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Sample records for missing covariate information

  1. Frailty models with missing covariates.

    PubMed

    Herring, Amy H; Ibrahim, Joseph G; Lipsitz, Stuart R

    2002-03-01

    We present a method for estimating the parameters in random effects models for survival data when covariates are subject to missingness. Our method is more general than the usual frailty model as it accommodates a wide range of distributions for the random effects, which are included as an offset in the linear predictor in a manner analogous to that used in generalized linear mixed models. We propose using a Monte Carlo EM algorithm along with the Gibbs sampler to obtain parameter estimates. This method is useful in reducing the bias that may be incurred using complete-case methods in this setting. The methodology is applied to data from Eastern Cooperative Oncology Group melanoma clinical trials in which observations were believed to be clustered and several tumor characteristics were not always observed.

  2. Sequential BART for imputation of missing covariates

    PubMed Central

    Xu, Dandan; Daniels, Michael J.; Winterstein, Almut G.

    2016-01-01

    To conduct comparative effectiveness research using electronic health records (EHR), many covariates are typically needed to adjust for selection and confounding biases. Unfortunately, it is typical to have missingness in these covariates. Just using cases with complete covariates will result in considerable efficiency losses and likely bias. Here, we consider the covariates missing at random with missing data mechanism either depending on the response or not. Standard methods for multiple imputation can either fail to capture nonlinear relationships or suffer from the incompatibility and uncongeniality issues. We explore a flexible Bayesian nonparametric approach to impute the missing covariates, which involves factoring the joint distribution of the covariates with missingness into a set of sequential conditionals and applying Bayesian additive regression trees to model each of these univariate conditionals. Using data augmentation, the posterior for each conditional can be sampled simultaneously. We provide details on the computational algorithm and make comparisons to other methods, including parametric sequential imputation and two versions of multiple imputation by chained equations. We illustrate the proposed approach on EHR data from an affiliated tertiary care institution to examine factors related to hyperglycemia. PMID:26980459

  3. Doubly robust estimates for binary longitudinal data analysis with missing response and missing covariates.

    PubMed

    Chen, Baojiang; Zhou, Xiao-Hua

    2011-09-01

    Longitudinal studies often feature incomplete response and covariate data. Likelihood-based methods such as the expectation-maximization algorithm give consistent estimators for model parameters when data are missing at random (MAR) provided that the response model and the missing covariate model are correctly specified; however, we do not need to specify the missing data mechanism. An alternative method is the weighted estimating equation, which gives consistent estimators if the missing data and response models are correctly specified; however, we do not need to specify the distribution of the covariates that have missing values. In this article, we develop a doubly robust estimation method for longitudinal data with missing response and missing covariate when data are MAR. This method is appealing in that it can provide consistent estimators if either the missing data model or the missing covariate model is correctly specified. Simulation studies demonstrate that this method performs well in a variety of situations.

  4. On analyzing ordinal data when responses and covariates are both missing at random.

    PubMed

    Rana, Subrata; Roy, Surupa; Das, Kalyan

    2016-08-01

    In many occasions, particularly in biomedical studies, data are unavailable for some responses and covariates. This leads to biased inference in the analysis when a substantial proportion of responses or a covariate or both are missing. Except a few situations, methods for missing data have earlier been considered either for missing response or for missing covariates, but comparatively little attention has been directed to account for both missing responses and missing covariates, which is partly attributable to complexity in modeling and computation. This seems to be important as the precise impact of substantial missing data depends on the association between two missing data processes as well. The real difficulty arises when the responses are ordinal by nature. We develop a joint model to take into account simultaneously the association between the ordinal response variable and covariates and also that between the missing data indicators. Such a complex model has been analyzed here by using the Markov chain Monte Carlo approach and also by the Monte Carlo relative likelihood approach. Their performance on estimating the model parameters in finite samples have been looked into. We illustrate the application of these two methods using data from an orthodontic study. Analysis of such data provides some interesting information on human habit.

  5. Model selection for marginal regression analysis of longitudinal data with missing observations and covariate measurement error.

    PubMed

    Shen, Chung-Wei; Chen, Yi-Hau

    2015-10-01

    Missing observations and covariate measurement error commonly arise in longitudinal data. However, existing methods for model selection in marginal regression analysis of longitudinal data fail to address the potential bias resulting from these issues. To tackle this problem, we propose a new model selection criterion, the Generalized Longitudinal Information Criterion, which is based on an approximately unbiased estimator for the expected quadratic error of a considered marginal model accounting for both data missingness and covariate measurement error. The simulation results reveal that the proposed method performs quite well in the presence of missing data and covariate measurement error. On the contrary, the naive procedures without taking care of such complexity in data may perform quite poorly. The proposed method is applied to data from the Taiwan Longitudinal Study on Aging to assess the relationship of depression with health and social status in the elderly, accommodating measurement error in the covariate as well as missing observations.

  6. Semiparametric approach for non-monotone missing covariates in a parametric regression model.

    PubMed

    Sinha, Samiran; Saha, Krishna K; Wang, Suojin

    2014-06-01

    Missing covariate data often arise in biomedical studies, and analysis of such data that ignores subjects with incomplete information may lead to inefficient and possibly biased estimates. A great deal of attention has been paid to handling a single missing covariate or a monotone pattern of missing data when the missingness mechanism is missing at random. In this article, we propose a semiparametric method for handling non-monotone patterns of missing data. The proposed method relies on the assumption that the missingness mechanism of a variable does not depend on the missing variable itself but may depend on the other missing variables. This mechanism is somewhat less general than the completely non-ignorable mechanism but is sometimes more flexible than the missing at random mechanism where the missingness mechansim is allowed to depend only on the completely observed variables. The proposed approach is robust to misspecification of the distribution of the missing covariates, and the proposed mechanism helps to nullify (or reduce) the problems due to non-identifiability that result from the non-ignorable missingness mechanism. The asymptotic properties of the proposed estimator are derived. Finite sample performance is assessed through simulation studies. Finally, for the purpose of illustration we analyze an endometrial cancer dataset and a hip fracture dataset.

  7. Analysis of missing mechanism in IVUS imaging clinical trials with missing covariates.

    PubMed

    Hu, Mingxiu; Zhou, Tianyue

    2011-03-01

    Intravascular ultrasound (IVUS) is a clinical imaging procedure used to assess drug effects on the progression of coronary atherosclerosis in clinical trials. It is an invasive medical procedure of measuring coronary artery atheroma (plaque) volume, and leads to high missing rates (often over 30%). This paper uses an IVUS Phase II clinical trial data to explore the missing mechanism of IVUS endpoint, the percent atheroma volume (PAV). We proposed a moving-window method to examine the relationship between continuous covariates such as lipid endpoint and the probability of missing IVUS values, which provides a general approach for missing mechanism exploration. The moving-window method is more intuitive and provides a fuller picture about the relationship. In the example, some covariates such as lipid measures also have high missing rates after 12 months because of compliance issues probably caused by fatigue of blood drawing. We found that if the method of last observation carried forward (LOCF) is used to impute the lipid endpoints, it leads to biologically unexplainable results. Using the multiple imputation approach for the missing covariates results in a more reasonable conclusion about the IVUS missing mechanism. Age, race, and baseline PAV are identified as key potential contributors to the probability of missing IVUS endpoint. This finding can be used to reduce missing values in future IVUS trials by setting up appropriate inclusion and exclusion criteria at the trial design stages.

  8. Handling Missing Covariates in Conditional Mixture Models Under Missing at Random Assumptions.

    PubMed

    Sterba, Sonya K

    2014-01-01

    Mixture modeling is a popular method that accounts for unobserved population heterogeneity using multiple latent classes that differ in response patterns. Psychologists use conditional mixture models to incorporate covariates into between-class and/or within-class regressions. Although psychologists often have missing covariate data, conditional mixtures are currently fit with a conditional likelihood, treating covariates as fixed and fully observed. Under this exogenous-x approach, missing covariates are handled primarily via listwise deletion. This sacrifices efficiency and does not allow missingness to depend on observed outcomes. Here we describe a modified joint likelihood approach that (a) allows inference about parameters of the exogenous-x conditional mixture even with nonnormal covariates, unlike a conventional multivariate mixture; (b) retains all cases under missing at random assumptions; (c) yields lower bias and higher efficiency than the exogenous-x approach under a variety of conditions with missing covariates; and (d) is straightforward to implement in available commercial software. The proposed approach is illustrated with an empirical analysis predicting membership in latent classes of conduct problems. Recommendations for practice are discussed.

  9. Multiple imputation of missing covariates in NONMEM and evaluation of the method's sensitivity to η-shrinkage.

    PubMed

    Johansson, Åsa M; Karlsson, Mats O

    2013-10-01

    Multiple imputation (MI) is an approach widely used in statistical analysis of incomplete data. However, its application to missing data problems in nonlinear mixed-effects modelling is limited. The objective was to implement a four-step MI method for handling missing covariate data in NONMEM and to evaluate the method's sensitivity to η-shrinkage. Four steps were needed; (1) estimation of empirical Bayes estimates (EBEs) using a base model without the partly missing covariate, (2) a regression model for the covariate values given the EBEs from subjects with covariate information, (3) imputation of covariates using the regression model and (4) estimation of the population model. Steps (3) and (4) were repeated several times. The procedure was automated in PsN and is now available as the mimp functionality ( http://psn.sourceforge.net/ ). The method's sensitivity to shrinkage in EBEs was evaluated in a simulation study where the covariate was missing according to a missing at random type of missing data mechanism. The η-shrinkage was increased in steps from 4.5 to 54%. Two hundred datasets were simulated and analysed for each scenario. When shrinkage was low the MI method gave unbiased and precise estimates of all population parameters. With increased shrinkage the estimates became less precise but remained unbiased.

  10. Multiple imputation for IPD meta-analysis: allowing for heterogeneity and studies with missing covariates.

    PubMed

    Quartagno, M; Carpenter, J R

    2016-07-30

    Recently, multiple imputation has been proposed as a tool for individual patient data meta-analysis with sporadically missing observations, and it has been suggested that within-study imputation is usually preferable. However, such within study imputation cannot handle variables that are completely missing within studies. Further, if some of the contributing studies are relatively small, it may be appropriate to share information across studies when imputing. In this paper, we develop and evaluate a joint modelling approach to multiple imputation of individual patient data in meta-analysis, with an across-study probability distribution for the study specific covariance matrices. This retains the flexibility to allow for between-study heterogeneity when imputing while allowing (i) sharing information on the covariance matrix across studies when this is appropriate, and (ii) imputing variables that are wholly missing from studies. Simulation results show both equivalent performance to the within-study imputation approach where this is valid, and good results in more general, practically relevant, scenarios with studies of very different sizes, non-negligible between-study heterogeneity and wholly missing variables. We illustrate our approach using data from an individual patient data meta-analysis of hypertension trials. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

  11. Comparison of Two Approaches for Handling Missing Covariates in Logistic Regression

    ERIC Educational Resources Information Center

    Peng, Chao-Ying Joanne; Zhu, Jin

    2008-01-01

    For the past 25 years, methodological advances have been made in missing data treatment. Most published work has focused on missing data in dependent variables under various conditions. The present study seeks to fill the void by comparing two approaches for handling missing data in categorical covariates in logistic regression: the…

  12. Comparison of Two Approaches for Handling Missing Covariates in Logistic Regression

    ERIC Educational Resources Information Center

    Peng, Chao-Ying Joanne; Zhu, Jin

    2008-01-01

    For the past 25 years, methodological advances have been made in missing data treatment. Most published work has focused on missing data in dependent variables under various conditions. The present study seeks to fill the void by comparing two approaches for handling missing data in categorical covariates in logistic regression: the…

  13. A New Approach to Handle Missing Covariate Data in Twin Research : With an Application to Educational Achievement Data.

    PubMed

    Schwabe, Inga; Boomsma, Dorret I; Zeeuw, Eveline L de; Berg, Stéphanie M van den

    2016-07-01

    The often-used ACE model which decomposes phenotypic variance into additive genetic (A), common-environmental (C) and unique-environmental (E) parts can be extended to include covariates. Collection of these variables however often leads to a large amount of missing data, for example when self-reports (e.g. questionnaires) are not fully completed. The usual approach to handle missing covariate data in twin research results in reduced power to detect statistical effects, as only phenotypic and covariate data of individual twins with complete data can be used. Here we present a full information approach to handle missing covariate data that makes it possible to use all available data. A simulation study shows that, independent of missingness scenario, number of covariates or amount of missingness, the full information approach is more powerful than the usual approach. To illustrate the new method, we applied it to test scores on a Dutch national school achievement test (Eindtoets Basisonderwijs) in the final grade of primary school of 990 twin pairs. The effects of school-aggregated measures (e.g. school denomination, pedagogical philosophy, school size) and the effect of the sex of a twin on these test scores were tested. None of the covariates had a significant effect on individual differences in test scores.

  14. Bias and efficiency of multiple imputation compared with complete-case analysis for missing covariate values.

    PubMed

    White, Ian R; Carlin, John B

    2010-12-10

    When missing data occur in one or more covariates in a regression model, multiple imputation (MI) is widely advocated as an improvement over complete-case analysis (CC). We use theoretical arguments and simulation studies to compare these methods with MI implemented under a missing at random assumption. When data are missing completely at random, both methods have negligible bias, and MI is more efficient than CC across a wide range of scenarios. For other missing data mechanisms, bias arises in one or both methods. In our simulation setting, CC is biased towards the null when data are missing at random. However, when missingness is independent of the outcome given the covariates, CC has negligible bias and MI is biased away from the null. With more general missing data mechanisms, bias tends to be smaller for MI than for CC. Since MI is not always better than CC for missing covariate problems, the choice of method should take into account what is known about the missing data mechanism in a particular substantive application. Importantly, the choice of method should not be based on comparison of standard errors. We propose new ways to understand empirical differences between MI and CC, which may provide insights into the appropriateness of the assumptions underlying each method, and we propose a new index for assessing the likely gain in precision from MI: the fraction of incomplete cases among the observed values of a covariate (FICO).

  15. Maximum Likelihood Inference for the Cox Regression Model with Applications to Missing Covariates.

    PubMed

    Chen, Ming-Hui; Ibrahim, Joseph G; Shao, Qi-Man

    2009-10-01

    In this paper, we carry out an in-depth theoretical investigation for existence of maximum likelihood estimates for the Cox model (Cox, 1972, 1975) both in the full data setting as well as in the presence of missing covariate data. The main motivation for this work arises from missing data problems, where models can easily become difficult to estimate with certain missing data configurations or large missing data fractions. We establish necessary and sufficient conditions for existence of the maximum partial likelihood estimate (MPLE) for completely observed data (i.e., no missing data) settings as well as sufficient conditions for existence of the maximum likelihood estimate (MLE) for survival data with missing covariates via a profile likelihood method. Several theorems are given to establish these conditions. A real dataset from a cancer clinical trial is presented to further illustrate the proposed methodology.

  16. Maximum Likelihood Inference for the Cox Regression Model with Applications to Missing Covariates

    PubMed Central

    Chen, Ming-Hui; Ibrahim, Joseph G.; Shao, Qi-Man

    2009-01-01

    In this paper, we carry out an in-depth theoretical investigation for existence of maximum likelihood estimates for the Cox model (Cox, 1972, 1975) both in the full data setting as well as in the presence of missing covariate data. The main motivation for this work arises from missing data problems, where models can easily become difficult to estimate with certain missing data configurations or large missing data fractions. We establish necessary and sufficient conditions for existence of the maximum partial likelihood estimate (MPLE) for completely observed data (i.e., no missing data) settings as well as sufficient conditions for existence of the maximum likelihood estimate (MLE) for survival data with missing covariates via a profile likelihood method. Several theorems are given to establish these conditions. A real dataset from a cancer clinical trial is presented to further illustrate the proposed methodology. PMID:19802375

  17. Missing continuous outcomes under covariate dependent missingness in cluster randomised trials.

    PubMed

    Hossain, Anower; Diaz-Ordaz, Karla; Bartlett, Jonathan W

    2016-05-13

    Attrition is a common occurrence in cluster randomised trials which leads to missing outcome data. Two approaches for analysing such trials are cluster-level analysis and individual-level analysis. This paper compares the performance of unadjusted cluster-level analysis, baseline covariate adjusted cluster-level analysis and linear mixed model analysis, under baseline covariate dependent missingness in continuous outcomes, in terms of bias, average estimated standard error and coverage probability. The methods of complete records analysis and multiple imputation are used to handle the missing outcome data. We considered four scenarios, with the missingness mechanism and baseline covariate effect on outcome either the same or different between intervention groups. We show that both unadjusted cluster-level analysis and baseline covariate adjusted cluster-level analysis give unbiased estimates of the intervention effect only if both intervention groups have the same missingness mechanisms and there is no interaction between baseline covariate and intervention group. Linear mixed model and multiple imputation give unbiased estimates under all four considered scenarios, provided that an interaction of intervention and baseline covariate is included in the model when appropriate. Cluster mean imputation has been proposed as a valid approach for handling missing outcomes in cluster randomised trials. We show that cluster mean imputation only gives unbiased estimates when missingness mechanism is the same between the intervention groups and there is no interaction between baseline covariate and intervention group. Multiple imputation shows overcoverage for small number of clusters in each intervention group.

  18. Missing continuous outcomes under covariate dependent missingness in cluster randomised trials

    PubMed Central

    Diaz-Ordaz, Karla; Bartlett, Jonathan W

    2016-01-01

    Attrition is a common occurrence in cluster randomised trials which leads to missing outcome data. Two approaches for analysing such trials are cluster-level analysis and individual-level analysis. This paper compares the performance of unadjusted cluster-level analysis, baseline covariate adjusted cluster-level analysis and linear mixed model analysis, under baseline covariate dependent missingness in continuous outcomes, in terms of bias, average estimated standard error and coverage probability. The methods of complete records analysis and multiple imputation are used to handle the missing outcome data. We considered four scenarios, with the missingness mechanism and baseline covariate effect on outcome either the same or different between intervention groups. We show that both unadjusted cluster-level analysis and baseline covariate adjusted cluster-level analysis give unbiased estimates of the intervention effect only if both intervention groups have the same missingness mechanisms and there is no interaction between baseline covariate and intervention group. Linear mixed model and multiple imputation give unbiased estimates under all four considered scenarios, provided that an interaction of intervention and baseline covariate is included in the model when appropriate. Cluster mean imputation has been proposed as a valid approach for handling missing outcomes in cluster randomised trials. We show that cluster mean imputation only gives unbiased estimates when missingness mechanism is the same between the intervention groups and there is no interaction between baseline covariate and intervention group. Multiple imputation shows overcoverage for small number of clusters in each intervention group. PMID:27177885

  19. Comparison of techniques for handling missing covariate data within prognostic modelling studies: a simulation study.

    PubMed

    Marshall, Andrea; Altman, Douglas G; Royston, Patrick; Holder, Roger L

    2010-01-19

    There is no consensus on the most appropriate approach to handle missing covariate data within prognostic modelling studies. Therefore a simulation study was performed to assess the effects of different missing data techniques on the performance of a prognostic model. Datasets were generated to resemble the skewed distributions seen in a motivating breast cancer example. Multivariate missing data were imposed on four covariates using four different mechanisms; missing completely at random (MCAR), missing at random (MAR), missing not at random (MNAR) and a combination of all three mechanisms. Five amounts of incomplete cases from 5% to 75% were considered. Complete case analysis (CC), single imputation (SI) and five multiple imputation (MI) techniques available within the R statistical software were investigated: a) data augmentation (DA) approach assuming a multivariate normal distribution, b) DA assuming a general location model, c) regression switching imputation, d) regression switching with predictive mean matching (MICE-PMM) and e) flexible additive imputation models. A Cox proportional hazards model was fitted and appropriate estimates for the regression coefficients and model performance measures were obtained. Performing a CC analysis produced unbiased regression estimates, but inflated standard errors, which affected the significance of the covariates in the model with 25% or more missingness. Using SI, underestimated the variability; resulting in poor coverage even with 10% missingness. Of the MI approaches, applying MICE-PMM produced, in general, the least biased estimates and better coverage for the incomplete covariates and better model performance for all mechanisms. However, this MI approach still produced biased regression coefficient estimates for the incomplete skewed continuous covariates when 50% or more cases had missing data imposed with a MCAR, MAR or combined mechanism. When the missingness depended on the incomplete covariates, i.e. MNAR

  20. ML Estimation of Mean and Covariance Structures with Missing Data Using Complete Data Routines.

    ERIC Educational Resources Information Center

    Jamshidian, Mortaza; Bentler, Peter M.

    1999-01-01

    Describes the maximum likelihood (ML) estimation of mean and covariance structure models when data are missing. Describes expectation maximization (EM), generalized expectation maximization, Fletcher-Powell, and Fisher-scoring algorithms for parameter estimation and shows how software can be used to implement each algorithm. (Author/SLD)

  1. Covariance Structure Model Fit Testing under Missing Data: An Application of the Supplemented EM Algorithm

    ERIC Educational Resources Information Center

    Cai, Li; Lee, Taehun

    2009-01-01

    We apply the Supplemented EM algorithm (Meng & Rubin, 1991) to address a chronic problem with the "two-stage" fitting of covariance structure models in the presence of ignorable missing data: the lack of an asymptotically chi-square distributed goodness-of-fit statistic. We show that the Supplemented EM algorithm provides a…

  2. Comparison of multiple imputation and complete-case in a simulated longitudinal data with missing covariate

    NASA Astrophysics Data System (ADS)

    Yoke, Chin Wan; Khalid, Zarina Mohd

    2014-07-01

    Along a continual process of collecting data, missing recorded datum always a main problem faced by the real application. It happens due to the carelessness or the unawareness of a recorder to the importance of data documentation. In this study, a random-effects analysis which simulates data from a proposed algorithm is presented with a missing covariate. It is an improved simulation method which involves first-order autoregressive (AR(1)) process in measuring the correlation between measurements of a subject across two time sequence. Complete-case analysis and multiple imputation method are comparatively implemented for the estimation procedure. This study shows that the multiple imputation method results in estimations which fit well to the data which are not only missing completely at random (MCAR) but also missing at random (MAR). However, the complete-case analysis results in estimators which fit well to the data which are only MCAR.

  3. TRANSPOSABLE REGULARIZED COVARIANCE MODELS WITH AN APPLICATION TO MISSING DATA IMPUTATION.

    PubMed

    Allen, Genevera I; Tibshirani, Robert

    2010-06-01

    Missing data estimation is an important challenge with high-dimensional data arranged in the form of a matrix. Typically this data matrix is transposable, meaning that either the rows, columns or both can be treated as features. To model transposable data, we present a modification of the matrix-variate normal, the mean-restricted matrix-variate normal, in which the rows and columns each have a separate mean vector and covariance matrix. By placing additive penalties on the inverse covariance matrices of the rows and columns, these so called transposable regularized covariance models allow for maximum likelihood estimation of the mean and non-singular covariance matrices. Using these models, we formulate EM-type algorithms for missing data imputation in both the multivariate and transposable frameworks. We present theoretical results exploiting the structure of our transposable models that allow these models and imputation methods to be applied to high-dimensional data. Simulations and results on microarray data and the Netflix data show that these imputation techniques often outperform existing methods and offer a greater degree of flexibility.

  4. Spatio-Temporal Regression Based Clustering of Precipitation Extremes in a Presence of Systematically Missing Covariates

    NASA Astrophysics Data System (ADS)

    Kaiser, Olga; Martius, Olivia; Horenko, Illia

    2017-04-01

    Regression based Generalized Pareto Distribution (GPD) models are often used to describe the dynamics of hydrological threshold excesses relying on the explicit availability of all of the relevant covariates. But, in real application the complete set of relevant covariates might be not available. In this context, it was shown that under weak assumptions the influence coming from systematically missing covariates can be reflected by a nonstationary and nonhomogenous dynamics. We present a data-driven, semiparametric and an adaptive approach for spatio-temporal regression based clustering of threshold excesses in a presence of systematically missing covariates. The nonstationary and nonhomogenous behavior of threshold excesses is describes by a set of local stationary GPD models, where the parameters are expressed as regression models, and a non-parametric spatio-temporal hidden switching process. Exploiting nonparametric Finite Element time-series analysis Methodology (FEM) with Bounded Variation of the model parameters (BV) for resolving the spatio-temporal switching process, the approach goes beyond strong a priori assumptions made is standard latent class models like Mixture Models and Hidden Markov Models. Additionally, the presented FEM-BV-GPD provides a pragmatic description of the corresponding spatial dependence structure by grouping together all locations that exhibit similar behavior of the switching process. The performance of the framework is demonstrated on daily accumulated precipitation series over 17 different locations in Switzerland from 1981 till 2013 - showing that the introduced approach allows for a better description of the historical data.

  5. TRANSPOSABLE REGULARIZED COVARIANCE MODELS WITH AN APPLICATION TO MISSING DATA IMPUTATION

    PubMed Central

    Allen, Genevera I.; Tibshirani, Robert

    2015-01-01

    Missing data estimation is an important challenge with high-dimensional data arranged in the form of a matrix. Typically this data matrix is transposable, meaning that either the rows, columns or both can be treated as features. To model transposable data, we present a modification of the matrix-variate normal, the mean-restricted matrix-variate normal, in which the rows and columns each have a separate mean vector and covariance matrix. By placing additive penalties on the inverse covariance matrices of the rows and columns, these so called transposable regularized covariance models allow for maximum likelihood estimation of the mean and non-singular covariance matrices. Using these models, we formulate EM-type algorithms for missing data imputation in both the multivariate and transposable frameworks. We present theoretical results exploiting the structure of our transposable models that allow these models and imputation methods to be applied to high-dimensional data. Simulations and results on microarray data and the Netflix data show that these imputation techniques often outperform existing methods and offer a greater degree of flexibility. PMID:26877823

  6. Missing clinical information during primary care visits.

    PubMed

    Smith, Peter C; Araya-Guerra, Rodrigo; Bublitz, Caroline; Parnes, Bennett; Dickinson, L Miriam; Van Vorst, Rebecca; Westfall, John M; Pace, Wilson D

    2005-02-02

    The coordinating function of primary care is information-intensive and may be impeded by missing clinical information. However, missing clinical information has not been explicitly investigated in the primary care setting. To describe primary care clinicians' reports of missing clinical information. Cross-sectional survey conducted in 32 primary care clinics within State Networks of Colorado Ambulatory Practices and Partners (SNOCAP), a consortium of practice-based research networks participating in the Applied Strategies for Improving Patient Safety medical error reporting study. Two hundred fifty-three clinicians were surveyed about 1614 patient visits between May and December 2003. For every visit during 1 half-day session, each clinician completed a questionnaire about patient and visit characteristics and stated whether important clinical information had been missing. Clinician characteristics were also recorded. Reports of missing clinical information frequency, type, and presumed location; perceived likelihood of adverse effects, delays in care, and additional services; and time spent looking for missing information. Multivariate analysis was conducted to assess the relationship of missing information to patient, visit, or clinician characteristics, adjusting for potential confounders and effects of clustering. Clinicians reported missing clinical information in 13.6% of visits; missing information included laboratory results (6.1% of all visits), letters/dictation (5.4%), radiology results (3.8%), history and physical examination (3.7%), and medications (3.2%). Missing clinical information was frequently reported to be located outside their clinical system but within the United States (52.3%), to be at least somewhat likely to adversely affect patients (44%), and to potentially result in delayed care or additional services (59.5%). Significant time was reportedly spent unsuccessfully searching for missing clinical information (5-10 minutes, 25.6%; >10

  7. Simultaneous inference and bias analysis for longitudinal data with covariate measurement error and missing responses.

    PubMed

    Yi, G Y; Liu, W; Wu, Lang

    2011-03-01

    Longitudinal data arise frequently in medical studies and it is common practice to analyze such data with generalized linear mixed models. Such models enable us to account for various types of heterogeneity, including between- and within-subjects ones. Inferential procedures complicate dramatically when missing observations or measurement error arise. In the literature, there has been considerable interest in accommodating either incompleteness or covariate measurement error under random effects models. However, there is relatively little work concerning both features simultaneously. There is a need to fill up this gap as longitudinal data do often have both characteristics. In this article, our objectives are to study simultaneous impact of missingness and covariate measurement error on inferential procedures and to develop a valid method that is both computationally feasible and theoretically valid. Simulation studies are conducted to assess the performance of the proposed method, and a real example is analyzed with the proposed method.

  8. Imputation of missing covariate values in epigenome-wide analysis of DNA methylation data

    PubMed Central

    Wu, Chong; Demerath, Ellen W.; Pankow, James S.; Bressler, Jan; Fornage, Myriam; Grove, Megan L.; Chen, Wei; Guan, Weihua

    2016-01-01

    ABSTRACT DNA methylation is a widely studied epigenetic mechanism and alterations in methylation patterns may be involved in the development of common diseases. Unlike inherited changes in genetic sequence, variation in site-specific methylation varies by tissue, developmental stage, and disease status, and may be impacted by aging and exposure to environmental factors, such as diet or smoking. These non-genetic factors are typically included in epigenome-wide association studies (EWAS) because they may be confounding factors to the association between methylation and disease. However, missing values in these variables can lead to reduced sample size and decrease the statistical power of EWAS. We propose a site selection and multiple imputation (MI) method to impute missing covariate values and to perform association tests in EWAS. Then, we compare this method to an alternative projection-based method. Through simulations, we show that the MI-based method is slightly conservative, but provides consistent estimates for effect size. We also illustrate these methods with data from the Atherosclerosis Risk in Communities (ARIC) study to carry out an EWAS between methylation levels and smoking status, in which missing cell type compositions and white blood cell counts are imputed. PMID:26890800

  9. Imputation of missing covariate values in epigenome-wide analysis of DNA methylation data.

    PubMed

    Wu, Chong; Demerath, Ellen W; Pankow, James S; Bressler, Jan; Fornage, Myriam; Grove, Megan L; Chen, Wei; Guan, Weihua

    2016-01-01

    DNA methylation is a widely studied epigenetic mechanism and alterations in methylation patterns may be involved in the development of common diseases. Unlike inherited changes in genetic sequence, variation in site-specific methylation varies by tissue, developmental stage, and disease status, and may be impacted by aging and exposure to environmental factors, such as diet or smoking. These non-genetic factors are typically included in epigenome-wide association studies (EWAS) because they may be confounding factors to the association between methylation and disease. However, missing values in these variables can lead to reduced sample size and decrease the statistical power of EWAS. We propose a site selection and multiple imputation (MI) method to impute missing covariate values and to perform association tests in EWAS. Then, we compare this method to an alternative projection-based method. Through simulations, we show that the MI-based method is slightly conservative, but provides consistent estimates for effect size. We also illustrate these methods with data from the Atherosclerosis Risk in Communities (ARIC) study to carry out an EWAS between methylation levels and smoking status, in which missing cell type compositions and white blood cell counts are imputed.

