validation, an independent dataset can assess how well the model performs. It is imperative that published models properly detail the study design and methods for both model building and validation. This provides readers the information necessary to assess the bias in a study, compare other published models, and determine the model's clinical usefulness. As editors, reviewers, and readers of the relevant literature, we should be cognizant of the needed statistical considerations and insist on their use. PMID:26657835

Assessing Discriminative Performance at External Validation of Clinical Prediction Models.

PubMed

Nieboer, Daan; van der Ploeg, Tjeerd; Steyerberg, Ewout W

2016-01-01

External validation studies are essential to study the generalizability of prediction models. Recently a permutation test, focusing on discrimination as quantified by the c-statistic, was proposed to judge whether a prediction model is transportable to a new setting. We aimed to evaluate this test and compare it to previously proposed procedures to judge any changes in c-statistic from development to external validation setting. We compared the use of the permutation test to the use of benchmark values of the c-statistic following from a previously proposed framework to judge transportability of a prediction model. In a simulation study we developed a prediction model with logistic regression on a development set and validated them in the validation set. We concentrated on two scenarios: 1) the case-mix was more heterogeneous and predictor effects were weaker in the validation set compared to the development set, and 2) the case-mix was less heterogeneous in the validation set and predictor effects were identical in the validation and development set. Furthermore we illustrated the methods in a case study using 15 datasets of patients suffering from traumatic brain injury. The permutation test indicated that the validation and development set were homogenous in scenario 1 (in almost all simulated samples) and heterogeneous in scenario 2 (in 17%-39% of simulated samples). Previously proposed benchmark values of the c-statistic and the standard deviation of the linear predictors correctly pointed at the more heterogeneous case-mix in scenario 1 and the less heterogeneous case-mix in scenario 2. The recently proposed permutation test may provide misleading results when externally validating prediction models in the presence of case-mix differences between the development and validation population. To correctly interpret the c-statistic found at external validation it is crucial to disentangle case-mix differences from incorrect regression coefficients.

Evaluation of Fuzzy-Logic Framework for Spatial Statistics Preserving Methods for Estimation of Missing Precipitation Data

NASA Astrophysics Data System (ADS)

El Sharif, H.; Teegavarapu, R. S.

2012-12-01

Spatial interpolation methods used for estimation of missing precipitation data at a site seldom check for their ability to preserve site and regional statistics. Such statistics are primarily defined by spatial correlations and other site-to-site statistics in a region. Preservation of site and regional statistics represents a means of assessing the validity of missing precipitation estimates at a site. This study evaluates the efficacy of a fuzzy-logic methodology for infilling missing historical daily precipitation data in preserving site and regional statistics. Rain gauge sites in the state of Kentucky, USA, are used as a case study for evaluation of this newly proposed method in comparison to traditional data infilling techniques. Several error and performance measures will be used to evaluate the methods and trade-offs in accuracy of estimation and preservation of site and regional statistics.

Trends in study design and the statistical methods employed in a leading general medicine journal.

PubMed

Gosho, M; Sato, Y; Nagashima, K; Takahashi, S

2018-02-01

Study design and statistical methods have become core components of medical research, and the methodology has become more multifaceted and complicated over time. The study of the comprehensive details and current trends of study design and statistical methods is required to support the future implementation of well-planned clinical studies providing information about evidence-based medicine. Our purpose was to illustrate study design and statistical methods employed in recent medical literature. This was an extension study of Sato et al. (N Engl J Med 2017; 376: 1086-1087), which reviewed 238 articles published in 2015 in the New England Journal of Medicine (NEJM) and briefly summarized the statistical methods employed in NEJM. Using the same database, we performed a new investigation of the detailed trends in study design and individual statistical methods that were not reported in the Sato study. Due to the CONSORT statement, prespecification and justification of sample size are obligatory in planning intervention studies. Although standard survival methods (eg Kaplan-Meier estimator and Cox regression model) were most frequently applied, the Gray test and Fine-Gray proportional hazard model for considering competing risks were sometimes used for a more valid statistical inference. With respect to handling missing data, model-based methods, which are valid for missing-at-random data, were more frequently used than single imputation methods. These methods are not recommended as a primary analysis, but they have been applied in many clinical trials. Group sequential design with interim analyses was one of the standard designs, and novel design, such as adaptive dose selection and sample size re-estimation, was sometimes employed in NEJM. Model-based approaches for handling missing data should replace single imputation methods for primary analysis in the light of the information found in some publications. Use of adaptive design with interim analyses is increasing after the presentation of the FDA guidance for adaptive design. © 2017 John Wiley & Sons Ltd.

Validation tools for image segmentation

NASA Astrophysics Data System (ADS)

Padfield, Dirk; Ross, James

2009-02-01

A large variety of image analysis tasks require the segmentation of various regions in an image. For example, segmentation is required to generate accurate models of brain pathology that are important components of modern diagnosis and therapy. While the manual delineation of such structures gives accurate information, the automatic segmentation of regions such as the brain and tumors from such images greatly enhances the speed and repeatability of quantifying such structures. The ubiquitous need for such algorithms has lead to a wide range of image segmentation algorithms with various assumptions, parameters, and robustness. The evaluation of such algorithms is an important step in determining their effectiveness. Therefore, rather than developing new segmentation algorithms, we here describe validation methods for segmentation algorithms. Using similarity metrics comparing the automatic to manual segmentations, we demonstrate methods for optimizing the parameter settings for individual cases and across a collection of datasets using the Design of Experiment framework. We then employ statistical analysis methods to compare the effectiveness of various algorithms. We investigate several region-growing algorithms from the Insight Toolkit and compare their accuracy to that of a separate statistical segmentation algorithm. The segmentation algorithms are used with their optimized parameters to automatically segment the brain and tumor regions in MRI images of 10 patients. The validation tools indicate that none of the ITK algorithms studied are able to outperform with statistical significance the statistical segmentation algorithm although they perform reasonably well considering their simplicity.

DBS-LC-MS/MS assay for caffeine: validation and neonatal application.

PubMed

Bruschettini, Matteo; Barco, Sebastiano; Romantsik, Olga; Risso, Francesco; Gennai, Iulian; Chinea, Benito; Ramenghi, Luca A; Tripodi, Gino; Cangemi, Giuliana

2016-09-01

DBS might be an appropriate microsampling technique for therapeutic drug monitoring of caffeine in infants. Nevertheless, its application presents several issues that still limit its use. This paper describes a validated DBS-LC-MS/MS method for caffeine. The results of the method validation showed an hematocrit dependence. In the analysis of 96 paired plasma and DBS clinical samples, caffeine levels measured in DBS were statistically significantly lower than in plasma but the observed differences were independent from hematocrit. These results clearly showed the need for extensive validation with real-life samples for DBS-based methods. DBS-LC-MS/MS can be considered to be a good alternative to traditional methods for therapeutic drug monitoring or PK studies in preterm infants.

Statistical Calibration and Validation of a Homogeneous Ventilated Wall-Interference Correction Method for the National Transonic Facility

NASA Technical Reports Server (NTRS)

Walker, Eric L.

2005-01-01

Wind tunnel experiments will continue to be a primary source of validation data for many types of mathematical and computational models in the aerospace industry. The increased emphasis on accuracy of data acquired from these facilities requires understanding of the uncertainty of not only the measurement data but also any correction applied to the data. One of the largest and most critical corrections made to these data is due to wall interference. In an effort to understand the accuracy and suitability of these corrections, a statistical validation process for wall interference correction methods has been developed. This process is based on the use of independent cases which, after correction, are expected to produce the same result. Comparison of these independent cases with respect to the uncertainty in the correction process establishes a domain of applicability based on the capability of the method to provide reasonable corrections with respect to customer accuracy requirements. The statistical validation method was applied to the version of the Transonic Wall Interference Correction System (TWICS) recently implemented in the National Transonic Facility at NASA Langley Research Center. The TWICS code generates corrections for solid and slotted wall interference in the model pitch plane based on boundary pressure measurements. Before validation could be performed on this method, it was necessary to calibrate the ventilated wall boundary condition parameters. Discrimination comparisons are used to determine the most representative of three linear boundary condition models which have historically been used to represent longitudinally slotted test section walls. Of the three linear boundary condition models implemented for ventilated walls, the general slotted wall model was the most representative of the data. The TWICS code using the calibrated general slotted wall model was found to be valid to within the process uncertainty for test section Mach numbers less than or equal to 0.60. The scatter among the mean corrected results of the bodies of revolution validation cases was within one count of drag on a typical transport aircraft configuration for Mach numbers at or below 0.80 and two counts of drag for Mach numbers at or below 0.90.

PCA as a practical indicator of OPLS-DA model reliability.

PubMed

Worley, Bradley; Powers, Robert

Principal Component Analysis (PCA) and Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) are powerful statistical modeling tools that provide insights into separations between experimental groups based on high-dimensional spectral measurements from NMR, MS or other analytical instrumentation. However, when used without validation, these tools may lead investigators to statistically unreliable conclusions. This danger is especially real for Partial Least Squares (PLS) and OPLS, which aggressively force separations between experimental groups. As a result, OPLS-DA is often used as an alternative method when PCA fails to expose group separation, but this practice is highly dangerous. Without rigorous validation, OPLS-DA can easily yield statistically unreliable group separation. A Monte Carlo analysis of PCA group separations and OPLS-DA cross-validation metrics was performed on NMR datasets with statistically significant separations in scores-space. A linearly increasing amount of Gaussian noise was added to each data matrix followed by the construction and validation of PCA and OPLS-DA models. With increasing added noise, the PCA scores-space distance between groups rapidly decreased and the OPLS-DA cross-validation statistics simultaneously deteriorated. A decrease in correlation between the estimated loadings (added noise) and the true (original) loadings was also observed. While the validity of the OPLS-DA model diminished with increasing added noise, the group separation in scores-space remained basically unaffected. Supported by the results of Monte Carlo analyses of PCA group separations and OPLS-DA cross-validation metrics, we provide practical guidelines and cross-validatory recommendations for reliable inference from PCA and OPLS-DA models.

Using the bootstrap to establish statistical significance for relative validity comparisons among patient-reported outcome measures

PubMed Central

2013-01-01

Background Relative validity (RV), a ratio of ANOVA F-statistics, is often used to compare the validity of patient-reported outcome (PRO) measures. We used the bootstrap to establish the statistical significance of the RV and to identify key factors affecting its significance. Methods Based on responses from 453 chronic kidney disease (CKD) patients to 16 CKD-specific and generic PRO measures, RVs were computed to determine how well each measure discriminated across clinically-defined groups of patients compared to the most discriminating (reference) measure. Statistical significance of RV was quantified by the 95% bootstrap confidence interval. Simulations examined the effects of sample size, denominator F-statistic, correlation between comparator and reference measures, and number of bootstrap replicates. Results The statistical significance of the RV increased as the magnitude of denominator F-statistic increased or as the correlation between comparator and reference measures increased. A denominator F-statistic of 57 conveyed sufficient power (80%) to detect an RV of 0.6 for two measures correlated at r = 0.7. Larger denominator F-statistics or higher correlations provided greater power. Larger sample size with a fixed denominator F-statistic or more bootstrap replicates (beyond 500) had minimal impact. Conclusions The bootstrap is valuable for establishing the statistical significance of RV estimates. A reasonably large denominator F-statistic (F > 57) is required for adequate power when using the RV to compare the validity of measures with small or moderate correlations (r < 0.7). Substantially greater power can be achieved when comparing measures of a very high correlation (r > 0.9). PMID:23721463

Is There a Critical Distance for Fickian Transport? - a Statistical Approach to Sub-Fickian Transport Modelling in Porous Media

NASA Astrophysics Data System (ADS)

Most, S.; Nowak, W.; Bijeljic, B.

2014-12-01

Transport processes in porous media are frequently simulated as particle movement. This process can be formulated as a stochastic process of particle position increments. At the pore scale, the geometry and micro-heterogeneities prohibit the commonly made assumption of independent and normally distributed increments to represent dispersion. Many recent particle methods seek to loosen this assumption. Recent experimental data suggest that we have not yet reached the end of the need to generalize, because particle increments show statistical dependency beyond linear correlation and over many time steps. The goal of this work is to better understand the validity regions of commonly made assumptions. We are investigating after what transport distances can we observe: A statistical dependence between increments, that can be modelled as an order-k Markov process, boils down to order 1. This would be the Markovian distance for the process, where the validity of yet-unexplored non-Gaussian-but-Markovian random walks would start. A bivariate statistical dependence that simplifies to a multi-Gaussian dependence based on simple linear correlation (validity of correlated PTRW). Complete absence of statistical dependence (validity of classical PTRW/CTRW). The approach is to derive a statistical model for pore-scale transport from a powerful experimental data set via copula analysis. The model is formulated as a non-Gaussian, mutually dependent Markov process of higher order, which allows us to investigate the validity ranges of simpler models.

Validation of Milliflex® Quantum for Bioburden Testing of Pharmaceutical Products.

PubMed

Gordon, Oliver; Goverde, Marcel; Staerk, Alexandra; Roesti, David

2017-01-01

This article reports the validation strategy used to demonstrate that the Milliflex ® Quantum yielded non-inferior results to the traditional bioburden method. It was validated according to USP <1223>, European Pharmacopoeia 5.1.6, and Parenteral Drug Association Technical Report No. 33 and comprised the validation parameters robustness, ruggedness, repeatability, specificity, limit of detection and quantification, accuracy, precision, linearity, range, and equivalence in routine operation. For the validation, a combination of pharmacopeial ATCC strains as well as a broad selection of in-house isolates were used. In-house isolates were used in stressed state. Results were statistically evaluated regarding the pharmacopeial acceptance criterion of ≥70% recovery compared to the traditional method. Post-hoc test power calculations verified the appropriateness of the used sample size to detect such a difference. Furthermore, equivalence tests verified non-inferiority of the rapid method as compared to the traditional method. In conclusion, the rapid bioburden on basis of the Milliflex ® Quantum was successfully validated as alternative method to the traditional bioburden test. LAY ABSTRACT: Pharmaceutical drug products must fulfill specified quality criteria regarding their microbial content in order to ensure patient safety. Drugs that are delivered into the body via injection, infusion, or implantation must be sterile (i.e., devoid of living microorganisms). Bioburden testing measures the levels of microbes present in the bulk solution of a drug before sterilization, and thus it provides important information for manufacturing a safe product. In general, bioburden testing has to be performed using the methods described in the pharmacopoeias (membrane filtration or plate count). These methods are well established and validated regarding their effectiveness; however, the incubation time required to visually identify microbial colonies is long. Thus, alternative methods that detect microbial contamination faster will improve control over the manufacturing process and speed up product release. Before alternative methods may be used, they must undergo a side-by-side comparison with pharmacopeial methods. In this comparison, referred to as validation, it must be shown in a statistically verified manner that the effectiveness of the alternative method is at least equivalent to that of the pharmacopeial methods. Here we describe the successful validation of an alternative bioburden testing method based on fluorescent staining of growing microorganisms applying the Milliflex ® Quantum system by MilliporeSigma. © PDA, Inc. 2017.

A Generalized Pivotal Quantity Approach to Analytical Method Validation Based on Total Error.

PubMed

Yang, Harry; Zhang, Jianchun

2015-01-01

The primary purpose of method validation is to demonstrate that the method is fit for its intended use. Traditionally, an analytical method is deemed valid if its performance characteristics such as accuracy and precision are shown to meet prespecified acceptance criteria. However, these acceptance criteria are not directly related to the method's intended purpose, which is usually a gurantee that a high percentage of the test results of future samples will be close to their true values. Alternate "fit for purpose" acceptance criteria based on the concept of total error have been increasingly used. Such criteria allow for assessing method validity, taking into account the relationship between accuracy and precision. Although several statistical test methods have been proposed in literature to test the "fit for purpose" hypothesis, the majority of the methods are not designed to protect the risk of accepting unsuitable methods, thus having the potential to cause uncontrolled consumer's risk. In this paper, we propose a test method based on generalized pivotal quantity inference. Through simulation studies, the performance of the method is compared to five existing approaches. The results show that both the new method and the method based on β-content tolerance interval with a confidence level of 90%, hereafter referred to as the β-content (0.9) method, control Type I error and thus consumer's risk, while the other existing methods do not. It is further demonstrated that the generalized pivotal quantity method is less conservative than the β-content (0.9) method when the analytical methods are biased, whereas it is more conservative when the analytical methods are unbiased. Therefore, selection of either the generalized pivotal quantity or β-content (0.9) method for an analytical method validation depends on the accuracy of the analytical method. It is also shown that the generalized pivotal quantity method has better asymptotic properties than all of the current methods. Analytical methods are often used to ensure safety, efficacy, and quality of medicinal products. According to government regulations and regulatory guidelines, these methods need to be validated through well-designed studies to minimize the risk of accepting unsuitable methods. This article describes a novel statistical test for analytical method validation, which provides better protection for the risk of accepting unsuitable analytical methods. © PDA, Inc. 2015.

Validation of a physical anthropology methodology using mandibles for gender estimation in a Brazilian population

PubMed Central

CARVALHO, Suzana Papile Maciel; BRITO, Liz Magalhães; de PAIVA, Luiz Airton Saavedra; BICUDO, Lucilene Arilho Ribeiro; CROSATO, Edgard Michel; de OLIVEIRA, Rogério Nogueira

2013-01-01

Validation studies of physical anthropology methods in the different population groups are extremely important, especially in cases in which the population variations may cause problems in the identification of a native individual by the application of norms developed for different communities. Objective This study aimed to estimate the gender of skeletons by application of the method of Oliveira, et al. (1995), previously used in a population sample from Northeast Brazil. Material and Methods The accuracy of this method was assessed for a population from Southeast Brazil and validated by statistical tests. The method used two mandibular measurements, namely the bigonial distance and the mandibular ramus height. The sample was composed of 66 skulls and the method was applied by two examiners. The results were statistically analyzed by the paired t test, logistic discriminant analysis and logistic regression. Results The results demonstrated that the application of the method of Oliveira, et al. (1995) in this population achieved very different outcomes between genders, with 100% for females and only 11% for males, which may be explained by ethnic differences. However, statistical adjustment of measurement data for the population analyzed allowed accuracy of 76.47% for males and 78.13% for females, with the creation of a new discriminant formula. Conclusion It was concluded that methods involving physical anthropology present high rate of accuracy for human identification, easy application, low cost and simplicity; however, the methodologies must be validated for the different populations due to differences in ethnic patterns, which are directly related to the phenotypic aspects. In this specific case, the method of Oliveira, et al. (1995) presented good accuracy and may be used for gender estimation in Brazil in two geographic regions, namely Northeast and Southeast; however, for other regions of the country (North, Central West and South), previous methodological adjustment is recommended as demonstrated in this study. PMID:24037076

Autoregressive statistical pattern recognition algorithms for damage detection in civil structures

NASA Astrophysics Data System (ADS)

Yao, Ruigen; Pakzad, Shamim N.

2012-08-01

Statistical pattern recognition has recently emerged as a promising set of complementary methods to system identification for automatic structural damage assessment. Its essence is to use well-known concepts in statistics for boundary definition of different pattern classes, such as those for damaged and undamaged structures. In this paper, several statistical pattern recognition algorithms using autoregressive models, including statistical control charts and hypothesis testing, are reviewed as potentially competitive damage detection techniques. To enhance the performance of statistical methods, new feature extraction techniques using model spectra and residual autocorrelation, together with resampling-based threshold construction methods, are proposed. Subsequently, simulated acceleration data from a multi degree-of-freedom system is generated to test and compare the efficiency of the existing and proposed algorithms. Data from laboratory experiments conducted on a truss and a large-scale bridge slab model are then used to further validate the damage detection methods and demonstrate the superior performance of proposed algorithms.

Cross-Validation of Survival Bump Hunting by Recursive Peeling Methods.

PubMed

Dazard, Jean-Eudes; Choe, Michael; LeBlanc, Michael; Rao, J Sunil

2014-08-01

We introduce a survival/risk bump hunting framework to build a bump hunting model with a possibly censored time-to-event type of response and to validate model estimates. First, we describe the use of adequate survival peeling criteria to build a survival/risk bump hunting model based on recursive peeling methods. Our method called "Patient Recursive Survival Peeling" is a rule-induction method that makes use of specific peeling criteria such as hazard ratio or log-rank statistics. Second, to validate our model estimates and improve survival prediction accuracy, we describe a resampling-based validation technique specifically designed for the joint task of decision rule making by recursive peeling (i.e. decision-box) and survival estimation. This alternative technique, called "combined" cross-validation is done by combining test samples over the cross-validation loops, a design allowing for bump hunting by recursive peeling in a survival setting. We provide empirical results showing the importance of cross-validation and replication.

Cross-Validation of Survival Bump Hunting by Recursive Peeling Methods

PubMed Central

Dazard, Jean-Eudes; Choe, Michael; LeBlanc, Michael; Rao, J. Sunil

2015-01-01

We introduce a survival/risk bump hunting framework to build a bump hunting model with a possibly censored time-to-event type of response and to validate model estimates. First, we describe the use of adequate survival peeling criteria to build a survival/risk bump hunting model based on recursive peeling methods. Our method called “Patient Recursive Survival Peeling” is a rule-induction method that makes use of specific peeling criteria such as hazard ratio or log-rank statistics. Second, to validate our model estimates and improve survival prediction accuracy, we describe a resampling-based validation technique specifically designed for the joint task of decision rule making by recursive peeling (i.e. decision-box) and survival estimation. This alternative technique, called “combined” cross-validation is done by combining test samples over the cross-validation loops, a design allowing for bump hunting by recursive peeling in a survival setting. We provide empirical results showing the importance of cross-validation and replication. PMID:26997922

Review of surface steam sterilization for validation purposes.

PubMed

van Doornmalen, Joost; Kopinga, Klaas

2008-03-01

Sterilization is an essential step in the process of producing sterile medical devices. To guarantee sterility, the process of sterilization must be validated. Because there is no direct way to measure sterility, the techniques applied to validate the sterilization process are based on statistical principles. Steam sterilization is the most frequently applied sterilization method worldwide and can be validated either by indicators (chemical or biological) or physical measurements. The steam sterilization conditions are described in the literature. Starting from these conditions, criteria for the validation of steam sterilization are derived and can be described in terms of physical parameters. Physical validation of steam sterilization appears to be an adequate and efficient validation method that could be considered as an alternative for indicator validation. Moreover, physical validation can be used for effective troubleshooting in steam sterilizing processes.

Quantitative analysis of γ-oryzanol content in cold pressed rice bran oil by TLC-image analysis method

PubMed Central

Sakunpak, Apirak; Suksaeree, Jirapornchai; Monton, Chaowalit; Pathompak, Pathamaporn; Kraisintu, Krisana

2014-01-01

Objective To develop and validate an image analysis method for quantitative analysis of γ-oryzanol in cold pressed rice bran oil. Methods TLC-densitometric and TLC-image analysis methods were developed, validated, and used for quantitative analysis of γ-oryzanol in cold pressed rice bran oil. The results obtained by these two different quantification methods were compared by paired t-test. Results Both assays provided good linearity, accuracy, reproducibility and selectivity for determination of γ-oryzanol. Conclusions The TLC-densitometric and TLC-image analysis methods provided a similar reproducibility, accuracy and selectivity for the quantitative determination of γ-oryzanol in cold pressed rice bran oil. A statistical comparison of the quantitative determinations of γ-oryzanol in samples did not show any statistically significant difference between TLC-densitometric and TLC-image analysis methods. As both methods were found to be equal, they therefore can be used for the determination of γ-oryzanol in cold pressed rice bran oil. PMID:25182282

Testing prediction methods: Earthquake clustering versus the Poisson model

USGS Publications Warehouse

Michael, A.J.

1997-01-01

Testing earthquake prediction methods requires statistical techniques that compare observed success to random chance. One technique is to produce simulated earthquake catalogs and measure the relative success of predicting real and simulated earthquakes. The accuracy of these tests depends on the validity of the statistical model used to simulate the earthquakes. This study tests the effect of clustering in the statistical earthquake model on the results. Three simulation models were used to produce significance levels for a VLF earthquake prediction method. As the degree of simulated clustering increases, the statistical significance drops. Hence, the use of a seismicity model with insufficient clustering can lead to overly optimistic results. A successful method must pass the statistical tests with a model that fully replicates the observed clustering. However, a method can be rejected based on tests with a model that contains insufficient clustering. U.S. copyright. Published in 1997 by the American Geophysical Union.

Statistical Methods for Rapid Aerothermal Analysis and Design Technology: Validation

NASA Technical Reports Server (NTRS)

DePriest, Douglas; Morgan, Carolyn

2003-01-01

The cost and safety goals for NASA s next generation of reusable launch vehicle (RLV) will require that rapid high-fidelity aerothermodynamic design tools be used early in the design cycle. To meet these requirements, it is desirable to identify adequate statistical models that quantify and improve the accuracy, extend the applicability, and enable combined analyses using existing prediction tools. The initial research work focused on establishing suitable candidate models for these purposes. The second phase is focused on assessing the performance of these models to accurately predict the heat rate for a given candidate data set. This validation work compared models and methods that may be useful in predicting the heat rate.

Experimental and statistical post-validation of positive example EST sequences carrying peroxisome targeting signals type 1 (PTS1)

PubMed Central

Lingner, Thomas; Kataya, Amr R. A.; Reumann, Sigrun

2012-01-01

We recently developed the first algorithms specifically for plants to predict proteins carrying peroxisome targeting signals type 1 (PTS1) from genome sequences.1 As validated experimentally, the prediction methods are able to correctly predict unknown peroxisomal Arabidopsis proteins and to infer novel PTS1 tripeptides. The high prediction performance is primarily determined by the large number and sequence diversity of the underlying positive example sequences, which mainly derived from EST databases. However, a few constructs remained cytosolic in experimental validation studies, indicating sequencing errors in some ESTs. To identify erroneous sequences, we validated subcellular targeting of additional positive example sequences in the present study. Moreover, we analyzed the distribution of prediction scores separately for each orthologous group of PTS1 proteins, which generally resembled normal distributions with group-specific mean values. The cytosolic sequences commonly represented outliers of low prediction scores and were located at the very tail of a fitted normal distribution. Three statistical methods for identifying outliers were compared in terms of sensitivity and specificity.” Their combined application allows elimination of erroneous ESTs from positive example data sets. This new post-validation method will further improve the prediction accuracy of both PTS1 and PTS2 protein prediction models for plants, fungi, and mammals. PMID:22415050

Experimental and statistical post-validation of positive example EST sequences carrying peroxisome targeting signals type 1 (PTS1).

PubMed

Lingner, Thomas; Kataya, Amr R A; Reumann, Sigrun

2012-02-01

We recently developed the first algorithms specifically for plants to predict proteins carrying peroxisome targeting signals type 1 (PTS1) from genome sequences. As validated experimentally, the prediction methods are able to correctly predict unknown peroxisomal Arabidopsis proteins and to infer novel PTS1 tripeptides. The high prediction performance is primarily determined by the large number and sequence diversity of the underlying positive example sequences, which mainly derived from EST databases. However, a few constructs remained cytosolic in experimental validation studies, indicating sequencing errors in some ESTs. To identify erroneous sequences, we validated subcellular targeting of additional positive example sequences in the present study. Moreover, we analyzed the distribution of prediction scores separately for each orthologous group of PTS1 proteins, which generally resembled normal distributions with group-specific mean values. The cytosolic sequences commonly represented outliers of low prediction scores and were located at the very tail of a fitted normal distribution. Three statistical methods for identifying outliers were compared in terms of sensitivity and specificity." Their combined application allows elimination of erroneous ESTs from positive example data sets. This new post-validation method will further improve the prediction accuracy of both PTS1 and PTS2 protein prediction models for plants, fungi, and mammals.

A statistical method for lung tumor segmentation uncertainty in PET images based on user inference.

PubMed

Zheng, Chaojie; Wang, Xiuying; Feng, Dagan

2015-01-01

PET has been widely accepted as an effective imaging modality for lung tumor diagnosis and treatment. However, standard criteria for delineating tumor boundary from PET are yet to develop largely due to relatively low quality of PET images, uncertain tumor boundary definition, and variety of tumor characteristics. In this paper, we propose a statistical solution to segmentation uncertainty on the basis of user inference. We firstly define the uncertainty segmentation band on the basis of segmentation probability map constructed from Random Walks (RW) algorithm; and then based on the extracted features of the user inference, we use Principle Component Analysis (PCA) to formulate the statistical model for labeling the uncertainty band. We validated our method on 10 lung PET-CT phantom studies from the public RIDER collections [1] and 16 clinical PET studies where tumors were manually delineated by two experienced radiologists. The methods were validated using Dice similarity coefficient (DSC) to measure the spatial volume overlap. Our method achieved an average DSC of 0.878 ± 0.078 on phantom studies and 0.835 ± 0.039 on clinical studies.

Valid statistical inference methods for a case-control study with missing data.

PubMed

Tian, Guo-Liang; Zhang, Chi; Jiang, Xuejun

2018-04-01

The main objective of this paper is to derive the valid sampling distribution of the observed counts in a case-control study with missing data under the assumption of missing at random by employing the conditional sampling method and the mechanism augmentation method. The proposed sampling distribution, called the case-control sampling distribution, can be used to calculate the standard errors of the maximum likelihood estimates of parameters via the Fisher information matrix and to generate independent samples for constructing small-sample bootstrap confidence intervals. Theoretical comparisons of the new case-control sampling distribution with two existing sampling distributions exhibit a large difference. Simulations are conducted to investigate the influence of the three different sampling distributions on statistical inferences. One finding is that the conclusion by the Wald test for testing independency under the two existing sampling distributions could be completely different (even contradictory) from the Wald test for testing the equality of the success probabilities in control/case groups under the proposed distribution. A real cervical cancer data set is used to illustrate the proposed statistical methods.

Spectral signature verification using statistical analysis and text mining

NASA Astrophysics Data System (ADS)

DeCoster, Mallory E.; Firpi, Alexe H.; Jacobs, Samantha K.; Cone, Shelli R.; Tzeng, Nigel H.; Rodriguez, Benjamin M.

2016-05-01

In the spectral science community, numerous spectral signatures are stored in databases representative of many sample materials collected from a variety of spectrometers and spectroscopists. Due to the variety and variability of the spectra that comprise many spectral databases, it is necessary to establish a metric for validating the quality of spectral signatures. This has been an area of great discussion and debate in the spectral science community. This paper discusses a method that independently validates two different aspects of a spectral signature to arrive at a final qualitative assessment; the textual meta-data and numerical spectral data. Results associated with the spectral data stored in the Signature Database1 (SigDB) are proposed. The numerical data comprising a sample material's spectrum is validated based on statistical properties derived from an ideal population set. The quality of the test spectrum is ranked based on a spectral angle mapper (SAM) comparison to the mean spectrum derived from the population set. Additionally, the contextual data of a test spectrum is qualitatively analyzed using lexical analysis text mining. This technique analyzes to understand the syntax of the meta-data to provide local learning patterns and trends within the spectral data, indicative of the test spectrum's quality. Text mining applications have successfully been implemented for security2 (text encryption/decryption), biomedical3 , and marketing4 applications. The text mining lexical analysis algorithm is trained on the meta-data patterns of a subset of high and low quality spectra, in order to have a model to apply to the entire SigDB data set. The statistical and textual methods combine to assess the quality of a test spectrum existing in a database without the need of an expert user. This method has been compared to other validation methods accepted by the spectral science community, and has provided promising results when a baseline spectral signature is present for comparison. The spectral validation method proposed is described from a practical application and analytical perspective.

An Improved LC-ESI-MS/MS Method to Quantify Pregabalin in Human Plasma and Dry Plasma Spot for Therapeutic Monitoring and Pharmacokinetic Applications.

PubMed

Dwivedi, Jaya; Namdev, Kuldeep K; Chilkoti, Deepak C; Verma, Surajpal; Sharma, Swapnil

2018-06-06

Therapeutic drug monitoring (TDM) of anti-epileptic drugs provides a valid clinical tool in optimization of overall therapy. However, TDM is challenging due to the high biological samples (plasma/blood) storage/shipment costs and the limited availability of laboratories providing TDM services. Sampling in the form of dry plasma spot (DPS) or dry blood spot (DBS) is a suitable alternative to overcome these issues. An improved, simple, rapid, and stability indicating method for quantification of pregabalin in human plasma and DPS has been developed and validated. Analyses were performed on liquid chromatography tandem mass spectrometer under positive ionization mode of electrospray interface. Pregabain-d4 was used as internal standard, and the chromatographic separations were performed on Poroshell 120 EC-C18 column using an isocratic mobile phase flow rate of 1 mL/min. Stability of pregabalin in DPS was evaluated under simulated real-time conditions. Extraction procedures from plasma and DPS samples were compared using statistical tests. The method was validated considering the FDA method validation guideline. The method was linear over the concentration range of 20-16000 ng/mL and 100-10000 ng/mL in plasma and DPS, respectively. DPS samples were found stable for only one week upon storage at room temperature and for at least four weeks at freezing temperature (-20 ± 5 °C). Method was applied for quantification of pregabalin in over 600 samples of a clinical study. Statistical analyses revealed that two extraction procedures in plasma and DPS samples showed statistically insignificant difference and can be used interchangeably without any bias. Proposed method involves simple and rapid steps of sample processing that do not require a pre- or post-column derivatization procedure. The method is suitable for routine pharmacokinetic analysis and therapeutic monitoring of pregabalin.

Validation of a physical anthropology methodology using mandibles for gender estimation in a Brazilian population.

PubMed

Carvalho, Suzana Papile Maciel; Brito, Liz Magalhães; Paiva, Luiz Airton Saavedra de; Bicudo, Lucilene Arilho Ribeiro; Crosato, Edgard Michel; Oliveira, Rogério Nogueira de

2013-01-01

Validation studies of physical anthropology methods in the different population groups are extremely important, especially in cases in which the population variations may cause problems in the identification of a native individual by the application of norms developed for different communities. This study aimed to estimate the gender of skeletons by application of the method of Oliveira, et al. (1995), previously used in a population sample from Northeast Brazil. The accuracy of this method was assessed for a population from Southeast Brazil and validated by statistical tests. The method used two mandibular measurements, namely the bigonial distance and the mandibular ramus height. The sample was composed of 66 skulls and the method was applied by two examiners. The results were statistically analyzed by the paired t test, logistic discriminant analysis and logistic regression. The results demonstrated that the application of the method of Oliveira, et al. (1995) in this population achieved very different outcomes between genders, with 100% for females and only 11% for males, which may be explained by ethnic differences. However, statistical adjustment of measurement data for the population analyzed allowed accuracy of 76.47% for males and 78.13% for females, with the creation of a new discriminant formula. It was concluded that methods involving physical anthropology present high rate of accuracy for human identification, easy application, low cost and simplicity; however, the methodologies must be validated for the different populations due to differences in ethnic patterns, which are directly related to the phenotypic aspects. In this specific case, the method of Oliveira, et al. (1995) presented good accuracy and may be used for gender estimation in Brazil in two geographic regions, namely Northeast and Southeast; however, for other regions of the country (North, Central West and South), previous methodological adjustment is recommended as demonstrated in this study.

Testing alternative ground water models using cross-validation and other methods

USGS Publications Warehouse

Foglia, L.; Mehl, S.W.; Hill, M.C.; Perona, P.; Burlando, P.

2007-01-01

Many methods can be used to test alternative ground water models. Of concern in this work are methods able to (1) rank alternative models (also called model discrimination) and (2) identify observations important to parameter estimates and predictions (equivalent to the purpose served by some types of sensitivity analysis). Some of the measures investigated are computationally efficient; others are computationally demanding. The latter are generally needed to account for model nonlinearity. The efficient model discrimination methods investigated include the information criteria: the corrected Akaike information criterion, Bayesian information criterion, and generalized cross-validation. The efficient sensitivity analysis measures used are dimensionless scaled sensitivity (DSS), composite scaled sensitivity, and parameter correlation coefficient (PCC); the other statistics are DFBETAS, Cook's D, and observation-prediction statistic. Acronyms are explained in the introduction. Cross-validation (CV) is a computationally intensive nonlinear method that is used for both model discrimination and sensitivity analysis. The methods are tested using up to five alternative parsimoniously constructed models of the ground water system of the Maggia Valley in southern Switzerland. The alternative models differ in their representation of hydraulic conductivity. A new method for graphically representing CV and sensitivity analysis results for complex models is presented and used to evaluate the utility of the efficient statistics. The results indicate that for model selection, the information criteria produce similar results at much smaller computational cost than CV. For identifying important observations, the only obviously inferior linear measure is DSS; the poor performance was expected because DSS does not include the effects of parameter correlation and PCC reveals large parameter correlations. ?? 2007 National Ground Water Association.

Quantitative analysis of γ-oryzanol content in cold pressed rice bran oil by TLC-image analysis method.

PubMed

Sakunpak, Apirak; Suksaeree, Jirapornchai; Monton, Chaowalit; Pathompak, Pathamaporn; Kraisintu, Krisana

2014-02-01

To develop and validate an image analysis method for quantitative analysis of γ-oryzanol in cold pressed rice bran oil. TLC-densitometric and TLC-image analysis methods were developed, validated, and used for quantitative analysis of γ-oryzanol in cold pressed rice bran oil. The results obtained by these two different quantification methods were compared by paired t-test. Both assays provided good linearity, accuracy, reproducibility and selectivity for determination of γ-oryzanol. The TLC-densitometric and TLC-image analysis methods provided a similar reproducibility, accuracy and selectivity for the quantitative determination of γ-oryzanol in cold pressed rice bran oil. A statistical comparison of the quantitative determinations of γ-oryzanol in samples did not show any statistically significant difference between TLC-densitometric and TLC-image analysis methods. As both methods were found to be equal, they therefore can be used for the determination of γ-oryzanol in cold pressed rice bran oil.

Statistical methods used to test for agreement of medical instruments measuring continuous variables in method comparison studies: a systematic review.

PubMed

Zaki, Rafdzah; Bulgiba, Awang; Ismail, Roshidi; Ismail, Noor Azina

2012-01-01

Accurate values are a must in medicine. An important parameter in determining the quality of a medical instrument is agreement with a gold standard. Various statistical methods have been used to test for agreement. Some of these methods have been shown to be inappropriate. This can result in misleading conclusions about the validity of an instrument. The Bland-Altman method is the most popular method judging by the many citations of the article proposing this method. However, the number of citations does not necessarily mean that this method has been applied in agreement research. No previous study has been conducted to look into this. This is the first systematic review to identify statistical methods used to test for agreement of medical instruments. The proportion of various statistical methods found in this review will also reflect the proportion of medical instruments that have been validated using those particular methods in current clinical practice. Five electronic databases were searched between 2007 and 2009 to look for agreement studies. A total of 3,260 titles were initially identified. Only 412 titles were potentially related, and finally 210 fitted the inclusion criteria. The Bland-Altman method is the most popular method with 178 (85%) studies having used this method, followed by the correlation coefficient (27%) and means comparison (18%). Some of the inappropriate methods highlighted by Altman and Bland since the 1980s are still in use. This study finds that the Bland-Altman method is the most popular method used in agreement research. There are still inappropriate applications of statistical methods in some studies. It is important for a clinician or medical researcher to be aware of this issue because misleading conclusions from inappropriate analyses will jeopardize the quality of the evidence, which in turn will influence quality of care given to patients in the future.

Statistical Methods Used to Test for Agreement of Medical Instruments Measuring Continuous Variables in Method Comparison Studies: A Systematic Review

PubMed Central

Zaki, Rafdzah; Bulgiba, Awang; Ismail, Roshidi; Ismail, Noor Azina

2012-01-01

Background Accurate values are a must in medicine. An important parameter in determining the quality of a medical instrument is agreement with a gold standard. Various statistical methods have been used to test for agreement. Some of these methods have been shown to be inappropriate. This can result in misleading conclusions about the validity of an instrument. The Bland-Altman method is the most popular method judging by the many citations of the article proposing this method. However, the number of citations does not necessarily mean that this method has been applied in agreement research. No previous study has been conducted to look into this. This is the first systematic review to identify statistical methods used to test for agreement of medical instruments. The proportion of various statistical methods found in this review will also reflect the proportion of medical instruments that have been validated using those particular methods in current clinical practice. Methodology/Findings Five electronic databases were searched between 2007 and 2009 to look for agreement studies. A total of 3,260 titles were initially identified. Only 412 titles were potentially related, and finally 210 fitted the inclusion criteria. The Bland-Altman method is the most popular method with 178 (85%) studies having used this method, followed by the correlation coefficient (27%) and means comparison (18%). Some of the inappropriate methods highlighted by Altman and Bland since the 1980s are still in use. Conclusions This study finds that the Bland-Altman method is the most popular method used in agreement research. There are still inappropriate applications of statistical methods in some studies. It is important for a clinician or medical researcher to be aware of this issue because misleading conclusions from inappropriate analyses will jeopardize the quality of the evidence, which in turn will influence quality of care given to patients in the future. PMID:22662248

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

PubMed

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

2006-01-01

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

Can Percentiles Replace Raw Scores in the Statistical Analysis of Test Data?

ERIC Educational Resources Information Center

Zimmerman, Donald W.; Zumbo, Bruno D.

2005-01-01

Educational and psychological testing textbooks typically warn of the inappropriateness of performing arithmetic operations and statistical analysis on percentiles instead of raw scores. This seems inconsistent with the well-established finding that transforming scores to ranks and using nonparametric methods often improves the validity and power…

A Statistical Method of Identifying Interactions in Neuron–Glia Systems Based on Functional Multicell Ca2+ Imaging

PubMed Central

Nakae, Ken; Ikegaya, Yuji; Ishikawa, Tomoe; Oba, Shigeyuki; Urakubo, Hidetoshi; Koyama, Masanori; Ishii, Shin

2014-01-01

Crosstalk between neurons and glia may constitute a significant part of information processing in the brain. We present a novel method of statistically identifying interactions in a neuron–glia network. We attempted to identify neuron–glia interactions from neuronal and glial activities via maximum-a-posteriori (MAP)-based parameter estimation by developing a generalized linear model (GLM) of a neuron–glia network. The interactions in our interest included functional connectivity and response functions. We evaluated the cross-validated likelihood of GLMs that resulted from the addition or removal of connections to confirm the existence of specific neuron-to-glia or glia-to-neuron connections. We only accepted addition or removal when the modification improved the cross-validated likelihood. We applied the method to a high-throughput, multicellular in vitro Ca2+ imaging dataset obtained from the CA3 region of a rat hippocampus, and then evaluated the reliability of connectivity estimates using a statistical test based on a surrogate method. Our findings based on the estimated connectivity were in good agreement with currently available physiological knowledge, suggesting our method can elucidate undiscovered functions of neuron–glia systems. PMID:25393874

Use of multiple cluster analysis methods to explore the validity of a community outcomes concept map.

PubMed

Orsi, Rebecca

2017-02-01

Concept mapping is now a commonly-used technique for articulating and evaluating programmatic outcomes. However, research regarding validity of knowledge and outcomes produced with concept mapping is sparse. The current study describes quantitative validity analyses using a concept mapping dataset. We sought to increase the validity of concept mapping evaluation results by running multiple cluster analysis methods and then using several metrics to choose from among solutions. We present four different clustering methods based on analyses using the R statistical software package: partitioning around medoids (PAM), fuzzy analysis (FANNY), agglomerative nesting (AGNES) and divisive analysis (DIANA). We then used the Dunn and Davies-Bouldin indices to assist in choosing a valid cluster solution for a concept mapping outcomes evaluation. We conclude that the validity of the outcomes map is high, based on the analyses described. Finally, we discuss areas for further concept mapping methods research. Copyright © 2016 Elsevier Ltd. All rights reserved.

A New Technique for Personality Scale Construction. Preliminary Findings.

ERIC Educational Resources Information Center

Schaffner, Paul E.; Darlington, Richard B.

Most methods of personality scale construction have clear statistical disadvantages. A hybrid method (Darlington and Bishop, 1966) was found to increase scale validity more than any other method, with large item pools. A simple modification of the Darlington-Bishop method (algebraically and conceptually similar to ridge regression, but…

Assessing Attitudes towards Statistics among Medical Students: Psychometric Properties of the Serbian Version of the Survey of Attitudes Towards Statistics (SATS)

PubMed Central

Stanisavljevic, Dejana; Trajkovic, Goran; Marinkovic, Jelena; Bukumiric, Zoran; Cirkovic, Andja; Milic, Natasa

2014-01-01

Background Medical statistics has become important and relevant for future doctors, enabling them to practice evidence based medicine. Recent studies report that students’ attitudes towards statistics play an important role in their statistics achievements. The aim of the study was to test the psychometric properties of the Serbian version of the Survey of Attitudes Towards Statistics (SATS) in order to acquire a valid instrument to measure attitudes inside the Serbian educational context. Methods The validation study was performed on a cohort of 417 medical students who were enrolled in an obligatory introductory statistics course. The SATS adaptation was based on an internationally accepted methodology for translation and cultural adaptation. Psychometric properties of the Serbian version of the SATS were analyzed through the examination of factorial structure and internal consistency. Results Most medical students held positive attitudes towards statistics. The average total SATS score was above neutral (4.3±0.8), and varied from 1.9 to 6.2. Confirmatory factor analysis validated the six-factor structure of the questionnaire (Affect, Cognitive Competence, Value, Difficulty, Interest and Effort). Values for fit indices TLI (0.940) and CFI (0.961) were above the cut-off of ≥0.90. The RMSEA value of 0.064 (0.051–0.078) was below the suggested value of ≤0.08. Cronbach’s alpha of the entire scale was 0.90, indicating scale reliability. In a multivariate regression model, self-rating of ability in mathematics and current grade point average were significantly associated with the total SATS score after adjusting for age and gender. Conclusion Present study provided the evidence for the appropriate metric properties of the Serbian version of SATS. Confirmatory factor analysis validated the six-factor structure of the scale. The SATS might be reliable and a valid instrument for identifying medical students’ attitudes towards statistics in the Serbian educational context. PMID:25405489

Uncertainty propagation for statistical impact prediction of space debris

NASA Astrophysics Data System (ADS)

Hoogendoorn, R.; Mooij, E.; Geul, J.

2018-01-01

Predictions of the impact time and location of space debris in a decaying trajectory are highly influenced by uncertainties. The traditional Monte Carlo (MC) method can be used to perform accurate statistical impact predictions, but requires a large computational effort. A method is investigated that directly propagates a Probability Density Function (PDF) in time, which has the potential to obtain more accurate results with less computational effort. The decaying trajectory of Delta-K rocket stages was used to test the methods using a six degrees-of-freedom state model. The PDF of the state of the body was propagated in time to obtain impact-time distributions. This Direct PDF Propagation (DPP) method results in a multi-dimensional scattered dataset of the PDF of the state, which is highly challenging to process. No accurate results could be obtained, because of the structure of the DPP data and the high dimensionality. Therefore, the DPP method is less suitable for practical uncontrolled entry problems and the traditional MC method remains superior. Additionally, the MC method was used with two improved uncertainty models to obtain impact-time distributions, which were validated using observations of true impacts. For one of the two uncertainty models, statistically more valid impact-time distributions were obtained than in previous research.

Measurement of patient safety: a systematic review of the reliability and validity of adverse event detection with record review

PubMed Central

Hanskamp-Sebregts, Mirelle; Zegers, Marieke; Vincent, Charles; van Gurp, Petra J; de Vet, Henrica C W; Wollersheim, Hub

2016-01-01

Objectives Record review is the most used method to quantify patient safety. We systematically reviewed the reliability and validity of adverse event detection with record review. Design A systematic review of the literature. Methods We searched PubMed, EMBASE, CINAHL, PsycINFO and the Cochrane Library and from their inception through February 2015. We included all studies that aimed to describe the reliability and/or validity of record review. Two reviewers conducted data extraction. We pooled κ values (κ) and analysed the differences in subgroups according to number of reviewers, reviewer experience and training level, adjusted for the prevalence of adverse events. Results In 25 studies, the psychometric data of the Global Trigger Tool (GTT) and the Harvard Medical Practice Study (HMPS) were reported and 24 studies were included for statistical pooling. The inter-rater reliability of the GTT and HMPS showed a pooled κ of 0.65 and 0.55, respectively. The inter-rater agreement was statistically significantly higher when the group of reviewers within a study consisted of a maximum five reviewers. We found no studies reporting on the validity of the GTT and HMPS. Conclusions The reliability of record review is moderate to substantial and improved when a small group of reviewers carried out record review. The validity of the record review method has never been evaluated, while clinical data registries, autopsy or direct observations of patient care are potential reference methods that can be used to test concurrent validity. PMID:27550650

Does bad inference drive out good?

PubMed

Marozzi, Marco

2015-07-01

The (mis)use of statistics in practice is widely debated, and a field where the debate is particularly active is medicine. Many scholars emphasize that a large proportion of published medical research contains statistical errors. It has been noted that top class journals like Nature Medicine and The New England Journal of Medicine publish a considerable proportion of papers that contain statistical errors and poorly document the application of statistical methods. This paper joins the debate on the (mis)use of statistics in the medical literature. Even though the validation process of a statistical result may be quite elusive, a careful assessment of underlying assumptions is central in medicine as well as in other fields where a statistical method is applied. Unfortunately, a careful assessment of underlying assumptions is missing in many papers, including those published in top class journals. In this paper, it is shown that nonparametric methods are good alternatives to parametric methods when the assumptions for the latter ones are not satisfied. A key point to solve the problem of the misuse of statistics in the medical literature is that all journals have their own statisticians to review the statistical method/analysis section in each submitted paper. © 2015 Wiley Publishing Asia Pty Ltd.

Towards Validation of an Adaptive Flight Control Simulation Using Statistical Emulation

NASA Technical Reports Server (NTRS)

He, Yuning; Lee, Herbert K. H.; Davies, Misty D.

2012-01-01

Traditional validation of flight control systems is based primarily upon empirical testing. Empirical testing is sufficient for simple systems in which a.) the behavior is approximately linear and b.) humans are in-the-loop and responsible for off-nominal flight regimes. A different possible concept of operation is to use adaptive flight control systems with online learning neural networks (OLNNs) in combination with a human pilot for off-nominal flight behavior (such as when a plane has been damaged). Validating these systems is difficult because the controller is changing during the flight in a nonlinear way, and because the pilot and the control system have the potential to co-adapt in adverse ways traditional empirical methods are unlikely to provide any guarantees in this case. Additionally, the time it takes to find unsafe regions within the flight envelope using empirical testing means that the time between adaptive controller design iterations is large. This paper describes a new concept for validating adaptive control systems using methods based on Bayesian statistics. This validation framework allows the analyst to build nonlinear models with modal behavior, and to have an uncertainty estimate for the difference between the behaviors of the model and system under test.

Degrees of separation as a statistical tool for evaluating candidate genes.

PubMed

Nelson, Ronald M; Pettersson, Mats E

2014-12-01

Selection of candidate genes is an important step in the exploration of complex genetic architecture. The number of gene networks available is increasing and these can provide information to help with candidate gene selection. It is currently common to use the degree of connectedness in gene networks as validation in Genome Wide Association (GWA) and Quantitative Trait Locus (QTL) mapping studies. However, it can cause misleading results if not validated properly. Here we present a method and tool for validating the gene pairs from GWA studies given the context of the network they co-occur in. It ensures that proposed interactions and gene associations are not statistical artefacts inherent to the specific gene network architecture. The CandidateBacon package provides an easy and efficient method to calculate the average degree of separation (DoS) between pairs of genes to currently available gene networks. We show how these empirical estimates of average connectedness are used to validate candidate gene pairs. Validation of interacting genes by comparing their connectedness with the average connectedness in the gene network will provide support for said interactions by utilising the growing amount of gene network information available. Copyright © 2014 Elsevier Ltd. All rights reserved.

The closure approximation in the hierarchy equations.

NASA Technical Reports Server (NTRS)

Adomian, G.

1971-01-01

The expectation of the solution process in a stochastic operator equation can be obtained from averaged equations only under very special circumstances. Conditions for validity are given and the significance and validity of the approximation in widely used hierarchy methods and the ?self-consistent field' approximation in nonequilibrium statistical mechanics are clarified. The error at any level of the hierarchy can be given and can be avoided by the use of the iterative method.

Statistical Methodologies to Integrate Experimental and Computational Research

NASA Technical Reports Server (NTRS)

Parker, P. A.; Johnson, R. T.; Montgomery, D. C.

2008-01-01

Development of advanced algorithms for simulating engine flow paths requires the integration of fundamental experiments with the validation of enhanced mathematical models. In this paper, we provide an overview of statistical methods to strategically and efficiently conduct experiments and computational model refinement. Moreover, the integration of experimental and computational research efforts is emphasized. With a statistical engineering perspective, scientific and engineering expertise is combined with statistical sciences to gain deeper insights into experimental phenomenon and code development performance; supporting the overall research objectives. The particular statistical methods discussed are design of experiments, response surface methodology, and uncertainty analysis and planning. Their application is illustrated with a coaxial free jet experiment and a turbulence model refinement investigation. Our goal is to provide an overview, focusing on concepts rather than practice, to demonstrate the benefits of using statistical methods in research and development, thereby encouraging their broader and more systematic application.

Spectroflurimetric estimation of the new antiviral agent ledipasvir in presence of sofosbuvir

NASA Astrophysics Data System (ADS)

Salama, Fathy M.; Attia, Khalid A.; Abouserie, Ahmed A.; El-Olemy, Ahmed; Abolmagd, Ebrahim

2018-02-01

A spectroflurimetric method has been developed and validated for the selective quantitative determination of ledipasvir in presence of sofosbuvir. In this method the native fluorescence of ledipasvir in ethanol at 405 nm was measured after excitation at 340 nm. The proposed method was validated according to ICH guidelines and show high sensitivity, accuracy and precision. Furthermore this method was successfully applied to the analysis of ledipasvir in pharmaceutical dosage form without interference from sofosbuvir and other additives and the results were statistically compared to a reported method and found no significant difference.

Developing and validating a nutrition knowledge questionnaire: key methods and considerations.

PubMed

Trakman, Gina Louise; Forsyth, Adrienne; Hoye, Russell; Belski, Regina

2017-10-01

To outline key statistical considerations and detailed methodologies for the development and evaluation of a valid and reliable nutrition knowledge questionnaire. Literature on questionnaire development in a range of fields was reviewed and a set of evidence-based guidelines specific to the creation of a nutrition knowledge questionnaire have been developed. The recommendations describe key qualitative methods and statistical considerations, and include relevant examples from previous papers and existing nutrition knowledge questionnaires. Where details have been omitted for the sake of brevity, the reader has been directed to suitable references. We recommend an eight-step methodology for nutrition knowledge questionnaire development as follows: (i) definition of the construct and development of a test plan; (ii) generation of the item pool; (iii) choice of the scoring system and response format; (iv) assessment of content validity; (v) assessment of face validity; (vi) purification of the scale using item analysis, including item characteristics, difficulty and discrimination; (vii) evaluation of the scale including its factor structure and internal reliability, or Rasch analysis, including assessment of dimensionality and internal reliability; and (viii) gathering of data to re-examine the questionnaire's properties, assess temporal stability and confirm construct validity. Several of these methods have previously been overlooked. The measurement of nutrition knowledge is an important consideration for individuals working in the nutrition field. Improved methods in the development of nutrition knowledge questionnaires, such as the use of factor analysis or Rasch analysis, will enable more confidence in reported measures of nutrition knowledge.

Predictive Validity of DSM-IV and ICD-10 Criteria for ADHD and Hyperkinetic Disorder

ERIC Educational Resources Information Center

Lee, Soyoung I.; Schachar, Russell J.; Chen, Shirley X.; Ornstein, Tisha J.; Charach, Alice; Barr, Cathy; Ickowicz, Abel

2008-01-01

Background: The goal of this study was to compare the predictive validity of the two main diagnostic schemata for childhood hyperactivity--attention-deficit hyperactivity disorder (ADHD; "Diagnostic and Statistical Manual"-IV) and hyperkinetic disorder (HKD; "International Classification of Diseases"-10th Edition). Methods: Diagnostic criteria for…

The Politics and Statistics of Value-Added Modeling for Accountability of Teacher Preparation Programs

ERIC Educational Resources Information Center

Lincove, Jane Arnold; Osborne, Cynthia; Dillon, Amanda; Mills, Nicholas

2014-01-01

Despite questions about validity and reliability, the use of value-added estimation methods has moved beyond academic research into state accountability systems for teachers, schools, and teacher preparation programs (TPPs). Prior studies of value-added measurement for TPPs test the validity of researcher-designed models and find that measuring…

Likert or Not, Survey (In)Validation Requires Explicit Theories and True Grit

ERIC Educational Resources Information Center

McGrane, Joshua A.; Nowland, Trisha

2017-01-01

From the time of Likert (1932) on, attitudes of expediency regarding both theory and methodology became apparent with reference to survey construction and validation practices. In place of theory and more--theoretically minded methods, such as those found in the early work of Thurstone (1928) and Coombs (1964), statistical models and…

Load Model Verification, Validation and Calibration Framework by Statistical Analysis on Field Data

NASA Astrophysics Data System (ADS)

Jiao, Xiangqing; Liao, Yuan; Nguyen, Thai

2017-11-01

Accurate load models are critical for power system analysis and operation. A large amount of research work has been done on load modeling. Most of the existing research focuses on developing load models, while little has been done on developing formal load model verification and validation (V&V) methodologies or procedures. Most of the existing load model validation is based on qualitative rather than quantitative analysis. In addition, not all aspects of model V&V problem have been addressed by the existing approaches. To complement the existing methods, this paper proposes a novel load model verification and validation framework that can systematically and more comprehensively examine load model's effectiveness and accuracy. Statistical analysis, instead of visual check, quantifies the load model's accuracy, and provides a confidence level of the developed load model for model users. The analysis results can also be used to calibrate load models. The proposed framework can be used as a guidance to systematically examine load models for utility engineers and researchers. The proposed method is demonstrated through analysis of field measurements collected from a utility system.

Methods for assessing reliability and validity for a measurement tool: a case study and critique using the WHO haemoglobin colour scale.

PubMed

White, Sarah A; van den Broek, Nynke R

2004-05-30

Before introducing a new measurement tool it is necessary to evaluate its performance. Several statistical methods have been developed, or used, to evaluate the reliability and validity of a new assessment method in such circumstances. In this paper we review some commonly used methods. Data from a study that was conducted to evaluate the usefulness of a specific measurement tool (the WHO Colour Scale) is then used to illustrate the application of these methods. The WHO Colour Scale was developed under the auspices of the WHO to provide a simple portable and reliable method of detecting anaemia. This Colour Scale is a discrete interval scale, whereas the actual haemoglobin values it is used to estimate are on a continuous interval scale and can be measured accurately using electrical laboratory equipment. The methods we consider are: linear regression, correlation coefficients, paired t-tests plotting differences against mean values and deriving limits of agreement; kappa and weighted kappa statistics, sensitivity and specificity, an intraclass correlation coefficient and the repeatability coefficient. We note that although the definition and properties of each of these methods is well established inappropriate methods continue to be used in medical literature for assessing reliability and validity, as evidenced in the context of the evaluation of the WHO Colour Scale. Copyright 2004 John Wiley & Sons, Ltd.

Validation of Statistical Sampling Algorithms in Visual Sample Plan (VSP): Summary Report

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

Nuffer, Lisa L; Sego, Landon H.; Wilson, John E.

2009-02-18

The U.S. Department of Homeland Security, Office of Technology Development (OTD) contracted with a set of U.S. Department of Energy national laboratories, including the Pacific Northwest National Laboratory (PNNL), to write a Remediation Guidance for Major Airports After a Chemical Attack. The report identifies key activities and issues that should be considered by a typical major airport following an incident involving release of a toxic chemical agent. Four experimental tasks were identified that would require further research in order to supplement the Remediation Guidance. One of the tasks, Task 4, OTD Chemical Remediation Statistical Sampling Design Validation, dealt with statisticalmore » sampling algorithm validation. This report documents the results of the sampling design validation conducted for Task 4. In 2005, the Government Accountability Office (GAO) performed a review of the past U.S. responses to Anthrax terrorist cases. Part of the motivation for this PNNL report was a major GAO finding that there was a lack of validated sampling strategies in the U.S. response to Anthrax cases. The report (GAO 2005) recommended that probability-based methods be used for sampling design in order to address confidence in the results, particularly when all sample results showed no remaining contamination. The GAO also expressed a desire that the methods be validated, which is the main purpose of this PNNL report. The objective of this study was to validate probability-based statistical sampling designs and the algorithms pertinent to within-building sampling that allow the user to prescribe or evaluate confidence levels of conclusions based on data collected as guided by the statistical sampling designs. Specifically, the designs found in the Visual Sample Plan (VSP) software were evaluated. VSP was used to calculate the number of samples and the sample location for a variety of sampling plans applied to an actual release site. Most of the sampling designs validated are probability based, meaning samples are located randomly (or on a randomly placed grid) so no bias enters into the placement of samples, and the number of samples is calculated such that IF the amount and spatial extent of contamination exceeds levels of concern, at least one of the samples would be taken from a contaminated area, at least X% of the time. Hence, "validation" of the statistical sampling algorithms is defined herein to mean ensuring that the "X%" (confidence) is actually met.« less

Quantification of integrated HIV DNA by repetitive-sampling Alu-HIV PCR on the basis of poisson statistics.

PubMed

De Spiegelaere, Ward; Malatinkova, Eva; Lynch, Lindsay; Van Nieuwerburgh, Filip; Messiaen, Peter; O'Doherty, Una; Vandekerckhove, Linos

2014-06-01

Quantification of integrated proviral HIV DNA by repetitive-sampling Alu-HIV PCR is a candidate virological tool to monitor the HIV reservoir in patients. However, the experimental procedures and data analysis of the assay are complex and hinder its widespread use. Here, we provide an improved and simplified data analysis method by adopting binomial and Poisson statistics. A modified analysis method on the basis of Poisson statistics was used to analyze the binomial data of positive and negative reactions from a 42-replicate Alu-HIV PCR by use of dilutions of an integration standard and on samples of 57 HIV-infected patients. Results were compared with the quantitative output of the previously described Alu-HIV PCR method. Poisson-based quantification of the Alu-HIV PCR was linearly correlated with the standard dilution series, indicating that absolute quantification with the Poisson method is a valid alternative for data analysis of repetitive-sampling Alu-HIV PCR data. Quantitative outputs of patient samples assessed by the Poisson method correlated with the previously described Alu-HIV PCR analysis, indicating that this method is a valid alternative for quantifying integrated HIV DNA. Poisson-based analysis of the Alu-HIV PCR data enables absolute quantification without the need of a standard dilution curve. Implementation of the CI estimation permits improved qualitative analysis of the data and provides a statistical basis for the required minimal number of technical replicates. © 2014 The American Association for Clinical Chemistry.

Errors in reporting on dissolution research: methodological and statistical implications.

PubMed

Jasińska-Stroschein, Magdalena; Kurczewska, Urszula; Orszulak-Michalak, Daria

2017-02-01

In vitro dissolution testing provides useful information at clinical and preclinical stages of the drug development process. The study includes pharmaceutical papers on dissolution research published in Polish journals between 2010 and 2015. They were analyzed with regard to information provided by authors about chosen methods, performed validation, statistical reporting or assumptions used to properly compare release profiles considering the present guideline documents addressed to dissolution methodology and its validation. Of all the papers included in the study, 23.86% presented at least one set of validation parameters, 63.64% gave the results of the weight uniformity test, 55.68% content determination, 97.73% dissolution testing conditions, and 50% discussed a comparison of release profiles. The assumptions for methods used to compare dissolution profiles were discussed in 6.82% of papers. By means of example analyses, we demonstrate that the outcome can be influenced by the violation of several assumptions or selection of an improper method to compare dissolution profiles. A clearer description of the procedures would undoubtedly increase the quality of papers in this area.

Validation of Methods to Predict Vibration of a Panel in the Near Field of a Hot Supersonic Rocket Plume

NASA Technical Reports Server (NTRS)

Bremner, P. G.; Blelloch, P. A.; Hutchings, A.; Shah, P.; Streett, C. L.; Larsen, C. E.

2011-01-01

This paper describes the measurement and analysis of surface fluctuating pressure level (FPL) data and vibration data from a plume impingement aero-acoustic and vibration (PIAAV) test to validate NASA s physics-based modeling methods for prediction of panel vibration in the near field of a hot supersonic rocket plume. For this test - reported more fully in a companion paper by Osterholt & Knox at 26th Aerospace Testing Seminar, 2011 - the flexible panel was located 2.4 nozzle diameters from the plume centerline and 4.3 nozzle diameters downstream from the nozzle exit. The FPL loading is analyzed in terms of its auto spectrum, its cross spectrum, its spatial correlation parameters and its statistical properties. The panel vibration data is used to estimate the in-situ damping under plume FPL loading conditions and to validate both finite element analysis (FEA) and statistical energy analysis (SEA) methods for prediction of panel response. An assessment is also made of the effects of non-linearity in the panel elasticity.

Statistical Validation of Surrogate Endpoints: Another Look at the Prentice Criterion and Other Criteria.

PubMed

Saraf, Sanatan; Mathew, Thomas; Roy, Anindya

2015-01-01

For the statistical validation of surrogate endpoints, an alternative formulation is proposed for testing Prentice's fourth criterion, under a bivariate normal model. In such a setup, the criterion involves inference concerning an appropriate regression parameter, and the criterion holds if the regression parameter is zero. Testing such a null hypothesis has been criticized in the literature since it can only be used to reject a poor surrogate, and not to validate a good surrogate. In order to circumvent this, an equivalence hypothesis is formulated for the regression parameter, namely the hypothesis that the parameter is equivalent to zero. Such an equivalence hypothesis is formulated as an alternative hypothesis, so that the surrogate endpoint is statistically validated when the null hypothesis is rejected. Confidence intervals for the regression parameter and tests for the equivalence hypothesis are proposed using bootstrap methods and small sample asymptotics, and their performances are numerically evaluated and recommendations are made. The choice of the equivalence margin is a regulatory issue that needs to be addressed. The proposed equivalence testing formulation is also adopted for other parameters that have been proposed in the literature on surrogate endpoint validation, namely, the relative effect and proportion explained.

Validating the simulation of large-scale parallel applications using statistical characteristics

DOE PAGES

Zhang, Deli; Wilke, Jeremiah; Hendry, Gilbert; ...

2016-03-01

Simulation is a widely adopted method to analyze and predict the performance of large-scale parallel applications. Validating the hardware model is highly important for complex simulations with a large number of parameters. Common practice involves calculating the percent error between the projected and the real execution time of a benchmark program. However, in a high-dimensional parameter space, this coarse-grained approach often suffers from parameter insensitivity, which may not be known a priori. Moreover, the traditional approach cannot be applied to the validation of software models, such as application skeletons used in online simulations. In this work, we present a methodologymore » and a toolset for validating both hardware and software models by quantitatively comparing fine-grained statistical characteristics obtained from execution traces. Although statistical information has been used in tasks like performance optimization, this is the first attempt to apply it to simulation validation. Lastly, our experimental results show that the proposed evaluation approach offers significant improvement in fidelity when compared to evaluation using total execution time, and the proposed metrics serve as reliable criteria that progress toward automating the simulation tuning process.« less

Standardization, evaluation and early-phase method validation of an analytical scheme for batch-consistency N-glycosylation analysis of recombinant produced glycoproteins.