  10. Evaluating model based imputation methods for missing covariates in regression models with interactions

    PubMed Central

    Kim, Soeun; Sugar, Catherine A.; Belin, Thomas R.

    2015-01-01

    Imputation strategies are widely used in settings that involve inference with incomplete data. However, implementation of a particular approach always rests on assumptions, and subtle distinctions between methods can have an impact on subsequent analyses. In this paper we are concerned with regression models in which the true underlying relationship includes interaction terms. We focus in particular on a linear model with one fully observed continuous predictor, a second partially observed continuous predictor, and their interaction. We derive the conditional distribution of the missing covariate and interaction term given the observed covariate and the outcome variable, and examine the performance of a multiple imputation procedure based on this distribution. We also investigate several alternative procedures that can be implemented by adapting multivariate normal multiple imputation software in ways that might be expected to perform well despite incompatibilities between model assumptions and true underlying relationships among the variables. The methods are compared in terms of bias, coverage and confidence interval width. As expected, the procedure based on the correct conditional distribution (CCD) performs well across all scenarios. Just as importantly for general practitioners, several of the approaches based on multivariate normality perform comparably to the CCD in a number of circumstances, although, interestingly, procedures that seek to preserve the multiplicative relationship between the interaction term and the main-effects are found to be substantially less reliable. For illustration, the various procedures are applied to an analysis of post-traumatic-stress-disorder symptoms in a study of childhood trauma. PMID:25630757

  11. Bias reduction and a solution for separation of logistic regression with missing covariates.

    PubMed

    Maiti, Tapabrata; Pradhan, Vivek

    2009-12-01

    Logistic regression is an important statistical procedure used in many disciplines. The standard software packages for data analysis are generally equipped with this procedure where the maximum likelihood estimates of the regression coefficients are obtained iteratively. It is well known that the estimates from the analyses of small- or medium-sized samples are biased. Also, in finding such estimates, often a separation is encountered in which the likelihood converges but at least one of the parameter estimates diverges to infinity. Standard approaches of finding such estimates do not take care of these problems. Moreover, the missingness in the covariates adds an extra layer of complexity to the whole process. In this article, we address these three practical issues--bias, separation, and missing covariates by means of simple adjustments. We have applied the proposed technique using real and simulated data. The proposed method always finds a solution and the estimates are less biased. A SAS macro that implements the proposed method can be obtained from the authors.

  12. A comparison of hospital performance with non-ignorable missing covariates: an application to trauma care data.

    PubMed

    Kirkham, Jamie J

    2008-11-29

    Trauma is a term used in medicine for describing physical injury. The prospective evaluation of the care of injured patients aims to improve the management of a trauma system and acts as an ongoing audit of trauma care. One of the principal techniques used to evaluate the effectiveness of trauma care at different hospitals is through a comparative outcome analysis. In such an analysis, a national 'league table' can be compiled to determine which hospitals are better at managing trauma care. One of the problems with the conventional analysis is that key covariates for measuring physiological injury can often be missing. It is also hypothesized that this missingness is not missing at random (NMAR). We describe the methods used to assess the performance of hospitals in a trauma setting and implement the method of weights for generalized linear models to account for the missing covariate data, when we suspect the missing data mechanism is NMAR using a Monte Carlo EM algorithm. Through simulation work and application to the trauma data we demonstrate the affect the missing covariate data can have on the performance of hospitals and how the conclusions we draw from the analysis can differ. We highlight the differences in hospital performance and the ranking of hospitals.

  13. Missing information can be more persuasive.

    PubMed

    Chebat, Jean-Charles; Gélinas-Chebat, Claire; Dorais, Suzie

    2003-06-01

    A 2 x 2 experiment (low/high self-relevance and complete/incomplete information about an advertised service) was designed to test a set of hypotheses related to inference-making from advertisements providing no information on the uses of the advertised service. Findings show that under high self-relevance conditions, viewers have more positive attitudes toward the advertisements mentioning no use at all of the advertised service, whereas under low self-relevance conditions, viewers have more positive attitudes toward the advertisements mentioning all the possible uses of the advertised service. Similar relations are found for the attitudes toward the service. The absence of specific uses allows the viewers to complement the missing information with their own relevant information.

  14. Semiparametric Bayesian analysis of gene-environment interactions with error in measurement of environmental covariates and missing genetic data.

    PubMed

    Lobach, Iryna; Mallick, Bani; Carroll, Raymond J

    2011-01-01

    Case-control studies are widely used to detect gene-environment interactions in the etiology of complex diseases. Many variables that are of interest to biomedical researchers are difficult to measure on an individual level, e.g. nutrient intake, cigarette smoking exposure, long-term toxic exposure. Measurement error causes bias in parameter estimates, thus masking key features of data and leading to loss of power and spurious/masked associations. We develop a Bayesian methodology for analysis of case-control studies for the case when measurement error is present in an environmental covariate and the genetic variable has missing data. This approach offers several advantages. It allows prior information to enter the model to make estimation and inference more precise. The environmental covariates measured exactly are modeled completely nonparametrically. Further, information about the probability of disease can be incorporated in the estimation procedure to improve quality of parameter estimates, what cannot be done in conventional case-control studies. A unique feature of the procedure under investigation is that the analysis is based on a pseudo-likelihood function therefore conventional Bayesian techniques may not be technically correct. We propose an approach using Markov Chain Monte Carlo sampling as well as a computationally simple method based on an asymptotic posterior distribution. Simulation experiments demonstrated that our method produced parameter estimates that are nearly unbiased even for small sample sizes. An application of our method is illustrated using a population-based case-control study of the association between calcium intake with the risk of colorectal adenoma development.

  15. Semiparametric Bayesian analysis of gene-environment interactions with error in measurement of environmental covariates and missing genetic data

    PubMed Central

    Mallick, Bani; Carroll, Raymond J.

    2011-01-01

    Case-control studies are widely used to detect gene-environment interactions in the etiology of complex diseases. Many variables that are of interest to biomedical researchers are difficult to measure on an individual level, e.g. nutrient intake, cigarette smoking exposure, long-term toxic exposure. Measurement error causes bias in parameter estimates, thus masking key features of data and leading to loss of power and spurious/masked associations. We develop a Bayesian methodology for analysis of case-control studies for the case when measurement error is present in an environmental covariate and the genetic variable has missing data. This approach offers several advantages. It allows prior information to enter the model to make estimation and inference more precise. The environmental covariates measured exactly are modeled completely nonparametrically. Further, information about the probability of disease can be incorporated in the estimation procedure to improve quality of parameter estimates, what cannot be done in conventional case-control studies. A unique feature of the procedure under investigation is that the analysis is based on a pseudo-likelihood function therefore conventional Bayesian techniques may not be technically correct. We propose an approach using Markov Chain Monte Carlo sampling as well as a computationally simple method based on an asymptotic posterior distribution. Simulation experiments demonstrated that our method produced parameter estimates that are nearly unbiased even for small sample sizes. An application of our method is illustrated using a population-based case-control study of the association between calcium intake with the risk of colorectal adenoma development. PMID:21949562

  16. 19 CFR 201.3a - Missing children information.

    Code of Federal Regulations, 2014 CFR

    2014-04-01

    ... 19 Customs Duties 3 2014-04-01 2014-04-01 false Missing children information. 201.3a Section 201... Miscellaneous § 201.3a Missing children information. (a) Pursuant to 39 U.S.C. 3220, penalty mail sent by the Commission may be used to assist in the location and recovery of missing children. This section...

  17. 19 CFR 201.3a - Missing children information.

    Code of Federal Regulations, 2013 CFR

    2013-04-01

    ... 19 Customs Duties 3 2013-04-01 2013-04-01 false Missing children information. 201.3a Section 201... Miscellaneous § 201.3a Missing children information. (a) Pursuant to 39 U.S.C. 3220, penalty mail sent by the Commission may be used to assist in the location and recovery of missing children. This section...

  18. 19 CFR 201.3a - Missing children information.

    Code of Federal Regulations, 2011 CFR

    2011-04-01

    ... 19 Customs Duties 3 2011-04-01 2011-04-01 false Missing children information. 201.3a Section 201... Miscellaneous § 201.3a Missing children information. (a) Pursuant to 39 U.S.C. 3220, penalty mail sent by the Commission may be used to assist in the location and recovery of missing children. This section...

  19. 19 CFR 201.3a - Missing children information.

    Code of Federal Regulations, 2010 CFR

    2010-04-01

    ... 19 Customs Duties 3 2010-04-01 2010-04-01 false Missing children information. 201.3a Section 201... Miscellaneous § 201.3a Missing children information. (a) Pursuant to 39 U.S.C. 3220, penalty mail sent by the Commission may be used to assist in the location and recovery of missing children. This section...

  20. 19 CFR 201.3a - Missing children information.

    Code of Federal Regulations, 2012 CFR

    2012-04-01

    ... 19 Customs Duties 3 2012-04-01 2012-04-01 false Missing children information. 201.3a Section 201... Miscellaneous § 201.3a Missing children information. (a) Pursuant to 39 U.S.C. 3220, penalty mail sent by the Commission may be used to assist in the location and recovery of missing children. This section...

  1. Bayesian semiparametric nonlinear mixed-effects joint models for data with skewness, missing responses, and measurement errors in covariates.

    PubMed

    Huang, Yangxin; Dagne, Getachew

    2012-09-01

    It is a common practice to analyze complex longitudinal data using semiparametric nonlinear mixed-effects (SNLME) models with a normal distribution. Normality assumption of model errors may unrealistically obscure important features of subject variations. To partially explain between- and within-subject variations, covariates are usually introduced in such models, but some covariates may often be measured with substantial errors. Moreover, the responses may be missing and the missingness may be nonignorable. Inferential procedures can be complicated dramatically when data with skewness, missing values, and measurement error are observed. In the literature, there has been considerable interest in accommodating either skewness, incompleteness or covariate measurement error in such models, but there has been relatively little study concerning all three features simultaneously. In this article, our objective is to address the simultaneous impact of skewness, missingness, and covariate measurement error by jointly modeling the response and covariate processes based on a flexible Bayesian SNLME model. The method is illustrated using a real AIDS data set to compare potential models with various scenarios and different distribution specifications.

  2. Experimental Uncertainty and Covariance Information in EXFOR Library

    NASA Astrophysics Data System (ADS)

    Otuka, N.; Capote, R.; Kopecky, S.; Plompen, A. J. M.; Pronyaev, V. G.; Schillebeeckx, P.; Smith, D. L.

    2012-05-01

    Compilation of experimental uncertainty and covariance information in the EXFOR Library is discussed. Following the presentation of a brief history of information provided in the EXFOR Library, the current EXFOR Formats and their limitations are reviewed. Proposed extensions for neutron-induced reaction cross sections in the fast neutron region and resonance region are also presented.

  3. Dealing with missing covariates in epidemiologic studies: a comparison between multiple imputation and a full Bayesian approach.

    PubMed

    Erler, Nicole S; Rizopoulos, Dimitris; Rosmalen, Joost van; Jaddoe, Vincent W V; Franco, Oscar H; Lesaffre, Emmanuel M E H

    2016-07-30

    Incomplete data are generally a challenge to the analysis of most large studies. The current gold standard to account for missing data is multiple imputation, and more specifically multiple imputation with chained equations (MICE). Numerous studies have been conducted to illustrate the performance of MICE for missing covariate data. The results show that the method works well in various situations. However, less is known about its performance in more complex models, specifically when the outcome is multivariate as in longitudinal studies. In current practice, the multivariate nature of the longitudinal outcome is often neglected in the imputation procedure, or only the baseline outcome is used to impute missing covariates. In this work, we evaluate the performance of MICE using different strategies to include a longitudinal outcome into the imputation models and compare it with a fully Bayesian approach that jointly imputes missing values and estimates the parameters of the longitudinal model. Results from simulation and a real data example show that MICE requires the analyst to correctly specify which components of the longitudinal process need to be included in the imputation models in order to obtain unbiased results. The full Bayesian approach, on the other hand, does not require the analyst to explicitly specify how the longitudinal outcome enters the imputation models. It performed well under different scenarios. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

  4. Multiple imputation of missing covariates with non-linear effects and interactions: an evaluation of statistical methods.

    PubMed

    Seaman, Shaun R; Bartlett, Jonathan W; White, Ian R

    2012-04-10

    Multiple imputation is often used for missing data. When a model contains as covariates more than one function of a variable, it is not obvious how best to impute missing values in these covariates. Consider a regression with outcome Y and covariates X and X2. In 'passive imputation' a value X* is imputed for X and then X2 is imputed as (X*)2. A recent proposal is to treat X2 as 'just another variable' (JAV) and impute X and X2 under multivariate normality. We use simulation to investigate the performance of three methods that can easily be implemented in standard software: 1) linear regression of X on Y to impute X then passive imputation of X2; 2) the same regression but with predictive mean matching (PMM); and 3) JAV. We also investigate the performance of analogous methods when the analysis involves an interaction, and study the theoretical properties of JAV. The application of the methods when complete or incomplete confounders are also present is illustrated using data from the EPIC Study. JAV gives consistent estimation when the analysis is linear regression with a quadratic or interaction term and X is missing completely at random. When X is missing at random, JAV may be biased, but this bias is generally less than for passive imputation and PMM. Coverage for JAV was usually good when bias was small. However, in some scenarios with a more pronounced quadratic effect, bias was large and coverage poor. When the analysis was logistic regression, JAV's performance was sometimes very poor. PMM generally improved on passive imputation, in terms of bias and coverage, but did not eliminate the bias. Given the current state of available software, JAV is the best of a set of imperfect imputation methods for linear regression with a quadratic or interaction effect, but should not be used for logistic regression.

  5. Multiple imputation of missing covariates with non-linear effects and interactions: an evaluation of statistical methods

    PubMed Central

    2012-01-01

    Background Multiple imputation is often used for missing data. When a model contains as covariates more than one function of a variable, it is not obvious how best to impute missing values in these covariates. Consider a regression with outcome Y and covariates X and X2. In 'passive imputation' a value X* is imputed for X and then X2 is imputed as (X*)2. A recent proposal is to treat X2 as 'just another variable' (JAV) and impute X and X2 under multivariate normality. Methods We use simulation to investigate the performance of three methods that can easily be implemented in standard software: 1) linear regression of X on Y to impute X then passive imputation of X2; 2) the same regression but with predictive mean matching (PMM); and 3) JAV. We also investigate the performance of analogous methods when the analysis involves an interaction, and study the theoretical properties of JAV. The application of the methods when complete or incomplete confounders are also present is illustrated using data from the EPIC Study. Results JAV gives consistent estimation when the analysis is linear regression with a quadratic or interaction term and X is missing completely at random. When X is missing at random, JAV may be biased, but this bias is generally less than for passive imputation and PMM. Coverage for JAV was usually good when bias was small. However, in some scenarios with a more pronounced quadratic effect, bias was large and coverage poor. When the analysis was logistic regression, JAV's performance was sometimes very poor. PMM generally improved on passive imputation, in terms of bias and coverage, but did not eliminate the bias. Conclusions Given the current state of available software, JAV is the best of a set of imperfect imputation methods for linear regression with a quadratic or interaction effect, but should not be used for logistic regression. PMID:22489953

  6. Addressing Item-Level Missing Data: A Comparison of Proration and Full Information Maximum Likelihood Estimation.

    PubMed

    Mazza, Gina L; Enders, Craig K; Ruehlman, Linda S

    2015-01-01

    Often when participants have missing scores on one or more of the items comprising a scale, researchers compute prorated scale scores by averaging the available items. Methodologists have cautioned that proration may make strict assumptions about the mean and covariance structures of the items comprising the scale (Schafer & Graham, 2002 ; Graham, 2009 ; Enders, 2010 ). We investigated proration empirically and found that it resulted in bias even under a missing completely at random (MCAR) mechanism. To encourage researchers to forgo proration, we describe a full information maximum likelihood (FIML) approach to item-level missing data handling that mitigates the loss in power due to missing scale scores and utilizes the available item-level data without altering the substantive analysis. Specifically, we propose treating the scale score as missing whenever one or more of the items are missing and incorporating items as auxiliary variables. Our simulations suggest that item-level missing data handling drastically increases power relative to scale-level missing data handling. These results have important practical implications, especially when recruiting more participants is prohibitively difficult or expensive. Finally, we illustrate the proposed method with data from an online chronic pain management program.

  7. Variable Selection and Inference Procedures for Marginal Analysis of Longitudinal Data with Missing Observations and Covariate Measurement Error

    PubMed Central

    Yi, Grace Y.; Tan, Xianming; Li, Runze

    2015-01-01

    Summary In contrast to extensive attention on model selection for univariate data, research on model selection for longitudinal data remains largely unexplored. This is particularly the case when data are subject to missingness and measurement error. To address this important problem, we propose marginal methods that simultaneously carry out model selection and estimation for longitudinal data with missing responses and error-prone covariates. Our method have several appealing features: the applicability is broad because the methods are developed for a unified framework with marginal generalized linear models; model assumptions are minimal in that no full distribution is required for the response process and the distribution of the mismeasured covariates is left unspecified; and the implementation is straightforward. To justify the proposed methods, we provide both theoretical properties and numerical assessments. PMID:26877582

  8. Bias resulting from missing information: some epidemiological findings.

    PubMed Central

    Cox, A; Rutter, M; Yule, B; Quinton, D

    1977-01-01

    The biases resulting from missing information were examined in three psychiatric epidemiological studies. In each study, cases with missing information could be compared with the main sample because data were available from several sources or at several points in time through a longitudinal study. In almost all instances, cases with missing data differed systematically in terms of variables crucial to the questions being studied. In general, they tended to include a higher proportion with problems of various kinds--such as, behavioural deviance, reading backwardness, child or adult psychiatric disorder, and marital discord. The characteristics or circumstances of those giving information were generally more strongly associated with co-operation in testing or interviewing than the characteristics of those about whom information was sought. In some situations, the nature and degree of distortion resulting from missing information could lead to biased results. PMID:884397

  9. MISSE in the Materials and Processes Technical Information System (MAPTIS )

    NASA Technical Reports Server (NTRS)

    Burns, DeWitt; Finckenor, Miria; Henrie, Ben

    2013-01-01

    Materials International Space Station Experiment (MISSE) data is now being collected and distributed through the Materials and Processes Technical Information System (MAPTIS) at Marshall Space Flight Center in Huntsville, Alabama. MISSE data has been instrumental in many programs and continues to be an important source of data for the space community. To facilitate great access to the MISSE data the International Space Station (ISS) program office and MAPTIS are working to gather this data into a central location. The MISSE database contains information about materials, samples, and flights along with pictures, pdfs, excel files, word documents, and other files types. Major capabilities of the system are: access control, browsing, searching, reports, and record comparison. The search capabilities will search within any searchable files so even if the desired meta-data has not been associated data can still be retrieved. Other functionality will continue to be added to the MISSE database as the Athena Platform is expanded

  10. Multiple imputation of missing covariates for the Cox proportional hazards cure model.

    PubMed

    Beesley, Lauren J; Bartlett, Jonathan W; Wolf, Gregory T; Taylor, Jeremy M G

    2016-11-20

    We explore several approaches for imputing partially observed covariates when the outcome of interest is a censored event time and when there is an underlying subset of the population that will never experience the event of interest. We call these subjects 'cured', and we consider the case where the data are modeled using a Cox proportional hazards (CPH) mixture cure model. We study covariate imputation approaches using fully conditional specification. We derive the exact conditional distribution and suggest a sampling scheme for imputing partially observed covariates in the CPH cure model setting. We also propose several approximations to the exact distribution that are simpler and more convenient to use for imputation. A simulation study demonstrates that the proposed imputation approaches outperform existing imputation approaches for survival data without a cure fraction in terms of bias in estimating CPH cure model parameters. We apply our multiple imputation techniques to a study of patients with head and neck cancer. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

  11. Information Gaps: The Missing Links to Learning.

    ERIC Educational Resources Information Center

    Adams, Carl R.

    Communication takes place when a speaker conveys new information to the listener. In second language teaching, information gaps motivate students to use and learn the target language in order to obtain information. The resulting interactive language use may develop affective bonds among the students. A variety of classroom techniques are available…

  12. Urban eddy covariance measurements reveal significant missing NOx emissions in Central Europe.

    PubMed

    Karl, T; Graus, M; Striednig, M; Lamprecht, C; Hammerle, A; Wohlfahrt, G; Held, A; von der Heyden, L; Deventer, M J; Krismer, A; Haun, C; Feichter, R; Lee, J

    2017-05-30

    Nitrogen oxide (NOx) pollution is emerging as a primary environmental concern across Europe. While some large European metropolitan areas are already in breach of EU safety limits for NO2, this phenomenon does not seem to be only restricted to large industrialized areas anymore. Many smaller scale populated agglomerations including their surrounding rural areas are seeing frequent NO2 concentration violations. The question of a quantitative understanding of different NOx emission sources is therefore of immanent relevance for climate and air chemistry models as well as air pollution management and health. Here we report simultaneous eddy covariance flux measurements of NOx, CO2, CO and non methane volatile organic compound tracers in a city that might be considered representative for Central Europe and the greater Alpine region. Our data show that NOx fluxes are largely at variance with modelled emission projections, suggesting an appreciable underestimation of the traffic related atmospheric NOx input in Europe, comparable to the weekend-weekday effect, which locally changes ozone production rates by 40%.

  13. On Obtaining Estimates of the Fraction of Missing Information from Full Information Maximum Likelihood

    ERIC Educational Resources Information Center

    Savalei, Victoria; Rhemtulla, Mijke

    2012-01-01

    Fraction of missing information [lambda][subscript j] is a useful measure of the impact of missing data on the quality of estimation of a particular parameter. This measure can be computed for all parameters in the model, and it communicates the relative loss of efficiency in the estimation of a particular parameter due to missing data. It has…

  14. On Obtaining Estimates of the Fraction of Missing Information from Full Information Maximum Likelihood

    ERIC Educational Resources Information Center

    Savalei, Victoria; Rhemtulla, Mijke

    2012-01-01

    Fraction of missing information [lambda][subscript j] is a useful measure of the impact of missing data on the quality of estimation of a particular parameter. This measure can be computed for all parameters in the model, and it communicates the relative loss of efficiency in the estimation of a particular parameter due to missing data. It has…

  15. Characterization of informational completeness for covariant phase space observables

    NASA Astrophysics Data System (ADS)

    Kiukas, J.; Lahti, P.; Schultz, J.; Werner, R. F.

    2012-10-01

    In the nonrelativistic setting with finitely many canonical degrees of freedom, a shift-covariant phase space observable is uniquely characterized by a positive operator of trace one and, in turn, by the Fourier-Weyl transform of this operator. We study three properties of such observables, and characterize them in terms of the zero set of this transform. The first is informational completeness, for which it is necessary and sufficient that the zero set has dense complement. The second is a version of informational completeness for the Hilbert-Schmidt class, equivalent to the zero set being of measure zero, and the third, known as regularity, is equivalent to the zero set being empty. We give examples demonstrating that all three conditions are distinct. The three conditions are the special cases for p = 1, 2, ∞ of a more general notion of p-regularity defined as the norm density of the span of translates of the operator in the Schatten-p class. We show that the relation between zero sets and p-regularity can be mapped completely to the corresponding relation for functions in classical harmonic analysis.

  16. A Simple Approach to Inference in Covariance Structure Modeling with Missing Data: Bayesian Analysis. Project 2.4, Quantitative Models To Monitor the Status and Progress of Learning and Performance and Their Antecedents.

    ERIC Educational Resources Information Center

    Muthen, Bengt

    This paper investigates methods that avoid using multiple groups to represent the missing data patterns in covariance structure modeling, attempting instead to do a single-group analysis where the only action the analyst has to take is to indicate that data is missing. A new covariance structure approach developed by B. Muthen and G. Arminger is…

  17. Missing Data Imputation versus Full Information Maximum Likelihood with Second-Level Dependencies

    ERIC Educational Resources Information Center

    Larsen, Ross

    2011-01-01

    Missing data in the presence of upper level dependencies in multilevel models have never been thoroughly examined. Whereas first-level subjects are independent over time, the second-level subjects might exhibit nonzero covariances over time. This study compares 2 missing data techniques in the presence of a second-level dependency: multiple…

  18. Missing Data Imputation versus Full Information Maximum Likelihood with Second-Level Dependencies

    ERIC Educational Resources Information Center

    Larsen, Ross

    2011-01-01

    Missing data in the presence of upper level dependencies in multilevel models have never been thoroughly examined. Whereas first-level subjects are independent over time, the second-level subjects might exhibit nonzero covariances over time. This study compares 2 missing data techniques in the presence of a second-level dependency: multiple…

  19. 38 CFR 1.705 - Restrictions on use of missing children information.

    Code of Federal Regulations, 2013 CFR

    2013-07-01

    ... missing children information. 1.705 Section 1.705 Pensions, Bonuses, and Veterans' Relief DEPARTMENT OF VETERANS AFFAIRS GENERAL PROVISIONS Use of Official Mail in the Location and Recovery of Missing Children § 1.705 Restrictions on use of missing children information. Missing children pictures...

  20. Measuring adequacy of prenatal care: does missing visit information matter?

    PubMed

    Kurtzman, Jordan H; Wasserman, Erin B; Suter, Barbara J; Glantz, J Christopher; Dozier, Ann M

    2014-09-01

    Kotelchuck's Adequacy of Prenatal Care Utilization (APNCU) Index is frequently used to classify levels of prenatal care. In the Finger Lakes Region (FLR) of upstate New York, prenatal care visit information late in pregnancy is often not documented on the birth certificate. We studied the extent of this missing information and its impact on the validity of regional APNCU scores. We calculated the "weeks between" a mother's last prenatal care visit and her infant's date of birth. We adjusted the APNCU algorithm creating the Last Visit Adequacy of Prenatal Care (LV-APNC) Index using the last recorded prenatal care visit date as the end point of care and the expected number of visits at that time. We compared maternal characteristics by care level with each index, examining rates of reclassification and number of "weeks between" by birth hospital. Stuart-Maxwell, McNemar, chi-square, and t-tests were used to determine statistical significance. Based on 58,462 births, the mean "weeks between" was 2.8 weeks. Compared with their APNCU Index score, 42.4 percent of mothers were reclassified using the LV-APNC Index. Major movement occurred from Intermediate (APNCU) to Adequate or Adequate Plus (LV-APNC) leaving the Intermediate Care group a more at-risk group of mothers. Those with Adequate or Adequate Plus Care (LV-APNC) increased by 31.6 percent, surpassing the Healthy People 2020 objective. In the FLR, missing visit information at the end of pregnancy results in an underestimation of mothers' prenatal care. Future research is needed to determine the extent of this missing visit information on the national level. © 2014 Wiley Periodicals, Inc.

  1. An ICU Preanesthesia Evaluation Form Reduces Missing Preoperative Key Information

    PubMed Central

    Chuy, Katherine; Yan, Zhe; Fleisher, Lee; Liu, Renyu

    2013-01-01

    Background A comprehensive preoperative evaluation is critical for providing anesthetic care for patients from the intensive care unit (ICU). There has been no preoperative evaluation form specific for ICU patients that allows for a rapid and focused evaluation by anesthesia providers, including junior residents. In this study, a specific preoperative form was designed for ICU patients and evaluated to allow residents to perform the most relevant and important preoperative evaluations efficiently. Methods The following steps were utilized for developing the preoperative evaluation form: 1) designed a new preoperative form specific for ICU patients; 2) had the form reviewed by attending physicians and residents, followed by multiple revisions; 3) conducted test releases and revisions; 4) released the final version and conducted a survey; 5) compared data collection from new ICU form with that from a previously used generic form. Each piece of information on the forms was assigned a score, and the score for the total missing information was determined. The score for each form was presented as mean ± standard deviation (SD), and compared by unpaired t test. A P value < 0.05 was considered statistically significant. Results Of 52 anesthesiologists (19 attending physicians, 33 residents) responding to the survey, 90% preferred the final new form; and 56% thought the new form would reduce perioperative risk for ICU patients. Forty percent were unsure whether the form would reduce perioperative risk. Over a three month period, we randomly collected 32 generic forms and 25 new forms. The average score for missing data was 23 ± 10 for the generic form and 8 ± 4 for the new form (P = 2.58E-11). Conclusions A preoperative evaluation form designed specifically for ICU patients is well accepted by anesthesia providers and helped to reduce missing key preoperative information. Such an approach is important for perioperative patient safety. PMID:23853741

  2. Informed conditioning on clinical covariates increases power in case-control association studies.

    PubMed

    Zaitlen, Noah; Lindström, Sara; Pasaniuc, Bogdan; Cornelis, Marilyn; Genovese, Giulio; Pollack, Samuela; Barton, Anne; Bickeböller, Heike; Bowden, Donald W; Eyre, Steve; Freedman, Barry I; Friedman, David J; Field, John K; Groop, Leif; Haugen, Aage; Heinrich, Joachim; Henderson, Brian E; Hicks, Pamela J; Hocking, Lynne J; Kolonel, Laurence N; Landi, Maria Teresa; Langefeld, Carl D; Le Marchand, Loic; Meister, Michael; Morgan, Ann W; Raji, Olaide Y; Risch, Angela; Rosenberger, Albert; Scherf, David; Steer, Sophia; Walshaw, Martin; Waters, Kevin M; Wilson, Anthony G; Wordsworth, Paul; Zienolddiny, Shanbeh; Tchetgen, Eric Tchetgen; Haiman, Christopher; Hunter, David J; Plenge, Robert M; Worthington, Jane; Christiani, David C; Schaumberg, Debra A; Chasman, Daniel I; Altshuler, David; Voight, Benjamin; Kraft, Peter; Patterson, Nick; Price, Alkes L

    2012-01-01

    Genetic case-control association studies often include data on clinical covariates, such as body mass index (BMI), smoking status, or age, that may modify the underlying genetic risk of case or control samples. For example, in type 2 diabetes, odds ratios for established variants estimated from low-BMI cases are larger than those estimated from high-BMI cases. An unanswered question is how to use this information to maximize statistical power in case-control studies that ascertain individuals on the basis of phenotype (case-control ascertainment) or phenotype and clinical covariates (case-control-covariate ascertainment). While current approaches improve power in studies with random ascertainment, they often lose power under case-control ascertainment and fail to capture available power increases under case-control-covariate ascertainment. We show that an informed conditioning approach, based on the liability threshold model with parameters informed by external epidemiological information, fully accounts for disease prevalence and non-random ascertainment of phenotype as well as covariates and provides a substantial increase in power while maintaining a properly controlled false-positive rate. Our method outperforms standard case-control association tests with or without covariates, tests of gene x covariate interaction, and previously proposed tests for dealing with covariates in ascertained data, with especially large improvements in the case of case-control-covariate ascertainment. We investigate empirical case-control studies of type 2 diabetes, prostate cancer, lung cancer, breast cancer, rheumatoid arthritis, age-related macular degeneration, and end-stage kidney disease over a total of 89,726 samples. In these datasets, informed conditioning outperforms logistic regression for 115 of the 157 known associated variants investigated (P-value = 1 × 10(-9)). The improvement varied across diseases with a 16% median increase in χ(2) test statistics and a

  3. Evaluation of covariance and information performance measures for dynamic object tracking

    NASA Astrophysics Data System (ADS)

    Yang, Chun; Blasch, Erik; Douville, Phil; Kaplan, Lance; Qiu, Di

    2010-04-01

    In surveillance and reconnaissance applications, dynamic objects are dynamically followed by track filters with sequential measurements. There are two popular implementations of tracking filters: one is the covariance or Kalman filter and the other is the information filter. Evaluation of tracking filters is important in performance optimization not only for tracking filter design but also for resource management. Typically, the information matrix is the inverse of the covariance matrix. The covariance filter-based approaches attempt to minimize the covariance matrix-based scalar indexes whereas the information filter-based methods aim at maximizing the information matrix-based scalar indexes. Such scalar performance measures include the trace, determinant, norms (1-norm, 2-norm, infinite-norm, and Forbenius norm), and eigenstructure of the covariance matrix or the information matrix and their variants. One natural question to ask is if the scalar track filter performance measures applied to the covariance matrix are equivalent to those applied to the information matrix? In this paper we show most of the scalar performance indexes are equivalent yet some are not. As a result, the indexes if used improperly would provide an "optimized" solution but in the wrong sense relative to track accuracy. The simulation indicated that all the seven indexes were successful when applied to the covariance matrix. However, the failed indexes for the information filter include the trace and the four norms (as defined in MATLAB) of the information matrix. Nevertheless, the determinant and the properly selected eigenvalue of the information matrix were successful to select the optimal sensor update configuration. The evaluation analysis of track measures can serve as a guideline to determine the suitability of performance measures for tracking filter design and resource management.