PubMed

Zietze, Stefan; Müller, Rainer H; Brecht, René

2008-03-01

In order to set up a batch-to-batch-consistency analytical scheme for N-glycosylation analysis, several sample preparation steps including enzyme digestions and fluorophore labelling and two HPLC-methods were established. The whole method scheme was standardized, evaluated and validated according to the requirements on analytical testing in early clinical drug development by usage of a recombinant produced reference glycoprotein (RGP). The standardization of the methods was performed by clearly defined standard operation procedures. During evaluation of the methods, the major interest was in the loss determination of oligosaccharides within the analytical scheme. Validation of the methods was performed with respect to specificity, linearity, repeatability, LOD and LOQ. Due to the fact that reference N-glycan standards were not available, a statistical approach was chosen to derive accuracy from the linearity data. After finishing the validation procedure, defined limits for method variability could be calculated and differences observed in consistency analysis could be separated into significant and incidental ones.

Statistically Validated Networks in Bipartite Complex Systems

PubMed Central

Tumminello, Michele; Miccichè, Salvatore; Lillo, Fabrizio; Piilo, Jyrki; Mantegna, Rosario N.

2011-01-01

Many complex systems present an intrinsic bipartite structure where elements of one set link to elements of the second set. In these complex systems, such as the system of actors and movies, elements of one set are qualitatively different than elements of the other set. The properties of these complex systems are typically investigated by constructing and analyzing a projected network on one of the two sets (for example the actor network or the movie network). Complex systems are often very heterogeneous in the number of relationships that the elements of one set establish with the elements of the other set, and this heterogeneity makes it very difficult to discriminate links of the projected network that are just reflecting system's heterogeneity from links relevant to unveil the properties of the system. Here we introduce an unsupervised method to statistically validate each link of a projected network against a null hypothesis that takes into account system heterogeneity. We apply the method to a biological, an economic and a social complex system. The method we propose is able to detect network structures which are very informative about the organization and specialization of the investigated systems, and identifies those relationships between elements of the projected network that cannot be explained simply by system heterogeneity. We also show that our method applies to bipartite systems in which different relationships might have different qualitative nature, generating statistically validated networks in which such difference is preserved. PMID:21483858

14 CFR 417.203 - Compliance.

Code of Federal Regulations, 2012 CFR

2012-01-01

... analysis method is based on accurate data and scientific principles and is statistically valid. The FAA... safety analysis must also meet the requirements for methods of analysis contained in appendices A and B... from an identical or similar launch if the analysis still applies to the later launch. (b) Method of...

14 CFR 417.203 - Compliance.

Code of Federal Regulations, 2014 CFR

2014-01-01

... analysis method is based on accurate data and scientific principles and is statistically valid. The FAA... safety analysis must also meet the requirements for methods of analysis contained in appendices A and B... from an identical or similar launch if the analysis still applies to the later launch. (b) Method of...

14 CFR 417.203 - Compliance.

Code of Federal Regulations, 2013 CFR

2013-01-01

... analysis method is based on accurate data and scientific principles and is statistically valid. The FAA... safety analysis must also meet the requirements for methods of analysis contained in appendices A and B... from an identical or similar launch if the analysis still applies to the later launch. (b) Method of...

40 CFR Appendix A to Part 63 - Test Methods

Code of Federal Regulations, 2010 CFR

2010-07-01

... components by a different analyst). 3.3Surrogate Reference Materials. The analyst may use surrogate compounds... the variance of the proposed method is significantly different from that of the validated method by... variables can be determined in eight experiments rather than 128 (W.J. Youden, Statistical Manual of the...

Validation of non-stationary precipitation series for site-specific impact assessment: comparison of two statistical downscaling techniques

NASA Astrophysics Data System (ADS)

Mullan, Donal; Chen, Jie; Zhang, Xunchang John

2016-02-01

Statistical downscaling (SD) methods have become a popular, low-cost and accessible means of bridging the gap between the coarse spatial resolution at which climate models output climate scenarios and the finer spatial scale at which impact modellers require these scenarios, with various different SD techniques used for a wide range of applications across the world. This paper compares the Generator for Point Climate Change (GPCC) model and the Statistical DownScaling Model (SDSM)—two contrasting SD methods—in terms of their ability to generate precipitation series under non-stationary conditions across ten contrasting global climates. The mean, maximum and a selection of distribution statistics as well as the cumulative frequencies of dry and wet spells for four different temporal resolutions were compared between the models and the observed series for a validation period. Results indicate that both methods can generate daily precipitation series that generally closely mirror observed series for a wide range of non-stationary climates. However, GPCC tends to overestimate higher precipitation amounts, whilst SDSM tends to underestimate these. This infers that GPCC is more likely to overestimate the effects of precipitation on a given impact sector, whilst SDSM is likely to underestimate the effects. GPCC performs better than SDSM in reproducing wet and dry day frequency, which is a key advantage for many impact sectors. Overall, the mixed performance of the two methods illustrates the importance of users performing a thorough validation in order to determine the influence of simulated precipitation on their chosen impact sector.

Statistical Approaches to Interpretation of Local, Regional, and National Highway-Runoff and Urban-Stormwater Data

USGS Publications Warehouse

Tasker, Gary D.; Granato, Gregory E.

2000-01-01

Decision makers need viable methods for the interpretation of local, regional, and national-highway runoff and urban-stormwater data including flows, concentrations and loads of chemical constituents and sediment, potential effects on receiving waters, and the potential effectiveness of various best management practices (BMPs). Valid (useful for intended purposes), current, and technically defensible stormwater-runoff models are needed to interpret data collected in field studies, to support existing highway and urban-runoffplanning processes, to meet National Pollutant Discharge Elimination System (NPDES) requirements, and to provide methods for computation of Total Maximum Daily Loads (TMDLs) systematically and economically. Historically, conceptual, simulation, empirical, and statistical models of varying levels of detail, complexity, and uncertainty have been used to meet various data-quality objectives in the decision-making processes necessary for the planning, design, construction, and maintenance of highways and for other land-use applications. Water-quality simulation models attempt a detailed representation of the physical processes and mechanisms at a given site. Empirical and statistical regional water-quality assessment models provide a more general picture of water quality or changes in water quality over a region. All these modeling techniques share one common aspect-their predictive ability is poor without suitable site-specific data for calibration. To properly apply the correct model, one must understand the classification of variables, the unique characteristics of water-resources data, and the concept of population structure and analysis. Classifying variables being used to analyze data may determine which statistical methods are appropriate for data analysis. An understanding of the characteristics of water-resources data is necessary to evaluate the applicability of different statistical methods, to interpret the results of these techniques, and to use tools and techniques that account for the unique nature of water-resources data sets. Populations of data on stormwater-runoff quantity and quality are often best modeled as logarithmic transformations. Therefore, these factors need to be considered to form valid, current, and technically defensible stormwater-runoff models. Regression analysis is an accepted method for interpretation of water-resources data and for prediction of current or future conditions at sites that fit the input data model. Regression analysis is designed to provide an estimate of the average response of a system as it relates to variation in one or more known variables. To produce valid models, however, regression analysis should include visual analysis of scatterplots, an examination of the regression equation, evaluation of the method design assumptions, and regression diagnostics. A number of statistical techniques are described in the text and in the appendixes to provide information necessary to interpret data by use of appropriate methods. Uncertainty is an important part of any decisionmaking process. In order to deal with uncertainty problems, the analyst needs to know the severity of the statistical uncertainty of the methods used to predict water quality. Statistical models need to be based on information that is meaningful, representative, complete, precise, accurate, and comparable to be deemed valid, up to date, and technically supportable. To assess uncertainty in the analytical tools, the modeling methods, and the underlying data set, all of these components need be documented and communicated in an accessible format within project publications.

Investigation of Super Learner Methodology on HIV-1 Small Sample: Application on Jaguar Trial Data.

PubMed

Houssaïni, Allal; Assoumou, Lambert; Marcelin, Anne Geneviève; Molina, Jean Michel; Calvez, Vincent; Flandre, Philippe

2012-01-01

Background. Many statistical models have been tested to predict phenotypic or virological response from genotypic data. A statistical framework called Super Learner has been introduced either to compare different methods/learners (discrete Super Learner) or to combine them in a Super Learner prediction method. Methods. The Jaguar trial is used to apply the Super Learner framework. The Jaguar study is an "add-on" trial comparing the efficacy of adding didanosine to an on-going failing regimen. Our aim was also to investigate the impact on the use of different cross-validation strategies and different loss functions. Four different repartitions between training set and validations set were tested through two loss functions. Six statistical methods were compared. We assess performance by evaluating R(2) values and accuracy by calculating the rates of patients being correctly classified. Results. Our results indicated that the more recent Super Learner methodology of building a new predictor based on a weighted combination of different methods/learners provided good performance. A simple linear model provided similar results to those of this new predictor. Slight discrepancy arises between the two loss functions investigated, and slight difference arises also between results based on cross-validated risks and results from full dataset. The Super Learner methodology and linear model provided around 80% of patients correctly classified. The difference between the lower and higher rates is around 10 percent. The number of mutations retained in different learners also varys from one to 41. Conclusions. The more recent Super Learner methodology combining the prediction of many learners provided good performance on our small dataset.

Cluster mass inference via random field theory.

PubMed

Zhang, Hui; Nichols, Thomas E; Johnson, Timothy D

2009-01-01

Cluster extent and voxel intensity are two widely used statistics in neuroimaging inference. Cluster extent is sensitive to spatially extended signals while voxel intensity is better for intense but focal signals. In order to leverage strength from both statistics, several nonparametric permutation methods have been proposed to combine the two methods. Simulation studies have shown that of the different cluster permutation methods, the cluster mass statistic is generally the best. However, to date, there is no parametric cluster mass inference available. In this paper, we propose a cluster mass inference method based on random field theory (RFT). We develop this method for Gaussian images, evaluate it on Gaussian and Gaussianized t-statistic images and investigate its statistical properties via simulation studies and real data. Simulation results show that the method is valid under the null hypothesis and demonstrate that it can be more powerful than the cluster extent inference method. Further, analyses with a single subject and a group fMRI dataset demonstrate better power than traditional cluster size inference, and good accuracy relative to a gold-standard permutation test.

Risk prediction models of breast cancer: a systematic review of model performances.

PubMed

Anothaisintawee, Thunyarat; Teerawattananon, Yot; Wiratkapun, Chollathip; Kasamesup, Vijj; Thakkinstian, Ammarin

2012-05-01

The number of risk prediction models has been increasingly developed, for estimating about breast cancer in individual women. However, those model performances are questionable. We therefore have conducted a study with the aim to systematically review previous risk prediction models. The results from this review help to identify the most reliable model and indicate the strengths and weaknesses of each model for guiding future model development. We searched MEDLINE (PubMed) from 1949 and EMBASE (Ovid) from 1974 until October 2010. Observational studies which constructed models using regression methods were selected. Information about model development and performance were extracted. Twenty-five out of 453 studies were eligible. Of these, 18 developed prediction models and 7 validated existing prediction models. Up to 13 variables were included in the models and sample sizes for each study ranged from 550 to 2,404,636. Internal validation was performed in four models, while five models had external validation. Gail and Rosner and Colditz models were the significant models which were subsequently modified by other scholars. Calibration performance of most models was fair to good (expected/observe ratio: 0.87-1.12), but discriminatory accuracy was poor to fair both in internal validation (concordance statistics: 0.53-0.66) and in external validation (concordance statistics: 0.56-0.63). Most models yielded relatively poor discrimination in both internal and external validation. This poor discriminatory accuracy of existing models might be because of a lack of knowledge about risk factors, heterogeneous subtypes of breast cancer, and different distributions of risk factors across populations. In addition the concordance statistic itself is insensitive to measure the improvement of discrimination. Therefore, the new method such as net reclassification index should be considered to evaluate the improvement of the performance of a new develop model.

Novel spectrophotometric methods for simultaneous determination of timolol and dorzolamide in their binary mixture.

PubMed

Lotfy, Hayam Mahmoud; Hegazy, Maha A; Rezk, Mamdouh R; Omran, Yasmin Rostom

2014-05-21

Two smart and novel spectrophotometric methods namely; absorbance subtraction (AS) and amplitude modulation (AM) were developed and validated for the determination of a binary mixture of timolol maleate (TIM) and dorzolamide hydrochloride (DOR) in presence of benzalkonium chloride without prior separation, using unified regression equation. Additionally, simple, specific, accurate and precise spectrophotometric methods manipulating ratio spectra were developed and validated for simultaneous determination of the binary mixture namely; simultaneous ratio subtraction (SRS), ratio difference (RD), ratio subtraction (RS) coupled with extended ratio subtraction (EXRS), constant multiplication method (CM) and mean centering of ratio spectra (MCR). The proposed spectrophotometric procedures do not require any separation steps. Accuracy, precision and linearity ranges of the proposed methods were determined and the specificity was assessed by analyzing synthetic mixtures of both drugs. They were applied to their pharmaceutical formulation and the results obtained were statistically compared to that of a reported spectrophotometric method. The statistical comparison showed that there is no significant difference between the proposed methods and the reported one regarding both accuracy and precision. Copyright © 2014 Elsevier B.V. All rights reserved.

Validated spectrophotometric method for the determination, spectroscopic characterization and thermal structural analysis of duloxetine with 1,2-naphthoquinone-4-sulphonate

NASA Astrophysics Data System (ADS)

Ulu, Sevgi Tatar; Elmali, Fikriye Tuncel

2012-03-01

A novel, selective, sensitive and simple spectrophotometric method was developed and validated for the determination of the antidepressant duloxetine hydrochloride in pharmaceutical preparation. The method was based on the reaction of duloxetine hydrochloride with 1,2-naphthoquinone-4-sulphonate (NQS) in alkaline media to yield orange colored product. The formation of this complex was also confirmed by UV-visible, FTIR, 1H NMR, Mass spectra techniques and thermal analysis. This method was validated for various parameters according to ICH guidelines. Beer's law is obeyed in a range of 5.0-60 μg/mL at the maximum absorption wavelength of 480 nm. The detection limit is 0.99 μg/mL and the recovery rate is in a range of 98.10-99.57%. The proposed methods was validated and applied to the determination of duloxetine hydrochloride in pharmaceutical preparation. The results were statistically analyzed and compared to those of a reference UV spectrophotometric method.

Connectedness DEOCS 4.1 Construct Validity Summary

DTIC Science & Technology

2017-08-01

Connectedness DEOCS 4.1 Construct Validity Summary DEFENSE EQUAL OPPORTUNITY MANAGEMENT INSTITUTE DIRECTORATE OF...appropriate statistical method to analyze these data. This EFA yielded a single factor solution. Refer to Table 6 for more information . Table 6...top management team perceptions of CEO charisma. Academy of Management Journal, 49(1), 161-174. Assessment to Solutions. (2016). Retrieved from https

Family Early Literacy Practices Questionnaire: A Validation Study for a Spanish-Speaking Population

ERIC Educational Resources Information Center

Lewis, Kandia

2012-01-01

The purpose of the current study was to evaluate the psychometric validity of a Spanish translated version of a family involvement questionnaire (the FELP) using a mixed-methods design. Thus, statistical analyses (i.e., factor analysis, reliability analysis, and item analysis) and qualitative analyses (i.e., focus group data) were assessed.…

The Impact of Model Parameterization and Estimation Methods on Tests of Measurement Invariance with Ordered Polytomous Data

ERIC Educational Resources Information Center

Koziol, Natalie A.; Bovaird, James A.

2018-01-01

Evaluations of measurement invariance provide essential construct validity evidence--a prerequisite for seeking meaning in psychological and educational research and ensuring fair testing procedures in high-stakes settings. However, the quality of such evidence is partly dependent on the validity of the resulting statistical conclusions. Type I or…

Time-of-Flight Measurements as a Possible Method to Observe Anyonic Statistics

NASA Astrophysics Data System (ADS)

Umucalılar, R. O.; Macaluso, E.; Comparin, T.; Carusotto, I.

2018-06-01

We propose a standard time-of-flight experiment as a method for observing the anyonic statistics of quasiholes in a fractional quantum Hall state of ultracold atoms. The quasihole states can be stably prepared by pinning the quasiholes with localized potentials and a measurement of the mean square radius of the freely expanding cloud, which is related to the average total angular momentum of the initial state, offers direct signatures of the statistical phase. Our proposed method is validated by Monte Carlo calculations for ν =1 /2 and 1 /3 fractional quantum Hall liquids containing a realistic number of particles. Extensions to quantum Hall liquids of light and to non-Abelian anyons are briefly discussed.

On vital aid: the why, what and how of validation

PubMed Central

Kleywegt, Gerard J.

2009-01-01

Limitations to the data and subjectivity in the structure-determination process may cause errors in macromolecular crystal structures. Appropriate validation techniques may be used to reveal problems in structures, ideally before they are analysed, published or deposited. Additionally, such tech­niques may be used a posteriori to assess the (relative) merits of a model by potential users. Weak validation methods and statistics assess how well a model reproduces the information that was used in its construction (i.e. experimental data and prior knowledge). Strong methods and statistics, on the other hand, test how well a model predicts data or information that were not used in the structure-determination process. These may be data that were excluded from the process on purpose, general knowledge about macromolecular structure, information about the biological role and biochemical activity of the molecule under study or its mutants or complexes and predictions that are based on the model and that can be tested experimentally. PMID:19171968

A user-targeted synthesis of the VALUE perfect predictor experiment

NASA Astrophysics Data System (ADS)

Maraun, Douglas; Widmann, Martin; Gutierrez, Jose; Kotlarski, Sven; Hertig, Elke; Wibig, Joanna; Rössler, Ole; Huth, Radan

2016-04-01

VALUE is an open European network to validate and compare downscaling methods for climate change research. A key deliverable of VALUE is the development of a systematic validation framework to enable the assessment and comparison of both dynamical and statistical downscaling methods. VALUE's main approach to validation is user-focused: starting from a specific user problem, a validation tree guides the selection of relevant validation indices and performance measures. We consider different aspects: (1) marginal aspects such as mean, variance and extremes; (2) temporal aspects such as spell length characteristics; (3) spatial aspects such as the de-correlation length of precipitation extremes; and multi-variate aspects such as the interplay of temperature and precipitation or scale-interactions. Several experiments have been designed to isolate specific points in the downscaling procedure where problems may occur. Experiment 1 (perfect predictors): what is the isolated downscaling skill? How do statistical and dynamical methods compare? How do methods perform at different spatial scales? Experiment 2 (Global climate model predictors): how is the overall representation of regional climate, including errors inherited from global climate models? Experiment 3 (pseudo reality): do methods fail in representing regional climate change? Here, we present a user-targeted synthesis of the results of the first VALUE experiment. In this experiment, downscaling methods are driven with ERA-Interim reanalysis data to eliminate global climate model errors, over the period 1979-2008. As reference data we use, depending on the question addressed, (1) observations from 86 meteorological stations distributed across Europe; (2) gridded observations at the corresponding 86 locations or (3) gridded spatially extended observations for selected European regions. With more than 40 contributing methods, this study is the most comprehensive downscaling inter-comparison project so far. The results clearly indicate that for several aspects, the downscaling skill varies considerably between different methods. For specific purposes, some methods can therefore clearly be excluded.

Statistical Methods and Tools for Uxo Characterization (SERDP Final Technical Report)

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

Pulsipher, Brent A.; Gilbert, Richard O.; Wilson, John E.

2004-11-15

The Strategic Environmental Research and Development Program (SERDP) issued a statement of need for FY01 titled Statistical Sampling for Unexploded Ordnance (UXO) Site Characterization that solicited proposals to develop statistically valid sampling protocols for cost-effective, practical, and reliable investigation of sites contaminated with UXO; protocols that could be validated through subsequent field demonstrations. The SERDP goal was the development of a sampling strategy for which a fraction of the site is initially surveyed by geophysical detectors to confidently identify clean areas and subsections (target areas, TAs) that had elevated densities of anomalous geophysical detector readings that could indicate the presencemore » of UXO. More detailed surveys could then be conducted to search the identified TAs for UXO. SERDP funded three projects: those proposed by the Pacific Northwest National Laboratory (PNNL) (SERDP Project No. UXO 1199), Sandia National Laboratory (SNL), and Oak Ridge National Laboratory (ORNL). The projects were closely coordinated to minimize duplication of effort and facilitate use of shared algorithms where feasible. This final report for PNNL Project 1199 describes the methods developed by PNNL to address SERDP's statement-of-need for the development of statistically-based geophysical survey methods for sites where 100% surveys are unattainable or cost prohibitive.« less

Polygenic scores via penalized regression on summary statistics.

PubMed

Mak, Timothy Shin Heng; Porsch, Robert Milan; Choi, Shing Wan; Zhou, Xueya; Sham, Pak Chung

2017-09-01

Polygenic scores (PGS) summarize the genetic contribution of a person's genotype to a disease or phenotype. They can be used to group participants into different risk categories for diseases, and are also used as covariates in epidemiological analyses. A number of possible ways of calculating PGS have been proposed, and recently there is much interest in methods that incorporate information available in published summary statistics. As there is no inherent information on linkage disequilibrium (LD) in summary statistics, a pertinent question is how we can use LD information available elsewhere to supplement such analyses. To answer this question, we propose a method for constructing PGS using summary statistics and a reference panel in a penalized regression framework, which we call lassosum. We also propose a general method for choosing the value of the tuning parameter in the absence of validation data. In our simulations, we showed that pseudovalidation often resulted in prediction accuracy that is comparable to using a dataset with validation phenotype and was clearly superior to the conservative option of setting the tuning parameter of lassosum to its lowest value. We also showed that lassosum achieved better prediction accuracy than simple clumping and P-value thresholding in almost all scenarios. It was also substantially faster and more accurate than the recently proposed LDpred. © 2017 WILEY PERIODICALS, INC.

Statistical Analysis of the Uncertainty in Pre-Flight Aerodynamic Database of a Hypersonic Vehicle

NASA Astrophysics Data System (ADS)

Huh, Lynn

The objective of the present research was to develop a new method to derive the aerodynamic coefficients and the associated uncertainties for flight vehicles via post- flight inertial navigation analysis using data from the inertial measurement unit. Statistical estimates of vehicle state and aerodynamic coefficients are derived using Monte Carlo simulation. Trajectory reconstruction using the inertial navigation system (INS) is a simple and well used method. However, deriving realistic uncertainties in the reconstructed state and any associated parameters is not so straight forward. Extended Kalman filters, batch minimum variance estimation and other approaches have been used. However, these methods generally depend on assumed physical models, assumed statistical distributions (usually Gaussian) or have convergence issues for non-linear problems. The approach here assumes no physical models, is applicable to any statistical distribution, and does not have any convergence issues. The new approach obtains the statistics directly from a sufficient number of Monte Carlo samples using only the generally well known gyro and accelerometer specifications and could be applied to the systems of non-linear form and non-Gaussian distribution. When redundant data are available, the set of Monte Carlo simulations are constrained to satisfy the redundant data within the uncertainties specified for the additional data. The proposed method was applied to validate the uncertainty in the pre-flight aerodynamic database of the X-43A Hyper-X research vehicle. In addition to gyro and acceleration data, the actual flight data include redundant measurements of position and velocity from the global positioning system (GPS). The criteria derived from the blend of the GPS and INS accuracy was used to select valid trajectories for statistical analysis. The aerodynamic coefficients were derived from the selected trajectories by either direct extraction method based on the equations in dynamics, or by the inquiry of the pre-flight aerodynamic database. After the application of the proposed method to the case of the X-43A Hyper-X research vehicle, it was found that 1) there were consistent differences in the aerodynamic coefficients from the pre-flight aerodynamic database and post-flight analysis, 2) the pre-flight estimation of the pitching moment coefficients was significantly different from the post-flight analysis, 3) the type of distribution of the states from the Monte Carlo simulation were affected by that of the perturbation parameters, 4) the uncertainties in the pre-flight model were overestimated, 5) the range where the aerodynamic coefficients from the pre-flight aerodynamic database and post-flight analysis are in closest agreement is between Mach *.* and *.* and more data points may be needed between Mach * and ** in the pre-flight aerodynamic database, 6) selection criterion for valid trajectories from the Monte Carlo simulations was mostly driven by the horizontal velocity error, 7) the selection criterion must be based on reasonable model to ensure the validity of the statistics from the proposed method, and 8) the results from the proposed method applied to the two different flights with the identical geometry and similar flight profile were consistent.

Detection of proximal caries using quantitative light-induced fluorescence-digital and laser fluorescence: a comparative study.

PubMed

Yoon, Hyung-In; Yoo, Min-Jeong; Park, Eun-Jin

2017-12-01

The purpose of this study was to evaluate the in vitro validity of quantitative light-induced fluorescence-digital (QLF-D) and laser fluorescence (DIAGNOdent) for assessing proximal caries in extracted premolars, using digital radiography as reference method. A total of 102 extracted premolars with similar lengths and shapes were used. A single operator conducted all the examinations using three different detection methods (bitewing radiography, QLF-D, and DIAGNOdent). The bitewing x-ray scale, QLF-D fluorescence loss (ΔF), and DIAGNOdent peak readings were compared and statistically analyzed. Each method showed an excellent reliability. The correlation coefficient between bitewing radiography and QLF-D, DIAGNOdent were -0.644 and 0.448, respectively, while the value between QLF-D and DIAGNOdent was -0.382. The kappa statistics for bitewing radiography and QLF-D had a higher diagnosis consensus than those for bitewing radiography and DIAGNOdent. The QLF-D was moderately to highly accurate (AUC = 0.753 - 0.908), while DIAGNOdent was moderately to less accurate (AUC = 0.622 - 0.784). All detection methods showed statistically significant correlation and high correlation between the bitewing radiography and QLF-D. QLF-D was found to be a valid and reliable alternative diagnostic method to digital bitewing radiography for in vitro detection of proximal caries.

Open-source platform to benchmark fingerprints for ligand-based virtual screening

PubMed Central

2013-01-01

Similarity-search methods using molecular fingerprints are an important tool for ligand-based virtual screening. A huge variety of fingerprints exist and their performance, usually assessed in retrospective benchmarking studies using data sets with known actives and known or assumed inactives, depends largely on the validation data sets used and the similarity measure used. Comparing new methods to existing ones in any systematic way is rather difficult due to the lack of standard data sets and evaluation procedures. Here, we present a standard platform for the benchmarking of 2D fingerprints. The open-source platform contains all source code, structural data for the actives and inactives used (drawn from three publicly available collections of data sets), and lists of randomly selected query molecules to be used for statistically valid comparisons of methods. This allows the exact reproduction and comparison of results for future studies. The results for 12 standard fingerprints together with two simple baseline fingerprints assessed by seven evaluation methods are shown together with the correlations between methods. High correlations were found between the 12 fingerprints and a careful statistical analysis showed that only the two baseline fingerprints were different from the others in a statistically significant way. High correlations were also found between six of the seven evaluation methods, indicating that despite their seeming differences, many of these methods are similar to each other. PMID:23721588

Development of a statistical method to help evaluating the transparency/opacity of decorative thin films

NASA Astrophysics Data System (ADS)

da Silva Oliveira, C. I.; Martinez-Martinez, D.; Al-Rjoub, A.; Rebouta, L.; Menezes, R.; Cunha, L.

2018-04-01

In this paper, we present a statistical method that allows evaluating the degree of a transparency of a thin film. To do so, the color coordinates are measured on different substrates, and the standard deviation is evaluated. In case of low values, the color depends on the film and not on the substrate, and intrinsic colors are obtained. In contrast, transparent films lead to high values of standard deviation, since the value of the color coordinates depends on the substrate. Between both extremes, colored films with a certain degree of transparency can be found. This method allows an objective and simple evaluation of the transparency of any film, improving the subjective visual inspection and avoiding the thickness problems related to optical spectroscopy evaluation. Zirconium oxynitride films deposited on three different substrates (Si, steel and glass) are used for testing the validity of this method, whose results have been validated with optical spectroscopy, and agree with the visual impression of the samples.

Technical Notes on the Multifactor Method of Elementary School Closing.

ERIC Educational Resources Information Center

Puleo, Vincent T.

This report provides preliminary technical information on a method for analyzing the factors involved in the closing of elementary schools. Included is a presentation of data and a brief discussion bearing on descriptive statistics, reliability, and validity. An intercorrelation matrix is also examined. The method employs 9 factors that have a…

Cover/Frequency (CF)

Treesearch

John F. Caratti

2006-01-01

The FIREMON Cover/Frequency (CF) method is used to assess changes in plant species cover and frequency for a macroplot. This method uses multiple quadrats to sample within-plot variation and quantify statistically valid changes in plant species cover, height, and frequency over time. Because it is difficult to estimate cover in quadrats for larger plants, this method...

Line Intercept (LI)

Treesearch

John F. Caratti

2006-01-01

The FIREMON Line Intercept (LI) method is used to assess changes in plant species cover for a macroplot. This method uses multiple line transects to sample within plot variation and quantify statistically valid changes in plant species cover and height over time. This method is suited for most forest and rangeland communities, but is especially useful for sampling...

Derivation and Validation of the Surgical Site Infections Risk Model Using Health Administrative Data.

PubMed

van Walraven, Carl; Jackson, Timothy D; Daneman, Nick

2016-04-01

OBJECTIVE Surgical site infections (SSIs) are common hospital-acquired infections. Tracking SSIs is important to monitor their incidence, and this process requires primary data collection. In this study, we derived and validated a method using health administrative data to predict the probability that a person who had surgery would develop an SSI within 30 days. METHODS All patients enrolled in the National Surgical Quality Improvement Program (NSQIP) from 2 sites were linked to population-based administrative datasets in Ontario, Canada. We derived a multivariate model, stratified by surgical specialty, to determine the independent association of SSI status with patient and hospitalization covariates as well as physician claim codes. This SSI risk model was validated in 2 cohorts. RESULTS The derivation cohort included 5,359 patients with a 30-day SSI incidence of 6.0% (n=118). The SSI risk model predicted the probability that a person had an SSI based on 7 covariates: index hospitalization diagnostic score; physician claims score; emergency visit diagnostic score; operation duration; surgical service; and potential SSI codes. More than 90% of patients had predicted SSI risks lower than 10%. In the derivation group, model discrimination and calibration was excellent (C statistic, 0.912; Hosmer-Lemeshow [H-L] statistic, P=.47). In the 2 validation groups, performance decreased slightly (C statistics, 0.853 and 0.812; H-L statistics, 26.4 [P=.0009] and 8.0 [P=.42]), but low-risk patients were accurately identified. CONCLUSION Health administrative data can effectively identify postoperative patients with a very low risk of surgical site infection within 30 days of their procedure. Records of higher-risk patients can be reviewed to confirm SSI status.

Comparing The Effectiveness of a90/95 Calculations (Preprint)

DTIC Science & Technology

2006-09-01

Nachtsheim, John Neter, William Li, Applied Linear Statistical Models , 5th ed., McGraw-Hill/Irwin, 2005 5. Mood, Graybill and Boes, Introduction...curves is based on methods that are only valid for ordinary linear regression. Requirements for a valid Ordinary Least-Squares Regression Model There... linear . For example is a linear model ; is not. 2. Uniform variance (homoscedasticity

Reconstruction of spatio-temporal temperature from sparse historical records using robust probabilistic principal component regression

USGS Publications Warehouse

Tipton, John; Hooten, Mevin B.; Goring, Simon

2017-01-01

Scientific records of temperature and precipitation have been kept for several hundred years, but for many areas, only a shorter record exists. To understand climate change, there is a need for rigorous statistical reconstructions of the paleoclimate using proxy data. Paleoclimate proxy data are often sparse, noisy, indirect measurements of the climate process of interest, making each proxy uniquely challenging to model statistically. We reconstruct spatially explicit temperature surfaces from sparse and noisy measurements recorded at historical United States military forts and other observer stations from 1820 to 1894. One common method for reconstructing the paleoclimate from proxy data is principal component regression (PCR). With PCR, one learns a statistical relationship between the paleoclimate proxy data and a set of climate observations that are used as patterns for potential reconstruction scenarios. We explore PCR in a Bayesian hierarchical framework, extending classical PCR in a variety of ways. First, we model the latent principal components probabilistically, accounting for measurement error in the observational data. Next, we extend our method to better accommodate outliers that occur in the proxy data. Finally, we explore alternatives to the truncation of lower-order principal components using different regularization techniques. One fundamental challenge in paleoclimate reconstruction efforts is the lack of out-of-sample data for predictive validation. Cross-validation is of potential value, but is computationally expensive and potentially sensitive to outliers in sparse data scenarios. To overcome the limitations that a lack of out-of-sample records presents, we test our methods using a simulation study, applying proper scoring rules including a computationally efficient approximation to leave-one-out cross-validation using the log score to validate model performance. The result of our analysis is a spatially explicit reconstruction of spatio-temporal temperature from a very sparse historical record.

Advancing a Model-Validated Statistical Method for Decomposing the Key Oceanic Drivers of Regional Climate: Focus on Northern and Tropical African Climate Variability in the Community Earth System Model (CESM)

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

Wang, Fuyao; Yu, Yan; Notaro, Michael

This study advances the practicality and stability of the traditional multivariate statistical method, generalized equilibrium feedback assessment (GEFA), for decomposing the key oceanic drivers of regional atmospheric variability, especially when available data records are short. An advanced stepwise GEFA methodology is introduced, in which unimportant forcings within the forcing matrix are eliminated through stepwise selection. Method validation of stepwise GEFA is performed using the CESM, with a focused application to northern and tropical Africa (NTA). First, a statistical assessment of the atmospheric response to each primary oceanic forcing is carried out by applying stepwise GEFA to a fully coupled controlmore » run. Then, a dynamical assessment of the atmospheric response to individual oceanic forcings is performed through ensemble experiments by imposing sea surface temperature anomalies over focal ocean basins. Finally, to quantify the reliability of stepwise GEFA, the statistical assessment is evaluated against the dynamical assessment in terms of four metrics: the percentage of grid cells with consistent response sign, the spatial correlation of atmospheric response patterns, the area-averaged seasonal cycle of response magnitude, and consistency in associated mechanisms between assessments. In CESM, tropical modes, namely El Niño–Southern Oscillation and the tropical Indian Ocean Basin, tropical Indian Ocean dipole, and tropical Atlantic Niño modes, are the dominant oceanic controls of NTA climate. In complementary studies, stepwise GEFA is validated in terms of isolating terrestrial forcings on the atmosphere, and observed oceanic and terrestrial drivers of NTA climate are extracted to establish an observational benchmark for subsequent coupled model evaluation and development of process-based weights for regional climate projections.« less

Advancing a Model-Validated Statistical Method for Decomposing the Key Oceanic Drivers of Regional Climate: Focus on Northern and Tropical African Climate Variability in the Community Earth System Model (CESM)

DOE PAGES

Wang, Fuyao; Yu, Yan; Notaro, Michael; ...

2017-09-27

This study advances the practicality and stability of the traditional multivariate statistical method, generalized equilibrium feedback assessment (GEFA), for decomposing the key oceanic drivers of regional atmospheric variability, especially when available data records are short. An advanced stepwise GEFA methodology is introduced, in which unimportant forcings within the forcing matrix are eliminated through stepwise selection. Method validation of stepwise GEFA is performed using the CESM, with a focused application to northern and tropical Africa (NTA). First, a statistical assessment of the atmospheric response to each primary oceanic forcing is carried out by applying stepwise GEFA to a fully coupled controlmore » run. Then, a dynamical assessment of the atmospheric response to individual oceanic forcings is performed through ensemble experiments by imposing sea surface temperature anomalies over focal ocean basins. Finally, to quantify the reliability of stepwise GEFA, the statistical assessment is evaluated against the dynamical assessment in terms of four metrics: the percentage of grid cells with consistent response sign, the spatial correlation of atmospheric response patterns, the area-averaged seasonal cycle of response magnitude, and consistency in associated mechanisms between assessments. In CESM, tropical modes, namely El Niño–Southern Oscillation and the tropical Indian Ocean Basin, tropical Indian Ocean dipole, and tropical Atlantic Niño modes, are the dominant oceanic controls of NTA climate. In complementary studies, stepwise GEFA is validated in terms of isolating terrestrial forcings on the atmosphere, and observed oceanic and terrestrial drivers of NTA climate are extracted to establish an observational benchmark for subsequent coupled model evaluation and development of process-based weights for regional climate projections.« less

Uniting statistical and individual-based approaches for animal movement modelling.

PubMed

Latombe, Guillaume; Parrott, Lael; Basille, Mathieu; Fortin, Daniel

2014-01-01

The dynamic nature of their internal states and the environment directly shape animals' spatial behaviours and give rise to emergent properties at broader scales in natural systems. However, integrating these dynamic features into habitat selection studies remains challenging, due to practically impossible field work to access internal states and the inability of current statistical models to produce dynamic outputs. To address these issues, we developed a robust method, which combines statistical and individual-based modelling. Using a statistical technique for forward modelling of the IBM has the advantage of being faster for parameterization than a pure inverse modelling technique and allows for robust selection of parameters. Using GPS locations from caribou monitored in Québec, caribou movements were modelled based on generative mechanisms accounting for dynamic variables at a low level of emergence. These variables were accessed by replicating real individuals' movements in parallel sub-models, and movement parameters were then empirically parameterized using Step Selection Functions. The final IBM model was validated using both k-fold cross-validation and emergent patterns validation and was tested for two different scenarios, with varying hardwood encroachment. Our results highlighted a functional response in habitat selection, which suggests that our method was able to capture the complexity of the natural system, and adequately provided projections on future possible states of the system in response to different management plans. This is especially relevant for testing the long-term impact of scenarios corresponding to environmental configurations that have yet to be observed in real systems.

Uniting Statistical and Individual-Based Approaches for Animal Movement Modelling

PubMed Central

Latombe, Guillaume; Parrott, Lael; Basille, Mathieu; Fortin, Daniel

2014-01-01

The dynamic nature of their internal states and the environment directly shape animals' spatial behaviours and give rise to emergent properties at broader scales in natural systems. However, integrating these dynamic features into habitat selection studies remains challenging, due to practically impossible field work to access internal states and the inability of current statistical models to produce dynamic outputs. To address these issues, we developed a robust method, which combines statistical and individual-based modelling. Using a statistical technique for forward modelling of the IBM has the advantage of being faster for parameterization than a pure inverse modelling technique and allows for robust selection of parameters. Using GPS locations from caribou monitored in Québec, caribou movements were modelled based on generative mechanisms accounting for dynamic variables at a low level of emergence. These variables were accessed by replicating real individuals' movements in parallel sub-models, and movement parameters were then empirically parameterized using Step Selection Functions. The final IBM model was validated using both k-fold cross-validation and emergent patterns validation and was tested for two different scenarios, with varying hardwood encroachment. Our results highlighted a functional response in habitat selection, which suggests that our method was able to capture the complexity of the natural system, and adequately provided projections on future possible states of the system in response to different management plans. This is especially relevant for testing the long-term impact of scenarios corresponding to environmental configurations that have yet to be observed in real systems. PMID:24979047

Strengthen forensic entomology in court--the need for data exploration and the validation of a generalised additive mixed model.

PubMed

Baqué, Michèle; Amendt, Jens

2013-01-01

Developmental data of juvenile blow flies (Diptera: Calliphoridae) are typically used to calculate the age of immature stages found on or around a corpse and thus to estimate a minimum post-mortem interval (PMI(min)). However, many of those data sets don't take into account that immature blow flies grow in a non-linear fashion. Linear models do not supply a sufficient reliability on age estimates and may even lead to an erroneous determination of the PMI(min). According to the Daubert standard and the need for improvements in forensic science, new statistic tools like smoothing methods and mixed models allow the modelling of non-linear relationships and expand the field of statistical analyses. The present study introduces into the background and application of these statistical techniques by analysing a model which describes the development of the forensically important blow fly Calliphora vicina at different temperatures. The comparison of three statistical methods (linear regression, generalised additive modelling and generalised additive mixed modelling) clearly demonstrates that only the latter provided regression parameters that reflect the data adequately. We focus explicitly on both the exploration of the data--to assure their quality and to show the importance of checking it carefully prior to conducting the statistical tests--and the validation of the resulting models. Hence, we present a common method for evaluating and testing forensic entomological data sets by using for the first time generalised additive mixed models.

Interpreting carnivore scent-station surveys

USGS Publications Warehouse

Sargeant, G.A.; Johnson, D.H.; Berg, W.E.

1998-01-01

The scent-station survey method has been widely used to estimate trends in carnivore abundance. However, statistical properties of scent-station data are poorly understood, and the relation between scent-station indices and carnivore abundance has not been adequately evaluated. We assessed properties of scent-station indices by analyzing data collected in Minnesota during 1986-03. Visits to stations separated by <2 km were correlated for all species because individual carnivores sometimes visited several stations in succession. Thus, visits to stations had an intractable statistical distribution. Dichotomizing results for lines of 10 stations (0 or 21 visits) produced binomially distributed data that were robust to multiple visits by individuals. We abandoned 2-way comparisons among years in favor of tests for population trend, which are less susceptible to bias, and analyzed results separately for biogeographic sections of Minnesota because trends differed among sections. Before drawing inferences about carnivore population trends, we reevaluated published validation experiments. Results implicated low statistical power and confounding as possible explanations for equivocal or conflicting results of validation efforts. Long-term trends in visitation rates probably reflect real changes in populations, but poor spatial and temporal resolution, susceptibility to confounding, and low statistical power limit the usefulness of this survey method.

Statistical methods for conducting agreement (comparison of clinical tests) and precision (repeatability or reproducibility) studies in optometry and ophthalmology.

PubMed

McAlinden, Colm; Khadka, Jyoti; Pesudovs, Konrad

2011-07-01

The ever-expanding choice of ocular metrology and imaging equipment has driven research into the validity of their measurements. Consequently, studies of the agreement between two instruments or clinical tests have proliferated in the ophthalmic literature. It is important that researchers apply the appropriate statistical tests in agreement studies. Correlation coefficients are hazardous and should be avoided. The 'limits of agreement' method originally proposed by Altman and Bland in 1983 is the statistical procedure of choice. Its step-by-step use and practical considerations in relation to optometry and ophthalmology are detailed in addition to sample size considerations and statistical approaches to precision (repeatability or reproducibility) estimates. Ophthalmic & Physiological Optics © 2011 The College of Optometrists.

On using summary statistics from an external calibration sample to correct for covariate measurement error.

PubMed

Guo, Ying; Little, Roderick J; McConnell, Daniel S

2012-01-01

Covariate measurement error is common in epidemiologic studies. Current methods for correcting measurement error with information from external calibration samples are insufficient to provide valid adjusted inferences. We consider the problem of estimating the regression of an outcome Y on covariates X and Z, where Y and Z are observed, X is unobserved, but a variable W that measures X with error is observed. Information about measurement error is provided in an external calibration sample where data on X and W (but not Y and Z) are recorded. We describe a method that uses summary statistics from the calibration sample to create multiple imputations of the missing values of X in the regression sample, so that the regression coefficients of Y on X and Z and associated standard errors can be estimated using simple multiple imputation combining rules, yielding valid statistical inferences under the assumption of a multivariate normal distribution. The proposed method is shown by simulation to provide better inferences than existing methods, namely the naive method, classical calibration, and regression calibration, particularly for correction for bias and achieving nominal confidence levels. We also illustrate our method with an example using linear regression to examine the relation between serum reproductive hormone concentrations and bone mineral density loss in midlife women in the Michigan Bone Health and Metabolism Study. Existing methods fail to adjust appropriately for bias due to measurement error in the regression setting, particularly when measurement error is substantial. The proposed method corrects this deficiency.

Performance Equivalence and Validation of the Soleris Automated System for Quantitative Microbial Content Testing Using Pure Suspension Cultures.

PubMed

Limberg, Brian J; Johnstone, Kevin; Filloon, Thomas; Catrenich, Carl

2016-09-01

Using United States Pharmacopeia-National Formulary (USP-NF) general method <1223> guidance, the Soleris(®) automated system and reagents (Nonfermenting Total Viable Count for bacteria and Direct Yeast and Mold for yeast and mold) were validated, using a performance equivalence approach, as an alternative to plate counting for total microbial content analysis using five representative microbes: Staphylococcus aureus, Bacillus subtilis, Pseudomonas aeruginosa, Candida albicans, and Aspergillus brasiliensis. Detection times (DTs) in the alternative automated system were linearly correlated to CFU/sample (R(2) = 0.94-0.97) with ≥70% accuracy per USP General Chapter <1223> guidance. The LOD and LOQ of the automated system were statistically similar to the traditional plate count method. This system was significantly more precise than plate counting (RSD 1.2-2.9% for DT, 7.8-40.6% for plate counts), was statistically comparable to plate counting with respect to variations in analyst, vial lots, and instruments, and was robust when variations in the operating detection thresholds (dTs; ±2 units) were used. The automated system produced accurate results, was more precise and less labor-intensive, and met or exceeded criteria for a valid alternative quantitative method, consistent with USP-NF general method <1223> guidance.

Linear regression models and k-means clustering for statistical analysis of fNIRS data.

PubMed

Bonomini, Viola; Zucchelli, Lucia; Re, Rebecca; Ieva, Francesca; Spinelli, Lorenzo; Contini, Davide; Paganoni, Anna; Torricelli, Alessandro

2015-02-01

We propose a new algorithm, based on a linear regression model, to statistically estimate the hemodynamic activations in fNIRS data sets. The main concern guiding the algorithm development was the minimization of assumptions and approximations made on the data set for the application of statistical tests. Further, we propose a K-means method to cluster fNIRS data (i.e. channels) as activated or not activated. The methods were validated both on simulated and in vivo fNIRS data. A time domain (TD) fNIRS technique was preferred because of its high performances in discriminating cortical activation and superficial physiological changes. However, the proposed method is also applicable to continuous wave or frequency domain fNIRS data sets.

Linear regression models and k-means clustering for statistical analysis of fNIRS data

PubMed Central

Bonomini, Viola; Zucchelli, Lucia; Re, Rebecca; Ieva, Francesca; Spinelli, Lorenzo; Contini, Davide; Paganoni, Anna; Torricelli, Alessandro

2015-01-01

We propose a new algorithm, based on a linear regression model, to statistically estimate the hemodynamic activations in fNIRS data sets. The main concern guiding the algorithm development was the minimization of assumptions and approximations made on the data set for the application of statistical tests. Further, we propose a K-means method to cluster fNIRS data (i.e. channels) as activated or not activated. The methods were validated both on simulated and in vivo fNIRS data. A time domain (TD) fNIRS technique was preferred because of its high performances in discriminating cortical activation and superficial physiological changes. However, the proposed method is also applicable to continuous wave or frequency domain fNIRS data sets. PMID:25780751

Predictive validity of the UK clinical aptitude test in the final years of medical school: a prospective cohort study

PubMed Central

2014-01-01

Background The UK Clinical Aptitude Test (UKCAT) was designed to address issues identified with traditional methods of selection. This study aims to examine the predictive validity of the UKCAT and compare this to traditional selection methods in the senior years of medical school. This was a follow-up study of two cohorts of students from two medical schools who had previously taken part in a study examining the predictive validity of the UKCAT in first year. Methods The sample consisted of 4th and 5th Year students who commenced their studies at the University of Aberdeen or University of Dundee medical schools in 2007. Data collected were: demographics (gender and age group), UKCAT scores; Universities and Colleges Admissions Service (UCAS) form scores; admission interview scores; Year 4 and 5 degree examination scores. Pearson’s correlations were used to examine the relationships between admissions variables, examination scores, gender and age group, and to select variables for multiple linear regression analysis to predict examination scores. Results Ninety-nine and 89 students at Aberdeen medical school from Years 4 and 5 respectively, and 51 Year 4 students in Dundee, were included in the analysis. Neither UCAS form nor interview scores were statistically significant predictors of examination performance. Conversely, the UKCAT yielded statistically significant validity coefficients between .24 and .36 in four of five assessments investigated. Multiple regression analysis showed the UKCAT made a statistically significant unique contribution to variance in examination performance in the senior years. Conclusions Results suggest the UKCAT appears to predict performance better in the later years of medical school compared to earlier years and provides modest supportive evidence for the UKCAT’s role in student selection within these institutions. Further research is needed to assess the predictive validity of the UKCAT against professional and behavioural outcomes as the cohort commences working life. PMID:24762134

Comparison of the predictive validity of diagnosis-based risk adjusters for clinical outcomes.

PubMed

Petersen, Laura A; Pietz, Kenneth; Woodard, LeChauncy D; Byrne, Margaret

2005-01-01

Many possible methods of risk adjustment exist, but there is a dearth of comparative data on their performance. We compared the predictive validity of 2 widely used methods (Diagnostic Cost Groups [DCGs] and Adjusted Clinical Groups [ACGs]) for 2 clinical outcomes using a large national sample of patients. We studied all patients who used Veterans Health Administration (VA) medical services in fiscal year (FY) 2001 (n = 3,069,168) and assigned both a DCG and an ACG to each. We used logistic regression analyses to compare predictive ability for death or long-term care (LTC) hospitalization for age/gender models, DCG models, and ACG models. We also assessed the effect of adding age to the DCG and ACG models. Patients in the highest DCG categories, indicating higher severity of illness, were more likely to die or to require LTC hospitalization. Surprisingly, the age/gender model predicted death slightly more accurately than the ACG model (c-statistic of 0.710 versus 0.700, respectively). The addition of age to the ACG model improved the c-statistic to 0.768. The highest c-statistic for prediction of death was obtained with a DCG/age model (0.830). The lowest c-statistics were obtained for age/gender models for LTC hospitalization (c-statistic 0.593). The c-statistic for use of ACGs to predict LTC hospitalization was 0.783, and improved to 0.792 with the addition of age. The c-statistics for use of DCGs and DCG/age to predict LTC hospitalization were 0.885 and 0.890, respectively, indicating the best prediction. We found that risk adjusters based upon diagnoses predicted an increased likelihood of death or LTC hospitalization, exhibiting good predictive validity. In this comparative analysis using VA data, DCG models were generally superior to ACG models in predicting clinical outcomes, although ACG model performance was enhanced by the addition of age.

How Valid are Estimates of Occupational Illness?

ERIC Educational Resources Information Center

Hilaski, Harvey J.; Wang, Chao Ling

1982-01-01

Examines some of the methods of estimating occupational diseases and suggests that a consensus on the adequacy and reliability of estimates by the Bureau of Labor Statistics and others is not likely. (SK)

External validation of a Cox prognostic model: principles and methods

PubMed Central

2013-01-01

Background A prognostic model should not enter clinical practice unless it has been demonstrated that it performs a useful role. External validation denotes evaluation of model performance in a sample independent of that used to develop the model. Unlike for logistic regression models, external validation of Cox models is sparsely treated in the literature. Successful validation of a model means achieving satisfactory discrimination and calibration (prediction accuracy) in the validation sample. Validating Cox models is not straightforward because event probabilities are estimated relative to an unspecified baseline function. Methods We describe statistical approaches to external validation of a published Cox model according to the level of published information, specifically (1) the prognostic index only, (2) the prognostic index together with Kaplan-Meier curves for risk groups, and (3) the first two plus the baseline survival curve (the estimated survival function at the mean prognostic index across the sample). The most challenging task, requiring level 3 information, is assessing calibration, for which we suggest a method of approximating the baseline survival function. Results We apply the methods to two comparable datasets in primary breast cancer, treating one as derivation and the other as validation sample. Results are presented for discrimination and calibration. We demonstrate plots of survival probabilities that can assist model evaluation. Conclusions Our validation methods are applicable to a wide range of prognostic studies and provide researchers with a toolkit for external validation of a published Cox model. PMID:23496923

Comparison and validation of statistical methods for predicting power outage durations in the event of hurricanes.

PubMed

Nateghi, Roshanak; Guikema, Seth D; Quiring, Steven M

2011-12-01

This article compares statistical methods for modeling power outage durations during hurricanes and examines the predictive accuracy of these methods. Being able to make accurate predictions of power outage durations is valuable because the information can be used by utility companies to plan their restoration efforts more efficiently. This information can also help inform customers and public agencies of the expected outage times, enabling better collective response planning, and coordination of restoration efforts for other critical infrastructures that depend on electricity. In the long run, outage duration estimates for future storm scenarios may help utilities and public agencies better allocate risk management resources to balance the disruption from hurricanes with the cost of hardening power systems. We compare the out-of-sample predictive accuracy of five distinct statistical models for estimating power outage duration times caused by Hurricane Ivan in 2004. The methods compared include both regression models (accelerated failure time (AFT) and Cox proportional hazard models (Cox PH)) and data mining techniques (regression trees, Bayesian additive regression trees (BART), and multivariate additive regression splines). We then validate our models against two other hurricanes. Our results indicate that BART yields the best prediction accuracy and that it is possible to predict outage durations with reasonable accuracy. © 2011 Society for Risk Analysis.

Alternating Renewal Process Models for Behavioral Observation: Simulation Methods, Software, and Validity Illustrations

ERIC Educational Resources Information Center

Pustejovsky, James E.; Runyon, Christopher

2014-01-01

Direct observation recording procedures produce reductive summary measurements of an underlying stream of behavior. Previous methodological studies of these recording procedures have employed simulation methods for generating random behavior streams, many of which amount to special cases of a statistical model known as the alternating renewal…

Reliability and Validity of the Research Methods Skills Assessment

ERIC Educational Resources Information Center

Smith, Tamarah; Smith, Samantha

2018-01-01

The Research Methods Skills Assessment (RMSA) was created to measure psychology majors' statistics knowledge and skills. The American Psychological Association's Guidelines for the Undergraduate Major in Psychology (APA, 2007, 2013) served as a framework for development. Results from a Rasch analysis with data from n = 330 undergraduates showed…

Density (DE)

Treesearch

John F. Caratti

2006-01-01

The FIREMON Density (DE) method is used to assess changes in plant species density and height for a macroplot. This method uses multiple quadrats and belt transects (transects having a width) to sample within plot variation and quantify statistically valid changes in plant species density and height over time. Herbaceous plant species are sampled with quadrats while...

Statistical Learning Theory for High Dimensional Prediction: Application to Criterion-Keyed Scale Development

PubMed Central

Chapman, Benjamin P.; Weiss, Alexander; Duberstein, Paul

2016-01-01

Statistical learning theory (SLT) is the statistical formulation of machine learning theory, a body of analytic methods common in “big data” problems. Regression-based SLT algorithms seek to maximize predictive accuracy for some outcome, given a large pool of potential predictors, without overfitting the sample. Research goals in psychology may sometimes call for high dimensional regression. One example is criterion-keyed scale construction, where a scale with maximal predictive validity must be built from a large item pool. Using this as a working example, we first introduce a core principle of SLT methods: minimization of expected prediction error (EPE). Minimizing EPE is fundamentally different than maximizing the within-sample likelihood, and hinges on building a predictive model of sufficient complexity to predict the outcome well, without undue complexity leading to overfitting. We describe how such models are built and refined via cross-validation. We then illustrate how three common SLT algorithms–Supervised Principal Components, Regularization, and Boosting—can be used to construct a criterion-keyed scale predicting all-cause mortality, using a large personality item pool within a population cohort. Each algorithm illustrates a different approach to minimizing EPE. Finally, we consider broader applications of SLT predictive algorithms, both as supportive analytic tools for conventional methods, and as primary analytic tools in discovery phase research. We conclude that despite their differences from the classic null-hypothesis testing approach—or perhaps because of them–SLT methods may hold value as a statistically rigorous approach to exploratory regression. PMID:27454257

Development and Validation of a Statistical Shape Modeling-Based Finite Element Model of the Cervical Spine Under Low-Level Multiple Direction Loading Conditions

PubMed Central

Bredbenner, Todd L.; Eliason, Travis D.; Francis, W. Loren; McFarland, John M.; Merkle, Andrew C.; Nicolella, Daniel P.

2014-01-01

Cervical spinal injuries are a significant concern in all trauma injuries. Recent military conflicts have demonstrated the substantial risk of spinal injury for the modern warfighter. Finite element models used to investigate injury mechanisms often fail to examine the effects of variation in geometry or material properties on mechanical behavior. The goals of this study were to model geometric variation for a set of cervical spines, to extend this model to a parametric finite element model, and, as a first step, to validate the parametric model against experimental data for low-loading conditions. Individual finite element models were created using cervical spine (C3–T1) computed tomography data for five male cadavers. Statistical shape modeling (SSM) was used to generate a parametric finite element model incorporating variability of spine geometry, and soft-tissue material property variation was also included. The probabilistic loading response of the parametric model was determined under flexion-extension, axial rotation, and lateral bending and validated by comparison to experimental data. Based on qualitative and quantitative comparison of the experimental loading response and model simulations, we suggest that the model performs adequately under relatively low-level loading conditions in multiple loading directions. In conclusion, SSM methods coupled with finite element analyses within a probabilistic framework, along with the ability to statistically validate the overall model performance, provide innovative and important steps toward describing the differences in vertebral morphology, spinal curvature, and variation in material properties. We suggest that these methods, with additional investigation and validation under injurious loading conditions, will lead to understanding and mitigating the risks of injury in the spine and other musculoskeletal structures. PMID:25506051

Statistical Validation of Image Segmentation Quality Based on a Spatial Overlap Index1

PubMed Central

Zou, Kelly H.; Warfield, Simon K.; Bharatha, Aditya; Tempany, Clare M.C.; Kaus, Michael R.; Haker, Steven J.; Wells, William M.; Jolesz, Ferenc A.; Kikinis, Ron

2005-01-01

Rationale and Objectives To examine a statistical validation method based on the spatial overlap between two sets of segmentations of the same anatomy. Materials and Methods The Dice similarity coefficient (DSC) was used as a statistical validation metric to evaluate the performance of both the reproducibility of manual segmentations and the spatial overlap accuracy of automated probabilistic fractional segmentation of MR images, illustrated on two clinical examples. Example 1: 10 consecutive cases of prostate brachytherapy patients underwent both preoperative 1.5T and intraoperative 0.5T MR imaging. For each case, 5 repeated manual segmentations of the prostate peripheral zone were performed separately on preoperative and on intraoperative images. Example 2: A semi-automated probabilistic fractional segmentation algorithm was applied to MR imaging of 9 cases with 3 types of brain tumors. DSC values were computed and logit-transformed values were compared in the mean with the analysis of variance (ANOVA). Results Example 1: The mean DSCs of 0.883 (range, 0.876–0.893) with 1.5T preoperative MRI and 0.838 (range, 0.819–0.852) with 0.5T intraoperative MRI (P < .001) were within and at the margin of the range of good reproducibility, respectively. Example 2: Wide ranges of DSC were observed in brain tumor segmentations: Meningiomas (0.519–0.893), astrocytomas (0.487–0.972), and other mixed gliomas (0.490–0.899). Conclusion The DSC value is a simple and useful summary measure of spatial overlap, which can be applied to studies of reproducibility and accuracy in image segmentation. We observed generally satisfactory but variable validation results in two clinical applications. This metric may be adapted for similar validation tasks. PMID:14974593

Simulation of anisoplanatic imaging through optical turbulence using numerical wave propagation with new validation analysis

NASA Astrophysics Data System (ADS)

Hardie, Russell C.; Power, Jonathan D.; LeMaster, Daniel A.; Droege, Douglas R.; Gladysz, Szymon; Bose-Pillai, Santasri

2017-07-01

We present a numerical wave propagation method for simulating imaging of an extended scene under anisoplanatic conditions. While isoplanatic simulation is relatively common, few tools are specifically designed for simulating the imaging of extended scenes under anisoplanatic conditions. We provide a complete description of the proposed simulation tool, including the wave propagation method used. Our approach computes an array of point spread functions (PSFs) for a two-dimensional grid on the object plane. The PSFs are then used in a spatially varying weighted sum operation, with an ideal image, to produce a simulated image with realistic optical turbulence degradation. The degradation includes spatially varying warping and blurring. To produce the PSF array, we generate a series of extended phase screens. Simulated point sources are numerically propagated from an array of positions on the object plane, through the phase screens, and ultimately to the focal plane of the simulated camera. Note that the optical path for each PSF will be different, and thus, pass through a different portion of the extended phase screens. These different paths give rise to a spatially varying PSF to produce anisoplanatic effects. We use a method for defining the individual phase screen statistics that we have not seen used in previous anisoplanatic simulations. We also present a validation analysis. In particular, we compare simulated outputs with the theoretical anisoplanatic tilt correlation and a derived differential tilt variance statistic. This is in addition to comparing the long- and short-exposure PSFs and isoplanatic angle. We believe this analysis represents the most thorough validation of an anisoplanatic simulation to date. The current work is also unique that we simulate and validate both constant and varying Cn2(z) profiles. Furthermore, we simulate sequences with both temporally independent and temporally correlated turbulence effects. Temporal correlation is introduced by generating even larger extended phase screens and translating this block of screens in front of the propagation area. Our validation analysis shows an excellent match between the simulation statistics and the theoretical predictions. Thus, we think this tool can be used effectively to study optical anisoplanatic turbulence and to aid in the development of image restoration methods.

Statistical error in simulations of Poisson processes: Example of diffusion in solids

NASA Astrophysics Data System (ADS)

Nilsson, Johan O.; Leetmaa, Mikael; Vekilova, Olga Yu.; Simak, Sergei I.; Skorodumova, Natalia V.

2016-08-01

Simulations of diffusion in solids often produce poor statistics of diffusion events. We present an analytical expression for the statistical error in ion conductivity obtained in such simulations. The error expression is not restricted to any computational method in particular, but valid in the context of simulation of Poisson processes in general. This analytical error expression is verified numerically for the case of Gd-doped ceria by running a large number of kinetic Monte Carlo calculations.

[Validation of measurement methods and estimation of uncertainty of measurement of chemical agents in the air at workstations].

PubMed

Dobecki, Marek

2012-01-01

This paper reviews the requirements for measurement methods of chemical agents in the air at workstations. European standards, which have a status of Polish standards, comprise some requirements and information on sampling strategy, measuring techniques, type of samplers, sampling pumps and methods of occupational exposure evaluation at a given technological process. Measurement methods, including air sampling and analytical procedure in a laboratory, should be appropriately validated before intended use. In the validation process, selected methods are tested and budget of uncertainty is set up. The validation procedure that should be implemented in the laboratory together with suitable statistical tools and major components of uncertainity to be taken into consideration, were presented in this paper. Methods of quality control, including sampling and laboratory analyses were discussed. Relative expanded uncertainty for each measurement expressed as a percentage, should not exceed the limit of values set depending on the type of occupational exposure (short-term or long-term) and the magnitude of exposure to chemical agents in the work environment.

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

NASA Astrophysics Data System (ADS)

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

2015-02-01

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

Often Asked but Rarely Answered: Can Asians Meet DSM-5/ICD-10 Autism Spectrum Disorder Criteria?

PubMed Central

Kim, So Hyun; Koh, Yun-Joo; Lim, Eun-Chung; Kim, Soo-Jeong; Leventhal, Bennett L.

2016-01-01

Abstract Objectives: To evaluate whether Asian (Korean children) populations can be validly diagnosed with autism spectrum disorder (ASD) using Western-based diagnostic instruments and criteria based on Diagnostic and Statistical Manual on Mental Disorders, 5th edition (DSM-5). Methods: Participants included an epidemiologically ascertained 7–14-year-old (N = 292) South Korean cohort from a larger prevalence study (N = 55,266). Main outcomes were based on Western-based diagnostic methods for Korean children using gold standard instruments, Autism Diagnostic Interview-Revised, and Autism Diagnostic Observation Schedule. Factor analysis and ANOVAs were performed to examine factor structure of autism symptoms and identify phenotypic differences between Korean children with ASD and non-ASD diagnoses. Results: Using Western-based diagnostic methods, Korean children with ASD were successfully identified with moderate-to-high diagnostic validity (sensitivities/specificities ranging 64%–93%), strong internal consistency, and convergent/concurrent validity. The patterns of autism phenotypes in a Korean population were similar to those observed in a Western population with two symptom domains (social communication and restricted and repetitive behavior factors). Statistically significant differences in the use of socially acceptable communicative behaviors (e.g., direct gaze, range of facial expressions) emerged between ASD versus non-ASD cases (mostly p < 0.001), ensuring that these can be a similarly valid part of the ASD phenotype in both Asian and Western populations. Conclusions: Despite myths, biases, and stereotypes about Asian social behavior, Asians (at least Korean children) typically use elements of reciprocal social interactions similar to those in the West. Therefore, standardized diagnostic methods widely used for ASD in Western culture can be validly used as part of the assessment process and research with Koreans and, possibly, other Asians. PMID:27315155

Sensitivity-Uncertainty Based Nuclear Criticality Safety Validation

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

Brown, Forrest B.

2016-09-20

These are slides from a seminar given to the University of Mexico Nuclear Engineering Department. Whisper is a statistical analysis package developed to support nuclear criticality safety validation. It uses the sensitivity profile data for an application as computed by MCNP6 along with covariance files for the nuclear data to determine a baseline upper-subcritical-limit for the application. Whisper and its associated benchmark files are developed and maintained as part of MCNP6, and will be distributed with all future releases of MCNP6. Although sensitivity-uncertainty methods for NCS validation have been under development for 20 years, continuous-energy Monte Carlo codes such asmore » MCNP could not determine the required adjoint-weighted tallies for sensitivity profiles. The recent introduction of the iterated fission probability method into MCNP led to the rapid development of sensitivity analysis capabilities for MCNP6 and the development of Whisper. Sensitivity-uncertainty based methods represent the future for NCS validation – making full use of today’s computer power to codify past approaches based largely on expert judgment. Validation results are defensible, auditable, and repeatable as needed with different assumptions and process models. The new methods can supplement, support, and extend traditional validation approaches.« less

Validated spectrofluorimetric method for the determination of clonazepam in pharmaceutical preparations.

PubMed

Ibrahim, Fawzia; El-Enany, Nahed; Shalan, Shereen; Elsharawy, Rasha

2016-05-01

A simple, highly sensitive and validated spectrofluorimetric method was applied in the determination of clonazepam (CLZ). The method is based on reduction of the nitro group of clonazepam with zinc/CaCl2, and the product is then reacted with 2-cyanoacetamide (2-CNA) in the presence of ammonia (25%) yielding a highly fluorescent product. The produced fluorophore exhibits strong fluorescence intensity at ʎ(em) = 383 nm after excitation at ʎ(ex) = 333 nm. The method was rectilinear over a concentration range of 0.1-0.5 ng/mL with a limit of detection (LOD) of 0.0057 ng/mL and a limit of quantification (LOQ) of 0.017 ng/mL. The method was fully validated and successfully applied to the determination of CLZ in its tablets with a mean percentage recovery of 100.10 ± 0.75%. Method validation according to ICH Guidelines was evaluated. Statistical analysis of the results obtained using the proposed method was successfully compared with those obtained using a reference method, and there was no significance difference between the two methods in terms of accuracy and precision. Copyright © 2015 John Wiley & Sons, Ltd.

Identification of abnormal accident patterns at intersections

DOT National Transportation Integrated Search

1999-08-01

This report presents the findings and recommendations based on the Identification of Abnormal Accident Patterns at Intersections. This project used a statistically valid sampling method to determine whether a specific intersection has an abnormally h...

Statistical Anomalies of Bitflips in SRAMs to Discriminate SBUs From MCUs

NASA Astrophysics Data System (ADS)

Clemente, Juan Antonio; Franco, Francisco J.; Villa, Francesca; Baylac, Maud; Rey, Solenne; Mecha, Hortensia; Agapito, Juan A.; Puchner, Helmut; Hubert, Guillaume; Velazco, Raoul

2016-08-01

Recently, the occurrence of multiple events in static tests has been investigated by checking the statistical distribution of the difference between the addresses of the words containing bitflips. That method has been successfully applied to Field Programmable Gate Arrays (FPGAs) and the original authors indicate that it is also valid for SRAMs. This paper presents a modified methodology that is based on checking the XORed addresses with bitflips, rather than on the difference. Irradiation tests on CMOS 130 & 90 nm SRAMs with 14-MeV neutrons have been performed to validate this methodology. Results in high-altitude environments are also presented and cross-checked with theoretical predictions. In addition, this methodology has also been used to detect modifications in the organization of said memories. Theoretical predictions have been validated with actual data provided by the manufacturer.

Dimension from covariance matrices.

PubMed

Carroll, T L; Byers, J M

2017-02-01

We describe a method to estimate embedding dimension from a time series. This method includes an estimate of the probability that the dimension estimate is valid. Such validity estimates are not common in algorithms for calculating the properties of dynamical systems. The algorithm described here compares the eigenvalues of covariance matrices created from an embedded signal to the eigenvalues for a covariance matrix of a Gaussian random process with the same dimension and number of points. A statistical test gives the probability that the eigenvalues for the embedded signal did not come from the Gaussian random process.

Introduction to Bayesian statistical approaches to compositional analyses of transgenic crops 1. Model validation and setting the stage.

PubMed

Harrison, Jay M; Breeze, Matthew L; Harrigan, George G

2011-08-01

Statistical comparisons of compositional data generated on genetically modified (GM) crops and their near-isogenic conventional (non-GM) counterparts typically rely on classical significance testing. This manuscript presents an introduction to Bayesian methods for compositional analysis along with recommendations for model validation. The approach is illustrated using protein and fat data from two herbicide tolerant GM soybeans (MON87708 and MON87708×MON89788) and a conventional comparator grown in the US in 2008 and 2009. Guidelines recommended by the US Food and Drug Administration (FDA) in conducting Bayesian analyses of clinical studies on medical devices were followed. This study is the first Bayesian approach to GM and non-GM compositional comparisons. The evaluation presented here supports a conclusion that a Bayesian approach to analyzing compositional data can provide meaningful and interpretable results. We further describe the importance of method validation and approaches to model checking if Bayesian approaches to compositional data analysis are to be considered viable by scientists involved in GM research and regulation. Copyright © 2011 Elsevier Inc. All rights reserved.

PyClone: statistical inference of clonal population structure in cancer.

PubMed

Roth, Andrew; Khattra, Jaswinder; Yap, Damian; Wan, Adrian; Laks, Emma; Biele, Justina; Ha, Gavin; Aparicio, Samuel; Bouchard-Côté, Alexandre; Shah, Sohrab P

2014-04-01

We introduce PyClone, a statistical model for inference of clonal population structures in cancers. PyClone is a Bayesian clustering method for grouping sets of deeply sequenced somatic mutations into putative clonal clusters while estimating their cellular prevalences and accounting for allelic imbalances introduced by segmental copy-number changes and normal-cell contamination. Single-cell sequencing validation demonstrates PyClone's accuracy.

Reliability and Validity of Isometric Knee Extensor Strength Test With Hand-Held Dynamometer Depending on Its Fixation: A Pilot Study

PubMed Central

Kim, Won Kuel; Seo, Kyung Mook; Kang, Si Hyun

2014-01-01

Objective To determine the reliability and validity of hand-held dynamometer (HHD) depending on its fixation in measuring isometric knee extensor strength by comparing the results with an isokinetic dynamometer. Methods Twenty-seven healthy female volunteers participated in this study. The subjects were tested in seated and supine position using three measurement methods: isometric knee extension by isokinetic dynamometer, non-fixed HHD, and fixed HHD. During the measurement, the knee joints of subjects were fixed at a 35° angle from the extended position. The fixed HHD measurement was conducted with the HHD fixed to distal tibia with a Velcro strap; non-fixed HHD was performed with a hand-held method without Velcro fixation. All the measurements were repeated three times and among them, the maximum values of peak torque were used for the analysis. Results The data from the fixed HHD method showed higher validity than the non-fixed method compared with the results of the isokinetic dynamometer. Pearson correlation coefficients (r) between fixed HHD and isokinetic dynamometer method were statistically significant (supine-right: r=0.806, p<0.05; seating-right: r=0.473, p<0.05; supine-left: r=0.524, p<0.05), whereas Pearson correlation coefficients between non-fixed dynamometer and isokinetic dynamometer methods were not statistically significant, except for the result of the supine position of the left leg (r=0.384, p<0.05). Both fixed and non-fixed HHD methods showed excellent inter-rater reliability. However, the fixed HHD method showed a higher reliability than the non-fixed HHD method by considering the intraclass correlation coefficient (fixed HHD, 0.952-0.984; non-fixed HHD, 0.940-0.963). Conclusion Fixation of HHD during measurement in the supine position increases the reliability and validity in measuring the quadriceps strength. PMID:24639931

Validation of a Survey Questionnaire on Organ Donation: An Arabic World Scenario

PubMed Central

Agarwal, Tulika Mehta; Al-Thani, Hassan; Al Maslamani, Yousuf

2018-01-01

Objective To validate a questionnaire for measuring factors influencing organ donation and transplant. Methods The constructed questionnaire was based on the theory of planned behavior by Ajzen Icek and had 45 questions including general inquiry and demographic information. Four experts on the topic, Arabic culture, and the Arabic and English languages established content validity through review. It was quantified by content validity index (CVI). Construct validity was established by principal component analysis (PCA), whereas internal consistency was checked by Cronbach's Alpha and intraclass correlation coefficient (ICC). Statistical analysis was performed by SPSS 22.0 statistical package. Results Content validity in the form of S-CVI/Average and S-CVI/UA was 0.95 and 0.82, respectively, suggesting adequate relevance content of the questionnaire. Factor analysis indicated that the construct validity for each domain (knowledge, attitudes, beliefs, and intention) was 65%, 71%, 77%, and 70%, respectively. Cronbach's Alpha and ICC coefficients were 0.90, 0.67, 0.75, and 0.74 and 0.82, 0.58, 0.61, and 0.74, respectively, for the domains. Conclusion The questionnaire consists of 39 items on knowledge, attitudes, beliefs, and intention domains which is valid and reliable tool to use for organ donation and transplant survey. PMID:29593894

Statistical power to detect violation of the proportional hazards assumption when using the Cox regression model.

PubMed

Austin, Peter C

2018-01-01

The use of the Cox proportional hazards regression model is widespread. A key assumption of the model is that of proportional hazards. Analysts frequently test the validity of this assumption using statistical significance testing. However, the statistical power of such assessments is frequently unknown. We used Monte Carlo simulations to estimate the statistical power of two different methods for detecting violations of this assumption. When the covariate was binary, we found that a model-based method had greater power than a method based on cumulative sums of martingale residuals. Furthermore, the parametric nature of the distribution of event times had an impact on power when the covariate was binary. Statistical power to detect a strong violation of the proportional hazards assumption was low to moderate even when the number of observed events was high. In many data sets, power to detect a violation of this assumption is likely to be low to modest.

Statistical power to detect violation of the proportional hazards assumption when using the Cox regression model

PubMed Central

Austin, Peter C.

2017-01-01

The use of the Cox proportional hazards regression model is widespread. A key assumption of the model is that of proportional hazards. Analysts frequently test the validity of this assumption using statistical significance testing. However, the statistical power of such assessments is frequently unknown. We used Monte Carlo simulations to estimate the statistical power of two different methods for detecting violations of this assumption. When the covariate was binary, we found that a model-based method had greater power than a method based on cumulative sums of martingale residuals. Furthermore, the parametric nature of the distribution of event times had an impact on power when the covariate was binary. Statistical power to detect a strong violation of the proportional hazards assumption was low to moderate even when the number of observed events was high. In many data sets, power to detect a violation of this assumption is likely to be low to modest. PMID:29321694

Using the Bootstrap Method for a Statistical Significance Test of Differences between Summary Histograms

NASA Technical Reports Server (NTRS)

Xu, Kuan-Man

2006-01-01

A new method is proposed to compare statistical differences between summary histograms, which are the histograms summed over a large ensemble of individual histograms. It consists of choosing a distance statistic for measuring the difference between summary histograms and using a bootstrap procedure to calculate the statistical significance level. Bootstrapping is an approach to statistical inference that makes few assumptions about the underlying probability distribution that describes the data. Three distance statistics are compared in this study. They are the Euclidean distance, the Jeffries-Matusita distance and the Kuiper distance. The data used in testing the bootstrap method are satellite measurements of cloud systems called cloud objects. Each cloud object is defined as a contiguous region/patch composed of individual footprints or fields of view. A histogram of measured values over footprints is generated for each parameter of each cloud object and then summary histograms are accumulated over all individual histograms in a given cloud-object size category. The results of statistical hypothesis tests using all three distances as test statistics are generally similar, indicating the validity of the proposed method. The Euclidean distance is determined to be most suitable after comparing the statistical tests of several parameters with distinct probability distributions among three cloud-object size categories. Impacts on the statistical significance levels resulting from differences in the total lengths of satellite footprint data between two size categories are also discussed.

NASA DOE POD NDE Capabilities Data Book

NASA Technical Reports Server (NTRS)

Generazio, Edward R.

2015-01-01

This data book contains the Directed Design of Experiments for Validating Probability of Detection (POD) Capability of NDE Systems (DOEPOD) analyses of the nondestructive inspection data presented in the NTIAC, Nondestructive Evaluation (NDE) Capabilities Data Book, 3rd ed., NTIAC DB-97-02. DOEPOD is designed as a decision support system to validate inspection system, personnel, and protocol demonstrating 0.90 POD with 95% confidence at critical flaw sizes, a90/95. The test methodology used in DOEPOD is based on the field of statistical sequential analysis founded by Abraham Wald. Sequential analysis is a method of statistical inference whose characteristic feature is that the number of observations required by the procedure is not determined in advance of the experiment. The decision to terminate the experiment depends, at each stage, on the results of the observations previously made. A merit of the sequential method, as applied to testing statistical hypotheses, is that test procedures can be constructed which require, on average, a substantially smaller number of observations than equally reliable test procedures based on a predetermined number of observations.

Validation Thin Layer Chromatography for the Determination of Acetaminophen in Tablets and Comparison with a Pharmacopeial Method

PubMed Central

Pyka, Alina; Budzisz, Marika; Dołowy, Małgorzata

2013-01-01

Adsorption thin layer chromatography (NP-TLC) with densitometry has been established for the identification and the quantification of acetaminophen in three leading commercial products of pharmaceutical tablets coded as brand: P1 (Product no. 1), P2 (Product no. 2), and P3 (Product no. 3). Applied chromatographic conditions have separated acetaminophen from its related substances, namely, 4-aminophenol and and 4′-chloroacetanilide. UV densitometry was performed in absorbance mode at 248 nm. The presented method was validated by specificity, range, linearity, accuracy, precision, detection limit, quantitative limit, and robustness. The TLC-densitometric method was also compared with a pharmacopeial UV-spectrophotometric method for the assay of acetaminophen, and the results confirmed statistically that the NP-TLC-densitometric method can be used as a substitute method. It could be said that the validated NP-TLC-densitometric method is suitable for the routine analysis of acetaminophen in quantity control laboratories. PMID:24063006

Comparison of on-site field measured inorganic arsenic in rice with laboratory measurements using a field deployable method: Method validation.

PubMed

Mlangeni, Angstone Thembachako; Vecchi, Valeria; Norton, Gareth J; Raab, Andrea; Krupp, Eva M; Feldmann, Joerg

2018-10-15

A commercial arsenic field kit designed to measure inorganic arsenic (iAs) in water was modified into a field deployable method (FDM) to measure iAs in rice. While the method has been validated to give precise and accurate results in the laboratory, its on-site field performance has not been evaluated. This study was designed to test the method on-site in Malawi in order to evaluate its accuracy and precision in determination of iAs on-site by comparing with a validated reference method and giving original data on inorganic arsenic in Malawian rice and rice-based products. The method was validated by using the established laboratory-based HPLC-ICPMS. Statistical tests indicated there were no significant differences between on-site and laboratory iAs measurements determined using the FDM (p = 0.263, ά = 0.05) and between on-site measurements and measurements determined using HPLC-ICP-MS (p = 0.299, ά = 0.05). This method allows quick (within 1 h) and efficient screening of rice containing iAs concentrations on-site. Copyright © 2018 Elsevier Ltd. All rights reserved.

A New Approach of Juvenile Age Estimation using Measurements of the Ilium and Multivariate Adaptive Regression Splines (MARS) Models for Better Age Prediction.

PubMed

Corron, Louise; Marchal, François; Condemi, Silvana; Chaumoître, Kathia; Adalian, Pascal

2017-01-01

Juvenile age estimation methods used in forensic anthropology generally lack methodological consistency and/or statistical validity. Considering this, a standard approach using nonparametric Multivariate Adaptive Regression Splines (MARS) models were tested to predict age from iliac biometric variables of male and female juveniles from Marseilles, France, aged 0-12 years. Models using unidimensional (length and width) and bidimensional iliac data (module and surface) were constructed on a training sample of 176 individuals and validated on an independent test sample of 68 individuals. Results show that MARS prediction models using iliac width, module and area give overall better and statistically valid age estimates. These models integrate punctual nonlinearities of the relationship between age and osteometric variables. By constructing valid prediction intervals whose size increases with age, MARS models take into account the normal increase of individual variability. MARS models can qualify as a practical and standardized approach for juvenile age estimation. © 2016 American Academy of Forensic Sciences.

Methods for Assessment of Memory Reactivation.

PubMed

Liu, Shizhao; Grosmark, Andres D; Chen, Zhe

2018-04-13

It has been suggested that reactivation of previously acquired experiences or stored information in declarative memories in the hippocampus and neocortex contributes to memory consolidation and learning. Understanding memory consolidation depends crucially on the development of robust statistical methods for assessing memory reactivation. To date, several statistical methods have seen established for assessing memory reactivation based on bursts of ensemble neural spike activity during offline states. Using population-decoding methods, we propose a new statistical metric, the weighted distance correlation, to assess hippocampal memory reactivation (i.e., spatial memory replay) during quiet wakefulness and slow-wave sleep. The new metric can be combined with an unsupervised population decoding analysis, which is invariant to latent state labeling and allows us to detect statistical dependency beyond linearity in memory traces. We validate the new metric using two rat hippocampal recordings in spatial navigation tasks. Our proposed analysis framework may have a broader impact on assessing memory reactivations in other brain regions under different behavioral tasks.

Validation of phenol red versus gravimetric method for water reabsorption correction and study of gender differences in Doluisio's absorption technique.

PubMed

Tuğcu-Demiröz, Fatmanur; Gonzalez-Alvarez, Isabel; Gonzalez-Alvarez, Marta; Bermejo, Marival

2014-10-01

The aim of the present study was to develop a method for water flux reabsorption measurement in Doluisio's Perfusion Technique based on the use of phenol red as a non-absorbable marker and to validate it by comparison with gravimetric procedure. The compounds selected for the study were metoprolol, atenolol, cimetidine and cefadroxil in order to include low, intermediate and high permeability drugs absorbed by passive diffusion and by carrier mediated mechanism. The intestinal permeabilities (Peff) of the drugs were obtained in male and female Wistar rats and calculated using both methods of water flux correction. The absorption rate coefficients of all the assayed compounds did not show statistically significant differences between male and female rats consequently all the individual values were combined to compare between reabsorption methods. The absorption rate coefficients and permeability values did not show statistically significant differences between the two strategies of concentration correction. The apparent zero order water absorption coefficients were also similar in both correction procedures. In conclusion gravimetric and phenol red method for water reabsorption correction are accurate and interchangeable for permeability estimation in closed loop perfusion method. Copyright © 2014 Elsevier B.V. All rights reserved.

Increased efficacy for in-house validation of real-time PCR GMO detection methods.

PubMed

Scholtens, I M J; Kok, E J; Hougs, L; Molenaar, B; Thissen, J T N M; van der Voet, H

2010-03-01

To improve the efficacy of the in-house validation of GMO detection methods (DNA isolation and real-time PCR, polymerase chain reaction), a study was performed to gain insight in the contribution of the different steps of the GMO detection method to the repeatability and in-house reproducibility. In the present study, 19 methods for (GM) soy, maize canola and potato were validated in-house of which 14 on the basis of an 8-day validation scheme using eight different samples and five on the basis of a more concise validation protocol. In this way, data was obtained with respect to the detection limit, accuracy and precision. Also, decision limits were calculated for declaring non-conformance (>0.9%) with 95% reliability. In order to estimate the contribution of the different steps in the GMO analysis to the total variation variance components were estimated using REML (residual maximum likelihood method). From these components, relative standard deviations for repeatability and reproducibility (RSD(r) and RSD(R)) were calculated. The results showed that not only the PCR reaction but also the factors 'DNA isolation' and 'PCR day' are important factors for the total variance and should therefore be included in the in-house validation. It is proposed to use a statistical model to estimate these factors from a large dataset of initial validations so that for similar GMO methods in the future, only the PCR step needs to be validated. The resulting data are discussed in the light of agreed European criteria for qualified GMO detection methods.

Capture approximations beyond a statistical quantum mechanical method for atom-diatom reactions

NASA Astrophysics Data System (ADS)

Barrios, Lizandra; Rubayo-Soneira, Jesús; González-Lezana, Tomás

2016-03-01

Statistical techniques constitute useful approaches to investigate atom-diatom reactions mediated by insertion dynamics which involves complex-forming mechanisms. Different capture schemes based on energy considerations regarding the specific diatom rovibrational states are suggested to evaluate the corresponding probabilities of formation of such collision species between reactants and products in an attempt to test reliable alternatives for computationally demanding processes. These approximations are tested in combination with a statistical quantum mechanical method for the S + H2(v = 0 ,j = 1) → SH + H and Si + O2(v = 0 ,j = 1) → SiO + O reactions, where this dynamical mechanism plays a significant role, in order to probe their validity.

Sea ice motion from low-resolution satellite sensors: An alternative method and its validation in the Arctic

NASA Astrophysics Data System (ADS)

Lavergne, T.; Eastwood, S.; Teffah, Z.; Schyberg, H.; Breivik, L.-A.

2010-10-01

The retrieval of sea ice motion with the Maximum Cross-Correlation (MCC) method from low-resolution (10-15 km) spaceborne imaging sensors is challenged by a dominating quantization noise as the time span of displacement vectors is shortened. To allow investigating shorter displacements from these instruments, we introduce an alternative sea ice motion tracking algorithm that builds on the MCC method but relies on a continuous optimization step for computing the motion vector. The prime effect of this method is to effectively dampen the quantization noise, an artifact of the MCC. It allows for retrieving spatially smooth 48 h sea ice motion vector fields in the Arctic. Strategies to detect and correct erroneous vectors as well as to optimally merge several polarization channels of a given instrument are also described. A test processing chain is implemented and run with several active and passive microwave imagers (Advanced Microwave Scanning Radiometer-EOS (AMSR-E), Special Sensor Microwave Imager, and Advanced Scatterometer) during three Arctic autumn, winter, and spring seasons. Ice motion vectors are collocated to and compared with GPS positions of in situ drifters. Error statistics are shown to be ranging from 2.5 to 4.5 km (standard deviation for components of the vectors) depending on the sensor, without significant bias. We discuss the relative contribution of measurement and representativeness errors by analyzing monthly validation statistics. The 37 GHz channels of the AMSR-E instrument allow for the best validation statistics. The operational low-resolution sea ice drift product of the EUMETSAT OSI SAF (European Organisation for the Exploitation of Meteorological Satellites Ocean and Sea Ice Satellite Application Facility) is based on the algorithms presented in this paper.

MO-G-12A-01: Quantitative Imaging Metrology: What Should Be Assessed and How?

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

Giger, M; Petrick, N; Obuchowski, N

The first two symposia in the Quantitative Imaging Track focused on 1) the introduction of quantitative imaging (QI) challenges and opportunities, and QI efforts of agencies and organizations such as the RSNA, NCI, FDA, and NIST, and 2) the techniques, applications, and challenges of QI, with specific examples from CT, PET/CT, and MR. This third symposium in the QI Track will focus on metrology and its importance in successfully advancing the QI field. While the specific focus will be on QI, many of the concepts presented are more broadly applicable to many areas of medical physics research and applications. Asmore » such, the topics discussed should be of interest to medical physicists involved in imaging as well as therapy. The first talk of the session will focus on the introduction to metrology and why it is critically important in QI. The second talk will focus on appropriate methods for technical performance assessment. The third talk will address statistically valid methods for algorithm comparison, a common problem not only in QI but also in other areas of medical physics. The final talk in the session will address strategies for publication of results that will allow statistically valid meta-analyses, which is critical for combining results of individual studies with typically small sample sizes in a manner that can best inform decisions and advance the field. Learning Objectives: Understand the importance of metrology in the QI efforts. Understand appropriate methods for technical performance assessment. Understand methods for comparing algorithms with or without reference data (i.e., “ground truth”). Understand the challenges and importance of reporting results in a manner that allows for statistically valid meta-analyses.« less

Probability of identification (POI): a statistical model for the validation of qualitative botanical identification methods

USDA-ARS?s Scientific Manuscript database

A qualitative botanical identification method (BIM) is an analytical procedure which returns a binary result (1 = Identified, 0 = Not Identified). A BIM may be used by a buyer, manufacturer, or regulator to determine whether a botanical material being tested is the same as the target (desired) mate...

Point Intercept (PO)

Treesearch

John F. Caratti

2006-01-01

The FIREMON Point Intercept (PO) method is used to assess changes in plant species cover or ground cover for a macroplot. This method uses a narrow diameter sampling pole or sampling pins, placed at systematic intervals along line transects to sample within plot variation and quantify statistically valid changes in plant species cover and height over time. Plant...

Principle Component Analysis with Incomplete Data: A simulation of R pcaMethods package in Constructing an Environmental Quality Index with Missing Data

EPA Science Inventory

Missing data is a common problem in the application of statistical techniques. In principal component analysis (PCA), a technique for dimensionality reduction, incomplete data points are either discarded or imputed using interpolation methods. Such approaches are less valid when ...

Interference detection and correction applied to incoherent-scatter radar power spectrum measurement

NASA Technical Reports Server (NTRS)

Ying, W. P.; Mathews, J. D.; Rastogi, P. K.

1986-01-01

A median filter based interference detection and correction technique is evaluated and the method applied to the Arecibo incoherent scatter radar D-region ionospheric power spectrum is discussed. The method can be extended to other kinds of data when the statistics involved in the process are still valid.

A Simple Method to Control Positive Baseline Trend within Data Nonoverlap

ERIC Educational Resources Information Center

Parker, Richard I.; Vannest, Kimberly J.; Davis, John L.

2014-01-01

Nonoverlap is widely used as a statistical summary of data; however, these analyses rarely correct unwanted positive baseline trend. This article presents and validates the graph rotation for overlap and trend (GROT) technique, a hand calculation method for controlling positive baseline trend within an analysis of data nonoverlap. GROT is…

Universal immunogenicity validation and assessment during early biotherapeutic development to support a green laboratory.

PubMed

Bautista, Ami C; Zhou, Lei; Jawa, Vibha

2013-10-01

Immunogenicity support during nonclinical biotherapeutic development can be resource intensive if supported by conventional methodologies. A universal indirect species-specific immunoassay can eliminate the need for biotherapeutic-specific anti-drug antibody immunoassays without compromising quality. By implementing the R's of sustainability (reduce, reuse, rethink), conservation of resources and greener laboratory practices were achieved in this study. Statistical analysis across four biotherapeutics supported identification of consistent product performance standards (cut points, sensitivity and reference limits) and a streamlined universal anti-drug antibody immunoassay method implementation strategy. We propose an efficient, fit-for-purpose, scientifically and statistically supported nonclinical immunogenicity assessment strategy. Utilization of a universal method and streamlined validation, while retaining comparability to conventional immunoassays and meeting the industry recommended standards, provides environmental credits in the scientific laboratory. Collectively, individual reductions in critical material consumption, energy usage, waste and non-environment friendly consumables, such as plastic and paper, support a greener laboratory environment.

An adaptive state of charge estimation approach for lithium-ion series-connected battery system

NASA Astrophysics Data System (ADS)

Peng, Simin; Zhu, Xuelai; Xing, Yinjiao; Shi, Hongbing; Cai, Xu; Pecht, Michael

2018-07-01

Due to the incorrect or unknown noise statistics of a battery system and its cell-to-cell variations, state of charge (SOC) estimation of a lithium-ion series-connected battery system is usually inaccurate or even divergent using model-based methods, such as extended Kalman filter (EKF) and unscented Kalman filter (UKF). To resolve this problem, an adaptive unscented Kalman filter (AUKF) based on a noise statistics estimator and a model parameter regulator is developed to accurately estimate the SOC of a series-connected battery system. An equivalent circuit model is first built based on the model parameter regulator that illustrates the influence of cell-to-cell variation on the battery system. A noise statistics estimator is then used to attain adaptively the estimated noise statistics for the AUKF when its prior noise statistics are not accurate or exactly Gaussian. The accuracy and effectiveness of the SOC estimation method is validated by comparing the developed AUKF and UKF when model and measurement statistics noises are inaccurate, respectively. Compared with the UKF and EKF, the developed method shows the highest SOC estimation accuracy.

Risk prediction model: Statistical and artificial neural network approach

NASA Astrophysics Data System (ADS)

Paiman, Nuur Azreen; Hariri, Azian; Masood, Ibrahim

2017-04-01

Prediction models are increasingly gaining popularity and had been used in numerous areas of studies to complement and fulfilled clinical reasoning and decision making nowadays. The adoption of such models assist physician's decision making, individual's behavior, and consequently improve individual outcomes and the cost-effectiveness of care. The objective of this paper is to reviewed articles related to risk prediction model in order to understand the suitable approach, development and the validation process of risk prediction model. A qualitative review of the aims, methods and significant main outcomes of the nineteen published articles that developed risk prediction models from numerous fields were done. This paper also reviewed on how researchers develop and validate the risk prediction models based on statistical and artificial neural network approach. From the review done, some methodological recommendation in developing and validating the prediction model were highlighted. According to studies that had been done, artificial neural network approached in developing the prediction model were more accurate compared to statistical approach. However currently, only limited published literature discussed on which approach is more accurate for risk prediction model development.

Enhancement and Validation of an Arab Surname Database

PubMed Central

Schwartz, Kendra; Beebani, Ganj; Sedki, Mai; Tahhan, Mamon; Ruterbusch, Julie J.

2015-01-01

Objectives Arab Americans constitute a large, heterogeneous, and quickly growing subpopulation in the United States. Health statistics for this group are difficult to find because US governmental offices do not recognize Arab as separate from white. The development and validation of an Arab- and Chaldean-American name database will enhance research efforts in this population subgroup. Methods A previously validated name database was supplemented with newly identified names gathered primarily from vital statistic records and then evaluated using a multistep process. This process included 1) review by 4 Arabic- and Chaldean-speaking reviewers, 2) ethnicity assessment by social media searches, and 3) self-report of ancestry obtained from a telephone survey. Results Our Arab- and Chaldean-American name algorithm has a positive predictive value of 91% and a negative predictive value of 100%. Conclusions This enhanced name database and algorithm can be used to identify Arab Americans in health statistics data, such as cancer and hospital registries, where they are often coded as white, to determine the extent of health disparities in this population. PMID:24625771

Significant Association of Urinary Toxic Metals and Autism-Related Symptoms—A Nonlinear Statistical Analysis with Cross Validation

PubMed Central

Adams, James; Kruger, Uwe; Geis, Elizabeth; Gehn, Eva; Fimbres, Valeria; Pollard, Elena; Mitchell, Jessica; Ingram, Julie; Hellmers, Robert; Quig, David; Hahn, Juergen

2017-01-01

Introduction A number of previous studies examined a possible association of toxic metals and autism, and over half of those studies suggest that toxic metal levels are different in individuals with Autism Spectrum Disorders (ASD). Additionally, several studies found that those levels correlate with the severity of ASD. Methods In order to further investigate these points, this paper performs the most detailed statistical analysis to date of a data set in this field. First morning urine samples were collected from 67 children and adults with ASD and 50 neurotypical controls of similar age and gender. The samples were analyzed to determine the levels of 10 urinary toxic metals (UTM). Autism-related symptoms were assessed with eleven behavioral measures. Statistical analysis was used to distinguish participants on the ASD spectrum and neurotypical participants based upon the UTM data alone. The analysis also included examining the association of autism severity with toxic metal excretion data using linear and nonlinear analysis. “Leave-one-out” cross-validation was used to ensure statistical independence of results. Results and Discussion Average excretion levels of several toxic metals (lead, tin, thallium, antimony) were significantly higher in the ASD group. However, ASD classification using univariate statistics proved difficult due to large variability, but nonlinear multivariate statistical analysis significantly improved ASD classification with Type I/II errors of 15% and 18%, respectively. These results clearly indicate that the urinary toxic metal excretion profiles of participants in the ASD group were significantly different from those of the neurotypical participants. Similarly, nonlinear methods determined a significantly stronger association between the behavioral measures and toxic metal excretion. The association was strongest for the Aberrant Behavior Checklist (including subscales on Irritability, Stereotypy, Hyperactivity, and Inappropriate Speech), but significant associations were found for UTM with all eleven autism-related assessments with cross-validation R2 values ranging from 0.12–0.48. PMID:28068407

Fast mean and variance computation of the diffuse sound transmission through finite-sized thick and layered wall and floor systems

NASA Astrophysics Data System (ADS)

Decraene, Carolina; Dijckmans, Arne; Reynders, Edwin P. B.

2018-05-01

A method is developed for computing the mean and variance of the diffuse field sound transmission loss of finite-sized layered wall and floor systems that consist of solid, fluid and/or poroelastic layers. This is achieved by coupling a transfer matrix model of the wall or floor to statistical energy analysis subsystem models of the adjacent room volumes. The modal behavior of the wall is approximately accounted for by projecting the wall displacement onto a set of sinusoidal lateral basis functions. This hybrid modal transfer matrix-statistical energy analysis method is validated on multiple wall systems: a thin steel plate, a polymethyl methacrylate panel, a thick brick wall, a sandwich panel, a double-leaf wall with poro-elastic material in the cavity, and a double glazing. The predictions are compared with experimental data and with results obtained using alternative prediction methods such as the transfer matrix method with spatial windowing, the hybrid wave based-transfer matrix method, and the hybrid finite element-statistical energy analysis method. These comparisons confirm the prediction accuracy of the proposed method and the computational efficiency against the conventional hybrid finite element-statistical energy analysis method.

A Psychometric Study of the Bayley Scales of Infant and Toddler Development in Persian Language Children.

PubMed

Azari, Nadia; Soleimani, Farin; Vameghi, Roshanak; Sajedi, Firoozeh; Shahshahani, Soheila; Karimi, Hossein; Kraskian, Adis; Shahrokhi, Amin; Teymouri, Robab; Gharib, Masoud

2017-01-01

Bayley Scales of infant & toddler development is a well-known diagnostic developmental assessment tool for children aged 1-42 months. Our aim was investigating the validity & reliability of this scale in Persian speaking children. The method was descriptive-analytic. Translation- back translation and cultural adaptation was done. Content & face validity of translated scale was determined by experts' opinions. Overall, 403 children aged 1 to 42 months were recruited from health centers of Tehran, during years of 2013-2014 for developmental assessment in cognitive, communicative (receptive & expressive) and motor (fine & gross) domains. Reliability of scale was calculated through three methods; internal consistency using Cronbach's alpha coefficient, test-retest and interrater methods. Construct validity was calculated using factor analysis and comparison of the mean scores methods. Cultural and linguistic changes were made in items of all domains especially on communication subscale. Content and face validity of the test were approved by experts' opinions. Cronbach's alpha coefficient was above 0.74 in all domains. Pearson correlation coefficient in various domains, were ≥ 0.982 in test retest method, and ≥0.993 in inter-rater method. Construct validity of the test was approved by factor analysis. Moreover, the mean scores for the different age groups were compared and statistically significant differences were observed between mean scores of different age groups, that confirms validity of the test. The Bayley Scales of Infant and Toddler Development is a valid and reliable tool for child developmental assessment in Persian language children.

History and development of the Schmidt-Hunter meta-analysis methods.

PubMed

Schmidt, Frank L

2015-09-01

In this article, I provide answers to the questions posed by Will Shadish about the history and development of the Schmidt-Hunter methods of meta-analysis. In the 1970s, I headed a research program on personnel selection at the US Office of Personnel Management (OPM). After our research showed that validity studies have low statistical power, OPM felt a need for a better way to demonstrate test validity, especially in light of court cases challenging selection methods. In response, we created our method of meta-analysis (initially called validity generalization). Results showed that most of the variability of validity estimates from study to study was because of sampling error and other research artifacts such as variations in range restriction and measurement error. Corrections for these artifacts in our research and in replications by others showed that the predictive validity of most tests was high and generalizable. This conclusion challenged long-standing beliefs and so provoked resistance, which over time was overcome. The 1982 book that we published extending these methods to research areas beyond personnel selection was positively received and was followed by expanded books in 1990, 2004, and 2014. Today, these methods are being applied in a wide variety of areas. Copyright © 2015 John Wiley & Sons, Ltd.

Data processing of qualitative results from an interlaboratory comparison for the detection of “Flavescence dorée” phytoplasma: How the use of statistics can improve the reliability of the method validation process in plant pathology

PubMed Central

Renaudin, Isabelle; Poliakoff, Françoise

2017-01-01

A working group established in the framework of the EUPHRESCO European collaborative project aimed to compare and validate diagnostic protocols for the detection of “Flavescence dorée” (FD) phytoplasma in grapevines. Seven molecular protocols were compared in an interlaboratory test performance study where each laboratory had to analyze the same panel of samples consisting of DNA extracts prepared by the organizing laboratory. The tested molecular methods consisted of universal and group-specific real-time and end-point nested PCR tests. Different statistical approaches were applied to this collaborative study. Firstly, there was the standard statistical approach consisting in analyzing samples which are known to be positive and samples which are known to be negative and reporting the proportion of false-positive and false-negative results to respectively calculate diagnostic specificity and sensitivity. This approach was supplemented by the calculation of repeatability and reproducibility for qualitative methods based on the notions of accordance and concordance. Other new approaches were also implemented, based, on the one hand, on the probability of detection model, and, on the other hand, on Bayes’ theorem. These various statistical approaches are complementary and give consistent results. Their combination, and in particular, the introduction of new statistical approaches give overall information on the performance and limitations of the different methods, and are particularly useful for selecting the most appropriate detection scheme with regards to the prevalence of the pathogen. Three real-time PCR protocols (methods M4, M5 and M6 respectively developed by Hren (2007), Pelletier (2009) and under patent oligonucleotides) achieved the highest levels of performance for FD phytoplasma detection. This paper also addresses the issue of indeterminate results and the identification of outlier results. The statistical tools presented in this paper and their combination can be applied to many other studies concerning plant pathogens and other disciplines that use qualitative detection methods. PMID:28384335

Data processing of qualitative results from an interlaboratory comparison for the detection of "Flavescence dorée" phytoplasma: How the use of statistics can improve the reliability of the method validation process in plant pathology.

PubMed

Chabirand, Aude; Loiseau, Marianne; Renaudin, Isabelle; Poliakoff, Françoise

2017-01-01

A working group established in the framework of the EUPHRESCO European collaborative project aimed to compare and validate diagnostic protocols for the detection of "Flavescence dorée" (FD) phytoplasma in grapevines. Seven molecular protocols were compared in an interlaboratory test performance study where each laboratory had to analyze the same panel of samples consisting of DNA extracts prepared by the organizing laboratory. The tested molecular methods consisted of universal and group-specific real-time and end-point nested PCR tests. Different statistical approaches were applied to this collaborative study. Firstly, there was the standard statistical approach consisting in analyzing samples which are known to be positive and samples which are known to be negative and reporting the proportion of false-positive and false-negative results to respectively calculate diagnostic specificity and sensitivity. This approach was supplemented by the calculation of repeatability and reproducibility for qualitative methods based on the notions of accordance and concordance. Other new approaches were also implemented, based, on the one hand, on the probability of detection model, and, on the other hand, on Bayes' theorem. These various statistical approaches are complementary and give consistent results. Their combination, and in particular, the introduction of new statistical approaches give overall information on the performance and limitations of the different methods, and are particularly useful for selecting the most appropriate detection scheme with regards to the prevalence of the pathogen. Three real-time PCR protocols (methods M4, M5 and M6 respectively developed by Hren (2007), Pelletier (2009) and under patent oligonucleotides) achieved the highest levels of performance for FD phytoplasma detection. This paper also addresses the issue of indeterminate results and the identification of outlier results. The statistical tools presented in this paper and their combination can be applied to many other studies concerning plant pathogens and other disciplines that use qualitative detection methods.

Statistical learning theory for high dimensional prediction: Application to criterion-keyed scale development.

PubMed

Chapman, Benjamin P; Weiss, Alexander; Duberstein, Paul R

2016-12-01

Statistical learning theory (SLT) is the statistical formulation of machine learning theory, a body of analytic methods common in "big data" problems. Regression-based SLT algorithms seek to maximize predictive accuracy for some outcome, given a large pool of potential predictors, without overfitting the sample. Research goals in psychology may sometimes call for high dimensional regression. One example is criterion-keyed scale construction, where a scale with maximal predictive validity must be built from a large item pool. Using this as a working example, we first introduce a core principle of SLT methods: minimization of expected prediction error (EPE). Minimizing EPE is fundamentally different than maximizing the within-sample likelihood, and hinges on building a predictive model of sufficient complexity to predict the outcome well, without undue complexity leading to overfitting. We describe how such models are built and refined via cross-validation. We then illustrate how 3 common SLT algorithms-supervised principal components, regularization, and boosting-can be used to construct a criterion-keyed scale predicting all-cause mortality, using a large personality item pool within a population cohort. Each algorithm illustrates a different approach to minimizing EPE. Finally, we consider broader applications of SLT predictive algorithms, both as supportive analytic tools for conventional methods, and as primary analytic tools in discovery phase research. We conclude that despite their differences from the classic null-hypothesis testing approach-or perhaps because of them-SLT methods may hold value as a statistically rigorous approach to exploratory regression. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

75 FR 56059 - Patent Examiner Technical Training Program

Federal Register 2010, 2011, 2012, 2013, 2014

2010-09-15

...); statistical methods in validation of microarry data; personalized medicine, manufacture of carbon nanospheres... processing, growing monocrystals, hydrogen production, liquid and gas purification and separation, making... Systems and Components: Mixed signal design and architecture, flexible displays, OLED display technology...

A combined pre-clinical meta-analysis and randomized confirmatory trial approach to improve data validity for therapeutic target validation.

PubMed

Kleikers, Pamela W M; Hooijmans, Carlijn; Göb, Eva; Langhauser, Friederike; Rewell, Sarah S J; Radermacher, Kim; Ritskes-Hoitinga, Merel; Howells, David W; Kleinschnitz, Christoph; Schmidt, Harald H H W

2015-08-27

Biomedical research suffers from a dramatically poor translational success. For example, in ischemic stroke, a condition with a high medical need, over a thousand experimental drug targets were unsuccessful. Here, we adopt methods from clinical research for a late-stage pre-clinical meta-analysis (MA) and randomized confirmatory trial (pRCT) approach. A profound body of literature suggests NOX2 to be a major therapeutic target in stroke. Systematic review and MA of all available NOX2(-/y) studies revealed a positive publication bias and lack of statistical power to detect a relevant reduction in infarct size. A fully powered multi-center pRCT rejects NOX2 as a target to improve neurofunctional outcomes or achieve a translationally relevant infarct size reduction. Thus stringent statistical thresholds, reporting negative data and a MA-pRCT approach can ensure biomedical data validity and overcome risks of bias.

VARSEDIG: an algorithm for morphometric characters selection and statistical validation in morphological taxonomy.

PubMed

Guisande, Cástor; Vari, Richard P; Heine, Jürgen; García-Roselló, Emilio; González-Dacosta, Jacinto; Perez-Schofield, Baltasar J García; González-Vilas, Luis; Pelayo-Villamil, Patricia

2016-09-12

We present and discuss VARSEDIG, an algorithm which identifies the morphometric features that significantly discriminate two taxa and validates the morphological distinctness between them via a Monte-Carlo test. VARSEDIG is freely available as a function of the RWizard application PlotsR (http://www.ipez.es/RWizard) and as R package on CRAN. The variables selected by VARSEDIG with the overlap method were very similar to those selected by logistic regression and discriminant analysis, but overcomes some shortcomings of these methods. VARSEDIG is, therefore, a good alternative by comparison to current classical classification methods for identifying morphometric features that significantly discriminate a taxon and for validating its morphological distinctness from other taxa. As a demonstration of the potential of VARSEDIG for this purpose, we analyze morphological discrimination among some species of the Neotropical freshwater family Characidae.

Validation of chemistry models employed in a particle simulation method

NASA Technical Reports Server (NTRS)

Haas, Brian L.; Mcdonald, Jeffrey D.

1991-01-01

The chemistry models employed in a statistical particle simulation method, as implemented in the Intel iPSC/860 multiprocessor computer, are validated and applied. Chemical relaxation of five-species air in these reservoirs involves 34 simultaneous dissociation, recombination, and atomic-exchange reactions. The reaction rates employed in the analytic solutions are obtained from Arrhenius experimental correlations as functions of temperature for adiabatic gas reservoirs in thermal equilibrium. Favorable agreement with the analytic solutions validates the simulation when applied to relaxation of O2 toward equilibrium in reservoirs dominated by dissociation and recombination, respectively, and when applied to relaxation of air in the temperature range 5000 to 30,000 K. A flow of O2 over a circular cylinder at high Mach number is simulated to demonstrate application of the method to multidimensional reactive flows.

Performance comparison of LUR and OK in PM2.5 concentration mapping: a multidimensional perspective

PubMed Central

Zou, Bin; Luo, Yanqing; Wan, Neng; Zheng, Zhong; Sternberg, Troy; Liao, Yilan

2015-01-01

Methods of Land Use Regression (LUR) modeling and Ordinary Kriging (OK) interpolation have been widely used to offset the shortcomings of PM2.5 data observed at sparse monitoring sites. However, traditional point-based performance evaluation strategy for these methods remains stagnant, which could cause unreasonable mapping results. To address this challenge, this study employs ‘information entropy’, an area-based statistic, along with traditional point-based statistics (e.g. error rate, RMSE) to evaluate the performance of LUR model and OK interpolation in mapping PM2.5 concentrations in Houston from a multidimensional perspective. The point-based validation reveals significant differences between LUR and OK at different test sites despite the similar end-result accuracy (e.g. error rate 6.13% vs. 7.01%). Meanwhile, the area-based validation demonstrates that the PM2.5 concentrations simulated by the LUR model exhibits more detailed variations than those interpolated by the OK method (i.e. information entropy, 7.79 vs. 3.63). Results suggest that LUR modeling could better refine the spatial distribution scenario of PM2.5 concentrations compared to OK interpolation. The significance of this study primarily lies in promoting the integration of point- and area-based statistics for model performance evaluation in air pollution mapping. PMID:25731103

Combining Multiple Hypothesis Testing with Machine Learning Increases the Statistical Power of Genome-wide Association Studies

PubMed Central

Mieth, Bettina; Kloft, Marius; Rodríguez, Juan Antonio; Sonnenburg, Sören; Vobruba, Robin; Morcillo-Suárez, Carlos; Farré, Xavier; Marigorta, Urko M.; Fehr, Ernst; Dickhaus, Thorsten; Blanchard, Gilles; Schunk, Daniel; Navarro, Arcadi; Müller, Klaus-Robert

2016-01-01

The standard approach to the analysis of genome-wide association studies (GWAS) is based on testing each position in the genome individually for statistical significance of its association with the phenotype under investigation. To improve the analysis of GWAS, we propose a combination of machine learning and statistical testing that takes correlation structures within the set of SNPs under investigation in a mathematically well-controlled manner into account. The novel two-step algorithm, COMBI, first trains a support vector machine to determine a subset of candidate SNPs and then performs hypothesis tests for these SNPs together with an adequate threshold correction. Applying COMBI to data from a WTCCC study (2007) and measuring performance as replication by independent GWAS published within the 2008–2015 period, we show that our method outperforms ordinary raw p-value thresholding as well as other state-of-the-art methods. COMBI presents higher power and precision than the examined alternatives while yielding fewer false (i.e. non-replicated) and more true (i.e. replicated) discoveries when its results are validated on later GWAS studies. More than 80% of the discoveries made by COMBI upon WTCCC data have been validated by independent studies. Implementations of the COMBI method are available as a part of the GWASpi toolbox 2.0. PMID:27892471

Combining Multiple Hypothesis Testing with Machine Learning Increases the Statistical Power of Genome-wide Association Studies.

PubMed

Mieth, Bettina; Kloft, Marius; Rodríguez, Juan Antonio; Sonnenburg, Sören; Vobruba, Robin; Morcillo-Suárez, Carlos; Farré, Xavier; Marigorta, Urko M; Fehr, Ernst; Dickhaus, Thorsten; Blanchard, Gilles; Schunk, Daniel; Navarro, Arcadi; Müller, Klaus-Robert

2016-11-28

The standard approach to the analysis of genome-wide association studies (GWAS) is based on testing each position in the genome individually for statistical significance of its association with the phenotype under investigation. To improve the analysis of GWAS, we propose a combination of machine learning and statistical testing that takes correlation structures within the set of SNPs under investigation in a mathematically well-controlled manner into account. The novel two-step algorithm, COMBI, first trains a support vector machine to determine a subset of candidate SNPs and then performs hypothesis tests for these SNPs together with an adequate threshold correction. Applying COMBI to data from a WTCCC study (2007) and measuring performance as replication by independent GWAS published within the 2008-2015 period, we show that our method outperforms ordinary raw p-value thresholding as well as other state-of-the-art methods. COMBI presents higher power and precision than the examined alternatives while yielding fewer false (i.e. non-replicated) and more true (i.e. replicated) discoveries when its results are validated on later GWAS studies. More than 80% of the discoveries made by COMBI upon WTCCC data have been validated by independent studies. Implementations of the COMBI method are available as a part of the GWASpi toolbox 2.0.

Combining Multiple Hypothesis Testing with Machine Learning Increases the Statistical Power of Genome-wide Association Studies

NASA Astrophysics Data System (ADS)

Mieth, Bettina; Kloft, Marius; Rodríguez, Juan Antonio; Sonnenburg, Sören; Vobruba, Robin; Morcillo-Suárez, Carlos; Farré, Xavier; Marigorta, Urko M.; Fehr, Ernst; Dickhaus, Thorsten; Blanchard, Gilles; Schunk, Daniel; Navarro, Arcadi; Müller, Klaus-Robert

2016-11-01

The standard approach to the analysis of genome-wide association studies (GWAS) is based on testing each position in the genome individually for statistical significance of its association with the phenotype under investigation. To improve the analysis of GWAS, we propose a combination of machine learning and statistical testing that takes correlation structures within the set of SNPs under investigation in a mathematically well-controlled manner into account. The novel two-step algorithm, COMBI, first trains a support vector machine to determine a subset of candidate SNPs and then performs hypothesis tests for these SNPs together with an adequate threshold correction. Applying COMBI to data from a WTCCC study (2007) and measuring performance as replication by independent GWAS published within the 2008-2015 period, we show that our method outperforms ordinary raw p-value thresholding as well as other state-of-the-art methods. COMBI presents higher power and precision than the examined alternatives while yielding fewer false (i.e. non-replicated) and more true (i.e. replicated) discoveries when its results are validated on later GWAS studies. More than 80% of the discoveries made by COMBI upon WTCCC data have been validated by independent studies. Implementations of the COMBI method are available as a part of the GWASpi toolbox 2.0.

Statistical-Dynamical Seasonal Forecasts of Central-Southwest Asian Winter Precipitation.

NASA Astrophysics Data System (ADS)

Tippett, Michael K.; Goddard, Lisa; Barnston, Anthony G.

2005-06-01

Interannual precipitation variability in central-southwest (CSW) Asia has been associated with East Asian jet stream variability and western Pacific tropical convection. However, atmospheric general circulation models (AGCMs) forced by observed sea surface temperature (SST) poorly simulate the region's interannual precipitation variability. The statistical-dynamical approach uses statistical methods to correct systematic deficiencies in the response of AGCMs to SST forcing. Statistical correction methods linking model-simulated Indo-west Pacific precipitation and observed CSW Asia precipitation result in modest, but statistically significant, cross-validated simulation skill in the northeast part of the domain for the period from 1951 to 1998. The statistical-dynamical method is also applied to recent (winter 1998/99 to 2002/03) multimodel, two-tier December-March precipitation forecasts initiated in October. This period includes 4 yr (winter of 1998/99 to 2001/02) of severe drought. Tercile probability forecasts are produced using ensemble-mean forecasts and forecast error estimates. The statistical-dynamical forecasts show enhanced probability of below-normal precipitation for the four drought years and capture the return to normal conditions in part of the region during the winter of 2002/03.May Kabul be without gold, but not without snow.—Traditional Afghan proverb

An entropy-based nonparametric test for the validation of surrogate endpoints.

PubMed

Miao, Xiaopeng; Wang, Yong-Cheng; Gangopadhyay, Ashis

2012-06-30

We present a nonparametric test to validate surrogate endpoints based on measure of divergence and random permutation. This test is a proposal to directly verify the Prentice statistical definition of surrogacy. The test does not impose distributional assumptions on the endpoints, and it is robust to model misspecification. Our simulation study shows that the proposed nonparametric test outperforms the practical test of the Prentice criterion in terms of both robustness of size and power. We also evaluate the performance of three leading methods that attempt to quantify the effect of surrogate endpoints. The proposed method is applied to validate magnetic resonance imaging lesions as the surrogate endpoint for clinical relapses in a multiple sclerosis trial. Copyright © 2012 John Wiley & Sons, Ltd.

Comparison of individual-based model output to data using a model of walleye pollock early life history in the Gulf of Alaska

NASA Astrophysics Data System (ADS)

Hinckley, Sarah; Parada, Carolina; Horne, John K.; Mazur, Michael; Woillez, Mathieu

2016-10-01

Biophysical individual-based models (IBMs) have been used to study aspects of early life history of marine fishes such as recruitment, connectivity of spawning and nursery areas, and marine reserve design. However, there is no consistent approach to validating the spatial outputs of these models. In this study, we hope to rectify this gap. We document additions to an existing individual-based biophysical model for Alaska walleye pollock (Gadus chalcogrammus), some simulations made with this model and methods that were used to describe and compare spatial output of the model versus field data derived from ichthyoplankton surveys in the Gulf of Alaska. We used visual methods (e.g. distributional centroids with directional ellipses), several indices (such as a Normalized Difference Index (NDI), and an Overlap Coefficient (OC), and several statistical methods: the Syrjala method, the Getis-Ord Gi* statistic, and a geostatistical method for comparing spatial indices. We assess the utility of these different methods in analyzing spatial output and comparing model output to data, and give recommendations for their appropriate use. Visual methods are useful for initial comparisons of model and data distributions. Metrics such as the NDI and OC give useful measures of co-location and overlap, but care must be taken in discretizing the fields into bins. The Getis-Ord Gi* statistic is useful to determine the patchiness of the fields. The Syrjala method is an easily implemented statistical measure of the difference between the fields, but does not give information on the details of the distributions. Finally, the geostatistical comparison of spatial indices gives good information of details of the distributions and whether they differ significantly between the model and the data. We conclude that each technique gives quite different information about the model-data distribution comparison, and that some are easy to apply and some more complex. We also give recommendations for a multistep process to validate spatial output from IBMs.

Simultaneous determination of some cholesterol-lowering drugs in their binary mixture by novel spectrophotometric methods

NASA Astrophysics Data System (ADS)

Lotfy, Hayam Mahmoud; Hegazy, Maha Abdel Monem

2013-09-01

Four simple, specific, accurate and precise spectrophotometric methods manipulating ratio spectra were developed and validated for simultaneous determination of simvastatin (SM) and ezetimibe (EZ) namely; extended ratio subtraction (EXRSM), simultaneous ratio subtraction (SRSM), ratio difference (RDSM) and absorption factor (AFM). The proposed spectrophotometric procedures do not require any preliminary separation step. The accuracy, precision and linearity ranges of the proposed methods were determined, and the methods were validated and the specificity was assessed by analyzing synthetic mixtures containing the cited drugs. The four methods were applied for the determination of the cited drugs in tablets and the obtained results were statistically compared with each other and with those of a reported HPLC method. The comparison showed that there is no significant difference between the proposed methods and the reported method regarding both accuracy and precision.

Methodological problems in the method used by IQWiG within early benefit assessment of new pharmaceuticals in Germany.

PubMed

Herpers, Matthias; Dintsios, Charalabos-Markos

2018-04-25

The decision matrix applied by the Institute for Quality and Efficiency in Health Care (IQWiG) for the quantification of added benefit within the early benefit assessment of new pharmaceuticals in Germany with its nine fields is quite complex and could be simplified. Furthermore, the method used by IQWiG is subject to manifold criticism: (1) it is implicitly weighting endpoints differently in its assessments favoring overall survival and, thereby, drug interventions in fatal diseases, (2) it is assuming that two pivotal trials are available when assessing the dossiers submitted by the pharmaceutical manufacturers, leading to far-reaching implications with respect to the quantification of added benefit, and, (3) it is basing the evaluation primarily on dichotomous endpoints and consequently leading to an information loss of usable evidence. To investigate if criticism is justified and to propose methodological adaptations. Analysis of the available dossiers up to the end of 2016 using statistical tests and multinomial logistic regression and simulations. It was shown that due to power losses, the method does not ensure that results are statistically valid and outcomes of the early benefit assessment may be compromised, though evidence on favoring overall survival remains unclear. Modifications, however, of the IQWiG method are possible to address the identified problems. By converging with the approach of approval authorities for confirmatory endpoints, the decision matrix could be simplified and the analysis method could be improved, to put the results on a more valid statistical basis.

A Psychometric Study of the Bayley Scales of Infant and Toddler Development in Persian Language Children

PubMed Central

AZARI, Nadia; SOLEIMANI, Farin; VAMEGHI, Roshanak; SAJEDI, Firoozeh; SHAHSHAHANI, Soheila; KARIMI, Hossein; KRASKIAN, Adis; SHAHROKHI, Amin; TEYMOURI, Robab; GHARIB, Masoud

2017-01-01

Objective Bayley Scales of infant & toddler development is a well-known diagnostic developmental assessment tool for children aged 1–42 months. Our aim was investigating the validity & reliability of this scale in Persian speaking children. Materials & Methods The method was descriptive-analytic. Translation- back translation and cultural adaptation was done. Content & face validity of translated scale was determined by experts’ opinions. Overall, 403 children aged 1 to 42 months were recruited from health centers of Tehran, during years of 2013-2014 for developmental assessment in cognitive, communicative (receptive & expressive) and motor (fine & gross) domains. Reliability of scale was calculated through three methods; internal consistency using Cronbach’s alpha coefficient, test-retest and interrater methods. Construct validity was calculated using factor analysis and comparison of the mean scores methods. Results Cultural and linguistic changes were made in items of all domains especially on communication subscale. Content and face validity of the test were approved by experts’ opinions. Cronbach’s alpha coefficient was above 0.74 in all domains. Pearson correlation coefficient in various domains, were ≥ 0.982 in test retest method, and ≥0.993 in inter-rater method. Construct validity of the test was approved by factor analysis. Moreover, the mean scores for the different age groups were compared and statistically significant differences were observed between mean scores of different age groups, that confirms validity of the test. Conclusion The Bayley Scales of Infant and Toddler Development is a valid and reliable tool for child developmental assessment in Persian language children. PMID:28277556

Update of Standard Practices for New Method Validation in Forensic Toxicology.

PubMed

Wille, Sarah M R; Coucke, Wim; De Baere, Thierry; Peters, Frank T

2017-01-01

International agreement concerning validation guidelines is important to obtain quality forensic bioanalytical research and routine applications as it all starts with the reporting of reliable analytical data. Standards for fundamental validation parameters are provided in guidelines as those from the US Food and Drug Administration (FDA), the European Medicines Agency (EMA), the German speaking Gesellschaft fur Toxikologie und Forensische Chemie (GTFCH) and the Scientific Working Group of Forensic Toxicology (SWGTOX). These validation parameters include selectivity, matrix effects, method limits, calibration, accuracy and stability, as well as other parameters such as carryover, dilution integrity and incurred sample reanalysis. It is, however, not easy for laboratories to implement these guidelines into practice as these international guidelines remain nonbinding protocols, that depend on the applied analytical technique, and that need to be updated according the analyst's method requirements and the application type. In this manuscript, a review of the current guidelines and literature concerning bioanalytical validation parameters in a forensic context is given and discussed. In addition, suggestions for the experimental set-up, the pros and cons of statistical approaches and adequate acceptance criteria for the validation of bioanalytical applications are given. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

A Public-Private Partnership Develops and Externally Validates a 30-Day Hospital Readmission Risk Prediction Model

PubMed Central

Choudhry, Shahid A.; Li, Jing; Davis, Darcy; Erdmann, Cole; Sikka, Rishi; Sutariya, Bharat

2013-01-01

Introduction: Preventing the occurrence of hospital readmissions is needed to improve quality of care and foster population health across the care continuum. Hospitals are being held accountable for improving transitions of care to avert unnecessary readmissions. Advocate Health Care in Chicago and Cerner (ACC) collaborated to develop all-cause, 30-day hospital readmission risk prediction models to identify patients that need interventional resources. Ideally, prediction models should encompass several qualities: they should have high predictive ability; use reliable and clinically relevant data; use vigorous performance metrics to assess the models; be validated in populations where they are applied; and be scalable in heterogeneous populations. However, a systematic review of prediction models for hospital readmission risk determined that most performed poorly (average C-statistic of 0.66) and efforts to improve their performance are needed for widespread usage. Methods: The ACC team incorporated electronic health record data, utilized a mixed-method approach to evaluate risk factors, and externally validated their prediction models for generalizability. Inclusion and exclusion criteria were applied on the patient cohort and then split for derivation and internal validation. Stepwise logistic regression was performed to develop two predictive models: one for admission and one for discharge. The prediction models were assessed for discrimination ability, calibration, overall performance, and then externally validated. Results: The ACC Admission and Discharge Models demonstrated modest discrimination ability during derivation, internal and external validation post-recalibration (C-statistic of 0.76 and 0.78, respectively), and reasonable model fit during external validation for utility in heterogeneous populations. Conclusions: The ACC Admission and Discharge Models embody the design qualities of ideal prediction models. The ACC plans to continue its partnership to further improve and develop valuable clinical models. PMID:24224068

Imputation of missing data in time series for air pollutants

NASA Astrophysics Data System (ADS)

Junger, W. L.; Ponce de Leon, A.

2015-02-01

Missing data are major concerns in epidemiological studies of the health effects of environmental air pollutants. This article presents an imputation-based method that is suitable for multivariate time series data, which uses the EM algorithm under the assumption of normal distribution. Different approaches are considered for filtering the temporal component. A simulation study was performed to assess validity and performance of proposed method in comparison with some frequently used methods. Simulations showed that when the amount of missing data was as low as 5%, the complete data analysis yielded satisfactory results regardless of the generating mechanism of the missing data, whereas the validity began to degenerate when the proportion of missing values exceeded 10%. The proposed imputation method exhibited good accuracy and precision in different settings with respect to the patterns of missing observations. Most of the imputations obtained valid results, even under missing not at random. The methods proposed in this study are implemented as a package called mtsdi for the statistical software system R.

Statistical limitations in functional neuroimaging. I. Non-inferential methods and statistical models.

PubMed Central

Petersson, K M; Nichols, T E; Poline, J B; Holmes, A P

1999-01-01

Functional neuroimaging (FNI) provides experimental access to the intact living brain making it possible to study higher cognitive functions in humans. In this review and in a companion paper in this issue, we discuss some common methods used to analyse FNI data. The emphasis in both papers is on assumptions and limitations of the methods reviewed. There are several methods available to analyse FNI data indicating that none is optimal for all purposes. In order to make optimal use of the methods available it is important to know the limits of applicability. For the interpretation of FNI results it is also important to take into account the assumptions, approximations and inherent limitations of the methods used. This paper gives a brief overview over some non-inferential descriptive methods and common statistical models used in FNI. Issues relating to the complex problem of model selection are discussed. In general, proper model selection is a necessary prerequisite for the validity of the subsequent statistical inference. The non-inferential section describes methods that, combined with inspection of parameter estimates and other simple measures, can aid in the process of model selection and verification of assumptions. The section on statistical models covers approaches to global normalization and some aspects of univariate, multivariate, and Bayesian models. Finally, approaches to functional connectivity and effective connectivity are discussed. In the companion paper we review issues related to signal detection and statistical inference. PMID:10466149

Improving the Validity and Reliability of a Health Promotion Survey for Physical Therapists

PubMed Central

Stephens, Jaca L.; Lowman, John D.; Graham, Cecilia L.; Morris, David M.; Kohler, Connie L.; Waugh, Jonathan B.

2013-01-01

Purpose Physical therapists (PTs) have a unique opportunity to intervene in the area of health promotion. However, no instrument has been validated to measure PTs’ views on health promotion in physical therapy practice. The purpose of this study was to evaluate the content validity and test-retest reliability of a health promotion survey designed for PTs. Methods An expert panel of PTs assessed the content validity of “The Role of Health Promotion in Physical Therapy Survey” and provided suggestions for revision. Item content validity was assessed using the content validity ratio (CVR) as well as the modified kappa statistic. Therapists then participated in the test-retest reliability assessment of the revised health promotion survey, which was assessed using a weighted kappa statistic. Results Based on feedback from the expert panelists, significant revisions were made to the original survey. The expert panel reached at least a majority consensus agreement for all items in the revised survey and the survey-CVR improved from 0.44 to 0.66. Only one item on the revised survey had substantial test-retest agreement, with 55% of the items having moderate agreement and 43% poor agreement. Conclusions All items on the revised health promotion survey demonstrated at least fair validity, but few items had reasonable test-retest reliability. Further modifications should be made to strengthen the validity and improve the reliability of this survey. PMID:23754935

The construction and assessment of a statistical model for the prediction of protein assay data.

PubMed

Pittman, J; Sacks, J; Young, S Stanley

2002-01-01

The focus of this work is the development of a statistical model for a bioinformatics database whose distinctive structure makes model assessment an interesting and challenging problem. The key components of the statistical methodology, including a fast approximation to the singular value decomposition and the use of adaptive spline modeling and tree-based methods, are described, and preliminary results are presented. These results are shown to compare favorably to selected results achieved using comparitive methods. An attempt to determine the predictive ability of the model through the use of cross-validation experiments is discussed. In conclusion a synopsis of the results of these experiments and their implications for the analysis of bioinformatic databases in general is presented.

Ecological content validation of the Information Assessment Method for parents (IAM-parent): A mixed methods study.

PubMed

Bujold, M; El Sherif, R; Bush, P L; Johnson-Lafleur, J; Doray, G; Pluye, P

2018-02-01

This mixed methods study content validated the Information Assessment Method for parents (IAM-parent) that allows users to systematically rate and comment on online parenting information. Quantitative data and results: 22,407 IAM ratings were collected; of the initial 32 items, descriptive statistics showed that 10 had low relevance. Qualitative data and results: IAM-based comments were collected, and 20 IAM users were interviewed (maximum variation sample); the qualitative data analysis assessed the representativeness of IAM items, and identified items with problematic wording. Researchers, the program director, and Web editors integrated quantitative and qualitative results, which led to a shorter and clearer IAM-parent. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Fall Risk Assessment Through Automatic Combination of Clinical Fall Risk Factors and Body-Worn Sensor Data.

PubMed

Greene, Barry R; Redmond, Stephen J; Caulfield, Brian

2017-05-01

Falls are the leading global cause of accidental death and disability in older adults and are the most common cause of injury and hospitalization. Accurate, early identification of patients at risk of falling, could lead to timely intervention and a reduction in the incidence of fall-related injury and associated costs. We report a statistical method for fall risk assessment using standard clinical fall risk factors (N = 748). We also report a means of improving this method by automatically combining it, with a fall risk assessment algorithm based on inertial sensor data and the timed-up-and-go test. Furthermore, we provide validation data on the sensor-based fall risk assessment method using a statistically independent dataset. Results obtained using cross-validation on a sample of 292 community dwelling older adults suggest that a combined clinical and sensor-based approach yields a classification accuracy of 76.0%, compared to either 73.6% for sensor-based assessment alone, or 68.8% for clinical risk factors alone. Increasing the cohort size by adding an additional 130 subjects from a separate recruitment wave (N = 422), and applying the same model building and validation method, resulted in a decrease in classification performance (68.5% for combined classifier, 66.8% for sensor data alone, and 58.5% for clinical data alone). This suggests that heterogeneity between cohorts may be a major challenge when attempting to develop fall risk assessment algorithms which generalize well. Independent validation of the sensor-based fall risk assessment algorithm on an independent cohort of 22 community dwelling older adults yielded a classification accuracy of 72.7%. Results suggest that the present method compares well to previously reported sensor-based fall risk assessment methods in assessing falls risk. Implementation of objective fall risk assessment methods on a large scale has the potential to improve quality of care and lead to a reduction in associated hospital costs, due to fewer admissions and reduced injuries due to falling.

Unavoidable Pressure Ulcers: Development and Testing of the Indiana University Health Pressure Ulcer Prevention Inventory.

PubMed

Pittman, Joyce; Beeson, Terrie; Terry, Colin; Dillon, Jill; Hampton, Charity; Kerley, Denise; Mosier, Judith; Gumiela, Ellen; Tucker, Jessica

2016-01-01

Despite prevention strategies, hospital-acquired pressure ulcers (HAPUs) continue to occur in the acute care setting. The purpose of this study was to develop an operational definition of and an instrument for identifying avoidable/unavoidable HAPUs in the acute care setting. The Indiana University Health Pressure Ulcer Prevention Inventory (PUPI) was developed and psychometric testing was performed. A retrospective pilot study of 31 adult hospitalized patients with an HAPU was conducted using the PUPI. Overall content validity index of 0.99 and individual item content validity index scores (0.9-1.0) demonstrated excellent content validity. Acceptable PUPI criterion validity was demonstrated with no statistically significant differences between wound specialists' and other panel experts' scoring. Construct validity findings were acceptable with no statistically significant differences among avoidable or unavoidable HAPU patients and their Braden Scale total scores. Interrater reliability was acceptable with perfect agreement on the total PUPI score between raters (κ = 1.0; P = .025). Raters were in total agreement 93% (242/260) of the time on all 12 individual PUPI items. No risk factors were found to be significantly associated with unavoidable HAPUs. An operational definition of and an instrument for identifying avoidable/unavoidable HAPUs in the acute care setting were developed and tested. The instrument provides an objective and structured method for identifying avoidable/unavoidable HAPUs. The PUPI provides an additional method that could be used in root-cause analyses and when reporting adverse pressure ulcer events.