  4. Estimating Missing Features to Improve Multimedia Information Retrieval

    SciTech Connect

    Bagherjeiran, A; Love, N S; Kamath, C

    2006-09-28

    Retrieval in a multimedia database usually involves combining information from different modalities of data, such as text and images. However, all modalities of the data may not be available to form the query. The retrieval results from such a partial query are often less than satisfactory. In this paper, we present an approach to complete a partial query by estimating the missing features in the query. Our experiments with a database of images and their associated captions show that, with an initial text-only query, our completion method has similar performance to a full query with both image and text features. In addition, when we use relevance feedback, our approach outperforms the results obtained using a full query.

  5. Training Neural Networks to See Beyond Missing Information

    NASA Astrophysics Data System (ADS)

    Howard, M. E.; Schradin, L. J.; Cizewski, J. A.

    2012-10-01

    While the human eye may easily see a distorted image and imagine the original image, a rigorous mathematical treatment of the reconstruction may turn out to be a programming nightmare. We present a case study of nuclear physics data for which a significant population of events from a microchannel plate (MCP) detector are missing information for one of four MCP corners. Using events with good data for all four MCP corners to train a neural network, events with only three good corners are treated on equal footing in the analysis of position measurements, recovering much needed statistics. As this neural network is available within the framework of standard physics analysis packages such as ROOT and PAW, implementation is quite straightforward. We conclude with a discussion of the obvious advantages and limitations of this method as compared with an analytic approach. Work supported in part by the National Science Foundation and the Department of Energy.

  6. Electronic pharmacopoeia: a missed opportunity for safe opioid prescribing information?

    PubMed

    Lapoint, Jeff; Perrone, Jeanmarie; Nelson, Lewis S

    2014-03-01

    Errors in prescribing of dangerous medications, such as extended release or long acting (ER/LA) opioid forlmulations, remain an important cause of patient harm. Prescribing errors often relate to the failure to note warnings regarding contraindications and drug interactions. Many prescribers utilize electronic pharmacopoeia (EP) to improve medication ordering. The purpose of this study is to assess the ability of commonly used apps to provide accurate safety information about the boxed warning for ER/LA opioids. We evaluated a convenience sample of six popular EP apps available for the iPhone and an online reference for the presence of relevant safety warnings. We accessed the dosing information for each of six ER/LA medications and assessed for the presence of an easily identifiable indication that a boxed warning was present, even if the warning itself was not provided. The prominence of precautionary drug information presented to the user was assessed for each app. Provided information was classified based on the presence of the warning in the ordering pathway, located separately but within the prescribers view, or available in a separate screen of the drug information but non-highlighted. Each program provided a consistent level of warning information for each of the six ER/LA medications. Only 2/7 programs placed a warning in line with dosing information (level 1); 3/7 programs offered level 2 warning and 1/7 offered level 3 warning. One program made no mention of a boxed warning. Most EP apps isolate important safety warnings, and this represents a missed opportunity to improve prescribing practices.

  7. Addressing Item-Level Missing Data: A Comparison of Proration and Full Information Maximum Likelihood Estimation

    PubMed Central

    Mazza, Gina L.; Enders, Craig K.; Ruehlman, Linda S.

    2015-01-01

    Often when participants have missing scores on one or more of the items comprising a scale, researchers compute prorated scale scores by averaging the available items. Methodologists have cautioned that proration may make strict assumptions about the mean and covariance structures of the items comprising the scale (Schafer & Graham, 2002; Graham, 2009; Enders, 2010). We investigated proration empirically and found that it resulted in bias even under a missing completely at random (MCAR) mechanism. To encourage researchers to forgo proration, we describe an FIML approach to item-level missing data handling that mitigates the loss in power due to missing scale scores and utilizes the available item-level data without altering the substantive analysis. Specifically, we propose treating the scale score as missing whenever one or more of the items are missing and incorporating items as auxiliary variables. Our simulations suggest that item-level missing data handling drastically increases power relative to scale-level missing data handling. These results have important practical implications, especially when recruiting more participants is prohibitively difficult or expensive. Finally, we illustrate the proposed method with data from an online chronic pain management program. PMID:26610249

  8. Informed Conditioning on Clinical Covariates Increases Power in Case-Control Association Studies

    PubMed Central

    Zaitlen, Noah; Lindström, Sara; Pasaniuc, Bogdan; Cornelis, Marilyn; Genovese, Giulio; Pollack, Samuela; Barton, Anne; Bickeböller, Heike; Bowden, Donald W.; Eyre, Steve; Freedman, Barry I.; Friedman, David J.; Field, John K.; Groop, Leif; Haugen, Aage; Heinrich, Joachim; Henderson, Brian E.; Hicks, Pamela J.; Hocking, Lynne J.; Kolonel, Laurence N.; Landi, Maria Teresa; Langefeld, Carl D.; Le Marchand, Loic; Meister, Michael; Morgan, Ann W.; Raji, Olaide Y.; Risch, Angela; Rosenberger, Albert; Scherf, David; Steer, Sophia; Walshaw, Martin; Waters, Kevin M.; Wilson, Anthony G.; Wordsworth, Paul; Zienolddiny, Shanbeh; Tchetgen, Eric Tchetgen; Haiman, Christopher; Hunter, David J.; Plenge, Robert M.; Worthington, Jane; Christiani, David C.; Schaumberg, Debra A.; Chasman, Daniel I.; Altshuler, David; Voight, Benjamin; Kraft, Peter; Patterson, Nick; Price, Alkes L.

    2012-01-01

    Genetic case-control association studies often include data on clinical covariates, such as body mass index (BMI), smoking status, or age, that may modify the underlying genetic risk of case or control samples. For example, in type 2 diabetes, odds ratios for established variants estimated from low–BMI cases are larger than those estimated from high–BMI cases. An unanswered question is how to use this information to maximize statistical power in case-control studies that ascertain individuals on the basis of phenotype (case-control ascertainment) or phenotype and clinical covariates (case-control-covariate ascertainment). While current approaches improve power in studies with random ascertainment, they often lose power under case-control ascertainment and fail to capture available power increases under case-control-covariate ascertainment. We show that an informed conditioning approach, based on the liability threshold model with parameters informed by external epidemiological information, fully accounts for disease prevalence and non-random ascertainment of phenotype as well as covariates and provides a substantial increase in power while maintaining a properly controlled false-positive rate. Our method outperforms standard case-control association tests with or without covariates, tests of gene x covariate interaction, and previously proposed tests for dealing with covariates in ascertained data, with especially large improvements in the case of case-control-covariate ascertainment. We investigate empirical case-control studies of type 2 diabetes, prostate cancer, lung cancer, breast cancer, rheumatoid arthritis, age-related macular degeneration, and end-stage kidney disease over a total of 89,726 samples. In these datasets, informed conditioning outperforms logistic regression for 115 of the 157 known associated variants investigated (P-value = 1×10−9). The improvement varied across diseases with a 16% median increase in χ2 test statistics and a

  9. Quantifying lost information due to covariance matrix estimation in parameter inference

    NASA Astrophysics Data System (ADS)

    Sellentin, Elena; Heavens, Alan F.

    2017-02-01

    Parameter inference with an estimated covariance matrix systematically loses information due to the remaining uncertainty of the covariance matrix. Here, we quantify this loss of precision and develop a framework to hypothetically restore it, which allows to judge how far away a given analysis is from the ideal case of a known covariance matrix. We point out that it is insufficient to estimate this loss by debiasing the Fisher matrix as previously done, due to a fundamental inequality that describes how biases arise in non-linear functions. We therefore develop direct estimators for parameter credibility contours and the figure of merit, finding that significantly fewer simulations than previously thought are sufficient to reach satisfactory precisions. We apply our results to DES Science Verification weak lensing data, detecting a 10 per cent loss of information that increases their credibility contours. No significant loss of information is found for KiDS. For a Euclid-like survey, with about 10 nuisance parameters we find that 2900 simulations are sufficient to limit the systematically lost information to 1 per cent, with an additional uncertainty of about 2 per cent. Without any nuisance parameters, 1900 simulations are sufficient to only lose 1 per cent of information. We further derive estimators for all quantities needed for forecasting with estimated covariance matrices. Our formalism allows to determine the sweetspot between running sophisticated simulations to reduce the number of nuisance parameters, and running as many fast simulations as possible.

  10. The Role of Mechanism and Covariation Information in Causal Belief Updating

    ERIC Educational Resources Information Center

    Perales, Jose C.; Catena, Andres; Maldonado, Antonio; Candido, Antonio

    2007-01-01

    The present study is aimed at identifying how prior causal beliefs and covariation information contribute to belief updating when evidence, either compatible or contradictory with those beliefs, is provided. Participants were presented with a cover story with which it was intended to activate or generate a causal belief. Variables related to the…

  11. 38 CFR 1.705 - Restrictions on use of missing children information.

    Code of Federal Regulations, 2014 CFR

    2014-07-01

    ... 38 Pensions, Bonuses, and Veterans' Relief 1 2014-07-01 2014-07-01 false Restrictions on use of missing children information. 1.705 Section 1.705 Pensions, Bonuses, and Veterans' Relief DEPARTMENT OF VETERANS AFFAIRS GENERAL PROVISIONS Use of Official Mail in the Location and Recovery of Missing...

  12. 38 CFR 1.705 - Restrictions on use of missing children information.

    Code of Federal Regulations, 2010 CFR

    2010-07-01

    ... 38 Pensions, Bonuses, and Veterans' Relief 1 2010-07-01 2010-07-01 false Restrictions on use of missing children information. 1.705 Section 1.705 Pensions, Bonuses, and Veterans' Relief DEPARTMENT OF VETERANS AFFAIRS GENERAL PROVISIONS Use of Official Mail in the Location and Recovery of Missing...

  13. 38 CFR 1.705 - Restrictions on use of missing children information.

    Code of Federal Regulations, 2011 CFR

    2011-07-01

    ... 38 Pensions, Bonuses, and Veterans' Relief 1 2011-07-01 2011-07-01 false Restrictions on use of missing children information. 1.705 Section 1.705 Pensions, Bonuses, and Veterans' Relief DEPARTMENT OF VETERANS AFFAIRS GENERAL PROVISIONS Use of Official Mail in the Location and Recovery of Missing...

  14. 38 CFR 1.705 - Restrictions on use of missing children information.

    Code of Federal Regulations, 2012 CFR

    2012-07-01

    ... 38 Pensions, Bonuses, and Veterans' Relief 1 2012-07-01 2012-07-01 false Restrictions on use of missing children information. 1.705 Section 1.705 Pensions, Bonuses, and Veterans' Relief DEPARTMENT OF VETERANS AFFAIRS GENERAL PROVISIONS Use of Official Mail in the Location and Recovery of Missing...

  15. Operation Reliability Assessment for Cutting Tools by Applying a Proportional Covariate Model to Condition Monitoring Information

    PubMed Central

    Cai, Gaigai; Chen, Xuefeng; Li, Bing; Chen, Baojia; He, Zhengjia

    2012-01-01

    The reliability of cutting tools is critical to machining precision and production efficiency. The conventional statistic-based reliability assessment method aims at providing a general and overall estimation of reliability for a large population of identical units under given and fixed conditions. However, it has limited effectiveness in depicting the operational characteristics of a cutting tool. To overcome this limitation, this paper proposes an approach to assess the operation reliability of cutting tools. A proportional covariate model is introduced to construct the relationship between operation reliability and condition monitoring information. The wavelet packet transform and an improved distance evaluation technique are used to extract sensitive features from vibration signals, and a covariate function is constructed based on the proportional covariate model. Ultimately, the failure rate function of the cutting tool being assessed is calculated using the baseline covariate function obtained from a small sample of historical data. Experimental results and a comparative study show that the proposed method is effective for assessing the operation reliability of cutting tools. PMID:23201980

  16. Operation reliability assessment for cutting tools by applying a proportional covariate model to condition monitoring information.

    PubMed

    Cai, Gaigai; Chen, Xuefeng; Li, Bing; Chen, Baojia; He, Zhengjia

    2012-09-25

    The reliability of cutting tools is critical to machining precision and production efficiency. The conventional statistic-based reliability assessment method aims at providing a general and overall estimation of reliability for a large population of identical units under given and fixed conditions. However, it has limited effectiveness in depicting the operational characteristics of a cutting tool. To overcome this limitation, this paper proposes an approach to assess the operation reliability of cutting tools. A proportional covariate model is introduced to construct the relationship between operation reliability and condition monitoring information. The wavelet packet transform and an improved distance evaluation technique are used to extract sensitive features from vibration signals, and a covariate function is constructed based on the proportional covariate model. Ultimately, the failure rate function of the cutting tool being assessed is calculated using the baseline covariate function obtained from a small sample of historical data. Experimental results and a comparative study show that the proposed method is effective for assessing the operation reliability of cutting tools.

  17. Statistical inference for Hardy-Weinberg proportions in the presence of missing genotype information.

    PubMed

    Graffelman, Jan; Sánchez, Milagros; Cook, Samantha; Moreno, Victor

    2013-01-01

    In genetic association studies, tests for Hardy-Weinberg proportions are often employed as a quality control checking procedure. Missing genotypes are typically discarded prior to testing. In this paper we show that inference for Hardy-Weinberg proportions can be biased when missing values are discarded. We propose to use multiple imputation of missing values in order to improve inference for Hardy-Weinberg proportions. For imputation we employ a multinomial logit model that uses information from allele intensities and/or neighbouring markers. Analysis of an empirical data set of single nucleotide polymorphisms possibly related to colon cancer reveals that missing genotypes are not missing completely at random. Deviation from Hardy-Weinberg proportions is mostly due to a lack of heterozygotes. Inbreeding coefficients estimated by multiple imputation of the missings are typically lowered with respect to inbreeding coefficients estimated by discarding the missings. Accounting for missings by multiple imputation qualitatively changed the results of 10 to 17% of the statistical tests performed. Estimates of inbreeding coefficients obtained by multiple imputation showed high correlation with estimates obtained by single imputation using an external reference panel. Our conclusion is that imputation of missing data leads to improved statistical inference for Hardy-Weinberg proportions.

  18. Covariance Manipulation for Conjunction Assessment

    NASA Technical Reports Server (NTRS)

    Hejduk, M. D.

    2016-01-01

    The manipulation of space object covariances to try to provide additional or improved information to conjunction risk assessment is not an uncommon practice. Types of manipulation include fabricating a covariance when it is missing or unreliable to force the probability of collision (Pc) to a maximum value ('PcMax'), scaling a covariance to try to improve its realism or see the effect of covariance volatility on the calculated Pc, and constructing the equivalent of an epoch covariance at a convenient future point in the event ('covariance forecasting'). In bringing these methods to bear for Conjunction Assessment (CA) operations, however, some do not remain fully consistent with best practices for conducting risk management, some seem to be of relatively low utility, and some require additional information before they can contribute fully to risk analysis. This study describes some basic principles of modern risk management (following the Kaplan construct) and then examines the PcMax and covariance forecasting paradigms for alignment with these principles; it then further examines the expected utility of these methods in the modern CA framework. Both paradigms are found to be not without utility, but only in situations that are somewhat carefully circumscribed.

  19. 78 FR 55123 - Submission for Review: We Need Information About Your Missing Payment, RI 38-31

    Federal Register 2010, 2011, 2012, 2013, 2014

    2013-09-09

    ... MANAGEMENT Submission for Review: We Need Information About Your Missing Payment, RI 38-31 AGENCY: U.S... (ICR) 3206-0187, We Need Information About Your Missing Payment, RI 38-31. As required by the Paperwork... Services, Office of Personnel Management. Title: We Need Information About Your Missing Payment. OMB:...

  20. Informing the Design of a New Pragmatic Registry to Stimulate Near Miss Reporting in Ambulatory Care.

    PubMed

    Pfoh, Elizabeth R; Engineer, Lilly; Singh, Hardeep; Hall, Laura Lee; Fried, Ethan D; Berger, Zackary; Wu, Albert W

    2017-02-28

    Ambulatory care safety is of emerging concern, especially in light of recent studies related to diagnostic errors and health information technology-related safety. Safety reporting systems in outpatient care must address the top safety concerns and be practical and simple to use. A registry that can identify common near misses in ambulatory care can be useful to facilitate safety improvements. We reviewed the literature on medical errors in the ambulatory setting to inform the design of a registry for collecting near miss incidents. This narrative review included articles from PubMed that were: 1) original research; 2) discussed near misses or adverse events in the ambulatory setting; 3) relevant to US health care; and 4) published between 2002 and 2013. After full text review, 38 studies were searched for information on near misses and associated factors. Additionally, we used expert opinion and current inpatient near miss registries to inform registry development. Studies included a variety of safety issues including diagnostic errors, treatment or management-related errors, communication errors, environmental/structural hazards, and health information technology (health IT)-related concerns. The registry, based on the results of the review, updates previous work by including specific sections for errors associated with diagnosis, communication, and environment structure and incorporates specific questions about the role of health information technology. Through use of this registry or future registries that incorporate newly identified categories, near misses in the ambulatory setting can be accurately captured, and that information can be used to improve patient safety.

  1. Information Literacy: The Missing Link in Early Childhood Education

    ERIC Educational Resources Information Center

    Heider, Kelly L.

    2009-01-01

    The rapid growth of information over the last 30 or 40 years has made it impossible for educators to prepare students for the future without teaching them how to be effective information managers. The American Library Association refers to those students who manage information effectively as "information literate." Information literacy instruction…

  2. Information Literacy: The Missing Link in Early Childhood Education

    ERIC Educational Resources Information Center

    Heider, Kelly L.

    2009-01-01

    The rapid growth of information over the last 30 or 40 years has made it impossible for educators to prepare students for the future without teaching them how to be effective information managers. The American Library Association refers to those students who manage information effectively as "information literate." Information literacy instruction…

  3. Sensitivity Analysis of Multiple Informant Models When Data are Not Missing at Random.

    PubMed

    Blozis, Shelley A; Ge, Xiaojia; Xu, Shu; Natsuaki, Misaki N; Shaw, Daniel S; Neiderhiser, Jenae; Scaramella, Laura; Leve, Leslie; Reiss, David

    2013-12-31

    Missing data are common in studies that rely on multiple informant data to evaluate relationships among variables for distinguishable individuals clustered within groups. Estimation of structural equation models using raw data allows for incomplete data, and so all groups may be retained even if only one member of a group contributes data. Statistical inference is based on the assumption that data are missing completely at random or missing at random. Importantly, whether or not data are missing is assumed to be independent of the missing data. A saturated correlates model that incorporates correlates of the missingness or the missing data into an analysis and multiple imputation that may also use such correlates offer advantages over the standard implementation of SEM when data are not missing at random because these approaches may result in a data analysis problem for which the missingness is ignorable. This paper considers these approaches in an analysis of family data to assess the sensitivity of parameter estimates to assumptions about missing data, a strategy that may be easily implemented using SEM software.

  4. Storage and computationally efficient permutations of factorized covariance and square-root information arrays

    NASA Technical Reports Server (NTRS)

    Muellerschoen, R. J.

    1988-01-01

    A unified method to permute vector stored Upper triangular Diagonal factorized covariance and vector stored upper triangular Square Root Information arrays is presented. The method involves cyclic permutation of the rows and columns of the arrays and retriangularization with fast (slow) Givens rotations (reflections). Minimal computation is performed, and a one dimensional scratch array is required. To make the method efficient for large arrays on a virtual memory machine, computations are arranged so as to avoid expensive paging faults. This method is potentially important for processing large volumes of radio metric data in the Deep Space Network.

  5. Exchanging Missing Information in Tasks: Old and New Interpretations

    ERIC Educational Resources Information Center

    Jenks, Christopher Joseph

    2009-01-01

    Information gap tasks have played a key role in applied linguistics (Pica, 2005). For example, extensive research has been conducted using information gap tasks to elicit second language data. Yet, despite their prominent role in research and pedagogy, there is still much to be investigated with regard to what information gap tasks offer research…

  6. Open Informational Ecosystems: The Missing Link for Sharing Educational Resources

    ERIC Educational Resources Information Center

    Kerres, Michael; Heinen, Richard

    2015-01-01

    Open educational resources are not available "as such". Their provision relies on a technological infrastructure of related services that can be described as an informational ecosystem. A closed informational ecosystem keeps educational resources within its boundary. An open informational ecosystem relies on the concurrence of…

  7. Fraction of Missing Information (γ) at Different Missing Data Fractions in the 2012 NAMCS Physician Workflow Mail Survey*

    PubMed Central

    Pan, Qiyuan; Wei, Rong

    2016-01-01

    In his 1987 classic book on multiple imputation (MI), Rubin used the fraction of missing information, γ, to define the relative efficiency (RE) of MI as RE = (1 + γ/m)−1/2, where m is the number of imputations, leading to the conclusion that a small m (≤5) would be sufficient for MI. However, evidence has been accumulating that many more imputations are needed. Why would the apparently sufficient m deduced from the RE be actually too small? The answer may lie with γ. In this research, γ was determined at the fractions of missing data (δ) of 4%, 10%, 20%, and 29% using the 2012 Physician Workflow Mail Survey of the National Ambulatory Medical Care Survey (NAMCS). The γ values were strikingly small, ranging in the order of 10−6 to 0.01. As δ increased, γ usually increased but sometimes decreased. How the data were analysed had the dominating effects on γ, overshadowing the effect of δ. The results suggest that it is impossible to predict γ using δ and that it may not be appropriate to use the γ-based RE to determine sufficient m. PMID:27398259

  8. Responsiveness-informed multiple imputation and inverse probability-weighting in cohort studies with missing data that are non-monotone or not missing at random.

    PubMed

    Doidge, James C

    2016-03-16

    Population-based cohort studies are invaluable to health research because of the breadth of data collection over time, and the representativeness of their samples. However, they are especially prone to missing data, which can compromise the validity of analyses when data are not missing at random. Having many waves of data collection presents opportunity for participants' responsiveness to be observed over time, which may be informative about missing data mechanisms and thus useful as an auxiliary variable. Modern approaches to handling missing data such as multiple imputation and maximum likelihood can be difficult to implement with the large numbers of auxiliary variables and large amounts of non-monotone missing data that occur in cohort studies. Inverse probability-weighting can be easier to implement but conventional wisdom has stated that it cannot be applied to non-monotone missing data. This paper describes two methods of applying inverse probability-weighting to non-monotone missing data, and explores the potential value of including measures of responsiveness in either inverse probability-weighting or multiple imputation. Simulation studies are used to compare methods and demonstrate that responsiveness in longitudinal studies can be used to mitigate bias induced by missing data, even when data are not missing at random. © The Author(s) 2016.

  9. The Missing Link: Evolving Accessibility To Formulary-Related Information

    PubMed Central

    Van Rossum, Alison; Holsopple, Megan; Karpinski, Julie; Dow, Jordan

    2016-01-01

    Background Formulary management is a key component to ensuring the safe, effective, and fiscally responsible use of medications for health systems. One challenge in the formulary management process is making the most relevant formulary information easily accessible to practitioners involved in medication therapy decisions at the point of care. In September 2014, Froedtert and the Medical College of Wisconsin (F&MCW) implemented a commercial formulary management tool (CFMT) to improve accessibility to the recently aligned health-system formulary. The CFMT replaced an internally developed formulary management tool. Objectives The primary objective was to determine pharmacist end-user satisfaction with accessibility to system formulary and formulary-related information through a new CMFT compared with the historical formulary management tool (HFMT). The secondary objective was to measure the use of formulary-related information in the CFMT and HFMT. Methods The primary objective was measured through pharmacist end-user satisfaction surveys before and after integration of formulary-related information into the CFMT. The secondary objective was measured by comparing monthly usage reports for the CFMT with monthly usage reports for the HFMT. Results Survey respondents reported being satisfied (52.5%) or very satisfied (18.8%) more frequently with the CFMT compared with the HFMT (31.7% satisfied and 2.5% very satisfied). Between October 2014 and January 2015 the frequency of access to formulary-related information increased from 92 to 104 requests per day through the CFMT and decreased from 47 to 33 requests per day through the HFMT. Conclusions Initial data suggest incorporating system formulary-related information and related resources into a single platform increases pharmacist end-user satisfaction and overall use of formulary-related information. PMID:27904302

  10. Model, properties and imputation method of missing SNP genotype data utilizing mutual information

    NASA Astrophysics Data System (ADS)

    Wang, Ying; Wan, Weiming; Wang, Rui-Sheng; Feng, Enmin

    2009-07-01

    Mutual information can be used as a measure for the association of a genetic marker or a combination of markers with the phenotype. In this paper, we study the imputation of missing genotype data. We first utilize joint mutual information to compute the dependence between SNP sites, then construct a mathematical model in order to find the two SNP sites having maximal dependence with missing SNP sites, and further study the properties of this model. Finally, an extension method to haplotype-based imputation is proposed to impute the missing values in genotype data. To verify our method, extensive experiments have been performed, and numerical results show that our method is superior to haplotype-based imputation methods. At the same time, numerical results also prove joint mutual information can better measure the dependence between SNP sites. According to experimental results, we also conclude that the dependence between the adjacent SNP sites is not necessarily strongest.

  11. Questions left unanswered: how the brain responds to missing information.

    PubMed

    Hoeks, John C J; Stowe, Laurie A; Hendriks, Petra; Brouwer, Harm

    2013-01-01

    It sometimes happens that when someone asks a question, the addressee does not give an adequate answer, for instance by leaving out part of the required information. The person who posed the question may wonder why the information was omitted, and engage in extensive processing to find out what the partial answer actually means. The present study looks at the neural correlates of the pragmatic processes invoked by partial answers to questions. Two experiments are presented in which participants read mini-dialogues while their Event-Related brain Potentials (ERPs) are being measured. In both experiments, violating the dependency between questions and answers was found to lead to an increase in the amplitude of the P600 component. We interpret these P600-effects as reflecting the increased effort in creating a coherent representation of what is communicated. This effortful processing might include the computation of what the dialogue participant meant to communicate by withholding information. Our study is one of few investigating language processing in conversation, be it that our participants were 'eavesdroppers' instead of real interactants. Our results contribute to the as of yet small range of pragmatic phenomena that modulate the processes underlying the P600 component, and suggest that people immediately attempt to regain cohesion if a question-answer dependency is violated in an ongoing conversation.

  12. Questions Left Unanswered: How the Brain Responds to Missing Information

    PubMed Central

    Hoeks, John C. J.; Stowe, Laurie A.; Hendriks, Petra; Brouwer, Harm

    2013-01-01

    It sometimes happens that when someone asks a question, the addressee does not give an adequate answer, for instance by leaving out part of the required information. The person who posed the question may wonder why the information was omitted, and engage in extensive processing to find out what the partial answer actually means. The present study looks at the neural correlates of the pragmatic processes invoked by partial answers to questions. Two experiments are presented in which participants read mini-dialogues while their Event-Related brain Potentials (ERPs) are being measured. In both experiments, violating the dependency between questions and answers was found to lead to an increase in the amplitude of the P600 component. We interpret these P600-effects as reflecting the increased effort in creating a coherent representation of what is communicated. This effortful processing might include the computation of what the dialogue participant meant to communicate by withholding information. Our study is one of few investigating language processing in conversation, be it that our participants were ‘eavesdroppers’ instead of real interactants. Our results contribute to the as of yet small range of pragmatic phenomena that modulate the processes underlying the P600 component, and suggest that people immediately attempt to regain cohesion if a question-answer dependency is violated in an ongoing conversation. PMID:24098327

  13. Storage and computationally efficient permutations of factorized covariance and square-root information matrices

    NASA Technical Reports Server (NTRS)

    Muellerschoen, R. J.

    1988-01-01

    A unified method to permute vector-stored upper-triangular diagonal factorized covariance (UD) and vector stored upper-triangular square-root information filter (SRIF) arrays is presented. The method involves cyclical permutation of the rows and columns of the arrays and retriangularization with appropriate square-root-free fast Givens rotations or elementary slow Givens reflections. A minimal amount of computation is performed and only one scratch vector of size N is required, where N is the column dimension of the arrays. To make the method efficient for large SRIF arrays on a virtual memory machine, three additional scratch vectors each of size N are used to avoid expensive paging faults. The method discussed is compared with the methods and routines of Bierman's Estimation Subroutine Library (ESL).

  14. Analyzing semi-competing risks data with missing cause of informative terminal event.

    PubMed

    Zhou, Renke; Zhu, Hong; Bondy, Melissa; Ning, Jing

    2017-02-28

    Cancer studies frequently yield multiple event times that correspond to landmarks in disease progression, including non-terminal events (i.e., cancer recurrence) and an informative terminal event (i.e., cancer-related death). Hence, we often observe semi-competing risks data. Work on such data has focused on scenarios in which the cause of the terminal event is known. However, in some circumstances, the information on cause for patients who experience the terminal event is missing; consequently, we are not able to differentiate an informative terminal event from a non-informative terminal event. In this article, we propose a method to handle missing data regarding the cause of an informative terminal event when analyzing the semi-competing risks data. We first consider the nonparametric estimation of the survival function for the terminal event time given missing cause-of-failure data via the expectation-maximization algorithm. We then develop an estimation method for semi-competing risks data with missing cause of the terminal event, under a pre-specified semiparametric copula model. We conduct simulation studies to investigate the performance of the proposed method. We illustrate our methodology using data from a study of early-stage breast cancer. Copyright © 2016 John Wiley & Sons, Ltd.