Simultaneous determination of rabeprazole sodium and itopride hydrochloride in capsule dosage form by spectrophotometry.

PubMed

Sabnis, Shweta S; Gandhi, Santosh V; Madgulkar, A R; Bothara, K G

Three methods viz. Absorbance Ratio Method (I), Dual Wavelength Method (II) and First Order Derivative Spectroscopic Method (III) for simultaneous estimation of Rabeprazole sodium and Itopride hydrochloride have been developed. The drugs obey Beer's law in the concentration range 2-20 microg/ml for RAB and 5-75 microg/ml for ITO. The results of analysis of drugs have been validated statistically and by recovery studies.

Measuring post-secondary stem majors' engagement in sustainability: The creation, assessment, and validation of an instrument for sustainability curricula evaluation

NASA Astrophysics Data System (ADS)

Little, David L., II

Ongoing changes in values, pedagogy, and curriculum concerning sustainability education necessitate that strong curricular elements are identified in sustainability education. However, quantitative research in sustainability education is largely undeveloped or relies on outdated instruments. In part, this is because no widespread quantitative instrument for measuring related educational outcomes has been developed for the field, though their development is pivotal for future efforts in sustainability education related to STEM majors. This research study details the creation, evaluation, and validation of an instrument -- the STEM Sustainability Engagement Instrument (STEMSEI) -- designed to measure sustainability engagement in post-secondary STEM majors. The study was conducted in three phases, using qualitative methods in phase 1, a concurrent mixed methods design in phase 2, and a sequential mixed methods design in phase 3. The STEMSEI was able to successfully predict statistically significant differences in the sample (n= 1017) that were predicted by prior research in environmental education. The STEMSEI also revealed statistically significant differences between STEM majors' sustainability engagement with a large effect size (.203 ≤ eta2 ≤ .211). As hypothesized, statistically significant differences were found on the environmental scales across gender and present religion. With respect to gender, self-perceived measures of emotional engagement with environmental sustainability was higher with females while males had higher measures in cognitive engagement with respect to knowing information related to environmental sustainability. With respect to present religion, self-perceived measures of general engagement and emotional engagement in environmental sustainability were higher for non-Christians as compared to Christians. On the economic scales, statistically significant differences were found across gender. Specifically, measures of males' self-perceived cognitive engagement in knowing information related to economic sustainability were greater than those of females. Future research should establish the generalizability of these results and further test the validity of the STEMSEI.

Improving qPCR telomere length assays: Controlling for well position effects increases statistical power.

PubMed

Eisenberg, Dan T A; Kuzawa, Christopher W; Hayes, M Geoffrey

2015-01-01

Telomere length (TL) is commonly measured using quantitative PCR (qPCR). Although, easier than the southern blot of terminal restriction fragments (TRF) TL measurement method, one drawback of qPCR is that it introduces greater measurement error and thus reduces the statistical power of analyses. To address a potential source of measurement error, we consider the effect of well position on qPCR TL measurements. qPCR TL data from 3,638 people run on a Bio-Rad iCycler iQ are reanalyzed here. To evaluate measurement validity, correspondence with TRF, age, and between mother and offspring are examined. First, we present evidence for systematic variation in qPCR TL measurements in relation to thermocycler well position. Controlling for these well-position effects consistently improves measurement validity and yields estimated improvements in statistical power equivalent to increasing sample sizes by 16%. We additionally evaluated the linearity of the relationships between telomere and single copy gene control amplicons and between qPCR and TRF measures. We find that, unlike some previous reports, our data exhibit linear relationships. We introduce the standard error in percent, a superior method for quantifying measurement error as compared to the commonly used coefficient of variation. Using this measure, we find that excluding samples with high measurement error does not improve measurement validity in our study. Future studies using block-based thermocyclers should consider well position effects. Since additional information can be gleaned from well position corrections, rerunning analyses of previous results with well position correction could serve as an independent test of the validity of these results. © 2015 Wiley Periodicals, Inc.

Statistically validated network of portfolio overlaps and systemic risk.

PubMed

Gualdi, Stanislao; Cimini, Giulio; Primicerio, Kevin; Di Clemente, Riccardo; Challet, Damien

2016-12-21

Common asset holding by financial institutions (portfolio overlap) is nowadays regarded as an important channel for financial contagion with the potential to trigger fire sales and severe losses at the systemic level. We propose a method to assess the statistical significance of the overlap between heterogeneously diversified portfolios, which we use to build a validated network of financial institutions where links indicate potential contagion channels. The method is implemented on a historical database of institutional holdings ranging from 1999 to the end of 2013, but can be applied to any bipartite network. We find that the proportion of validated links (i.e. of significant overlaps) increased steadily before the 2007-2008 financial crisis and reached a maximum when the crisis occurred. We argue that the nature of this measure implies that systemic risk from fire sales liquidation was maximal at that time. After a sharp drop in 2008, systemic risk resumed its growth in 2009, with a notable acceleration in 2013. We finally show that market trends tend to be amplified in the portfolios identified by the algorithm, such that it is possible to have an informative signal about institutions that are about to suffer (enjoy) the most significant losses (gains).

Statistically validated network of portfolio overlaps and systemic risk

PubMed Central

Gualdi, Stanislao; Cimini, Giulio; Primicerio, Kevin; Di Clemente, Riccardo; Challet, Damien

2016-01-01

Common asset holding by financial institutions (portfolio overlap) is nowadays regarded as an important channel for financial contagion with the potential to trigger fire sales and severe losses at the systemic level. We propose a method to assess the statistical significance of the overlap between heterogeneously diversified portfolios, which we use to build a validated network of financial institutions where links indicate potential contagion channels. The method is implemented on a historical database of institutional holdings ranging from 1999 to the end of 2013, but can be applied to any bipartite network. We find that the proportion of validated links (i.e. of significant overlaps) increased steadily before the 2007–2008 financial crisis and reached a maximum when the crisis occurred. We argue that the nature of this measure implies that systemic risk from fire sales liquidation was maximal at that time. After a sharp drop in 2008, systemic risk resumed its growth in 2009, with a notable acceleration in 2013. We finally show that market trends tend to be amplified in the portfolios identified by the algorithm, such that it is possible to have an informative signal about institutions that are about to suffer (enjoy) the most significant losses (gains). PMID:28000764

A Confirmatory Factor Analysis of the Structure of Statistics Anxiety Measure: An examination of four alternative models

PubMed Central

Vahedi, Shahram; Farrokhi, Farahman

2011-01-01

Objective The aim of this study is to explore the confirmatory factor analysis results of the Persian adaptation of Statistics Anxiety Measure (SAM), proposed by Earp. Method The validity and reliability assessments of the scale were performed on 298 college students chosen randomly from Tabriz University in Iran. Confirmatory factor analysis (CFA) was carried out to determine the factor structures of the Persian adaptation of SAM. Results As expected, the second order model provided a better fit to the data than the three alternative models. Conclusions Hence, SAM provides an equally valid measure for use among college students. The study both expands and adds support to the existing body of math anxiety literature. PMID:22952530

A robust bayesian estimate of the concordance correlation coefficient.

PubMed

Feng, Dai; Baumgartner, Richard; Svetnik, Vladimir

2015-01-01

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

Evaluating the quality of a cell counting measurement process via a dilution series experimental design.

PubMed

Sarkar, Sumona; Lund, Steven P; Vyzasatya, Ravi; Vanguri, Padmavathy; Elliott, John T; Plant, Anne L; Lin-Gibson, Sheng

2017-12-01

Cell counting measurements are critical in the research, development and manufacturing of cell-based products, yet determining cell quantity with accuracy and precision remains a challenge. Validating and evaluating a cell counting measurement process can be difficult because of the lack of appropriate reference material. Here we describe an experimental design and statistical analysis approach to evaluate the quality of a cell counting measurement process in the absence of appropriate reference materials or reference methods. The experimental design is based on a dilution series study with replicate samples and observations as well as measurement process controls. The statistical analysis evaluates the precision and proportionality of the cell counting measurement process and can be used to compare the quality of two or more counting methods. As an illustration of this approach, cell counting measurement processes (automated and manual methods) were compared for a human mesenchymal stromal cell (hMSC) preparation. For the hMSC preparation investigated, results indicated that the automated method performed better than the manual counting methods in terms of precision and proportionality. By conducting well controlled dilution series experimental designs coupled with appropriate statistical analysis, quantitative indicators of repeatability and proportionality can be calculated to provide an assessment of cell counting measurement quality. This approach does not rely on the use of a reference material or comparison to "gold standard" methods known to have limited assurance of accuracy and precision. The approach presented here may help the selection, optimization, and/or validation of a cell counting measurement process. Published by Elsevier Inc.

Assessment of dietary sodium intake using a food frequency questionnaire and 24-hour urinary sodium excretion: a systematic literature review.

PubMed

McLean, Rachael M; Farmer, Victoria L; Nettleton, Alice; Cameron, Claire M; Cook, Nancy R; Campbell, Norman R C

2017-12-01

Food frequency questionnaires (FFQs) are often used to assess dietary sodium intake, although 24-hour urinary excretion is the most accurate measure of intake. The authors conducted a systematic review to investigate whether FFQs are a reliable and valid way of measuring usual dietary sodium intake. Results from 18 studies are described in this review, including 16 validation studies. The methods of study design and analysis varied widely with respect to FFQ instrument, number of 24-hour urine collections collected per participant, methods used to assess completeness of urine collections, and statistical analysis. Overall, there was poor agreement between estimates from FFQ and 24-hour urine. The authors suggest a framework for validation and reporting based on a consensus statement (2004), and recommend that all FFQs used to estimate dietary sodium intake undergo validation against multiple 24-hour urine collections. ©2017 Wiley Periodicals, Inc.

External validation of ADO, DOSE, COTE and CODEX at predicting death in primary care patients with COPD using standard and machine learning approaches.

PubMed

Morales, Daniel R; Flynn, Rob; Zhang, Jianguo; Trucco, Emmanuel; Quint, Jennifer K; Zutis, Kris

2018-05-01

Several models for predicting the risk of death in people with chronic obstructive pulmonary disease (COPD) exist but have not undergone large scale validation in primary care. The objective of this study was to externally validate these models using statistical and machine learning approaches. We used a primary care COPD cohort identified using data from the UK Clinical Practice Research Datalink. Age-standardised mortality rates were calculated for the population by gender and discrimination of ADO (age, dyspnoea, airflow obstruction), COTE (COPD-specific comorbidity test), DOSE (dyspnoea, airflow obstruction, smoking, exacerbations) and CODEX (comorbidity, dyspnoea, airflow obstruction, exacerbations) at predicting death over 1-3 years measured using logistic regression and a support vector machine learning (SVM) method of analysis. The age-standardised mortality rate was 32.8 (95%CI 32.5-33.1) and 25.2 (95%CI 25.4-25.7) per 1000 person years for men and women respectively. Complete data were available for 54879 patients to predict 1-year mortality. ADO performed the best (c-statistic of 0.730) compared with DOSE (c-statistic 0.645), COTE (c-statistic 0.655) and CODEX (c-statistic 0.649) at predicting 1-year mortality. Discrimination of ADO and DOSE improved at predicting 1-year mortality when combined with COTE comorbidities (c-statistic 0.780 ADO + COTE; c-statistic 0.727 DOSE + COTE). Discrimination did not change significantly over 1-3 years. Comparable results were observed using SVM. In primary care, ADO appears superior at predicting death in COPD. Performance of ADO and DOSE improved when combined with COTE comorbidities suggesting better models may be generated with additional data facilitated using novel approaches. Copyright © 2018. Published by Elsevier Ltd.

Reliability and validity of a nutrition and physical activity environmental self-assessment for child care

PubMed Central

Benjamin, Sara E; Neelon, Brian; Ball, Sarah C; Bangdiwala, Shrikant I; Ammerman, Alice S; Ward, Dianne S

2007-01-01

Background Few assessment instruments have examined the nutrition and physical activity environments in child care, and none are self-administered. Given the emerging focus on child care settings as a target for intervention, a valid and reliable measure of the nutrition and physical activity environment is needed. Methods To measure inter-rater reliability, 59 child care center directors and 109 staff completed the self-assessment concurrently, but independently. Three weeks later, a repeat self-assessment was completed by a sub-sample of 38 directors to assess test-retest reliability. To assess criterion validity, a researcher-administered environmental assessment was conducted at 69 centers and was compared to a self-assessment completed by the director. A weighted kappa test statistic and percent agreement were calculated to assess agreement for each question on the self-assessment. Results For inter-rater reliability, kappa statistics ranged from 0.20 to 1.00 across all questions. Test-retest reliability of the self-assessment yielded kappa statistics that ranged from 0.07 to 1.00. The inter-quartile kappa statistic ranges for inter-rater and test-retest reliability were 0.45 to 0.63 and 0.27 to 0.45, respectively. When percent agreement was calculated, questions ranged from 52.6% to 100% for inter-rater reliability and 34.3% to 100% for test-retest reliability. Kappa statistics for validity ranged from -0.01 to 0.79, with an inter-quartile range of 0.08 to 0.34. Percent agreement for validity ranged from 12.9% to 93.7%. Conclusion This study provides estimates of criterion validity, inter-rater reliability and test-retest reliability for an environmental nutrition and physical activity self-assessment instrument for child care. Results indicate that the self-assessment is a stable and reasonably accurate instrument for use with child care interventions. We therefore recommend the Nutrition and Physical Activity Self-Assessment for Child Care (NAP SACC) instrument to researchers and practitioners interested in conducting healthy weight intervention in child care. However, a more robust, less subjective measure would be more appropriate for researchers seeking an outcome measure to assess intervention impact. PMID:17615078

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

PubMed

Ceppi, Marcello; Gallo, Fabio; Bonassi, Stefano

2011-01-01

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

Open-access programs for injury categorization using ICD-9 or ICD-10.

PubMed

Clark, David E; Black, Adam W; Skavdahl, David H; Hallagan, Lee D

2018-04-09

The article introduces Programs for Injury Categorization, using the International Classification of Diseases (ICD) and R statistical software (ICDPIC-R). Starting with ICD-8, methods have been described to map injury diagnosis codes to severity scores, especially the Abbreviated Injury Scale (AIS) and Injury Severity Score (ISS). ICDPIC was originally developed for this purpose using Stata, and ICDPIC-R is an open-access update that accepts both ICD-9 and ICD-10 codes. Data were obtained from the National Trauma Data Bank (NTDB), Admission Year 2015. ICDPIC-R derives CDC injury mechanism categories and an approximate ISS ("RISS") from either ICD-9 or ICD-10 codes. For ICD-9-coded cases, RISS is derived similar to the Stata package (with some improvements reflecting user feedback). For ICD-10-coded cases, RISS may be calculated in several ways: The "GEM" methods convert ICD-10 to ICD-9 (using General Equivalence Mapping tables from CMS) and then calculate ISS with options similar to the Stata package; a "ROCmax" method calculates RISS directly from ICD-10 codes, based on diagnosis-specific mortality in the NTDB, maximizing the C-statistic for predicting NTDB mortality while attempting to minimize the difference between RISS and ISS submitted by NTDB registrars (ISSAIS). Findings were validated using data from the National Inpatient Survey (NIS, 2015). NTDB contained 917,865 cases, of which 86,878 had valid ICD-10 injury codes. For a random 100,000 ICD-9-coded cases in NTDB, RISS using the GEM methods was nearly identical to ISS calculated by the Stata version, which has been previously validated. For ICD-10-coded cases in NTDB, categorized ISS using any version of RISS was similar to ISSAIS; for both NTDB and NIS cases, increasing ISS was associated with increasing mortality. Prediction of NTDB mortality was associated with C-statistics of 0.81 for ISSAIS, 0.75 for RISS using the GEM methods, and 0.85 for RISS using the ROCmax method; prediction of NIS mortality was associated with C-statistics of 0.75-0.76 for RISS using the GEM methods, and 0.78 for RISS using the ROCmax method. Instructions are provided for accessing ICDPIC-R at no cost. The ideal methods of injury categorization and injury severity scoring involve trained personnel with access to injured persons or their medical records. ICDPIC-R may be a useful substitute when this ideal cannot be obtained.

MIMoSA: An Automated Method for Intermodal Segmentation Analysis of Multiple Sclerosis Brain Lesions.

PubMed

Valcarcel, Alessandra M; Linn, Kristin A; Vandekar, Simon N; Satterthwaite, Theodore D; Muschelli, John; Calabresi, Peter A; Pham, Dzung L; Martin, Melissa Lynne; Shinohara, Russell T

2018-03-08

Magnetic resonance imaging (MRI) is crucial for in vivo detection and characterization of white matter lesions (WMLs) in multiple sclerosis. While WMLs have been studied for over two decades using MRI, automated segmentation remains challenging. Although the majority of statistical techniques for the automated segmentation of WMLs are based on single imaging modalities, recent advances have used multimodal techniques for identifying WMLs. Complementary modalities emphasize different tissue properties, which help identify interrelated features of lesions. Method for Inter-Modal Segmentation Analysis (MIMoSA), a fully automatic lesion segmentation algorithm that utilizes novel covariance features from intermodal coupling regression in addition to mean structure to model the probability lesion is contained in each voxel, is proposed. MIMoSA was validated by comparison with both expert manual and other automated segmentation methods in two datasets. The first included 98 subjects imaged at Johns Hopkins Hospital in which bootstrap cross-validation was used to compare the performance of MIMoSA against OASIS and LesionTOADS, two popular automatic segmentation approaches. For a secondary validation, a publicly available data from a segmentation challenge were used for performance benchmarking. In the Johns Hopkins study, MIMoSA yielded average Sørensen-Dice coefficient (DSC) of .57 and partial AUC of .68 calculated with false positive rates up to 1%. This was superior to performance using OASIS and LesionTOADS. The proposed method also performed competitively in the segmentation challenge dataset. MIMoSA resulted in statistically significant improvements in lesion segmentation performance compared with LesionTOADS and OASIS, and performed competitively in an additional validation study. Copyright © 2018 by the American Society of Neuroimaging.

A Possible Tool for Checking Errors in the INAA Results, Based on Neutron Data and Method Validation

NASA Astrophysics Data System (ADS)

Cincu, Em.; Grigore, Ioana Manea; Barbos, D.; Cazan, I. L.; Manu, V.

2008-08-01

This work presents preliminary results of a new type of possible application in the INAA experiments of elemental analysis, useful to check errors occurred during investigation of unknown samples; it relies on the INAA method validation experiments and accuracy of the neutron data from the literature. The paper comprises 2 sections, the first one presents—in short—the steps of the experimental tests carried out for INAA method validation and for establishing the `ACTIVA-N' laboratory performance, which is-at the same time-an illustration of the laboratory evolution on the way to get performance. Section 2 presents our recent INAA results on CRMs, of which interpretation opens discussions about the usefulness of using a tool for checking possible errors, different from the usual statistical procedures. The questionable aspects and the requirements to develop a practical checking tool are discussed.

Predictive validity of the UK clinical aptitude test in the final years of medical school: a prospective cohort study.

PubMed

Husbands, Adrian; Mathieson, Alistair; Dowell, Jonathan; Cleland, Jennifer; MacKenzie, Rhoda

2014-04-23

The UK Clinical Aptitude Test (UKCAT) was designed to address issues identified with traditional methods of selection. This study aims to examine the predictive validity of the UKCAT and compare this to traditional selection methods in the senior years of medical school. This was a follow-up study of two cohorts of students from two medical schools who had previously taken part in a study examining the predictive validity of the UKCAT in first year. The sample consisted of 4th and 5th Year students who commenced their studies at the University of Aberdeen or University of Dundee medical schools in 2007. Data collected were: demographics (gender and age group), UKCAT scores; Universities and Colleges Admissions Service (UCAS) form scores; admission interview scores; Year 4 and 5 degree examination scores. Pearson's correlations were used to examine the relationships between admissions variables, examination scores, gender and age group, and to select variables for multiple linear regression analysis to predict examination scores. Ninety-nine and 89 students at Aberdeen medical school from Years 4 and 5 respectively, and 51 Year 4 students in Dundee, were included in the analysis. Neither UCAS form nor interview scores were statistically significant predictors of examination performance. Conversely, the UKCAT yielded statistically significant validity coefficients between .24 and .36 in four of five assessments investigated. Multiple regression analysis showed the UKCAT made a statistically significant unique contribution to variance in examination performance in the senior years. Results suggest the UKCAT appears to predict performance better in the later years of medical school compared to earlier years and provides modest supportive evidence for the UKCAT's role in student selection within these institutions. Further research is needed to assess the predictive validity of the UKCAT against professional and behavioural outcomes as the cohort commences working life.

Evidence-based dentistry: analysis of dental anxiety scales for children.

PubMed

Al-Namankany, A; de Souza, M; Ashley, P

2012-03-09

To review paediatric dental anxiety measures (DAMs) and assess the statistical methods used for validation and their clinical implications. A search of four computerised databases between 1960 and January 2011 associated with DAMs, using pre-specified search terms, to assess the method of validation including the reliability as intra-observer agreement 'repeatability or stability' and inter-observer agreement 'reproducibility' and all types of validity. Fourteen paediatric DAMs were predominantly validated in schools and not in the clinical setting while five of the DAMs were not validated at all. The DAMs that were validated were done so against other paediatric DAMs which may not have been validated previously. Reliability was not assessed in four of the DAMs. However, all of the validated studies assessed reliability which was usually 'good' or 'acceptable'. None of the current DAMs used a formal sample size technique. Diversity was seen between the studies ranging from a few simple pictograms to lists of questions reported by either the individual or an observer. To date there is no scale that can be considered as a gold standard, and there is a need to further develop an anxiety scale with a cognitive component for children and adolescents.

Epidemiological cut-off values for Flavobacterium psychrophilum MIC data generated by a standard test protocol.

PubMed

Smith, P; Endris, R; Kronvall, G; Thomas, V; Verner-Jeffreys, D; Wilhelm, C; Dalsgaard, I

2016-02-01

Epidemiological cut-off values were developed for application to antibiotic susceptibility data for Flavobacterium psychrophilum generated by standard CLSI test protocols. The MIC values for ten antibiotic agents against Flavobacterium psychrophilum were determined in two laboratories. For five antibiotics, the data sets were of sufficient quality and quantity to allow the setting of valid epidemiological cut-off values. For these agents, the cut-off values, calculated by the application of the statistically based normalized resistance interpretation method, were ≤16 mg L(-1) for erythromycin, ≤2 mg L(-1) for florfenicol, ≤0.025 mg L(-1) for oxolinic acid (OXO), ≤0.125 mg L(-1) for oxytetracycline and ≤20 (1/19) mg L(-1) for trimethoprim/sulphamethoxazole. For ampicillin and amoxicillin, the majority of putative wild-type observations were 'off scale', and therefore, statistically valid cut-off values could not be calculated. For ormetoprim/sulphadimethoxine, the data were excessively diverse and a valid cut-off could not be determined. For flumequine, the putative wild-type data were extremely skewed, and for enrofloxacin, there was inadequate separation in the MIC values for putative wild-type and non-wild-type strains. It is argued that the adoption of OXO as a class representative for the quinolone group would be a valid method of determining susceptibilities to these agents. © 2014 John Wiley & Sons Ltd.

Primary Sclerosing Cholangitis Risk Estimate Tool (PREsTo) Predicts Outcomes in PSC: A Derivation & Validation Study Using Machine Learning.

PubMed

Eaton, John E; Vesterhus, Mette; McCauley, Bryan M; Atkinson, Elizabeth J; Schlicht, Erik M; Juran, Brian D; Gossard, Andrea A; LaRusso, Nicholas F; Gores, Gregory J; Karlsen, Tom H; Lazaridis, Konstantinos N

2018-05-09

Improved methods are needed to risk stratify and predict outcomes in patients with primary sclerosing cholangitis (PSC). Therefore, we sought to derive and validate a new prediction model and compare its performance to existing surrogate markers. The model was derived using 509 subjects from a multicenter North American cohort and validated in an international multicenter cohort (n=278). Gradient boosting, a machine based learning technique, was used to create the model. The endpoint was hepatic decompensation (ascites, variceal hemorrhage or encephalopathy). Subjects with advanced PSC or cholangiocarcinoma at baseline were excluded. The PSC risk estimate tool (PREsTo) consists of 9 variables: bilirubin, albumin, serum alkaline phosphatase (SAP) times the upper limit of normal (ULN), platelets, AST, hemoglobin, sodium, patient age and the number of years since PSC was diagnosed. Validation in an independent cohort confirms PREsTo accurately predicts decompensation (C statistic 0.90, 95% confidence interval (CI) 0.84-0.95) and performed well compared to MELD score (C statistic 0.72, 95% CI 0.57-0.84), Mayo PSC risk score (C statistic 0.85, 95% CI 0.77-0.92) and SAP < 1.5x ULN (C statistic 0.65, 95% CI 0.55-0.73). PREsTo continued to be accurate among individuals with a bilirubin < 2.0 mg/dL (C statistic 0.90, 95% CI 0.82-0.96) and when the score was re-applied at a later course in the disease (C statistic 0.82, 95% CI 0.64-0.95). PREsTo accurately predicts hepatic decompensation in PSC and exceeds the performance among other widely available, noninvasive prognostic scoring systems. This article is protected by copyright. All rights reserved. © 2018 by the American Association for the Study of Liver Diseases.

History and Development of the Schmidt-Hunter Meta-Analysis Methods

ERIC Educational Resources Information Center

Schmidt, Frank L.

2015-01-01

In this article, I provide answers to the questions posed by Will Shadish about the history and development of the Schmidt-Hunter methods of meta-analysis. In the 1970s, I headed a research program on personnel selection at the US Office of Personnel Management (OPM). After our research showed that validity studies have low statistical power, OPM…

A statistically valid method for using FIA plots to guide spectral class rejection in producing stratification maps

Treesearch

Michael L. Hoppus; Andrew J. Lister

2002-01-01

A Landsat TM classification method (iterative guided spectral class rejection) produced a forest cover map of southern West Virginia that provided the stratification layer for producing estimates of timberland area from Forest Service FIA ground plots using a stratified sampling technique. These same high quality and expensive FIA ground plots provided ground reference...

[Respondent-Driven Sampling: a new sampling method to study visible and hidden populations].

PubMed

Mantecón, Alejandro; Juan, Montse; Calafat, Amador; Becoña, Elisardo; Román, Encarna

2008-01-01

The paper introduces a variant of chain-referral sampling: respondent-driven sampling (RDS). This sampling method shows that methods based on network analysis can be combined with the statistical validity of standard probability sampling methods. In this sense, RDS appears to be a mathematical improvement of snowball sampling oriented to the study of hidden populations. However, we try to prove its validity with populations that are not within a sampling frame but can nonetheless be contacted without difficulty. The basics of RDS are explained through our research on young people (aged 14 to 25) who go clubbing, consume alcohol and other drugs, and have sex. Fieldwork was carried out between May and July 2007 in three Spanish regions: Baleares, Galicia and Comunidad Valenciana. The presentation of the study shows the utility of this type of sampling when the population is accessible but there is a difficulty deriving from the lack of a sampling frame. However, the sample obtained is not a random representative one in statistical terms of the target population. It must be acknowledged that the final sample is representative of a 'pseudo-population' that approximates to the target population but is not identical to it.

Development and validation of multivariate calibration methods for simultaneous estimation of Paracetamol, Enalapril maleate and hydrochlorothiazide in pharmaceutical dosage form

NASA Astrophysics Data System (ADS)

Singh, Veena D.; Daharwal, Sanjay J.

2017-01-01

Three multivariate calibration spectrophotometric methods were developed for simultaneous estimation of Paracetamol (PARA), Enalapril maleate (ENM) and Hydrochlorothiazide (HCTZ) in tablet dosage form; namely multi-linear regression calibration (MLRC), trilinear regression calibration method (TLRC) and classical least square (CLS) method. The selectivity of the proposed methods were studied by analyzing the laboratory prepared ternary mixture and successfully applied in their combined dosage form. The proposed methods were validated as per ICH guidelines and good accuracy; precision and specificity were confirmed within the concentration range of 5-35 μg mL- 1, 5-40 μg mL- 1 and 5-40 μg mL- 1of PARA, HCTZ and ENM, respectively. The results were statistically compared with reported HPLC method. Thus, the proposed methods can be effectively useful for the routine quality control analysis of these drugs in commercial tablet dosage form.

A wind proxy based on migrating dunes at the Baltic coast: statistical analysis of the link between wind conditions and sand movement

NASA Astrophysics Data System (ADS)

Bierstedt, Svenja E.; Hünicke, Birgit; Zorita, Eduardo; Ludwig, Juliane

2017-07-01

We statistically analyse the relationship between the structure of migrating dunes in the southern Baltic and the driving wind conditions over the past 26 years, with the long-term aim of using migrating dunes as a proxy for past wind conditions at an interannual resolution. The present analysis is based on the dune record derived from geo-radar measurements by Ludwig et al. (2017). The dune system is located at the Baltic Sea coast of Poland and is migrating from west to east along the coast. The dunes present layers with different thicknesses that can be assigned to absolute dates at interannual timescales and put in relation to seasonal wind conditions. To statistically analyse this record and calibrate it as a wind proxy, we used a gridded regional meteorological reanalysis data set (coastDat2) covering recent decades. The identified link between the dune annual layers and wind conditions was additionally supported by the co-variability between dune layers and observed sea level variations in the southern Baltic Sea. We include precipitation and temperature into our analysis, in addition to wind, to learn more about the dependency between these three atmospheric factors and their common influence on the dune system. We set up a statistical linear model based on the correlation between the frequency of days with specific wind conditions in a given season and dune migration velocities derived for that season. To some extent, the dune records can be seen as analogous to tree-ring width records, and hence we use a proxy validation method usually applied in dendrochronology, cross-validation with the leave-one-out method, when the observational record is short. The revealed correlations between the wind record from the reanalysis and the wind record derived from the dune structure is in the range between 0.28 and 0.63, yielding similar statistical validation skill as dendroclimatological records.

An entropy-based statistic for genomewide association studies.

PubMed

Zhao, Jinying; Boerwinkle, Eric; Xiong, Momiao

2005-07-01

Efficient genotyping methods and the availability of a large collection of single-nucleotide polymorphisms provide valuable tools for genetic studies of human disease. The standard chi2 statistic for case-control studies, which uses a linear function of allele frequencies, has limited power when the number of marker loci is large. We introduce a novel test statistic for genetic association studies that uses Shannon entropy and a nonlinear function of allele frequencies to amplify the differences in allele and haplotype frequencies to maintain statistical power with large numbers of marker loci. We investigate the relationship between the entropy-based test statistic and the standard chi2 statistic and show that, in most cases, the power of the entropy-based statistic is greater than that of the standard chi2 statistic. The distribution of the entropy-based statistic and the type I error rates are validated using simulation studies. Finally, we apply the new entropy-based test statistic to two real data sets, one for the COMT gene and schizophrenia and one for the MMP-2 gene and esophageal carcinoma, to evaluate the performance of the new method for genetic association studies. The results show that the entropy-based statistic obtained smaller P values than did the standard chi2 statistic.

Gene network inherent in genomic big data improves the accuracy of prognostic prediction for cancer patients.

PubMed

Kim, Yun Hak; Jeong, Dae Cheon; Pak, Kyoungjune; Goh, Tae Sik; Lee, Chi-Seung; Han, Myoung-Eun; Kim, Ji-Young; Liangwen, Liu; Kim, Chi Dae; Jang, Jeon Yeob; Cha, Wonjae; Oh, Sae-Ock

2017-09-29

Accurate prediction of prognosis is critical for therapeutic decisions regarding cancer patients. Many previously developed prognostic scoring systems have limitations in reflecting recent progress in the field of cancer biology such as microarray, next-generation sequencing, and signaling pathways. To develop a new prognostic scoring system for cancer patients, we used mRNA expression and clinical data in various independent breast cancer cohorts (n=1214) from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and Gene Expression Omnibus (GEO). A new prognostic score that reflects gene network inherent in genomic big data was calculated using Network-Regularized high-dimensional Cox-regression (Net-score). We compared its discriminatory power with those of two previously used statistical methods: stepwise variable selection via univariate Cox regression (Uni-score) and Cox regression via Elastic net (Enet-score). The Net scoring system showed better discriminatory power in prediction of disease-specific survival (DSS) than other statistical methods (p=0 in METABRIC training cohort, p=0.000331, 4.58e-06 in two METABRIC validation cohorts) when accuracy was examined by log-rank test. Notably, comparison of C-index and AUC values in receiver operating characteristic analysis at 5 years showed fewer differences between training and validation cohorts with the Net scoring system than other statistical methods, suggesting minimal overfitting. The Net-based scoring system also successfully predicted prognosis in various independent GEO cohorts with high discriminatory power. In conclusion, the Net-based scoring system showed better discriminative power than previous statistical methods in prognostic prediction for breast cancer patients. This new system will mark a new era in prognosis prediction for cancer patients.

Gene network inherent in genomic big data improves the accuracy of prognostic prediction for cancer patients

PubMed Central

Kim, Yun Hak; Jeong, Dae Cheon; Pak, Kyoungjune; Goh, Tae Sik; Lee, Chi-Seung; Han, Myoung-Eun; Kim, Ji-Young; Liangwen, Liu; Kim, Chi Dae; Jang, Jeon Yeob; Cha, Wonjae; Oh, Sae-Ock

2017-01-01

Accurate prediction of prognosis is critical for therapeutic decisions regarding cancer patients. Many previously developed prognostic scoring systems have limitations in reflecting recent progress in the field of cancer biology such as microarray, next-generation sequencing, and signaling pathways. To develop a new prognostic scoring system for cancer patients, we used mRNA expression and clinical data in various independent breast cancer cohorts (n=1214) from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and Gene Expression Omnibus (GEO). A new prognostic score that reflects gene network inherent in genomic big data was calculated using Network-Regularized high-dimensional Cox-regression (Net-score). We compared its discriminatory power with those of two previously used statistical methods: stepwise variable selection via univariate Cox regression (Uni-score) and Cox regression via Elastic net (Enet-score). The Net scoring system showed better discriminatory power in prediction of disease-specific survival (DSS) than other statistical methods (p=0 in METABRIC training cohort, p=0.000331, 4.58e-06 in two METABRIC validation cohorts) when accuracy was examined by log-rank test. Notably, comparison of C-index and AUC values in receiver operating characteristic analysis at 5 years showed fewer differences between training and validation cohorts with the Net scoring system than other statistical methods, suggesting minimal overfitting. The Net-based scoring system also successfully predicted prognosis in various independent GEO cohorts with high discriminatory power. In conclusion, the Net-based scoring system showed better discriminative power than previous statistical methods in prognostic prediction for breast cancer patients. This new system will mark a new era in prognosis prediction for cancer patients. PMID:29100405

Influence of process parameters on content uniformity of a low dose active pharmaceutical ingredient in a tablet formulation according to GMP.

PubMed

Muselík, Jan; Franc, Aleš; Doležel, Petr; Goněc, Roman; Krondlová, Anna; Lukášová, Ivana

2014-09-01

The article describes the development and production of tablets using direct compression of powder mixtures. The aim was to describe the impact of filler particle size and the time of lubricant addition during mixing on content uniformity according to the Good Manufacturing Practice (GMP) process validation requirements. Processes are regulated by complex directives, forcing the producers to validate, using sophisticated methods, the content uniformity of intermediates as well as final products. Cutting down of production time and material, shortening of analyses, and fast and reliable statistic evaluation of results can reduce the final price without affecting product quality. The manufacturing process of directly compressed tablets containing the low dose active pharmaceutical ingredient (API) warfarin, with content uniformity passing validation criteria, is used as a model example. Statistic methods have proved that the manufacturing process is reproducible. Methods suitable for elucidation of various properties of the final blend, e.g., measurement of electrostatic charge by Faraday pail and evaluation of mutual influences of researched variables by partial least square (PLS) regression, were used. Using these methods, it was proved that the filler with higher particle size increased the content uniformity of both blends and the ensuing tablets. Addition of the lubricant, magnesium stearate, during the blending process improved the content uniformity of blends containing the filler with larger particles. This seems to be caused by reduced sampling error due to the suppression of electrostatic charge.

Improved statistical method for temperature and salinity quality control

NASA Astrophysics Data System (ADS)

Gourrion, Jérôme; Szekely, Tanguy

2017-04-01

Climate research and Ocean monitoring benefit from the continuous development of global in-situ hydrographic networks in the last decades. Apart from the increasing volume of observations available on a large range of temporal and spatial scales, a critical aspect concerns the ability to constantly improve the quality of the datasets. In the context of the Coriolis Dataset for ReAnalysis (CORA) version 4.2, a new quality control method based on a local comparison to historical extreme values ever observed is developed, implemented and validated. Temperature, salinity and potential density validity intervals are directly estimated from minimum and maximum values from an historical reference dataset, rather than from traditional mean and standard deviation estimates. Such an approach avoids strong statistical assumptions on the data distributions such as unimodality, absence of skewness and spatially homogeneous kurtosis. As a new feature, it also allows addressing simultaneously the two main objectives of an automatic quality control strategy, i.e. maximizing the number of good detections while minimizing the number of false alarms. The reference dataset is presently built from the fusion of 1) all ARGO profiles up to late 2015, 2) 3 historical CTD datasets and 3) the Sea Mammals CTD profiles from the MEOP database. All datasets are extensively and manually quality controlled. In this communication, the latest method validation results are also presented. The method has already been implemented in the latest version of the delayed-time CMEMS in-situ dataset and will be deployed soon in the equivalent near-real time products.

Improved Statistical Method For Hydrographic Climatic Records Quality Control

NASA Astrophysics Data System (ADS)

Gourrion, J.; Szekely, T.

2016-02-01

Climate research benefits from the continuous development of global in-situ hydrographic networks in the last decades. Apart from the increasing volume of observations available on a large range of temporal and spatial scales, a critical aspect concerns the ability to constantly improve the quality of the datasets. In the context of the Coriolis Dataset for ReAnalysis (CORA) version 4.2, a new quality control method based on a local comparison to historical extreme values ever observed is developed, implemented and validated. Temperature, salinity and potential density validity intervals are directly estimated from minimum and maximum values from an historical reference dataset, rather than from traditional mean and standard deviation estimates. Such an approach avoids strong statistical assumptions on the data distributions such as unimodality, absence of skewness and spatially homogeneous kurtosis. As a new feature, it also allows addressing simultaneously the two main objectives of a quality control strategy, i.e. maximizing the number of good detections while minimizing the number of false alarms. The reference dataset is presently built from the fusion of 1) all ARGO profiles up to early 2014, 2) 3 historical CTD datasets and 3) the Sea Mammals CTD profiles from the MEOP database. All datasets are extensively and manually quality controlled. In this communication, the latest method validation results are also presented. The method has been implemented in the latest version of the CORA dataset and will benefit to the next version of the Copernicus CMEMS dataset.

Classical Statistics and Statistical Learning in Imaging Neuroscience

PubMed Central

Bzdok, Danilo

2017-01-01

Brain-imaging research has predominantly generated insight by means of classical statistics, including regression-type analyses and null-hypothesis testing using t-test and ANOVA. Throughout recent years, statistical learning methods enjoy increasing popularity especially for applications in rich and complex data, including cross-validated out-of-sample prediction using pattern classification and sparsity-inducing regression. This concept paper discusses the implications of inferential justifications and algorithmic methodologies in common data analysis scenarios in neuroimaging. It is retraced how classical statistics and statistical learning originated from different historical contexts, build on different theoretical foundations, make different assumptions, and evaluate different outcome metrics to permit differently nuanced conclusions. The present considerations should help reduce current confusion between model-driven classical hypothesis testing and data-driven learning algorithms for investigating the brain with imaging techniques. PMID:29056896

Simulation of spatially evolving turbulence and the applicability of Taylor's hypothesis in compressible flow

NASA Technical Reports Server (NTRS)

Lee, Sangsan; Lele, Sanjiva K.; Moin, Parviz

1992-01-01

For the numerical simulation of inhomogeneous turbulent flows, a method is developed for generating stochastic inflow boundary conditions with a prescribed power spectrum. Turbulence statistics from spatial simulations using this method with a low fluctuation Mach number are in excellent agreement with the experimental data, which validates the procedure. Turbulence statistics from spatial simulations are also compared to those from temporal simulations using Taylor's hypothesis. Statistics such as turbulence intensity, vorticity, and velocity derivative skewness compare favorably with the temporal simulation. However, the statistics of dilatation show a significant departure from those obtained in the temporal simulation. To directly check the applicability of Taylor's hypothesis, space-time correlations of fluctuations in velocity, vorticity, and dilatation are investigated. Convection velocities based on vorticity and velocity fluctuations are computed as functions of the spatial and temporal separations. The profile of the space-time correlation of dilatation fluctuations is explained via a wave propagation model.

Fusion of Local Statistical Parameters for Buried Underwater Mine Detection in Sonar Imaging

NASA Astrophysics Data System (ADS)

Maussang, F.; Rombaut, M.; Chanussot, J.; Hétet, A.; Amate, M.

2008-12-01

Detection of buried underwater objects, and especially mines, is a current crucial strategic task. Images provided by sonar systems allowing to penetrate in the sea floor, such as the synthetic aperture sonars (SASs), are of great interest for the detection and classification of such objects. However, the signal-to-noise ratio is fairly low and advanced information processing is required for a correct and reliable detection of the echoes generated by the objects. The detection method proposed in this paper is based on a data-fusion architecture using the belief theory. The input data of this architecture are local statistical characteristics extracted from SAS data corresponding to the first-, second-, third-, and fourth-order statistical properties of the sonar images, respectively. The interest of these parameters is derived from a statistical model of the sonar data. Numerical criteria are also proposed to estimate the detection performances and to validate the method.

Enhancing predictive accuracy and reproducibility in clinical evaluation research: Commentary on the special section of the Journal of Evaluation in Clinical Practice.

PubMed

Bryant, Fred B

2016-12-01

This paper introduces a special section of the current issue of the Journal of Evaluation in Clinical Practice that includes a set of 6 empirical articles showcasing a versatile, new machine-learning statistical method, known as optimal data (or discriminant) analysis (ODA), specifically designed to produce statistical models that maximize predictive accuracy. As this set of papers clearly illustrates, ODA offers numerous important advantages over traditional statistical methods-advantages that enhance the validity and reproducibility of statistical conclusions in empirical research. This issue of the journal also includes a review of a recently published book that provides a comprehensive introduction to the logic, theory, and application of ODA in empirical research. It is argued that researchers have much to gain by using ODA to analyze their data. © 2016 John Wiley & Sons, Ltd.

Adaptation and Validation of a Burnout Inventory in a Survey of the Staff of a Correctional Institution in Bulgaria.

PubMed

Harizanova, Stanislava N; Mateva, Nonka G; Tarnovska, Tanya Ch

2016-12-01

Burnout syndrome is a phenomenon that seems to be studied globally in relation to all types of populations. The staff in the system of correctional institutions in Bulgaria, however, is oddly left out of this tendency. There is no standardized model in Bulgaria that can be used to detect possible susceptibility to professional burnout. The methods available at present only register the irreversible changes that have already set in the functioning of the individual. V. Boyko's method for burnout assessment allows clinicians to use individual approach to patients and affords easy comparability of results with data from other psychodiagnostic instruments. Adaptation of the assessment instruments to fit the specificities of a study population (linguistic, ethno-cultural, etc.) is obligatory so that the instrument could be correctly used and yield valid results. Validation is one of the most frequently used technique to achieve this. The aim of the present study was to adapt and validate V. Boyko's burnout inventory for diagnosing burnout and assessment of the severity of the burnout syndrome in correctional officers. We conducted a pilot study with 50 officers working in the Plovdiv Regional Correction Facility by test-retest survey performed at an interval of 2 to 4 months. All participants completed the adapted questionnaire translated into Bulgarian voluntarily and anonymously. Statistical analysis was performed using SPSS v.17. We found a mild-to-strong statistically significant correlation (P<0.01) across all subscales between the most frequently used questionnaire for assessing the burnout syndrome, the Maslach Burnout Inventory, and the tool we propose here. The high Cronbach's α coefficient (α=0.94) and Spearman-Brown coefficient (rsb=0.86), and the low mean between-item correlation (r=0.30) demonstrated the instrument's good reliability and validity. With the validation herein presented we offer a highly reliable Bulgarian variant of Boyko's method for burnout assessment and research.

A shape-based statistical method to retrieve 2D TRUS-MR slice correspondence for prostate biopsy

NASA Astrophysics Data System (ADS)

Mitra, Jhimli; Srikantha, Abhilash; Sidibé, Désiré; Martí, Robert; Oliver, Arnau; Lladó, Xavier; Ghose, Soumya; Vilanova, Joan C.; Comet, Josep; Meriaudeau, Fabrice

2012-02-01

This paper presents a method based on shape-context and statistical measures to match interventional 2D Trans Rectal Ultrasound (TRUS) slice during prostate biopsy to a 2D Magnetic Resonance (MR) slice of a pre-acquired prostate volume. Accurate biopsy tissue sampling requires translation of the MR slice information on the TRUS guided biopsy slice. However, this translation or fusion requires the knowledge of the spatial position of the TRUS slice and this is only possible with the use of an electro-magnetic (EM) tracker attached to the TRUS probe. Since, the use of EM tracker is not common in clinical practice and 3D TRUS is not used during biopsy, we propose to perform an analysis based on shape and information theory to reach close enough to the actual MR slice as validated by experts. The Bhattacharyya distance is used to find point correspondences between shape-context representations of the prostate contours. Thereafter, Chi-square distance is used to find out those MR slices where the prostates closely match with that of the TRUS slice. Normalized Mutual Information (NMI) values of the TRUS slice with each of the axial MR slices are computed after rigid alignment and consecutively a strategic elimination based on a set of rules between the Chi-square distances and the NMI leads to the required MR slice. We validated our method for TRUS axial slices of 15 patients, of which 11 results matched at least one experts validation and the remaining 4 are at most one slice away from the expert validations.

Dynamic TIMI Risk Score for STEMI

PubMed Central

Amin, Sameer T.; Morrow, David A.; Braunwald, Eugene; Sloan, Sarah; Contant, Charles; Murphy, Sabina; Antman, Elliott M.

2013-01-01

Background Although there are multiple methods of risk stratification for ST‐elevation myocardial infarction (STEMI), this study presents a prospectively validated method for reclassification of patients based on in‐hospital events. A dynamic risk score provides an initial risk stratification and reassessment at discharge. Methods and Results The dynamic TIMI risk score for STEMI was derived in ExTRACT‐TIMI 25 and validated in TRITON‐TIMI 38. Baseline variables were from the original TIMI risk score for STEMI. New variables were major clinical events occurring during the index hospitalization. Each variable was tested individually in a univariate Cox proportional hazards regression. Variables with P<0.05 were incorporated into a full multivariable Cox model to assess the risk of death at 1 year. Each variable was assigned an integer value based on the odds ratio, and the final score was the sum of these values. The dynamic score included the development of in‐hospital MI, arrhythmia, major bleed, stroke, congestive heart failure, recurrent ischemia, and renal failure. The C‐statistic produced by the dynamic score in the derivation database was 0.76, with a net reclassification improvement (NRI) of 0.33 (P<0.0001) from the inclusion of dynamic events to the original TIMI risk score. In the validation database, the C‐statistic was 0.81, with a NRI of 0.35 (P=0.01). Conclusions This score is a prospectively derived, validated means of estimating 1‐year mortality of STEMI at hospital discharge and can serve as a clinically useful tool. By incorporating events during the index hospitalization, it can better define risk and help to guide treatment decisions. PMID:23525425

Cross-validation and Peeling Strategies for Survival Bump Hunting using Recursive Peeling Methods

PubMed Central

Dazard, Jean-Eudes; Choe, Michael; LeBlanc, Michael; Rao, J. Sunil

2015-01-01

We introduce a framework to build a survival/risk bump hunting model with a censored time-to-event response. Our Survival Bump Hunting (SBH) method is based on a recursive peeling procedure that uses a specific survival peeling criterion derived from non/semi-parametric statistics such as the hazards-ratio, the log-rank test or the Nelson--Aalen estimator. To optimize the tuning parameter of the model and validate it, we introduce an objective function based on survival or prediction-error statistics, such as the log-rank test and the concordance error rate. We also describe two alternative cross-validation techniques adapted to the joint task of decision-rule making by recursive peeling and survival estimation. Numerical analyses show the importance of replicated cross-validation and the differences between criteria and techniques in both low and high-dimensional settings. Although several non-parametric survival models exist, none addresses the problem of directly identifying local extrema. We show how SBH efficiently estimates extreme survival/risk subgroups unlike other models. This provides an insight into the behavior of commonly used models and suggests alternatives to be adopted in practice. Finally, our SBH framework was applied to a clinical dataset. In it, we identified subsets of patients characterized by clinical and demographic covariates with a distinct extreme survival outcome, for which tailored medical interventions could be made. An R package PRIMsrc (Patient Rule Induction Method in Survival, Regression and Classification settings) is available on CRAN (Comprehensive R Archive Network) and GitHub. PMID:27034730

Statistical prediction of dynamic distortion of inlet flow using minimum dynamic measurement. An application to the Melick statistical method and inlet flow dynamic distortion prediction without RMS measurements

NASA Technical Reports Server (NTRS)

Schweikhard, W. G.; Chen, Y. S.

1986-01-01

The Melick method of inlet flow dynamic distortion prediction by statistical means is outlined. A hypothetic vortex model is used as the basis for the mathematical formulations. The main variables are identified by matching the theoretical total pressure rms ratio with the measured total pressure rms ratio. Data comparisons, using the HiMAT inlet test data set, indicate satisfactory prediction of the dynamic peak distortion for cases with boundary layer control device vortex generators. A method for the dynamic probe selection was developed. Validity of the probe selection criteria is demonstrated by comparing the reduced-probe predictions with the 40-probe predictions. It is indicated that the the number of dynamic probes can be reduced to as few as two and still retain good accuracy.

Polypropylene Production Optimization in Fluidized Bed Catalytic Reactor (FBCR): Statistical Modeling and Pilot Scale Experimental Validation

PubMed Central

Khan, Mohammad Jakir Hossain; Hussain, Mohd Azlan; Mujtaba, Iqbal Mohammed

2014-01-01

Propylene is one type of plastic that is widely used in our everyday life. This study focuses on the identification and justification of the optimum process parameters for polypropylene production in a novel pilot plant based fluidized bed reactor. This first-of-its-kind statistical modeling with experimental validation for the process parameters of polypropylene production was conducted by applying ANNOVA (Analysis of variance) method to Response Surface Methodology (RSM). Three important process variables i.e., reaction temperature, system pressure and hydrogen percentage were considered as the important input factors for the polypropylene production in the analysis performed. In order to examine the effect of process parameters and their interactions, the ANOVA method was utilized among a range of other statistical diagnostic tools such as the correlation between actual and predicted values, the residuals and predicted response, outlier t plot, 3D response surface and contour analysis plots. The statistical analysis showed that the proposed quadratic model had a good fit with the experimental results. At optimum conditions with temperature of 75°C, system pressure of 25 bar and hydrogen percentage of 2%, the highest polypropylene production obtained is 5.82% per pass. Hence it is concluded that the developed experimental design and proposed model can be successfully employed with over a 95% confidence level for optimum polypropylene production in a fluidized bed catalytic reactor (FBCR). PMID:28788576

Quantifying prognosis with risk predictions.

PubMed

Pace, Nathan L; Eberhart, Leopold H J; Kranke, Peter R

2012-01-01

Prognosis is a forecast, based on present observations in a patient, of their probable outcome from disease, surgery and so on. Research methods for the development of risk probabilities may not be familiar to some anaesthesiologists. We briefly describe methods for identifying risk factors and risk scores. A probability prediction rule assigns a risk probability to a patient for the occurrence of a specific event. Probability reflects the continuum between absolute certainty (Pi = 1) and certified impossibility (Pi = 0). Biomarkers and clinical covariates that modify risk are known as risk factors. The Pi as modified by risk factors can be estimated by identifying the risk factors and their weighting; these are usually obtained by stepwise logistic regression. The accuracy of probabilistic predictors can be separated into the concepts of 'overall performance', 'discrimination' and 'calibration'. Overall performance is the mathematical distance between predictions and outcomes. Discrimination is the ability of the predictor to rank order observations with different outcomes. Calibration is the correctness of prediction probabilities on an absolute scale. Statistical methods include the Brier score, coefficient of determination (Nagelkerke R2), C-statistic and regression calibration. External validation is the comparison of the actual outcomes to the predicted outcomes in a new and independent patient sample. External validation uses the statistical methods of overall performance, discrimination and calibration and is uniformly recommended before acceptance of the prediction model. Evidence from randomised controlled clinical trials should be obtained to show the effectiveness of risk scores for altering patient management and patient outcomes.

The Application of FT-IR Spectroscopy for Quality Control of Flours Obtained from Polish Producers

PubMed Central

Ceglińska, Alicja; Reder, Magdalena; Ciemniewska-Żytkiewicz, Hanna

2017-01-01

Samples of wheat, spelt, rye, and triticale flours produced by different Polish mills were studied by both classic chemical methods and FT-IR MIR spectroscopy. An attempt was made to statistically correlate FT-IR spectral data with reference data with regard to content of various components, for example, proteins, fats, ash, and fatty acids as well as properties such as moisture, falling number, and energetic value. This correlation resulted in calibrated and validated statistical models for versatile evaluation of unknown flour samples. The calibration data set was used to construct calibration models with use of the CSR and the PLS with the leave one-out, cross-validation techniques. The calibrated models were validated with a validation data set. The results obtained confirmed that application of statistical models based on MIR spectral data is a robust, accurate, precise, rapid, inexpensive, and convenient methodology for determination of flour characteristics, as well as for detection of content of selected flour ingredients. The obtained models' characteristics were as follows: R2 = 0.97, PRESS = 2.14; R2 = 0.96, PRESS = 0.69; R2 = 0.95, PRESS = 1.27; R2 = 0.94, PRESS = 0.76, for content of proteins, lipids, ash, and moisture level, respectively. Best results of CSR models were obtained for protein, ash, and crude fat (R2 = 0.86; 0.82; and 0.78, resp.). PMID:28243483

78 FR 73839 - 1,1,1,2 Tetrafluoroethane From the People's Republic of China: Initiation of Countervailing Duty...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-12-09

... using a statistically valid sampling method to poll the industry. Section 771(4)(A) of the Act defines... subheading 2903.39.2020. Although the HTSUS subheading and CAS registry number are provided for convenience...

Labor Productivity Standards in Texas School Foodservice Operations

ERIC Educational Resources Information Center

Sherrin, A. Rachelle; Bednar, Carolyn; Kwon, Junehee

2009-01-01

Purpose: Purpose of this research was to investigate utilization of labor productivity standards and variables that affect productivity in Texas school foodservice operations. Methods: A questionnaire was developed, validated, and pilot tested, then mailed to 200 randomly selected Texas school foodservice directors. Descriptive statistics for…

Assessing Creative Problem-Solving with Automated Text Grading

ERIC Educational Resources Information Center

Wang, Hao-Chuan; Chang, Chun-Yen; Li, Tsai-Yen

2008-01-01

The work aims to improve the assessment of creative problem-solving in science education by employing language technologies and computational-statistical machine learning methods to grade students' natural language responses automatically. To evaluate constructs like creative problem-solving with validity, open-ended questions that elicit…

Parametric vs. non-parametric statistics of low resolution electromagnetic tomography (LORETA).

PubMed

Thatcher, R W; North, D; Biver, C

2005-01-01

This study compared the relative statistical sensitivity of non-parametric and parametric statistics of 3-dimensional current sources as estimated by the EEG inverse solution Low Resolution Electromagnetic Tomography (LORETA). One would expect approximately 5% false positives (classification of a normal as abnormal) at the P < .025 level of probability (two tailed test) and approximately 1% false positives at the P < .005 level. EEG digital samples (2 second intervals sampled 128 Hz, 1 to 2 minutes eyes closed) from 43 normal adult subjects were imported into the Key Institute's LORETA program. We then used the Key Institute's cross-spectrum and the Key Institute's LORETA output files (*.lor) as the 2,394 gray matter pixel representation of 3-dimensional currents at different frequencies. The mean and standard deviation *.lor files were computed for each of the 2,394 gray matter pixels for each of the 43 subjects. Tests of Gaussianity and different transforms were computed in order to best approximate a normal distribution for each frequency and gray matter pixel. The relative sensitivity of parametric vs. non-parametric statistics were compared using a "leave-one-out" cross validation method in which individual normal subjects were withdrawn and then statistically classified as being either normal or abnormal based on the remaining subjects. Log10 transforms approximated Gaussian distribution in the range of 95% to 99% accuracy. Parametric Z score tests at P < .05 cross-validation demonstrated an average misclassification rate of approximately 4.25%, and range over the 2,394 gray matter pixels was 27.66% to 0.11%. At P < .01 parametric Z score cross-validation false positives were 0.26% and ranged from 6.65% to 0% false positives. The non-parametric Key Institute's t-max statistic at P < .05 had an average misclassification error rate of 7.64% and ranged from 43.37% to 0.04% false positives. The nonparametric t-max at P < .01 had an average misclassification rate of 6.67% and ranged from 41.34% to 0% false positives of the 2,394 gray matter pixels for any cross-validated normal subject. In conclusion, adequate approximation to Gaussian distribution and high cross-validation can be achieved by the Key Institute's LORETA programs by using a log10 transform and parametric statistics, and parametric normative comparisons had lower false positive rates than the non-parametric tests.

Challenges of Big Data Analysis.

PubMed

Fan, Jianqing; Han, Fang; Liu, Han

2014-06-01

Big Data bring new opportunities to modern society and challenges to data scientists. On one hand, Big Data hold great promises for discovering subtle population patterns and heterogeneities that are not possible with small-scale data. On the other hand, the massive sample size and high dimensionality of Big Data introduce unique computational and statistical challenges, including scalability and storage bottleneck, noise accumulation, spurious correlation, incidental endogeneity, and measurement errors. These challenges are distinguished and require new computational and statistical paradigm. This article gives overviews on the salient features of Big Data and how these features impact on paradigm change on statistical and computational methods as well as computing architectures. We also provide various new perspectives on the Big Data analysis and computation. In particular, we emphasize on the viability of the sparsest solution in high-confidence set and point out that exogeneous assumptions in most statistical methods for Big Data can not be validated due to incidental endogeneity. They can lead to wrong statistical inferences and consequently wrong scientific conclusions.

Challenges of Big Data Analysis

PubMed Central

Fan, Jianqing; Han, Fang; Liu, Han

2014-01-01

Big Data bring new opportunities to modern society and challenges to data scientists. On one hand, Big Data hold great promises for discovering subtle population patterns and heterogeneities that are not possible with small-scale data. On the other hand, the massive sample size and high dimensionality of Big Data introduce unique computational and statistical challenges, including scalability and storage bottleneck, noise accumulation, spurious correlation, incidental endogeneity, and measurement errors. These challenges are distinguished and require new computational and statistical paradigm. This article gives overviews on the salient features of Big Data and how these features impact on paradigm change on statistical and computational methods as well as computing architectures. We also provide various new perspectives on the Big Data analysis and computation. In particular, we emphasize on the viability of the sparsest solution in high-confidence set and point out that exogeneous assumptions in most statistical methods for Big Data can not be validated due to incidental endogeneity. They can lead to wrong statistical inferences and consequently wrong scientific conclusions. PMID:25419469

Evolving forecasting classifications and applications in health forecasting

PubMed Central

Soyiri, Ireneous N; Reidpath, Daniel D

2012-01-01

Health forecasting forewarns the health community about future health situations and disease episodes so that health systems can better allocate resources and manage demand. The tools used for developing and measuring the accuracy and validity of health forecasts commonly are not defined although they are usually adapted forms of statistical procedures. This review identifies previous typologies used in classifying the forecasting methods commonly used in forecasting health conditions or situations. It then discusses the strengths and weaknesses of these methods and presents the choices available for measuring the accuracy of health-forecasting models, including a note on the discrepancies in the modes of validation. PMID:22615533

Gamma-ray Full Spectrum Analysis for Environmental Radioactivity by HPGe Detector

NASA Astrophysics Data System (ADS)

Jeong, Meeyoung; Lee, Kyeong Beom; Kim, Kyeong Ja; Lee, Min-Kie; Han, Ju-Bong

2014-12-01

Odyssey, one of the NASA¡¯s Mars exploration program and SELENE (Kaguya), a Japanese lunar orbiting spacecraft have a payload of Gamma-Ray Spectrometer (GRS) for analyzing radioactive chemical elements of the atmosphere and the surface. In these days, gamma-ray spectroscopy with a High-Purity Germanium (HPGe) detector has been widely used for the activity measurements of natural radionuclides contained in the soil of the Earth. The energy spectra obtained by the HPGe detectors have been generally analyzed by means of the Window Analysis (WA) method. In this method, activity concentrations are determined by using the net counts of energy window around individual peaks. Meanwhile, an alternative method, the so-called Full Spectrum Analysis (FSA) method uses count numbers not only from full-absorption peaks but from the contributions of Compton scattering due to gamma-rays. Consequently, while it takes a substantial time to obtain a statistically significant result in the WA method, the FSA method requires a much shorter time to reach the same level of the statistical significance. This study shows the validation results of FSA method. We have compared the concentration of radioactivity of 40K, 232Th and 238U in the soil measured by the WA method and the FSA method, respectively. The gamma-ray spectrum of reference materials (RGU and RGTh, KCl) and soil samples were measured by the 120% HPGe detector with cosmic muon veto detector. According to the comparison result of activity concentrations between the FSA and the WA, we could conclude that FSA method is validated against the WA method. This study implies that the FSA method can be used in a harsh measurement environment, such as the gamma-ray measurement in the Moon, in which the level of statistical significance is usually required in a much shorter data acquisition time than the WA method.

Reliability and validity of the workplace harassment questionnaire for Korean finance and service workers.

PubMed

Lee, Myeongjun; Kim, Hyunjung; Shin, Donghee; Lee, Sangyun

2016-01-01

Harassment means systemic and repeated unethical acts. Research on workplace harassment have been conducted widely and the NAQ-R has been widely used for the researches. But this tool, however the limitations in revealing differended in sub-factors depending on the culture and in reflecting that unique characteristics of the Koren society. So, The workplace harassment questionnaire for Korean finace and service workers has been developed to assess the level of personal harassment at work. This study aims to develop a tool to assess the level of personal harassment at work and to test its validity and reliability while examining specific characteristics of workplace harassment against finance and service workers in Korea. The framework of survey was established based on literature review, focused-group interview for the Korean finance and service workers. To verify its reliability, Cronbach's alpha coefficient was calculated; and to verify its validity, items and factors of the tool were analyzed. The correlation matrix analysis was examined to verify the tool's convergent validity and discriminant validity. Structural validity was verified by checking statistical significance in relation to the BDI-K. Cronbach's alpha coefficient of this survey was 0.93, which indicates a quite high level of reliability. To verify the appropriateness of this survey tool, its construct validity was examined through factor analysis. As a result of the factor analysis, 3 factors were extracted, explaining 56.5 % of the total variance. The loading values and communalities of the 20 items were 0.85 to 0.48 and 0.71 to 0.46. The convergent validity and discriminant validity were analyzed and rate of item discriminant validity was 100 %. Finally, for the concurrent validity, We examined the relationship between the WHI-KFSW and pschosocial stress by examining the correlation with the BDI-K. The results of chi-square test and multiple logistic analysis indicated that the correlation with the BDI-K was satatisctically significant. Workplace harassment in actual workplaces were investigated based on interviews, and the statistical analysis contributed to systematizing the types of actual workplace harassment. By statistical method, we developed the questionare, 20 items of 3 categories.

LandScape: a simple method to aggregate p-values and other stochastic variables without a priori grouping.

PubMed

Wiuf, Carsten; Schaumburg-Müller Pallesen, Jonatan; Foldager, Leslie; Grove, Jakob

2016-08-01

In many areas of science it is custom to perform many, potentially millions, of tests simultaneously. To gain statistical power it is common to group tests based on a priori criteria such as predefined regions or by sliding windows. However, it is not straightforward to choose grouping criteria and the results might depend on the chosen criteria. Methods that summarize, or aggregate, test statistics or p-values, without relying on a priori criteria, are therefore desirable. We present a simple method to aggregate a sequence of stochastic variables, such as test statistics or p-values, into fewer variables without assuming a priori defined groups. We provide different ways to evaluate the significance of the aggregated variables based on theoretical considerations and resampling techniques, and show that under certain assumptions the FWER is controlled in the strong sense. Validity of the method was demonstrated using simulations and real data analyses. Our method may be a useful supplement to standard procedures relying on evaluation of test statistics individually. Moreover, by being agnostic and not relying on predefined selected regions, it might be a practical alternative to conventionally used methods of aggregation of p-values over regions. The method is implemented in Python and freely available online (through GitHub, see the Supplementary information).

The discounting model selector: Statistical software for delay discounting applications.

PubMed

Gilroy, Shawn P; Franck, Christopher T; Hantula, Donald A

2017-05-01

Original, open-source computer software was developed and validated against established delay discounting methods in the literature. The software executed approximate Bayesian model selection methods from user-supplied temporal discounting data and computed the effective delay 50 (ED50) from the best performing model. Software was custom-designed to enable behavior analysts to conveniently apply recent statistical methods to temporal discounting data with the aid of a graphical user interface (GUI). The results of independent validation of the approximate Bayesian model selection methods indicated that the program provided results identical to that of the original source paper and its methods. Monte Carlo simulation (n = 50,000) confirmed that true model was selected most often in each setting. Simulation code and data for this study were posted to an online repository for use by other researchers. The model selection approach was applied to three existing delay discounting data sets from the literature in addition to the data from the source paper. Comparisons of model selected ED50 were consistent with traditional indices of discounting. Conceptual issues related to the development and use of computer software by behavior analysts and the opportunities afforded by free and open-sourced software are discussed and a review of possible expansions of this software are provided. © 2017 Society for the Experimental Analysis of Behavior.

Methods for detecting, quantifying, and adjusting for dissemination bias in meta-analysis are described.