  15. Handling Missing Data With Multilevel Structural Equation Modeling and Full Information Maximum Likelihood Techniques.

    PubMed

    Schminkey, Donna L; von Oertzen, Timo; Bullock, Linda

    2016-08-01

    With increasing access to population-based data and electronic health records for secondary analysis, missing data are common. In the social and behavioral sciences, missing data frequently are handled with multiple imputation methods or full information maximum likelihood (FIML) techniques, but healthcare researchers have not embraced these methodologies to the same extent and more often use either traditional imputation techniques or complete case analysis, which can compromise power and introduce unintended bias. This article is a review of options for handling missing data, concluding with a case study demonstrating the utility of multilevel structural equation modeling using full information maximum likelihood (MSEM with FIML) to handle large amounts of missing data. MSEM with FIML is a parsimonious and hypothesis-driven strategy to cope with large amounts of missing data without compromising power or introducing bias. This technique is relevant for nurse researchers faced with ever-increasing amounts of electronic data and decreasing research budgets. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

  16. Analyzing disease recurrence with missing at risk information.

    PubMed

    Štupnik, Tomaž; Pohar Perme, Maja

    2016-03-30

    When analyzing time to disease recurrence, we sometimes need to work with data where all the recurrences are recorded, but no information is available on the possible deaths. This may occur when studying diseases of benign nature where patients are only seen at disease recurrences or in poorly-designed registries of benign diseases or medical device implantations without sufficient patient identifiers to obtain their dead/alive status at a later date. When the average time to disease recurrence is long enough in comparison with the expected survival of the patients, statistical analysis of such data can be significantly biased. Under the assumption that the expected survival of an individual is not influenced by the disease itself, general population mortality tables may be used to remove this bias. We show why the intuitive solution of simply imputing the patient's expected survival time does not give unbiased estimates of the usual quantities of interest in survival analysis and further explain that cumulative incidence function analysis does not require additional assumptions on general population mortality. We provide an alternative framework that allows unbiased estimation and introduce two new approaches: an iterative imputation method and a mortality adjusted at risk function. Their properties are carefully studied, with the results supported by simulations and illustrated on a real-world example.

  17. The Relative Performance of Full Information Maximum Likelihood Estimation for Missing Data in Structural Equation Models.

    ERIC Educational Resources Information Center

    Enders, Craig K.; Bandalos, Deborah L.

    2001-01-01

    Used Monte Carlo simulation to examine the performance of four missing data methods in structural equation models: (1)full information maximum likelihood (FIML); (2) listwise deletion; (3) pairwise deletion; and (4) similar response pattern imputation. Results show that FIML estimation is superior across all conditions of the design. (SLD)

  18. The Performance of the Full Information Maximum Likelihood Estimator in Multiple Regression Models with Missing Data.

    ERIC Educational Resources Information Center

    Enders, Craig K.

    2001-01-01

    Examined the performance of a recently available full information maximum likelihood (FIML) estimator in a multiple regression model with missing data using Monte Carlo simulation and considering the effects of four independent variables. Results indicate that FIML estimation was superior to that of three ad hoc techniques, with less bias and less…

  19. Sensitivity Analysis of Multiple Informant Models When Data Are Not Missing at Random

    ERIC Educational Resources Information Center

    Blozis, Shelley A.; Ge, Xiaojia; Xu, Shu; Natsuaki, Misaki N.; Shaw, Daniel S.; Neiderhiser, Jenae M.; Scaramella, Laura V.; Leve, Leslie D.; Reiss, David

    2013-01-01

    Missing data are common in studies that rely on multiple informant data to evaluate relationships among variables for distinguishable individuals clustered within groups. Estimation of structural equation models using raw data allows for incomplete data, and so all groups can be retained for analysis even if only 1 member of a group contributes…

  20. The Relative Performance of Full Information Maximum Likelihood Estimation for Missing Data in Structural Equation Models.

    ERIC Educational Resources Information Center

    Enders, Craig K.; Bandalos, Deborah L.

    2001-01-01

    Used Monte Carlo simulation to examine the performance of four missing data methods in structural equation models: (1)full information maximum likelihood (FIML); (2) listwise deletion; (3) pairwise deletion; and (4) similar response pattern imputation. Results show that FIML estimation is superior across all conditions of the design. (SLD)

  1. Individual Information-Centered Approach for Handling Physical Activity Missing Data

    ERIC Educational Resources Information Center

    Kang, Minsoo; Rowe, David A.; Barreira, Tiago V.; Robinson, Terrance S.; Mahar, Matthew T.

    2009-01-01

    The purpose of this study was to validate individual information (II)-centered methods for handling missing data, using data samples of 118 middle-aged adults and 91 older adults equipped with Yamax SW-200 pedometers and Actigraph accelerometers for 7 days. We used a semisimulation approach to create six data sets: three physical activity outcome…

  2. Individual Information-Centered Approach for Handling Physical Activity Missing Data

    ERIC Educational Resources Information Center

    Kang, Minsoo; Rowe, David A.; Barreira, Tiago V.; Robinson, Terrance S.; Mahar, Matthew T.

    2009-01-01

    The purpose of this study was to validate individual information (II)-centered methods for handling missing data, using data samples of 118 middle-aged adults and 91 older adults equipped with Yamax SW-200 pedometers and Actigraph accelerometers for 7 days. We used a semisimulation approach to create six data sets: three physical activity outcome…

  3. Sensitivity Analysis of Multiple Informant Models When Data Are Not Missing at Random

    ERIC Educational Resources Information Center

    Blozis, Shelley A.; Ge, Xiaojia; Xu, Shu; Natsuaki, Misaki N.; Shaw, Daniel S.; Neiderhiser, Jenae M.; Scaramella, Laura V.; Leve, Leslie D.; Reiss, David

    2013-01-01

    Missing data are common in studies that rely on multiple informant data to evaluate relationships among variables for distinguishable individuals clustered within groups. Estimation of structural equation models using raw data allows for incomplete data, and so all groups can be retained for analysis even if only 1 member of a group contributes…

  4. The Performance of the Full Information Maximum Likelihood Estimator in Multiple Regression Models with Missing Data.

    ERIC Educational Resources Information Center

    Enders, Craig K.

    2001-01-01

    Examined the performance of a recently available full information maximum likelihood (FIML) estimator in a multiple regression model with missing data using Monte Carlo simulation and considering the effects of four independent variables. Results indicate that FIML estimation was superior to that of three ad hoc techniques, with less bias and less…

  5. Relying on Your Own Best Judgment: Imputing Values to Missing Information in Decision Making.

    ERIC Educational Resources Information Center

    Johnson, Richard D.; And Others

    Processes involved in making estimates of the value of missing information that could help in a decision making process were studied. Hypothetical purchases of ground beef were selected for the study as such purchases have the desirable property of quantifying both the price and quality. A total of 150 students at the University of Iowa rated the…

  6. Using an EM Covariance Matrix to Estimate Structural Equation Models with Missing Data: Choosing an Adjusted Sample Size to Improve the Accuracy of Inferences

    ERIC Educational Resources Information Center

    Enders, Craig K.; Peugh, James L.

    2004-01-01

    Two methods, direct maximum likelihood (ML) and the expectation maximization (EM) algorithm, can be used to obtain ML parameter estimates for structural equation models with missing data (MD). Although the 2 methods frequently produce identical parameter estimates, it may be easier to satisfy missing at random assumptions using EM. However, no…

  7. Using an EM Covariance Matrix to Estimate Structural Equation Models with Missing Data: Choosing an Adjusted Sample Size to Improve the Accuracy of Inferences

    ERIC Educational Resources Information Center

    Enders, Craig K.; Peugh, James L.

    2004-01-01

    Two methods, direct maximum likelihood (ML) and the expectation maximization (EM) algorithm, can be used to obtain ML parameter estimates for structural equation models with missing data (MD). Although the 2 methods frequently produce identical parameter estimates, it may be easier to satisfy missing at random assumptions using EM. However, no…

  8. Modeling Achievement Trajectories when Attrition Is Informative

    ERIC Educational Resources Information Center

    Feldman, Betsy J.; Rabe-Hesketh, Sophia

    2012-01-01

    In longitudinal education studies, assuming that dropout and missing data occur completely at random is often unrealistic. When the probability of dropout depends on covariates and observed responses (called "missing at random" [MAR]), or on values of responses that are missing (called "informative" or "not missing at random" [NMAR]),…

  9. Modeling Achievement Trajectories when Attrition Is Informative

    ERIC Educational Resources Information Center

    Feldman, Betsy J.; Rabe-Hesketh, Sophia

    2012-01-01

    In longitudinal education studies, assuming that dropout and missing data occur completely at random is often unrealistic. When the probability of dropout depends on covariates and observed responses (called "missing at random" [MAR]), or on values of responses that are missing (called "informative" or "not missing at random" [NMAR]),…

  10. 20 CFR 364.3 - Publication of missing children information in the Railroad Retirement Board's in-house...

    Code of Federal Regulations, 2010 CFR

    2010-04-01

    ... in the Railroad Retirement Board's in-house publications. 364.3 Section 364.3 Employees' Benefits... the Railroad Retirement Board's in-house publications. (a) All-A-Board. Information about missing... publication. (b) Other in-house publications. The Board may publish missing children information in other...

  11. Adverse events and near misses relating to information management in a hospital.

    PubMed

    Jylhä, Virpi; Bates, David W; Saranto, Kaija

    2016-08-01

    This study described information management incidents and adverse event reporting choices of health professionals. Hospital adverse events reported in an anonymous electronic reporting system were analysed using directed content analysis and descriptive and inferential statistics. The data consisted of near miss and adverse event incident reports (n = 3075) that occurred between January 2008 and the end of December 2009. A total of 824 incidents were identified. The most common information management incident was failure in written information transfer and communication, when patient data were copied or documented incorrectly. Often patient data were transferred using paper even though an electronic patient record was in use. Reporting choices differed significantly among professional groups; in particular, registered nurses reported more events than other health professionals. A broad spectrum of information management incidents was identified, which indicates that preventing adverse events requires the development of safe practices, especially in documentation and information transfer. © The Author(s) 2016.

  12. A comparison of multiple imputation methods for handling missing values in longitudinal data in the presence of a time-varying covariate with a non-linear association with time: a simulation study.

    PubMed

    De Silva, Anurika Priyanjali; Moreno-Betancur, Margarita; De Livera, Alysha Madhu; Lee, Katherine Jane; Simpson, Julie Anne

    2017-07-25

    Missing data is a common problem in epidemiological studies, and is particularly prominent in longitudinal data, which involve multiple waves of data collection. Traditional multiple imputation (MI) methods (fully conditional specification (FCS) and multivariate normal imputation (MVNI)) treat repeated measurements of the same time-dependent variable as just another 'distinct' variable for imputation and therefore do not make the most of the longitudinal structure of the data. Only a few studies have explored extensions to the standard approaches to account for the temporal structure of longitudinal data. One suggestion is the two-fold fully conditional specification (two-fold FCS) algorithm, which restricts the imputation of a time-dependent variable to time blocks where the imputation model includes measurements taken at the specified and adjacent times. To date, no study has investigated the performance of two-fold FCS and standard MI methods for handling missing data in a time-varying covariate with a non-linear trajectory over time - a commonly encountered scenario in epidemiological studies. We simulated 1000 datasets of 5000 individuals based on the Longitudinal Study of Australian Children (LSAC). Three missing data mechanisms: missing completely at random (MCAR), and a weak and a strong missing at random (MAR) scenarios were used to impose missingness on body mass index (BMI) for age z-scores; a continuous time-varying exposure variable with a non-linear trajectory over time. We evaluated the performance of FCS, MVNI, and two-fold FCS for handling up to 50% of missing data when assessing the association between childhood obesity and sleep problems. The standard two-fold FCS produced slightly more biased and less precise estimates than FCS and MVNI. We observed slight improvements in bias and precision when using a time window width of two for the two-fold FCS algorithm compared to the standard width of one. We recommend the use of FCS or MVNI in a similar

  13. Weakly Informative Prior for Point Estimation of Covariance Matrices in Hierarchical Models

    ERIC Educational Resources Information Center

    Chung, Yeojin; Gelman, Andrew; Rabe-Hesketh, Sophia; Liu, Jingchen; Dorie, Vincent

    2015-01-01

    When fitting hierarchical regression models, maximum likelihood (ML) estimation has computational (and, for some users, philosophical) advantages compared to full Bayesian inference, but when the number of groups is small, estimates of the covariance matrix (S) of group-level varying coefficients are often degenerate. One can do better, even from…

  14. Weakly Informative Prior for Point Estimation of Covariance Matrices in Hierarchical Models

    ERIC Educational Resources Information Center

    Chung, Yeojin; Gelman, Andrew; Rabe-Hesketh, Sophia; Liu, Jingchen; Dorie, Vincent

    2015-01-01

    When fitting hierarchical regression models, maximum likelihood (ML) estimation has computational (and, for some users, philosophical) advantages compared to full Bayesian inference, but when the number of groups is small, estimates of the covariance matrix (S) of group-level varying coefficients are often degenerate. One can do better, even from…

  15. Variance decomposition of MRI-based covariance maps using genetically informative samples and structural equation modeling.

    PubMed

    Schmitt, J Eric; Lenroot, Rhoshel K; Ordaz, Sarah E; Wallace, Gregory L; Lerch, Jason P; Evans, Alan C; Prom, Elizabeth C; Kendler, Kenneth S; Neale, Michael C; Giedd, Jay N

    2009-08-01

    The role of genetics in driving intracortical relationships is an important question that has rarely been studied in humans. In particular, there are no extant high-resolution imaging studies on genetic covariance. In this article, we describe a novel method that combines classical quantitative genetic methodologies for variance decomposition with recently developed semi-multivariate algorithms for high-resolution measurement of phenotypic covariance. Using these tools, we produced correlational maps of genetic and environmental (i.e. nongenetic) relationships between several regions of interest and the cortical surface in a large pediatric sample of 600 twins, siblings, and singletons. These analyses demonstrated high, fairly uniform, statistically significant genetic correlations between the entire cortex and global mean cortical thickness. In agreement with prior reports on phenotypic covariance using similar methods, we found that mean cortical thickness was most strongly correlated with association cortices. However, the present study suggests that genetics plays a large role in global brain patterning of cortical thickness in this manner. Further, using specific gyri with known high heritabilities as seed regions, we found a consistent pattern of high bilateral genetic correlations between structural homologues, with environmental correlations more restricted to the same hemisphere as the seed region, suggesting that interhemispheric covariance is largely genetically mediated. These findings are consistent with the limited existing knowledge on the genetics of cortical variability as well as our prior multivariate studies on cortical gyri.

  16. Background Error Covariance Estimation Using Information from a Single Model Trajectory with Application to Ocean Data Assimilation

    NASA Technical Reports Server (NTRS)

    Keppenne, Christian L.; Rienecker, Michele; Kovach, Robin M.; Vernieres, Guillaume

    2014-01-01

    An attractive property of ensemble data assimilation methods is that they provide flow dependent background error covariance estimates which can be used to update fields of observed variables as well as fields of unobserved model variables. Two methods to estimate background error covariances are introduced which share the above property with ensemble data assimilation methods but do not involve the integration of multiple model trajectories. Instead, all the necessary covariance information is obtained from a single model integration. The Space Adaptive Forecast error Estimation (SAFE) algorithm estimates error covariances from the spatial distribution of model variables within a single state vector. The Flow Adaptive error Statistics from a Time series (FAST) method constructs an ensemble sampled from a moving window along a model trajectory.SAFE and FAST are applied to the assimilation of Argo temperature profiles into version 4.1 of the Modular Ocean Model (MOM4.1) coupled to the GEOS-5 atmospheric model and to the CICE sea ice model. The results are validated against unassimilated Argo salinity data. They show that SAFE and FAST are competitive with the ensemble optimal interpolation (EnOI) used by the Global Modeling and Assimilation Office (GMAO) to produce its ocean analysis. Because of their reduced cost, SAFE and FAST hold promise for high-resolution data assimilation applications.

  17. Improvement of Modeling HTGR Neutron Physics by Uncertainty Analysis with the Use of Cross-Section Covariance Information

    NASA Astrophysics Data System (ADS)

    Boyarinov, V. F.; Grol, A. V.; Fomichenko, P. A.; Ternovykh, M. Yu

    2017-01-01

    This work is aimed at improvement of HTGR neutron physics design calculations by application of uncertainty analysis with the use of cross-section covariance information. Methodology and codes for preparation of multigroup libraries of covariance information for individual isotopes from the basic 44-group library of SCALE-6 code system were developed. A 69-group library of covariance information in a special format for main isotopes and elements typical for high temperature gas cooled reactors (HTGR) was generated. This library can be used for estimation of uncertainties, associated with nuclear data, in analysis of HTGR neutron physics with design codes. As an example, calculations of one-group cross-section uncertainties for fission and capture reactions for main isotopes of the MHTGR-350 benchmark, as well as uncertainties of the multiplication factor (k∞) for the MHTGR-350 fuel compact cell model and fuel block model were performed. These uncertainties were estimated by the developed technology with the use of WIMS-D code and modules of SCALE-6 code system, namely, by TSUNAMI, KENO-VI and SAMS. Eight most important reactions on isotopes for MHTGR-350 benchmark were identified, namely: 10B(capt), 238U(n,γ), ν5, 235U(n,γ), 238U(el), natC(el), 235U(fiss)-235U(n,γ), 235U(fiss).

  18. On the role of covariance information for GRACE K-band observations in the Celestial Mechanics Approach

    NASA Astrophysics Data System (ADS)

    Bentel, Katrin; Meyer, Ulrich; Arnold, Daniel; Jean, Yoomin; Jäggi, Adrian

    2017-04-01

    The Astronomical Institute at the University of Bern (AIUB) derives static and time-variable gravity fields by means of the Celestial Mechanics Approach (CMA) from GRACE (level 1B) data. This approach makes use of the close link between orbit and gravity field determination. GPS-derived kinematic GRACE orbit positions, inter-satellite K-band observations, which are the core observations of GRACE, and accelerometer data are combined to rigorously estimate orbit and spherical harmonic gravity field coefficients in one adjustment step. Pseudo-stochastic orbit parameters are set up to absorb unmodeled noise. The K-band range measurements in along-track direction lead to a much higher correlation of the observations in this direction compared to the other directions and thus, to north-south stripes in the unconstrained gravity field solutions, so-called correlated errors. By using a full covariance matrix for the K-band observations the correlation can be taken into account. One possibility is to derive correlation information from post-processing K-band residuals. This is then used in a second iteration step to derive an improved gravity field solution. We study the effects of pre-defined covariance matrices and residual-derived covariance matrices on the final gravity field product with the CMA.

  19. Comparing the performance of geostatistical models with additional information from covariates for sewage plume characterization.

    PubMed

    Del Monego, Maurici; Ribeiro, Paulo Justiniano; Ramos, Patrícia

    2015-04-01

    In this work, kriging with covariates is used to model and map the spatial distribution of salinity measurements gathered by an autonomous underwater vehicle in a sea outfall monitoring campaign aiming to distinguish the effluent plume from the receiving waters and characterize its spatial variability in the vicinity of the discharge. Four different geostatistical linear models for salinity were assumed, where the distance to diffuser, the west-east positioning, and the south-north positioning were used as covariates. Sample variograms were fitted by the Matèrn models using weighted least squares and maximum likelihood estimation methods as a way to detect eventual discrepancies. Typically, the maximum likelihood method estimated very low ranges which have limited the kriging process. So, at least for these data sets, weighted least squares showed to be the most appropriate estimation method for variogram fitting. The kriged maps show clearly the spatial variation of salinity, and it is possible to identify the effluent plume in the area studied. The results obtained show some guidelines for sewage monitoring if a geostatistical analysis of the data is in mind. It is important to treat properly the existence of anomalous values and to adopt a sampling strategy that includes transects parallel and perpendicular to the effluent dispersion.

  20. Estimating hazard ratios in cohort data with missing disease information due to death.

    PubMed

    Binder, Nadine; Herrnböck, Anne-Sophie; Schumacher, Martin

    2017-03-01

    In clinical and epidemiological studies information on the primary outcome of interest, that is, the disease status, is usually collected at a limited number of follow-up visits. The disease status can often only be retrieved retrospectively in individuals who are alive at follow-up, but will be missing for those who died before. Right-censoring the death cases at the last visit (ad-hoc analysis) yields biased hazard ratio estimates of a potential risk factor, and the bias can be substantial and occur in either direction. In this work, we investigate three different approaches that use the same likelihood contributions derived from an illness-death multistate model in order to more adequately estimate the hazard ratio by including the death cases into the analysis: a parametric approach, a penalized likelihood approach, and an imputation-based approach. We investigate to which extent these approaches allow for an unbiased regression analysis by evaluating their performance in simulation studies and on a real data example. In doing so, we use the full cohort with complete illness-death data as reference and artificially induce missing information due to death by setting discrete follow-up visits. Compared to an ad-hoc analysis, all considered approaches provide less biased or even unbiased results, depending on the situation studied. In the real data example, the parametric approach is seen to be too restrictive, whereas the imputation-based approach could almost reconstruct the original event history information. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  1. Reweighting estimators for Cox regression with missing covariate data: Analysis of insulin resistance and risk of stroke in the Northern Manhattan Study

    PubMed Central

    Xu, Qiang; Paik, Myunghee Cho; Rundek, Tatjana; Elkind, Mitchell S. V.; Sacco, Ralph L.

    2015-01-01

    Incomplete covariates often obscure analysis results from a Cox regression. In an analysis of the Northern Manhattan Study (NOMAS) to determine the influence of insulin resistance on the incidence of stroke in non-diabetic individuals, insulin level is unknown for 34.1% of the subjects. The available data suggest that the missingness mechanism depends on outcome variables, which may generate biases in estimating the parameters of interest if only using the complete observations. This article aimed to introduce practical strategies to analyze the NOMAS data and present sensitivity analyses by using the reweighting method in standard statistical packages. When the data set structure is in counting process style, the reweighting estimates can be obtained by built-in procedures with variance estimated by the jackknife method. Simulation results indicate that the jackknife variance estimate provides reasonable coverage probability in moderate sample sizes. We subsequently conducted sensitivity analyses for the NOMAS data, showing that the risk estimates are robust to a variety of missingness mechanisms. At the end of this article, we present the core SAS and R programs used in the analysis. PMID:21965165

  2. Revising estimates of global GPP using new information from eddy covariance and satellite datasets

    NASA Astrophysics Data System (ADS)

    Wang, Y.; Jin, Y.; Goulden, M. L.; Randerson, J. T.

    2009-12-01

    Gross Primary Production (GPP) is defined as the sum of photosynthesis by all leaves from all plants per unit of ground area and is typically measured at the scale of an ecosystem over a period of hours to years. Obtaining precise estimates of contemporary GPP at regional and global scales is an important first step towards developing realistic prognostic models that can be used to understand the effects of climate change on terrestrial ecosystems and feedbacks between climate and the carbon cycle. Major technical and theoretical advances have improved our understanding of GPP over the past decade. The proliferation of eddy covariance towers and the systematic organization of these data through Fluxnet provide an important new constraint on the distribution of GPP across ecosystems and the sensitivity GPP to variability to climate and stand age. Concurrently, the availability of high quality remote sensing products has increased significantly as a result of instruments on Terra and Aqua satellites, making it possible to monitor biosphere continuously at a global scale on a time span of a week to 10 days. Here we revise global estimates of GPP using a light-use-efficiency (LUE) model. We used enhanced vegetation index (EVI) measurements from MODIS to estimate the fraction of absorbed photosynthetically active radiation (fAPAR). PAR was derived as a product of surface shortwave radiation measurements from Goddard Earth Observing System (GEOS) version 5 and conversion factor from International Satellite Cloud Climatology Program (ISCCP). LUE was optimized using Ameriflux GPP estimates and other estimates from other published eddy covariance studies. Scalars for temperature and moisture stress were applied locally using reanalysis observations from GEOS 5. In our analysis we tested different model structures, evaluating their success at predicting GPP at an independent set of measurement sites. We find that the global estimates of GPP of 120 Pg C/yr that are widely

  3. Covariance Manipulation for Conjunction Assessment

    NASA Technical Reports Server (NTRS)

    Hejduk, M. D.

    2016-01-01

    Use of probability of collision (Pc) has brought sophistication to CA. Made possible by JSpOC precision catalogue because provides covariance. Has essentially replaced miss distance as basic CA parameter. Embrace of Pc has elevated methods to 'manipulate' covariance to enable/improve CA calculations. Two such methods to be examined here; compensation for absent or unreliable covariances through 'Maximum Pc' calculation constructs, projection (not propagation) of epoch covariances forward in time to try to enable better risk assessments. Two questions to be answered about each; situations to which such approaches are properly applicable, amount of utility that such methods offer.

  4. A Cautionary Note on the Use of Information Fit Indexes in Covariance Structure Modeling with Means

    ERIC Educational Resources Information Center

    Wicherts, Jelte M.; Dolan, Conor V.

    2004-01-01

    Information fit indexes such as Akaike Information Criterion, Consistent Akaike Information Criterion, Bayesian Information Criterion, and the expected cross validation index can be valuable in assessing the relative fit of structural equation models that differ regarding restrictiveness. In cases in which models without mean restrictions (i.e.,…

  5. FW: An R Package for Finlay-Wilkinson Regression that Incorporates Genomic/Pedigree Information and Covariance Structures Between Environments.

    PubMed

    Lian, Lian; de Los Campos, Gustavo

    2015-12-29

    The Finlay-Wilkinson regression (FW) is a popular method among plant breeders to describe genotype by environment interaction. The standard implementation is a two-step procedure that uses environment (sample) means as covariates in a within-line ordinary least squares (OLS) regression. This procedure can be suboptimal for at least four reasons: (1) in the first step environmental means are typically estimated without considering genetic-by-environment interactions, (2) in the second step uncertainty about the environmental means is ignored, (3) estimation is performed regarding lines and environment as fixed effects, and (4) the procedure does not incorporate genetic (either pedigree-derived or marker-derived) relationships. Su et al. proposed to address these problems using a Bayesian method that allows simultaneous estimation of environmental and genotype parameters, and allows incorporation of pedigree information. In this article we: (1) extend the model presented by Su et al. to allow integration of genomic information [e.g., single nucleotide polymorphism (SNP)] and covariance between environments, (2) present an R package (FW) that implements these methods, and (3) illustrate the use of the package using examples based on real data. The FW R package implements both the two-step OLS method and a full Bayesian approach for Finlay-Wilkinson regression with a very simple interface. Using a real wheat data set we demonstrate that the prediction accuracy of the Bayesian approach is consistently higher than the one achieved by the two-step OLS method. Copyright © 2016 Lian and Campos.

  6. Missing information caused by death leads to bias in relative risk estimates.

    PubMed

    Binder, Nadine; Schumacher, Martin

    2014-10-01

    In most clinical and epidemiologic studies, information on disease status is usually collected at regular follow-up visits. Often, this information can only be retrieved in individuals who are alive at follow-up, and studies frequently right censor individuals with missing information because of death in the analysis. Such ad hoc analyses can lead to seriously biased hazard ratio estimates of potential risk factors. We systematically investigate this bias. We illustrate under which conditions the bias can occur. Considering three numerical studies, we characterize the bias, its magnitude, and direction as well as its real-world relevance. Depending on the situation studied, the bias can be substantial and in both directions. It is mainly caused by differential mortality: if deaths without occurrence of the disease are more pronounced, the risk factor effect is overestimated. However, if the risk for dying after being diseased is prevailing, the effect is mostly underestimated and might even change signs. The bias is a result of both, a too coarse follow-up and an ad hoc Cox analysis in which the data sample is restricted to the observed and known event history. This is especially relevant for studies in which a considerable number of death cases are expected. Copyright © 2014 Elsevier Inc. All rights reserved.

  7. Miss Heroin.

    ERIC Educational Resources Information Center

    Riley, Bernice

    This script, with music, lyrics and dialog, was written especially for youngsters to inform them of the potential dangers of various drugs. The author, who teaches in an elementary school in Harlem, New York, offers Miss Heroin as her answer to the expressed opinion that most drug and alcohol information available is either too simplified and…

  8. Reconstructing missing information on precipitation datasets: impact of tails on adopted statistical distributions.

    NASA Astrophysics Data System (ADS)

    Pedretti, Daniele; Beckie, Roger Daniel

    2014-05-01

    Missing data in hydrological time-series databases are ubiquitous in practical applications, yet it is of fundamental importance to make educated decisions in problems involving exhaustive time-series knowledge. This includes precipitation datasets, since recording or human failures can produce gaps in these time series. For some applications, directly involving the ratio between precipitation and some other quantity, lack of complete information can result in poor understanding of basic physical and chemical dynamics involving precipitated water. For instance, the ratio between precipitation (recharge) and outflow rates at a discharge point of an aquifer (e.g. rivers, pumping wells, lysimeters) can be used to obtain aquifer parameters and thus to constrain model-based predictions. We tested a suite of methodologies to reconstruct missing information in rainfall datasets. The goal was to obtain a suitable and versatile method to reduce the errors given by the lack of data in specific time windows. Our analyses included both a classical chronologically-pairing approach between rainfall stations and a probability-based approached, which accounted for the probability of exceedence of rain depths measured at two or multiple stations. Our analyses proved that it is not clear a priori which method delivers the best methodology. Rather, this selection should be based considering the specific statistical properties of the rainfall dataset. In this presentation, our emphasis is to discuss the effects of a few typical parametric distributions used to model the behavior of rainfall. Specifically, we analyzed the role of distributional "tails", which have an important control on the occurrence of extreme rainfall events. The latter strongly affect several hydrological applications, including recharge-discharge relationships. The heavy-tailed distributions we considered were parametric Log-Normal, Generalized Pareto, Generalized Extreme and Gamma distributions. The methods were

  9. Missed bleeding events after ticagrelor in PEGASUS trial: Massive non-compliance, information censoring, or both?

    PubMed

    Serebruany, Victor; Tomek, Ales

    2016-07-15

    PEGASUS trial reported reduction of composite primary endpoint after conventional 180mg/daily ticagrelor (CT), and lower 120mg/daily dose ticagrelor (LT) at expense of extra bleeding. Following approval of CT and LT for long-term secondary prevention indication, recent FDA review verified some bleeding outcomes in PEGASUS. To compare the risks after CT and LT against placebo by seven TIMI scale variables, and 9 bleeding categories considered as serious adverse events (SAE) in light of PEGASUS drug discontinuation rates (DDR). The DDR in all PEGASUS arms was high reaching astronomical 32% for CT. The distribution of some outcomes (TIMI major, trauma, epistaxis, iron deficiency, hemoptysis, and anemia) was reasonable. However, the TIMI minor events were heavily underreported when compared to similar trials. Other bleedings (intracranial, spontaneous, hematuria, and gastrointestinal) appear sporadic, lacking expected dose-dependent impact of CT and LT. Few SAE outcomes (fatal, ecchymosis, hematoma, bruises, bleeding) paradoxically reported more bleeding after LT than after CT. Many bleeding outcomes were probably missed in PEGASUS potentially due to massive non-compliance, information censoring, or both. The FDA must improve reporting of trial outcomes especially in the sponsor-controlled environment when DDR and incomplete follow-up rates are high.