PubMed

Mueller, Katharina Felicitas; Meerpohl, Joerg J; Briel, Matthias; Antes, Gerd; von Elm, Erik; Lang, Britta; Motschall, Edith; Schwarzer, Guido; Bassler, Dirk

2016-12-01

To systematically review methodological articles which focus on nonpublication of studies and to describe methods of detecting and/or quantifying and/or adjusting for dissemination in meta-analyses. To evaluate whether the methods have been applied to an empirical data set for which one can be reasonably confident that all studies conducted have been included. We systematically searched Medline, the Cochrane Library, and Web of Science, for methodological articles that describe at least one method of detecting and/or quantifying and/or adjusting for dissemination bias in meta-analyses. The literature search retrieved 2,224 records, of which we finally included 150 full-text articles. A great variety of methods to detect, quantify, or adjust for dissemination bias were described. Methods included graphical methods mainly based on funnel plot approaches, statistical methods, such as regression tests, selection models, sensitivity analyses, and a great number of more recent statistical approaches. Only few methods have been validated in empirical evaluations using unpublished studies obtained from regulators (Food and Drug Administration, European Medicines Agency). We present an overview of existing methods to detect, quantify, or adjust for dissemination bias. It remains difficult to advise which method should be used as they are all limited and their validity has rarely been assessed. Therefore, a thorough literature search remains crucial in systematic reviews, and further steps to increase the availability of all research results need to be taken. Copyright © 2016 Elsevier Inc. All rights reserved.

Postcraniometric sex and ancestry estimation in South Africa: a validation study.

PubMed

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

2018-05-24

With the acceptance of the Daubert criteria as the standards for best practice in forensic anthropological research, more emphasis is being placed on the validation of published methods. Methods, both traditional and novel, need to be validated, adjusted, and refined for optimal performance within forensic anthropological analyses. Recently, a custom postcranial database of modern South Africans was created for use in Fordisc 3.1. Classification accuracies of up to 85% for ancestry estimation and 98% for sex estimation were achieved using a multivariate approach. To measure the external validity and report more realistic performance statistics, an independent sample was tested. The postcrania from 180 black, white, and colored South Africans were measured and classified using the custom postcranial database. A decrease in accuracy was observed for both ancestry estimation (79%) and sex estimation (95%) of the validation sample. When incorporating both sex and ancestry simultaneously, the method achieved 70% accuracy, and 79% accuracy when sex-specific ancestry analyses were run. Classification matrices revealed that postcrania were more likely to misclassify as a result of ancestry rather than sex. While both sex and ancestry influence the size of an individual, sex differences are more marked in the postcranial skeleton and are therefore easier to identify. The external validity of the postcranial database was verified and therefore shown to be a useful tool for forensic casework in South Africa. While the classification rates were slightly lower than the original method, this is expected when a method is generalized.

One Hundred Ways to be Non-Fickian - A Rigorous Multi-Variate Statistical Analysis of Pore-Scale Transport

NASA Astrophysics Data System (ADS)

Most, Sebastian; Nowak, Wolfgang; Bijeljic, Branko

2015-04-01

Fickian transport in groundwater flow is the exception rather than the rule. Transport in porous media is frequently simulated via particle methods (i.e. particle tracking random walk (PTRW) or continuous time random walk (CTRW)). These methods formulate transport as a stochastic process of particle position increments. At the pore scale, geometry and micro-heterogeneities prohibit the commonly made assumption of independent and normally distributed increments to represent dispersion. Many recent particle methods seek to loosen this assumption. Hence, it is important to get a better understanding of the processes at pore scale. For our analysis we track the positions of 10.000 particles migrating through the pore space over time. The data we use come from micro CT scans of a homogeneous sandstone and encompass about 10 grain sizes. Based on those images we discretize the pore structure and simulate flow at the pore scale based on the Navier-Stokes equation. This flow field realistically describes flow inside the pore space and we do not need to add artificial dispersion during the transport simulation. Next, we use particle tracking random walk and simulate pore-scale transport. Finally, we use the obtained particle trajectories to do a multivariate statistical analysis of the particle motion at the pore scale. Our analysis is based on copulas. Every multivariate joint distribution is a combination of its univariate marginal distributions. The copula represents the dependence structure of those univariate marginals and is therefore useful to observe correlation and non-Gaussian interactions (i.e. non-Fickian transport). The first goal of this analysis is to better understand the validity regions of commonly made assumptions. We are investigating three different transport distances: 1) The distance where the statistical dependence between particle increments can be modelled as an order-one Markov process. This would be the Markovian distance for the process, where the validity of yet-unexplored non-Gaussian-but-Markovian random walks start. 2) The distance where bivariate statistical dependence simplifies to a multi-Gaussian dependence based on simple linear correlation (validity of correlated PTRW/CTRW). 3) The distance of complete statistical independence (validity of classical PTRW/CTRW). The second objective is to reveal characteristic dependencies influencing transport the most. Those dependencies can be very complex. Copulas are highly capable of representing linear dependence as well as non-linear dependence. With that tool we are able to detect persistent characteristics dominating transport even across different scales. The results derived from our experimental data set suggest that there are many more non-Fickian aspects of pore-scale transport than the univariate statistics of longitudinal displacements. Non-Fickianity can also be found in transverse displacements, and in the relations between increments at different time steps. Also, the found dependence is non-linear (i.e. beyond simple correlation) and persists over long distances. Thus, our results strongly support the further refinement of techniques like correlated PTRW or correlated CTRW towards non-linear statistical relations.

Parametric study of statistical bias in laser Doppler velocimetry

NASA Technical Reports Server (NTRS)

Gould, Richard D.; Stevenson, Warren H.; Thompson, H. Doyle

1989-01-01

Analytical studies have often assumed that LDV velocity bias depends on turbulence intensity in conjunction with one or more characteristic time scales, such as the time between validated signals, the time between data samples, and the integral turbulence time-scale. These parameters are presently varied independently, in an effort to quantify the biasing effect. Neither of the post facto correction methods employed is entirely accurate. The mean velocity bias error is found to be nearly independent of data validation rate.

Realizing the Potential of Mobile Mental Health: New Methods for New Data in Psychiatry

PubMed Central

Staples, Patrick; Onnela, Jukka-Pekka

2015-01-01

Smartphones are now ubiquitous and can be harnessed to offer psychiatry a wealth of real-time data regarding patient behavior, self-reported symptoms, and even physiology. The data collected from smartphones meet the three criteria of big data: velocity, volume, and variety. Although these data have tremendous potential, transforming them into clinically valid and useful information requires using new tools and methods as a part of assessment in psychiatry. In this paper, we introduce and explore numerous analytical methods and tools from the computational and statistical sciences that appear readily applicable to psychiatric data collected using smartphones. By matching smartphone data with appropriate statistical methods, psychiatry can better realize the potential of mobile mental health and empower both patients and providers with novel clinical tools. PMID:26073363

Validation of Alternative In Vitro Methods to Animal Testing: Concepts, Challenges, Processes and Tools.

PubMed

Griesinger, Claudius; Desprez, Bertrand; Coecke, Sandra; Casey, Warren; Zuang, Valérie

This chapter explores the concepts, processes, tools and challenges relating to the validation of alternative methods for toxicity and safety testing. In general terms, validation is the process of assessing the appropriateness and usefulness of a tool for its intended purpose. Validation is routinely used in various contexts in science, technology, the manufacturing and services sectors. It serves to assess the fitness-for-purpose of devices, systems, software up to entire methodologies. In the area of toxicity testing, validation plays an indispensable role: "alternative approaches" are increasingly replacing animal models as predictive tools and it needs to be demonstrated that these novel methods are fit for purpose. Alternative approaches include in vitro test methods, non-testing approaches such as predictive computer models up to entire testing and assessment strategies composed of method suites, data sources and decision-aiding tools. Data generated with alternative approaches are ultimately used for decision-making on public health and the protection of the environment. It is therefore essential that the underlying methods and methodologies are thoroughly characterised, assessed and transparently documented through validation studies involving impartial actors. Importantly, validation serves as a filter to ensure that only test methods able to produce data that help to address legislative requirements (e.g. EU's REACH legislation) are accepted as official testing tools and, owing to the globalisation of markets, recognised on international level (e.g. through inclusion in OECD test guidelines). Since validation creates a credible and transparent evidence base on test methods, it provides a quality stamp, supporting companies developing and marketing alternative methods and creating considerable business opportunities. Validation of alternative methods is conducted through scientific studies assessing two key hypotheses, reliability and relevance of the test method for a given purpose. Relevance encapsulates the scientific basis of the test method, its capacity to predict adverse effects in the "target system" (i.e. human health or the environment) as well as its applicability for the intended purpose. In this chapter we focus on the validation of non-animal in vitro alternative testing methods and review the concepts, challenges, processes and tools fundamental to the validation of in vitro methods intended for hazard testing of chemicals. We explore major challenges and peculiarities of validation in this area. Based on the notion that validation per se is a scientific endeavour that needs to adhere to key scientific principles, namely objectivity and appropriate choice of methodology, we examine basic aspects of study design and management, and provide illustrations of statistical approaches to describe predictive performance of validated test methods as well as their reliability.

Differential privacy-based evaporative cooling feature selection and classification with relief-F and random forests.

PubMed

Le, Trang T; Simmons, W Kyle; Misaki, Masaya; Bodurka, Jerzy; White, Bill C; Savitz, Jonathan; McKinney, Brett A

2017-09-15

Classification of individuals into disease or clinical categories from high-dimensional biological data with low prediction error is an important challenge of statistical learning in bioinformatics. Feature selection can improve classification accuracy but must be incorporated carefully into cross-validation to avoid overfitting. Recently, feature selection methods based on differential privacy, such as differentially private random forests and reusable holdout sets, have been proposed. However, for domains such as bioinformatics, where the number of features is much larger than the number of observations p≫n , these differential privacy methods are susceptible to overfitting. We introduce private Evaporative Cooling, a stochastic privacy-preserving machine learning algorithm that uses Relief-F for feature selection and random forest for privacy preserving classification that also prevents overfitting. We relate the privacy-preserving threshold mechanism to a thermodynamic Maxwell-Boltzmann distribution, where the temperature represents the privacy threshold. We use the thermal statistical physics concept of Evaporative Cooling of atomic gases to perform backward stepwise privacy-preserving feature selection. On simulated data with main effects and statistical interactions, we compare accuracies on holdout and validation sets for three privacy-preserving methods: the reusable holdout, reusable holdout with random forest, and private Evaporative Cooling, which uses Relief-F feature selection and random forest classification. In simulations where interactions exist between attributes, private Evaporative Cooling provides higher classification accuracy without overfitting based on an independent validation set. In simulations without interactions, thresholdout with random forest and private Evaporative Cooling give comparable accuracies. We also apply these privacy methods to human brain resting-state fMRI data from a study of major depressive disorder. Code available at http://insilico.utulsa.edu/software/privateEC . brett-mckinney@utulsa.edu. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Scoring Methods for Building Genotypic Scores: An Application to Didanosine Resistance in a Large Derivation Set

PubMed Central

Houssaini, Allal; Assoumou, Lambert; Miller, Veronica; Calvez, Vincent; Marcelin, Anne-Geneviève; Flandre, Philippe

2013-01-01

Background Several attempts have been made to determine HIV-1 resistance from genotype resistance testing. We compare scoring methods for building weighted genotyping scores and commonly used systems to determine whether the virus of a HIV-infected patient is resistant. Methods and Principal Findings Three statistical methods (linear discriminant analysis, support vector machine and logistic regression) are used to determine the weight of mutations involved in HIV resistance. We compared these weighted scores with known interpretation systems (ANRS, REGA and Stanford HIV-db) to classify patients as resistant or not. Our methodology is illustrated on the Forum for Collaborative HIV Research didanosine database (N = 1453). The database was divided into four samples according to the country of enrolment (France, USA/Canada, Italy and Spain/UK/Switzerland). The total sample and the four country-based samples allow external validation (one sample is used to estimate a score and the other samples are used to validate it). We used the observed precision to compare the performance of newly derived scores with other interpretation systems. Our results show that newly derived scores performed better than or similar to existing interpretation systems, even with external validation sets. No difference was found between the three methods investigated. Our analysis identified four new mutations associated with didanosine resistance: D123S, Q207K, H208Y and K223Q. Conclusions We explored the potential of three statistical methods to construct weighted scores for didanosine resistance. Our proposed scores performed at least as well as already existing interpretation systems and previously unrecognized didanosine-resistance associated mutations were identified. This approach could be used for building scores of genotypic resistance to other antiretroviral drugs. PMID:23555613

Validated Spectrophotometric and RP-HPLC-DAD Methods for the Determination of Ursodeoxycholic Acid Based on Derivatization with 2-Nitrophenylhydrazine.

PubMed

El-Kafrawy, Dina S; Belal, Tarek S; Mahrous, Mohamed S; Abdel-Khalek, Magdi M; Abo-Gharam, Amira H

2017-05-01

This work describes the development, validation, and application of two simple, accurate, and reliable methods for the determination of ursodeoxycholic acid (UDCA) in bulk powder and in pharmaceutical dosage forms. The carboxylic acid group in UDCA was exploited for the development of these novel methods. Method 1 is the colorimetric determination of the drug based on its reaction with 2-nitrophenylhydrazine hydrochloride in the presence of a water-soluble carbodiimide coupler [1-ethyl-3-(3-dimethylaminopropyl)-carbodiimide hydrochloride] and pyridine to produce an acid hydrazide derivative, which ionizes to yield an intense violet color with maximum absorption at 553 nm. Method 2 uses reversed-phase HPLC with diode-array detection for the determination of UDCA after precolumn derivatization using the same reaction mentioned above. The acid hydrazide reaction product was separated using a Pinnacle DB C8 column (4.6 × 150 mm, 5 μm particle size) and a mobile phase consisting of 0.01 M acetate buffer (pH 3)-methanol-acetonitrile (30 + 30 + 40, v/v/v) isocratically pumped at a flow rate of 1 mL/min. Ibuprofen was used as the internal standard (IS). The peaks of the reaction product and IS were monitored at 400 nm. Different experimental parameters for both methods were carefully optimized. Analytical performance of the developed methods were statistically validated for linearity, range, precision, accuracy, specificity, robustness, LOD, and LOQ. Calibration curves showed good linear relationships for concentration ranges 32-192 and 60-600 μg/mL for methods 1 and 2, respectively. The proposed methods were successfully applied for the assay of UDCA in bulk form, capsules, and oral suspension with good accuracy and precision. Assay results were statistically compared with a reference pharmacopeial HPLC method, and no significant differences were observed between proposed and reference methods.

Surveys Assessing Students' Attitudes toward Statistics: A Systematic Review of Validity and Reliability

ERIC Educational Resources Information Center

Nolan, Meaghan M.; Beran, Tanya; Hecker, Kent G.

2012-01-01

Students with positive attitudes toward statistics are likely to show strong academic performance in statistics courses. Multiple surveys measuring students' attitudes toward statistics exist; however, a comparison of the validity and reliability of interpretations based on their scores is needed. A systematic review of relevant electronic…

Validation of spatial variability in downscaling results from the VALUE perfect predictor experiment

NASA Astrophysics Data System (ADS)

Widmann, Martin; Bedia, Joaquin; Gutiérrez, Jose Manuel; Maraun, Douglas; Huth, Radan; Fischer, Andreas; Keller, Denise; Hertig, Elke; Vrac, Mathieu; Wibig, Joanna; Pagé, Christian; Cardoso, Rita M.; Soares, Pedro MM; Bosshard, Thomas; Casado, Maria Jesus; Ramos, Petra

2016-04-01

VALUE is an open European network to validate and compare downscaling methods for climate change research. Within VALUE a systematic validation framework to enable the assessment and comparison of both dynamical and statistical downscaling methods has been developed. In the first validation experiment the downscaling methods are validated in a setup with perfect predictors taken from the ERA-interim reanalysis for the period 1997 - 2008. This allows to investigate the isolated skill of downscaling methods without further error contributions from the large-scale predictors. One aspect of the validation is the representation of spatial variability. As part of the VALUE validation we have compared various properties of the spatial variability of downscaled daily temperature and precipitation with the corresponding properties in observations. We have used two test validation datasets, one European-wide set of 86 stations, and one higher-density network of 50 stations in Germany. Here we present results based on three approaches, namely the analysis of i.) correlation matrices, ii.) pairwise joint threshold exceedances, and iii.) regions of similar variability. We summarise the information contained in correlation matrices by calculating the dependence of the correlations on distance and deriving decorrelation lengths, as well as by determining the independent degrees of freedom. Probabilities for joint threshold exceedances and (where appropriate) non-exceedances are calculated for various user-relevant thresholds related for instance to extreme precipitation or frost and heat days. The dependence of these probabilities on distance is again characterised by calculating typical length scales that separate dependent from independent exceedances. Regionalisation is based on rotated Principal Component Analysis. The results indicate which downscaling methods are preferable if the dependency of variability at different locations is relevant for the user.

Validated spectrophotometric methods for simultaneous determination of troxerutin and carbazochrome in dosage form

NASA Astrophysics Data System (ADS)

Khattab, Fatma I.; Ramadan, Nesrin K.; Hegazy, Maha A.; Al-Ghobashy, Medhat A.; Ghoniem, Nermine S.

2015-03-01

Four simple, accurate, sensitive and precise spectrophotometric methods were developed and validated for simultaneous determination of Troxerutin (TXN) and Carbazochrome (CZM) in their bulk powders, laboratory prepared mixtures and pharmaceutical dosage forms. Method A is first derivative spectrophotometry (D1) where TXN and CZM were determined at 294 and 483.5 nm, respectively. Method B is first derivative of ratio spectra (DD1) where the peak amplitude at 248 for TXN and 439 nm for CZM were used for their determination. Method C is ratio subtraction (RS); in which TXN was determined at its λmax (352 nm) in the presence of CZM which was determined by D1 at 483.5 nm. While, method D is mean centering of the ratio spectra (MCR) in which the mean centered values at 300 nm and 340.0 nm were used for the two drugs in a respective order. The two compounds were simultaneously determined in the concentration ranges of 5.00-50.00 μg mL-1 and 0.5-10.0 μg mL-1 for TXN and CZM, respectively. The methods were validated according to the ICH guidelines and the results were statistically compared to the manufacturer's method.

Validation of four different spectrophotometric methods for simultaneous determination of Domperidone and Ranitidine in bulk and pharmaceutical formulation.

PubMed

Abdel-Ghany, Maha F; Abdel-Aziz, Omar; Mohammed, Yomna Y

2015-01-01

Four simple, specific, accurate and precise spectrophotometric methods were developed and validated for simultaneous determination of Domperidone (DP) and Ranitidine Hydrochloride (RT) in bulk powder and pharmaceutical formulation. The first method was simultaneous ratio subtraction (SRS), the second was ratio subtraction (RS) coupled with zero order spectrophotometry (D(0)), the third was first derivative of the ratio spectra ((1)DD) and the fourth method was mean centering of ratio spectra (MCR). The calibration curve is linear over the concentration range of 0.5-5 and 1-45 μg mL(-1) for DP and RT, respectively. The proposed spectrophotometric methods can analyze both drugs without any prior separation steps. The selectivity of the adopted methods was tested by analyzing synthetic mixtures of the investigated drugs, also in their pharmaceutical formulation. The suggested methods were validated according to International Conference of Harmonization (ICH) guidelines and the results revealed that; they were precise and reproducible. All the obtained results were statistically compared with those of the reported method, where there was no significant difference. Copyright © 2015 Elsevier B.V. All rights reserved.

On the radiated EMI current extraction of dc transmission line based on corona current statistical measurements

NASA Astrophysics Data System (ADS)

Yi, Yong; Chen, Zhengying; Wang, Liming

2018-05-01

Corona-originated discharge of DC transmission lines is the main reason for the radiated electromagnetic interference (EMI) field in the vicinity of transmission lines. A joint time-frequency analysis technique was proposed to extract the radiated EMI current (excitation current) of DC corona based on corona current statistical measurements. A reduced-scale experimental platform was setup to measure the statistical distributions of current waveform parameters of aluminum conductor steel reinforced. Based on the measured results, the peak value, root-mean-square value and average value with 9 kHz and 200 Hz band-with of 0.5 MHz radiated EMI current were calculated by the technique proposed and validated with conventional excitation function method. Radio interference (RI) was calculated based on the radiated EMI current and a wire-to-plate platform was built for the validity of the RI computation results. The reason for the certain deviation between the computations and measurements was detailed analyzed.

[Quantitative structure-gas chromatographic retention relationship of polycyclic aromatic sulfur heterocycles using molecular electronegativity-distance vector].

PubMed

Li, Zhenghua; Cheng, Fansheng; Xia, Zhining

2011-01-01

The chemical structures of 114 polycyclic aromatic sulfur heterocycles (PASHs) have been studied by molecular electronegativity-distance vector (MEDV). The linear relationships between gas chromatographic retention index and the MEDV have been established by a multiple linear regression (MLR) model. The results of variable selection by stepwise multiple regression (SMR) and the powerful predictive abilities of the optimization model appraised by leave-one-out cross-validation showed that the optimization model with the correlation coefficient (R) of 0.994 7 and the cross-validated correlation coefficient (Rcv) of 0.994 0 possessed the best statistical quality. Furthermore, when the 114 PASHs compounds were divided into calibration and test sets in the ratio of 2:1, the statistical analysis showed our models possesses almost equal statistical quality, the very similar regression coefficients and the good robustness. The quantitative structure-retention relationship (QSRR) model established may provide a convenient and powerful method for predicting the gas chromatographic retention of PASHs.

New Sentinel-2 radiometric validation approaches (SEOM program)

NASA Astrophysics Data System (ADS)

Bruniquel, Véronique; Lamquin, Nicolas; Ferron, Stéphane; Govaerts, Yves; Woolliams, Emma; Dilo, Arta; Gascon, Ferran

2016-04-01

SEOM is an ESA program element whose one of the objectives aims at launching state-of-the-art studies for the scientific exploitation of operational missions. In the frame of this program, ESA awarded ACRI-ST and its partners Rayference and National Physical Laboratory (NPL) early 2016 for a R&D study on the development and intercomparison of algorithms for validating the Sentinel-2 radiometric L1 data products beyond the baseline algorithms used operationally in the frame of the S2 Mission Performance Centre. In this context, several algorithms have been proposed and are currently in development: The first one is based on the exploitation of Deep Convective Cloud (DCC) observations over ocean. This method allows an inter-band radiometry validation from the blue to the NIR (typically from B1 to B8a) from a reference band already validated for example with the well-known Rayleigh method. Due to their physical properties, DCCs appear from the remote sensing point of view to have bright and cold tops and they can be used as invariant targets to monitor the radiometric response degradation of reflective solar bands. The DCC approach is statistical i.e. the method shall be applied on a large number of measurements to derive reliable statistics and decrease the impact of the perturbing contributors. The second radiometric validation method is based on the exploitation of matchups combining both concomitant in-situ measurements and Sentinel-2 observations. The in-situ measurements which are used here correspond to measurements acquired in the frame of the RadCalNet networks. The validation is performed for the Sentinel-2 bands similar to the bands of the instruments equipping the validation site. The measurements from the Cimel CE 318 12-filters BRDF Sun Photometer installed recently in the Gobabeb site near the Namib desert are used for this method. A comprehensive verification of the calibration requires an analysis of MSI radiances over the full dynamic range, including low radiances, as extreme values are more subject to instrument response non-linearity. The third method developed in the frame of this project aims to address this point. It is based on a comparison of Sentinel-2 observations over coastal waters which have low radiometry and corresponding Radiative Transfer (RT) simulations using AERONET-OC measurements. Finally, a last method is developed using RadCalNet measurements and Sentinel-2 observations to validate the radiometry of mid/low resolution sensors such as Sentinel-3/OLCI. The RadCalNet measurements are transferred from the RadCalNet sites to Pseudo Invariant Calibration Sites (PICS) using Sentinel-2, and then these larger sites are used to validate mid- and low-resolution sensors to the RadCalNet reference. For all the developed methods, an uncertainty budget is derived following QA4EO guidelines. A last step of this ESA project is dedicated to an Inter-comparison Workshop open to entities involved in Sentinel-2 radiometric validation activities. Blind inter-comparison tests over a series of images will be proposed and the results will be discussed during the workshop.

A comparative study of the novel spectrophotometric methods versus conventional ones for the simultaneous determination of Esomeprazole magnesium trihydrate and Naproxen in their binary mixture.

PubMed

Lotfy, Hayam M; Amer, Sawsan M; Zaazaa, Hala E; Mostafa, Noha S

2015-01-01

Two novel simple, specific, accurate and precise spectrophotometric methods manipulating ratio spectra are developed and validated for simultaneous determination of Esomeprazole magnesium trihydrate (ESO) and Naproxen (NAP) namely; absorbance subtraction and ratio difference. The results were compared to that of the conventional spectrophotometric methods namely; dual wavelength and isoabsorptive point coupled with first derivative of ratio spectra and derivative ratio. The suggested methods were validated in compliance with the ICH guidelines and were successfully applied for determination of ESO and NAP in their laboratory prepared mixtures and pharmaceutical preparation. No preliminary separation steps are required for the proposed spectrophotometeric procedures. The statistical comparison showed that there is no significant difference between the proposed methods and the reported method with respect to both accuracy and precision. Copyright © 2015 Elsevier B.V. All rights reserved.

An experimental validation of a statistical-based damage detection approach.

DOT National Transportation Integrated Search

2011-01-01

In this work, a previously-developed, statistical-based, damage-detection approach was validated for its ability to : autonomously detect damage in bridges. The damage-detection approach uses statistical differences in the actual and : predicted beha...

Software for Statistical Analysis of Weibull Distributions with Application to Gear Fatigue Data: User Manual with Verification

NASA Technical Reports Server (NTRS)

Krantz, Timothy L.

2002-01-01

The Weibull distribution has been widely adopted for the statistical description and inference of fatigue data. This document provides user instructions, examples, and verification for software to analyze gear fatigue test data. The software was developed presuming the data are adequately modeled using a two-parameter Weibull distribution. The calculations are based on likelihood methods, and the approach taken is valid for data that include type 1 censoring. The software was verified by reproducing results published by others.

Software for Statistical Analysis of Weibull Distributions with Application to Gear Fatigue Data: User Manual with Verification

NASA Technical Reports Server (NTRS)

Kranz, Timothy L.

2002-01-01

The Weibull distribution has been widely adopted for the statistical description and inference of fatigue data. This document provides user instructions, examples, and verification for software to analyze gear fatigue test data. The software was developed presuming the data are adequately modeled using a two-parameter Weibull distribution. The calculations are based on likelihood methods, and the approach taken is valid for data that include type I censoring. The software was verified by reproducing results published by others.

How Attitudes towards Statistics Courses and the Field of Statistics Predicts Statistics Anxiety among Undergraduate Social Science Majors: A Validation of the Statistical Anxiety Scale

ERIC Educational Resources Information Center

O'Bryant, Monique J.

2017-01-01

The aim of this study was to validate an instrument that can be used by instructors or social scientist who are interested in evaluating statistics anxiety. The psychometric properties of the English version of the Statistical Anxiety Scale (SAS) was examined through a confirmatory factor analysis of scores from a sample of 323 undergraduate…

Validation of a low cost computer-based method for quantification of immunohistochemistry-stained sections.

PubMed

Montgomery, Jill D; Hensler, Heather R; Jacobson, Lisa P; Jenkins, Frank J

2008-07-01

The aim of the present study was to determine if the Alpha DigiDoc RT system would be an effective method of quantifying immunohistochemical staining as compared with a manual counting method, which is considered the gold standard. Two readers were used to count 31 samples by both methods. The results obtained using the Bland-Altman for concordance deemed no statistical difference between the 2 methods. Thus, the Alpha DigiDoc RT system is an effective, low cost method to quantify immunohistochemical data.

Practical Bias Correction in Aerial Surveys of Large Mammals: Validation of Hybrid Double-Observer with Sightability Method against Known Abundance of Feral Horse (Equus caballus) Populations

PubMed Central

2016-01-01

Reliably estimating wildlife abundance is fundamental to effective management. Aerial surveys are one of the only spatially robust tools for estimating large mammal populations, but statistical sampling methods are required to address detection biases that affect accuracy and precision of the estimates. Although various methods for correcting aerial survey bias are employed on large mammal species around the world, these have rarely been rigorously validated. Several populations of feral horses (Equus caballus) in the western United States have been intensively studied, resulting in identification of all unique individuals. This provided a rare opportunity to test aerial survey bias correction on populations of known abundance. We hypothesized that a hybrid method combining simultaneous double-observer and sightability bias correction techniques would accurately estimate abundance. We validated this integrated technique on populations of known size and also on a pair of surveys before and after a known number was removed. Our analysis identified several covariates across the surveys that explained and corrected biases in the estimates. All six tests on known populations produced estimates with deviations from the known value ranging from -8.5% to +13.7% and <0.7 standard errors. Precision varied widely, from 6.1% CV to 25.0% CV. In contrast, the pair of surveys conducted around a known management removal produced an estimated change in population between the surveys that was significantly larger than the known reduction. Although the deviation between was only 9.1%, the precision estimate (CV = 1.6%) may have been artificially low. It was apparent that use of a helicopter in those surveys perturbed the horses, introducing detection error and heterogeneity in a manner that could not be corrected by our statistical models. Our results validate the hybrid method, highlight its potentially broad applicability, identify some limitations, and provide insight and guidance for improving survey designs. PMID:27139732

Practical Bias Correction in Aerial Surveys of Large Mammals: Validation of Hybrid Double-Observer with Sightability Method against Known Abundance of Feral Horse (Equus caballus) Populations.

PubMed

Lubow, Bruce C; Ransom, Jason I

2016-01-01

Reliably estimating wildlife abundance is fundamental to effective management. Aerial surveys are one of the only spatially robust tools for estimating large mammal populations, but statistical sampling methods are required to address detection biases that affect accuracy and precision of the estimates. Although various methods for correcting aerial survey bias are employed on large mammal species around the world, these have rarely been rigorously validated. Several populations of feral horses (Equus caballus) in the western United States have been intensively studied, resulting in identification of all unique individuals. This provided a rare opportunity to test aerial survey bias correction on populations of known abundance. We hypothesized that a hybrid method combining simultaneous double-observer and sightability bias correction techniques would accurately estimate abundance. We validated this integrated technique on populations of known size and also on a pair of surveys before and after a known number was removed. Our analysis identified several covariates across the surveys that explained and corrected biases in the estimates. All six tests on known populations produced estimates with deviations from the known value ranging from -8.5% to +13.7% and <0.7 standard errors. Precision varied widely, from 6.1% CV to 25.0% CV. In contrast, the pair of surveys conducted around a known management removal produced an estimated change in population between the surveys that was significantly larger than the known reduction. Although the deviation between was only 9.1%, the precision estimate (CV = 1.6%) may have been artificially low. It was apparent that use of a helicopter in those surveys perturbed the horses, introducing detection error and heterogeneity in a manner that could not be corrected by our statistical models. Our results validate the hybrid method, highlight its potentially broad applicability, identify some limitations, and provide insight and guidance for improving survey designs.

A systematic review of the asymmetric inheritance of cellular organelles in eukaryotes: A critique of basic science validity and imprecision

PubMed Central

Collins, Anne; Ross, Janine

2017-01-01

We performed a systematic review to identify all original publications describing the asymmetric inheritance of cellular organelles in normal animal eukaryotic cells and to critique the validity and imprecision of the evidence. Searches were performed in Embase, MEDLINE and Pubmed up to November 2015. Screening of titles, abstracts and full papers was performed by two independent reviewers. Data extraction and validity were performed by one reviewer and checked by a second reviewer. Study quality was assessed using the SYRCLE risk of bias tool, for animal studies and by developing validity tools for the experimental model, organelle markers and imprecision. A narrative data synthesis was performed. We identified 31 studies (34 publications) of the asymmetric inheritance of organelles after mitotic or meiotic division. Studies for the asymmetric inheritance of centrosomes (n = 9); endosomes (n = 6), P granules (n = 4), the midbody (n = 3), mitochondria (n = 3), proteosomes (n = 2), spectrosomes (n = 2), cilia (n = 2) and endoplasmic reticulum (n = 2) were identified. Asymmetry was defined and quantified by variable methods. Assessment of the statistical reliability of the results indicated only two studies (7%) were judged to have low concern, the majority of studies (77%) were 'unclear' and five (16%) were judged to have 'high concerns'; the main reasons were low technical repeats (<10). Assessment of model validity indicated that the majority of studies (61%) were judged to be valid, ten studies (32%) were unclear and two studies (7%) were judged to have 'high concerns'; both described 'stem cells' without providing experimental evidence to confirm this (pluripotency and self-renewal). Assessment of marker validity indicated that no studies had low concern, most studies were unclear (96.5%), indicating there were insufficient details to judge if the markers were appropriate. One study had high concern for marker validity due to the contradictory results of two markers for the same organelle. For most studies the validity and imprecision of results could not be confirmed. In particular, data were limited due to a lack of reporting of interassay variability, sample size calculations, controls and functional validation of organelle markers. An evaluation of 16 systematic reviews containing cell assays found that only 50% reported adherence to PRISMA or ARRIVE reporting guidelines and 38% reported a formal risk of bias assessment. 44% of the reviews did not consider how relevant or valid the models were to the research question. 75% reviews did not consider how valid the markers were. 69% of reviews did not consider the impact of the statistical reliability of the results. Future systematic reviews in basic or preclinical research should ensure the rigorous reporting of the statistical reliability of the results in addition to the validity of the methods. Increased awareness of the importance of reporting guidelines and validation tools is needed for the scientific community. PMID:28562636

Survey of statistical techniques used in validation studies of air pollution prediction models

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

Bornstein, R D; Anderson, S F

1979-03-01

Statistical techniques used by meteorologists to validate predictions made by air pollution models are surveyed. Techniques are divided into the following three groups: graphical, tabular, and summary statistics. Some of the practical problems associated with verification are also discussed. Characteristics desired in any validation program are listed and a suggested combination of techniques that possesses many of these characteristics is presented.

Validating MEDIQUAL Constructs

NASA Astrophysics Data System (ADS)

Lee, Sang-Gun; Min, Jae H.

In this paper, we validate MEDIQUAL constructs through the different media users in help desk service. In previous research, only two end-users' constructs were used: assurance and responsiveness. In this paper, we extend MEDIQUAL constructs to include reliability, empathy, assurance, tangibles, and responsiveness, which are based on the SERVQUAL theory. The results suggest that: 1) five MEDIQUAL constructs are validated through the factor analysis. That is, importance of the constructs have relatively high correlations between measures of the same construct using different methods and low correlations between measures of the constructs that are expected to differ; and 2) five MEDIQUAL constructs are statistically significant on media users' satisfaction in help desk service by regression analysis.

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

PubMed

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

2012-03-15

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

Random forests for classification in ecology

USGS Publications Warehouse

Cutler, D.R.; Edwards, T.C.; Beard, K.H.; Cutler, A.; Hess, K.T.; Gibson, J.; Lawler, J.J.

2007-01-01

Classification procedures are some of the most widely used statistical methods in ecology. Random forests (RF) is a new and powerful statistical classifier that is well established in other disciplines but is relatively unknown in ecology. Advantages of RF compared to other statistical classifiers include (1) very high classification accuracy; (2) a novel method of determining variable importance; (3) ability to model complex interactions among predictor variables; (4) flexibility to perform several types of statistical data analysis, including regression, classification, survival analysis, and unsupervised learning; and (5) an algorithm for imputing missing values. We compared the accuracies of RF and four other commonly used statistical classifiers using data on invasive plant species presence in Lava Beds National Monument, California, USA, rare lichen species presence in the Pacific Northwest, USA, and nest sites for cavity nesting birds in the Uinta Mountains, Utah, USA. We observed high classification accuracy in all applications as measured by cross-validation and, in the case of the lichen data, by independent test data, when comparing RF to other common classification methods. We also observed that the variables that RF identified as most important for classifying invasive plant species coincided with expectations based on the literature. ?? 2007 by the Ecological Society of America.

29 CFR 1910.1001 - Asbestos.

Code of Federal Regulations, 2010 CFR

2010-07-01

.... The designation of a material as “PACM” may be rebutted pursuant to paragraph (j)(8) of this section... level as demonstrated by a statistically valid protocol; and (C) The equivalent method is documented and... PACM are in excess of the TWA and/or excursion limit prescribed in paragraph (c) of this section. (2...

29 CFR 1910.1001 - Asbestos.

Code of Federal Regulations, 2011 CFR

2011-07-01

.... The designation of a material as “PACM” may be rebutted pursuant to paragraph (j)(8) of this section... level as demonstrated by a statistically valid protocol; and (C) The equivalent method is documented and... PACM are in excess of the TWA and/or excursion limit prescribed in paragraph (c) of this section. (2...

A Deterministic Annealing Approach to Clustering AIRS Data

NASA Technical Reports Server (NTRS)

Guillaume, Alexandre; Braverman, Amy; Ruzmaikin, Alexander

2012-01-01

We will examine the validity of means and standard deviations as a basis for climate data products. We will explore the conditions under which these two simple statistics are inadequate summaries of the underlying empirical probability distributions by contrasting them with a nonparametric, method called Deterministic Annealing technique

Mapping the Classroom Emotional Environment

ERIC Educational Resources Information Center

Harvey, Shane T.; Bimler, David; Evans, Ian M.; Kirkland, John; Pechtel, Pia

2012-01-01

Harvey and Evans (2003) have proposed that teachers' emotional skills, as required in the classroom, can be organized into a five-dimensional model. Further research is necessary to validate this model and evaluate the importance of each dimension of teacher emotion competence for educational practice. Using a statistical method for mapping…

Assessing oral health-related quality of life in general dental practice in Scotland: validation of the OHIP-14.

PubMed

Fernandes, Marcelo José; Ruta, Danny Adolph; Ogden, Graham Richard; Pitts, Nigel Berry; Ogston, Simon Alexander

2006-02-01

To validate the Oral Health Impact Profile (OHIP)-14 in a sample of patients attending general dental practice. Patients with pathology-free impacted wisdom teeth were recruited from six general dental practices in Tayside, Scotland, and followed for a year to assess the development of problems related to impaction. The OHIP-14 was completed at baseline and at 1-year follow-up, and analysed using three different scoring methods: a summary score, a weighted and standardized score and the total number of problems reported. Instrument reliability was measured by assessing internal consistency and test-retest reliability. Construct validity was assessed using a number of variables. Linear regression was then used to model the relationship between OHIP-14 and all significantly correlated variables. Responsiveness was measured using the standardized response mean (SRM). Adjusted R(2)s and SRMs were calculated for each of the three scoring methods. Estimates for the differences between adjusted R(2)s and the differences between SRMs were obtained with 95% confidence intervals. A total of 278 and 169 patients completed the questionnaire at baseline and follow-up, respectively. Reliability - Cronbach's alpha coefficients ranged from 0.30 to 0.75. Alpha coefficients for all 14 items were 0.88 and 0.87 for baseline and follow-up, respectively. Test-retest coefficients ranged from 0.72 to 0.78. Validity - OHIP-14 scores were significantly correlated with number of teeth, education, main activity, the use of mouthwash, frequency of seeing a dentist, the reason for the last dental appointment, smoking, alcohol intake, pain and symptoms. Adjusted R(2)s ranged from 0.123 to 0.202 and there were no statistically significant differences between those for the three different scoring methods. Responsiveness - The SRMs ranged from 0.37 to 0.56 and there was a statistically significant difference between the summary scores method and the total number of problems method for symptomatic patients. The OHIP-14 is a valid and reliable measure of oral health-related quality of life in general dental practice and is responsive to third molar clinical change. The summary score method demonstrated performance as good as, or better than, the other methods studied.

On the Use of Biomineral Oxygen Isotope Data to Identify Human Migrants in the Archaeological Record: Intra-Sample Variation, Statistical Methods and Geographical Considerations

PubMed Central

Lightfoot, Emma; O’Connell, Tamsin C.

2016-01-01

Oxygen isotope analysis of archaeological skeletal remains is an increasingly popular tool to study past human migrations. It is based on the assumption that human body chemistry preserves the δ18O of precipitation in such a way as to be a useful technique for identifying migrants and, potentially, their homelands. In this study, the first such global survey, we draw on published human tooth enamel and bone bioapatite data to explore the validity of using oxygen isotope analyses to identify migrants in the archaeological record. We use human δ18O results to show that there are large variations in human oxygen isotope values within a population sample. This may relate to physiological factors influencing the preservation of the primary isotope signal, or due to human activities (such as brewing, boiling, stewing, differential access to water sources and so on) causing variation in ingested water and food isotope values. We compare the number of outliers identified using various statistical methods. We determine that the most appropriate method for identifying migrants is dependent on the data but is likely to be the IQR or median absolute deviation from the median under most archaeological circumstances. Finally, through a spatial assessment of the dataset, we show that the degree of overlap in human isotope values from different locations across Europe is such that identifying individuals’ homelands on the basis of oxygen isotope analysis alone is not possible for the regions analysed to date. Oxygen isotope analysis is a valid method for identifying first-generation migrants from an archaeological site when used appropriately, however it is difficult to identify migrants using statistical methods for a sample size of less than c. 25 individuals. In the absence of local previous analyses, each sample should be treated as an individual dataset and statistical techniques can be used to identify migrants, but in most cases pinpointing a specific homeland should not be attempted. PMID:27124001

Statistical Validation of Automatic Methods for Hippocampus Segmentation in MR Images of Epileptic Patients

PubMed Central

Hosseini, Mohammad-Parsa; Nazem-Zadeh, Mohammad R.; Pompili, Dario; Soltanian-Zadeh, Hamid

2015-01-01

Hippocampus segmentation is a key step in the evaluation of mesial Temporal Lobe Epilepsy (mTLE) by MR images. Several automated segmentation methods have been introduced for medical image segmentation. Because of multiple edges, missing boundaries, and shape changing along its longitudinal axis, manual outlining still remains the benchmark for hippocampus segmentation, which however, is impractical for large datasets due to time constraints. In this study, four automatic methods, namely FreeSurfer, Hammer, Automatic Brain Structure Segmentation (ABSS), and LocalInfo segmentation, are evaluated to find the most accurate and applicable method that resembles the bench-mark of hippocampus. Results from these four methods are compared against those obtained using manual segmentation for T1-weighted images of 157 symptomatic mTLE patients. For performance evaluation of automatic segmentation, Dice coefficient, Hausdorff distance, Precision, and Root Mean Square (RMS) distance are extracted and compared. Among these four automated methods, ABSS generates the most accurate results and the reproducibility is more similar to expert manual outlining by statistical validation. By considering p-value<0.05, the results of performance measurement for ABSS reveal that, Dice is 4%, 13%, and 17% higher, Hausdorff is 23%, 87%, and 70% lower, precision is 5%, -5%, and 12% higher, and RMS is 19%, 62%, and 65% lower compared to LocalInfo, FreeSurfer, and Hammer, respectively. PMID:25571043

Validated spectrofluorimetric method for determination of two phosphodiesterase inhibitors tadalafil and vardenafil in pharmaceutical preparations and spiked human plasma.

PubMed

Abu El-Enin, Mohammed Abu Bakr; Al-Ghaffar Hammouda, Mohammed El-Sayed Abd; El-Sherbiny, Dina Tawfik; El-Wasseef, Dalia Rashad; El-Ashry, Saadia Mahmoud

2016-02-01

A valid, sensitive and rapid spectrofluorimetric method has been developed and validated for determination of both tadalafil (TAD) and vardenafil (VAR) either in their pure form, in their tablet dosage forms or spiked in human plasma. This method is based on measurement of the native fluorescence of both drugs in acetonitrile at λem 330 and 470 nm after excitation at 280 and 275 nm for tadalafil and vardenafil, respectively. Linear relationships were obtained over the concentration range 4-40 and 10-250 ng/mL with a minimum detection of 1 and 3 ng/mL for tadalafil and vardenafil, respectively. Various experimental parameters affecting the fluorescence intensity were carefully studied and optimized. The developed method was applied successfully for the determination of tadalafil and vardenafil in bulk drugs and tablet dosage forms. Moreover, the high sensitivity of the proposed method permitted their determination in spiked human plasma. The developed method was validated in terms of specificity, linearity, lower limit of quantification (LOQ), lower limit of detection (LOD), precision and accuracy. The mean recoveries of the analytes in pharmaceutical preparations were in agreement with those obtained from the comparison methods, as revealed by statistical analysis of the obtained results using Student's t-test and the variance ratio F-test. Copyright © 2015 John Wiley & Sons, Ltd.

A statistical method for the conservative adjustment of false discovery rate (q-value).

PubMed

Lai, Yinglei

2017-03-14

q-value is a widely used statistical method for estimating false discovery rate (FDR), which is a conventional significance measure in the analysis of genome-wide expression data. q-value is a random variable and it may underestimate FDR in practice. An underestimated FDR can lead to unexpected false discoveries in the follow-up validation experiments. This issue has not been well addressed in literature, especially in the situation when the permutation procedure is necessary for p-value calculation. We proposed a statistical method for the conservative adjustment of q-value. In practice, it is usually necessary to calculate p-value by a permutation procedure. This was also considered in our adjustment method. We used simulation data as well as experimental microarray or sequencing data to illustrate the usefulness of our method. The conservativeness of our approach has been mathematically confirmed in this study. We have demonstrated the importance of conservative adjustment of q-value, particularly in the situation that the proportion of differentially expressed genes is small or the overall differential expression signal is weak.

Principles for valid histopathologic scoring in research

PubMed Central

Gibson-Corley, Katherine N.; Olivier, Alicia K.; Meyerholz, David K.

2013-01-01

Histopathologic scoring is a tool by which semi-quantitative data can be obtained from tissues. Initially, a thorough understanding of the experimental design, study objectives and methods are required to allow the pathologist to appropriately examine tissues and develop lesion scoring approaches. Many principles go into the development of a scoring system such as tissue examination, lesion identification, scoring definitions and consistency in interpretation. Masking (a.k.a. “blinding”) of the pathologist to experimental groups is often necessary to constrain bias and multiple mechanisms are available. Development of a tissue scoring system requires appreciation of the attributes and limitations of the data (e.g. nominal, ordinal, interval and ratio data) to be evaluated. Incidence, ordinal and rank methods of tissue scoring are demonstrated along with key principles for statistical analyses and reporting. Validation of a scoring system occurs through two principal measures: 1) validation of repeatability and 2) validation of tissue pathobiology. Understanding key principles of tissue scoring can help in the development and/or optimization of scoring systems so as to consistently yield meaningful and valid scoring data. PMID:23558974

Development and validation of challenge materials for double-blind, placebo-controlled food challenges in children.

PubMed

Vlieg-Boerstra, Berber J; Bijleveld, Charles M A; van der Heide, Sicco; Beusekamp, Berta J; Wolt-Plompen, Saskia A A; Kukler, Jeanet; Brinkman, Joep; Duiverman, Eric J; Dubois, Anthony E J

2004-02-01

The use of double-blind, placebo-controlled food challenges (DBPCFCs) is considered the gold standard for the diagnosis of food allergy. Despite this, materials and methods used in DBPCFCs have not been standardized. The purpose of this study was to develop and validate recipes for use in DBPCFCs in children by using allergenic foods, preferably in their usual edible form. Recipes containing milk, soy, cooked egg, raw whole egg, peanut, hazelnut, and wheat were developed. For each food, placebo and active test food recipes were developed that met the requirements of acceptable taste, allowance of a challenge dose high enough to elicit reactions in an acceptable volume, optimal matrix ingredients, and good matching of sensory properties of placebo and active test food recipes. Validation was conducted on the basis of sensory tests for difference by using the triangle test and the paired comparison test. Recipes were first tested by volunteers from the hospital staff and subsequently by a professional panel of food tasters in a food laboratory designed for sensory testing. Recipes were considered to be validated if no statistically significant differences were found. Twenty-seven recipes were developed and found to be valid by the volunteer panel. Of these 27 recipes, 17 could be validated by the professional panel. Sensory testing with appropriate statistical analysis allows for objective validation of challenge materials. We recommend the use of professional tasters in the setting of a food laboratory for best results.

Evaluation of methyl methanesulfonate, 2,6-diaminotoluene and 5-fluorouracil: Part of the Japanese center for the validation of alternative methods (JaCVAM) international validation study of the in vivo rat alkaline comet assay.

PubMed

Plappert-Helbig, Ulla; Junker-Walker, Ursula; Martus, Hans-Joerg

2015-07-01

As a part of the Japanese Center for the Validation of Alternative Methods (JaCVAM)-initiative international validation study of the in vivo rat alkaline comet assay (comet assay), we examined methyl methanesulfonate, 2,6-diaminotoluene, and 5-fluorouracil under coded test conditions. Rats were treated orally with the maximum tolerated dose (MTD) and two additional descending doses of the respective compounds. In the MMS treated groups liver and stomach showed significantly elevated DNA damage at each dose level and a significant dose-response relationship. 2,6-diaminotoluene induced significantly elevated DNA damage in the liver at each dose and a statistically significant dose-response relationship whereas no DNA damage was obtained in the stomach. 5-fluorouracil did not induce DNA damage in either liver or stomach. Copyright © 2015 Elsevier B.V. All rights reserved.

Using Optimal Test Assembly Methods for Shortening Patient-Reported Outcome Measures: Development and Validation of the Cochin Hand Function Scale-6: A Scleroderma Patient-Centered Intervention Network Cohort Study.

PubMed

Levis, Alexander W; Harel, Daphna; Kwakkenbos, Linda; Carrier, Marie-Eve; Mouthon, Luc; Poiraudeau, Serge; Bartlett, Susan J; Khanna, Dinesh; Malcarne, Vanessa L; Sauve, Maureen; van den Ende, Cornelia H M; Poole, Janet L; Schouffoer, Anne A; Welling, Joep; Thombs, Brett D

2016-11-01

To develop and validate a short form of the Cochin Hand Function Scale (CHFS), which measures hand disability, for use in systemic sclerosis, using objective criteria and reproducible techniques. Responses on the 18-item CHFS were obtained from English-speaking patients enrolled in the Scleroderma Patient-Centered Intervention Network Cohort. CHFS unidimensionality was verified using confirmatory factor analysis, and an item response theory model was fit to CHFS items. Optimal test assembly (OTA) methods identified a maximally precise short form for each possible form length between 1 and 17 items. The final short form selected was the form with the least number of items that maintained statistically equivalent convergent validity, compared to the full-length CHFS, with the Health Assessment Questionnaire (HAQ) disability index (DI) and the physical function domain of the 29-item Patient-Reported Outcomes Measurement Information System (PROMIS-29). There were 601 patients included. A 6-item short form of the CHFS (CHFS-6) was selected. The CHFS-6 had a Cronbach's alpha of 0.93. Correlations of the CHFS-6 summed score with HAQ DI (r = 0.79) and PROMIS-29 physical function (r = -0.54) were statistically equivalent to the CHFS (r = 0.81 and r = -0.56). The correlation with the full CHFS was high (r = 0.98). The OTA procedure generated a valid short form of the CHFS with minimal loss of information compared to the full-length form. The OTA method used was based on objective, prespecified criteria, but should be further studied for viability as a general procedure for shortening patient-reported outcome measures in health research. © 2016, American College of Rheumatology.

Use of the Monte Carlo Method for OECD Principles-Guided QSAR Modeling of SIRT1 Inhibitors.

PubMed

Kumar, Ashwani; Chauhan, Shilpi

2017-01-01

SIRT1 inhibitors offer therapeutic potential for the treatment of a number of diseases including cancer and human immunodeficiency virus infection. A diverse series of 45 compounds with reported SIRT1 inhibitory activity has been employed for the development of quantitative structure-activity relationship (QSAR) models using the Monte Carlo optimization method. This method makes use of simplified molecular input line entry system notation of the molecular structure. The QSAR models were built up according to OECD principles. Three subsets of three splits were examined and validated by respective external sets. All the three described models have good statistical quality. The best model has the following statistical characteristics: R 2  = 0.8350, Q 2 test  = 0.7491 for the test set and R 2  = 0.9655, Q 2 ext  = 0.9261 for the validation set. In the mechanistic interpretation, structural attributes responsible for the endpoint increase and decrease are defined. Further, the design of some prospective SIRT1 inhibitors is also presented on the basis of these structural attributes. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Bayesian statistical ionospheric tomography improved by incorporating ionosonde measurements

NASA Astrophysics Data System (ADS)

Norberg, Johannes; Virtanen, Ilkka I.; Roininen, Lassi; Vierinen, Juha; Orispää, Mikko; Kauristie, Kirsti; Lehtinen, Markku S.

2016-04-01

We validate two-dimensional ionospheric tomography reconstructions against EISCAT incoherent scatter radar measurements. Our tomography method is based on Bayesian statistical inversion with prior distribution given by its mean and covariance. We employ ionosonde measurements for the choice of the prior mean and covariance parameters and use the Gaussian Markov random fields as a sparse matrix approximation for the numerical computations. This results in a computationally efficient tomographic inversion algorithm with clear probabilistic interpretation. We demonstrate how this method works with simultaneous beacon satellite and ionosonde measurements obtained in northern Scandinavia. The performance is compared with results obtained with a zero-mean prior and with the prior mean taken from the International Reference Ionosphere 2007 model. In validating the results, we use EISCAT ultra-high-frequency incoherent scatter radar measurements as the ground truth for the ionization profile shape. We find that in comparison to the alternative prior information sources, ionosonde measurements improve the reconstruction by adding accurate information about the absolute value and the altitude distribution of electron density. With an ionosonde at continuous disposal, the presented method enhances stand-alone near-real-time ionospheric tomography for the given conditions significantly.

Interrelationships Between Receiver/Relative Operating Characteristics Display, Binomial, Logit, and Bayes' Rule Probability of Detection Methodologies

NASA Technical Reports Server (NTRS)

Generazio, Edward R.

2014-01-01

Unknown risks are introduced into failure critical systems when probability of detection (POD) capabilities are accepted without a complete understanding of the statistical method applied and the interpretation of the statistical results. The presence of this risk in the nondestructive evaluation (NDE) community is revealed in common statements about POD. These statements are often interpreted in a variety of ways and therefore, the very existence of the statements identifies the need for a more comprehensive understanding of POD methodologies. Statistical methodologies have data requirements to be met, procedures to be followed, and requirements for validation or demonstration of adequacy of the POD estimates. Risks are further enhanced due to the wide range of statistical methodologies used for determining the POD capability. Receiver/Relative Operating Characteristics (ROC) Display, simple binomial, logistic regression, and Bayes' rule POD methodologies are widely used in determining POD capability. This work focuses on Hit-Miss data to reveal the framework of the interrelationships between Receiver/Relative Operating Characteristics Display, simple binomial, logistic regression, and Bayes' Rule methodologies as they are applied to POD. Knowledge of these interrelationships leads to an intuitive and global understanding of the statistical data, procedural and validation requirements for establishing credible POD estimates.

Fault detection, isolation, and diagnosis of self-validating multifunctional sensors.

PubMed

Yang, Jing-Li; Chen, Yin-Sheng; Zhang, Li-Li; Sun, Zhen

2016-06-01

A novel fault detection, isolation, and diagnosis (FDID) strategy for self-validating multifunctional sensors is presented in this paper. The sparse non-negative matrix factorization-based method can effectively detect faults by using the squared prediction error (SPE) statistic, and the variables contribution plots based on SPE statistic can help to locate and isolate the faulty sensitive units. The complete ensemble empirical mode decomposition is employed to decompose the fault signals to a series of intrinsic mode functions (IMFs) and a residual. The sample entropy (SampEn)-weighted energy values of each IMFs and the residual are estimated to represent the characteristics of the fault signals. Multi-class support vector machine is introduced to identify the fault mode with the purpose of diagnosing status of the faulty sensitive units. The performance of the proposed strategy is compared with other fault detection strategies such as principal component analysis, independent component analysis, and fault diagnosis strategies such as empirical mode decomposition coupled with support vector machine. The proposed strategy is fully evaluated in a real self-validating multifunctional sensors experimental system, and the experimental results demonstrate that the proposed strategy provides an excellent solution to the FDID research topic of self-validating multifunctional sensors.

Application of a Subspace-Based Fault Detection Method to Industrial Structures

NASA Astrophysics Data System (ADS)

Mevel, L.; Hermans, L.; van der Auweraer, H.

1999-11-01

Early detection and localization of damage allow increased expectations of reliability, safety and reduction of the maintenance cost. This paper deals with the industrial validation of a technique to monitor the health of a structure in operating conditions (e.g. rotating machinery, civil constructions subject to ambient excitations, etc.) and to detect slight deviations in a modal model derived from in-operation measured data. In this paper, a statistical local approach based on covariance-driven stochastic subspace identification is proposed. The capabilities and limitations of the method with respect to health monitoring and damage detection are discussed and it is explained how the method can be practically used in industrial environments. After the successful validation of the proposed method on a few laboratory structures, its application to a sports car is discussed. The example illustrates that the method allows the early detection of a vibration-induced fatigue problem of a sports car.

A Methodological Approach to Small Area Estimation for the Behavioral Risk Factor Surveillance System

PubMed Central

Xu, Fang; Wallace, Robyn C.; Garvin, William; Greenlund, Kurt J.; Bartoli, William; Ford, Derek; Eke, Paul; Town, G. Machell

2016-01-01

Public health researchers have used a class of statistical methods to calculate prevalence estimates for small geographic areas with few direct observations. Many researchers have used Behavioral Risk Factor Surveillance System (BRFSS) data as a basis for their models. The aims of this study were to 1) describe a new BRFSS small area estimation (SAE) method and 2) investigate the internal and external validity of the BRFSS SAEs it produced. The BRFSS SAE method uses 4 data sets (the BRFSS, the American Community Survey Public Use Microdata Sample, Nielsen Claritas population totals, and the Missouri Census Geographic Equivalency File) to build a single weighted data set. Our findings indicate that internal and external validity tests were successful across many estimates. The BRFSS SAE method is one of several methods that can be used to produce reliable prevalence estimates in small geographic areas. PMID:27418213

Development and Validation of a Job Exposure Matrix for Physical Risk Factors in Low Back Pain

PubMed Central

Solovieva, Svetlana; Pehkonen, Irmeli; Kausto, Johanna; Miranda, Helena; Shiri, Rahman; Kauppinen, Timo; Heliövaara, Markku; Burdorf, Alex; Husgafvel-Pursiainen, Kirsti; Viikari-Juntura, Eira

2012-01-01

Objectives The aim was to construct and validate a gender-specific job exposure matrix (JEM) for physical exposures to be used in epidemiological studies of low back pain (LBP). Materials and Methods We utilized two large Finnish population surveys, one to construct the JEM and another to test matrix validity. The exposure axis of the matrix included exposures relevant to LBP (heavy physical work, heavy lifting, awkward trunk posture and whole body vibration) and exposures that increase the biomechanical load on the low back (arm elevation) or those that in combination with other known risk factors could be related to LBP (kneeling or squatting). Job titles with similar work tasks and exposures were grouped. Exposure information was based on face-to-face interviews. Validity of the matrix was explored by comparing the JEM (group-based) binary measures with individual-based measures. The predictive validity of the matrix against LBP was evaluated by comparing the associations of the group-based (JEM) exposures with those of individual-based exposures. Results The matrix includes 348 job titles, representing 81% of all Finnish job titles in the early 2000s. The specificity of the constructed matrix was good, especially in women. The validity measured with kappa-statistic ranged from good to poor, being fair for most exposures. In men, all group-based (JEM) exposures were statistically significantly associated with one-month prevalence of LBP. In women, four out of six group-based exposures showed an association with LBP. Conclusions The gender-specific JEM for physical exposures showed relatively high specificity without compromising sensitivity. The matrix can therefore be considered as a valid instrument for exposure assessment in large-scale epidemiological studies, when more precise but more labour-intensive methods are not feasible. Although the matrix was based on Finnish data we foresee that it could be applicable, with some modifications, in other countries with a similar level of technology. PMID:23152793

Validation of the Hospital Ethical Climate Survey for older people care.

PubMed

Suhonen, Riitta; Stolt, Minna; Katajisto, Jouko; Charalambous, Andreas; Olson, Linda L

2015-08-01

The exploration of the ethical climate in the care settings for older people is highlighted in the literature, and it has been associated with various aspects of clinical practice and nurses' jobs. However, ethical climate is seldom studied in the older people care context. Valid, reliable, feasible measures are needed for the measurement of ethical climate. This study aimed to test the reliability, validity, and sensitivity of the Hospital Ethical Climate Survey in healthcare settings for older people. A non-experimental cross-sectional study design was employed, and a survey using questionnaires, including the Hospital Ethical Climate Survey was used for data collection. Data were analyzed using descriptive statistics, inferential statistics, and multivariable methods. Survey data were collected from a sample of nurses working in the care settings for older people in Finland (N = 1513, n = 874, response rate = 58%) in 2011. This study was conducted according to good scientific inquiry guidelines, and ethical approval was obtained from the university ethics committee. The mean score for the Hospital Ethical Climate Survey total was 3.85 (standard deviation = 0.56). Cronbach's alpha was 0.92. Principal component analysis provided evidence for factorial validity. LISREL provided evidence for construct validity based on goodness-of-fit statistics. Pearson's correlations of 0.68-0.90 were found between the sub-scales and the Hospital Ethical Climate Survey. The Hospital Ethical Climate Survey was found able to reveal discrimination across care settings and proved to be a valid and reliable tool for measuring ethical climate in care settings for older people and sensitive enough to reveal variations across various clinical settings. The Finnish version of the Hospital Ethical Climate Survey, used mainly in the hospital settings previously, proved to be a valid instrument to be used in the care settings for older people. Further studies are due to analyze the factor structure and some items of the Hospital Ethical Climate Survey. © The Author(s) 2014.

Empirical validation of statistical parametric mapping for group imaging of fast neural activity using electrical impedance tomography.

PubMed

Packham, B; Barnes, G; Dos Santos, G Sato; Aristovich, K; Gilad, O; Ghosh, A; Oh, T; Holder, D

2016-06-01

Electrical impedance tomography (EIT) allows for the reconstruction of internal conductivity from surface measurements. A change in conductivity occurs as ion channels open during neural activity, making EIT a potential tool for functional brain imaging. EIT images can have  >10 000 voxels, which means statistical analysis of such images presents a substantial multiple testing problem. One way to optimally correct for these issues and still maintain the flexibility of complicated experimental designs is to use random field theory. This parametric method estimates the distribution of peaks one would expect by chance in a smooth random field of a given size. Random field theory has been used in several other neuroimaging techniques but never validated for EIT images of fast neural activity, such validation can be achieved using non-parametric techniques. Both parametric and non-parametric techniques were used to analyze a set of 22 images collected from 8 rats. Significant group activations were detected using both techniques (corrected p  <  0.05). Both parametric and non-parametric analyses yielded similar results, although the latter was less conservative. These results demonstrate the first statistical analysis of such an image set and indicate that such an analysis is an approach for EIT images of neural activity.

Empirical validation of statistical parametric mapping for group imaging of fast neural activity using electrical impedance tomography

PubMed Central

Packham, B; Barnes, G; dos Santos, G Sato; Aristovich, K; Gilad, O; Ghosh, A; Oh, T; Holder, D

2016-01-01

Abstract Electrical impedance tomography (EIT) allows for the reconstruction of internal conductivity from surface measurements. A change in conductivity occurs as ion channels open during neural activity, making EIT a potential tool for functional brain imaging. EIT images can have  >10 000 voxels, which means statistical analysis of such images presents a substantial multiple testing problem. One way to optimally correct for these issues and still maintain the flexibility of complicated experimental designs is to use random field theory. This parametric method estimates the distribution of peaks one would expect by chance in a smooth random field of a given size. Random field theory has been used in several other neuroimaging techniques but never validated for EIT images of fast neural activity, such validation can be achieved using non-parametric techniques. Both parametric and non-parametric techniques were used to analyze a set of 22 images collected from 8 rats. Significant group activations were detected using both techniques (corrected p  <  0.05). Both parametric and non-parametric analyses yielded similar results, although the latter was less conservative. These results demonstrate the first statistical analysis of such an image set and indicate that such an analysis is an approach for EIT images of neural activity. PMID:27203477

Confirmatory Factor Analysis of the Malay Version of the Confusion, Hubbub and Order Scale (CHAOS-6) among Myocardial Infarction Survivors in a Malaysian Cardiac Healthcare Facility

PubMed Central

Ganasegeran, Kurubaran; Selvaraj, Kamaraj; Rashid, Abdul

2017-01-01

Background The six item Confusion, Hubbub and Order Scale (CHAOS-6) has been validated as a reliable tool to measure levels of household disorder. We aimed to investigate the goodness of fit and reliability of a new Malay version of the CHAOS-6. Methods The original English version of the CHAOS-6 underwent forward-backward translation into the Malay language. The finalised Malay version was administered to 105 myocardial infarction survivors in a Malaysian cardiac health facility. We performed confirmatory factor analyses (CFAs) using structural equation modelling. A path diagram and fit statistics were yielded to determine the Malay version’s validity. Composite reliability was tested to determine the scale’s reliability. Results All 105 myocardial infarction survivors participated in the study. The CFA yielded a six-item, one-factor model with excellent fit statistics. Composite reliability for the single factor CHAOS-6 was 0.65, confirming that the scale is reliable for Malay speakers. Conclusion The Malay version of the CHAOS-6 was reliable and showed the best fit statistics for our study sample. We thus offer a simple, brief, validated, reliable and novel instrument to measure chaos, the Skala Kecelaruan, Keriuhan & Tertib Terubahsuai (CHAOS-6), for the Malaysian population. PMID:28951688

Analysis of Nonequivalent Assessments across Different Linguistic Groups Using a Mixed Methods Approach: Understanding the Causes of Differential Item Functioning by Cognitive Interviewing

ERIC Educational Resources Information Center

Benítez, Isabel; Padilla, José-Luis

2014-01-01

Differential item functioning (DIF) can undermine the validity of cross-lingual comparisons. While a lot of efficient statistics for detecting DIF are available, few general findings have been found to explain DIF results. The objective of the article was to study DIF sources by using a mixed method design. The design involves a quantitative phase…

Direct statistical modeling and its implications for predictive mapping in mining exploration

NASA Astrophysics Data System (ADS)

Sterligov, Boris; Gumiaux, Charles; Barbanson, Luc; Chen, Yan; Cassard, Daniel; Cherkasov, Sergey; Zolotaya, Ludmila

2010-05-01

Recent advances in geosciences make more and more multidisciplinary data available for mining exploration. This allowed developing methodologies for computing forecast ore maps from the statistical combination of such different input parameters, all based on an inverse problem theory. Numerous statistical methods (e.g. algebraic method, weight of evidence, Siris method, etc) with varying degrees of complexity in their development and implementation, have been proposed and/or adapted for ore geology purposes. In literature, such approaches are often presented through applications on natural examples and the results obtained can present specificities due to local characteristics. Moreover, though crucial for statistical computations, "minimum requirements" needed for input parameters (number of minimum data points, spatial distribution of objects, etc) are often only poorly expressed. From these, problems often arise when one has to choose between one and the other method for her/his specific question. In this study, a direct statistical modeling approach is developed in order to i) evaluate the constraints on the input parameters and ii) test the validity of different existing inversion methods. The approach particularly focused on the analysis of spatial relationships between location of points and various objects (e.g. polygons and /or polylines) which is particularly well adapted to constrain the influence of intrusive bodies - such as a granite - and faults or ductile shear-zones on spatial location of ore deposits (point objects). The method is designed in a way to insure a-dimensionality with respect to scale. In this approach, both spatial distribution and topology of objects (polygons and polylines) can be parametrized by the user (e.g. density of objects, length, surface, orientation, clustering). Then, the distance of points with respect to a given type of objects (polygons or polylines) is given using a probability distribution. The location of points is computed assuming either independency or different grades of dependency between the two probability distributions. The results show that i)polygons surface mean value, polylines length mean value, the number of objects and their clustering are critical and ii) the validity of the different tested inversion methods strongly depends on the relative importance and on the dependency between the parameters used. In addition, this combined approach of direct and inverse modeling offers an opportunity to test the robustness of the inferred distribution point laws with respect to the quality of the input data set.

Validation of stationary phases in (111)In-pentetreotide planar chromatography.

PubMed

Moreno-Ortega, E; Mena-Bares, L M; Maza-Muret, F R; Hidalgo-Ramos, F J; Vallejo-Casas, J A

2013-01-01

Since Pall-German stopped manufacturing ITLC-SG, it has become necessary to validate alternative stationary phases. To validate different stationary phases versus ITLC-SG Pall-Gelman in the determination of the radiochemical purity (RCP) of (111)In-pentetreotide ((111)In-Octreoscan) by planar chromatography. We conducted a case-control study, which included 66 (111)In-pentetreotide preparations. We determined the RCP by planar chromatography, using a freshly prepared solution of 0,1M sodium citrate (pH 5) and the following stationary phases: ITLC-SG (Pall-Gelman) (reference method), iTLC-SG (Varian), HPTLC silica gel 60 (Merck), Whatman 1, Whatman 3MM and Whatman 17. For each of the methods, we calculated: PRQ, relative front values (RF) of the radiopharmaceutical and free (111)In, chromatographic development time, resolution between peaks. We compared the results obtained with the reference method. The statistical analysis was performed using the SPSS program. The p value was calculated for the study of statistical significance. The highest resolution is obtained with HPTLC silica gel 60 (Merck). However, the chromatographic development time is too long (mean=33.62minutes). Greater resolution is obtained with iTLC-SG (Varian) than with the reference method, with lower chromatographic development time (mean=3.61minutes). Very low resolutions are obtained with Whatman paper, essentially with Whatman 1 and 3MM. Therefore, we do not recommend their use. Although iTLC-SG (Varian) and HPTLC silica gel 60 (Merck) are suitable alternatives to ITLC-SG (Pall-Gelman) in determining the RCP of (111)In-pentetreotide, iTLC-SG (Varian) is the method of choice due to its lower chromatographic development time. Copyright © 2012 Elsevier España, S.L. and SEMNIM. All rights reserved.

Validation of Statistical Predictive Models Meant to Select Melanoma Patients for Sentinel Lymph Node Biopsy

PubMed Central

Sabel, Michael S.; Rice, John D.; Griffith, Kent A.; Lowe, Lori; Wong, Sandra L.; Chang, Alfred E.; Johnson, Timothy M.; Taylor, Jeremy M.G.

2013-01-01

Introduction To identify melanoma patients at sufficiently low risk of nodal metastases who could avoid SLN biopsy (SLNB). Several statistical models have been proposed based upon patient/tumor characteristics, including logistic regression, classification trees, random forests and support vector machines. We sought to validate recently published models meant to predict sentinel node status. Methods We queried our comprehensive, prospectively-collected melanoma database for consecutive melanoma patients undergoing SLNB. Prediction values were estimated based upon 4 published models, calculating the same reported metrics: negative predictive value (NPV), rate of negative predictions (RNP), and false negative rate (FNR). Results Logistic regression performed comparably with our data when considering NPV (89.4% vs. 93.6%); however the model’s specificity was not high enough to significantly reduce the rate of biopsies (SLN reduction rate of 2.9%). When applied to our data, the classification tree produced NPV and reduction in biopsies rates that were lower 87.7% vs. 94.1% and 29.8% vs. 14.3%, respectively. Two published models could not be applied to our data due to model complexity and the use of proprietary software. Conclusions Published models meant to reduce the SLNB rate among patients with melanoma either underperformed when applied to our larger dataset, or could not be validated. Differences in selection criteria and histopathologic interpretation likely resulted in underperformance. Development of statistical predictive models must be created in a clinically applicable manner to allow for both validation and ultimately clinical utility. PMID:21822550

A powerful approach for association analysis incorporating imprinting effects

PubMed Central

Xia, Fan; Zhou, Ji-Yuan; Fung, Wing Kam

2011-01-01

Motivation: For a diallelic marker locus, the transmission disequilibrium test (TDT) is a simple and powerful design for genetic studies. The TDT was originally proposed for use in families with both parents available (complete nuclear families) and has further been extended to 1-TDT for use in families with only one of the parents available (incomplete nuclear families). Currently, the increasing interest of the influence of parental imprinting on heritability indicates the importance of incorporating imprinting effects into the mapping of association variants. Results: In this article, we extend the TDT-type statistics to incorporate imprinting effects and develop a series of new test statistics in a general two-stage framework for association studies. Our test statistics enjoy the nature of family-based designs that need no assumption of Hardy–Weinberg equilibrium. Also, the proposed methods accommodate complete and incomplete nuclear families with one or more affected children. In the simulation study, we verify the validity of the proposed test statistics under various scenarios, and compare the powers of the proposed statistics with some existing test statistics. It is shown that our methods greatly improve the power for detecting association in the presence of imprinting effects. We further demonstrate the advantage of our methods by the application of the proposed test statistics to a rheumatoid arthritis dataset. Contact: wingfung@hku.hk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:21798962

A powerful approach for association analysis incorporating imprinting effects.

PubMed

Xia, Fan; Zhou, Ji-Yuan; Fung, Wing Kam

2011-09-15

For a diallelic marker locus, the transmission disequilibrium test (TDT) is a simple and powerful design for genetic studies. The TDT was originally proposed for use in families with both parents available (complete nuclear families) and has further been extended to 1-TDT for use in families with only one of the parents available (incomplete nuclear families). Currently, the increasing interest of the influence of parental imprinting on heritability indicates the importance of incorporating imprinting effects into the mapping of association variants. In this article, we extend the TDT-type statistics to incorporate imprinting effects and develop a series of new test statistics in a general two-stage framework for association studies. Our test statistics enjoy the nature of family-based designs that need no assumption of Hardy-Weinberg equilibrium. Also, the proposed methods accommodate complete and incomplete nuclear families with one or more affected children. In the simulation study, we verify the validity of the proposed test statistics under various scenarios, and compare the powers of the proposed statistics with some existing test statistics. It is shown that our methods greatly improve the power for detecting association in the presence of imprinting effects. We further demonstrate the advantage of our methods by the application of the proposed test statistics to a rheumatoid arthritis dataset. wingfung@hku.hk Supplementary data are available at Bioinformatics online.

Software phantom with realistic speckle modeling for validation of image analysis methods in echocardiography

NASA Astrophysics Data System (ADS)

Law, Yuen C.; Tenbrinck, Daniel; Jiang, Xiaoyi; Kuhlen, Torsten

2014-03-01

Computer-assisted processing and interpretation of medical ultrasound images is one of the most challenging tasks within image analysis. Physical phenomena in ultrasonographic images, e.g., the characteristic speckle noise and shadowing effects, make the majority of standard methods from image analysis non optimal. Furthermore, validation of adapted computer vision methods proves to be difficult due to missing ground truth information. There is no widely accepted software phantom in the community and existing software phantoms are not exible enough to support the use of specific speckle models for different tissue types, e.g., muscle and fat tissue. In this work we propose an anatomical software phantom with a realistic speckle pattern simulation to _ll this gap and provide a exible tool for validation purposes in medical ultrasound image analysis. We discuss the generation of speckle patterns and perform statistical analysis of the simulated textures to obtain quantitative measures of the realism and accuracy regarding the resulting textures.

Model selection and assessment for multi­-species occupancy models

USGS Publications Warehouse

Broms, Kristin M.; Hooten, Mevin B.; Fitzpatrick, Ryan M.

2016-01-01

While multi-species occupancy models (MSOMs) are emerging as a popular method for analyzing biodiversity data, formal checking and validation approaches for this class of models have lagged behind. Concurrent with the rise in application of MSOMs among ecologists, a quiet regime shift is occurring in Bayesian statistics where predictive model comparison approaches are experiencing a resurgence. Unlike single-species occupancy models that use integrated likelihoods, MSOMs are usually couched in a Bayesian framework and contain multiple levels. Standard model checking and selection methods are often unreliable in this setting and there is only limited guidance in the ecological literature for this class of models. We examined several different contemporary Bayesian hierarchical approaches for checking and validating MSOMs and applied these methods to a freshwater aquatic study system in Colorado, USA, to better understand the diversity and distributions of plains fishes. Our findings indicated distinct differences among model selection approaches, with cross-validation techniques performing the best in terms of prediction.

Validity and Interrater Reliability of the Visual Quarter-Waste Method for Assessing Food Waste in Middle School and High School Cafeteria Settings.

PubMed

Getts, Katherine M; Quinn, Emilee L; Johnson, Donna B; Otten, Jennifer J

2017-11-01

Measuring food waste (ie, plate waste) in school cafeterias is an important tool to evaluate the effectiveness of school nutrition policies and interventions aimed at increasing consumption of healthier meals. Visual assessment methods are frequently applied in plate waste studies because they are more convenient than weighing. The visual quarter-waste method has become a common tool in studies of school meal waste and consumption, but previous studies of its validity and reliability have used correlation coefficients, which measure association but not necessarily agreement. The aims of this study were to determine, using a statistic measuring interrater agreement, whether the visual quarter-waste method is valid and reliable for assessing food waste in a school cafeteria setting when compared with the gold standard of weighed plate waste. To evaluate validity, researchers used the visual quarter-waste method and weighed food waste from 748 trays at four middle schools and five high schools in one school district in Washington State during May 2014. To assess interrater reliability, researcher pairs independently assessed 59 of the same trays using the visual quarter-waste method. Both validity and reliability were assessed using a weighted κ coefficient. For validity, as compared with the measured weight, 45% of foods assessed using the visual quarter-waste method were in almost perfect agreement, 42% of foods were in substantial agreement, 10% were in moderate agreement, and 3% were in slight agreement. For interrater reliability between pairs of visual assessors, 46% of foods were in perfect agreement, 31% were in almost perfect agreement, 15% were in substantial agreement, and 8% were in moderate agreement. These results suggest that the visual quarter-waste method is a valid and reliable tool for measuring plate waste in school cafeteria settings. Copyright © 2017 Academy of Nutrition and Dietetics. Published by Elsevier Inc. All rights reserved.

Risk-based Methodology for Validation of Pharmaceutical Batch Processes.

PubMed

Wiles, Frederick

2013-01-01

In January 2011, the U.S. Food and Drug Administration published new process validation guidance for pharmaceutical processes. The new guidance debunks the long-held industry notion that three consecutive validation batches or runs are all that are required to demonstrate that a process is operating in a validated state. Instead, the new guidance now emphasizes that the level of monitoring and testing performed during process performance qualification (PPQ) studies must be sufficient to demonstrate statistical confidence both within and between batches. In some cases, three qualification runs may not be enough. Nearly two years after the guidance was first published, little has been written defining a statistical methodology for determining the number of samples and qualification runs required to satisfy Stage 2 requirements of the new guidance. This article proposes using a combination of risk assessment, control charting, and capability statistics to define the monitoring and testing scheme required to show that a pharmaceutical batch process is operating in a validated state. In this methodology, an assessment of process risk is performed through application of a process failure mode, effects, and criticality analysis (PFMECA). The output of PFMECA is used to select appropriate levels of statistical confidence and coverage which, in turn, are used in capability calculations to determine when significant Stage 2 (PPQ) milestones have been met. The achievement of Stage 2 milestones signals the release of batches for commercial distribution and the reduction of monitoring and testing to commercial production levels. Individuals, moving range, and range/sigma charts are used in conjunction with capability statistics to demonstrate that the commercial process is operating in a state of statistical control. The new process validation guidance published by the U.S. Food and Drug Administration in January of 2011 indicates that the number of process validation batches or runs required to demonstrate that a pharmaceutical process is operating in a validated state should be based on sound statistical principles. The old rule of "three consecutive batches and you're done" is no longer sufficient. The guidance, however, does not provide any specific methodology for determining the number of runs required, and little has been published to augment this shortcoming. The paper titled "Risk-based Methodology for Validation of Pharmaceutical Batch Processes" describes a statistically sound methodology for determining when a statistically valid number of validation runs has been acquired based on risk assessment and calculation of process capability.

FLiGS Score: A New Method of Outcome Assessment for Lip Carcinoma–Treated Patients

PubMed Central

Grassi, Rita; Toia, Francesca; Di Rosa, Luigi; Cordova, Adriana

2015-01-01

Background: Lip cancer and its treatment have considerable functional and cosmetic effects with resultant nutritional and physical detriments. As we continue to investigate new treatment regimens, we are simultaneously required to assess postoperative outcomes to design interventions that lessen the adverse impact of this disease process. We wish to introduce Functional Lip Glasgow Scale (FLiGS) score as a new method of outcome assessment to measure the effect of lip cancer and its treatment on patients’ daily functioning. Methods: Fifty patients affected by lip squamous cell carcinoma were recruited between 2009 and 2013. Patients were asked to fill the FLiGS questionnaire before surgery, 1 month, 6 months, and 1 year after surgery. The subscores were used to calculate a total FLiGS score of global oral disability. Statistical analysis was performed to test validity and reliability. Results: FLiGS scores improved significantly from preoperative to 12 months postoperative values (P = 0.000). Statistical evidence of validity was provided through rs (Spearman correlation coefficient) that resulted >0.30 for all surveys and for which P < 0.001. FLiGS score reliability was shown through examination of internal consistency and test-retest reliability. Conclusions: FLiGS score is a simple way of assessing functional impairment related to lip cancer before and after surgery; it is sensitive, valid, reliable, and clinically relevant: it provides useful information to orient the physician in the postoperative management and in the rehabilitation program. PMID:26034652

Development and validation of LC-MS/MS method for the quantification of oxcarbazepine in human plasma using an experimental design.

PubMed

Srinubabu, Gedela; Ratnam, Bandaru Veera Venkata; Rao, Allam Appa; Rao, Medicherla Narasimha

2008-01-01

A rapid tandem mass spectrometric (MS-MS) method for the quantification of Oxcarbazepine (OXB) in human plasma using imipramine as an internal standard (IS) has been developed and validated. Chromatographic separation was achieved isocratically on a C18 reversed-phase column within 3.0 min, using a mobile phase of acetonitrile-10 mM ammonium formate (90 : 10 v/v) at a flow rate of 0.3 ml/min. Quantitation was achieved using multiple reaction monitoring (MRM) scan at MRM transitions m/z 253>208 and m/z 281>86 for OXB and the IS respectively. Calibration curves were linear over the concentration range of 0.2-16 mug/ml (r>0.999) with a limit of quantification of 0.2 mug/ml. Analytical recoveries of OXB from spiked human plasma were in the range of 74.9 to 76.3%. Plackett-Burman design was applied for screening of chromatographic and mass spectrometric factors; factorial design was applied for optimization of essential factors for the robustness study. A linear model was postulated and a 2(3) full factorial design was employed to estimate the model coefficients for intermediate precision. More specifically, experimental design helps the researcher to verify if changes in factor values produce a statistically significant variation of the observed response. The strategy is most effective if statistical design is used in most or all stages of the screening and optimizing process for future method validation of pharmacokinetic and bioequivalence studies.

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

PubMed

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

2012-08-01

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

Methodology and issues of integral experiments selection for nuclear data validation

NASA Astrophysics Data System (ADS)

Tatiana, Ivanova; Ivanov, Evgeny; Hill, Ian

2017-09-01

Nuclear data validation involves a large suite of Integral Experiments (IEs) for criticality, reactor physics and dosimetry applications. [1] Often benchmarks are taken from international Handbooks. [2, 3] Depending on the application, IEs have different degrees of usefulness in validation, and usually the use of a single benchmark is not advised; indeed, it may lead to erroneous interpretation and results. [1] This work aims at quantifying the importance of benchmarks used in application dependent cross section validation. The approach is based on well-known General Linear Least Squared Method (GLLSM) extended to establish biases and uncertainties for given cross sections (within a given energy interval). The statistical treatment results in a vector of weighting factors for the integral benchmarks. These factors characterize the value added by a benchmark for nuclear data validation for the given application. The methodology is illustrated by one example, selecting benchmarks for 239Pu cross section validation. The studies were performed in the framework of Subgroup 39 (Methods and approaches to provide feedback from nuclear and covariance data adjustment for improvement of nuclear data files) established at the Working Party on International Nuclear Data Evaluation Cooperation (WPEC) of the Nuclear Science Committee under the Nuclear Energy Agency (NEA/OECD).

Systematic survey of the design, statistical analysis, and reporting of studies published in the 2008 volume of the Journal of Cerebral Blood Flow and Metabolism.

PubMed

Vesterinen, Hanna M; Vesterinen, Hanna V; Egan, Kieren; Deister, Amelie; Schlattmann, Peter; Macleod, Malcolm R; Dirnagl, Ulrich

2011-04-01

Translating experimental findings into clinically effective therapies is one of the major bottlenecks of modern medicine. As this has been particularly true for cerebrovascular research, attention has turned to the quality and validity of experimental cerebrovascular studies. We set out to assess the study design, statistical analyses, and reporting of cerebrovascular research. We assessed all original articles published in the Journal of Cerebral Blood Flow and Metabolism during the year 2008 against a checklist designed to capture the key attributes relating to study design, statistical analyses, and reporting. A total of 156 original publications were included (animal, in vitro, human). Few studies reported a primary research hypothesis, statement of purpose, or measures to safeguard internal validity (such as randomization, blinding, exclusion or inclusion criteria). Many studies lacked sufficient information regarding methods and results to form a reasonable judgment about their validity. In nearly 20% of studies, statistical tests were either not appropriate or information to allow assessment of appropriateness was lacking. This study identifies a number of factors that should be addressed if the quality of research in basic and translational biomedicine is to be improved. We support the widespread implementation of the ARRIVE (Animal Research Reporting In Vivo Experiments) statement for the reporting of experimental studies in biomedicine, for improving training in proper study design and analysis, and that reviewers and editors adopt a more constructively critical approach in the assessment of manuscripts for publication.

Systematic survey of the design, statistical analysis, and reporting of studies published in the 2008 volume of the Journal of Cerebral Blood Flow and Metabolism

PubMed Central

Vesterinen, Hanna V; Egan, Kieren; Deister, Amelie; Schlattmann, Peter; Macleod, Malcolm R; Dirnagl, Ulrich

2011-01-01

Translating experimental findings into clinically effective therapies is one of the major bottlenecks of modern medicine. As this has been particularly true for cerebrovascular research, attention has turned to the quality and validity of experimental cerebrovascular studies. We set out to assess the study design, statistical analyses, and reporting of cerebrovascular research. We assessed all original articles published in the Journal of Cerebral Blood Flow and Metabolism during the year 2008 against a checklist designed to capture the key attributes relating to study design, statistical analyses, and reporting. A total of 156 original publications were included (animal, in vitro, human). Few studies reported a primary research hypothesis, statement of purpose, or measures to safeguard internal validity (such as randomization, blinding, exclusion or inclusion criteria). Many studies lacked sufficient information regarding methods and results to form a reasonable judgment about their validity. In nearly 20% of studies, statistical tests were either not appropriate or information to allow assessment of appropriateness was lacking. This study identifies a number of factors that should be addressed if the quality of research in basic and translational biomedicine is to be improved. We support the widespread implementation of the ARRIVE (Animal Research Reporting In Vivo Experiments) statement for the reporting of experimental studies in biomedicine, for improving training in proper study design and analysis, and that reviewers and editors adopt a more constructively critical approach in the assessment of manuscripts for publication. PMID:21157472

Usefulness and limitations of various guinea-pig test methods in detecting human skin sensitizers-validation of guinea-pig tests for skin hypersensitivity.

PubMed

Marzulli, F; Maguire, H C

1982-02-01

Several guinea-pig predictive test methods were evaluated by comparison of results with those obtained with human predictive tests, using ten compounds that have been used in cosmetics. The method involves the statistical analysis of the frequency with which guinea-pig tests agree with the findings of tests in humans. In addition, the frequencies of false positive and false negative predictive findings are considered and statistically analysed. The results clearly demonstrate the superiority of adjuvant tests (complete Freund's adjuvant) in determining skin sensitizers and the overall superiority of the guinea-pig maximization test in providing results similar to those obtained by human testing. A procedure is suggested for utilizing adjuvant and non-adjuvant test methods for characterizing compounds as of weak, moderate or strong sensitizing potential.

Guidelines for the design and statistical analysis of experiments in papers submitted to ATLA.

PubMed

Festing, M F

2001-01-01

In vitro experiments need to be well designed and correctly analysed if they are to achieve their full potential to replace the use of animals in research. An "experiment" is a procedure for collecting scientific data in order to answer a hypothesis, or to provide material for generating new hypotheses, and differs from a survey because the scientist has control over the treatments that can be applied. Most experiments can be classified into one of a few formal designs, the most common being completely randomised, and randomised block designs. These are quite common with in vitro experiments, which are often replicated in time. Some experiments involve a single independent (treatment) variable, while other "factorial" designs simultaneously vary two or more independent variables, such as drug treatment and cell line. Factorial designs often provide additional information at little extra cost. Experiments need to be carefully planned to avoid bias, be powerful yet simple, provide for a valid statistical analysis and, in some cases, have a wide range of applicability. Virtually all experiments need some sort of statistical analysis in order to take account of biological variation among the experimental subjects. Parametric methods using the t test or analysis of variance are usually more powerful than non-parametric methods, provided the underlying assumptions of normality of the residuals and equal variances are approximately valid. The statistical analyses of data from a completely randomised design, and from a randomised-block design are demonstrated in Appendices 1 and 2, and methods of determining sample size are discussed in Appendix 3. Appendix 4 gives a checklist for authors submitting papers to ATLA.

Development and Validation of an Agency for Healthcare Research and Quality Indicator for Mortality After Congenital Heart Surgery Harmonized With Risk Adjustment for Congenital Heart Surgery (RACHS-1) Methodology.

PubMed

Jenkins, Kathy J; Koch Kupiec, Jennifer; Owens, Pamela L; Romano, Patrick S; Geppert, Jeffrey J; Gauvreau, Kimberlee

2016-05-20

The National Quality Forum previously approved a quality indicator for mortality after congenital heart surgery developed by the Agency for Healthcare Research and Quality (AHRQ). Several parameters of the validated Risk Adjustment for Congenital Heart Surgery (RACHS-1) method were included, but others differed. As part of the National Quality Forum endorsement maintenance process, developers were asked to harmonize the 2 methodologies. Parameters that were identical between the 2 methods were retained. AHRQ's Healthcare Cost and Utilization Project State Inpatient Databases (SID) 2008 were used to select optimal parameters where differences existed, with a goal to maximize model performance and face validity. Inclusion criteria were not changed and included all discharges for patients <18 years with International Classification of Diseases, Ninth Revision, Clinical Modification procedure codes for congenital heart surgery or nonspecific heart surgery combined with congenital heart disease diagnosis codes. The final model includes procedure risk group, age (0-28 days, 29-90 days, 91-364 days, 1-17 years), low birth weight (500-2499 g), other congenital anomalies (Clinical Classifications Software 217, except for 758.xx), multiple procedures, and transfer-in status. Among 17 945 eligible cases in the SID 2008, the c statistic for model performance was 0.82. In the SID 2013 validation data set, the c statistic was 0.82. Risk-adjusted mortality rates by center ranged from 0.9% to 4.1% (5th-95th percentile). Congenital heart surgery programs can now obtain national benchmarking reports by applying AHRQ Quality Indicator software to hospital administrative data, based on the harmonized RACHS-1 method, with high discrimination and face validity. © 2016 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley Blackwell.

Assessment of the Validity of the Research Diagnostic Criteria for Temporomandibular Disorders: Overview and Methodology

PubMed Central

Schiffman, Eric L.; Truelove, Edmond L.; Ohrbach, Richard; Anderson, Gary C.; John, Mike T.; List, Thomas; Look, John O.

2011-01-01

AIMS The purpose of the Research Diagnostic Criteria for Temporomandibular Disorders (RDC/TMD) Validation Project was to assess the diagnostic validity of this examination protocol. An overview is presented, including Axis I and II methodology and descriptive statistics for the study participant sample. This paper details the development of reliable methods to establish the reference standards for assessing criterion validity of the Axis I RDC/TMD diagnoses. Validity testing for the Axis II biobehavioral instruments was based on previously validated reference standards. METHODS The Axis I reference standards were based on the consensus of 2 criterion examiners independently performing a comprehensive history, clinical examination, and evaluation of imaging. Intersite reliability was assessed annually for criterion examiners and radiologists. Criterion exam reliability was also assessed within study sites. RESULTS Study participant demographics were comparable to those of participants in previous studies using the RDC/TMD. Diagnostic agreement of the criterion examiners with each other and with the consensus-based reference standards was excellent with all kappas ≥ 0.81, except for osteoarthrosis (moderate agreement, k = 0.53). Intrasite criterion exam agreement with reference standards was excellent (k ≥ 0.95). Intersite reliability of the radiologists for detecting computed tomography-disclosed osteoarthrosis and magnetic resonance imaging-disclosed disc displacement was good to excellent (k = 0.71 and 0.84, respectively). CONCLUSION The Validation Project study population was appropriate for assessing the reliability and validity of the RDC/TMD Axis I and II. The reference standards used to assess the validity of Axis I TMD were based on reliable and clinically credible methods. PMID:20213028

A hybrid model for traffic flow and crowd dynamics with random individual properties.

PubMed

Schleper, Veronika

2015-04-01

Based on an established mathematical model for the behavior of large crowds, a new model is derived that is able to take into account the statistical variation of individual maximum walking speeds. The same model is shown to be valid also in traffic flow situations, where for instance the statistical variation of preferred maximum speeds can be considered. The model involves explicit bounds on the state variables, such that a special Riemann solver is derived that is proved to respect the state constraints. Some care is devoted to a valid construction of random initial data, necessary for the use of the new model. The article also includes a numerical method that is shown to respect the bounds on the state variables and illustrative numerical examples, explaining the properties of the new model in comparison with established models.

Mass spectrometry-based protein identification with accurate statistical significance assignment.

PubMed

Alves, Gelio; Yu, Yi-Kuo

2015-03-01

Assigning statistical significance accurately has become increasingly important as metadata of many types, often assembled in hierarchies, are constructed and combined for further biological analyses. Statistical inaccuracy of metadata at any level may propagate to downstream analyses, undermining the validity of scientific conclusions thus drawn. From the perspective of mass spectrometry-based proteomics, even though accurate statistics for peptide identification can now be achieved, accurate protein level statistics remain challenging. We have constructed a protein ID method that combines peptide evidences of a candidate protein based on a rigorous formula derived earlier; in this formula the database P-value of every peptide is weighted, prior to the final combination, according to the number of proteins it maps to. We have also shown that this protein ID method provides accurate protein level E-value, eliminating the need of using empirical post-processing methods for type-I error control. Using a known protein mixture, we find that this protein ID method, when combined with the Sorić formula, yields accurate values for the proportion of false discoveries. In terms of retrieval efficacy, the results from our method are comparable with other methods tested. The source code, implemented in C++ on a linux system, is available for download at ftp://ftp.ncbi.nlm.nih.gov/pub/qmbp/qmbp_ms/RAId/RAId_Linux_64Bit. Published by Oxford University Press 2014. This work is written by US Government employees and is in the public domain in the US.

Methods for flexible sample-size design in clinical trials: Likelihood, weighted, dual test, and promising zone approaches.

PubMed

Shih, Weichung Joe; Li, Gang; Wang, Yining

2016-03-01

Sample size plays a crucial role in clinical trials. Flexible sample-size designs, as part of the more general category of adaptive designs that utilize interim data, have been a popular topic in recent years. In this paper, we give a comparative review of four related methods for such a design. The likelihood method uses the likelihood ratio test with an adjusted critical value. The weighted method adjusts the test statistic with given weights rather than the critical value. The dual test method requires both the likelihood ratio statistic and the weighted statistic to be greater than the unadjusted critical value. The promising zone approach uses the likelihood ratio statistic with the unadjusted value and other constraints. All four methods preserve the type-I error rate. In this paper we explore their properties and compare their relationships and merits. We show that the sample size rules for the dual test are in conflict with the rules of the promising zone approach. We delineate what is necessary to specify in the study protocol to ensure the validity of the statistical procedure and what can be kept implicit in the protocol so that more flexibility can be attained for confirmatory phase III trials in meeting regulatory requirements. We also prove that under mild conditions, the likelihood ratio test still preserves the type-I error rate when the actual sample size is larger than the re-calculated one. Copyright © 2015 Elsevier Inc. All rights reserved.

Development of a Self-Rated Mixed Methods Skills Assessment: The National Institutes of Health Mixed Methods Research Training Program for the Health Sciences.

PubMed

Guetterman, Timothy C; Creswell, John W; Wittink, Marsha; Barg, Fran K; Castro, Felipe G; Dahlberg, Britt; Watkins, Daphne C; Deutsch, Charles; Gallo, Joseph J

2017-01-01

Demand for training in mixed methods is high, with little research on faculty development or assessment in mixed methods. We describe the development of a self-rated mixed methods skills assessment and provide validity evidence. The instrument taps six research domains: "Research question," "Design/approach," "Sampling," "Data collection," "Analysis," and "Dissemination." Respondents are asked to rate their ability to define or explain concepts of mixed methods under each domain, their ability to apply the concepts to problems, and the extent to which they need to improve. We administered the questionnaire to 145 faculty and students using an internet survey. We analyzed descriptive statistics and performance characteristics of the questionnaire using the Cronbach alpha to assess reliability and an analysis of variance that compared a mixed methods experience index with assessment scores to assess criterion relatedness. Internal consistency reliability was high for the total set of items (0.95) and adequate (≥0.71) for all but one subscale. Consistent with establishing criterion validity, respondents who had more professional experiences with mixed methods (eg, published a mixed methods article) rated themselves as more skilled, which was statistically significant across the research domains. This self-rated mixed methods assessment instrument may be a useful tool to assess skills in mixed methods for training programs. It can be applied widely at the graduate and faculty level. For the learner, assessment may lead to enhanced motivation to learn and training focused on self-identified needs. For faculty, the assessment may improve curriculum and course content planning.

Evaluation of Informed Choice for contraceptive methods among women attending a family planning program: conceptual development; a case study in Chile.

PubMed

Valdés, Patricio R; Alarcon, Ana M; Munoz, Sergio R

2013-03-01

To generate and validate a scale to measure the Informed Choice of contraceptive methods among women attending a family health care service in Chile. The study follows a multimethod design that combined expert opinions from 13 physicians, 3 focus groups of 21 women each, and a sample survey of 1,446 women. Data analysis consisted of a qualitative text analysis of group interviews, a factor analysis for construct validity, and kappa statistic and Cronbach alpha to assess scale reliability. The instrument comprises 25 items grouped into six categories: information and orientation, quality of treatment, communication, participation in decision making, expression of reproductive rights, and method access and availability. Internal consistency measured with Cronbach alpha ranged from 0.75 to 0.89 for all subscales (kappa, 0.62; standard deviation, 0.06), and construct validity was demonstrated from the testing of several hypotheses. The use of mixed methods contributed to developing a scale of Informed Choice that was culturally appropriate for assessing the women who participated in the family planning program. Copyright © 2013 Elsevier Inc. All rights reserved.

Pattern statistics on Markov chains and sensitivity to parameter estimation

PubMed Central

Nuel, Grégory

2006-01-01

Background: In order to compute pattern statistics in computational biology a Markov model is commonly used to take into account the sequence composition. Usually its parameter must be estimated. The aim of this paper is to determine how sensitive these statistics are to parameter estimation, and what are the consequences of this variability on pattern studies (finding the most over-represented words in a genome, the most significant common words to a set of sequences,...). Results: In the particular case where pattern statistics (overlap counting only) computed through binomial approximations we use the delta-method to give an explicit expression of σ, the standard deviation of a pattern statistic. This result is validated using simulations and a simple pattern study is also considered. Conclusion: We establish that the use of high order Markov model could easily lead to major mistakes due to the high sensitivity of pattern statistics to parameter estimation. PMID:17044916

Pattern statistics on Markov chains and sensitivity to parameter estimation.

PubMed

Nuel, Grégory

2006-10-17

In order to compute pattern statistics in computational biology a Markov model is commonly used to take into account the sequence composition. Usually its parameter must be estimated. The aim of this paper is to determine how sensitive these statistics are to parameter estimation, and what are the consequences of this variability on pattern studies (finding the most over-represented words in a genome, the most significant common words to a set of sequences,...). In the particular case where pattern statistics (overlap counting only) computed through binomial approximations we use the delta-method to give an explicit expression of sigma, the standard deviation of a pattern statistic. This result is validated using simulations and a simple pattern study is also considered. We establish that the use of high order Markov model could easily lead to major mistakes due to the high sensitivity of pattern statistics to parameter estimation.

Mass detection, localization and estimation for wind turbine blades based on statistical pattern recognition

NASA Astrophysics Data System (ADS)

Colone, L.; Hovgaard, M. K.; Glavind, L.; Brincker, R.

2018-07-01

A method for mass change detection on wind turbine blades using natural frequencies is presented. The approach is based on two statistical tests. The first test decides if there is a significant mass change and the second test is a statistical group classification based on Linear Discriminant Analysis. The frequencies are identified by means of Operational Modal Analysis using natural excitation. Based on the assumption of Gaussianity of the frequencies, a multi-class statistical model is developed by combining finite element model sensitivities in 10 classes of change location on the blade, the smallest area being 1/5 of the span. The method is experimentally validated for a full scale wind turbine blade in a test setup and loaded by natural wind. Mass change from natural causes was imitated with sand bags and the algorithm was observed to perform well with an experimental detection rate of 1, localization rate of 0.88 and mass estimation rate of 0.72.

The use of multiple imputation method for the validation of 24-h food recalls by part-time observation of dietary intake in school.

PubMed

Kupek, Emil; de Assis, Maria Alice A

2016-09-01

External validation of food recall over 24 h in schoolchildren is often restricted to eating events in schools and is based on direct observation as the reference method. The aim of this study was to estimate the dietary intake out of school, and consequently the bias in such research design based on only part-time validated food recall, using multiple imputation (MI) conditioned on the information on child age, sex, BMI, family income, parental education and the school attended. The previous-day, web-based questionnaire WebCAAFE, structured as six meals/snacks and thirty-two foods/beverage, was answered by a sample of 7-11-year-old Brazilian schoolchildren (n 602) from five public schools. Food/beverage intake recalled by children was compared with the records provided by trained observers during school meals. Sensitivity analysis was performed with artificial data emulating those recalled by children on WebCAAFE in order to evaluate the impact of both differential and non-differential bias. Estimated bias was within ±30 % interval for 84·4 % of the thirty-two foods/beverages evaluated in WebCAAFE, and half of the latter reached statistical significance (P<0·05). Rarely (<3 %) consumed dietary items were often under-reported (fish/seafood, vegetable soup, cheese bread, French fries), whereas some of those most frequently reported (meat, bread/biscuits, fruits) showed large overestimation. Compared with the analysis restricted to fully validated data, MI reduced differential bias in sensitivity analysis but the bias still remained large in most cases. MI provided a suitable statistical framework for part-time validation design of dietary intake over six daily eating events.

Propensity score to detect baseline imbalance in cluster randomized trials: the role of the c-statistic.

PubMed

Leyrat, Clémence; Caille, Agnès; Foucher, Yohann; Giraudeau, Bruno

2016-01-22

Despite randomization, baseline imbalance and confounding bias may occur in cluster randomized trials (CRTs). Covariate imbalance may jeopardize the validity of statistical inferences if they occur on prognostic factors. Thus, the diagnosis of a such imbalance is essential to adjust statistical analysis if required. We developed a tool based on the c-statistic of the propensity score (PS) model to detect global baseline covariate imbalance in CRTs and assess the risk of confounding bias. We performed a simulation study to assess the performance of the proposed tool and applied this method to analyze the data from 2 published CRTs. The proposed method had good performance for large sample sizes (n =500 per arm) and when the number of unbalanced covariates was not too small as compared with the total number of baseline covariates (≥40% of unbalanced covariates). We also provide a strategy for pre selection of the covariates needed to be included in the PS model to enhance imbalance detection. The proposed tool could be useful in deciding whether covariate adjustment is required before performing statistical analyses of CRTs.

Statistical process control: A feasibility study of the application of time-series measurement in early neurorehabilitation after acquired brain injury.

PubMed

Markovic, Gabriela; Schult, Marie-Louise; Bartfai, Aniko; Elg, Mattias

2017-01-31

Progress in early cognitive recovery after acquired brain injury is uneven and unpredictable, and thus the evaluation of rehabilitation is complex. The use of time-series measurements is susceptible to statistical change due to process variation. To evaluate the feasibility of using a time-series method, statistical process control, in early cognitive rehabilitation. Participants were 27 patients with acquired brain injury undergoing interdisciplinary rehabilitation of attention within 4 months post-injury. The outcome measure, the Paced Auditory Serial Addition Test, was analysed using statistical process control. Statistical process control identifies if and when change occurs in the process according to 3 patterns: rapid, steady or stationary performers. The statistical process control method was adjusted, in terms of constructing the baseline and the total number of measurement points, in order to measure a process in change. Statistical process control methodology is feasible for use in early cognitive rehabilitation, since it provides information about change in a process, thus enabling adjustment of the individual treatment response. Together with the results indicating discernible subgroups that respond differently to rehabilitation, statistical process control could be a valid tool in clinical decision-making. This study is a starting-point in understanding the rehabilitation process using a real-time-measurements approach.

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

Klein, Levente

Interpreting sensor data require knowledge about sensor placement and the surrounding environment. For a single sensor measurement, it is easy to document the context by visual observation, however for millions of sensors reporting data back to a server, the contextual information needs to be automatically extracted from either data analysis or leveraging complimentary data sources. Data layers that overlap spatially or temporally with sensor locations, can be used to extract the context and to validate the measurement. To minimize the amount of data transmitted through the internet, while preserving signal information content, two methods are explored; computation at the edgemore » and compressed sensing. We validate the above methods on wind and chemical sensor data (1) eliminate redundant measurement from wind sensors and (2) extract peak value of a chemical sensor measuring a methane plume. We present a general cloud based framework to validate sensor data based on statistical and physical modeling and contextual data extracted from geospatial data.« less

Validation of Physics Standardized Test Items

NASA Astrophysics Data System (ADS)

Marshall, Jill

2008-10-01

The Texas Physics Assessment Team (TPAT) examined the Texas Assessment of Knowledge and Skills (TAKS) to determine whether it is a valid indicator of physics preparation for future course work and employment, and of the knowledge and skills needed to act as an informed citizen in a technological society. We categorized science items from the 2003 and 2004 10th and 11th grade TAKS by content area(s) covered, knowledge and skills required to select the correct answer, and overall quality. We also analyzed a 5000 student sample of item-level results from the 2004 11th grade exam using standard statistical methods employed by test developers (factor analysis and Item Response Theory). Triangulation of our results revealed strengths and weaknesses of the different methods of analysis. The TAKS was found to be only weakly indicative of physics preparation and we make recommendations for increasing the validity of standardized physics testing..

The R package "sperrorest" : Parallelized spatial error estimation and variable importance assessment for geospatial machine learning

NASA Astrophysics Data System (ADS)

Schratz, Patrick; Herrmann, Tobias; Brenning, Alexander

2017-04-01

Computational and statistical prediction methods such as the support vector machine have gained popularity in remote-sensing applications in recent years and are often compared to more traditional approaches like maximum-likelihood classification. However, the accuracy assessment of such predictive models in a spatial context needs to account for the presence of spatial autocorrelation in geospatial data by using spatial cross-validation and bootstrap strategies instead of their now more widely used non-spatial equivalent. The R package sperrorest by A. Brenning [IEEE International Geoscience and Remote Sensing Symposium, 1, 374 (2012)] provides a generic interface for performing (spatial) cross-validation of any statistical or machine-learning technique available in R. Since spatial statistical models as well as flexible machine-learning algorithms can be computationally expensive, parallel computing strategies are required to perform cross-validation efficiently. The most recent major release of sperrorest therefore comes with two new features (aside from improved documentation): The first one is the parallelized version of sperrorest(), parsperrorest(). This function features two parallel modes to greatly speed up cross-validation runs. Both parallel modes are platform independent and provide progress information. par.mode = 1 relies on the pbapply package and calls interactively (depending on the platform) parallel::mclapply() or parallel::parApply() in the background. While forking is used on Unix-Systems, Windows systems use a cluster approach for parallel execution. par.mode = 2 uses the foreach package to perform parallelization. This method uses a different way of cluster parallelization than the parallel package does. In summary, the robustness of parsperrorest() is increased with the implementation of two independent parallel modes. A new way of partitioning the data in sperrorest is provided by partition.factor.cv(). This function gives the user the possibility to perform cross-validation at the level of some grouping structure. As an example, in remote sensing of agricultural land uses, pixels from the same field contain nearly identical information and will thus be jointly placed in either the test set or the training set. Other spatial sampling resampling strategies are already available and can be extended by the user.

Systematic review of prediction models for delirium in the older adult inpatient.

PubMed

Lindroth, Heidi; Bratzke, Lisa; Purvis, Suzanne; Brown, Roger; Coburn, Mark; Mrkobrada, Marko; Chan, Matthew T V; Davis, Daniel H J; Pandharipande, Pratik; Carlsson, Cynthia M; Sanders, Robert D

2018-04-28

To identify existing prognostic delirium prediction models and evaluate their validity and statistical methodology in the older adult (≥60 years) acute hospital population. Systematic review. PubMed, CINAHL, PsychINFO, SocINFO, Cochrane, Web of Science and Embase were searched from 1 January 1990 to 31 December 2016. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses and CHARMS Statement guided protocol development. age >60 years, inpatient, developed/validated a prognostic delirium prediction model. alcohol-related delirium, sample size ≤50. The primary performance measures were calibration and discrimination statistics. Two authors independently conducted search and extracted data. The synthesis of data was done by the first author. Disagreement was resolved by the mentoring author. The initial search resulted in 7,502 studies. Following full-text review of 192 studies, 33 were excluded based on age criteria (<60 years) and 27 met the defined criteria. Twenty-three delirium prediction models were identified, 14 were externally validated and 3 were internally validated. The following populations were represented: 11 medical, 3 medical/surgical and 13 surgical. The assessment of delirium was often non-systematic, resulting in varied incidence. Fourteen models were externally validated with an area under the receiver operating curve range from 0.52 to 0.94. Limitations in design, data collection methods and model metric reporting statistics were identified. Delirium prediction models for older adults show variable and typically inadequate predictive capabilities. Our review highlights the need for development of robust models to predict delirium in older inpatients. We provide recommendations for the development of such 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.

Goodness-of-Fit Assessment of Item Response Theory Models

ERIC Educational Resources Information Center

Maydeu-Olivares, Alberto

2013-01-01

The article provides an overview of goodness-of-fit assessment methods for item response theory (IRT) models. It is now possible to obtain accurate "p"-values of the overall fit of the model if bivariate information statistics are used. Several alternative approaches are described. As the validity of inferences drawn on the fitted model…

3D Texture Analysis in Renal Cell Carcinoma Tissue Image Grading

PubMed Central

Cho, Nam-Hoon; Choi, Heung-Kook

2014-01-01

One of the most significant processes in cancer cell and tissue image analysis is the efficient extraction of features for grading purposes. This research applied two types of three-dimensional texture analysis methods to the extraction of feature values from renal cell carcinoma tissue images, and then evaluated the validity of the methods statistically through grade classification. First, we used a confocal laser scanning microscope to obtain image slices of four grades of renal cell carcinoma, which were then reconstructed into 3D volumes. Next, we extracted quantitative values using a 3D gray level cooccurrence matrix (GLCM) and a 3D wavelet based on two types of basis functions. To evaluate their validity, we predefined 6 different statistical classifiers and applied these to the extracted feature sets. In the grade classification results, 3D Haar wavelet texture features combined with principal component analysis showed the best discrimination results. Classification using 3D wavelet texture features was significantly better than 3D GLCM, suggesting that the former has potential for use in a computer-based grading system. PMID:25371701

Integrating Symbolic and Statistical Methods for Testing Intelligent Systems Applications to Machine Learning and Computer Vision

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

Jha, Sumit Kumar; Pullum, Laura L; Ramanathan, Arvind

Embedded intelligent systems ranging from tiny im- plantable biomedical devices to large swarms of autonomous un- manned aerial systems are becoming pervasive in our daily lives. While we depend on the flawless functioning of such intelligent systems, and often take their behavioral correctness and safety for granted, it is notoriously difficult to generate test cases that expose subtle errors in the implementations of machine learning algorithms. Hence, the validation of intelligent systems is usually achieved by studying their behavior on representative data sets, using methods such as cross-validation and bootstrapping.In this paper, we present a new testing methodology for studyingmore » the correctness of intelligent systems. Our approach uses symbolic decision procedures coupled with statistical hypothesis testing to. We also use our algorithm to analyze the robustness of a human detection algorithm built using the OpenCV open-source computer vision library. We show that the human detection implementation can fail to detect humans in perturbed video frames even when the perturbations are so small that the corresponding frames look identical to the naked eye.« less

The Earthquake‐Source Inversion Validation (SIV) Project

USGS Publications Warehouse

Mai, P. Martin; Schorlemmer, Danijel; Page, Morgan T.; Ampuero, Jean-Paul; Asano, Kimiyuki; Causse, Mathieu; Custodio, Susana; Fan, Wenyuan; Festa, Gaetano; Galis, Martin; Gallovic, Frantisek; Imperatori, Walter; Käser, Martin; Malytskyy, Dmytro; Okuwaki, Ryo; Pollitz, Fred; Passone, Luca; Razafindrakoto, Hoby N. T.; Sekiguchi, Haruko; Song, Seok Goo; Somala, Surendra N.; Thingbaijam, Kiran K. S.; Twardzik, Cedric; van Driel, Martin; Vyas, Jagdish C.; Wang, Rongjiang; Yagi, Yuji; Zielke, Olaf

2016-01-01

Finite‐fault earthquake source inversions infer the (time‐dependent) displacement on the rupture surface from geophysical data. The resulting earthquake source models document the complexity of the rupture process. However, multiple source models for the same earthquake, obtained by different research teams, often exhibit remarkable dissimilarities. To address the uncertainties in earthquake‐source inversion methods and to understand strengths and weaknesses of the various approaches used, the Source Inversion Validation (SIV) project conducts a set of forward‐modeling exercises and inversion benchmarks. In this article, we describe the SIV strategy, the initial benchmarks, and current SIV results. Furthermore, we apply statistical tools for quantitative waveform comparison and for investigating source‐model (dis)similarities that enable us to rank the solutions, and to identify particularly promising source inversion approaches. All SIV exercises (with related data and descriptions) and statistical comparison tools are available via an online collaboration platform, and we encourage source modelers to use the SIV benchmarks for developing and testing new methods. We envision that the SIV efforts will lead to new developments for tackling the earthquake‐source imaging problem.

Comparing Thermal Process Validation Methods for Salmonella Inactivation on Almond Kernels.

PubMed

Jeong, Sanghyup; Marks, Bradley P; James, Michael K

2017-01-01

Ongoing regulatory changes are increasing the need for reliable process validation methods for pathogen reduction processes involving low-moisture products; however, the reliability of various validation methods has not been evaluated. Therefore, the objective was to quantify accuracy and repeatability of four validation methods (two biologically based and two based on time-temperature models) for thermal pasteurization of almonds. Almond kernels were inoculated with Salmonella Enteritidis phage type 30 or Enterococcus faecium (NRRL B-2354) at ~10 8 CFU/g, equilibrated to 0.24, 0.45, 0.58, or 0.78 water activity (a w ), and then heated in a pilot-scale, moist-air impingement oven (dry bulb 121, 149, or 177°C; dew point <33.0, 69.4, 81.6, or 90.6°C; v air = 2.7 m/s) to a target lethality of ~4 log. Almond surface temperatures were measured in two ways, and those temperatures were used to calculate Salmonella inactivation using a traditional (D, z) model and a modified model accounting for process humidity. Among the process validation methods, both methods based on time-temperature models had better repeatability, with replication errors approximately half those of the surrogate ( E. faecium ). Additionally, the modified model yielded the lowest root mean squared error in predicting Salmonella inactivation (1.1 to 1.5 log CFU/g); in contrast, E. faecium yielded a root mean squared error of 1.2 to 1.6 log CFU/g, and the traditional model yielded an unacceptably high error (3.4 to 4.4 log CFU/g). Importantly, the surrogate and modified model both yielded lethality predictions that were statistically equivalent (α = 0.05) to actual Salmonella lethality. The results demonstrate the importance of methodology, a w , and process humidity when validating thermal pasteurization processes for low-moisture foods, which should help processors select and interpret validation methods to ensure product safety.

Statistical analysis of water-quality data containing multiple detection limits II: S-language software for nonparametric distribution modeling and hypothesis testing

USGS Publications Warehouse

Lee, L.; Helsel, D.

2007-01-01

Analysis of low concentrations of trace contaminants in environmental media often results in left-censored data that are below some limit of analytical precision. Interpretation of values becomes complicated when there are multiple detection limits in the data-perhaps as a result of changing analytical precision over time. Parametric and semi-parametric methods, such as maximum likelihood estimation and robust regression on order statistics, can be employed to model distributions of multiply censored data and provide estimates of summary statistics. However, these methods are based on assumptions about the underlying distribution of data. Nonparametric methods provide an alternative that does not require such assumptions. A standard nonparametric method for estimating summary statistics of multiply-censored data is the Kaplan-Meier (K-M) method. This method has seen widespread usage in the medical sciences within a general framework termed "survival analysis" where it is employed with right-censored time-to-failure data. However, K-M methods are equally valid for the left-censored data common in the geosciences. Our S-language software provides an analytical framework based on K-M methods that is tailored to the needs of the earth and environmental sciences community. This includes routines for the generation of empirical cumulative distribution functions, prediction or exceedance probabilities, and related confidence limits computation. Additionally, our software contains K-M-based routines for nonparametric hypothesis testing among an unlimited number of grouping variables. A primary characteristic of K-M methods is that they do not perform extrapolation and interpolation. Thus, these routines cannot be used to model statistics beyond the observed data range or when linear interpolation is desired. For such applications, the aforementioned parametric and semi-parametric methods must be used.

Novel Spectrofluorimetric Method for the Determination of Perindopril Erbumine Based on Fluorescence Quenching of Rhodamine B.

PubMed

Fael, Hanan; Sakur, Amir Al-Haj

2015-11-01

A novel, simple and specific spectrofluorimetric method was developed and validated for the determination of perindopril erbumine (PDE). The method is based on the fluorescence quenching of Rhodamine B upon adding perindopril erbumine. The quenched fluorescence was monitored at 578 nm after excitation at 500 nm. The optimization of the reaction conditions such as the solvent, reagent concentration, and reaction time were investigated. Under the optimum conditions, the fluorescence quenching was linear over a concentration range of 1.0-6.0 μg/mL. The proposed method was fully validated and successfully applied to the analysis of perindopril erbumine in pure form and tablets. Statistical comparison of the results obtained by the developed and reference methods revealed no significant differences between the methods compared in terms of accuracy and precision. The method was shown to be highly specific in the presence of indapamide, a diuretic that is commonly combined with perindopril erbumine. The mechanism of rhodamine B quenching was also discussed.

A validation method for near-infrared spectroscopy based tissue oximeters for cerebral and somatic tissue oxygen saturation measurements.

PubMed

Benni, Paul B; MacLeod, David; Ikeda, Keita; Lin, Hung-Mo

2018-04-01

We describe the validation methodology for the NIRS based FORE-SIGHT ELITE ® (CAS Medical Systems, Inc., Branford, CT, USA) tissue oximeter for cerebral and somatic tissue oxygen saturation (StO 2 ) measurements for adult subjects submitted to the United States Food and Drug Administration (FDA) to obtain clearance for clinical use. This validation methodology evolved from a history of NIRS validations in the literature and FDA recommended use of Deming regression and bootstrapping statistical validation methods. For cerebral validation, forehead cerebral StO 2 measurements were compared to a weighted 70:30 reference (REF CX B ) of co-oximeter internal jugular venous and arterial blood saturation of healthy adult subjects during a controlled hypoxia sequence, with a sensor placed on the forehead. For somatic validation, somatic StO 2 measurements were compared to a weighted 70:30 reference (REF CX S ) of co-oximetry central venous and arterial saturation values following a similar protocol, with sensors place on the flank, quadriceps muscle, and calf muscle. With informed consent, 25 subjects successfully completed the cerebral validation study. The bias and precision (1 SD) of cerebral StO 2 compared to REF CX B was -0.14 ± 3.07%. With informed consent, 24 subjects successfully completed the somatic validation study. The bias and precision of somatic StO 2 compared to REF CX S was 0.04 ± 4.22% from the average of flank, quadriceps, and calf StO 2 measurements to best represent the global whole body REF CX S . The NIRS validation methods presented potentially provide a reliable means to test NIRS monitors and qualify them for clinical use.

Validated spectrophotometric methods for simultaneous determination of troxerutin and carbazochrome in dosage form.

PubMed

Khattab, Fatma I; Ramadan, Nesrin K; Hegazy, Maha A; Al-Ghobashy, Medhat A; Ghoniem, Nermine S

2015-03-15

Four simple, accurate, sensitive and precise spectrophotometric methods were developed and validated for simultaneous determination of Troxerutin (TXN) and Carbazochrome (CZM) in their bulk powders, laboratory prepared mixtures and pharmaceutical dosage forms. Method A is first derivative spectrophotometry (D(1)) where TXN and CZM were determined at 294 and 483.5 nm, respectively. Method B is first derivative of ratio spectra (DD(1)) where the peak amplitude at 248 for TXN and 439 nm for CZM were used for their determination. Method C is ratio subtraction (RS); in which TXN was determined at its λmax (352 nm) in the presence of CZM which was determined by D(1) at 483.5 nm. While, method D is mean centering of the ratio spectra (MCR) in which the mean centered values at 300 nm and 340.0 nm were used for the two drugs in a respective order. The two compounds were simultaneously determined in the concentration ranges of 5.00-50.00 μg mL(-1) and 0.5-10.0 μg mL(-1) for TXN and CZM, respectively. The methods were validated according to the ICH guidelines and the results were statistically compared to the manufacturer's method. Copyright © 2015 Elsevier B.V. All rights reserved.

Lung Cancer Risk Prediction Model Incorporating Lung Function: Development and Validation in the UK Biobank Prospective Cohort Study.

PubMed

Muller, David C; Johansson, Mattias; Brennan, Paul

2017-03-10

Purpose Several lung cancer risk prediction models have been developed, but none to date have assessed the predictive ability of lung function in a population-based cohort. We sought to develop and internally validate a model incorporating lung function using data from the UK Biobank prospective cohort study. Methods This analysis included 502,321 participants without a previous diagnosis of lung cancer, predominantly between 40 and 70 years of age. We used flexible parametric survival models to estimate the 2-year probability of lung cancer, accounting for the competing risk of death. Models included predictors previously shown to be associated with lung cancer risk, including sex, variables related to smoking history and nicotine addiction, medical history, family history of lung cancer, and lung function (forced expiratory volume in 1 second [FEV1]). Results During accumulated follow-up of 1,469,518 person-years, there were 738 lung cancer diagnoses. A model incorporating all predictors had excellent discrimination (concordance (c)-statistic [95% CI] = 0.85 [0.82 to 0.87]). Internal validation suggested that the model will discriminate well when applied to new data (optimism-corrected c-statistic = 0.84). The full model, including FEV1, also had modestly superior discriminatory power than one that was designed solely on the basis of questionnaire variables (c-statistic = 0.84 [0.82 to 0.86]; optimism-corrected c-statistic = 0.83; p FEV1 = 3.4 × 10 -13 ). The full model had better discrimination than standard lung cancer screening eligibility criteria (c-statistic = 0.66 [0.64 to 0.69]). Conclusion A risk prediction model that includes lung function has strong predictive ability, which could improve eligibility criteria for lung cancer screening programs.

The thresholds for statistical and clinical significance – a five-step procedure for evaluation of intervention effects in randomised clinical trials

PubMed Central

2014-01-01

Background Thresholds for statistical significance are insufficiently demonstrated by 95% confidence intervals or P-values when assessing results from randomised clinical trials. First, a P-value only shows the probability of getting a result assuming that the null hypothesis is true and does not reflect the probability of getting a result assuming an alternative hypothesis to the null hypothesis is true. Second, a confidence interval or a P-value showing significance may be caused by multiplicity. Third, statistical significance does not necessarily result in clinical significance. Therefore, assessment of intervention effects in randomised clinical trials deserves more rigour in order to become more valid. Methods Several methodologies for assessing the statistical and clinical significance of intervention effects in randomised clinical trials were considered. Balancing simplicity and comprehensiveness, a simple five-step procedure was developed. Results For a more valid assessment of results from a randomised clinical trial we propose the following five-steps: (1) report the confidence intervals and the exact P-values; (2) report Bayes factor for the primary outcome, being the ratio of the probability that a given trial result is compatible with a ‘null’ effect (corresponding to the P-value) divided by the probability that the trial result is compatible with the intervention effect hypothesised in the sample size calculation; (3) adjust the confidence intervals and the statistical significance threshold if the trial is stopped early or if interim analyses have been conducted; (4) adjust the confidence intervals and the P-values for multiplicity due to number of outcome comparisons; and (5) assess clinical significance of the trial results. Conclusions If the proposed five-step procedure is followed, this may increase the validity of assessments of intervention effects in randomised clinical trials. PMID:24588900

Uncertainty Quantification and Statistical Engineering for Hypersonic Entry Applications

NASA Technical Reports Server (NTRS)

Cozmuta, Ioana

2011-01-01

NASA has invested significant resources in developing and validating a mathematical construct for TPS margin management: a) Tailorable for low/high reliability missions; b) Tailorable for ablative/reusable TPS; c) Uncertainty Quantification and Statistical Engineering are valuable tools not exploited enough; and d) Need to define strategies combining both Theoretical Tools and Experimental Methods. The main reason for this lecture is to give a flavor of where UQ and SE could contribute and hope that the broader community will work with us to improve in these areas.

Portfolio optimization problem with nonidentical variances of asset returns using statistical mechanical informatics.

PubMed

Shinzato, Takashi

2016-12-01

The portfolio optimization problem in which the variances of the return rates of assets are not identical is analyzed in this paper using the methodology of statistical mechanical informatics, specifically, replica analysis. We defined two characteristic quantities of an optimal portfolio, namely, minimal investment risk and investment concentration, in order to solve the portfolio optimization problem and analytically determined their asymptotical behaviors using replica analysis. Numerical experiments were also performed, and a comparison between the results of our simulation and those obtained via replica analysis validated our proposed method.

Portfolio optimization problem with nonidentical variances of asset returns using statistical mechanical informatics

NASA Astrophysics Data System (ADS)

Shinzato, Takashi

2016-12-01

The portfolio optimization problem in which the variances of the return rates of assets are not identical is analyzed in this paper using the methodology of statistical mechanical informatics, specifically, replica analysis. We defined two characteristic quantities of an optimal portfolio, namely, minimal investment risk and investment concentration, in order to solve the portfolio optimization problem and analytically determined their asymptotical behaviors using replica analysis. Numerical experiments were also performed, and a comparison between the results of our simulation and those obtained via replica analysis validated our proposed method.

Application of the Analog Method to Modelling Heat Waves: A Case Study with Power Transformers

DTIC Science & Technology

2017-04-21

UNCLASSIFIED Massachusetts Institute of Technology Lincoln Laboratory APPLICATION OF THE ANALOG METHOD TO MODELLING HEAT WAVES: A CASE STUDY WITH...18 2 Calibration and validation statistics with the use of five atmospheric vari- ables to construct analogue diagnostics for JJA of transformer T2...electrical grid as a series of nodes (transformers) and edges (transmission lines) so that basic mathematical anal- ysis can be performed. The mathematics

Development and validation of an ionic chromatography method for the determination of nitrate, nitrite and chloride in meat.

PubMed

Lopez-Moreno, Cristina; Perez, Isabel Viera; Urbano, Ana M

2016-03-01

The purpose of this study is to develop the validation of a method for the analysis of certain preservatives in meat and to obtain a suitable Certified Reference Material (CRM) to achieve this task. The preservatives studied were NO3(-), NO2(-) and Cl(-) as they serve as important antimicrobial agents in meat to inhibit the growth of bacteria spoilage. The meat samples were prepared using a treatment that allowed the production of a known CRM concentration that is highly homogeneous and stable in time. The matrix effects were also studied to evaluate the influence on the analytical signal for the ions of interest, showing that the matrix influence does not affect the final result. An assessment of the signal variation in time was carried out for the ions. In this regard, although the chloride and nitrate signal remained stable for the duration of the study, the nitrite signal decreased appreciably with time. A mathematical treatment of the data gave a stable nitrite signal, obtaining a method suitable for the validation of these anions in meat. A statistical study was needed for the validation of the method, where the precision, accuracy, uncertainty and other mathematical parameters were evaluated obtaining satisfactory results. Copyright © 2015 Elsevier Ltd. All rights reserved.

Bias correction for selecting the minimal-error classifier from many machine learning models.

PubMed

Ding, Ying; Tang, Shaowu; Liao, Serena G; Jia, Jia; Oesterreich, Steffi; Lin, Yan; Tseng, George C

2014-11-15

Supervised machine learning is commonly applied in genomic research to construct a classifier from the training data that is generalizable to predict independent testing data. When test datasets are not available, cross-validation is commonly used to estimate the error rate. Many machine learning methods are available, and it is well known that no universally best method exists in general. It has been a common practice to apply many machine learning methods and report the method that produces the smallest cross-validation error rate. Theoretically, such a procedure produces a selection bias. Consequently, many clinical studies with moderate sample sizes (e.g. n = 30-60) risk reporting a falsely small cross-validation error rate that could not be validated later in independent cohorts. In this article, we illustrated the probabilistic framework of the problem and explored the statistical and asymptotic properties. We proposed a new bias correction method based on learning curve fitting by inverse power law (IPL) and compared it with three existing methods: nested cross-validation, weighted mean correction and Tibshirani-Tibshirani procedure. All methods were compared in simulation datasets, five moderate size real datasets and two large breast cancer datasets. The result showed that IPL outperforms the other methods in bias correction with smaller variance, and it has an additional advantage to extrapolate error estimates for larger sample sizes, a practical feature to recommend whether more samples should be recruited to improve the classifier and accuracy. An R package 'MLbias' and all source files are publicly available. tsenglab.biostat.pitt.edu/software.htm. ctseng@pitt.edu Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

A Complex Systems Approach to Causal Discovery in Psychiatry.

PubMed

Saxe, Glenn N; Statnikov, Alexander; Fenyo, David; Ren, Jiwen; Li, Zhiguo; Prasad, Meera; Wall, Dennis; Bergman, Nora; Briggs, Ernestine C; Aliferis, Constantin

2016-01-01

Conventional research methodologies and data analytic approaches in psychiatric research are unable to reliably infer causal relations without experimental designs, or to make inferences about the functional properties of the complex systems in which psychiatric disorders are embedded. This article describes a series of studies to validate a novel hybrid computational approach--the Complex Systems-Causal Network (CS-CN) method-designed to integrate causal discovery within a complex systems framework for psychiatric research. The CS-CN method was first applied to an existing dataset on psychopathology in 163 children hospitalized with injuries (validation study). Next, it was applied to a much larger dataset of traumatized children (replication study). Finally, the CS-CN method was applied in a controlled experiment using a 'gold standard' dataset for causal discovery and compared with other methods for accurately detecting causal variables (resimulation controlled experiment). The CS-CN method successfully detected a causal network of 111 variables and 167 bivariate relations in the initial validation study. This causal network had well-defined adaptive properties and a set of variables was found that disproportionally contributed to these properties. Modeling the removal of these variables resulted in significant loss of adaptive properties. The CS-CN method was successfully applied in the replication study and performed better than traditional statistical methods, and similarly to state-of-the-art causal discovery algorithms in the causal detection experiment. The CS-CN method was validated, replicated, and yielded both novel and previously validated findings related to risk factors and potential treatments of psychiatric disorders. The novel approach yields both fine-grain (micro) and high-level (macro) insights and thus represents a promising approach for complex systems-oriented research in psychiatry.

Monte Carlo investigation of transient acoustic fields in partially or completely bounded medium. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Thanedar, B. D.

1972-01-01

A simple repetitive calculation was used to investigate what happens to the field in terms of the signal paths of disturbances originating from the energy source. The computation allowed the field to be reconstructed as a function of space and time on a statistical basis. The suggested Monte Carlo method is in response to the need for a numerical method to supplement analytical methods of solution which are only valid when the boundaries have simple shapes, rather than for a medium that is bounded. For the analysis, a suitable model was created from which was developed an algorithm for the estimation of acoustic pressure variations in the region under investigation. The validity of the technique was demonstrated by analysis of simple physical models with the aid of a digital computer. The Monte Carlo method is applicable to a medium which is homogeneous and is enclosed by either rectangular or curved boundaries.

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

PubMed

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

2018-03-01

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

A method for deterministic statistical downscaling of daily precipitation at a monsoonal site in Eastern China

NASA Astrophysics Data System (ADS)

Liu, Yonghe; Feng, Jinming; Liu, Xiu; Zhao, Yadi

2017-12-01

Statistical downscaling (SD) is a method that acquires the local information required for hydrological impact assessment from large-scale atmospheric variables. Very few statistical and deterministic downscaling models for daily precipitation have been conducted for local sites influenced by the East Asian monsoon. In this study, SD models were constructed by selecting the best predictors and using generalized linear models (GLMs) for Feixian, a site in the Yishu River Basin and Shandong Province. By calculating and mapping Spearman rank correlation coefficients between the gridded standardized values of five large-scale variables and daily observed precipitation, different cyclonic circulation patterns were found for monsoonal precipitation in summer (June-September) and winter (November-December and January-March); the values of the gridded boxes with the highest absolute correlations for observed precipitation were selected as predictors. Data for predictors and predictands covered the period 1979-2015, and different calibration and validation periods were divided when fitting and validating the models. Meanwhile, the bootstrap method was also used to fit the GLM. All the above thorough validations indicated that the models were robust and not sensitive to different samples or different periods. Pearson's correlations between downscaled and observed precipitation (logarithmically transformed) on a daily scale reached 0.54-0.57 in summer and 0.56-0.61 in winter, and the Nash-Sutcliffe efficiency between downscaled and observed precipitation reached 0.1 in summer and 0.41 in winter. The downscaled precipitation partially reflected exact variations in winter and main trends in summer for total interannual precipitation. For the number of wet days, both winter and summer models were able to reflect interannual variations. Other comparisons were also made in this study. These results demonstrated that when downscaling, it is appropriate to combine a correlation-based predictor selection across a spatial domain with GLM modeling.