  10. [The Hospital Information System of the Brazilian Unified National Health System: a performance evaluation for auditing maternal near miss].

    PubMed

    Nakamura-Pereira, Marcos; Mendes-Silva, Wallace; Dias, Marcos Augusto Bastos; Reichenheim, Michael E; Lobato, Gustavo

    2013-07-01

    This study aimed to investigate the performance of the Hospital Information System of the Brazilian Unified National Health System (SIH-SUS) in identifying cases of maternal near miss in a hospital in Rio de Janeiro, Brazil, in 2008. Cases were identified by reviewing medical records of pregnant and postpartum women admitted to the hospital. The search for potential near miss events in the SIH-SUS database relied on a list of procedures and codes from the International Classification of Diseases, 10th revision (ICD-10) that were consistent with this diagnosis. The patient chart review identified 27 cases, while 70 potential occurrences of near miss were detected in the SIH-SUS database. However, only 5 of 70 were "true cases" of near miss according to the chart review, which corresponds to a sensitivity of 18.5% (95%CI: 6.3-38.1), specificity of 94.3% (95%CI: 92.8-95.6), area under the ROC of 0.56 (95%CI: 0.48-0.63), and positive predictive value of 10.1% (IC95%: 4.7-20.3). These findings suggest that SIH-SUS does not appear appropriate for monitoring maternal near miss.

  11. Incorporating Person Covariates and Response Times as Collateral Information to Improve Person and Item Parameter Estimations

    ERIC Educational Resources Information Center

    Wang, Shudong; Jiao, Hong

    2011-01-01

    For decades, researchers and practitioners have made a great deal of effort to study a variety of methods to increase parameter accuracy, but only recently can researchers start focusing on improving parameter estimations by using a joint model that could incorporate RT and students information as CI. Given that many tests are currently…

  12. Change blindness for cast shadows in natural scenes: Even informative shadow changes are missed.

    PubMed

    Ehinger, Krista A; Allen, Kala; Wolfe, Jeremy M

    2016-05-01

    Previous work has shown that human observers discount or neglect cast shadows in natural and artificial scenes across a range of visual tasks. This is a reasonable strategy for a visual system designed to recognize objects under a range of lighting conditions, since cast shadows are not intrinsic properties of the scene-they look different (or disappear entirely) under different lighting conditions. However, cast shadows can convey useful information about the three-dimensional shapes of objects and their spatial relations. In this study, we investigated how well people detect changes to cast shadows, presented in natural scenes in a change blindness paradigm, and whether shadow changes that imply the movement or disappearance of an object are more easily noticed than shadow changes that imply a change in lighting. In Experiment 1, a critical object's shadow was removed, rotated to another direction, or shifted down to suggest that the object was floating. All of these shadow changes were noticed less often than changes to physical objects or surfaces in the scene, and there was no difference in the detection rates for the three types of changes. In Experiment 2, the shadows of visible or occluded objects were removed from the scenes. Although removing the cast shadow of an occluded object could be seen as an object deletion, both types of shadow changes were noticed less often than deletions of the visible, physical objects in the scene. These results show that even informative shadow changes are missed, suggesting that cast shadows are discounted fairly early in the processing of natural scenes.

  13. Predicting New Hampshire Indoor Radon Concentrations from geologic information and other covariates

    SciTech Connect

    Apte, M.G.; Price, P.N.; Nero, A.V.; Revzan, K.L.

    1998-05-01

    Generalized geologic province information and data on house construction were used to predict indoor radon concentrations in New Hampshire (NH). A mixed-effects regression model was used to predict the geometric mean (GM) short-term radon concentrations in 259 NH towns. Bayesian methods were used to avoid over-fitting and to minimize the effects of small sample variation within towns. Data from a random survey of short-term radon measurements, individual residence building characteristics, along with geologic unit information, and average surface radium concentration by town, were variables used in the model. Predicted town GM short-term indoor radon concentrations for detached houses with usable basements range from 34 Bq/m{sup 3} (1 pCi/l) to 558 Bq/m{sup 3} (15 pCi/l), with uncertainties of about 30%. A geologic province consisting of glacial deposits and marine sediments, was associated with significantly elevated radon levels, after adjustment for radium concentration, and building type. Validation and interpretation of results are discussed.

  14. Using Incidence Sampling to Estimate Covariances.

    ERIC Educational Resources Information Center

    Knapp, Thomas R.

    1979-01-01

    This paper presents the generalized symmetric means approach to the estimation of population covariances, complete with derivations and examples. Particular attention is paid to the problem of missing data, which is handled very naturally in the incidence sampling framework. (CTM)

  15. A Cautious Note on Auxiliary Variables That Can Increase Bias in Missing Data Problems.

    PubMed

    Thoemmes, Felix; Rose, Norman

    2014-01-01

    The treatment of missing data in the social sciences has changed tremendously during the last decade. Modern missing data techniques such as multiple imputation and full-information maximum likelihood are used much more frequently. These methods assume that data are missing at random. One very common approach to increase the likelihood that missing at random is achieved consists of including many covariates as so-called auxiliary variables. These variables are either included based on data considerations or in an inclusive fashion; that is, taking all available auxiliary variables. In this article, we point out that there are some instances in which auxiliary variables exhibit the surprising property of increasing bias in missing data problems. In a series of focused simulation studies, we highlight some situations in which this type of biasing behavior can occur. We briefly discuss possible ways how one can avoid selecting bias-inducing covariates as auxiliary variables.

  16. Estimation of surface O3 from lower-troposphere partial-column information: Vertical correlations and covariances in ozonesonde profiles

    NASA Astrophysics Data System (ADS)

    Chatfield, Robert B.; Esswein, Robert F.

    2012-12-01

    Analysis of the spatial correlation of ozone mixing ratio in the vertical provides information useful for several purposes: (a) it aids description of the degree of regionality of the ozone transport-transformation processes, (b) the information provided in the form of a priori covariance matrices for remote retrieval algorithms can simplify and sharpen accuracy of the resulting estimates, and most importantly, (c) it allows a first evaluation of the improvement that remote retrievals can give over boundary-layer climatology. Vertical profiles of mean, variance, and vertical autocovariance, and vertical autocorrelation of ozone mixing ratios were estimated and given parameterizations. The WOUDC ozonesonde network database was used. During the years 2004-2006, these were considerably augmented by sondes taken by NASA, NOAA, and Canadian agencies during recent summertime intensive periods in North America. There are large differences across the North American continent in the patterns and magnitudes of correlation, especially in the lowest 2-3 km of the troposphere. This is especially significant for the near-surface layers (100's of meters deep) which determine actual surface O3 smog exposure and phytotoxicity, since satellite retrievals typically characterize at best a thick layer extending 3 km or more from the surface. The relative variation of O3 decreases in the vertical, particularly for the somewhat polluted launch stations, and this affects inference of surface O3 significantly. We outline a simple synthesis of mixed-layer and ozone-chemistry behavior to aid discussion of this and similar phenomena. Regional differences suggest broad if qualitative explanations in terms of larger-scale (interstate-transport) and local-scale phenomena (lake and sea breezes, degree/frequency of subsidence), inviting future study. The character of near-surface-to-full-layer covariance suggests that remote retrieval can describe surface ozone surprisingly well using 0-3 km

  17. Missing data in longitudinal studies: cross-sectional multiple imputation provides similar estimates to full-information maximum likelihood.

    PubMed

    Ferro, Mark A

    2014-01-01

    The aim of this research was to examine, in an exploratory manner, whether cross-sectional multiple imputation generates valid parameter estimates for a latent growth curve model in a longitudinal data set with nonmonotone missingness. A simulated longitudinal data set of N = 5000 was generated and consisted of a continuous dependent variable, assessed at three measurement occasions and a categorical time-invariant independent variable. Missing data had a nonmonotone pattern and the proportion of missingness increased from the initial to the final measurement occasion (5%-20%). Three methods were considered to deal with missing data: listwise deletion, full-information maximum likelihood, and multiple imputation. A latent growth curve model was specified and analysis of variance was used to compare parameter estimates between the full data set and missing data approaches. Multiple imputation resulted in significantly lower slope variance compared with the full data set. There were no differences in any parameter estimates between the multiple imputation and full-information maximum likelihood approaches. This study suggested that in longitudinal studies with nonmonotone missingness, cross-sectional imputation at each time point may be viable and produces estimates comparable with those obtained with full-information maximum likelihood. Future research pursuing the validity of this method is warranted. Copyright © 2014 Elsevier Inc. All rights reserved.

  18. Combining Datum-free Normal Equation Systems and Solutions with Full Covariance Information for Upcoming CRF Realizations

    NASA Astrophysics Data System (ADS)

    Iddink, Andreas; Artz, Thomas; Nothnagel, Axel

    2014-12-01

    Combination approaches within the International VLBI Service for Geodesy and Astrometry (IVS) are solely performed on the level of datum-free normal equations (NEQs). The procedure is used to compute the two major products of the IVS, i.e., time series of Earth orientation parameters (EOPs) and station coordinates. One shortcoming of the datum-free NEQ-based combination is the limitation to contributions based on the classical least squares adjustment and to analysis software packages supporting the output of datum-free NEQs. Hence, in order to increase the potential number of contributions, it would be a big gain to be able to include contributions based on solutions of a constrained NEQ. In this paper, we present a method to mix the combination on the level of datum-free NEQ and on the solution level with full covariance information. We show the implementation of this approach in our existing software environment BonnSolutionCombination (BoSC) and discuss the prerequisites and the limitations. Furthermore, we show the benefits for upcoming Celestial Reference Frame (CRF) realizations.

  19. Impact of Violation of the Missing-at-Random Assumption on Full-Information Maximum Likelihood Method in Multidimensional Adaptive Testing

    ERIC Educational Resources Information Center

    Han, Kyung T.; Guo, Fanmin

    2014-01-01

    The full-information maximum likelihood (FIML) method makes it possible to estimate and analyze structural equation models (SEM) even when data are partially missing, enabling incomplete data to contribute to model estimation. The cornerstone of FIML is the missing-at-random (MAR) assumption. In (unidimensional) computerized adaptive testing…

  20. Impact of Violation of the Missing-at-Random Assumption on Full-Information Maximum Likelihood Method in Multidimensional Adaptive Testing

    ERIC Educational Resources Information Center

    Han, Kyung T.; Guo, Fanmin

    2014-01-01

    The full-information maximum likelihood (FIML) method makes it possible to estimate and analyze structural equation models (SEM) even when data are partially missing, enabling incomplete data to contribute to model estimation. The cornerstone of FIML is the missing-at-random (MAR) assumption. In (unidimensional) computerized adaptive testing…

  1. Treating the Einstein-Hilbert action as a higher derivative Lagrangian: revealing the missing information about conformal non-invariance

    NASA Astrophysics Data System (ADS)

    Nikolić, Branislav

    2017-08-01

    The Hamiltonian formulation of conformally invariant Weyl-squared higher derivative theory teaches us that conformal symmetry is expressed through particular first class constraints related to the absence of the three-metric determinant and the trace of the extrinsic curvature from the theory. Any term depending on them which is added to this theory breaks conformal invariance and turns these constraints into second class ones. Such second class constraints are missing in the standard canonical formulation of the conformally non-invariant Einstein-Hilbert theory. It is demonstrated that such constraints do appear if the theory is treated as a higher derivative one: if the extrinsic curvature is promoted to an independent variable, the apparently missing information about conformal behavior is revealed.

  2. Missing data exploration: highlighting graphical presentation of missing pattern.

    PubMed

    Zhang, Zhongheng

    2015-12-01

    Functions shipped with R base can fulfill many tasks of missing data handling. However, because the data volume of electronic medical record (EMR) system is always very large, more sophisticated methods may be helpful in data management. The article focuses on missing data handling by using advanced techniques. There are three types of missing data, that is, missing completely at random (MCAR), missing at random (MAR) and not missing at random (NMAR). This classification system depends on how missing values are generated. Two packages, Multivariate Imputation by Chained Equations (MICE) and Visualization and Imputation of Missing Values (VIM), provide sophisticated functions to explore missing data pattern. In particular, the VIM package is especially helpful in visual inspection of missing data. Finally, correlation analysis provides information on the dependence of missing data on other variables. Such information is useful in subsequent imputations.

  3. Accuracy of growth model parameters: effects of frequency and duration of data collection, and missing information.

    PubMed

    Aggrey, Samuel E

    2008-01-01

    This study was done to compare the accuracy of prediction of growth parameters using the Gompertz model when (1) data was collected infrequently, (2) data collection was truncated, and (3) data was missing. Initial growth rate and rate of decay were reduced by half when the model was fitted to data collected biweekly compared to data collected weekly. This reduction led to an increase in age of maximum growth and subsequently over-predicted the asymptotic body weight. When only part of the growth duration was used for prediction, both the initial growth rate and rate of decay were reduced. The degree of data truncation also affected sexual dimorphism of the parameters estimated. Using pre-asymptotic data for growth parameter prediction does not allow the intrinsic efficiency of growth to be determined accurately. However, using growth data with body weights missing at different phases of the growth curve does not seem to significantly affect the predicted growth parameters. Speculative or diagnostic conclusions on intrinsic growth should be done with data collected at short intervals to avoid potential inaccuracies in the prediction of initial growth rate, exponential decay rate, age of maximum growth and asymptotic weight.

  4. Background Error Covariance Estimation using Information from a Single Model Trajectory with Application to Ocean Data Assimilation into the GEOS-5 Coupled Model

    NASA Technical Reports Server (NTRS)

    Keppenne, Christian L.; Rienecker, Michele M.; Kovach, Robin M.; Vernieres, Guillaume; Koster, Randal D. (Editor)

    2014-01-01

    An attractive property of ensemble data assimilation methods is that they provide flow dependent background error covariance estimates which can be used to update fields of observed variables as well as fields of unobserved model variables. Two methods to estimate background error covariances are introduced which share the above property with ensemble data assimilation methods but do not involve the integration of multiple model trajectories. Instead, all the necessary covariance information is obtained from a single model integration. The Space Adaptive Forecast error Estimation (SAFE) algorithm estimates error covariances from the spatial distribution of model variables within a single state vector. The Flow Adaptive error Statistics from a Time series (FAST) method constructs an ensemble sampled from a moving window along a model trajectory. SAFE and FAST are applied to the assimilation of Argo temperature profiles into version 4.1 of the Modular Ocean Model (MOM4.1) coupled to the GEOS-5 atmospheric model and to the CICE sea ice model. The results are validated against unassimilated Argo salinity data. They show that SAFE and FAST are competitive with the ensemble optimal interpolation (EnOI) used by the Global Modeling and Assimilation Office (GMAO) to produce its ocean analysis. Because of their reduced cost, SAFE and FAST hold promise for high-resolution data assimilation applications.

  5. A fully covariant information-theoretic ultraviolet cutoff for scalar fields in expanding Friedmann Robertson Walker spacetimes

    NASA Astrophysics Data System (ADS)

    Kempf, A.; Chatwin-Davies, A.; Martin, R. T. W.

    2013-02-01

    While a natural ultraviolet cutoff, presumably at the Planck length, is widely assumed to exist in nature, it is nontrivial to implement a minimum length scale covariantly. This is because the presence of a fixed minimum length needs to be reconciled with the ability of Lorentz transformations to contract lengths. In this paper, we implement a fully covariant Planck scale cutoff by cutting off the spectrum of the d'Alembertian. In this scenario, consistent with Lorentz contractions, wavelengths that are arbitrarily smaller than the Planck length continue to exist. However, the dynamics of modes of wavelengths that are significantly smaller than the Planck length possess a very small bandwidth. This has the effect of freezing the dynamics of such modes. While both wavelengths and bandwidths are frame dependent, Lorentz contraction and time dilation conspire to make the freezing of modes of trans-Planckian wavelengths covariant. In particular, we show that this ultraviolet cutoff can be implemented covariantly also in curved spacetimes. We focus on Friedmann Robertson Walker spacetimes and their much-discussed trans-Planckian question: The physical wavelength of each comoving mode was smaller than the Planck scale at sufficiently early times. What was the mode's dynamics then? Here, we show that in the presence of the covariant UV cutoff, the dynamical bandwidth of a comoving mode is essentially zero up until its physical wavelength starts exceeding the Planck length. In particular, we show that under general assumptions, the number of dynamical degrees of freedom of each comoving mode all the way up to some arbitrary finite time is actually finite. Our results also open the way to calculating the impact of this natural UV cutoff on inflationary predictions for the cosmic microwave background.

  6. Predicting top-L missing links with node and link clustering information in large-scale networks

    NASA Astrophysics Data System (ADS)

    Wu, Zhihao; Lin, Youfang; Wan, Huaiyu; Jamil, Waleed

    2016-08-01

    Networks are mathematical structures that are universally used to describe a large variety of complex systems, such as social, biological, and technological systems. The prediction of missing links in incomplete complex networks aims to estimate the likelihood of the existence of a link between a pair of nodes. Various topological features of networks have been applied to develop link prediction methods. However, the exploration of features of links is still limited. In this paper, we demonstrate the power of node and link clustering information in predicting top -L missing links. In the existing literature, link prediction algorithms have only been tested on small-scale and middle-scale networks. The network scale factor has not attracted the same level of attention. In our experiments, we test the proposed method on three groups of networks. For small-scale networks, since the structures are not very complex, advanced methods cannot perform significantly better than classical methods. For middle-scale networks, the proposed index, combining both node and link clustering information, starts to demonstrate its advantages. In many networks, combining both node and link clustering information can improve the link prediction accuracy a great deal. Large-scale networks with more than 100 000 links have rarely been tested previously. Our experiments on three large-scale networks show that local clustering information based methods outperform other methods, and link clustering information can further improve the accuracy of node clustering information based methods, in particular for networks with a broad distribution of the link clustering coefficient.

  7. Principled Missing Data Treatments.

    PubMed

    Lang, Kyle M; Little, Todd D

    2016-04-04

    We review a number of issues regarding missing data treatments for intervention and prevention researchers. Many of the common missing data practices in prevention research are still, unfortunately, ill-advised (e.g., use of listwise and pairwise deletion, insufficient use of auxiliary variables). Our goal is to promote better practice in the handling of missing data. We review the current state of missing data methodology and recent missing data reporting in prevention research. We describe antiquated, ad hoc missing data treatments and discuss their limitations. We discuss two modern, principled missing data treatments: multiple imputation and full information maximum likelihood, and we offer practical tips on how to best employ these methods in prevention research. The principled missing data treatments that we discuss are couched in terms of how they improve causal and statistical inference in the prevention sciences. Our recommendations are firmly grounded in missing data theory and well-validated statistical principles for handling the missing data issues that are ubiquitous in biosocial and prevention research. We augment our broad survey of missing data analysis with references to more exhaustive resources.

  8. Spatio-temporal rectification of tower-based eddy-covariance flux measurements for consistently informing process-based models

    NASA Astrophysics Data System (ADS)

    Metzger, S.; Xu, K.; Desai, A. R.; Taylor, J. R.; Kljun, N.; Schneider, D.; Kampe, T. U.; Fox, A. M.

    2013-12-01

    Process-based models, such as land surface models (LSMs), allow insight in the spatio-temporal distribution of stocks and the exchange of nutrients, trace gases etc. among environmental compartments. More recently, LSMs also become capable of assimilating time-series of in-situ reference observations. This enables calibrating the underlying functional relationships to site-specific characteristics, or to constrain the model results after each time-step in an attempt to minimize drift. The spatial resolution of LSMs is typically on the order of 10^2-10^4 km2, which is suitable for linking regional to continental scales and beyond. However, continuous in-situ observations of relevant stock and exchange variables, such as tower-based eddy-covariance (EC) fluxes, represent orders of magnitude smaller spatial scales (10^-6-10^1 km2). During data assimilation, this significant gap in spatial representativeness is typically either neglected, or side-stepped using simple tiling approaches. Moreover, at ';coarse' resolutions, a single LSM evaluation per time-step implies linearity among the underlying functional relationships as well as among the sub-grid land cover fractions. This, however, is not warranted for land-atmosphere exchange processes over more complex terrain. Hence, it is desirable to explicitly consider spatial variability at LSM sub-grid scales. Here we present a procedure that determines from a single EC tower the spatially integrated probability density function (PDF) of the surface-atmosphere exchange for individual land covers. These PDFs allow quantifying the expected value, as well as spatial variability over a target domain, can be assimilated in tiling-capable LSMs, and mitigate linearity assumptions at ';coarse' resolutions. The procedure is based on the extraction and extrapolation of environmental response functions (ERFs), for which a technical-oriented companion poster is submitted. In short, the subsequent steps are: (i) Time

  9. An Upper Bound on High Speed Satellite Collision Probability When Only One Object has Position Uncertainty Information

    NASA Technical Reports Server (NTRS)

    Frisbee, Joseph H., Jr.

    2015-01-01

    Upper bounds on high speed satellite collision probability, PC †, have been investigated. Previous methods assume an individual position error covariance matrix is available for each object. The two matrices being combined into a single, relative position error covariance matrix. Components of the combined error covariance are then varied to obtain a maximum PC. If error covariance information for only one of the two objects was available, either some default shape has been used or nothing could be done. An alternative is presented that uses the known covariance information along with a critical value of the missing covariance to obtain an approximate but potentially useful Pc upper bound.

  10. Slide Presentations as Speech Suppressors: When and Why Learners Miss Oral Information

    ERIC Educational Resources Information Center

    Wecker, Christof

    2012-01-01

    The objective of this study was to test whether information presented on slides during presentations is retained at the expense of information presented only orally, and to investigate part of the conditions under which this effect occurs, and how it can be avoided. Such an effect could be expected and explained either as a kind of redundancy…

  11. Slide Presentations as Speech Suppressors: When and Why Learners Miss Oral Information

    ERIC Educational Resources Information Center

    Wecker, Christof

    2012-01-01

    The objective of this study was to test whether information presented on slides during presentations is retained at the expense of information presented only orally, and to investigate part of the conditions under which this effect occurs, and how it can be avoided. Such an effect could be expected and explained either as a kind of redundancy…

  12. Missing genetic information in case-control family data with general semi-parametric shared frailty model.

    PubMed

    Graber-Naidich, Anna; Gorfine, Malka; Malone, Kathleen E; Hsu, Li

    2011-04-01

    Case-control family data are now widely used to examine the role of gene-environment interactions in the etiology of complex diseases. In these types of studies, exposure levels are obtained retrospectively and, frequently, information on most risk factors of interest is available on the probands but not on their relatives. In this work we consider correlated failure time data arising from population-based case-control family studies with missing genotypes of relatives. We present a new method for estimating the age-dependent marginalized hazard function. The proposed technique has two major advantages: (1) it is based on the pseudo full likelihood function rather than a pseudo composite likelihood function, which usually suffers from substantial efficiency loss; (2) the cumulative baseline hazard function is estimated using a two-stage estimator instead of an iterative process. We assess the performance of the proposed methodology with simulation studies, and illustrate its utility on a real data example.

  13. Media Education and Information Literacy: Are We Missing Most of the Real Lessons?

    ERIC Educational Resources Information Center

    Duncan, Barry

    1997-01-01

    Discusses cultural issues and implications of media education and information literacy. Presents examples of the social impact of new technologies. Outlines insights from research on audience research on the effects of media. Lists Les Browns' the "Seven Deadly Sins of the Digital Age." (AEF)

  14. The Impact of Information and Communication Technology on Education: The Missing Discourse between Three Different Paradigms

    ERIC Educational Resources Information Center

    Aviram, Aharon; Talmi, Deborah

    2005-01-01

    Using a new methodological tool, the authors analyzed a large number of texts on information and communication technology (ICT) and education, and identified three clusters of views that guide educationists "in the field" and in more academic contexts. The clusters reflect different fundamental assumptions on ICT and education. The authors argue…

  15. Case reports describing treatments in the emergency medicine literature: missing and misleading information

    PubMed Central

    Richason, Tiffany P; Paulson, Stephen M; Lowenstein, Steven R; Heard, Kennon J

    2009-01-01

    Background Although randomized trials and systematic reviews provide the "best evidence" for guiding medical practice, many emergency medicine journals still publish case reports (CRs). The quality of the reporting in these publications has not been assessed. Objectives In this study we sought to determine the proportion of treatment-related case reports that adequately reported information about the patient, disease, interventions, co-interventions, outcomes and other critical information. Methods We identified CRs published in 4 emergency medicine journals in 2000–2005 and categorized them according to their purpose (disease description, overdose or adverse drug reactioin, diagnostic test or treatment effect). Treatment-related CRs were reviewed for the presence or absence of 11 reporting elements. Results All told, 1,316 CRs were identified; of these, 85 (6.5%; 95CI = 66, 84) were about medical or surgical treatments. Most contained adequate descriptions of the patient (99%; 95CI = 95, 100), the stage and severity of the patient's disease (88%; 95CI = 79, 93), the intervention (80%; 95CI = 70, 87) and the outcomes of treatment (90%; 95CI = 82, 95). Fewer CRs reported the patient's co-morbidities (45%; 95CI = 35, 56), concurrent medications (30%; 95CI = 21, 40) or co-interventions (57%; 95CI = 46, 67) or mentioned any possible treatment side-effects (33%; 95CI = 24, 44). Only 37% (95CI = 19, 38) discussed alternative explanations for favorable outcomes. Generalizability of treatment effects to other patients was mentioned in only 29% (95CI = 20, 39). Just 2 CRs (2.3%; 95CI = 1, 8) reported a 'denominator" (number of patients subjected to the same intervention, whether or not successful. Conclusion Treatment-related CRs in emergency medicine journals often omit critical details about treatments, co-interventions, outcomes, generalizability, causality and denominators. As a result, the information may be misleading to providers, and the clinical applications may

  16. What's missing? Discussing stem cell translational research in educational information on stem cell "tourism".

    PubMed

    Master, Zubin; Zarzeczny, Amy; Rachul, Christen; Caulfield, Timothy

    2013-01-01

    Stem cell tourism is a growing industry in which patients pursue unproven stem cell therapies for a wide variety of illnesses and conditions. It is a challenging market to regulate due to a number of factors including its international, online, direct-to-consumer approach. Calls to provide education and information to patients, their families, physicians, and the general public about the risks associated with stem cell tourism are mounting. Initial studies examining the perceptions of patients who have pursued stem cell tourism indicate many are highly critical of the research and regulatory systems in their home countries and believe them to be stagnant and unresponsive to patient needs. We suggest that educational material should include an explanation of the translational research process, in addition to other aspects of stem cell tourism, as one means to help promote greater understanding and, ideally, curb patient demand for unproven stem cell interventions. The material provided must stress that strong scientific research is required in order for therapies to be safe and have a greater chance at being effective. Through an analysis of educational material on stem cell tourism and translational stem cell research from patient groups and scientific societies, we describe essential elements that should be conveyed in educational material provided to patients. Although we support the broad dissemination of educational material on stem cell translational research, we also acknowledge that education may simply not be enough to engender patient and public trust in domestic research and regulatory systems. However, promoting patient autonomy by providing good quality information to patients so they can make better informed decisions is valuable in itself, irrespective of whether it serves as an effective deterrent of stem cell tourism. © 2013 American Society of Law, Medicine & Ethics, Inc.

  17. Working with Missing Values

    ERIC Educational Resources Information Center

    Acock, Alan C.

    2005-01-01

    Less than optimum strategies for missing values can produce biased estimates, distorted statistical power, and invalid conclusions. After reviewing traditional approaches (listwise, pairwise, and mean substitution), selected alternatives are covered including single imputation, multiple imputation, and full information maximum likelihood…

  18. Working with Missing Values

    ERIC Educational Resources Information Center

    Acock, Alan C.

    2005-01-01

    Less than optimum strategies for missing values can produce biased estimates, distorted statistical power, and invalid conclusions. After reviewing traditional approaches (listwise, pairwise, and mean substitution), selected alternatives are covered including single imputation, multiple imputation, and full information maximum likelihood…

  19. A Note on the Use of Missing Auxiliary Variables in Full Information Maximum Likelihood-Based Structural Equation Models

    ERIC Educational Resources Information Center

    Enders, Craig K.

    2008-01-01

    Recent missing data studies have argued in favor of an "inclusive analytic strategy" that incorporates auxiliary variables into the estimation routine, and Graham (2003) outlined methods for incorporating auxiliary variables into structural equation analyses. In practice, the auxiliary variables often have missing values, so it is reasonable to…

  20. A Note on the Use of Missing Auxiliary Variables in Full Information Maximum Likelihood-Based Structural Equation Models

    ERIC Educational Resources Information Center

    Enders, Craig K.

    2008-01-01

    Recent missing data studies have argued in favor of an "inclusive analytic strategy" that incorporates auxiliary variables into the estimation routine, and Graham (2003) outlined methods for incorporating auxiliary variables into structural equation analyses. In practice, the auxiliary variables often have missing values, so it is reasonable to…

  1. Modeling Lung Carcinogenesis in Radon-Exposed Miner Cohorts: Accounting for Missing Information on Smoking.

    PubMed

    van Dillen, Teun; Dekkers, Fieke; Bijwaard, Harmen; Brüske, Irene; Wichmann, H-Erich; Kreuzer, Michaela; Grosche, Bernd

    2016-05-01

    Epidemiological miner cohort data used to estimate lung cancer risks related to occupational radon exposure often lack cohort-wide information on exposure to tobacco smoke, a potential confounder and important effect modifier. We have developed a method to project data on smoking habits from a case-control study onto an entire cohort by means of a Monte Carlo resampling technique. As a proof of principle, this method is tested on a subcohort of 35,084 former uranium miners employed at the WISMUT company (Germany), with 461 lung cancer deaths in the follow-up period 1955-1998. After applying the proposed imputation technique, a biologically-based carcinogenesis model is employed to analyze the cohort's lung cancer mortality data. A sensitivity analysis based on a set of 200 independent projections with subsequent model analyses yields narrow distributions of the free model parameters, indicating that parameter values are relatively stable and independent of individual projections. This technique thus offers a possibility to account for unknown smoking habits, enabling us to unravel risks related to radon, to smoking, and to the combination of both.

  2. Covariation neglect among novice investors.

    PubMed

    Hedesström, Ted Martin; Svedsäter, Henrik; Gärling, Tommy

    2006-09-01

    In 4 experiments, undergraduates made hypothetical investment choices. In Experiment 1, participants paid more attention to the volatility of individual assets than to the volatility of aggregated portfolios. The results of Experiment 2 show that most participants diversified even when this increased risk because of covariation between the returns of individual assets. In Experiment 3, nearly half of those who seemingly attempted to minimize risk diversified even when this increased risk. These results indicate that novice investors neglect covariation when diversifying across investment alternatives. Experiment 4 established that naive diversification follows from motivation to minimize risk and showed that covariation neglect was not significantly reduced by informing participants about how covariation affects portfolio risk but was reduced by making participants systematically calculate aggregate returns for diversified portfolios. In order to counteract naive diversification, novice investors need to be better informed about the rationale underlying recommendations to diversify.

  3. Estimating Landfill Methane Oxidation Using the Information of CO2/CH4 Fluxes Measured By the Eddy Covariance Method

    NASA Astrophysics Data System (ADS)

    Xu, L.; McDermitt, D. K.; Li, J.; Green, R. B.

    2016-12-01

    Methane plays a critical role in the radiation balance and chemistry of the atmosphere. Globally, landfill methane emission contributes about 10-19% of the anthropogenic methane burden into the atmosphere. In the United States, 18% of annual anthropogenic methane emissions come from landfills, which represent the third largest source of anthropogenic methane emissions, behind enteric fermentation and natural gas and oil production. One uncertainty in estimating landfill methane emissions is the fraction of methane oxidized when methane produced under anaerobic conditions passes through the cover soil. We developed a simple stoichiometric model to estimate the landfill methane oxidation fraction when the anaerobic CO2/CH4 production ratio is known. The model predicts a linear relationship between CO2 emission rates and CH4 emission rates, where the slope depends on anaerobic CO2/CH4 production ratio and the fraction of methane oxidized, and the intercept depends on non-methane-dependent oxidation processes. The model was tested with eddy covariance CO2 and CH4 emission rates at Bluff Road Landfill in Lincoln Nebraska. It predicted zero oxidation rate in the northern portion of this landfill where a membrane and vents were present. The zero oxidation rate was expected because there would be little opportunity for methane to encounter oxidizing conditions before leaving the vents. We also applied the model at the Turkey Run Landfill in Georgia to estimate the CH4 oxidation rate over a one year period. In contrast to Bluff Road Landfill, the Turkey Run Landfill did not have a membrane or vents. Instead, methane produced in the landfill had to diffuse through a 0.5 m soil cap before release to the atmosphere. We observed evidence for methane oxidation ranging from about 18% to above 60% depending upon the age of deposited waste material. The model will be briefly described, and results from the two contrasting landfills will be discussed in this presentation.

  4. Missing Mechanism Information

    ERIC Educational Resources Information Center

    Tryon, Warren W.

    2009-01-01

    The first recommendation Kazdin made for advancing the psychotherapy research knowledge base, improving patient care, and reducing the gulf between research and practice was to study the mechanisms of therapeutic change. He noted, "The study of mechanisms of change has received the least attention even though understanding mechanisms may well be…

  5. Covariant electromagnetic field lines

    NASA Astrophysics Data System (ADS)

    Hadad, Y.; Cohen, E.; Kaminer, I.; Elitzur, A. C.

    2017-08-01

    Faraday introduced electric field lines as a powerful tool for understanding the electric force, and these field lines are still used today in classrooms and textbooks teaching the basics of electromagnetism within the electrostatic limit. However, despite attempts at generalizing this concept beyond the electrostatic limit, such a fully relativistic field line theory still appears to be missing. In this work, we propose such a theory and define covariant electromagnetic field lines that naturally extend electric field lines to relativistic systems and general electromagnetic fields. We derive a closed-form formula for the field lines curvature in the vicinity of a charge, and show that it is related to the world line of the charge. This demonstrates how the kinematics of a charge can be derived from the geometry of the electromagnetic field lines. Such a theory may also provide new tools in modeling and analyzing electromagnetic phenomena, and may entail new insights regarding long-standing problems such as radiation-reaction and self-force. In particular, the electromagnetic field lines curvature has the attractive property of being non-singular everywhere, thus eliminating all self-field singularities without using renormalization techniques.

  6. Covariance Models for Hydrological Applications

    NASA Astrophysics Data System (ADS)

    Hristopulos, Dionissios

    2014-05-01

    a new class of generalized Gibbs random fields, IEEE Transactions on Information Theory, 53(12), 4667 - 4679. [2] D. T. Hristopulos and M. Zukovic, 2011. Relationships between correlation lengths and integral scales for covariance models with more than two parameters, Stochastic Environmental Research and Risk Assessment, 25(1), 11-19. [3] D. T. Hristopulos, 2014. Radial Covariance Functions Motivated by Spatial Random Field Models with Local Interactions, arXiv:1401.2823 [math.ST] .

  7. Accounting for interactions and complex inter-subject dependency in estimating treatment effect in cluster randomized trials with missing outcomes

    PubMed Central

    Prague, Melanie; Wang, Rui; Stephens, Alisa; Tchetgen Tchetgen, Eric; DeGruttola, Victor

    2016-01-01

    Summary Semi-parametric methods are often used for the estimation of intervention effects on correlated outcomes in cluster-randomized trials (CRTs). When outcomes are missing at random (MAR), Inverse Probability Weighted (IPW) methods incorporating baseline covariates can be used to deal with informative missingness. Also, augmented generalized estimating equations (AUG) correct for imbalance in baseline covariates but need to be extended for MAR outcomes. However, in the presence of interactions between treatment and baseline covariates, neither method alone produces consistent estimates for the marginal treatment effect if the model for interaction is not correctly specified. We propose an AUG-IPW estimator that weights by the inverse of the probability of being a complete case and allows different outcome models in each intervention arm. This estimator is doubly robust (DR), it gives correct estimates whether the missing data process or the outcome model is correctly specified. We consider the problem of covariate interference which arises when the outcome of an individual may depend on covariates of other individuals. When interfering covariates are not modeled, the DR property prevents bias as long as covariate interference is not present simultaneously for the outcome and the missingness. An R package is developed implementing the proposed method. An extensive simulation study and an application to a CRT of HIV risk reduction-intervention in South Africa illustrate the method. PMID:27060877

  8. Imputation by the mean score should be avoided when validating a Patient Reported Outcomes questionnaire by a Rasch model in presence of informative missing data

    PubMed Central

    2011-01-01

    Background Nowadays, more and more clinical scales consisting in responses given by the patients to some items (Patient Reported Outcomes - PRO), are validated with models based on Item Response Theory, and more specifically, with a Rasch model. In the validation sample, presence of missing data is frequent. The aim of this paper is to compare sixteen methods for handling the missing data (mainly based on simple imputation) in the context of psychometric validation of PRO by a Rasch model. The main indexes used for validation by a Rasch model are compared. Methods A simulation study was performed allowing to consider several cases, notably the possibility for the missing values to be informative or not and the rate of missing data. Results Several imputations methods produce bias on psychometrical indexes (generally, the imputation methods artificially improve the psychometric qualities of the scale). In particular, this is the case with the method based on the Personal Mean Score (PMS) which is the most commonly used imputation method in practice. Conclusions Several imputation methods should be avoided, in particular PMS imputation. From a general point of view, it is important to use an imputation method that considers both the ability of the patient (measured for example by his/her score), and the difficulty of the item (measured for example by its rate of favourable responses). Another recommendation is to always consider the addition of a random process in the imputation method, because such a process allows reducing the bias. Last, the analysis realized without imputation of the missing data (available case analyses) is an interesting alternative to the simple imputation in this context. PMID:21756330

  9. Imputation by the mean score should be avoided when validating a Patient Reported Outcomes questionnaire by a Rasch model in presence of informative missing data.

    PubMed

    Hardouin, Jean-Benoit; Conroy, Ronán; Sébille, Véronique

    2011-07-14

    Nowadays, more and more clinical scales consisting in responses given by the patients to some items (Patient Reported Outcomes - PRO), are validated with models based on Item Response Theory, and more specifically, with a Rasch model. In the validation sample, presence of missing data is frequent. The aim of this paper is to compare sixteen methods for handling the missing data (mainly based on simple imputation) in the context of psychometric validation of PRO by a Rasch model. The main indexes used for validation by a Rasch model are compared. A simulation study was performed allowing to consider several cases, notably the possibility for the missing values to be informative or not and the rate of missing data. Several imputations methods produce bias on psychometrical indexes (generally, the imputation methods artificially improve the psychometric qualities of the scale). In particular, this is the case with the method based on the Personal Mean Score (PMS) which is the most commonly used imputation method in practice. Several imputation methods should be avoided, in particular PMS imputation. From a general point of view, it is important to use an imputation method that considers both the ability of the patient (measured for example by his/her score), and the difficulty of the item (measured for example by its rate of favourable responses). Another recommendation is to always consider the addition of a random process in the imputation method, because such a process allows reducing the bias. Last, the analysis realized without imputation of the missing data (available case analyses) is an interesting alternative to the simple imputation in this context.

  10. Missing data methods for dealing with missing items in quality of life questionnaires. A comparison by simulation of personal mean score, full information maximum likelihood, multiple imputation, and hot deck techniques applied to the SF-36 in the French 2003 decennial health survey.

    PubMed

    Peyre, Hugo; Leplège, Alain; Coste, Joël

    2011-03-01

    Missing items are common in quality of life (QoL) questionnaires and present a challenge for research in this field. It remains unclear which of the various methods proposed to deal with missing data performs best in this context. We compared personal mean score, full information maximum likelihood, multiple imputation, and hot deck techniques using various realistic simulation scenarios of item missingness in QoL questionnaires constructed within the framework of classical test theory. Samples of 300 and 1,000 subjects were randomly drawn from the 2003 INSEE Decennial Health Survey (of 23,018 subjects representative of the French population and having completed the SF-36) and various patterns of missing data were generated according to three different item non-response rates (3, 6, and 9%) and three types of missing data (Little and Rubin's "missing completely at random," "missing at random," and "missing not at random"). The missing data methods were evaluated in terms of accuracy and precision for the analysis of one descriptive and one association parameter for three different scales of the SF-36. For all item non-response rates and types of missing data, multiple imputation and full information maximum likelihood appeared superior to the personal mean score and especially to hot deck in terms of accuracy and precision; however, the use of personal mean score was associated with insignificant bias (relative bias <2%) in all studied situations. Whereas multiple imputation and full information maximum likelihood are confirmed as reference methods, the personal mean score appears nonetheless appropriate for dealing with items missing from completed SF-36 questionnaires in most situations of routine use. These results can reasonably be extended to other questionnaires constructed according to classical test theory.

  11. A class of covariate-dependent spatiotemporal covariance functions.

    PubMed

    Reich, Brian J; Eidsvik, Jo; Guindani, Michele; Nail, Amy J; Schmidt, Alexandra M

    2011-12-01

    In geostatistics, it is common to model spatially distributed phenomena through an underlying stationary and isotropic spatial process. However, these assumptions are often untenable in practice because of the influence of local effects in the correlation structure. Therefore, it has been of prolonged interest in the literature to provide flexible and effective ways to model non-stationarity in the spatial effects. Arguably, due to the local nature of the problem, we might envision that the correlation structure would be highly dependent on local characteristics of the domain of study, namely the latitude, longitude and altitude of the observation sites, as well as other locally defined covariate information. In this work, we provide a flexible and computationally feasible way for allowing the correlation structure of the underlying processes to depend on local covariate information. We discuss the properties of the induced covariance functions and discuss methods to assess its dependence on local covariate information by means of a simulation study and the analysis of data observed at ozone-monitoring stations in the Southeast United States.

  12. Robust covariance estimation of galaxy-galaxy weak lensing: validation and limitation of jackknife covariance

    NASA Astrophysics Data System (ADS)

    Shirasaki, Masato; Takada, Masahiro; Miyatake, Hironao; Takahashi, Ryuichi; Hamana, Takashi; Nishimichi, Takahiro; Murata, Ryoma

    2017-09-01

    We develop a method to simulate galaxy-galaxy weak lensing by utilizing all-sky, light-cone simulations and their inherent halo catalogues. Using the mock catalogue to study the error covariance matrix of galaxy-galaxy weak lensing, we compare the full covariance with the 'jackknife' (JK) covariance, the method often used in the literature that estimates the covariance from the resamples of the data itself. We show that there exists the variation of JK covariance over realizations of mock lensing measurements, while the average JK covariance over mocks can give a reasonably accurate estimation of the true covariance up to separations comparable with the size of JK subregion. The scatter in JK covariances is found to be ∼10 per cent after we subtract the lensing measurement around random points. However, the JK method tends to underestimate the covariance at the larger separations, more increasingly for a survey with a higher number density of source galaxies. We apply our method to the Sloan Digital Sky Survey (SDSS) data, and show that the 48 mock SDSS catalogues nicely reproduce the signals and the JK covariance measured from the real data. We then argue that the use of the accurate covariance, compared to the JK covariance, allows us to use the lensing signals at large scales beyond a size of the JK subregion, which contains cleaner cosmological information in the linear regime.

  13. The effect of using approximate gametic variance covariance matrices on marker assisted selection by BLUP.

    PubMed

    Totir, Liviu R; Fernando, Rohan L; Dekkers, Jack C M; Fernández, Soledad A; Guldbrandtsen, Bernt

    2004-01-01

    Under additive inheritance, the Henderson mixed model equations (HMME) provide an efficient approach to obtaining genetic evaluations by marker assisted best linear unbiased prediction (MABLUP) given pedigree relationships, trait and marker data. For large pedigrees with many missing markers, however, it is not feasible to calculate the exact gametic variance covariance matrix required to construct HMME. The objective of this study was to investigate the consequences of using approximate gametic variance covariance matrices on response to selection by MABLUP. Two methods were used to generate approximate variance covariance matrices. The first method (Method A) completely discards the marker information for individuals with an unknown linkage phase between two flanking markers. The second method (Method B) makes use of the marker information at only the most polymorphic marker locus for individuals with an unknown linkage phase. Data sets were simulated with and without missing marker data for flanking markers with 2, 4, 6, 8 or 12 alleles. Several missing marker data patterns were considered. The genetic variability explained by marked quantitative trait loci (MQTL) was modeled with one or two MQTL of equal effect. Response to selection by MABLUP using Method A or Method B were compared with that obtained by MABLUP using the exact genetic variance covariance matrix, which was estimated using 15,000 samples from the conditional distribution of genotypic values given the observed marker data. For the simulated conditions, the superiority of MABLUP over BLUP based only on pedigree relationships and trait data varied between 0.1% and 13.5% for Method A, between 1.7% and 23.8% for Method B, and between 7.6% and 28.9% for the exact method. The relative performance of the methods under investigation was not affected by the number of MQTL in the model.

  14. Auto covariance computer

    NASA Technical Reports Server (NTRS)

    Hepner, T. E.; Meyers, J. F. (Inventor)

    1985-01-01

    A laser velocimeter covariance processor which calculates the auto covariance and cross covariance functions for a turbulent flow field based on Poisson sampled measurements in time from a laser velocimeter is described. The device will process a block of data that is up to 4096 data points in length and return a 512 point covariance function with 48-bit resolution along with a 512 point histogram of the interarrival times which is used to normalize the covariance function. The device is designed to interface and be controlled by a minicomputer from which the data is received and the results returned. A typical 4096 point computation takes approximately 1.5 seconds to receive the data, compute the covariance function, and return the results to the computer.

  15. Use of imputed population-based cancer registry data as a method of accounting for missing information: application to estrogen receptor status for breast cancer.

    PubMed

    Howlader, Nadia; Noone, Anne-Michelle; Yu, Mandi; Cronin, Kathleen A

    2012-08-15

    The National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) Program provides a rich source of data stratified according to tumor biomarkers that play an important role in cancer surveillance research. These data are useful for analyzing trends in cancer incidence and survival. These tumor markers, however, are often prone to missing observations. To address the problem of missing data, the authors employed sequential regression multivariate imputation for breast cancer variables, with a particular focus on estrogen receptor status, using data from 13 SEER registries covering the period 1992-2007. In this paper, they present an approach to accounting for missing information through the creation of imputed data sets that can be analyzed using existing software (e.g., SEER*Stat) developed for analyzing cancer registry data. Bias in age-adjusted trends in female breast cancer incidence is shown graphically before and after imputation of estrogen receptor status, stratified by age and race. The imputed data set will be made available in SEER*Stat (http://seer.cancer.gov/analysis/index.html) to facilitate accurate estimation of breast cancer incidence trends. To ensure that the imputed data set is used correctly, the authors provide detailed, step-by-step instructions for conducting analyses. This is the first time that a nationally representative, population-based cancer registry data set has been imputed and made available to researchers for conducting a variety of analyses of breast cancer incidence trends.

  16. 'Miss Frances', 'Miss Gail' and 'Miss Sandra' Crapemyrtles

    USDA-ARS?s Scientific Manuscript database

    The Agricultural Research Service, United States Department of Agriculture, announces the release to nurserymen of three new crapemyrtle cultivars named 'Miss Gail', 'Miss Frances', and 'Miss Sandra'. ‘Miss Gail’ resulted from a cross-pollination between ‘Catawba’ as the female parent and ‘Arapaho’ ...

  17. Help for Finding Missing Children.

    ERIC Educational Resources Information Center

    McCormick, Kathleen

    1984-01-01

    Efforts to locate missing children have expanded from a federal law allowing for entry of information into an F.B.I. computer system to companion bills before Congress for establishing a national missing child clearinghouse and a Justice Department center to help in conducting searches. Private organizations are also involved. (KS)

  18. Model Selection Criteria for Missing-Data Problems Using the EM Algorithm.

    PubMed

    Ibrahim, Joseph G; Zhu, Hongtu; Tang, Niansheng

    2008-12-01

    We consider novel methods for the computation of model selection criteria in missing-data problems based on the output of the EM algorithm. The methodology is very general and can be applied to numerous situations involving incomplete data within an EM framework, from covariates missing at random in arbitrary regression models to nonignorably missing longitudinal responses and/or covariates. Toward this goal, we develop a class of information criteria for missing-data problems, called IC(H) (,) (Q), which yields the Akaike information criterion and the Bayesian information criterion as special cases. The computation of IC(H) (,) (Q) requires an analytic approximation to a complicated function, called the H-function, along with output from the EM algorithm used in obtaining maximum likelihood estimates. The approximation to the H-function leads to a large class of information criteria, called IC(H̃) (() (k) (),) (Q). Theoretical properties of IC(H̃) (() (k) (),) (Q), including consistency, are investigated in detail. To eliminate the analytic approximation to the H-function, a computationally simpler approximation to IC(H) (,) (Q), called IC(Q), is proposed, the computation of which depends solely on the Q-function of the EM algorithm. Advantages and disadvantages of IC(H̃) (() (k) (),) (Q) and IC(Q) are discussed and examined in detail in the context of missing-data problems. Extensive simulations are given to demonstrate the methodology and examine the small-sample and large-sample performance of IC(H̃) (() (k) (),) (Q) and IC(Q) in missing-data problems. An AIDS data set also is presented to illustrate the proposed methodology.

  19. Propensity score analysis with missing data.

    PubMed

    Cham, Heining; West, Stephen G

    2016-09-01

    Propensity score analysis is a method that equates treatment and control groups on a comprehensive set of measured confounders in observational (nonrandomized) studies. A successful propensity score analysis reduces bias in the estimate of the average treatment effect in a nonrandomized study, making the estimate more comparable with that obtained from a randomized experiment. This article reviews and discusses an important practical issue in propensity analysis, in which the baseline covariates (potential confounders) and the outcome have missing values (incompletely observed). We review the statistical theory of propensity score analysis and estimation methods for propensity scores with incompletely observed covariates. Traditional logistic regression and modern machine learning methods (e.g., random forests, generalized boosted modeling) as estimation methods for incompletely observed covariates are reviewed. Balance diagnostics and equating methods for incompletely observed covariates are briefly described. Using an empirical example, the propensity score estimation methods for incompletely observed covariates are illustrated and compared. (PsycINFO Database Record

  20. Galilean covariant harmonic oscillator

    NASA Technical Reports Server (NTRS)

    Horzela, Andrzej; Kapuscik, Edward

    1993-01-01

    A Galilean covariant approach to classical mechanics of a single particle is described. Within the proposed formalism, all non-covariant force laws defining acting forces which become to be defined covariantly by some differential equations are rejected. Such an approach leads out of the standard classical mechanics and gives an example of non-Newtonian mechanics. It is shown that the exactly solvable linear system of differential equations defining forces contains the Galilean covariant description of harmonic oscillator as its particular case. Additionally, it is demonstrated that in Galilean covariant classical mechanics the validity of the second Newton law of dynamics implies the Hooke law and vice versa. It is shown that the kinetic and total energies transform differently with respect to the Galilean transformations.

  1. One‐stage individual participant data meta‐analysis models: estimation of treatment‐covariate interactions must avoid ecological bias by separating out within‐trial and across‐trial information

    PubMed Central

    Hua, Hairui; Burke, Danielle L.; Crowther, Michael J.; Ensor, Joie; Tudur Smith, Catrin

    2016-01-01

    Stratified medicine utilizes individual‐level covariates that are associated with a differential treatment effect, also known as treatment‐covariate interactions. When multiple trials are available, meta‐analysis is used to help detect true treatment‐covariate interactions by combining their data. Meta‐regression of trial‐level information is prone to low power and ecological bias, and therefore, individual participant data (IPD) meta‐analyses are preferable to examine interactions utilizing individual‐level information. However, one‐stage IPD models are often wrongly specified, such that interactions are based on amalgamating within‐ and across‐trial information. We compare, through simulations and an applied example, fixed‐effect and random‐effects models for a one‐stage IPD meta‐analysis of time‐to‐event data where the goal is to estimate a treatment‐covariate interaction. We show that it is crucial to centre patient‐level covariates by their mean value in each trial, in order to separate out within‐trial and across‐trial information. Otherwise, bias and coverage of interaction estimates may be adversely affected, leading to potentially erroneous conclusions driven by ecological bias. We revisit an IPD meta‐analysis of five epilepsy trials and examine age as a treatment effect modifier. The interaction is −0.011 (95% CI: −0.019 to −0.003; p = 0.004), and thus highly significant, when amalgamating within‐trial and across‐trial information. However, when separating within‐trial from across‐trial information, the interaction is −0.007 (95% CI: −0.019 to 0.005; p = 0.22), and thus its magnitude and statistical significance are greatly reduced. We recommend that meta‐analysts should only use within‐trial information to examine individual predictors of treatment effect and that one‐stage IPD models should separate within‐trial from across‐trial information to avoid ecological bias. © 2016

  2. On the joys of missing data.

    PubMed

    Little, Todd D; Jorgensen, Terrence D; Lang, Kyle M; Moore, E Whitney G

    2014-03-01

    We provide conceptual introductions to missingness mechanisms--missing completely at random, missing at random, and missing not at random--and state-of-the-art methods of handling missing data--full-information maximum likelihood and multiple imputation--followed by a discussion of planned missing designs: Multiform questionnaire protocols, 2-method measurement models, and wave-missing longitudinal designs. We reviewed 80 articles of empirical studies published in the 2012 issues of the Journal of Pediatric Psychology to present a picture of how adequately missing data are currently handled in this field. To illustrate the benefits of using multiple imputation or full-information maximum likelihood and incorporating planned missingness into study designs, we provide example analyses of empirical data gathered using a 3-form planned missing design.

  3. Covariant mutually unbiased bases

    NASA Astrophysics Data System (ADS)

    Carmeli, Claudio; Schultz, Jussi; Toigo, Alessandro

    2016-06-01

    The connection between maximal sets of mutually unbiased bases (MUBs) in a prime-power dimensional Hilbert space and finite phase-space geometries is well known. In this article, we classify MUBs according to their degree of covariance with respect to the natural symmetries of a finite phase-space, which are the group of its affine symplectic transformations. We prove that there exist maximal sets of MUBs that are covariant with respect to the full group only in odd prime-power dimensional spaces, and in this case, their equivalence class is actually unique. Despite this limitation, we show that in dimension 2r covariance can still be achieved by restricting to proper subgroups of the symplectic group, that constitute the finite analogues of the oscillator group. For these subgroups, we explicitly construct the unitary operators yielding the covariance.

  4. Covariant Noncommutative Field Theory

    SciTech Connect

    Estrada-Jimenez, S.; Garcia-Compean, H.; Obregon, O.; Ramirez, C.

    2008-07-02

    The covariant approach to noncommutative field and gauge theories is revisited. In the process the formalism is applied to field theories invariant under diffeomorphisms. Local differentiable forms are defined in this context. The lagrangian and hamiltonian formalism is consistently introduced.

  5. An Upper Bound on Orbital Debris Collision Probability When Only One Object has Position Uncertainty Information

    NASA Technical Reports Server (NTRS)

    Frisbee, Joseph H., Jr.

    2015-01-01

    Upper bounds on high speed satellite collision probability, P (sub c), have been investigated. Previous methods assume an individual position error covariance matrix is available for each object. The two matrices being combined into a single, relative position error covariance matrix. Components of the combined error covariance are then varied to obtain a maximum P (sub c). If error covariance information for only one of the two objects was available, either some default shape has been used or nothing could be done. An alternative is presented that uses the known covariance information along with a critical value of the missing covariance to obtain an approximate but useful P (sub c) upper bound. There are various avenues along which an upper bound on the high speed satellite collision probability has been pursued. Typically, for the collision plane representation of the high speed collision probability problem, the predicted miss position in the collision plane is assumed fixed. Then the shape (aspect ratio of ellipse), the size (scaling of standard deviations) or the orientation (rotation of ellipse principal axes) of the combined position error ellipse is varied to obtain a maximum P (sub c). Regardless as to the exact details of the approach, previously presented methods all assume that an individual position error covariance matrix is available for each object and the two are combined into a single, relative position error covariance matrix. This combined position error covariance matrix is then modified according to the chosen scheme to arrive at a maximum P (sub c). But what if error covariance information for one of the two objects is not available? When error covariance information for one of the objects is not available the analyst has commonly defaulted to the situation in which only the relative miss position and velocity are known without any corresponding state error covariance information. The various usual methods of finding a maximum P (sub c) do

  6. Matrix of the covariance of covariance of acceleration responses for damage detection from ambient vibration measurements

    NASA Astrophysics Data System (ADS)

    Li, X. Y.; Law, S. S.

    2010-05-01

    A new matrix on the covariance of covariance is formed from the auto/cross-correlation function of acceleration responses of a structure under white noise ambient excitation. The components of the covariance matrix are proved to be function of the modal parameters (modal frequency, mode shape, and damping parameter) of the structure. Information from all the vibration modes of the structure limited by the sampling frequency contributes to these components. The formulated covariance matrix contains more information on the vibration modes of the structure that cannot be obtained by the general methods for extracting modal parameters. When the component of the covariance matrix is used for damage detection, it is found more sensitive to local stiffness reduction than the first few modal frequencies and mode shapes obtained from ambient excitation. A simply supported 31 bar plane truss structure is studied numerically where a multiple damage scenario with different noise levels is identified with satisfactory results.

  7. A hierarchical nest survival model integrating incomplete temporally varying covariates

    USGS Publications Warehouse

    Converse, Sarah J.; Royle, J. Andrew; Adler, Peter H.; Urbanek, Richard P.; Barzan, Jeb A.

    2013-01-01

    Nest success is a critical determinant of the dynamics of avian populations, and nest survival modeling has played a key role in advancing avian ecology and management. Beginning with the development of daily nest survival models, and proceeding through subsequent extensions, the capacity for modeling the effects of hypothesized factors on nest survival has expanded greatly. We extend nest survival models further by introducing an approach to deal with incompletely observed, temporally varying covariates using a hierarchical model. Hierarchical modeling offers a way to separate process and observational components of demographic models to obtain estimates of the parameters of primary interest, and to evaluate structural effects of ecological and management interest. We built a hierarchical model for daily nest survival to analyze nest data from reintroduced whooping cranes (Grus americana) in the Eastern Migratory Population. This reintroduction effort has been beset by poor reproduction, apparently due primarily to nest abandonment by breeding birds. We used the model to assess support for the hypothesis that nest abandonment is caused by harassment from biting insects. We obtained indices of blood-feeding insect populations based on the spatially interpolated counts of insects captured in carbon dioxide traps. However, insect trapping was not conducted daily, and so we had incomplete information on a temporally variable covariate of interest. We therefore supplemented our nest survival model with a parallel model for estimating the values of the missing insect covariates. We used Bayesian model selection to identify the best predictors of daily nest survival. Our results suggest that the black fly Simulium annulus may be negatively affecting nest survival of reintroduced whooping cranes, with decreasing nest survival as abundance of S. annulus increases. The modeling framework we have developed will be applied in the future to a larger data set to evaluate the

  8. A hierarchical nest survival model integrating incomplete temporally varying covariates

    PubMed Central

    Converse, Sarah J; Royle, J Andrew; Adler, Peter H; Urbanek, Richard P; Barzen, Jeb A

    2013-01-01

    Nest success is a critical determinant of the dynamics of avian populations, and nest survival modeling has played a key role in advancing avian ecology and management. Beginning with the development of daily nest survival models, and proceeding through subsequent extensions, the capacity for modeling the effects of hypothesized factors on nest survival has expanded greatly. We extend nest survival models further by introducing an approach to deal with incompletely observed, temporally varying covariates using a hierarchical model. Hierarchical modeling offers a way to separate process and observational components of demographic models to obtain estimates of the parameters of primary interest, and to evaluate structural effects of ecological and management interest. We built a hierarchical model for daily nest survival to analyze nest data from reintroduced whooping cranes (Grus americana) in the Eastern Migratory Population. This reintroduction effort has been beset by poor reproduction, apparently due primarily to nest abandonment by breeding birds. We used the model to assess support for the hypothesis that nest abandonment is caused by harassment from biting insects. We obtained indices of blood-feeding insect populations based on the spatially interpolated counts of insects captured in carbon dioxide traps. However, insect trapping was not conducted daily, and so we had incomplete information on a temporally variable covariate of interest. We therefore supplemented our nest survival model with a parallel model for estimating the values of the missing insect covariates. We used Bayesian model selection to identify the best predictors of daily nest survival. Our results suggest that the black fly Simulium annulus may be negatively affecting nest survival of reintroduced whooping cranes, with decreasing nest survival as abundance of S. annulus increases. The modeling framework we have developed will be applied in the future to a larger data set to evaluate the

  9. A hierarchical nest survival model integrating incomplete temporally varying covariates.

    PubMed

    Converse, Sarah J; Royle, J Andrew; Adler, Peter H; Urbanek, Richard P; Barzen, Jeb A

    2013-11-01

    Nest success is a critical determinant of the dynamics of avian populations, and nest survival modeling has played a key role in advancing avian ecology and management. Beginning with the development of daily nest survival models, and proceeding through subsequent extensions, the capacity for modeling the effects of hypothesized factors on nest survival has expanded greatly. We extend nest survival models further by introducing an approach to deal with incompletely observed, temporally varying covariates using a hierarchical model. Hierarchical modeling offers a way to separate process and observational components of demographic models to obtain estimates of the parameters of primary interest, and to evaluate structural effects of ecological and management interest. We built a hierarchical model for daily nest survival to analyze nest data from reintroduced whooping cranes (Grus americana) in the Eastern Migratory Population. This reintroduction effort has been beset by poor reproduction, apparently due primarily to nest abandonment by breeding birds. We used the model to assess support for the hypothesis that nest abandonment is caused by harassment from biting insects. We obtained indices of blood-feeding insect populations based on the spatially interpolated counts of insects captured in carbon dioxide traps. However, insect trapping was not conducted daily, and so we had incomplete information on a temporally variable covariate of interest. We therefore supplemented our nest survival model with a parallel model for estimating the values of the missing insect covariates. We used Bayesian model selection to identify the best predictors of daily nest survival. Our results suggest that the black fly Simulium annulus may be negatively affecting nest survival of reintroduced whooping cranes, with decreasing nest survival as abundance of S. annulus increases. The modeling framework we have developed will be applied in the future to a larger data set to evaluate the

  10. Covariant Bardeen perturbation formalism

    NASA Astrophysics Data System (ADS)

    Vitenti, S. D. P.; Falciano, F. T.; Pinto-Neto, N.