Statistical shape analysis using 3D Poisson equation--A quantitatively validated approach.

PubMed

Gao, Yi; Bouix, Sylvain

2016-05-01

Statistical shape analysis has been an important area of research with applications in biology, anatomy, neuroscience, agriculture, paleontology, etc. Unfortunately, the proposed methods are rarely quantitatively evaluated, and as shown in recent studies, when they are evaluated, significant discrepancies exist in their outputs. In this work, we concentrate on the problem of finding the consistent location of deformation between two population of shapes. We propose a new shape analysis algorithm along with a framework to perform a quantitative evaluation of its performance. Specifically, the algorithm constructs a Signed Poisson Map (SPoM) by solving two Poisson equations on the volumetric shapes of arbitrary topology, and statistical analysis is then carried out on the SPoMs. The method is quantitatively evaluated on synthetic shapes and applied on real shape data sets in brain structures. Copyright © 2016 Elsevier B.V. All rights reserved.

A Science and Risk-Based Pragmatic Methodology for Blend and Content Uniformity Assessment.

PubMed

Sayeed-Desta, Naheed; Pazhayattil, Ajay Babu; Collins, Jordan; Doshi, Chetan

2018-04-01

This paper describes a pragmatic approach that can be applied in assessing powder blend and unit dosage uniformity of solid dose products at Process Design, Process Performance Qualification, and Continued/Ongoing Process Verification stages of the Process Validation lifecycle. The statistically based sampling, testing, and assessment plan was developed due to the withdrawal of the FDA draft guidance for industry "Powder Blends and Finished Dosage Units-Stratified In-Process Dosage Unit Sampling and Assessment." This paper compares the proposed Grouped Area Variance Estimate (GAVE) method with an alternate approach outlining the practicality and statistical rationalization using traditional sampling and analytical methods. The approach is designed to fit solid dose processes assuring high statistical confidence in both powder blend uniformity and dosage unit uniformity during all three stages of the lifecycle complying with ASTM standards as recommended by the US FDA.

Testing Pairwise Association between Spatially Autocorrelated Variables: A New Approach Using Surrogate Lattice Data

PubMed Central

Deblauwe, Vincent; Kennel, Pol; Couteron, Pierre

2012-01-01

Background Independence between observations is a standard prerequisite of traditional statistical tests of association. This condition is, however, violated when autocorrelation is present within the data. In the case of variables that are regularly sampled in space (i.e. lattice data or images), such as those provided by remote-sensing or geographical databases, this problem is particularly acute. Because analytic derivation of the null probability distribution of the test statistic (e.g. Pearson's r) is not always possible when autocorrelation is present, we propose instead the use of a Monte Carlo simulation with surrogate data. Methodology/Principal Findings The null hypothesis that two observed mapped variables are the result of independent pattern generating processes is tested here by generating sets of random image data while preserving the autocorrelation function of the original images. Surrogates are generated by matching the dual-tree complex wavelet spectra (and hence the autocorrelation functions) of white noise images with the spectra of the original images. The generated images can then be used to build the probability distribution function of any statistic of association under the null hypothesis. We demonstrate the validity of a statistical test of association based on these surrogates with both actual and synthetic data and compare it with a corrected parametric test and three existing methods that generate surrogates (randomization, random rotations and shifts, and iterative amplitude adjusted Fourier transform). Type I error control was excellent, even with strong and long-range autocorrelation, which is not the case for alternative methods. Conclusions/Significance The wavelet-based surrogates are particularly appropriate in cases where autocorrelation appears at all scales or is direction-dependent (anisotropy). We explore the potential of the method for association tests involving a lattice of binary data and discuss its potential for validation of species distribution models. An implementation of the method in Java for the generation of wavelet-based surrogates is available online as supporting material. PMID:23144961

An Empirical Taxonomy of Hospital Governing Board Roles

PubMed Central

Lee, Shoou-Yih D; Alexander, Jeffrey A; Wang, Virginia; Margolin, Frances S; Combes, John R

2008-01-01

Objective To develop a taxonomy of governing board roles in U.S. hospitals. Data Sources 2005 AHA Hospital Governance Survey, 2004 AHA Annual Survey of Hospitals, and Area Resource File. Study Design A governing board taxonomy was developed using cluster analysis. Results were validated and reviewed by industry experts. Differences in hospital and environmental characteristics across clusters were examined. Data Extraction Methods One-thousand three-hundred thirty-four hospitals with complete information on the study variables were included in the analysis. Principal Findings Five distinct clusters of hospital governing boards were identified. Statistical tests showed that the five clusters had high internal reliability and high internal validity. Statistically significant differences in hospital and environmental conditions were found among clusters. Conclusions The developed taxonomy provides policy makers, health care executives, and researchers a useful way to describe and understand hospital governing board roles. The taxonomy may also facilitate valid and systematic assessment of governance performance. Further, the taxonomy could be used as a framework for governing boards themselves to identify areas for improvement and direction for change. PMID:18355260

Geographical origin discrimination of lentils (Lens culinaris Medik.) using 1H NMR fingerprinting and multivariate statistical analyses.

PubMed

Longobardi, Francesco; Innamorato, Valentina; Di Gioia, Annalisa; Ventrella, Andrea; Lippolis, Vincenzo; Logrieco, Antonio F; Catucci, Lucia; Agostiano, Angela

2017-12-15

Lentil samples coming from two different countries, i.e. Italy and Canada, were analysed using untargeted 1 H NMR fingerprinting in combination with chemometrics in order to build models able to classify them according to their geographical origin. For such aim, Soft Independent Modelling of Class Analogy (SIMCA), k-Nearest Neighbor (k-NN), Principal Component Analysis followed by Linear Discriminant Analysis (PCA-LDA) and Partial Least Squares-Discriminant Analysis (PLS-DA) were applied to the NMR data and the results were compared. The best combination of average recognition (100%) and cross-validation prediction abilities (96.7%) was obtained for the PCA-LDA. All the statistical models were validated both by using a test set and by carrying out a Monte Carlo Cross Validation: the obtained performances were found to be satisfying for all the models, with prediction abilities higher than 95% demonstrating the suitability of the developed methods. Finally, the metabolites that mostly contributed to the lentil discrimination were indicated. Copyright © 2017 Elsevier Ltd. All rights reserved.

Quantitative validation of carbon-fiber laminate low velocity impact simulations

DOE PAGES

English, Shawn A.; Briggs, Timothy M.; Nelson, Stacy M.

2015-09-26

Simulations of low velocity impact with a flat cylindrical indenter upon a carbon fiber fabric reinforced polymer laminate are rigorously validated. Comparison of the impact energy absorption between the model and experiment is used as the validation metric. Additionally, non-destructive evaluation, including ultrasonic scans and three-dimensional computed tomography, provide qualitative validation of the models. The simulations include delamination, matrix cracks and fiber breaks. An orthotropic damage and failure constitutive model, capable of predicting progressive damage and failure, is developed in conjunction and described. An ensemble of simulations incorporating model parameter uncertainties is used to predict a response distribution which ismore » then compared to experimental output using appropriate statistical methods. Lastly, the model form errors are exposed and corrected for use in an additional blind validation analysis. The result is a quantifiable confidence in material characterization and model physics when simulating low velocity impact in structures of interest.« less

Corpus-based Statistical Screening for Phrase Identification

PubMed Central

Kim, Won; Wilbur, W. John

2000-01-01

Purpose: The authors study the extraction of useful phrases from a natural language database by statistical methods. The aim is to leverage human effort by providing preprocessed phrase lists with a high percentage of useful material. Method: The approach is to develop six different scoring methods that are based on different aspects of phrase occurrence. The emphasis here is not on lexical information or syntactic structure but rather on the statistical properties of word pairs and triples that can be obtained from a large database. Measurements: The Unified Medical Language System (UMLS) incorporates a large list of humanly acceptable phrases in the medical field as a part of its structure. The authors use this list of phrases as a gold standard for validating their methods. A good method is one that ranks the UMLS phrases high among all phrases studied. Measurements are 11-point average precision values and precision-recall curves based on the rankings. Result: The authors find of six different scoring methods that each proves effective in identifying UMLS quality phrases in a large subset of MEDLINE. These methods are applicable both to word pairs and word triples. All six methods are optimally combined to produce composite scoring methods that are more effective than any single method. The quality of the composite methods appears sufficient to support the automatic placement of hyperlinks in text at the site of highly ranked phrases. Conclusion: Statistical scoring methods provide a promising approach to the extraction of useful phrases from a natural language database for the purpose of indexing or providing hyperlinks in text. PMID:10984469

Validating Variational Bayes Linear Regression Method With Multi-Central Datasets.

PubMed

Murata, Hiroshi; Zangwill, Linda M; Fujino, Yuri; Matsuura, Masato; Miki, Atsuya; Hirasawa, Kazunori; Tanito, Masaki; Mizoue, Shiro; Mori, Kazuhiko; Suzuki, Katsuyoshi; Yamashita, Takehiro; Kashiwagi, Kenji; Shoji, Nobuyuki; Asaoka, Ryo

2018-04-01

To validate the prediction accuracy of variational Bayes linear regression (VBLR) with two datasets external to the training dataset. The training dataset consisted of 7268 eyes of 4278 subjects from the University of Tokyo Hospital. The Japanese Archive of Multicentral Databases in Glaucoma (JAMDIG) dataset consisted of 271 eyes of 177 patients, and the Diagnostic Innovations in Glaucoma Study (DIGS) dataset includes 248 eyes of 173 patients, which were used for validation. Prediction accuracy was compared between the VBLR and ordinary least squared linear regression (OLSLR). First, OLSLR and VBLR were carried out using total deviation (TD) values at each of the 52 test points from the second to fourth visual fields (VFs) (VF2-4) to 2nd to 10th VF (VF2-10) of each patient in JAMDIG and DIGS datasets, and the TD values of the 11th VF test were predicted every time. The predictive accuracy of each method was compared through the root mean squared error (RMSE) statistic. OLSLR RMSEs with the JAMDIG and DIGS datasets were between 31 and 4.3 dB, and between 19.5 and 3.9 dB. On the other hand, VBLR RMSEs with JAMDIG and DIGS datasets were between 5.0 and 3.7, and between 4.6 and 3.6 dB. There was statistically significant difference between VBLR and OLSLR for both datasets at every series (VF2-4 to VF2-10) (P < 0.01 for all tests). However, there was no statistically significant difference in VBLR RMSEs between JAMDIG and DIGS datasets at any series of VFs (VF2-2 to VF2-10) (P > 0.05). VBLR outperformed OLSLR to predict future VF progression, and the VBLR has a potential to be a helpful tool at clinical settings.

Statistical Considerations of Food Allergy Prevention Studies.

PubMed

Bahnson, Henry T; du Toit, George; Lack, Gideon

Clinical studies to prevent the development of food allergy have recently helped reshape public policy recommendations on the early introduction of allergenic foods. These trials are also prompting new research, and it is therefore important to address the unique design and analysis challenges of prevention trials. We highlight statistical concepts and give recommendations that clinical researchers may wish to adopt when designing future study protocols and analysis plans for prevention studies. Topics include selecting a study sample, addressing internal and external validity, improving statistical power, choosing alpha and beta, analysis innovations to address dilution effects, and analysis methods to deal with poor compliance, dropout, and missing data. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Statistical inference, the bootstrap, and neural-network modeling with application to foreign exchange rates.

PubMed

White, H; Racine, J

2001-01-01

We propose tests for individual and joint irrelevance of network inputs. Such tests can be used to determine whether an input or group of inputs "belong" in a particular model, thus permitting valid statistical inference based on estimated feedforward neural-network models. The approaches employ well-known statistical resampling techniques. We conduct a small Monte Carlo experiment showing that our tests have reasonable level and power behavior, and we apply our methods to examine whether there are predictable regularities in foreign exchange rates. We find that exchange rates do appear to contain information that is exploitable for enhanced point prediction, but the nature of the predictive relations evolves through time.

The relationship between knowledge of leadership and knowledge management practices in the food industry in Kurdistan province, Iran.

PubMed

Jad, Seyyed Mohammad Moosavi; Geravandi, Sahar; Mohammadi, Mohammad Javad; Alizadeh, Rashin; Sarvarian, Mohammad; Rastegarimehr, Babak; Afkar, Abolhasan; Yari, Ahmad Reza; Momtazan, Mahboobeh; Valipour, Aliasghar; Mahboubi, Mohammad; Karimyan, Azimeh; Mazraehkar, Alireza; Nejad, Ali Soleimani; Mohammadi, Hafez

2017-12-01

The aim of this study was to identify the relationship between the knowledge of leadership and knowledge management practices. This research strategy, in terms of quantity, procedure and obtain information, is descriptive and correlational. Statistical population, consist of all employees of a food industry in Kurdistan province of Iran, who were engaged in 2016 and their total number is about 1800 people. 316 employees in the Kurdistan food industry (Kurdistan FI) were selected, using Cochran formula. Non-random method and valid questions (standard) for measurement of the data are used. Reliability and validity were confirmed. Statistical analysis of the data was carried out, using SPSS 16. The statistical analysis of collected data showed the relationship between knowledge-oriented of leadership and knowledge management activities as mediator variables. The results of the data and test hypotheses suggest that knowledge management activities play an important role in the functioning of product innovation and the results showed that the activities of Knowledge Management (knowledge transfer, storage knowledge, application of knowledge, creation of knowledge) on performance of product innovation.

A simplified approach to the pooled analysis of calibration of clinical prediction rules for systematic reviews of validation studies

PubMed Central

Dimitrov, Borislav D; Motterlini, Nicola; Fahey, Tom

2015-01-01

Objective Estimating calibration performance of clinical prediction rules (CPRs) in systematic reviews of validation studies is not possible when predicted values are neither published nor accessible or sufficient or no individual participant or patient data are available. Our aims were to describe a simplified approach for outcomes prediction and calibration assessment and evaluate its functionality and validity. Study design and methods: Methodological study of systematic reviews of validation studies of CPRs: a) ABCD2 rule for prediction of 7 day stroke; and b) CRB-65 rule for prediction of 30 day mortality. Predicted outcomes in a sample validation study were computed by CPR distribution patterns (“derivation model”). As confirmation, a logistic regression model (with derivation study coefficients) was applied to CPR-based dummy variables in the validation study. Meta-analysis of validation studies provided pooled estimates of “predicted:observed” risk ratios (RRs), 95% confidence intervals (CIs), and indexes of heterogeneity (I2) on forest plots (fixed and random effects models), with and without adjustment of intercepts. The above approach was also applied to the CRB-65 rule. Results Our simplified method, applied to ABCD2 rule in three risk strata (low, 0–3; intermediate, 4–5; high, 6–7 points), indicated that predictions are identical to those computed by univariate, CPR-based logistic regression model. Discrimination was good (c-statistics =0.61–0.82), however, calibration in some studies was low. In such cases with miscalibration, the under-prediction (RRs =0.73–0.91, 95% CIs 0.41–1.48) could be further corrected by intercept adjustment to account for incidence differences. An improvement of both heterogeneities and P-values (Hosmer-Lemeshow goodness-of-fit test) was observed. Better calibration and improved pooled RRs (0.90–1.06), with narrower 95% CIs (0.57–1.41) were achieved. Conclusion Our results have an immediate clinical implication in situations when predicted outcomes in CPR validation studies are lacking or deficient by describing how such predictions can be obtained by everyone using the derivation study alone, without any need for highly specialized knowledge or sophisticated statistics. PMID:25931829

Comparative study of novel versus conventional two-wavelength spectrophotometric methods for analysis of spectrally overlapping binary mixture.

PubMed

Lotfy, Hayam M; Hegazy, Maha A; Rezk, Mamdouh R; Omran, Yasmin Rostom

2015-09-05

Smart spectrophotometric methods have been applied and validated for the simultaneous determination of a binary mixture of chloramphenicol (CPL) and prednisolone acetate (PA) without preliminary separation. Two novel methods have been developed; the first method depends upon advanced absorbance subtraction (AAS), while the other method relies on advanced amplitude modulation (AAM); in addition to the well established dual wavelength (DW), ratio difference (RD) and constant center coupled with spectrum subtraction (CC-SS) methods. Accuracy, precision and linearity ranges of these methods were determined. Moreover, selectivity was assessed by analyzing synthetic mixtures of both drugs. The proposed methods were successfully applied to the assay of drugs in their pharmaceutical formulations. No interference was observed from common additives and the validity of the methods was tested. The obtained results have been statistically compared to that of official spectrophotometric methods to give a conclusion that there is no significant difference between the proposed methods and the official ones with respect to accuracy and precision. Copyright © 2015 Elsevier B.V. All rights reserved.

On the statistical equivalence of restrained-ensemble simulations with the maximum entropy method

PubMed Central

Roux, Benoît; Weare, Jonathan

2013-01-01

An issue of general interest in computer simulations is to incorporate information from experiments into a structural model. An important caveat in pursuing this goal is to avoid corrupting the resulting model with spurious and arbitrary biases. While the problem of biasing thermodynamic ensembles can be formulated rigorously using the maximum entropy method introduced by Jaynes, the approach can be cumbersome in practical applications with the need to determine multiple unknown coefficients iteratively. A popular alternative strategy to incorporate the information from experiments is to rely on restrained-ensemble molecular dynamics simulations. However, the fundamental validity of this computational strategy remains in question. Here, it is demonstrated that the statistical distribution produced by restrained-ensemble simulations is formally consistent with the maximum entropy method of Jaynes. This clarifies the underlying conditions under which restrained-ensemble simulations will yield results that are consistent with the maximum entropy method. PMID:23464140

Dynamic TIMI risk score for STEMI.

PubMed

Amin, Sameer T; Morrow, David A; Braunwald, Eugene; Sloan, Sarah; Contant, Charles; Murphy, Sabina; Antman, Elliott M

2013-01-29

Although there are multiple methods of risk stratification for ST-elevation myocardial infarction (STEMI), this study presents a prospectively validated method for reclassification of patients based on in-hospital events. A dynamic risk score provides an initial risk stratification and reassessment at discharge. The dynamic TIMI risk score for STEMI was derived in ExTRACT-TIMI 25 and validated in TRITON-TIMI 38. Baseline variables were from the original TIMI risk score for STEMI. New variables were major clinical events occurring during the index hospitalization. Each variable was tested individually in a univariate Cox proportional hazards regression. Variables with P<0.05 were incorporated into a full multivariable Cox model to assess the risk of death at 1 year. Each variable was assigned an integer value based on the odds ratio, and the final score was the sum of these values. The dynamic score included the development of in-hospital MI, arrhythmia, major bleed, stroke, congestive heart failure, recurrent ischemia, and renal failure. The C-statistic produced by the dynamic score in the derivation database was 0.76, with a net reclassification improvement (NRI) of 0.33 (P<0.0001) from the inclusion of dynamic events to the original TIMI risk score. In the validation database, the C-statistic was 0.81, with a NRI of 0.35 (P=0.01). This score is a prospectively derived, validated means of estimating 1-year mortality of STEMI at hospital discharge and can serve as a clinically useful tool. By incorporating events during the index hospitalization, it can better define risk and help to guide treatment decisions.

Time series modeling of human operator dynamics in manual control tasks

NASA Technical Reports Server (NTRS)

Biezad, D. J.; Schmidt, D. K.

1984-01-01

A time-series technique is presented for identifying the dynamic characteristics of the human operator in manual control tasks from relatively short records of experimental data. Control of system excitation signals used in the identification is not required. The approach is a multi-channel identification technique for modeling multi-input/multi-output situations. The method presented includes statistical tests for validity, is designed for digital computation, and yields estimates for the frequency responses of the human operator. A comprehensive relative power analysis may also be performed for validated models. This method is applied to several sets of experimental data; the results are discussed and shown to compare favorably with previous research findings. New results are also presented for a multi-input task that has not been previously modeled to demonstrate the strengths of the method.

Time Series Modeling of Human Operator Dynamics in Manual Control Tasks

NASA Technical Reports Server (NTRS)

Biezad, D. J.; Schmidt, D. K.

1984-01-01

A time-series technique is presented for identifying the dynamic characteristics of the human operator in manual control tasks from relatively short records of experimental data. Control of system excitation signals used in the identification is not required. The approach is a multi-channel identification technique for modeling multi-input/multi-output situations. The method presented includes statistical tests for validity, is designed for digital computation, and yields estimates for the frequency response of the human operator. A comprehensive relative power analysis may also be performed for validated models. This method is applied to several sets of experimental data; the results are discussed and shown to compare favorably with previous research findings. New results are also presented for a multi-input task that was previously modeled to demonstrate the strengths of the method.

Predictive modeling of outcomes following definitive chemoradiotherapy for oropharyngeal cancer based on FDG-PET image characteristics

NASA Astrophysics Data System (ADS)

Folkert, Michael R.; Setton, Jeremy; Apte, Aditya P.; Grkovski, Milan; Young, Robert J.; Schöder, Heiko; Thorstad, Wade L.; Lee, Nancy Y.; Deasy, Joseph O.; Oh, Jung Hun

2017-07-01

In this study, we investigate the use of imaging feature-based outcomes research (‘radiomics’) combined with machine learning techniques to develop robust predictive models for the risk of all-cause mortality (ACM), local failure (LF), and distant metastasis (DM) following definitive chemoradiation therapy (CRT). One hundred seventy four patients with stage III-IV oropharyngeal cancer (OC) treated at our institution with CRT with retrievable pre- and post-treatment 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) scans were identified. From pre-treatment PET scans, 24 representative imaging features of FDG-avid disease regions were extracted. Using machine learning-based feature selection methods, multiparameter logistic regression models were built incorporating clinical factors and imaging features. All model building methods were tested by cross validation to avoid overfitting, and final outcome models were validated on an independent dataset from a collaborating institution. Multiparameter models were statistically significant on 5 fold cross validation with the area under the receiver operating characteristic curve (AUC)  =  0.65 (p  =  0.004), 0.73 (p  =  0.026), and 0.66 (p  =  0.015) for ACM, LF, and DM, respectively. The model for LF retained significance on the independent validation cohort with AUC  =  0.68 (p  =  0.029) whereas the models for ACM and DM did not reach statistical significance, but resulted in comparable predictive power to the 5 fold cross validation with AUC  =  0.60 (p  =  0.092) and 0.65 (p  =  0.062), respectively. In the largest study of its kind to date, predictive features including increasing metabolic tumor volume, increasing image heterogeneity, and increasing tumor surface irregularity significantly correlated to mortality, LF, and DM on 5 fold cross validation in a relatively uniform single-institution cohort. The LF model also retained significance in an independent population.

Validation of asthma recording in electronic health records: a systematic review

PubMed Central

Nissen, Francis; Quint, Jennifer K; Wilkinson, Samantha; Mullerova, Hana; Smeeth, Liam; Douglas, Ian J

2017-01-01

Objective To describe the methods used to validate asthma diagnoses in electronic health records and summarize the results of the validation studies. Background Electronic health records are increasingly being used for research on asthma to inform health services and health policy. Validation of the recording of asthma diagnoses in electronic health records is essential to use these databases for credible epidemiological asthma research. Methods We searched EMBASE and MEDLINE databases for studies that validated asthma diagnoses detected in electronic health records up to October 2016. Two reviewers independently assessed the full text against the predetermined inclusion criteria. Key data including author, year, data source, case definitions, reference standard, and validation statistics (including sensitivity, specificity, positive predictive value [PPV], and negative predictive value [NPV]) were summarized in two tables. Results Thirteen studies met the inclusion criteria. Most studies demonstrated a high validity using at least one case definition (PPV >80%). Ten studies used a manual validation as the reference standard; each had at least one case definition with a PPV of at least 63%, up to 100%. We also found two studies using a second independent database to validate asthma diagnoses. The PPVs of the best performing case definitions ranged from 46% to 58%. We found one study which used a questionnaire as the reference standard to validate a database case definition; the PPV of the case definition algorithm in this study was 89%. Conclusion Attaining high PPVs (>80%) is possible using each of the discussed validation methods. Identifying asthma cases in electronic health records is possible with high sensitivity, specificity or PPV, by combining multiple data sources, or by focusing on specific test measures. Studies testing a range of case definitions show wide variation in the validity of each definition, suggesting this may be important for obtaining asthma definitions with optimal validity. PMID:29238227

Development of a Self-Rated Mixed Methods Skills Assessment: The NIH Mixed Methods Research Training Program for the Health Sciences

PubMed Central

Guetterman, Timothy C.; Creswell, John W.; Wittink, Marsha; Barg, Fran K.; Castro, Felipe G.; Dahlberg, Britt; Watkins, Daphne C.; Deutsch, Charles; Gallo, Joseph J.

2017-01-01

Introduction Demand for training in mixed methods is high, with little research on faculty development or assessment in mixed methods. We describe the development of a Self-Rated Mixed Methods Skills Assessment and provide validity evidence. The instrument taps six research domains: “Research question,” “Design/approach,” “Sampling,” “Data collection,” “Analysis,” and “Dissemination.” Respondents are asked to rate their ability to define or explain concepts of mixed methods under each domain, their ability to apply the concepts to problems, and the extent to which they need to improve. Methods We administered the questionnaire to 145 faculty and students using an internet survey. We analyzed descriptive statistics and performance characteristics of the questionnaire using Cronbach’s alpha to assess reliability and an ANOVA that compared a mixed methods experience index with assessment scores to assess criterion-relatedness. Results Internal consistency reliability was high for the total set of items (.95) and adequate (>=.71) for all but one subscale. Consistent with establishing criterion validity, respondents who had more professional experiences with mixed methods (e.g., published a mixed methods paper) rated themselves as more skilled, which was statistically significant across the research domains. Discussion This Self-Rated Mixed Methods Assessment instrument may be a useful tool to assess skills in mixed methods for training programs. It can be applied widely at the graduate and faculty level. For the learner, assessment may lead to enhanced motivation to learn and training focused on self-identified needs. For faculty, the assessment may improve curriculum and course content planning. PMID:28562495

A three-step approach for the derivation and validation of high-performing predictive models using an operational dataset: congestive heart failure readmission case study.

PubMed

AbdelRahman, Samir E; Zhang, Mingyuan; Bray, Bruce E; Kawamoto, Kensaku

2014-05-27

The aim of this study was to propose an analytical approach to develop high-performing predictive models for congestive heart failure (CHF) readmission using an operational dataset with incomplete records and changing data over time. Our analytical approach involves three steps: pre-processing, systematic model development, and risk factor analysis. For pre-processing, variables that were absent in >50% of records were removed. Moreover, the dataset was divided into a validation dataset and derivation datasets which were separated into three temporal subsets based on changes to the data over time. For systematic model development, using the different temporal datasets and the remaining explanatory variables, the models were developed by combining the use of various (i) statistical analyses to explore the relationships between the validation and the derivation datasets; (ii) adjustment methods for handling missing values; (iii) classifiers; (iv) feature selection methods; and (iv) discretization methods. We then selected the best derivation dataset and the models with the highest predictive performance. For risk factor analysis, factors in the highest-performing predictive models were analyzed and ranked using (i) statistical analyses of the best derivation dataset, (ii) feature rankers, and (iii) a newly developed algorithm to categorize risk factors as being strong, regular, or weak. The analysis dataset consisted of 2,787 CHF hospitalizations at University of Utah Health Care from January 2003 to June 2013. In this study, we used the complete-case analysis and mean-based imputation adjustment methods; the wrapper subset feature selection method; and four ranking strategies based on information gain, gain ratio, symmetrical uncertainty, and wrapper subset feature evaluators. The best-performing models resulted from the use of a complete-case analysis derivation dataset combined with the Class-Attribute Contingency Coefficient discretization method and a voting classifier which averaged the results of multi-nominal logistic regression and voting feature intervals classifiers. Of 42 final model risk factors, discharge disposition, discretized age, and indicators of anemia were the most significant. This model achieved a c-statistic of 86.8%. The proposed three-step analytical approach enhanced predictive model performance for CHF readmissions. It could potentially be leveraged to improve predictive model performance in other areas of clinical medicine.

Estimation of Global Network Statistics from Incomplete Data

PubMed Central

Bliss, Catherine A.; Danforth, Christopher M.; Dodds, Peter Sheridan

2014-01-01

Complex networks underlie an enormous variety of social, biological, physical, and virtual systems. A profound complication for the science of complex networks is that in most cases, observing all nodes and all network interactions is impossible. Previous work addressing the impacts of partial network data is surprisingly limited, focuses primarily on missing nodes, and suggests that network statistics derived from subsampled data are not suitable estimators for the same network statistics describing the overall network topology. We generate scaling methods to predict true network statistics, including the degree distribution, from only partial knowledge of nodes, links, or weights. Our methods are transparent and do not assume a known generating process for the network, thus enabling prediction of network statistics for a wide variety of applications. We validate analytical results on four simulated network classes and empirical data sets of various sizes. We perform subsampling experiments by varying proportions of sampled data and demonstrate that our scaling methods can provide very good estimates of true network statistics while acknowledging limits. Lastly, we apply our techniques to a set of rich and evolving large-scale social networks, Twitter reply networks. Based on 100 million tweets, we use our scaling techniques to propose a statistical characterization of the Twitter Interactome from September 2008 to November 2008. Our treatment allows us to find support for Dunbar's hypothesis in detecting an upper threshold for the number of active social contacts that individuals maintain over the course of one week. PMID:25338183

Validation of 31 of the most commonly used immunohistochemical antibodies in cytology prepared using the Cellient(®) automated cell block system.

PubMed

Montgomery, Eric; Gao, Chen; de Luca, Julie; Bower, Jessie; Attwood, Kristropher; Ylagan, Lourdes

2014-12-01

The Cellient(®) cell block system has become available as an alternative, partially automated method to create cell blocks in cytology. We sought to show a validation method for immunohistochemical (IHC) staining on the Cellient cell block system (CCB) in comparison with the formalin fixed paraffin embedded traditional cell block (TCB). Immunohistochemical staining was performed using 31 antibodies on 38 patient samples for a total of 326 slides. Split samples were processed using both methods by following the Cellient(®) manufacturer's recommendations for the Cellient cell block (CCB) and the Histogel method for preparing the traditional cell block (TCB). Interpretation was performed by three pathologists and two cytotechnologists. Immunohistochemical stains were scored as: 0/1+ (negative) and 2/3+ (positive). Inter-rater agreement for each antibody was evaluated for CCB and TCB, as well as the intra-rater agreement between TCB and CCB between observers. Interobserver staining concordance for the TCB was obtained with statistical significance (P < 0.05) in 24 of 31 antibodies. Interobserver staining concordance for the CCB was obtained with statistical significance in 27 of 31 antibodies. Intra-observer staining concordance between TCB and CCB was obtained with statistical significance in 24 of 31 antibodies tested. In conclusions, immunohistochemical stains on cytologic specimens processed by the Cellient system are reliable and concordant with stains performed on the same split samples processed via a formalin fixed-paraffin embedded (FFPE) block. The Cellient system is a welcome adjunct to cytology work-flow by producing cell block material of sufficient quality to allow the use of routine IHC. © 2014 Wiley Periodicals, Inc.

Predicting Survival of De Novo Metastatic Breast Cancer in Asian Women: Systematic Review and Validation Study

PubMed Central

Miao, Hui; Hartman, Mikael; Bhoo-Pathy, Nirmala; Lee, Soo-Chin; Taib, Nur Aishah; Tan, Ern-Yu; Chan, Patrick; Moons, Karel G. M.; Wong, Hoong-Seam; Goh, Jeremy; Rahim, Siti Mastura; Yip, Cheng-Har; Verkooijen, Helena M.

2014-01-01

Background In Asia, up to 25% of breast cancer patients present with distant metastases at diagnosis. Given the heterogeneous survival probabilities of de novo metastatic breast cancer, individual outcome prediction is challenging. The aim of the study is to identify existing prognostic models for patients with de novo metastatic breast cancer and validate them in Asia. Materials and Methods We performed a systematic review to identify prediction models for metastatic breast cancer. Models were validated in 642 women with de novo metastatic breast cancer registered between 2000 and 2010 in the Singapore Malaysia Hospital Based Breast Cancer Registry. Survival curves for low, intermediate and high-risk groups according to each prognostic score were compared by log-rank test and discrimination of the models was assessed by concordance statistic (C-statistic). Results We identified 16 prediction models, seven of which were for patients with brain metastases only. Performance status, estrogen receptor status, metastatic site(s) and disease-free interval were the most common predictors. We were able to validate nine prediction models. The capacity of the models to discriminate between poor and good survivors varied from poor to fair with C-statistics ranging from 0.50 (95% CI, 0.48–0.53) to 0.63 (95% CI, 0.60–0.66). Conclusion The discriminatory performance of existing prediction models for de novo metastatic breast cancer in Asia is modest. Development of an Asian-specific prediction model is needed to improve prognostication and guide decision making. PMID:24695692

Definitions and validation criteria for biomarkers and surrogate endpoints: development and testing of a quantitative hierarchical levels of evidence schema.

PubMed

Lassere, Marissa N; Johnson, Kent R; Boers, Maarten; Tugwell, Peter; Brooks, Peter; Simon, Lee; Strand, Vibeke; Conaghan, Philip G; Ostergaard, Mikkel; Maksymowych, Walter P; Landewe, Robert; Bresnihan, Barry; Tak, Paul-Peter; Wakefield, Richard; Mease, Philip; Bingham, Clifton O; Hughes, Michael; Altman, Doug; Buyse, Marc; Galbraith, Sally; Wells, George

2007-03-01

There are clear advantages to using biomarkers and surrogate endpoints, but concerns about clinical and statistical validity and systematic methods to evaluate these aspects hinder their efficient application. Our objective was to review the literature on biomarkers and surrogates to develop a hierarchical schema that systematically evaluates and ranks the surrogacy status of biomarkers and surrogates; and to obtain feedback from stakeholders. After a systematic search of Medline and Embase on biomarkers, surrogate (outcomes, endpoints, markers, indicators), intermediate endpoints, and leading indicators, a quantitative surrogate validation schema was developed and subsequently evaluated at a stakeholder workshop. The search identified several classification schema and definitions. Components of these were incorporated into a new quantitative surrogate validation level of evidence schema that evaluates biomarkers along 4 domains: Target, Study Design, Statistical Strength, and Penalties. Scores derived from 3 domains the Target that the marker is being substituted for, the Design of the (best) evidence, and the Statistical strength are additive. Penalties are then applied if there is serious counterevidence. A total score (0 to 15) determines the level of evidence, with Level 1 the strongest and Level 5 the weakest. It was proposed that the term "surrogate" be restricted to markers attaining Levels 1 or 2 only. Most stakeholders agreed that this operationalization of the National Institutes of Health definitions of biomarker, surrogate endpoint, and clinical endpoint was useful. Further development and application of this schema provides incentives and guidance for effective biomarker and surrogate endpoint research, and more efficient drug discovery, development, and approval.

Modern modeling techniques had limited external validity in predicting mortality from traumatic brain injury.

PubMed

van der Ploeg, Tjeerd; Nieboer, Daan; Steyerberg, Ewout W

2016-10-01

Prediction of medical outcomes may potentially benefit from using modern statistical modeling techniques. We aimed to externally validate modeling strategies for prediction of 6-month mortality of patients suffering from traumatic brain injury (TBI) with predictor sets of increasing complexity. We analyzed individual patient data from 15 different studies including 11,026 TBI patients. We consecutively considered a core set of predictors (age, motor score, and pupillary reactivity), an extended set with computed tomography scan characteristics, and a further extension with two laboratory measurements (glucose and hemoglobin). With each of these sets, we predicted 6-month mortality using default settings with five statistical modeling techniques: logistic regression (LR), classification and regression trees, random forests (RFs), support vector machines (SVM) and neural nets. For external validation, a model developed on one of the 15 data sets was applied to each of the 14 remaining sets. This process was repeated 15 times for a total of 630 validations. The area under the receiver operating characteristic curve (AUC) was used to assess the discriminative ability of the models. For the most complex predictor set, the LR models performed best (median validated AUC value, 0.757), followed by RF and support vector machine models (median validated AUC value, 0.735 and 0.732, respectively). With each predictor set, the classification and regression trees models showed poor performance (median validated AUC value, <0.7). The variability in performance across the studies was smallest for the RF- and LR-based models (inter quartile range for validated AUC values from 0.07 to 0.10). In the area of predicting mortality from TBI, nonlinear and nonadditive effects are not pronounced enough to make modern prediction methods beneficial. Copyright © 2016 Elsevier Inc. All rights reserved.

Are Education Cost Functions Ready for Prime Time? An Examination of Their Validity and Reliability

ERIC Educational Resources Information Center

Duncombe, William; Yinger, John

2011-01-01

This article makes the case that cost functions are the best available methodology for ensuring consistency between a state's educational accountability system and its education finance system. Because they are based on historical data and well-known statistical methods, cost functions are a particularly flexible and low-cost way to forecast what…

The Association of Social Work Boards' Licensure Examinations: A Review of Reliability and Validity Processes

ERIC Educational Resources Information Center

Marson, Stephen M.; DeAngelis, Donna; Mittal, Nisha

2010-01-01

Objectives: The purpose of this article is to create transparency for the psychometric methods employed for the development of the Association of Social Work Boards' (ASWB) exams. Results: The article includes an assessment of the macro (political) and micro (statistical) environments of testing social work competence. The seven-step process used…

Investigation of a Nonparametric Procedure for Assessing Goodness-of-Fit in Item Response Theory

ERIC Educational Resources Information Center

Wells, Craig S.; Bolt, Daniel M.

2008-01-01

Tests of model misfit are often performed to validate the use of a particular model in item response theory. Douglas and Cohen (2001) introduced a general nonparametric approach for detecting misfit under the two-parameter logistic model. However, the statistical properties of their approach, and empirical comparisons to other methods, have not…

Using Person Response Functions to Investigate Areas of Person Misfit Related to Item Characteristics

ERIC Educational Resources Information Center

Walker, A. Adrienne; Jennings, Jeremy Kyle; Engelhard, George, Jr.

2018-01-01

Individual person fit analyses provide important information regarding the validity of test score inferences for an "individual" test taker. In this study, we use data from an undergraduate statistics test (N = 1135) to illustrate a two-step method that researchers and practitioners can use to examine individual person fit. First, person…

Statistical Approach To Estimate Vaccinia-Specific Neutralizing Antibody Titers Using a High-Throughput Assay▿

PubMed Central

Kennedy, Richard; Pankratz, V. Shane; Swanson, Eric; Watson, David; Golding, Hana; Poland, Gregory A.

2009-01-01

Because of the bioterrorism threat posed by agents such as variola virus, considerable time, resources, and effort have been devoted to biodefense preparation. One avenue of this research has been the development of rapid, sensitive, high-throughput assays to validate immune responses to poxviruses. Here we describe the adaptation of a β-galactosidase reporter-based vaccinia virus neutralization assay to large-scale use in a study that included over 1,000 subjects. We also describe the statistical methods involved in analyzing the large quantity of data generated. The assay and its associated methods should prove useful tools in monitoring immune responses to next-generation smallpox vaccines, studying poxvirus immunity, and evaluating therapeutic agents such as vaccinia virus immune globulin. PMID:19535540

Medical cost analysis: application to colorectal cancer data from the SEER Medicare database.

PubMed

Bang, Heejung

2005-10-01

Incompleteness is a key feature of most survival data. Numerous well established statistical methodologies and algorithms exist for analyzing life or failure time data. However, induced censorship invalidates the use of those standard analytic tools for some survival-type data such as medical costs. In this paper, some valid methods currently available for analyzing censored medical cost data are reviewed. Some cautionary findings under different assumptions are envisioned through application to medical costs from colorectal cancer patients. Cost analysis should be suitably planned and carefully interpreted under various meaningful scenarios even with judiciously selected statistical methods. This approach would be greatly helpful to policy makers who seek to prioritize health care expenditures and to assess the elements of resource use.

Extracting and Converting Quantitative Data into Human Error Probabilities

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

Tuan Q. Tran; Ronald L. Boring; Jeffrey C. Joe

2007-08-01

This paper discusses a proposed method using a combination of advanced statistical approaches (e.g., meta-analysis, regression, structural equation modeling) that will not only convert different empirical results into a common metric for scaling individual PSFs effects, but will also examine the complex interrelationships among PSFs. Furthermore, the paper discusses how the derived statistical estimates (i.e., effect sizes) can be mapped onto a HRA method (e.g. SPAR-H) to generate HEPs that can then be use in probabilistic risk assessment (PRA). The paper concludes with a discussion of the benefits of using academic literature in assisting HRA analysts in generating sound HEPsmore » and HRA developers in validating current HRA models and formulating new HRA models.« less

Frequentist Model Averaging in Structural Equation Modelling.

PubMed

Jin, Shaobo; Ankargren, Sebastian

2018-06-04

Model selection from a set of candidate models plays an important role in many structural equation modelling applications. However, traditional model selection methods introduce extra randomness that is not accounted for by post-model selection inference. In the current study, we propose a model averaging technique within the frequentist statistical framework. Instead of selecting an optimal model, the contributions of all candidate models are acknowledged. Valid confidence intervals and a [Formula: see text] test statistic are proposed. A simulation study shows that the proposed method is able to produce a robust mean-squared error, a better coverage probability, and a better goodness-of-fit test compared to model selection. It is an interesting compromise between model selection and the full model.

Assessment of NDE reliability data

NASA Technical Reports Server (NTRS)

Yee, B. G. W.; Couchman, J. C.; Chang, F. H.; Packman, D. F.

1975-01-01

Twenty sets of relevant nondestructive test (NDT) reliability data were identified, collected, compiled, and categorized. A criterion for the selection of data for statistical analysis considerations was formulated, and a model to grade the quality and validity of the data sets was developed. Data input formats, which record the pertinent parameters of the defect/specimen and inspection procedures, were formulated for each NDE method. A comprehensive computer program was written and debugged to calculate the probability of flaw detection at several confidence limits by the binomial distribution. This program also selects the desired data sets for pooling and tests the statistical pooling criteria before calculating the composite detection reliability. An example of the calculated reliability of crack detection in bolt holes by an automatic eddy current method is presented.

Translation and Validation of the Knee Society Score - KSS for Brazilian Portuguese

PubMed Central

Silva, Adriana Lucia Pastore e; Demange, Marco Kawamura; Gobbi, Riccardo Gomes; da Silva, Tânia Fernanda Cardoso; Pécora, José Ricardo; Croci, Alberto Tesconi

2012-01-01

Objective To translate, culturally adapt and validate the "Knee Society Score"(KSS) for the Portuguese language and determine its measurement properties, reproducibility and validity. Methods We analyzed 70 patients of both sexes, aged between 55 and 85 years, in a cross-sectional clinical trial, with diagnosis of primary osteoarthritis ,undergoing total knee arthroplasty surgery. We assessed the patients with the English version of the KSS questionnaire and after 30 minutes with the Portuguese version of the KSS questionnaire, done by a different evaluator. All the patients were assessed preoperatively, and again at three, and six months postoperatively. Results There was no statistical difference, using Cronbach's alpha index and the Bland-Altman graphical analysis, for the knees core during the preoperative period (p =1), and at three months (p =0.991) and six months postoperatively (p =0.985). There was no statistical difference for knee function score for all three periods (p =1.0). Conclusion The Brazilian version of the Knee Society Score is easy to apply, as well providing as a valid and reliable instrument for measuring the knee score and function of Brazilian patients undergoing TKA. Level of Evidence: Level I - Diagnostic Studies- Investigating a Diagnostic Test- Testing of previously developed diagnostic criteria on consecutive patients (with universally applied 'gold' reference standard). PMID:24453576

The validation of a swimming turn wall-contact-time measurement system: a touchpad application reliability study.

PubMed

Brackley, Victoria; Ball, Kevin; Tor, Elaine

2018-05-12

The effectiveness of the swimming turn is highly influential to overall performance in competitive swimming. The push-off or wall contact, within the turn phase, is directly involved in determining the speed the swimmer leaves the wall. Therefore, it is paramount to develop reliable methods to measure the wall-contact-time during the turn phase for training and research purposes. The aim of this study was to determine the concurrent validity and reliability of the Pool Pad App to measure wall-contact-time during the freestyle and backstroke tumble turn. The wall-contact-times of nine elite and sub-elite participants were recorded during their regular training sessions. Concurrent validity statistics included the standardised typical error estimate, linear analysis and effect sizes while the intraclass correlating coefficient (ICC) was used for the reliability statistics. The standardised typical error estimate resulted in a moderate Cohen's d effect size with an R 2 value of 0.80 and the ICC between the Pool Pad and 2D video footage was 0.89. Despite these measurement differences, the results from this concurrent validity and reliability analyses demonstrated that the Pool Pad is suitable for measuring wall-contact-time during the freestyle and backstroke tumble turn within a training environment.

Stereological Analysis of Liver Biopsy Histology Sections as a Reference Standard for Validating Non-Invasive Liver Fat Fraction Measurements by MRI

PubMed Central

St. Pierre, Tim G.; House, Michael J.; Bangma, Sander J.; Pang, Wenjie; Bathgate, Andrew; Gan, Eng K.; Ayonrinde, Oyekoya T.; Bhathal, Prithi S.; Clouston, Andrew; Olynyk, John K.; Adams, Leon A.

2016-01-01

Background and Aims Validation of non-invasive methods of liver fat quantification requires a reference standard. However, using standard histopathology assessment of liver biopsies is problematical because of poor repeatability. We aimed to assess a stereological method of measuring volumetric liver fat fraction (VLFF) in liver biopsies and to use the method to validate a magnetic resonance imaging method for measurement of VLFF. Methods VLFFs were measured in 59 subjects (1) by three independent analysts using a stereological point counting technique combined with the Delesse principle on liver biopsy histological sections and (2) by three independent analysts using the HepaFat-Scan® technique on magnetic resonance images of the liver. Bland Altman statistics and intraclass correlation (IC) were used to assess the repeatability of each method and the bias between the methods of liver fat fraction measurement. Results Inter-analyst repeatability coefficients for the stereology and HepaFat-Scan® methods were 8.2 (95% CI 7.7–8.8)% and 2.4 (95% CI 2.2–2.5)% VLFF respectively. IC coefficients were 0.86 (95% CI 0.69–0.93) and 0.990 (95% CI 0.985–0.994) respectively. Small biases (≤3.4%) were observable between two pairs of analysts using stereology while no significant biases were observable between any of the three pairs of analysts using HepaFat-Scan®. A bias of 1.4±0.5% VLFF was observed between the HepaFat-Scan® method and the stereological method. Conclusions Repeatability of the stereological method is superior to the previously reported performance of assessment of hepatic steatosis by histopathologists and is a suitable reference standard for validating non-invasive methods of measurement of VLFF. PMID:27501242

Establishing glucose- and ABA-regulated transcription networks in Arabidopsis by microarray analysis and promoter classification using a Relevance Vector Machine.

PubMed

Li, Yunhai; Lee, Kee Khoon; Walsh, Sean; Smith, Caroline; Hadingham, Sophie; Sorefan, Karim; Cawley, Gavin; Bevan, Michael W

2006-03-01

Establishing transcriptional regulatory networks by analysis of gene expression data and promoter sequences shows great promise. We developed a novel promoter classification method using a Relevance Vector Machine (RVM) and Bayesian statistical principles to identify discriminatory features in the promoter sequences of genes that can correctly classify transcriptional responses. The method was applied to microarray data obtained from Arabidopsis seedlings treated with glucose or abscisic acid (ABA). Of those genes showing >2.5-fold changes in expression level, approximately 70% were correctly predicted as being up- or down-regulated (under 10-fold cross-validation), based on the presence or absence of a small set of discriminative promoter motifs. Many of these motifs have known regulatory functions in sugar- and ABA-mediated gene expression. One promoter motif that was not known to be involved in glucose-responsive gene expression was identified as the strongest classifier of glucose-up-regulated gene expression. We show it confers glucose-responsive gene expression in conjunction with another promoter motif, thus validating the classification method. We were able to establish a detailed model of glucose and ABA transcriptional regulatory networks and their interactions, which will help us to understand the mechanisms linking metabolism with growth in Arabidopsis. This study shows that machine learning strategies coupled to Bayesian statistical methods hold significant promise for identifying functionally significant promoter sequences.

Further elucidation of nanofluid thermal conductivity measurement using a transient hot-wire method apparatus

NASA Astrophysics Data System (ADS)

Yoo, Donghoon; Lee, Joohyun; Lee, Byeongchan; Kwon, Suyong; Koo, Junemo

2018-02-01

The Transient Hot-Wire Method (THWM) was developed to measure the absolute thermal conductivity of gases, liquids, melts, and solids with low uncertainty. The majority of nanofluid researchers used THWM to measure the thermal conductivity of test fluids. Several reasons have been suggested for the discrepancies in these types of measurements, including nanofluid generation, nanofluid stability, and measurement challenges. The details of the transient hot-wire method such as the test cell size, the temperature coefficient of resistance (TCR) and the sampling number are further investigated to improve the accuracy and consistency of the measurements of different researchers. It was observed that smaller test apparatuses were better because they can delay the onset of natural convection. TCR values of a coated platinum wire were measured and statistically analyzed to reduce the uncertainty in thermal conductivity measurements. For validation, ethylene glycol (EG) and water thermal conductivity were measured and analyzed in the temperature range between 280 and 310 K. Furthermore, a detailed statistical analysis was conducted for such measurements, and the results confirmed the minimum number of samples required to achieve the desired resolution and precision of the measurements. It is further proposed that researchers fully report the information related to their measurements to validate the measurements and to avoid future inconsistent nanofluid data.

Detection of ground fog in mountainous areas from MODIS (Collection 051) daytime data using a statistical approach

NASA Astrophysics Data System (ADS)

Schulz, Hans Martin; Thies, Boris; Chang, Shih-Chieh; Bendix, Jörg

2016-03-01

The mountain cloud forest of Taiwan can be delimited from other forest types using a map of the ground fog frequency. In order to create such a frequency map from remotely sensed data, an algorithm able to detect ground fog is necessary. Common techniques for ground fog detection based on weather satellite data cannot be applied to fog occurrences in Taiwan as they rely on several assumptions regarding cloud properties. Therefore a new statistical method for the detection of ground fog in mountainous terrain from MODIS Collection 051 data is presented. Due to the sharpening of input data using MODIS bands 1 and 2, the method provides fog masks in a resolution of 250 m per pixel. The new technique is based on negative correlations between optical thickness and terrain height that can be observed if a cloud that is relatively plane-parallel is truncated by the terrain. A validation of the new technique using camera data has shown that the quality of fog detection is comparable to that of another modern fog detection scheme developed and validated for the temperate zones. The method is particularly applicable to optically thinner water clouds. Beyond a cloud optical thickness of ≈ 40, classification errors significantly increase.

Experimental methods to validate measures of emotional state and readiness for duty in critical operations.

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

Weston, Louise Marie

2007-09-01

A recent report on criticality accidents in nuclear facilities indicates that human error played a major role in a significant number of incidents with serious consequences and that some of these human errors may be related to the emotional state of the individual. A pre-shift test to detect a deleterious emotional state could reduce the occurrence of such errors in critical operations. The effectiveness of pre-shift testing is a challenge because of the need to gather predictive data in a relatively short test period and the potential occurrence of learning effects due to a requirement for frequent testing. This reportmore » reviews the different types of reliability and validity methods and testing and statistical analysis procedures to validate measures of emotional state. The ultimate value of a validation study depends upon the percentage of human errors in critical operations that are due to the emotional state of the individual. A review of the literature to identify the most promising predictors of emotional state for this application is highly recommended.« less

R package PRIMsrc: Bump Hunting by Patient Rule Induction Method for Survival, Regression and Classification

PubMed Central

Dazard, Jean-Eudes; Choe, Michael; LeBlanc, Michael; Rao, J. Sunil

2015-01-01

PRIMsrc is a novel implementation of a non-parametric bump hunting procedure, based on the Patient Rule Induction Method (PRIM), offering a unified treatment of outcome variables, including censored time-to-event (Survival), continuous (Regression) and discrete (Classification) responses. To fit the model, it uses a recursive peeling procedure with specific peeling criteria and stopping rules depending on the response. To validate the model, it provides an objective function based on prediction-error or other specific statistic, as well as two alternative cross-validation techniques, adapted to the task of decision-rule making and estimation in the three types of settings. PRIMsrc comes as an open source R package, including at this point: (i) a main function for fitting a Survival Bump Hunting model with various options allowing cross-validated model selection to control model size (#covariates) and model complexity (#peeling steps) and generation of cross-validated end-point estimates; (ii) parallel computing; (iii) various S3-generic and specific plotting functions for data visualization, diagnostic, prediction, summary and display of results. It is available on CRAN and GitHub. PMID:26798326

Forensic Science Research and Development at the National Institute of Justice: Opportunities in Applied Physics

NASA Astrophysics Data System (ADS)

Dutton, Gregory

Forensic science is a collection of applied disciplines that draws from all branches of science. A key question in forensic analysis is: to what degree do a piece of evidence and a known reference sample share characteristics? Quantification of similarity, estimation of uncertainty, and determination of relevant population statistics are of current concern. A 2016 PCAST report questioned the foundational validity and the validity in practice of several forensic disciplines, including latent fingerprints, firearms comparisons and DNA mixture interpretation. One recommendation was the advancement of objective, automated comparison methods based on image analysis and machine learning. These concerns parallel the National Institute of Justice's ongoing R&D investments in applied chemistry, biology and physics. NIJ maintains a funding program spanning fundamental research with potential for forensic application to the validation of novel instruments and methods. Since 2009, NIJ has funded over 179M in external research to support the advancement of accuracy, validity and efficiency in the forensic sciences. An overview of NIJ's programs will be presented, with examples of relevant projects from fluid dynamics, 3D imaging, acoustics, and materials science.

An ultraviolet-spectrophotometric method for the determination of glimepiride in solid dosage forms.

PubMed

Afieroho, Ozadheoghene E; Okorie, Ogbonna; Okonkwo, Tochukwu J N

2011-06-01

Considering the cost of acquiring a liquid chromatographic instrument in underdeveloped economies, the rising incidence of diabetes mellitus, the need to evaluate the quality performance of glimepiride generics, and the need for less toxic processes, this research is an imperative. The method was validated for linearity, recovery accuracy, intra- and inter-day precision, specificity in the presence of excipients, and inter-day stability under laboratory conditions. Student's t test at the 95% confidence limit was used for statistics. Using 96% ethanol as solvent, a less toxic and cost-effective spectrophotometric method for the determination of glimepiride in solid dosage forms was developed and validated. The results of the validated parameters showed a λ(max) of 231 nm, linearity range of 0.5-22 μg/mL, precision with relative SD of <1.0%, recovery accuracy of 100.8%, regression equation of y = 45.741x + 0.0202, R(2) = 0.999, limit of detection of 0.35 μg/mL, and negligible interference from common excipients and colorants. The method was found to be accurate at the 95% confidence limit compared with the standard liquid chromatographic method with comparable reproducibility when used to assay the formulated products Amaryl(®) (sanofi-aventis, Paris, France) and Mepyril(®) (May & Baker Nigeria PLC, Ikeja, Nigeria). The results obtained for the validated parameters were within allowable limits. This method is recommended for routine quality control analysis.

Statistical properties of nonlinear one-dimensional wave fields

NASA Astrophysics Data System (ADS)

Chalikov, D.

2005-06-01

A numerical model for long-term simulation of gravity surface waves is described. The model is designed as a component of a coupled Wave Boundary Layer/Sea Waves model, for investigation of small-scale dynamic and thermodynamic interactions between the ocean and atmosphere. Statistical properties of nonlinear wave fields are investigated on a basis of direct hydrodynamical modeling of 1-D potential periodic surface waves. The method is based on a nonstationary conformal surface-following coordinate transformation; this approach reduces the principal equations of potential waves to two simple evolutionary equations for the elevation and the velocity potential on the surface. The numerical scheme is based on a Fourier transform method. High accuracy was confirmed by validation of the nonstationary model against known solutions, and by comparison between the results obtained with different resolutions in the horizontal. The scheme allows reproduction of the propagation of steep Stokes waves for thousands of periods with very high accuracy. The method here developed is applied to simulation of the evolution of wave fields with large number of modes for many periods of dominant waves. The statistical characteristics of nonlinear wave fields for waves of different steepness were investigated: spectra, curtosis and skewness, dispersion relation, life time. The prime result is that wave field may be presented as a superposition of linear waves is valid only for small amplitudes. It is shown as well, that nonlinear wave fields are rather a superposition of Stokes waves not linear waves. Potential flow, free surface, conformal mapping, numerical modeling of waves, gravity waves, Stokes waves, breaking waves, freak waves, wind-wave interaction.

Validated chromatographic and spectrophotometric methods for analysis of some amoebicide drugs in their combined pharmaceutical preparation.

PubMed

Abdelaleem, Eglal Adelhamid; Abdelwahab, Nada Sayed

2013-01-01

This work is concerned with development and validation of chromatographic and spectrophotometric methods for analysis of mebeverine HCl (MEH), diloxanide furoate (DF) and metronidazole (MET) in Dimetrol® tablets - spectrophotometric and RP-HPLC methods using UV detection. The developed spectrophotometric methods depend on determination of MEH and DF in the combined dosage form using the successive derivative ratio spectra method which depends on derivatization of the obtained ratio spectra in two steps using methanol as a solvent and measuring MEH at 226.4-232.2 nm (peak to peak) and DF at 260.6-264.8 nm (peak to peak). While MET concentrations were determined using first derivative (1D) at λ = 327 nm using the same solvent. The chromatographic method depends on HPLC separation on ODS column and elution with a mobile phase consisting water: methanol: triethylamine (25: 75: 0.5, by volume, orthophosphoric acid to pH =4). Pumping the mobile phase at 0.7 ml min-1 with UV at 230 nm. Factors affecting the developed methods were studied and optimized, moreover, they have been validated as per ICH guideline and the results demonstrated that the suggested methods are reproducible, reliable and can be applied for routine use with short time of analysis. Statistical analysis of the two developed methods with each other using F and student's-t tests showed no significant difference.

Development and Validation of a Teaching Practice Scale (TISS) for Instructors of Introductory Statistics at the College Level

ERIC Educational Resources Information Center

Hassad, Rossi A.

2009-01-01

This study examined the teaching practices of 227 college instructors of introductory statistics (from the health and behavioral sciences). Using primarily multidimensional scaling (MDS) techniques, a two-dimensional, 10-item teaching practice scale, TISS (Teaching of Introductory Statistics Scale), was developed and validated. The two dimensions…

Combined 3D-QSAR, molecular docking and molecular dynamics study on thyroid hormone activity of hydroxylated polybrominated diphenyl ethers to thyroid receptors β

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

Li, Xiaolin; Ye, Li; Wang, Xiaoxiang

2012-12-15

Several recent reports suggested that hydroxylated polybrominated diphenyl ethers (HO-PBDEs) may disturb thyroid hormone homeostasis. To illuminate the structural features for thyroid hormone activity of HO-PBDEs and the binding mode between HO-PBDEs and thyroid hormone receptor (TR), the hormone activity of a series of HO-PBDEs to thyroid receptors β was studied based on the combination of 3D-QSAR, molecular docking, and molecular dynamics (MD) methods. The ligand- and receptor-based 3D-QSAR models were obtained using Comparative Molecular Similarity Index Analysis (CoMSIA) method. The optimum CoMSIA model with region focusing yielded satisfactory statistical results: leave-one-out cross-validation correlation coefficient (q{sup 2}) was 0.571 andmore » non-cross-validation correlation coefficient (r{sup 2}) was 0.951. Furthermore, the results of internal validation such as bootstrapping, leave-many-out cross-validation, and progressive scrambling as well as external validation indicated the rationality and good predictive ability of the best model. In addition, molecular docking elucidated the conformations of compounds and key amino acid residues at the docking pocket, MD simulation further determined the binding process and validated the rationality of docking results. -- Highlights: ► The thyroid hormone activities of HO-PBDEs were studied by 3D-QSAR. ► The binding modes between HO-PBDEs and TRβ were explored. ► 3D-QSAR, molecular docking, and molecular dynamics (MD) methods were performed.« less

A systematic review of validated methods to capture acute bronchospasm using administrative or claims data.

PubMed

Sharifi, Mona; Krishanswami, Shanthi; McPheeters, Melissa L

2013-12-30

To identify and assess billing, procedural, or diagnosis code, or pharmacy claim-based algorithms used to identify acute bronchospasm in administrative and claims databases. We searched the MEDLINE database from 1991 to September 2012 using controlled vocabulary and key terms related to bronchospasm, wheeze and acute asthma. We also searched the reference lists of included studies. Two investigators independently assessed the full text of studies against pre-determined inclusion criteria. Two reviewers independently extracted data regarding participant and algorithm characteristics. Our searches identified 677 citations of which 38 met our inclusion criteria. In these 38 studies, the most commonly used ICD-9 code was 493.x. Only 3 studies reported any validation methods for the identification of bronchospasm, wheeze or acute asthma in administrative and claims databases; all were among pediatric populations and only 2 offered any validation statistics. Some of the outcome definitions utilized were heterogeneous and included other disease based diagnoses, such as bronchiolitis and pneumonia, which are typically of an infectious etiology. One study offered the validation of algorithms utilizing Emergency Department triage chief complaint codes to diagnose acute asthma exacerbations with ICD-9 786.07 (wheezing) revealing the highest sensitivity (56%), specificity (97%), PPV (93.5%) and NPV (76%). There is a paucity of studies reporting rigorous methods to validate algorithms for the identification of bronchospasm in administrative data. The scant validated data available are limited in their generalizability to broad-based populations. Copyright © 2013 Elsevier Ltd. All rights reserved.

Student engagement in mathematics: Development of instrument and validation of construct

NASA Astrophysics Data System (ADS)

Kong, Qi-Ping; Wong, Ngai-Ying; Lam, Chi-Chung

2003-05-01

Universal education has aggravated the problems of students' disengagement in learning, highlighting in particular, a greater range of motivations to learn and wider diversification in students' interests. Students' engagement with curriculum has become a crucial element in classroom learning. How we cultivate their involvement in the curriculum may be seen as being far more important than the epistemological consideration in the design of the school curriculum. Though aspects of behavioural, affective and cognitive engagements have been revealed in literature, we are still in need of a validated instrument that measures student engagement for further research. In the present study, an instrument of student engagement in the subject area of mathematics was developed through grounded research. Its validity was established by statistical methods

A comparison of the validity of the Demirjian, Willems, Nolla and Häävikko methods in determination of chronological age of 5-15 year-old Indian children.

PubMed

Hegde, Sapna; Patodia, Akash; Dixit, Uma

2017-08-01

Demirjian's method has been the most popular and extensively tested radiographic method of age estimation. More recently, Willems' method has been reported to be a better predictor of age. Nolla's and Häävikko's methods have been used to a lesser extent. Very few studies have compared all four methods in non-Indian and Indian populations. Most Indian research is limited by inadequate sample sizes, age structures and grouping and different approaches to statistical analysis. The present study aimed to evaluate and compare the validity of the Demirjian, Willems, Nolla and Häävikko methods in determination of chronological age of 5 to 15 year-old Indian children. In this cross-sectional observational study, four methods were compared for validity in estimating the age of 1200 Indian children aged 5-15 years. Demirjian's method overestimated age by +0.24 ± 0.80, +0.11 ± 0.81 and +0.19 ± 0.80 years in boys, girls and the total sample, respectively. With Willems' method, overestimations of +0.09 ± 0.80, +0.08 ± 0.80 and +0.09 ± 0.80 years were obtained in boys, girls and the total sample, respectively. Nolla's method underestimated age by -0.13 ± 0.80, -0.30 ± 0.82 and -0.20 ± 0.81 years in boys, girls and the total sample, respectively. Häävikko's method underestimated age by -0.17 ± 0.80, -0.29 ± 0.83 and -0.22 ± 0.82 years in boys, girls and the total sample, respectively. Statistically significant differences were observed between dental and chronological ages with all methods (p < 0.001). Significant gender-based differences were observed with all methods except Willems' (p < 0.05). Gender-specific regression formulae were derived for all methods. Willems' method most accurately estimated age, followed by Demirjian's, Nolla's and Häävikko's methods. All four methods could be applicable for estimating age in the present population, mean prediction errors being lower than 0.30 years (3.6 months). Copyright © 2017 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

Empirical evaluation of data normalization methods for molecular classification.

PubMed

Huang, Huei-Chung; Qin, Li-Xuan

2018-01-01

Data artifacts due to variations in experimental handling are ubiquitous in microarray studies, and they can lead to biased and irreproducible findings. A popular approach to correct for such artifacts is through post hoc data adjustment such as data normalization. Statistical methods for data normalization have been developed and evaluated primarily for the discovery of individual molecular biomarkers. Their performance has rarely been studied for the development of multi-marker molecular classifiers-an increasingly important application of microarrays in the era of personalized medicine. In this study, we set out to evaluate the performance of three commonly used methods for data normalization in the context of molecular classification, using extensive simulations based on re-sampling from a unique pair of microRNA microarray datasets for the same set of samples. The data and code for our simulations are freely available as R packages at GitHub. In the presence of confounding handling effects, all three normalization methods tended to improve the accuracy of the classifier when evaluated in an independent test data. The level of improvement and the relative performance among the normalization methods depended on the relative level of molecular signal, the distributional pattern of handling effects (e.g., location shift vs scale change), and the statistical method used for building the classifier. In addition, cross-validation was associated with biased estimation of classification accuracy in the over-optimistic direction for all three normalization methods. Normalization may improve the accuracy of molecular classification for data with confounding handling effects; however, it cannot circumvent the over-optimistic findings associated with cross-validation for assessing classification accuracy.

A Pilot Study of a Novel Method of Measuring Stigma about Depression Developed for Latinos in the Faith-Based Setting.

PubMed

Caplan, Susan

2016-08-01

In order to understand the effects of interventions designed to reduce stigma about mental illness, we need valid measures. However, the validity of commonly used measures is compromised by social desirability bias. The purpose of this pilot study was to test an anonymous method of measuring stigma in the community setting. The method of data collection, Preguntas con Cartas (Questions with Cards) used numbered playing cards to conduct anonymous group polling about stigmatizing beliefs during a mental health literacy intervention. An analysis of the difference between Preguntas con Cartas stigma votes and corresponding face-to-face individual survey results for the same seven stigma questions indicated that there was a statistically significant differences in the distributions between the two methods of data collection (χ(2) = 8.27, p = 0.016). This exploratory study has shown the potential effectiveness of Preguntas con Cartas as a novel method of measuring stigma in the community-based setting.

Optimization and validation of a method for the determination of the refractive index of milk serum based on the reaction between milk and copper(II) sulfate to detect milk dilutions.