    2014-05-01

    In a previous work we obtained a set of necessary conditions for the linear approximation in cosmology. Here we discuss the relations of this approach with the so-called covariant perturbations. It is often argued in the literature that one of the main advantages of the covariant approach to describe cosmological perturbations is that the Bardeen formalism is coordinate dependent. In this paper we will reformulate the Bardeen approach in a completely covariant manner. For that, we introduce the notion of pure and mixed tensors, which yields an adequate language to treat both perturbative approaches in a common framework. We then stress that in the referred covariant approach, one necessarily introduces an additional hypersurface choice to the problem. Using our mixed and pure tensors approach, we are able to construct a one-to-one map relating the usual gauge dependence of the Bardeen formalism with the hypersurface dependence inherent to the covariant approach. Finally, through the use of this map, we define full nonlinear tensors that at first order correspond to the three known gauge invariant variables Φ, Ψ and Ξ, which are simultaneously foliation and gauge invariant. We then stress that the use of the proposed mixed tensors allows one to construct simultaneously gauge and hypersurface invariant variables at any order.

  11. Covariance mapping techniques

    NASA Astrophysics Data System (ADS)

    Frasinski, Leszek J.

    2016-08-01

    Recent technological advances in the generation of intense femtosecond pulses have made covariance mapping an attractive analytical technique. The laser pulses available are so intense that often thousands of ionisation and Coulomb explosion events will occur within each pulse. To understand the physics of these processes the photoelectrons and photoions need to be correlated, and covariance mapping is well suited for operating at the high counting rates of these laser sources. Partial covariance is particularly useful in experiments with x-ray free electron lasers, because it is capable of suppressing pulse fluctuation effects. A variety of covariance mapping methods is described: simple, partial (single- and multi-parameter), sliced, contingent and multi-dimensional. The relationship to coincidence techniques is discussed. Covariance mapping has been used in many areas of science and technology: inner-shell excitation and Auger decay, multiphoton and multielectron ionisation, time-of-flight and angle-resolved spectrometry, infrared spectroscopy, nuclear magnetic resonance imaging, stimulated Raman scattering, directional gamma ray sensing, welding diagnostics and brain connectivity studies (connectomics). This review gives practical advice for implementing the technique and interpreting the results, including its limitations and instrumental constraints. It also summarises recent theoretical studies, highlights unsolved problems and outlines a personal view on the most promising research directions.

  12. A systematic review of titles and abstracts of experimental studies in medical education: many informative elements missing.

    PubMed

    Cook, David A; Beckman, Thomas J; Bordage, Georges

    2007-11-01

    Informative titles and abstracts facilitate reading and searching the literature. To evaluate the quality of titles and abstracts of full-length reports of experimental studies in medical education. We used a random sample of 110 articles (of 185 eligible articles) describing education experiments. Articles were published in 2003 and 2004 in Academic Medicine, Advances in Health Sciences Education, American Journal of Surgery, Journal of General Internal Medicine, Medical Education and Teaching and Learning in Medicine. Titles were categorised as informative, indicative, neither, or both. Abstracts were evaluated for the presence of a rationale, objective, descriptions of study design, setting, participants, study intervention and comparison group, main outcomes, results and conclusions. Of the 105 articles suitable for review, 86 (82%) had an indicative title and 10 (10%) had a title that was both indicative and informative. A rationale was present in 66 abstracts (63%), objectives were present in 84 (80%), descriptions of study design in 20 (19%), setting in 29 (28%), and number and stage of training of participants in 42 (40%). The study intervention was defined in 55 (52%) abstracts. Among the 48 studies with a control or comparison group, this group was defined in 21 abstracts (44%). Study outcomes were defined in 64 abstracts (61%). Data were presented in 48 (46%) abstracts. Conclusions were presented in 97 abstracts (92%). Reports of experimental studies in medical education frequently lack the essential elements of informative titles and abstracts. More informative reporting is needed.

  13. Missing semantic annotation in databases. The root cause for data integration and migration problems in information systems.

    PubMed

    Dugas, M

    2014-01-01

    Data integration is a well-known grand challenge in information systems. It is highly relevant in medicine because of the multitude of patient data sources. Semantic annotations of data items regarding concept and value domain, based on comprehensive terminologies can facilitate data integration and migration. Therefore it should be implemented in databases from the very beginning.

  14. Missing the Target: We Need to Focus on Informal Care Rather than Preschool. Evidence Speaks Reports, Vol 1, #19

    ERIC Educational Resources Information Center

    Loeb, Susanna

    2016-01-01

    Despite the widely-recognized benefits of early childhood experiences in formal settings that enrich the social and cognitive environments of children, many children--particularly infants and toddlers--spend their days in unregulated (or very lightly regulated) "informal" childcare settings. Over half of all one- and two-year-olds are…

  15. 20 CFR 364.3 - Publication of missing children information in the Railroad Retirement Board's in-house...

    Code of Federal Regulations, 2011 CFR

    2011-04-01

    ... in the Railroad Retirement Board's in-house publications. 364.3 Section 364.3 Employees' Benefits RAILROAD RETIREMENT BOARD INTERNAL ADMINISTRATION, POLICY AND PROCEDURES USE OF PENALTY MAIL TO ASSIST IN... the Railroad Retirement Board's in-house publications. (a) All-A-Board. Information about...

  16. Missing the Target: We Need to Focus on Informal Care Rather than Preschool. Evidence Speaks Reports, Vol 1, #19

    ERIC Educational Resources Information Center

    Loeb, Susanna

    2016-01-01

    Despite the widely-recognized benefits of early childhood experiences in formal settings that enrich the social and cognitive environments of children, many children--particularly infants and toddlers--spend their days in unregulated (or very lightly regulated) "informal" childcare settings. Over half of all one- and two-year-olds are…

  17. Spatiotemporal noise covariance estimation from limited empirical magnetoencephalographic data

    NASA Astrophysics Data System (ADS)

    Jun, Sung C.; Plis, Sergey M.; Ranken, Doug M.; Schmidt, David M.

    2006-11-01

    The performance of parametric magnetoencephalography (MEG) and electroencephalography (EEG) source localization approaches can be degraded by the use of poor background noise covariance estimates. In general, estimation of the noise covariance for spatiotemporal analysis is difficult mainly due to the limited noise information available. Furthermore, its estimation requires a large amount of storage and a one-time but very large (and sometimes intractable) calculation or its inverse. To overcome these difficulties, noise covariance models consisting of one pair or a sum of multi-pairs of Kronecker products of spatial covariance and temporal covariance have been proposed. However, these approaches cannot be applied when the noise information is very limited, i.e., the amount of noise information is less than the degrees of freedom of the noise covariance models. A common example of this is when only averaged noise data are available for a limited prestimulus region (typically at most a few hundred milliseconds duration). For such cases, a diagonal spatiotemporal noise covariance model consisting of sensor variances with no spatial or temporal correlation has been the common choice for spatiotemporal analysis. In this work, we propose a different noise covariance model which consists of diagonal spatial noise covariance and Toeplitz temporal noise covariance. It can easily be estimated from limited noise information, and no time-consuming optimization and data-processing are required. Thus, it can be used as an alternative choice when one-pair or multi-pair noise covariance models cannot be estimated due to lack of noise information. To verify its capability we used Bayesian inference dipole analysis and a number of simulated and empirical datasets. We compared this covariance model with other existing covariance models such as conventional diagonal covariance, one-pair and multi-pair noise covariance models, when noise information is sufficient to estimate them. We

  18. Spatiotemporal noise covariance estimation from limited empirical magnetoencephalographic data.

    PubMed

    Jun, Sung C; Plis, Sergey M; Ranken, Doug M; Schmidt, David M

    2006-11-07

    The performance of parametric magnetoencephalography (MEG) and electroencephalography (EEG) source localization approaches can be degraded by the use of poor background noise covariance estimates. In general, estimation of the noise covariance for spatiotemporal analysis is difficult mainly due to the limited noise information available. Furthermore, its estimation requires a large amount of storage and a one-time but very large (and sometimes intractable) calculation or its inverse. To overcome these difficulties, noise covariance models consisting of one pair or a sum of multi-pairs of Kronecker products of spatial covariance and temporal covariance have been proposed. However, these approaches cannot be applied when the noise information is very limited, i.e., the amount of noise information is less than the degrees of freedom of the noise covariance models. A common example of this is when only averaged noise data are available for a limited prestimulus region (typically at most a few hundred milliseconds duration). For such cases, a diagonal spatiotemporal noise covariance model consisting of sensor variances with no spatial or temporal correlation has been the common choice for spatiotemporal analysis. In this work, we propose a different noise covariance model which consists of diagonal spatial noise covariance and Toeplitz temporal noise covariance. It can easily be estimated from limited noise information, and no time-consuming optimization and data-processing are required. Thus, it can be used as an alternative choice when one-pair or multi-pair noise covariance models cannot be estimated due to lack of noise information. To verify its capability we used Bayesian inference dipole analysis and a number of simulated and empirical datasets. We compared this covariance model with other existing covariance models such as conventional diagonal covariance, one-pair and multi-pair noise covariance models, when noise information is sufficient to estimate them. We

  19. Covariance Applications with Kiwi

    NASA Astrophysics Data System (ADS)

    Mattoon, C. M.; Brown, D.; Elliott, J. B.

    2012-05-01

    The Computational Nuclear Physics group at Lawrence Livermore National Laboratory (LLNL) is developing a new tool, named `Kiwi', that is intended as an interface between the covariance data increasingly available in major nuclear reaction libraries (including ENDF and ENDL) and large-scale Uncertainty Quantification (UQ) studies. Kiwi is designed to integrate smoothly into large UQ studies, using the covariance matrix to generate multiple variations of nuclear data. The code has been tested using critical assemblies as a test case, and is being integrated into LLNL's quality assurance and benchmarking for nuclear data.

  20. The missing link: information literacy and evidence-based practice as a new challenge for nurse educators.

    PubMed

    Courey, Tamra; Benson-Soros, Johnett; Deemer, Kevin; Zeller, Richard A

    2006-01-01

    The evolution of nursing as a profession requires the development of evidence-based practice based on outcomes and the ability by nurses to access and evaluate professional literature, both in print and on the Internet. To educate nurses to apply current research outcomes to nursing practice, an information literacy program was designed and implemented for first-semester associate degree nursing students in conjunction with a foundations in nursing course. The effectiveness of the program was evaluated using a 22-item questionnaire, both prior to the course and immediately after. A control group, students who did not receive the intervention, was also tested at both time points. Data analysis revealed that the information literacy program had both a positive effect on students' literacy skills and a negative effect on their attitudes toward the need for using these skills in nursing practice.

  1. A simulation-based marginal method for longitudinal data with dropout and mismeasured covariates.

    PubMed

    Yi, Grace Y

    2008-07-01

    Longitudinal data often contain missing observations and error-prone covariates. Extensive attention has been directed to analysis methods to adjust for the bias induced by missing observations. There is relatively little work on investigating the effects of covariate measurement error on estimation of the response parameters, especially on simultaneously accounting for the biases induced by both missing values and mismeasured covariates. It is not clear what the impact of ignoring measurement error is when analyzing longitudinal data with both missing observations and error-prone covariates. In this article, we study the effects of covariate measurement error on estimation of the response parameters for longitudinal studies. We develop an inference method that adjusts for the biases induced by measurement error as well as by missingness. The proposed method does not require the full specification of the distribution of the response vector but only requires modeling its mean and variance structures. Furthermore, the proposed method employs the so-called functional modeling strategy to handle the covariate process, with the distribution of covariates left unspecified. These features, plus the simplicity of implementation, make the proposed method very attractive. In this paper, we establish the asymptotic properties for the resulting estimators. With the proposed method, we conduct sensitivity analyses on a cohort data set arising from the Framingham Heart Study. Simulation studies are carried out to evaluate the impact of ignoring covariate measurement error and to assess the performance of the proposed method.

  2. Simulation-Extrapolation for Estimating Means and Causal Effects with Mismeasured Covariates

    ERIC Educational Resources Information Center

    Lockwood, J. R.; McCaffrey, Daniel F.

    2015-01-01

    Regression, weighting and related approaches to estimating a population mean from a sample with nonrandom missing data often rely on the assumption that conditional on covariates, observed samples can be treated as random. Standard methods using this assumption generally will fail to yield consistent estimators when covariates are measured with…

  3. Simulation-Extrapolation for Estimating Means and Causal Effects with Mismeasured Covariates

    ERIC Educational Resources Information Center

    Lockwood, J. R.; McCaffrey, Daniel F.

    2015-01-01

    Regression, weighting and related approaches to estimating a population mean from a sample with nonrandom missing data often rely on the assumption that conditional on covariates, observed samples can be treated as random. Standard methods using this assumption generally will fail to yield consistent estimators when covariates are measured with…

  4. Generalized Linear Covariance Analysis

    NASA Technical Reports Server (NTRS)

    Carpenter, J. Russell; Markley, F. Landis

    2008-01-01

    We review and extend in two directions the results of prior work on generalized covariance analysis methods. This prior work allowed for partitioning of the state space into "solve-for" and "consider" parameters, allowed for differences between the formal values and the true values of the measurement noise, process noise, and a priori solve-for and consider covariances, and explicitly partitioned the errors into subspaces containing only the influence of the measurement noise, process noise, and a priori solve-for and consider covariances. In this work, we explicitly add sensitivity analysis to this prior work, and relax an implicit assumption that the batch estimator s anchor time occurs prior to the definitive span. We also apply the method to an integrated orbit and attitude problem, in which gyro and accelerometer errors, though not estimated, influence the orbit determination performance. We illustrate our results using two graphical presentations, which we call the "variance sandpile" and the "sensitivity mosaic," and we compare the linear covariance results to confidence intervals associated with ensemble statistics from a Monte Carlo analysis.

  5. Covariant canonical superstrings

    SciTech Connect

    Aratyn, H.; Ingermanson, R.; Niemi, A.J.

    1987-12-01

    A covariant canonical formulation of generic superstrings is presented. The (super)geometry emerges dynamically and supergravity transformations are identified with particular canonical transformations. By construction these transformations are off-shell closed, and the necessary auxiliary fields can be identified with canonical momenta.

  6. Generalized Linear Covariance Analysis

    NASA Technical Reports Server (NTRS)

    Carpenter, James R.; Markley, F. Landis

    2014-01-01

    This talk presents a comprehensive approach to filter modeling for generalized covariance analysis of both batch least-squares and sequential estimators. We review and extend in two directions the results of prior work that allowed for partitioning of the state space into solve-for'' and consider'' parameters, accounted for differences between the formal values and the true values of the measurement noise, process noise, and textita priori solve-for and consider covariances, and explicitly partitioned the errors into subspaces containing only the influence of the measurement noise, process noise, and solve-for and consider covariances. In this work, we explicitly add sensitivity analysis to this prior work, and relax an implicit assumption that the batch estimator's epoch time occurs prior to the definitive span. We also apply the method to an integrated orbit and attitude problem, in which gyro and accelerometer errors, though not estimated, influence the orbit determination performance. We illustrate our results using two graphical presentations, which we call the variance sandpile'' and the sensitivity mosaic,'' and we compare the linear covariance results to confidence intervals associated with ensemble statistics from a Monte Carlo analysis.

  7. Missing the target: including perspectives of women with overweight and obesity to inform stigma-reduction strategies.

    PubMed

    Puhl, R M; Himmelstein, M S; Gorin, A A; Suh, Y J

    2017-03-01

    Pervasive weight stigma and discrimination have led to ongoing calls for efforts to reduce this bias. Despite increasing research on stigma-reduction strategies, perspectives of individuals who have experienced weight stigma have rarely been included to inform this research. The present study conducted a systematic examination of women with high body weight to assess their perspectives about a broad range of strategies to reduce weight-based stigma. Women with overweight or obesity (N = 461) completed an online survey in which they evaluated the importance, feasibility and potential impact of 35 stigma-reduction strategies in diverse settings. Participants (91.5% who reported experiencing weight stigma) also completed self-report measures assessing experienced and internalized weight stigma. Most participants assigned high importance to all stigma-reduction strategies, with school-based and healthcare approaches accruing the highest ratings. Adding weight stigma to existing anti-harassment workplace training was rated as the most impactful and feasible strategy. The family environment was viewed as an important intervention target, regardless of participants' experienced or internalized stigma. These findings underscore the importance of including people with stigmatized identities in stigma-reduction research; their insights provide a necessary and valuable contribution that can inform ways to reduce weight-based inequities and prioritize such efforts.

  8. Missing the target: including perspectives of women with overweight and obesity to inform stigma‐reduction strategies

    PubMed Central

    Himmelstein, M. S.; Gorin, A. A.; Suh, Y. J.

    2017-01-01

    Summary Objective Pervasive weight stigma and discrimination have led to ongoing calls for efforts to reduce this bias. Despite increasing research on stigma‐reduction strategies, perspectives of individuals who have experienced weight stigma have rarely been included to inform this research. The present study conducted a systematic examination of women with high body weight to assess their perspectives about a broad range of strategies to reduce weight‐based stigma. Methods Women with overweight or obesity (N = 461) completed an online survey in which they evaluated the importance, feasibility and potential impact of 35 stigma‐reduction strategies in diverse settings. Participants (91.5% who reported experiencing weight stigma) also completed self‐report measures assessing experienced and internalized weight stigma. Results Most participants assigned high importance to all stigma‐reduction strategies, with school‐based and healthcare approaches accruing the highest ratings. Adding weight stigma to existing anti‐harassment workplace training was rated as the most impactful and feasible strategy. The family environment was viewed as an important intervention target, regardless of participants' experienced or internalized stigma. Conclusion These findings underscore the importance of including people with stigmatized identities in stigma‐reduction research; their insights provide a necessary and valuable contribution that can inform ways to reduce weight‐based inequities and prioritize such efforts. PMID:28392929

  9. Missing Funds

    ERIC Educational Resources Information Center

    Hassenpflug, Ann

    2012-01-01

    A high school drama coach informs assistant principal Laura Madison that the money students earned through fund-raising activities seems to have vanished and that the male assistant principal may be involved in the disappearance of the funds. Laura has to determine how to address this situation. She considers her past experiences with problematic…

  10. Missing Funds

    ERIC Educational Resources Information Center

    Hassenpflug, Ann

    2012-01-01

    A high school drama coach informs assistant principal Laura Madison that the money students earned through fund-raising activities seems to have vanished and that the male assistant principal may be involved in the disappearance of the funds. Laura has to determine how to address this situation. She considers her past experiences with problematic…

  11. Missing persons-missing data: the need to collect antemortem dental records of missing persons.

    PubMed

    Blau, Soren; Hill, Anthony; Briggs, Christopher A; Cordner, Stephen M

    2006-03-01

    incorporated into the National Coroners Information System (NCIS) managed, on behalf of Australia's Coroners, by the Victorian Institute of Forensic Medicine. The existence of the NCIS would ensure operational collaboration in the implementation of the system and cost savings to Australian policing agencies involved in missing person inquiries. The implementation of such a database would facilitate timely and efficient reconciliation of clinical and postmortem dental records and have subsequent social and financial benefits.

  12. VARIABLE SELECTION FOR REGRESSION MODELS WITH MISSING DATA

    PubMed Central

    Garcia, Ramon I.; Ibrahim, Joseph G.; Zhu, Hongtu

    2009-01-01

    We consider the variable selection problem for a class of statistical models with missing data, including missing covariate and/or response data. We investigate the smoothly clipped absolute deviation penalty (SCAD) and adaptive LASSO and propose a unified model selection and estimation procedure for use in the presence of missing data. We develop a computationally attractive algorithm for simultaneously optimizing the penalized likelihood function and estimating the penalty parameters. Particularly, we propose to use a model selection criterion, called the ICQ statistic, for selecting the penalty parameters. We show that the variable selection procedure based on ICQ automatically and consistently selects the important covariates and leads to efficient estimates with oracle properties. The methodology is very general and can be applied to numerous situations involving missing data, from covariates missing at random in arbitrary regression models to nonignorably missing longitudinal responses and/or covariates. Simulations are given to demonstrate the methodology and examine the finite sample performance of the variable selection procedures. Melanoma data from a cancer clinical trial is presented to illustrate the proposed methodology. PMID:20336190

  13. Meta-analysis with missing study-level sample variance data.

    PubMed

    Chowdhry, Amit K; Dworkin, Robert H; McDermott, Michael P

    2016-07-30

    We consider a study-level meta-analysis with a normally distributed outcome variable and possibly unequal study-level variances, where the object of inference is the difference in means between a treatment and control group. A common complication in such an analysis is missing sample variances for some studies. A frequently used approach is to impute the weighted (by sample size) mean of the observed variances (mean imputation). Another approach is to include only those studies with variances reported (complete case analysis). Both mean imputation and complete case analysis are only valid under the missing-completely-at-random assumption, and even then the inverse variance weights produced are not necessarily optimal. We propose a multiple imputation method employing gamma meta-regression to impute the missing sample variances. Our method takes advantage of study-level covariates that may be used to provide information about the missing data. Through simulation studies, we show that multiple imputation, when the imputation model is correctly specified, is superior to competing methods in terms of confidence interval coverage probability and type I error probability when testing a specified group difference. Finally, we describe a similar approach to handling missing variances in cross-over studies. Copyright © 2016 John Wiley & Sons, Ltd.

  14. Covariant approximation averaging

    NASA Astrophysics Data System (ADS)

    Shintani, Eigo; Arthur, Rudy; Blum, Thomas; Izubuchi, Taku; Jung, Chulwoo; Lehner, Christoph

    2015-06-01

    We present a new class of statistical error reduction techniques for Monte Carlo simulations. Using covariant symmetries, we show that correlation functions can be constructed from inexpensive approximations without introducing any systematic bias in the final result. We introduce a new class of covariant approximation averaging techniques, known as all-mode averaging (AMA), in which the approximation takes account of contributions of all eigenmodes through the inverse of the Dirac operator computed from the conjugate gradient method with a relaxed stopping condition. In this paper we compare the performance and computational cost of our new method with traditional methods using correlation functions and masses of the pion, nucleon, and vector meson in Nf=2 +1 lattice QCD using domain-wall fermions. This comparison indicates that AMA significantly reduces statistical errors in Monte Carlo calculations over conventional methods for the same cost.

  15. Using Analysis of Covariance (ANCOVA) with Fallible Covariates

    ERIC Educational Resources Information Center

    Culpepper, Steven Andrew; Aguinis, Herman

    2011-01-01

    Analysis of covariance (ANCOVA) is used widely in psychological research implementing nonexperimental designs. However, when covariates are fallible (i.e., measured with error), which is the norm, researchers must choose from among 3 inadequate courses of action: (a) know that the assumption that covariates are perfectly reliable is violated but…

  16. Using Analysis of Covariance (ANCOVA) with Fallible Covariates

    ERIC Educational Resources Information Center

    Culpepper, Steven Andrew; Aguinis, Herman

    2011-01-01

    Analysis of covariance (ANCOVA) is used widely in psychological research implementing nonexperimental designs. However, when covariates are fallible (i.e., measured with error), which is the norm, researchers must choose from among 3 inadequate courses of action: (a) know that the assumption that covariates are perfectly reliable is violated but…

  17. Covariant deformed oscillator algebras

    NASA Technical Reports Server (NTRS)

    Quesne, Christiane

    1995-01-01

    The general form and associativity conditions of deformed oscillator algebras are reviewed. It is shown how the latter can be fulfilled in terms of a solution of the Yang-Baxter equation when this solution has three distinct eigenvalues and satisfies a Birman-Wenzl-Murakami condition. As an example, an SU(sub q)(n) x SU(sub q)(m)-covariant q-bosonic algebra is discussed in some detail.

  18. Covariance based outlier detection with feature selection.

    PubMed

    Zwilling, Chris E; Wang, Michelle Y

    2016-08-01

    The present covariance based outlier detection algorithm selects from a candidate set of feature vectors that are best at identifying outliers. Features extracted from biomedical and health informatics data can be more informative in disease assessment and there are no restrictions on the nature and number of features that can be tested. But an important challenge for an algorithm operating on a set of features is for it to winnow the effective features from the ineffective ones. The powerful algorithm described in this paper leverages covariance information from the time series data to identify features with the highest sensitivity for outlier identification. Empirical results demonstrate the efficacy of the method.

  19. The Bayesian Covariance Lasso

    PubMed Central

    Khondker, Zakaria S; Zhu, Hongtu; Chu, Haitao; Lin, Weili; Ibrahim, Joseph G.

    2012-01-01

    Estimation of sparse covariance matrices and their inverse subject to positive definiteness constraints has drawn a lot of attention in recent years. The abundance of high-dimensional data, where the sample size (n) is less than the dimension (d), requires shrinkage estimation methods since the maximum likelihood estimator is not positive definite in this case. Furthermore, when n is larger than d but not sufficiently larger, shrinkage estimation is more stable than maximum likelihood as it reduces the condition number of the precision matrix. Frequentist methods have utilized penalized likelihood methods, whereas Bayesian approaches rely on matrix decompositions or Wishart priors for shrinkage. In this paper we propose a new method, called the Bayesian Covariance Lasso (BCLASSO), for the shrinkage estimation of a precision (covariance) matrix. We consider a class of priors for the precision matrix that leads to the popular frequentist penalties as special cases, develop a Bayes estimator for the precision matrix, and propose an efficient sampling scheme that does not precalculate boundaries for positive definiteness. The proposed method is permutation invariant and performs shrinkage and estimation simultaneously for non-full rank data. Simulations show that the proposed BCLASSO performs similarly as frequentist methods for non-full rank data. PMID:24551316

  20. Inadequacy of internal covariance estimation for super-sample covariance

    NASA Astrophysics Data System (ADS)

    Lacasa, Fabien; Kunz, Martin

    2017-08-01

    We give an analytical interpretation of how subsample-based internal covariance estimators lead to biased estimates of the covariance, due to underestimating the super-sample covariance (SSC). This includes the jackknife and bootstrap methods as estimators for the full survey area, and subsampling as an estimator of the covariance of subsamples. The limitations of the jackknife covariance have been previously presented in the literature because it is effectively a rescaling of the covariance of the subsample area. However we point out that subsampling is also biased, but for a different reason: the subsamples are not independent, and the corresponding lack of power results in SSC underprediction. We develop the formalism in the case of cluster counts that allows the bias of each covariance estimator to be exactly predicted. We find significant effects for a small-scale area or when a low number of subsamples is used, with auto-redshift biases ranging from 0.4% to 15% for subsampling and from 5% to 75% for jackknife covariance estimates. The cross-redshift covariance is even more affected; biases range from 8% to 25% for subsampling and from 50% to 90% for jackknife. Owing to the redshift evolution of the probe, the covariances cannot be debiased by a simple rescaling factor, and an exact debiasing has the same requirements as the full SSC prediction. These results thus disfavour the use of internal covariance estimators on data itself or a single simulation, leaving analytical prediction and simulations suites as possible SSC predictors.

  1. What Is Missing in Counseling Research? Reporting Missing Data

    ERIC Educational Resources Information Center

    Sterner, William R.

    2011-01-01

    Missing data have long been problematic in quantitative research. Despite the statistical and methodological advances made over the past 3 decades, counseling researchers fail to provide adequate information on this phenomenon. Interpreting the complex statistical procedures and esoteric language seems to be a contributing factor. An overview of…

  2. Optimal covariant quantum networks

    NASA Astrophysics Data System (ADS)

    Chiribella, Giulio; D'Ariano, Giacomo Mauro; Perinotti, Paolo

    2009-04-01

    A sequential network of quantum operations is efficiently described by its quantum comb [1], a non-negative operator with suitable normalization constraints. Here we analyze the case of networks enjoying symmetry with respect to the action of a given group of physical transformations, introducing the notion of covariant combs and testers, and proving the basic structure theorems for these objects. As an application, we discuss the optimal alignment of reference frames (without pre-established common references) with multiple rounds of quantum communication, showing that i) allowing an arbitrary amount of classical communication does not improve the alignment, and ii) a single round of quantum communication is sufficient.

  3. Covariant magnetic connection hypersurfaces

    NASA Astrophysics Data System (ADS)

    Pegoraro, F.

    2016-04-01

    > In the single fluid, non-relativistic, ideal magnetohydrodynamic (MHD) plasma description, magnetic field lines play a fundamental role by defining dynamically preserved `magnetic connections' between plasma elements. Here we show how the concept of magnetic connection needs to be generalized in the case of a relativistic MHD description where we require covariance under arbitrary Lorentz transformations. This is performed by defining 2-D magnetic connection hypersurfaces in the 4-D Minkowski space. This generalization accounts for the loss of simultaneity between spatially separated events in different frames and is expected to provide a powerful insight into the 4-D geometry of electromagnetic fields when .

  4. Earth Observing System Covariance Realism

    NASA Technical Reports Server (NTRS)

    Zaidi, Waqar H.; Hejduk, Matthew D.

    2016-01-01

    The purpose of covariance realism is to properly size a primary object's covariance in order to add validity to the calculation of the probability of collision. The covariance realism technique in this paper consists of three parts: collection/calculation of definitive state estimates through orbit determination, calculation of covariance realism test statistics at each covariance propagation point, and proper assessment of those test statistics. An empirical cumulative distribution function (ECDF) Goodness-of-Fit (GOF) method is employed to determine if a covariance is properly sized by comparing the empirical distribution of Mahalanobis distance calculations to the hypothesized parent 3-DoF chi-squared distribution. To realistically size a covariance for collision probability calculations, this study uses a state noise compensation algorithm that adds process noise to the definitive epoch covariance to account for uncertainty in the force model. Process noise is added until the GOF tests pass a group significance level threshold. The results of this study indicate that when outliers attributed to persistently high or extreme levels of solar activity are removed, the aforementioned covariance realism compensation method produces a tuned covariance with up to 80 to 90% of the covariance propagation timespan passing (against a 60% minimum passing threshold) the GOF tests-a quite satisfactory and useful result.