PubMed

Rezende, Patrícia Sueli; Carmo, Geraldo Paulo do; Esteves, Eduardo Gonçalves

2015-06-01

We report the use of a method to determine the refractive index of copper(II) serum (RICS) in milk as a tool to detect the fraudulent addition of water. This practice is highly profitable, unlawful, and difficult to deter. The method was optimized and validated and is simple, fast and robust. The optimized method yielded statistically equivalent results compared to the reference method with an accuracy of 0.4% and quadrupled analytical throughput. Trueness, precision (repeatability and intermediate precision) and ruggedness are determined to be satisfactory at a 95.45% confidence level. The expanded uncertainty of the measurement was ±0.38°Zeiss at the 95.45% confidence level (k=3.30), corresponding to 1.03% of the minimum measurement expected in adequate samples (>37.00°Zeiss). Copyright © 2015 Elsevier B.V. All rights reserved.

Spectrophotometric methods for simultaneous determination of betamethasone valerate and fusidic acid in their binary mixture.

PubMed

Lotfy, Hayam Mahmoud; Salem, Hesham; Abdelkawy, Mohammad; Samir, Ahmed

2015-04-05

Five spectrophotometric methods were successfully developed and validated for the determination of betamethasone valerate and fusidic acid in their binary mixture. Those methods are isoabsorptive point method combined with the first derivative (ISO Point--D1) and the recently developed and well established methods namely ratio difference (RD) and constant center coupled with spectrum subtraction (CC) methods, in addition to derivative ratio (1DD) and mean centering of ratio spectra (MCR). New enrichment technique called spectrum addition technique was used instead of traditional spiking technique. The proposed spectrophotometric procedures do not require any separation steps. Accuracy, precision and linearity ranges of the proposed methods were determined and the specificity was assessed by analyzing synthetic mixtures of both drugs. They were applied to their pharmaceutical formulation and the results obtained were statistically compared to that of official methods. The statistical comparison showed that there is no significant difference between the proposed methods and the official ones regarding both accuracy and precision. Copyright © 2015 Elsevier B.V. All rights reserved.

Guidelines for Design and Analysis of Large, Brittle Spacecraft Components

NASA Technical Reports Server (NTRS)

Robinson, E. Y.

1993-01-01

There were two related parts to this work. The first, conducted at The Aerospace Corporation was to develop and define methods for integrating the statistical theory of brittle strength with conventional finite element stress analysis, and to carry out a limited laboratory test program to illustrate the methods. The second part, separately funded at Aerojet Electronic Systems Division, was to create the finite element postprocessing program for integrating the statistical strength analysis with the structural analysis. The second part was monitored by Capt. Jeff McCann of USAF/SMC, as Special Study No.11, which authorized Aerojet to support Aerospace on this work requested by NASA. This second part is documented in Appendix A. The activity at Aerojet was guided by the Aerospace methods developed in the first part of this work. This joint work of Aerospace and Aerojet stemmed from prior related work for the Defense Support Program (DSP) Program Office, to qualify the DSP sensor main mirror and corrector lens for flight as part of a shuttle payload. These large brittle components of the DSP sensor are provided by Aerojet. This document defines rational methods for addressing the structural integrity and safety of large, brittle, payload components, which have low and variable tensile strength and can suddenly break or shatter. The methods are applicable to the evaluation and validation of such components, which, because of size and configuration restrictions, cannot be validated by direct proof test.

Quantitative determination and validation of octreotide acetate using 1 H-NMR spectroscopy with internal standard method.

PubMed

Yu, Chen; Zhang, Qian; Xu, Peng-Yao; Bai, Yin; Shen, Wen-Bin; Di, Bin; Su, Meng-Xiang

2018-01-01

Quantitative nuclear magnetic resonance (qNMR) is a well-established technique in quantitative analysis. We presented a validated 1 H-qNMR method for assay of octreotide acetate, a kind of cyclic octopeptide. Deuterium oxide was used to remove the undesired exchangeable peaks, which was referred to as proton exchange, in order to make the quantitative signals isolated in the crowded spectrum of the peptide and ensure precise quantitative analysis. Gemcitabine hydrochloride was chosen as the suitable internal standard. Experimental conditions, including relaxation delay time, the numbers of scans, and pulse angle, were optimized first. Then method validation was carried out in terms of selectivity, stability, linearity, precision, and robustness. The assay result was compared with that by means of high performance liquid chromatography, which is provided by Chinese Pharmacopoeia. The statistical F test, Student's t test, and nonparametric test at 95% confidence level indicate that there was no significant difference between these two methods. qNMR is a simple and accurate quantitative tool with no need for specific corresponding reference standards. It has the potential of the quantitative analysis of other peptide drugs and standardization of the corresponding reference standards. Copyright © 2017 John Wiley & Sons, Ltd.

Statistical inference for noisy nonlinear ecological dynamic systems.

PubMed

Wood, Simon N

2010-08-26

Chaotic ecological dynamic systems defy conventional statistical analysis. Systems with near-chaotic dynamics are little better. Such systems are almost invariably driven by endogenous dynamic processes plus demographic and environmental process noise, and are only observable with error. Their sensitivity to history means that minute changes in the driving noise realization, or the system parameters, will cause drastic changes in the system trajectory. This sensitivity is inherited and amplified by the joint probability density of the observable data and the process noise, rendering it useless as the basis for obtaining measures of statistical fit. Because the joint density is the basis for the fit measures used by all conventional statistical methods, this is a major theoretical shortcoming. The inability to make well-founded statistical inferences about biological dynamic models in the chaotic and near-chaotic regimes, other than on an ad hoc basis, leaves dynamic theory without the methods of quantitative validation that are essential tools in the rest of biological science. Here I show that this impasse can be resolved in a simple and general manner, using a method that requires only the ability to simulate the observed data on a system from the dynamic model about which inferences are required. The raw data series are reduced to phase-insensitive summary statistics, quantifying local dynamic structure and the distribution of observations. Simulation is used to obtain the mean and the covariance matrix of the statistics, given model parameters, allowing the construction of a 'synthetic likelihood' that assesses model fit. This likelihood can be explored using a straightforward Markov chain Monte Carlo sampler, but one further post-processing step returns pure likelihood-based inference. I apply the method to establish the dynamic nature of the fluctuations in Nicholson's classic blowfly experiments.

Machine Learning Algorithms Outperform Conventional Regression Models in Predicting Development of Hepatocellular Carcinoma

PubMed Central

Singal, Amit G.; Mukherjee, Ashin; Elmunzer, B. Joseph; Higgins, Peter DR; Lok, Anna S.; Zhu, Ji; Marrero, Jorge A; Waljee, Akbar K

2015-01-01

Background Predictive models for hepatocellular carcinoma (HCC) have been limited by modest accuracy and lack of validation. Machine learning algorithms offer a novel methodology, which may improve HCC risk prognostication among patients with cirrhosis. Our study's aim was to develop and compare predictive models for HCC development among cirrhotic patients, using conventional regression analysis and machine learning algorithms. Methods We enrolled 442 patients with Child A or B cirrhosis at the University of Michigan between January 2004 and September 2006 (UM cohort) and prospectively followed them until HCC development, liver transplantation, death, or study termination. Regression analysis and machine learning algorithms were used to construct predictive models for HCC development, which were tested on an independent validation cohort from the Hepatitis C Antiviral Long-term Treatment against Cirrhosis (HALT-C) Trial. Both models were also compared to the previously published HALT-C model. Discrimination was assessed using receiver operating characteristic curve analysis and diagnostic accuracy was assessed with net reclassification improvement and integrated discrimination improvement statistics. Results After a median follow-up of 3.5 years, 41 patients developed HCC. The UM regression model had a c-statistic of 0.61 (95%CI 0.56-0.67), whereas the machine learning algorithm had a c-statistic of 0.64 (95%CI 0.60–0.69) in the validation cohort. The machine learning algorithm had significantly better diagnostic accuracy as assessed by net reclassification improvement (p<0.001) and integrated discrimination improvement (p=0.04). The HALT-C model had a c-statistic of 0.60 (95%CI 0.50-0.70) in the validation cohort and was outperformed by the machine learning algorithm (p=0.047). Conclusion Machine learning algorithms improve the accuracy of risk stratifying patients with cirrhosis and can be used to accurately identify patients at high-risk for developing HCC. PMID:24169273

Statistical modeling of software reliability

NASA Technical Reports Server (NTRS)

Miller, Douglas R.

1992-01-01

This working paper discusses the statistical simulation part of a controlled software development experiment being conducted under the direction of the System Validation Methods Branch, Information Systems Division, NASA Langley Research Center. The experiment uses guidance and control software (GCS) aboard a fictitious planetary landing spacecraft: real-time control software operating on a transient mission. Software execution is simulated to study the statistical aspects of reliability and other failure characteristics of the software during development, testing, and random usage. Quantification of software reliability is a major goal. Various reliability concepts are discussed. Experiments are described for performing simulations and collecting appropriate simulated software performance and failure data. This data is then used to make statistical inferences about the quality of the software development and verification processes as well as inferences about the reliability of software versions and reliability growth under random testing and debugging.

Air Combat Training: Good Stick Index Validation. Final Report for Period 3 April 1978-1 April 1979.

ERIC Educational Resources Information Center

Moore, Samuel B.; And Others

A study was conducted to investigate and statistically validate a performance measuring system (the Good Stick Index) in the Tactical Air Command Combat Engagement Simulator I (TAC ACES I) Air Combat Maneuvering (ACM) training program. The study utilized a twelve-week sample of eighty-nine student pilots to statistically validate the Good Stick…

Parameter Estimation and Model Validation of Nonlinear Dynamical Networks

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

Abarbanel, Henry; Gill, Philip

In the performance period of this work under a DOE contract, the co-PIs, Philip Gill and Henry Abarbanel, developed new methods for statistical data assimilation for problems of DOE interest, including geophysical and biological problems. This included numerical optimization algorithms for variational principles, new parallel processing Monte Carlo routines for performing the path integrals of statistical data assimilation. These results have been summarized in the monograph: “Predicting the Future: Completing Models of Observed Complex Systems” by Henry Abarbanel, published by Spring-Verlag in June 2013. Additional results and details have appeared in the peer reviewed literature.

Statistical turbulence theory and turbulence phenomenology

NASA Technical Reports Server (NTRS)

Herring, J. R.

1973-01-01

The application of deductive turbulence theory for validity determination of turbulence phenomenology at the level of second-order, single-point moments is considered. Particular emphasis is placed on the phenomenological formula relating the dissipation to the turbulence energy and the Rotta-type formula for the return to isotropy. Methods which deal directly with most or all the scales of motion explicitly are reviewed briefly. The statistical theory of turbulence is presented as an expansion about randomness. Two concepts are involved: (1) a modeling of the turbulence as nearly multipoint Gaussian, and (2) a simultaneous introduction of a generalized eddy viscosity operator.

Wang-Landau Reaction Ensemble Method: Simulation of Weak Polyelectrolytes and General Acid-Base Reactions.

PubMed

Landsgesell, Jonas; Holm, Christian; Smiatek, Jens

2017-02-14

We present a novel method for the study of weak polyelectrolytes and general acid-base reactions in molecular dynamics and Monte Carlo simulations. The approach combines the advantages of the reaction ensemble and the Wang-Landau sampling method. Deprotonation and protonation reactions are simulated explicitly with the help of the reaction ensemble method, while the accurate sampling of the corresponding phase space is achieved by the Wang-Landau approach. The combination of both techniques provides a sufficient statistical accuracy such that meaningful estimates for the density of states and the partition sum can be obtained. With regard to these estimates, several thermodynamic observables like the heat capacity or reaction free energies can be calculated. We demonstrate that the computation times for the calculation of titration curves with a high statistical accuracy can be significantly decreased when compared to the original reaction ensemble method. The applicability of our approach is validated by the study of weak polyelectrolytes and their thermodynamic properties.

Critical discussion of evaluation parameters for inter-observer variability in target definition for radiation therapy.

PubMed

Fotina, I; Lütgendorf-Caucig, C; Stock, M; Pötter, R; Georg, D

2012-02-01

Inter-observer studies represent a valid method for the evaluation of target definition uncertainties and contouring guidelines. However, data from the literature do not yet give clear guidelines for reporting contouring variability. Thus, the purpose of this work was to compare and discuss various methods to determine variability on the basis of clinical cases and a literature review. In this study, 7 prostate and 8 lung cases were contoured on CT images by 8 experienced observers. Analysis of variability included descriptive statistics, calculation of overlap measures, and statistical measures of agreement. Cross tables with ratios and correlations were established for overlap parameters. It was shown that the minimal set of parameters to be reported should include at least one of three volume overlap measures (i.e., generalized conformity index, Jaccard coefficient, or conformation number). High correlation between these parameters and scatter of the results was observed. A combination of descriptive statistics, overlap measure, and statistical measure of agreement or reliability analysis is required to fully report the interrater variability in delineation.

Structural damage detection based on stochastic subspace identification and statistical pattern recognition: I. Theory

NASA Astrophysics Data System (ADS)

Ren, W. X.; Lin, Y. Q.; Fang, S. E.

2011-11-01

One of the key issues in vibration-based structural health monitoring is to extract the damage-sensitive but environment-insensitive features from sampled dynamic response measurements and to carry out the statistical analysis of these features for structural damage detection. A new damage feature is proposed in this paper by using the system matrices of the forward innovation model based on the covariance-driven stochastic subspace identification of a vibrating system. To overcome the variations of the system matrices, a non-singularity transposition matrix is introduced so that the system matrices are normalized to their standard forms. For reducing the effects of modeling errors, noise and environmental variations on measured structural responses, a statistical pattern recognition paradigm is incorporated into the proposed method. The Mahalanobis and Euclidean distance decision functions of the damage feature vector are adopted by defining a statistics-based damage index. The proposed structural damage detection method is verified against one numerical signal and two numerical beams. It is demonstrated that the proposed statistics-based damage index is sensitive to damage and shows some robustness to the noise and false estimation of the system ranks. The method is capable of locating damage of the beam structures under different types of excitations. The robustness of the proposed damage detection method to the variations in environmental temperature is further validated in a companion paper by a reinforced concrete beam tested in the laboratory and a full-scale arch bridge tested in the field.

Variable Selection in the Presence of Missing Data: Imputation-based Methods.

PubMed

Zhao, Yize; Long, Qi

2017-01-01

Variable selection plays an essential role in regression analysis as it identifies important variables that associated with outcomes and is known to improve predictive accuracy of resulting models. Variable selection methods have been widely investigated for fully observed data. However, in the presence of missing data, methods for variable selection need to be carefully designed to account for missing data mechanisms and statistical techniques used for handling missing data. Since imputation is arguably the most popular method for handling missing data due to its ease of use, statistical methods for variable selection that are combined with imputation are of particular interest. These methods, valid used under the assumptions of missing at random (MAR) and missing completely at random (MCAR), largely fall into three general strategies. The first strategy applies existing variable selection methods to each imputed dataset and then combine variable selection results across all imputed datasets. The second strategy applies existing variable selection methods to stacked imputed datasets. The third variable selection strategy combines resampling techniques such as bootstrap with imputation. Despite recent advances, this area remains under-developed and offers fertile ground for further research.

Publication Bias ( The "File-Drawer Problem") in Scientific Inference

NASA Technical Reports Server (NTRS)

Scargle, Jeffrey D.; DeVincenzi, Donald (Technical Monitor)

1999-01-01

Publication bias arises whenever the probability that a study is published depends on the statistical significance of its results. This bias, often called the file-drawer effect since the unpublished results are imagined to be tucked away in researchers' file cabinets, is potentially a severe impediment to combining the statistical results of studies collected from the literature. With almost any reasonable quantitative model for publication bias, only a small number of studies lost in the file-drawer will produce a significant bias. This result contradicts the well known Fail Safe File Drawer (FSFD) method for setting limits on the potential harm of publication bias, widely used in social, medical and psychic research. This method incorrectly treats the file drawer as unbiased, and almost always miss-estimates the seriousness of publication bias. A large body of not only psychic research, but medical and social science studies, has mistakenly relied on this method to validate claimed discoveries. Statistical combination can be trusted only if it is known with certainty that all studies that have been carried out are included. Such certainty is virtually impossible to achieve in literature surveys.

Adaptation and Assessment of Reliability and Validity of the Greek Version of the Ohkuma Questionnaire for Dysphagia Screening.

PubMed

Papadopoulou, Soultana L; Exarchakos, Georgios; Christodoulou, Dimitrios; Theodorou, Stavroula; Beris, Alexandre; Ploumis, Avraam

2017-01-01

Introduction  The Ohkuma questionnaire is a validated screening tool originally used to detect dysphagia among patients hospitalized in Japanese nursing facilities. Objective  The purpose of this study is to evaluate the reliability and validity of the adapted Greek version of the Ohkuma questionnaire. Methods  Following the steps for cross-cultural adaptation, we delivered the validated Ohkuma questionnaire to 70 patients (53 men, 17 women) who were either suffering from dysphagia or not. All of them completed the questionnaire a second time within a month. For all of them, we performed a bedside and VFSS study of dysphagia and asked participants to undergo a second VFSS screening, with the exception of nine individuals. Statistical analysis included measurement of internal consistency with Cronbach's α coefficient, reliability with Cohen's Kappa, Pearson's correlation coefficient and construct validity with categorical components, and One-Way Anova test. Results  According to Cronbach's α coefficient (0.976) for total score, there was high internal consistency for the Ohkuma Dysphagia questionnaire. Test-retest reliability (Cohen's Kappa) ranged from 0.586 to 1.00, exhibiting acceptable stability. We also estimated the Pearson's correlation coefficient for the test-retest total score, which reached high levels (0.952; p  = 0.000). The One-Way Anova test in the two measurement times showed statistically significant correlation in both measurements ( p  = 0.02 and p  = 0.016). Conclusion  The adapted Greek version of the questionnaire is valid and reliable and can be used for the screening of dysphagia in the Greek-speaking patients.

An NCME Instructional Module on Item-Fit Statistics for Item Response Theory Models

ERIC Educational Resources Information Center

Ames, Allison J.; Penfield, Randall D.

2015-01-01

Drawing valid inferences from item response theory (IRT) models is contingent upon a good fit of the data to the model. Violations of model-data fit have numerous consequences, limiting the usefulness and applicability of the model. This instructional module provides an overview of methods used for evaluating the fit of IRT models. Upon completing…

A Simple Tool for Diet Evaluation in Primary Health Care: Validation of a 16-Item Food Intake Questionnaire

PubMed Central

Hemiö, Katri; Pölönen, Auli; Ahonen, Kirsti; Kosola, Mikko; Viitasalo, Katriina; Lindström, Jaana

2014-01-01

Our aim was to validate a 16-item food intake questionnaire (16-FIQ) and create an easy to use method to estimate patients’ nutrient intake in primary health care. Participants (52 men, 25 women) completed a 7-day food record and a 16-FIQ. Food and nutrient intakes were calculated and compared using Spearman correlation. Further, nutrient intakes were compared using kappa-statistics and exact and opposite agreement of intake tertiles. The results indicated that the 16-FIQ reliably categorized individuals according to their nutrient intakes. Methods to estimate nutrient intake based on the answers given in 16-FIQ were created. In linear regression models nutrient intake estimates from the food records were used as the dependent variables and sum variables derived from the 16-FIQ were used as the independent variables. Valid regression models were created for the energy proportion of fat, saturated fat, and sucrose and the amount of fibre (g), vitamin C (mg), iron (mg), and vitamin D (μg) intake. The 16-FIQ is a valid method for estimating nutrient intakes in group level. In addition, the 16-FIQ could be a useful tool to facilitate identification of people in need of dietary counselling and to monitor the effect of counselling in primary health care. PMID:24599042

EDGE COMPUTING AND CONTEXTUAL INFORMATION FOR THE INTERNET OF THINGS SENSORS

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

Klein, Levente

Interpreting sensor data require knowledge about sensor placement and the surrounding environment. For a single sensor measurement, it is easy to document the context by visual observation, however for millions of sensors reporting data back to a server, the contextual information needs to be automatically extracted from either data analysis or leveraging complimentary data sources. Data layers that overlap spatially or temporally with sensor locations, can be used to extract the context and to validate the measurement. To minimize the amount of data transmitted through the internet, while preserving signal information content, two methods are explored; computation at the edgemore » and compressed sensing. We validate the above methods on wind and chemical sensor data (1) eliminate redundant measurement from wind sensors and (2) extract peak value of a chemical sensor measuring a methane plume. We present a general cloud based framework to validate sensor data based on statistical and physical modeling and contextual data extracted from geospatial data.« less

Hypersensitivity to sound in tinnitus patients: an analysis of a construct based on questionnaire and audiological data.

PubMed

Bläsing, Lena; Goebel, Gerhard; Flötzinger, Uta; Berthold, Anke; Kröner-Herwig, Birgit

2010-07-01

The purpose of this study was to analyse the Questionnaire on Hypersensitivity to Sound (GUF; Nelting & Finlayson, 2004 ) and to improve its validity based on the analysis of intercorrelations (single item level) with other methods of assessing hyperacusis (uncomfortable loudness level, individual loudness function, self-rated severity of hyperacusis). Subjects consisted of 91 inpatients with tinnitus and hyperacusis. The GUF showed a good reliability (alpha = .92). The factorial structure of the questionnaire reported by Nelting et al (2002) was not completely supported by the evidence in this study. The total score and the single items showed small to moderate correlations with the other modes of measuring hyperacusis. Evidence for convergent and discriminant validity were found, but overall the results corroborate the conceptual heterogeneity of the construct hyperacusis and its dependency on the assessment method. Four items of the GUF with particularly low correlations were excluded from the questionnaire. The revised GUF total score showed slightly but not statistically significant higher convergent and discriminant validity.

Scaling field data to calibrate and validate moderate spatial resolution remote sensing models

USGS Publications Warehouse

Baccini, A.; Friedl, M.A.; Woodcock, C.E.; Zhu, Z.

2007-01-01

Validation and calibration are essential components of nearly all remote sensing-based studies. In both cases, ground measurements are collected and then related to the remote sensing observations or model results. In many situations, and particularly in studies that use moderate resolution remote sensing, a mismatch exists between the sensor's field of view and the scale at which in situ measurements are collected. The use of in situ measurements for model calibration and validation, therefore, requires a robust and defensible method to spatially aggregate ground measurements to the scale at which the remotely sensed data are acquired. This paper examines this challenge and specifically considers two different approaches for aggregating field measurements to match the spatial resolution of moderate spatial resolution remote sensing data: (a) landscape stratification; and (b) averaging of fine spatial resolution maps. The results show that an empirically estimated stratification based on a regression tree method provides a statistically defensible and operational basis for performing this type of procedure. 

Relations between Brain Structure and Attentional Function in Spina Bifida: Utilization of Robust Statistical Approaches

PubMed Central

Kulesz, Paulina A.; Tian, Siva; Juranek, Jenifer; Fletcher, Jack M.; Francis, David J.

2015-01-01

Objective Weak structure-function relations for brain and behavior may stem from problems in estimating these relations in small clinical samples with frequently occurring outliers. In the current project, we focused on the utility of using alternative statistics to estimate these relations. Method Fifty-four children with spina bifida meningomyelocele performed attention tasks and received MRI of the brain. Using a bootstrap sampling process, the Pearson product moment correlation was compared with four robust correlations: the percentage bend correlation, the Winsorized correlation, the skipped correlation using the Donoho-Gasko median, and the skipped correlation using the minimum volume ellipsoid estimator Results All methods yielded similar estimates of the relations between measures of brain volume and attention performance. The similarity of estimates across correlation methods suggested that the weak structure-function relations previously found in many studies are not readily attributable to the presence of outlying observations and other factors that violate the assumptions behind the Pearson correlation. Conclusions Given the difficulty of assembling large samples for brain-behavior studies, estimating correlations using multiple, robust methods may enhance the statistical conclusion validity of studies yielding small, but often clinically significant, correlations. PMID:25495830

A multi-time-step noise reduction method for measuring velocity statistics from particle tracking velocimetry

NASA Astrophysics Data System (ADS)

Machicoane, Nathanaël; López-Caballero, Miguel; Bourgoin, Mickael; Aliseda, Alberto; Volk, Romain

2017-10-01

We present a method to improve the accuracy of velocity measurements for fluid flow or particles immersed in it, based on a multi-time-step approach that allows for cancellation of noise in the velocity measurements. Improved velocity statistics, a critical element in turbulent flow measurements, can be computed from the combination of the velocity moments computed using standard particle tracking velocimetry (PTV) or particle image velocimetry (PIV) techniques for data sets that have been collected over different values of time intervals between images. This method produces Eulerian velocity fields and Lagrangian velocity statistics with much lower noise levels compared to standard PIV or PTV measurements, without the need of filtering and/or windowing. Particle displacement between two frames is computed for multiple different time-step values between frames in a canonical experiment of homogeneous isotropic turbulence. The second order velocity structure function of the flow is computed with the new method and compared to results from traditional measurement techniques in the literature. Increased accuracy is also demonstrated by comparing the dissipation rate of turbulent kinetic energy measured from this function against previously validated measurements.

Physiotherapy for patients with soft tissue shoulder disorders: a systematic review of randomised clinical trials.

PubMed Central

van der Heijden, G. J.; van der Windt, D. A.; de Winter, A. F.

1997-01-01

OBJECTIVE: To assess the effectiveness of physiotherapy for patients with soft tissue shoulder disorders. DESIGN: A systematic computerised literature search of Medline and Embase, supplemented with citation tracking, for relevant trials with random allocation published before 1996. SUBJECTS: Patients treated with physiotherapy for disorders of soft tissue of the shoulder. MAIN OUTCOME MEASURES: Success rates, mobility, pain, functional status. RESULTS: Six of the 20 assessed trials satisfied at least five of eight validity criteria. Assessment of methods was often hampered by insufficient information on various validity criteria, and trials were often flawed by lack of blinding, high proportions of withdrawals from treatment, and high proportions of missing values. Trial sizes were small: only six trials included intervention groups of more than 25 patients. Ultrasound therapy, evaluated in six trials, was not shown to be effective. Four other trials favoured physiotherapy (laser therapy or manipulation), but the validity of their methods was unsatisfactory. CONCLUSIONS: There is evidence that ultrasound therapy is ineffective in the treatment of soft tissue shoulder disorders. Due to small trial sizes and unsatisfactory methods, evidence for the effectiveness of other methods of physiotherapy is inconclusive. For all methods of treatment, trials were too heterogeneous with respect to included patients, index and reference treatments, and follow up to merit valid statistical pooling. Future studies should show whether physiotherapy is superior to treatment with drugs, steroid injections, or a wait and see policy. PMID:9233322

Validation of the Combined Comorbidity Index of Charlson and Elixhauser to Predict 30-Day Mortality Across ICD-9 and ICD-10.

PubMed

Simard, Marc; Sirois, Caroline; Candas, Bernard

2018-05-01

To validate and compare performance of an International Classification of Diseases, tenth revision (ICD-10) version of a combined comorbidity index merging conditions of Charlson and Elixhauser measures against individual measures in the prediction of 30-day mortality. To select a weight derivation method providing optimal performance across ICD-9 and ICD-10 coding systems. Using 2 adult population-based cohorts of patients with hospital admissions in ICD-9 (2005, n=337,367) and ICD-10 (2011, n=348,820), we validated a combined comorbidity index by predicting 30-day mortality with logistic regression. To appreciate performance of the Combined index and both individual measures, factors impacting indices performance such as population characteristics and weight derivation methods were accounted for. We applied 3 scoring methods (Van Walraven, Schneeweiss, and Charlson) and determined which provides best predictive values. Combined index [c-statistics: 0.853 (95% confidence interval: CI, 0.848-0.856)] performed better than original Charlson [0.841 (95% CI, 0.835-0.844)] or Elixhauser [0.841 (95% CI, 0.837-0.844)] measures on ICD-10 cohort. All weight derivation methods provided close high discrimination results for the Combined index (Van Walraven: 0.852, Schneeweiss: 0.851, Charlson: 0.849). Results were consistent across both coding systems. The Combined index remains valid with both ICD-9 and ICD-10 coding systems and the 3 weight derivation methods evaluated provided consistent high performance across those coding systems.

Assessment of protein set coherence using functional annotations

PubMed Central

Chagoyen, Monica; Carazo, Jose M; Pascual-Montano, Alberto

2008-01-01

Background Analysis of large-scale experimental datasets frequently produces one or more sets of proteins that are subsequently mined for functional interpretation and validation. To this end, a number of computational methods have been devised that rely on the analysis of functional annotations. Although current methods provide valuable information (e.g. significantly enriched annotations, pairwise functional similarities), they do not specifically measure the degree of homogeneity of a protein set. Results In this work we present a method that scores the degree of functional homogeneity, or coherence, of a set of proteins on the basis of the global similarity of their functional annotations. The method uses statistical hypothesis testing to assess the significance of the set in the context of the functional space of a reference set. As such, it can be used as a first step in the validation of sets expected to be homogeneous prior to further functional interpretation. Conclusion We evaluate our method by analysing known biologically relevant sets as well as random ones. The known relevant sets comprise macromolecular complexes, cellular components and pathways described for Saccharomyces cerevisiae, which are mostly significantly coherent. Finally, we illustrate the usefulness of our approach for validating 'functional modules' obtained from computational analysis of protein-protein interaction networks. Matlab code and supplementary data are available at PMID:18937846

Construction of measurement uncertainty profiles for quantitative analysis of genetically modified organisms based on interlaboratory validation data.

PubMed

Macarthur, Roy; Feinberg, Max; Bertheau, Yves

2010-01-01

A method is presented for estimating the size of uncertainty associated with the measurement of products derived from genetically modified organisms (GMOs). The method is based on the uncertainty profile, which is an extension, for the estimation of uncertainty, of a recent graphical statistical tool called an accuracy profile that was developed for the validation of quantitative analytical methods. The application of uncertainty profiles as an aid to decision making and assessment of fitness for purpose is also presented. Results of the measurement of the quantity of GMOs in flour by PCR-based methods collected through a number of interlaboratory studies followed the log-normal distribution. Uncertainty profiles built using the results generally give an expected range for measurement results of 50-200% of reference concentrations for materials that contain at least 1% GMO. This range is consistent with European Network of GM Laboratories and the European Union (EU) Community Reference Laboratory validation criteria and can be used as a fitness for purpose criterion for measurement methods. The effect on the enforcement of EU labeling regulations is that, in general, an individual analytical result needs to be < 0.45% to demonstrate compliance, and > 1.8% to demonstrate noncompliance with a labeling threshold of 0.9%.

T2* Mapping Provides Information That Is Statistically Comparable to an Arthroscopic Evaluation of Acetabular Cartilage.

PubMed

Morgan, Patrick; Nissi, Mikko J; Hughes, John; Mortazavi, Shabnam; Ellerman, Jutta

2017-07-01

Objectives The purpose of this study was to validate T2* mapping as an objective, noninvasive method for the prediction of acetabular cartilage damage. Methods This is the second step in the validation of T2*. In a previous study, we established a quantitative predictive model for identifying and grading acetabular cartilage damage. In this study, the model was applied to a second cohort of 27 consecutive hips to validate the model. A clinical 3.0-T imaging protocol with T2* mapping was used. Acetabular regions of interest (ROI) were identified on magnetic resonance and graded using the previously established model. Each ROI was then graded in a blinded fashion by arthroscopy. Accurate surgical location of ROIs was facilitated with a 2-dimensional map projection of the acetabulum. A total of 459 ROIs were studied. Results When T2* mapping and arthroscopic assessment were compared, 82% of ROIs were within 1 Beck group (of a total 6 possible) and 32% of ROIs were classified identically. Disease prediction based on receiver operating characteristic curve analysis demonstrated a sensitivity of 0.713 and a specificity of 0.804. Model stability evaluation required no significant changes to the predictive model produced in the initial study. Conclusions These results validate that T2* mapping provides statistically comparable information regarding acetabular cartilage when compared to arthroscopy. In contrast to arthroscopy, T2* mapping is quantitative, noninvasive, and can be used in follow-up. Unlike research quantitative magnetic resonance protocols, T2* takes little time and does not require a contrast agent. This may facilitate its use in the clinical sphere.

Measuring benefits and patients' satisfaction when glasses are not needed after cataract and presbyopia surgery: scoring and psychometric validation of the Freedom from Glasses Value Scale (FGVS©)

PubMed Central

2010-01-01

Background The purpose of this study was to reduce the number of items, create a scoring method and assess the psychometric properties of the Freedom from Glasses Value Scale (FGVS), which measures benefits of freedom from glasses perceived by cataract and presbyopic patients after multifocal intraocular lens (IOL) surgery. Methods The 21-item FGVS, developed simultaneously in French and Spanish, was administered by phone during an observational study to 152 French and 152 Spanish patients who had undergone cataract or presbyopia surgery at least 1 year before the study. Reduction of items and creation of the scoring method employed statistical methods (principal component analysis, multitrait analysis) and content analysis. Psychometric properties (validation of the structure, internal consistency reliability, and known-group validity) of the resulting version were assessed in the pooled population and per country. Results One item was deleted and 3 were kept but not aggregated in a dimension. The other 17 items were grouped into 2 dimensions ('global evaluation', 9 items; 'advantages', 8 items) and divided into 5 sub-dimensions, with higher scores indicating higher benefit of surgery. The structure was validated (good item convergent and discriminant validity). Internal consistency reliability was good for all dimensions and sub-dimensions (Cronbach's alphas above 0.70). The FGVS was able to discriminate between patients wearing glasses or not after surgery (higher scores for patients not wearing glasses). FGVS scores were significantly higher in Spain than France; however, the measure had similar psychometric performances in both countries. Conclusions The FGVS is a valid and reliable instrument measuring benefits of freedom from glasses perceived by cataract and presbyopic patients after multifocal IOL surgery. PMID:20497555

Extractive-spectrophotometric determination of disopyramide and irbesartan in their pharmaceutical formulation

NASA Astrophysics Data System (ADS)

Abdellatef, Hisham E.

2007-04-01

Picric acid, bromocresol green, bromothymol blue, cobalt thiocyanate and molybdenum(V) thiocyanate have been tested as spectrophotometric reagents for the determination of disopyramide and irbesartan. Reaction conditions have been optimized to obtain coloured comoplexes of higher sensitivity and longer stability. The absorbance of ion-pair complexes formed were found to increases linearity with increases in concentrations of disopyramide and irbesartan which were corroborated by correction coefficient values. The developed methods have been successfully applied for the determination of disopyramide and irbesartan in bulk drugs and pharmaceutical formulations. The common excipients and additives did not interfere in their determination. The results obtained by the proposed methods have been statistically compared by means of student t-test and by the variance ratio F-test. The validity was assessed by applying the standard addition technique. The results were compared statistically with the official or reference methods showing a good agreement with high precision and accuracy.

Active Learning with Rationales for Identifying Operationally Significant Anomalies in Aviation

NASA Technical Reports Server (NTRS)

Sharma, Manali; Das, Kamalika; Bilgic, Mustafa; Matthews, Bryan; Nielsen, David Lynn; Oza, Nikunj C.

2016-01-01

A major focus of the commercial aviation community is discovery of unknown safety events in flight operations data. Data-driven unsupervised anomaly detection methods are better at capturing unknown safety events compared to rule-based methods which only look for known violations. However, not all statistical anomalies that are discovered by these unsupervised anomaly detection methods are operationally significant (e.g., represent a safety concern). Subject Matter Experts (SMEs) have to spend significant time reviewing these statistical anomalies individually to identify a few operationally significant ones. In this paper we propose an active learning algorithm that incorporates SME feedback in the form of rationales to build a classifier that can distinguish between uninteresting and operationally significant anomalies. Experimental evaluation on real aviation data shows that our approach improves detection of operationally significant events by as much as 75% compared to the state-of-the-art. The learnt classifier also generalizes well to additional validation data sets.

Landslide Susceptibility Statistical Methods: A Critical and Systematic Literature Review

NASA Astrophysics Data System (ADS)

Mihir, Monika; Malamud, Bruce; Rossi, Mauro; Reichenbach, Paola; Ardizzone, Francesca

2014-05-01

Landslide susceptibility assessment, the subject of this systematic review, is aimed at understanding the spatial probability of slope failures under a set of geomorphological and environmental conditions. It is estimated that about 375 landslides that occur globally each year are fatal, with around 4600 people killed per year. Past studies have brought out the increasing cost of landslide damages which primarily can be attributed to human occupation and increased human activities in the vulnerable environments. Many scientists, to evaluate and reduce landslide risk, have made an effort to efficiently map landslide susceptibility using different statistical methods. In this paper, we do a critical and systematic landslide susceptibility literature review, in terms of the different statistical methods used. For each of a broad set of studies reviewed we note: (i) study geography region and areal extent, (ii) landslide types, (iii) inventory type and temporal period covered, (iv) mapping technique (v) thematic variables used (vi) statistical models, (vii) assessment of model skill, (viii) uncertainty assessment methods, (ix) validation methods. We then pulled out broad trends within our review of landslide susceptibility, particularly regarding the statistical methods. We found that the most common statistical methods used in the study of landslide susceptibility include logistic regression, artificial neural network, discriminant analysis and weight of evidence. Although most of the studies we reviewed assessed the model skill, very few assessed model uncertainty. In terms of geographic extent, the largest number of landslide susceptibility zonations were in Turkey, Korea, Spain, Italy and Malaysia. However, there are also many landslides and fatalities in other localities, particularly India, China, Philippines, Nepal and Indonesia, Guatemala, and Pakistan, where there are much fewer landslide susceptibility studies available in the peer-review literature. This raises some concern that existing studies do not always cover all the regions globally that currently experience landslides and landslide fatalities.

AMOVA ["Accumulative Manifold Validation Analysis"]: An Advanced Statistical Methodology Designed to Measure and Test the Validity, Reliability, and Overall Efficacy of Inquiry-Based Psychometric Instruments

ERIC Educational Resources Information Center

Osler, James Edward, II

2015-01-01

This monograph provides an epistemological rational for the Accumulative Manifold Validation Analysis [also referred by the acronym "AMOVA"] statistical methodology designed to test psychometric instruments. This form of inquiry is a form of mathematical optimization in the discipline of linear stochastic modelling. AMOVA is an in-depth…

Development and Validation of UV-Visible Spectrophotometric Method for Simultaneous Determination of Eperisone and Paracetamol in Solid Dosage Form.

PubMed

Khanage, Shantaram Gajanan; Mohite, Popat Baban; Jadhav, Sandeep

2013-01-01

Eperisone Hydrochloride (EPE) is a potent new generation antispasmodic drug which is used in the treatment of moderate to severe pain in combination with Paracetamol (PAR). Both drugs are available in tablet dosage form in combination with a dose of 50 mg for EPE and 325 mg PAR respectively. The method is based upon Q-absorption ratio method for the simultaneous determination of the EPE and PAR. Absorption ratio method is used for the ratio of the absorption at two selected wavelength one of which is the iso-absorptive point and other being the λmax of one of the two components. EPE and PAR shows their iso-absorptive point at 260 nm in methanol, the second wavelength used is 249 nm which is the λmax of PAR in methanol. The linearity was obtained in the concentration range of 5-25 μg/mL for EPE and 2-10 μg/mL for PAR. The proposed method was effectively applied to tablet dosage form for estimation of both drugs. The accuracy and reproducibility results are close to 100% with 2% RSD. RESULTS of the analysis were validated statistically and found to be satisfactory. The results of proposed method have been validated as per ICH guidelines. A simple, precise and economical spectrophotometric method has been developed for the estimation of EPE and PAR in pharmaceutical formulation.

Reviewing Reliability and Validity of Information for University Educational Evaluation

NASA Astrophysics Data System (ADS)

Otsuka, Yusaku

To better utilize evaluations in higher education, it is necessary to share the methods of reviewing reliability and validity of examination scores and grades, and to accumulate and share data for confirming results. Before the GPA system is first introduced into a university or college, the reliability of examination scores and grades, especially for essay examinations, must be assured. Validity is a complicated concept, so should be assured in various ways, including using professional audits, theoretical models, and statistical data analysis. Because individual students and teachers are continually improving, using evaluations to appraise their progress is not always compatible with using evaluations in appraising the implementation of accountability in various departments or the university overall. To better utilize evaluations and improve higher education, evaluations should be integrated into the current system by sharing the vision of an academic learning community and promoting interaction between students and teachers based on sufficiently reliable and validated evaluation tools.

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

English, Shawn A.; Briggs, Timothy M.; Nelson, Stacy M.

Simulations of low velocity impact with a flat cylindrical indenter upon a carbon fiber fabric reinforced polymer laminate are rigorously validated. Comparison of the impact energy absorption between the model and experiment is used as the validation metric. Additionally, non-destructive evaluation, including ultrasonic scans and three-dimensional computed tomography, provide qualitative validation of the models. The simulations include delamination, matrix cracks and fiber breaks. An orthotropic damage and failure constitutive model, capable of predicting progressive damage and failure, is developed in conjunction and described. An ensemble of simulations incorporating model parameter uncertainties is used to predict a response distribution which ismore » then compared to experimental output using appropriate statistical methods. Lastly, the model form errors are exposed and corrected for use in an additional blind validation analysis. The result is a quantifiable confidence in material characterization and model physics when simulating low velocity impact in structures of interest.« less

Development and validation of risk prediction equations to estimate future risk of blindness and lower limb amputation in patients with diabetes: cohort study

PubMed Central

Coupland, Carol

2015-01-01

Study question Is it possible to develop and externally validate risk prediction equations to estimate the 10 year risk of blindness and lower limb amputation in patients with diabetes aged 25-84 years? Methods This was a prospective cohort study using routinely collected data from general practices in England contributing to the QResearch and Clinical Practice Research Datalink (CPRD) databases during the study period 1998-2014. The equations were developed using 763 QResearch practices (n=454 575 patients with diabetes) and validated in 254 different QResearch practices (n=142 419) and 357 CPRD practices (n=206 050). Cox proportional hazards models were used to derive separate risk equations for blindness and amputation in men and women that could be evaluated at 10 years. Measures of calibration and discrimination were calculated in the two validation cohorts. Study answer and limitations Risk prediction equations to quantify absolute risk of blindness and amputation in men and women with diabetes have been developed and externally validated. In the QResearch derivation cohort, 4822 new cases of lower limb amputation and 8063 new cases of blindness occurred during follow-up. The risk equations were well calibrated in both validation cohorts. Discrimination was good in men in the external CPRD cohort for amputation (D statistic 1.69, Harrell’s C statistic 0.77) and blindness (D statistic 1.40, Harrell’s C statistic 0.73), with similar results in women and in the QResearch validation cohort. The algorithms are based on variables that patients are likely to know or that are routinely recorded in general practice computer systems. They can be used to identify patients at high risk for prevention or further assessment. Limitations include lack of formally adjudicated outcomes, information bias, and missing data. What this study adds Patients with type 1 or type 2 diabetes are at increased risk of blindness and amputation but generally do not have accurate assessments of the magnitude of their individual risks. The new algorithms calculate the absolute risk of developing these complications over a 10 year period in patients with diabetes, taking account of their individual risk factors. Funding, competing interests, data sharing JH-C is co-director of QResearch, a not for profit organisation which is a joint partnership between the University of Nottingham and Egton Medical Information Systems, and is also a paid director of ClinRisk Ltd. CC is a paid consultant statistician for ClinRisk Ltd. PMID:26560308

A rheumatoid arthritis study by Fourier transform infrared spectroscopy

NASA Astrophysics Data System (ADS)

Carvalho, Carolina S.; Silva, Ana Carla A.; Santos, Tatiano J. P. S.; Martin, Airton A.; dos Santos Fernandes, Ana Célia; Andrade, Luís E.; Raniero, Leandro

2012-01-01

Rheumatoid arthritis is a systemic inflammatory disease of unknown causes and a new methods to identify it in early stages are needed. The main purpose of this work is the biochemical differentiation of sera between normal and RA patients, through the establishment of a statistical method that can be appropriately used for serological analysis. The human sera from 39 healthy donors and 39 rheumatics donors were collected and analyzed by Fourier Transform Infrared Spectroscopy. The results show significant spectral variations with p<0.05 in regions corresponding to protein, lipids and immunoglobulins. The technique of latex particles, coated with human IgG and monoclonal anti-CRP by indirect agglutination known as FR and CRP, was performed to confirm possible false-negative results within the groups, facilitating the statistical interpretation and validation of the technique.

Empirical evaluation of data normalization methods for molecular classification

PubMed Central

Huang, Huei-Chung

2018-01-01

Background Data artifacts due to variations in experimental handling are ubiquitous in microarray studies, and they can lead to biased and irreproducible findings. A popular approach to correct for such artifacts is through post hoc data adjustment such as data normalization. Statistical methods for data normalization have been developed and evaluated primarily for the discovery of individual molecular biomarkers. Their performance has rarely been studied for the development of multi-marker molecular classifiers—an increasingly important application of microarrays in the era of personalized medicine. Methods In this study, we set out to evaluate the performance of three commonly used methods for data normalization in the context of molecular classification, using extensive simulations based on re-sampling from a unique pair of microRNA microarray datasets for the same set of samples. The data and code for our simulations are freely available as R packages at GitHub. Results In the presence of confounding handling effects, all three normalization methods tended to improve the accuracy of the classifier when evaluated in an independent test data. The level of improvement and the relative performance among the normalization methods depended on the relative level of molecular signal, the distributional pattern of handling effects (e.g., location shift vs scale change), and the statistical method used for building the classifier. In addition, cross-validation was associated with biased estimation of classification accuracy in the over-optimistic direction for all three normalization methods. Conclusion Normalization may improve the accuracy of molecular classification for data with confounding handling effects; however, it cannot circumvent the over-optimistic findings associated with cross-validation for assessing classification accuracy. PMID:29666754

A Stochastic Framework for Evaluating Seizure Prediction Algorithms Using Hidden Markov Models

PubMed Central

Wong, Stephen; Gardner, Andrew B.; Krieger, Abba M.; Litt, Brian

2007-01-01

Responsive, implantable stimulation devices to treat epilepsy are now in clinical trials. New evidence suggests that these devices may be more effective when they deliver therapy before seizure onset. Despite years of effort, prospective seizure prediction, which could improve device performance, remains elusive. In large part, this is explained by lack of agreement on a statistical framework for modeling seizure generation and a method for validating algorithm performance. We present a novel stochastic framework based on a three-state hidden Markov model (HMM) (representing interictal, preictal, and seizure states) with the feature that periods of increased seizure probability can transition back to the interictal state. This notion reflects clinical experience and may enhance interpretation of published seizure prediction studies. Our model accommodates clipped EEG segments and formalizes intuitive notions regarding statistical validation. We derive equations for type I and type II errors as a function of the number of seizures, duration of interictal data, and prediction horizon length and we demonstrate the model’s utility with a novel seizure detection algorithm that appeared to predicted seizure onset. We propose this framework as a vital tool for designing and validating prediction algorithms and for facilitating collaborative research in this area. PMID:17021032

The validity of ultrasound estimation of muscle volumes.

PubMed

Infantolino, Benjamin W; Gales, Daniel J; Winter, Samantha L; Challis, John H

2007-08-01

The purpose of this study was to validate ultrasound muscle volume estimation in vivo. To examine validity, vastus lateralis ultrasound images were collected from cadavers before muscle dissection; after dissection, the volumes were determined by hydrostatic weighing. Seven thighs from cadaver specimens were scanned using a 7.5-MHz ultrasound probe (SSD-1000, Aloka, Japan). The perimeter of the vastus lateralis was identified in the ultrasound images and manually digitized. Volumes were then estimated using the Cavalieri principle, by measuring the image areas of sets of parallel two-dimensional slices through the muscles. The muscles were then dissected from the cadavers, and muscle volume was determined via hydrostatic weighing. There was no statistically significant difference between the ultrasound estimation of muscle volume and that estimated using hydrostatic weighing (p > 0.05). The mean percentage error between the two volume estimates was 0.4% +/- 6.9. Three operators all performed four digitizations of all images from one randomly selected muscle; there was no statistical difference between operators or trials and the intraclass correlation was high (>0.8). The results of this study indicate that ultrasound is an accurate method for estimating muscle volumes in vivo.

Virtual Model Validation of Complex Multiscale Systems: Applications to Nonlinear Elastostatics

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

Oden, John Tinsley; Prudencio, Ernest E.; Bauman, Paul T.

We propose a virtual statistical validation process as an aid to the design of experiments for the validation of phenomenological models of the behavior of material bodies, with focus on those cases in which knowledge of the fabrication process used to manufacture the body can provide information on the micro-molecular-scale properties underlying macroscale behavior. One example is given by models of elastomeric solids fabricated using polymerization processes. We describe a framework for model validation that involves Bayesian updates of parameters in statistical calibration and validation phases. The process enables the quanti cation of uncertainty in quantities of interest (QoIs) andmore » the determination of model consistency using tools of statistical information theory. We assert that microscale information drawn from molecular models of the fabrication of the body provides a valuable source of prior information on parameters as well as a means for estimating model bias and designing virtual validation experiments to provide information gain over calibration posteriors.« less

Textural Analysis and Substrate Classification in the Nearshore Region of Lake Superior Using High-Resolution Multibeam Bathymetry

NASA Astrophysics Data System (ADS)

Dennison, Andrew G.

Classification of the seafloor substrate can be done with a variety of methods. These methods include Visual (dives, drop cameras); mechanical (cores, grab samples); acoustic (statistical analysis of echosounder returns). Acoustic methods offer a more powerful and efficient means of collecting useful information about the bottom type. Due to the nature of an acoustic survey, larger areas can be sampled, and by combining the collected data with visual and mechanical survey methods provide greater confidence in the classification of a mapped region. During a multibeam sonar survey, both bathymetric and backscatter data is collected. It is well documented that the statistical characteristic of a sonar backscatter mosaic is dependent on bottom type. While classifying the bottom-type on the basis on backscatter alone can accurately predict and map bottom-type, i.e a muddy area from a rocky area, it lacks the ability to resolve and capture fine textural details, an important factor in many habitat mapping studies. Statistical processing of high-resolution multibeam data can capture the pertinent details about the bottom-type that are rich in textural information. Further multivariate statistical processing can then isolate characteristic features, and provide the basis for an accurate classification scheme. The development of a new classification method is described here. It is based upon the analysis of textural features in conjunction with ground truth sampling. The processing and classification result of two geologically distinct areas in nearshore regions of Lake Superior; off the Lester River,MN and Amnicon River, WI are presented here, using the Minnesota Supercomputer Institute's Mesabi computing cluster for initial processing. Processed data is then calibrated using ground truth samples to conduct an accuracy assessment of the surveyed areas. From analysis of high-resolution bathymetry data collected at both survey sites is was possible to successfully calculate a series of measures that describe textural information about the lake floor. Further processing suggests that the features calculated capture a significant amount of statistical information about the lake floor terrain as well. Two sources of error, an anomalous heave and refraction error significantly deteriorated the quality of the processed data and resulting validate results. Ground truth samples used to validate the classification methods utilized for both survey sites, however, resulted in accuracy values ranging from 5 -30 percent at the Amnicon River, and between 60-70 percent for the Lester River. The final results suggest that this new processing methodology does adequately capture textural information about the lake floor and does provide an acceptable classification in the absence of significant data quality issues.

Validated flow-injection method for rapid aluminium determination in anti-perspirants.

PubMed

López-Gonzálvez, A; Ruiz, M A; Barbas, C

2008-09-29

A flow-injection (FI) method for the rapid determination of aluminium in anti-perspirants has been developed. The method is based on the spectrophotometric detection at 535nm of the complex formed between Al ions and the chromogenic reagent eriochrome cyanine R. Both the batch and FI methods were validated by checking the parameters included in the ISO-3543-1 regulation. Variables involved in the FI method were optimized by using appropriate statistical tools. The method does not exhibit interference from other substances present in anti-perspirants and it shows a high precision with a R.S.D. value (n=6) of 0.9%. Moreover, the accuracy of the method was evaluated by comparison with a back complexometric titration method, which is currently used for routine analysis in pharmaceutical laboratories. The Student's t-test showed that the results obtained by both methods were not significantly different for a significance level of 95%. A response time of 12s and a sample analysis time, by performing triplicate injections, of 60s were achieved. The analytical figures of merit make the method highly appropriate to substitute the time-consuming complexometric method for this kind of analysis.

Break and trend analysis of EUMETSAT Climate Data Records

NASA Astrophysics Data System (ADS)

Doutriaux-Boucher, Marie; Zeder, Joel; Lattanzio, Alessio; Khlystova, Iryna; Graw, Kathrin

2016-04-01

EUMETSAT reprocessed imagery acquired by the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board Meteosat 8-9. The data covers the period from 2004 to 2012. Climate Data Records (CDRs) of atmospheric parameters such as Atmospheric Motion Vectors (AMV) as well as Clear and All Sky Radiances (CSR and ASR) have been generated. Such CDRs are mainly ingested by ECMWF to produce a reanalysis data. In addition, EUMETSAT produced a long CDR (1982-2004) of land surface albedo exploiting imagery acquired by the Meteosat Visible and Infrared Imager (MVIRI) on board Meteosat 2-7. Such CDR is key information in climate analysis and climate models. Extensive validation has been performed for the surface albedo record and a first validation of the winds and clear sky radiances have been done. All validation results demonstrated that the time series of all parameter appear homogeneous at first sight. Statistical science offers a variety of analyses methods that have been applied to further analyse the homogeneity of the CDRs. Many breakpoint analysis techniques depend on the comparison of two time series which incorporates the issue that both may have breakpoints. This paper will present a quantitative and statistical analysis of eventual breakpoints found in the MVIRI and SEVIRI CDRs that includes attribution of breakpoints to changes of instruments and other events in the data series compared. The value of different methods applied will be discussed with suggestions how to further develop this type of analysis for quality evaluation of CDRs.

The ACTA PORT-score for predicting perioperative risk of blood transfusion for adult cardiac surgery.

PubMed

Klein, A A; Collier, T; Yeates, J; Miles, L F; Fletcher, S N; Evans, C; Richards, T

2017-09-01

A simple and accurate scoring system to predict risk of transfusion for patients undergoing cardiac surgery is lacking. We identified independent risk factors associated with transfusion by performing univariate analysis, followed by logistic regression. We then simplified the score to an integer-based system and tested it using the area under the receiver operator characteristic (AUC) statistic with a Hosmer-Lemeshow goodness-of-fit test. Finally, the scoring system was applied to the external validation dataset and the same statistical methods applied to test the accuracy of the ACTA-PORT score. Several factors were independently associated with risk of transfusion, including age, sex, body surface area, logistic EuroSCORE, preoperative haemoglobin and creatinine, and type of surgery. In our primary dataset, the score accurately predicted risk of perioperative transfusion in cardiac surgery patients with an AUC of 0.76. The external validation confirmed accuracy of the scoring method with an AUC of 0.84 and good agreement across all scores, with a minor tendency to under-estimate transfusion risk in very high-risk patients. The ACTA-PORT score is a reliable, validated tool for predicting risk of transfusion for patients undergoing cardiac surgery. This and other scores can be used in research studies for risk adjustment when assessing outcomes, and might also be incorporated into a Patient Blood Management programme. © The Author 2017. Published by Oxford University Press on behalf of the British Journal of Anaesthesia. All rights reserved. For Permissions, please email: journals.permissions@oup.com

A new framework to enhance the interpretation of external validation studies of clinical prediction models.

PubMed

Debray, Thomas P A; Vergouwe, Yvonne; Koffijberg, Hendrik; Nieboer, Daan; Steyerberg, Ewout W; Moons, Karel G M

2015-03-01

It is widely acknowledged that the performance of diagnostic and prognostic prediction models should be assessed in external validation studies with independent data from "different but related" samples as compared with that of the development sample. We developed a framework of methodological steps and statistical methods for analyzing and enhancing the interpretation of results from external validation studies of prediction models. We propose to quantify the degree of relatedness between development and validation samples on a scale ranging from reproducibility to transportability by evaluating their corresponding case-mix differences. We subsequently assess the models' performance in the validation sample and interpret the performance in view of the case-mix differences. Finally, we may adjust the model to the validation setting. We illustrate this three-step framework with a prediction model for diagnosing deep venous thrombosis using three validation samples with varying case mix. While one external validation sample merely assessed the model's reproducibility, two other samples rather assessed model transportability. The performance in all validation samples was adequate, and the model did not require extensive updating to correct for miscalibration or poor fit to the validation settings. The proposed framework enhances the interpretation of findings at external validation of prediction models. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

Psychometric properties of the Vulnerability to Abuse Screening Scale for screening abuse of older adults

PubMed Central

Dantas, Raquel Batista; Oliveira, Graziella Lage; Silveira, Andréa Maria

2017-01-01

ABSTRACT OBJECTIVE Adapt and evaluate the psychometric properties of the Vulnerability to Abuse Screening Scale to identify risk of domestic violence against older adults in Brazil. METHODS The instrument was adapted and validated in a sample of 151 older adults from a geriatric reference center in the municipality of Belo Horizonte, State of Minas Gerais, in 2014. We collected sociodemographic, clinical, and abuse-related information, and verified reliability by reproducibility in a sample of 55 older people, who underwent re-testing of the instrument seven days after the first application. Descriptive and comparative analyses were performed for all variables, with a significance level of 5%. The construct validity was analyzed by the principal components method with a tetrachoric correlation matrix, the reliability of the scale by the weighted Kappa (Kp) statistic, and the internal consistency by the Kuder-Richardson estimator formula 20 (KR-20). RESULTS The average age of the participants was 72.1 years (DP = 6.96; 95%CI 70.94–73.17), with a maximum of 92 years, and they were predominantly female (76.2%; 95%CI 69.82–83.03). When analyzing the relationship between the scores of the Vulnerability to Abuse Screening Scale, categorized by presence (score > 3) or absence (score < 3) of vulnerability to abuse, with clinical and health conditions, we found statistically significant differences for self-perception of health (p = 0.002), depressive symptoms (p = 0.000), and presence of rheumatism (p = 0.003). There were no statistically significant differences between sexes. The Vulnerability to Abuse Screening Scale acceptably evaluated validity in the transcultural adaptation process, demonstrating dimensionality coherent with the original proposal (four factors). In the internal consistency analysis, the instrument presented good results (KR-20 = 0.69) and the reliability via reproducibility was considered excellent for the global scale (Kp = 0.92). CONCLUSIONS The Vulnerability to Abuse Screening Scale proved to be a valid instrument with good psychometric capacity for screening domestic abuse against older adults in Brazil. PMID:28423137

Measuring the Autocorrelation Function of Nanoscale Three-Dimensional Density Distribution in Individual Cells Using Scanning Transmission Electron Microscopy, Atomic Force Microscopy, and a New Deconvolution Algorithm.

PubMed

Li, Yue; Zhang, Di; Capoglu, Ilker; Hujsak, Karl A; Damania, Dhwanil; Cherkezyan, Lusik; Roth, Eric; Bleher, Reiner; Wu, Jinsong S; Subramanian, Hariharan; Dravid, Vinayak P; Backman, Vadim

2017-06-01

Essentially all biological processes are highly dependent on the nanoscale architecture of the cellular components where these processes take place. Statistical measures, such as the autocorrelation function (ACF) of the three-dimensional (3D) mass-density distribution, are widely used to characterize cellular nanostructure. However, conventional methods of reconstruction of the deterministic 3D mass-density distribution, from which these statistical measures can be calculated, have been inadequate for thick biological structures, such as whole cells, due to the conflict between the need for nanoscale resolution and its inverse relationship with thickness after conventional tomographic reconstruction. To tackle the problem, we have developed a robust method to calculate the ACF of the 3D mass-density distribution without tomography. Assuming the biological mass distribution is isotropic, our method allows for accurate statistical characterization of the 3D mass-density distribution by ACF with two data sets: a single projection image by scanning transmission electron microscopy and a thickness map by atomic force microscopy. Here we present validation of the ACF reconstruction algorithm, as well as its application to calculate the statistics of the 3D distribution of mass-density in a region containing the nucleus of an entire mammalian cell. This method may provide important insights into architectural changes that accompany cellular processes.

Measuring the Autocorrelation Function of Nanoscale Three-Dimensional Density Distribution in Individual Cells Using Scanning Transmission Electron Microscopy, Atomic Force Microscopy, and a New Deconvolution Algorithm

PubMed Central

Li, Yue; Zhang, Di; Capoglu, Ilker; Hujsak, Karl A.; Damania, Dhwanil; Cherkezyan, Lusik; Roth, Eric; Bleher, Reiner; Wu, Jinsong S.; Subramanian, Hariharan; Dravid, Vinayak P.; Backman, Vadim

2018-01-01

Essentially all biological processes are highly dependent on the nanoscale architecture of the cellular components where these processes take place. Statistical measures, such as the autocorrelation function (ACF) of the three-dimensional (3D) mass–density distribution, are widely used to characterize cellular nanostructure. However, conventional methods of reconstruction of the deterministic 3D mass–density distribution, from which these statistical measures can be calculated, have been inadequate for thick biological structures, such as whole cells, due to the conflict between the need for nanoscale resolution and its inverse relationship with thickness after conventional tomographic reconstruction. To tackle the problem, we have developed a robust method to calculate the ACF of the 3D mass–density distribution without tomography. Assuming the biological mass distribution is isotropic, our method allows for accurate statistical characterization of the 3D mass–density distribution by ACF with two data sets: a single projection image by scanning transmission electron microscopy and a thickness map by atomic force microscopy. Here we present validation of the ACF reconstruction algorithm, as well as its application to calculate the statistics of the 3D distribution of mass–density in a region containing the nucleus of an entire mammalian cell. This method may provide important insights into architectural changes that accompany cellular processes. PMID:28416035

A semi-automatic method for left ventricle volume estimate: an in vivo validation study

NASA Technical Reports Server (NTRS)

Corsi, C.; Lamberti, C.; Sarti, A.; Saracino, G.; Shiota, T.; Thomas, J. D.

2001-01-01

This study aims to the validation of the left ventricular (LV) volume estimates obtained by processing volumetric data utilizing a segmentation model based on level set technique. The validation has been performed by comparing real-time volumetric echo data (RT3DE) and magnetic resonance (MRI) data. A validation protocol has been defined. The validation protocol was applied to twenty-four estimates (range 61-467 ml) obtained from normal and pathologic subjects, which underwent both RT3DE and MRI. A statistical analysis was performed on each estimate and on clinical parameters as stroke volume (SV) and ejection fraction (EF). Assuming MRI estimates (x) as a reference, an excellent correlation was found with volume measured by utilizing the segmentation procedure (y) (y=0.89x + 13.78, r=0.98). The mean error on SV was 8 ml and the mean error on EF was 2%. This study demonstrated that the segmentation technique is reliably applicable on human hearts in clinical practice.

Validation of survey information on smoking and alcohol consumption against import statistics, Greenland 1993-2010.

PubMed

Bjerregaard, Peter; Becker, Ulrik

2013-01-01

Questionnaires are widely used to obtain information on health-related behaviour, and they are more often than not the only method that can be used to assess the distribution of behaviour in subgroups of the population. No validation studies of reported consumption of tobacco or alcohol have been published from circumpolar indigenous communities. The purpose of the study is to compare information on the consumption of tobacco and alcohol obtained from 3 population surveys in Greenland with import statistics. Estimates of consumption of cigarettes and alcohol using several different survey instruments in cross-sectional population studies from 1993-1994, 1999-2001 and 2005-2010 were compared with import statistics from the same years. For cigarettes, survey results accounted for virtually the total import. Alcohol consumption was significantly under-reported with reporting completeness ranging from 40% to 51% for different estimates of habitual weekly consumption in the 3 study periods. Including an estimate of binge drinking increased the estimated total consumption to 78% of the import. Compared with import statistics, questionnaire-based population surveys capture the consumption of cigarettes well in Greenland. Consumption of alcohol is under-reported, but asking about binge episodes in addition to the usual intake considerably increased the reported intake in this population and made it more in agreement with import statistics. It is unknown to what extent these findings at the population level can be inferred to population subgroups.

Comment on Hall et al. (2017), "How to Choose Between Measures of Tinnitus Loudness for Clinical Research? A Report on the Reliability and Validity of an Investigator-Administered Test and a Patient-Reported Measure Using Baseline Data Collected in a Phase IIa Drug Trial".

PubMed

Sabour, Siamak

2018-03-08

The purpose of this letter, in response to Hall, Mehta, and Fackrell (2017), is to provide important knowledge about methodology and statistical issues in assessing the reliability and validity of an audiologist-administered tinnitus loudness matching test and a patient-reported tinnitus loudness rating. The author uses reference textbooks and published articles regarding scientific assessment of the validity and reliability of a clinical test to discuss the statistical test and the methodological approach in assessing validity and reliability in clinical research. Depending on the type of the variable (qualitative or quantitative), well-known statistical tests can be applied to assess reliability and validity. The qualitative variables of sensitivity, specificity, positive predictive value, negative predictive value, false positive and false negative rates, likelihood ratio positive and likelihood ratio negative, as well as odds ratio (i.e., ratio of true to false results), are the most appropriate estimates to evaluate validity of a test compared to a gold standard. In the case of quantitative variables, depending on distribution of the variable, Pearson r or Spearman rho can be applied. Diagnostic accuracy (validity) and diagnostic precision (reliability or agreement) are two completely different methodological issues. Depending on the type of the variable (qualitative or quantitative), well-known statistical tests can be applied to assess validity.

A novel multi-target regression framework for time-series prediction of drug efficacy.

PubMed

Li, Haiqing; Zhang, Wei; Chen, Ying; Guo, Yumeng; Li, Guo-Zheng; Zhu, Xiaoxin

2017-01-18

Excavating from small samples is a challenging pharmacokinetic problem, where statistical methods can be applied. Pharmacokinetic data is special due to the small samples of high dimensionality, which makes it difficult to adopt conventional methods to predict the efficacy of traditional Chinese medicine (TCM) prescription. The main purpose of our study is to obtain some knowledge of the correlation in TCM prescription. Here, a novel method named Multi-target Regression Framework to deal with the problem of efficacy prediction is proposed. We employ the correlation between the values of different time sequences and add predictive targets of previous time as features to predict the value of current time. Several experiments are conducted to test the validity of our method and the results of leave-one-out cross-validation clearly manifest the competitiveness of our framework. Compared with linear regression, artificial neural networks, and partial least squares, support vector regression combined with our framework demonstrates the best performance, and appears to be more suitable for this task.

Development and in house validation of a new thermogravimetric method for water content analysis in soft brown sugar.

PubMed

Ducat, Giseli; Felsner, Maria L; da Costa Neto, Pedro R; Quináia, Sueli P

2015-06-15

Recently the use of brown sugar has increased due to its nutritional characteristics, thus requiring a more rigid quality control. The development of a method for water content analysis in soft brown sugar is carried out for the first time by TG/DTA with application of different statistical tests. The results of the optimization study suggest that heating rates of 5°C min(-1) and an alumina sample holder improve the efficiency of the drying process. The validation study showed that thermo gravimetry presents good accuracy and precision for water content analysis in soft brown sugar samples. This technique offers advantages over other analytical methods as it does not use toxic and costly reagents or solvents, it does not need any sample preparation, and it allows the identification of the temperature at which water is completely eliminated in relation to other volatile degradation products. This is an important advantage over the official method (loss on drying). Copyright © 2015 Elsevier Ltd. All rights reserved.