  5. Observed Score Linear Equating with Covariates

    ERIC Educational Resources Information Center

    Branberg, Kenny; Wiberg, Marie

    2011-01-01

    This paper examined observed score linear equating in two different data collection designs, the equivalent groups design and the nonequivalent groups design, when information from covariates (i.e., background variables correlated with the test scores) was included. The main purpose of the study was to examine the effect (i.e., bias, variance, and…

  6. Observed Score Linear Equating with Covariates

    ERIC Educational Resources Information Center

    Branberg, Kenny; Wiberg, Marie

    2011-01-01

    This paper examined observed score linear equating in two different data collection designs, the equivalent groups design and the nonequivalent groups design, when information from covariates (i.e., background variables correlated with the test scores) was included. The main purpose of the study was to examine the effect (i.e., bias, variance, and…

  7. Covariance Analysis of Gamma Ray Spectra

    SciTech Connect

    Trainham, R.; Tinsley, J.

    2013-01-01

    The covariance method exploits fluctuations in signals to recover information encoded in correlations which are usually lost when signal averaging occurs. In nuclear spectroscopy it can be regarded as a generalization of the coincidence technique. The method can be used to extract signal from uncorrelated noise, to separate overlapping spectral peaks, to identify escape peaks, to reconstruct spectra from Compton continua, and to generate secondary spectral fingerprints. We discuss a few statistical considerations of the covariance method and present experimental examples of its use in gamma spectroscopy.

  8. Covariance analysis of gamma ray spectra

    SciTech Connect

    Trainham, R.; Tinsley, J.

    2013-01-15

    The covariance method exploits fluctuations in signals to recover information encoded in correlations which are usually lost when signal averaging occurs. In nuclear spectroscopy it can be regarded as a generalization of the coincidence technique. The method can be used to extract signal from uncorrelated noise, to separate overlapping spectral peaks, to identify escape peaks, to reconstruct spectra from Compton continua, and to generate secondary spectral fingerprints. We discuss a few statistical considerations of the covariance method and present experimental examples of its use in gamma spectroscopy.

  9. Hospital variation in missed nursing care.

    PubMed

    Kalisch, Beatrice J; Tschannen, Dana; Lee, Hyunhwa; Friese, Christopher R

    2011-01-01

    Quality of nursing care across hospitals is variable, and this variation can result in poor patient outcomes. One aspect of quality nursing care is the amount of necessary care that is omitted. This article reports on the extent and type of nursing care missed and the reasons for missed care. The MISSCARE Survey was administered to nursing staff (n = 4086) who provide direct patient care in 10 acute care hospitals. Missed nursing care patterns as well as reasons for missing care (labor resources, material resources, and communication) were common across all hospitals. Job title (ie, registered nurse vs nursing assistant), shift worked, absenteeism, perceived staffing adequacy, and patient work loads were significantly associated with missed care. The data from this study can inform quality improvement efforts to reduce missed nursing care and promote favorable patient outcomes.

  10. Impact of the 235U Covariance Data in Benchmark Calculations

    SciTech Connect

    Leal, Luiz C; Mueller, Don; Arbanas, Goran; Wiarda, Dorothea; Derrien, Herve

    2008-01-01

    The error estimation for calculated quantities relies on nuclear data uncertainty information available in the basic nuclear data libraries such as the U.S. Evaluated Nuclear Data File (ENDF/B). The uncertainty files (covariance matrices) in the ENDF/B library are generally obtained from analysis of experimental data. In the resonance region, the computer code SAMMY is used for analyses of experimental data and generation of resonance parameters. In addition to resonance parameters evaluation, SAMMY also generates resonance parameter covariance matrices (RPCM). SAMMY uses the generalized least-squares formalism (Bayes method) together with the resonance formalism (R-matrix theory) for analysis of experimental data. Two approaches are available for creation of resonance-parameter covariance data. (1) During the data-evaluation process, SAMMY generates both a set of resonance parameters that fit the experimental data and the associated resonance-parameter covariance matrix. (2) For existing resonance-parameter evaluations for which no resonance-parameter covariance data are available, SAMMY can retroactively create a resonance-parameter covariance matrix. The retroactive method was used to generate covariance data for 235U. The resulting 235U covariance matrix was then used as input to the PUFF-IV code, which processed the covariance data into multigroup form, and to the TSUNAMI code, which calculated the uncertainty in the multiplication factor due to uncertainty in the experimental cross sections. The objective of this work is to demonstrate the use of the 235U covariance data in calculations of critical benchmark systems.

  11. A Nonparametric Prior for Simultaneous Covariance Estimation

    PubMed Central

    Gaskins, Jeremy T.; Daniels, Michael J.

    2013-01-01

    Summary In the modeling of longitudinal data from several groups, appropriate handling of the dependence structure is of central importance. Standard methods include specifying a single covariance matrix for all groups or independently estimating the covariance matrix for each group without regard to the others, but when these model assumptions are incorrect, these techniques can lead to biased mean effects or loss of efficiency, respectively. Thus, it is desirable to develop methods to simultaneously estimate the covariance matrix for each group that will borrow strength across groups in a way that is ultimately informed by the data. In addition, for several groups with covariance matrices of even medium dimension, it is difficult to manually select a single best parametric model among the huge number of possibilities given by incorporating structural zeros and/or commonality of individual parameters across groups. In this paper we develop a family of nonparametric priors using the matrix stick-breaking process of Dunson et al. (2008) that seeks to accomplish this task by parameterizing the covariance matrices in terms of the parameters of their modified Cholesky decomposition (Pourahmadi, 1999). We establish some theoretic properties of these priors, examine their effectiveness via a simulation study, and illustrate the priors using data from a longitudinal clinical trial. PMID:24324281

  12. Interdomain climatic covariability, 1870-1997

    NASA Astrophysics Data System (ADS)

    Carl, P.

    2003-04-01

    further mutual relationships suggest organized climate dynamics, with solar signal transfer preferentially via the atmosphere--land (monsoon) system, whereas the signature of thermal evolutions is more pronounced in the atmosphere--ocean system. The strictly univariate analysis covers 128 annual values (1870--1997) for each of 11 time series, with a few missing data; the whole multivariate set includes 1.333 entries. The method does not impose any mutual information among the series and/or their individual components.

  13. A note on MAR, identifying restrictions, model comparison, and sensitivity analysis in pattern mixture models with and without covariates for incomplete data.

    PubMed

    Wang, Chenguang; Daniels, Michael J

    2011-09-01

    Pattern mixture modeling is a popular approach for handling incomplete longitudinal data. Such models are not identifiable by construction. Identifying restrictions is one approach to mixture model identification (Little, 1995, Journal of the American Statistical Association 90, 1112-1121; Little and Wang, 1996, Biometrics 52, 98-111; Thijs et al., 2002, Biostatistics 3, 245-265; Kenward, Molenberghs, and Thijs, 2003, Biometrika 90, 53-71; Daniels and Hogan, 2008, in Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity Analysis) and is a natural starting point for missing not at random sensitivity analysis (Thijs et al., 2002, Biostatistics 3, 245-265; Daniels and Hogan, 2008, in Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity Analysis). However, when the pattern specific models are multivariate normal, identifying restrictions corresponding to missing at random (MAR) may not exist. Furthermore, identification strategies can be problematic in models with covariates (e.g., baseline covariates with time-invariant coefficients). In this article, we explore conditions necessary for identifying restrictions that result in MAR to exist under a multivariate normality assumption and strategies for identifying sensitivity parameters for sensitivity analysis or for a fully Bayesian analysis with informative priors. In addition, we propose alternative modeling and sensitivity analysis strategies under a less restrictive assumption for the distribution of the observed response data. We adopt the deviance information criterion for model comparison and perform a simulation study to evaluate the performances of the different modeling approaches. We also apply the methods to a longitudinal clinical trial. Problems caused by baseline covariates with time-invariant coefficients are investigated and an alternative identifying restriction based on residuals is proposed as a solution.

  14. To Covary or Not to Covary, That is the Question

    NASA Astrophysics Data System (ADS)

    Oehlert, A. M.; Swart, P. K.

    2016-12-01

    The meaning of covariation between the δ13C values of carbonate carbon and that of organic material is classically interpreted as reflecting original variations in the δ13C values of the dissolved inorganic carbon in the depositional environment. However, recently it has been shown by the examination of a core from Great Bahama Bank (Clino) that during exposure not only do the rocks become altered acquiring a negative δ13C value, but at the same time terrestrial vegetation adds organic carbon to the system masking the original marine values. These processes yield a strong positive covariation between δ13Corg and δ13Ccar values even though the signals are clearly not original and unrelated to the marine δ13C values. Examining the correlation between the organic and inorganic system in a stratigraphic sense at Clino and in a second more proximally located core (Unda) using a windowed correlation coefficient technique reveals that the correlation is even more complex. Changes in slope and the magnitude of the correlation are associated with exposure surfaces, facies changes, dolomitized bodies, and non-depositional surfaces. Finally other isotopic systems such as the δ13C value of specific organic compounds as well as δ15N values of bulk and individual compounds can provide additional information. In the case of δ15N values, decreases reflect a changes in the influence of terrestrial organic material and an increase contribution of organic material from the platform surface where the main source of nitrogen is derived from the activities of cyanobacteria.

  15. The Use of Covariation as a Principle of Causal Analysis

    ERIC Educational Resources Information Center

    Shultz, Thomas R.; Mendelson, Rosyln

    1975-01-01

    This study investigated the use of covariation as a principle of causal analysis in children 3-4, 6-7, and 9-11 years of age. The results indicated that children as young as 3 years were capable of using covariation information in their attributions of simple physical effects. (Author/CS)

  16. OD Covariance in Conjunction Assessment: Introduction and Issues

    NASA Technical Reports Server (NTRS)

    Hejduk, M. D.; Duncan, M.

    2015-01-01

    Primary and secondary covariances combined and projected into conjunction plane (plane perpendicular to relative velocity vector at TCA) Primary placed on x-axis at (miss distance, 0) and represented by circle of radius equal to sum of both spacecraft circumscribing radiiZ-axis perpendicular to x-axis in conjunction plane Pc is portion of combined error ellipsoid that falls within the hard-body radius circle

  17. Stardust Navigation Covariance Analysis

    NASA Technical Reports Server (NTRS)

    Menon, Premkumar R.

    2000-01-01

    The Stardust spacecraft was launched on February 7, 1999 aboard a Boeing Delta-II rocket. Mission participants include the National Aeronautics and Space Administration (NASA), the Jet Propulsion Laboratory (JPL), Lockheed Martin Astronautics (LMA) and the University of Washington. The primary objective of the mission is to collect in-situ samples of the coma of comet Wild-2 and return those samples to the Earth for analysis. Mission design and operational navigation for Stardust is performed by the Jet Propulsion Laboratory (JPL). This paper will describe the extensive JPL effort in support of the Stardust pre-launch analysis of the orbit determination component of the mission covariance study. A description of the mission and it's trajectory will be provided first, followed by a discussion of the covariance procedure and models. Predicted accuracy's will be examined as they relate to navigation delivery requirements for specific critical events during the mission. Stardust was launched into a heliocentric trajectory in early 1999. It will perform an Earth Gravity Assist (EGA) on January 15, 2001 to acquire an orbit for the eventual rendezvous with comet Wild-2. The spacecraft will fly through the coma (atmosphere) on the dayside of Wild-2 on January 2, 2004. At that time samples will be obtained using an aerogel collector. After the comet encounter Stardust will return to Earth when the Sample Return Capsule (SRC) will separate and land at the Utah Test Site (UTTR) on January 15, 2006. The spacecraft will however be deflected off into a heliocentric orbit. The mission is divided into three phases for the covariance analysis. They are 1) Launch to EGA, 2) EGA to Wild-2 encounter and 3) Wild-2 encounter to Earth reentry. Orbit determination assumptions for each phase are provided. These include estimated and consider parameters and their associated a-priori uncertainties. Major perturbations to the trajectory include 19 deterministic and statistical maneuvers

  18. Deriving covariant holographic entanglement

    NASA Astrophysics Data System (ADS)

    Dong, Xi; Lewkowycz, Aitor; Rangamani, Mukund

    2016-11-01

    We provide a gravitational argument in favour of the covariant holographic entanglement entropy proposal. In general time-dependent states, the proposal asserts that the entanglement entropy of a region in the boundary field theory is given by a quarter of the area of a bulk extremal surface in Planck units. The main element of our discussion is an implementation of an appropriate Schwinger-Keldysh contour to obtain the reduced density matrix (and its powers) of a given region, as is relevant for the replica construction. We map this contour into the bulk gravitational theory, and argue that the saddle point solutions of these replica geometries lead to a consistent prescription for computing the field theory Rényi entropies. In the limiting case where the replica index is taken to unity, a local analysis suffices to show that these saddles lead to the extremal surfaces of interest. We also comment on various properties of holographic entanglement that follow from this construction.

  19. Stardust Navigation Covariance Analysis

    NASA Technical Reports Server (NTRS)

    Menon, Premkumar R.

    2000-01-01

    The Stardust spacecraft was launched on February 7, 1999 aboard a Boeing Delta-II rocket. Mission participants include the National Aeronautics and Space Administration (NASA), the Jet Propulsion Laboratory (JPL), Lockheed Martin Astronautics (LMA) and the University of Washington. The primary objective of the mission is to collect in-situ samples of the coma of comet Wild-2 and return those samples to the Earth for analysis. Mission design and operational navigation for Stardust is performed by the Jet Propulsion Laboratory (JPL). This paper will describe the extensive JPL effort in support of the Stardust pre-launch analysis of the orbit determination component of the mission covariance study. A description of the mission and it's trajectory will be provided first, followed by a discussion of the covariance procedure and models. Predicted accuracy's will be examined as they relate to navigation delivery requirements for specific critical events during the mission. Stardust was launched into a heliocentric trajectory in early 1999. It will perform an Earth Gravity Assist (EGA) on January 15, 2001 to acquire an orbit for the eventual rendezvous with comet Wild-2. The spacecraft will fly through the coma (atmosphere) on the dayside of Wild-2 on January 2, 2004. At that time samples will be obtained using an aerogel collector. After the comet encounter Stardust will return to Earth when the Sample Return Capsule (SRC) will separate and land at the Utah Test Site (UTTR) on January 15, 2006. The spacecraft will however be deflected off into a heliocentric orbit. The mission is divided into three phases for the covariance analysis. They are 1) Launch to EGA, 2) EGA to Wild-2 encounter and 3) Wild-2 encounter to Earth reentry. Orbit determination assumptions for each phase are provided. These include estimated and consider parameters and their associated a-priori uncertainties. Major perturbations to the trajectory include 19 deterministic and statistical maneuvers

  20. Covariant genetic dynamics.

    PubMed

    Chryssomalakos, Chryssomalis; Stephens, Christopher R

    2007-01-01

    We present a covariant form for the dynamics of a canonical GA of arbitrary cardinality, showing how each genetic operator can be uniquely represented by a mathematical object - a tensor - that transforms simply under a general linear coordinate transformation. For mutation and recombination these tensors can be written as tensor products of the analogous tensors for one-bit strings thus giving a greatly simplified formulation of the dynamics. We analyze the three most well known coordinate systems -- string, Walsh and Building Block - discussing their relative advantages and disadvantages with respect to the different operators, showing how one may transform from one to the other, and that the associated coordinate transformation matrices can be written as a tensor product of the corresponding one-bit matrices. We also show that in the Building Block basis the dynamical equations for all Building Blocks can be generated from the equation for the most fine-grained block (string) by a certain projection ("zapping").

  1. Covariantly quantum Galileon

    NASA Astrophysics Data System (ADS)

    Saltas, Ippocratis D.; Vitagliano, Vincenzo

    2017-05-01

    We derive the 1-loop effective action of the cubic Galileon coupled to quantum-gravitational fluctuations in a background and gauge-independent manner, employing the covariant framework of DeWitt and Vilkovisky. Although the bare action respects shift symmetry, the coupling to gravity induces an effective mass to the scalar, of the order of the cosmological constant, as a direct result of the nonflat field-space metric, the latter ensuring the field-reparametrization invariance of the formalism. Within a gauge-invariant regularization scheme, we discover novel, gravitationally induced non-Galileon higher-derivative interactions in the effective action. These terms, previously unnoticed within standard, noncovariant frameworks, are not Planck suppressed. Unless tuned to be subdominant, their presence could have important implications for the classical and quantum phenomenology of the theory.

  2. Nonlinear multiple imputation for continuous covariate within semiparametric Cox model: application to HIV data in Senegal.

    PubMed

    Mbougua, Jules Brice Tchatchueng; Laurent, Christian; Ndoye, Ibra; Delaporte, Eric; Gwet, Henri; Molinari, Nicolas

    2013-11-20

    Multiple imputation is commonly used to impute missing covariate in Cox semiparametric regression setting. It is to fill each missing data with more plausible values, via a Gibbs sampling procedure, specifying an imputation model for each missing variable. This imputation method is implemented in several softwares that offer imputation models steered by the shape of the variable to be imputed, but all these imputation models make an assumption of linearity on covariates effect. However, this assumption is not often verified in practice as the covariates can have a nonlinear effect. Such a linear assumption can lead to a misleading conclusion because imputation model should be constructed to reflect the true distributional relationship between the missing values and the observed values. To estimate nonlinear effects of continuous time invariant covariates in imputation model, we propose a method based on B-splines function. To assess the performance of this method, we conducted a simulation study, where we compared the multiple imputation method using Bayesian splines imputation model with multiple imputation using Bayesian linear imputation model in survival analysis setting. We evaluated the proposed method on the motivated data set collected in HIV-infected patients enrolled in an observational cohort study in Senegal, which contains several incomplete variables. We found that our method performs well to estimate hazard ratio compared with the linear imputation methods, when data are missing completely at random, or missing at random. Copyright © 2013 John Wiley & Sons, Ltd.

  3. General Galilei Covariant Gaussian Maps

    NASA Astrophysics Data System (ADS)

    Gasbarri, Giulio; Toroš, Marko; Bassi, Angelo

    2017-09-01

    We characterize general non-Markovian Gaussian maps which are covariant under Galilean transformations. In particular, we consider translational and Galilean covariant maps and show that they reduce to the known Holevo result in the Markovian limit. We apply the results to discuss measures of macroscopicity based on classicalization maps, specifically addressing dissipation, Galilean covariance and non-Markovianity. We further suggest a possible generalization of the macroscopicity measure defined by Nimmrichter and Hornberger [Phys. Rev. Lett. 110, 16 (2013)].

  4. COVARIANCE ASSISTED SCREENING AND ESTIMATION

    PubMed Central

    Ke, By Tracy; Jin, Jiashun; Fan, Jianqing

    2014-01-01

    Consider a linear model Y = X β + z, where X = Xn,p and z ~ N(0, In). The vector β is unknown and it is of interest to separate its nonzero coordinates from the zero ones (i.e., variable selection). Motivated by examples in long-memory time series (Fan and Yao, 2003) and the change-point problem (Bhattacharya, 1994), we are primarily interested in the case where the Gram matrix G = X′X is non-sparse but sparsifiable by a finite order linear filter. We focus on the regime where signals are both rare and weak so that successful variable selection is very challenging but is still possible. We approach this problem by a new procedure called the Covariance Assisted Screening and Estimation (CASE). CASE first uses a linear filtering to reduce the original setting to a new regression model where the corresponding Gram (covariance) matrix is sparse. The new covariance matrix induces a sparse graph, which guides us to conduct multivariate screening without visiting all the submodels. By interacting with the signal sparsity, the graph enables us to decompose the original problem into many separated small-size subproblems (if only we know where they are!). Linear filtering also induces a so-called problem of information leakage, which can be overcome by the newly introduced patching technique. Together, these give rise to CASE, which is a two-stage Screen and Clean (Fan and Song, 2010; Wasserman and Roeder, 2009) procedure, where we first identify candidates of these submodels by patching and screening, and then re-examine each candidate to remove false positives. For any procedure β̂ for variable selection, we measure the performance by the minimax Hamming distance between the sign vectors of β̂ and β. We show that in a broad class of situations where the Gram matrix is non-sparse but sparsifiable, CASE achieves the optimal rate of convergence. The results are successfully applied to long-memory time series and the change-point model. PMID:25541567

  5. Principled missing data methods for researchers.

    PubMed

    Dong, Yiran; Peng, Chao-Ying Joanne

    2013-12-01

    The impact of missing data on quantitative research can be serious, leading to biased estimates of parameters, loss of information, decreased statistical power, increased standard errors, and weakened generalizability of findings. In this paper, we discussed and demonstrated three principled missing data methods: multiple imputation, full information maximum likelihood, and expectation-maximization algorithm, applied to a real-world data set. Results were contrasted with those obtained from the complete data set and from the listwise deletion method. The relative merits of each method are noted, along with common features they share. The paper concludes with an emphasis on the importance of statistical assumptions, and recommendations for researchers. Quality of research will be enhanced if (a) researchers explicitly acknowledge missing data problems and the conditions under which they occurred, (b) principled methods are employed to handle missing data, and (c) the appropriate treatment of missing data is incorporated into review standards of manuscripts submitted for publication.

  6. Missing data and multiple imputation in clinical epidemiological research

    PubMed Central

    Pedersen, Alma B; Mikkelsen, Ellen M; Cronin-Fenton, Deirdre; Kristensen, Nickolaj R; Pham, Tra My; Pedersen, Lars; Petersen, Irene

    2017-01-01

    Missing data are ubiquitous in clinical epidemiological research. Individuals with missing data may differ from those with no missing data in terms of the outcome of interest and prognosis in general. Missing data are often categorized into the following three types: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR). In clinical epidemiological research, missing data are seldom MCAR. Missing data can constitute considerable challenges in the analyses and interpretation of results and can potentially weaken the validity of results and conclusions. A number of methods have been developed for dealing with missing data. These include complete-case analyses, missing indicator method, single value imputation, and sensitivity analyses incorporating worst-case and best-case scenarios. If applied under the MCAR assumption, some of these methods can provide unbiased but often less precise estimates. Multiple imputation is an alternative method to deal with missing data, which accounts for the uncertainty associated with missing data. Multiple imputation is implemented in most statistical software under the MAR assumption and provides unbiased and valid estimates of associations based on information from the available data. The method affects not only the coefficient estimates for variables with missing data but also the estimates for other variables with no missing data. PMID:28352203

  7. Missing data and multiple imputation in clinical epidemiological research.

    PubMed

    Pedersen, Alma B; Mikkelsen, Ellen M; Cronin-Fenton, Deirdre; Kristensen, Nickolaj R; Pham, Tra My; Pedersen, Lars; Petersen, Irene

    2017-01-01

    Missing data are ubiquitous in clinical epidemiological research. Individuals with missing data may differ from those with no missing data in terms of the outcome of interest and prognosis in general. Missing data are often categorized into the following three types: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR). In clinical epidemiological research, missing data are seldom MCAR. Missing data can constitute considerable challenges in the analyses and interpretation of results and can potentially weaken the validity of results and conclusions. A number of methods have been developed for dealing with missing data. These include complete-case analyses, missing indicator method, single value imputation, and sensitivity analyses incorporating worst-case and best-case scenarios. If applied under the MCAR assumption, some of these methods can provide unbiased but often less precise estimates. Multiple imputation is an alternative method to deal with missing data, which accounts for the uncertainty associated with missing data. Multiple imputation is implemented in most statistical software under the MAR assumption and provides unbiased and valid estimates of associations based on information from the available data. The method affects not only the coefficient estimates for variables with missing data but also the estimates for other variables with no missing data.

  8. CERAMIC: Case-Control Association Testing in Samples with Related Individuals, Based on Retrospective Mixed Model Analysis with Adjustment for Covariates

    PubMed Central

    Zhong, Sheng; McPeek, Mary Sara

    2016-01-01

    We consider the problem of genetic association testing of a binary trait in a sample that contains related individuals, where we adjust for relevant covariates and allow for missing data. We propose CERAMIC, an estimating equation approach that can be viewed as a hybrid of logistic regression and linear mixed-effects model (LMM) approaches. CERAMIC extends the recently proposed CARAT method to allow samples with related individuals and to incorporate partially missing data. In simulations, we show that CERAMIC outperforms existing LMM and generalized LMM approaches, maintaining high power and correct type 1 error across a wider range of scenarios. CERAMIC results in a particularly large power increase over existing methods when the sample includes related individuals with some missing data (e.g., when some individuals with phenotype and covariate information have missing genotype), because CERAMIC is able to make use of the relationship information to incorporate partially missing data in the analysis while correcting for dependence. Because CERAMIC is based on a retrospective analysis, it is robust to misspecification of the phenotype model, resulting in better control of type 1 error and higher power than that of prospective methods, such as GMMAT, when the phenotype model is misspecified. CERAMIC is computationally efficient for genomewide analysis in samples of related individuals of almost any configuration, including small families, unrelated individuals and even large, complex pedigrees. We apply CERAMIC to data on type 2 diabetes (T2D) from the Framingham Heart Study. In a genome scan, 9 of the 10 smallest CERAMIC p-values occur in or near either known T2D susceptibility loci or plausible candidates, verifying that CERAMIC is able to home in on the important loci in a genome scan. PMID:27695091

  9. National Center for Missing and Exploited Children

    MedlinePlus

    ... Team HOPE provides peer and emotional support to families. Contact Us Legal Information DONATE Careers Site Index Copyright © 2016 National Center for Missing & Exploited Children. All rights reserved. This Web site ...

  10. Tests of Homoscedasticity, Normality, and Missing Completely at Random for Incomplete Multivariate Data

    ERIC Educational Resources Information Center

    Jamshidian, Mortaza; Jalal, Siavash

    2010-01-01

    Test of homogeneity of covariances (or homoscedasticity) among several groups has many applications in statistical analysis. In the context of incomplete data analysis, tests of homoscedasticity among groups of cases with identical missing data patterns have been proposed to test whether data are missing completely at random (MCAR). These tests of…

  11. Tests of Homoscedasticity, Normality, and Missing Completely at Random for Incomplete Multivariate Data

    ERIC Educational Resources Information Center

    Jamshidian, Mortaza; Jalal, Siavash

    2010-01-01

    Test of homogeneity of covariances (or homoscedasticity) among several groups has many applications in statistical analysis. In the context of incomplete data analysis, tests of homoscedasticity among groups of cases with identical missing data patterns have been proposed to test whether data are missing completely at random (MCAR). These tests of…

  12. Covariant harmonic oscillators: 1973 revisited

    NASA Technical Reports Server (NTRS)

    Noz, M. E.

    1993-01-01

    Using the relativistic harmonic oscillator, a physical basis is given to the phenomenological wave function of Yukawa which is covariant and normalizable. It is shown that this wave function can be interpreted in terms of the unitary irreducible representations of the Poincare group. The transformation properties of these covariant wave functions are also demonstrated.

  13. Covariance hypotheses for LANDSAT data

    NASA Technical Reports Server (NTRS)

    Decell, H. P.; Peters, C.

    1983-01-01

    Two covariance hypotheses are considered for LANDSAT data acquired by sampling fields, one an autoregressive covariance structure and the other the hypothesis of exchangeability. A minimum entropy approximation of the first structure by the second is derived and shown to have desirable properties for incorporation into a mixture density estimation procedure. Results of a rough test of the exchangeability hypothesis are presented.

  14. Low-Fidelity Covariances: Neutron Cross Section Covariance Estimates for 387 Materials

    DOE Data Explorer

    The Low-fidelity Covariance Project (Low-Fi) was funded in FY07-08 by DOEÆs Nuclear Criticality Safety Program (NCSP). The project was a collaboration among ANL, BNL, LANL, and ORNL. The motivation for the Low-Fi project stemmed from an imbalance in supply and demand of covariance data. The interest in, and demand for, covariance data has been in a continual uptrend over the past few years. Requirements to understand application-dependent uncertainties in simulated quantities of interest have led to the development of sensitivity / uncertainty and data adjustment software such as TSUNAMI [1] at Oak Ridge. To take full advantage of the capabilities of TSUNAMI requires general availability of covariance data. However, the supply of covariance data has not been able to keep up with the demand. This fact is highlighted by the observation that the recent release of the much-heralded ENDF/B-VII.0 included covariance data for only 26 of the 393 neutron evaluations (which is, in fact, considerably less covariance data than was included in the final ENDF/B-VI release).[Copied from R.C. Little et al., "Low-Fidelity Covariance Project", Nuclear Data Sheets 109 (2008) 2828-2833] The Low-Fi covariance data are now available at the National Nuclear Data Center. They are separate from ENDF/B-VII.0 and the NNDC warns that this information is not approved by CSEWG. NNDC describes the contents of this collection as: "Covariance data are provided for radiative capture (or (n,ch.p.) for light nuclei), elastic scattering (or total for some actinides), inelastic scattering, (n,2n) reactions, fission and nubars over the energy range from 10(-5{super}) eV to 20 MeV. The library contains 387 files including almost all (383 out of 393) materials of the ENDF/B-VII.0. Absent are data for (7{super})Li, (232{super})Th, (233,235,238{super})U and (239{super})Pu as well as (223,224,225,226{super})Ra, while (nat{super})Zn is replaced by (64,66,67,68,70{super})Zn

  15. Minimax Rate-optimal Estimation of High-dimensional Covariance Matrices with Incomplete Data.

    PubMed

    Cai, T Tony; Zhang, Anru

    2016-09-01

    Missing data occur frequently in a wide range of applications. In this paper, we consider estimation of high-dimensional covariance matrices in the presence of missing observations under a general missing completely at random model in the sense that the missingness is not dependent on the values of the data. Based on incomplete data, estimators for bandable and sparse covariance matrices are proposed and their theoretical and numerical properties are investigated. Minimax rates of convergence are established under the spectral norm loss and the proposed estimators are shown to be rate-optimal under mild regularity conditions. Simulation studies demonstrate that the estimators perform well numerically. The methods are also illustrated through an application to data from four ovarian cancer studies. The key technical tools developed in this paper are of independent interest and potentially useful for a range of related problems in high-dimensional statistical inference with missing data.

  16. Minimax Rate-optimal Estimation of High-dimensional Covariance Matrices with Incomplete Data*

    PubMed Central

    Cai, T. Tony; Zhang, Anru

    2016-01-01

    Missing data occur frequently in a wide range of applications. In this paper, we consider estimation of high-dimensional covariance matrices in the presence of missing observations under a general missing completely at random model in the sense that the missingness is not dependent on the values of the data. Based on incomplete data, estimators for bandable and sparse covariance matrices are proposed and their theoretical and numerical properties are investigated. Minimax rates of convergence are established under the spectral norm loss and the proposed estimators are shown to be rate-optimal under mild regularity conditions. Simulation studies demonstrate that the estimators perform well numerically. The methods are also illustrated through an application to data from four ovarian cancer studies. The key technical tools developed in this paper are of independent interest and potentially useful for a range of related problems in high-dimensional statistical inference with missing data. PMID:27777471

  17. General Galilei Covariant Gaussian Maps.

    PubMed

    Gasbarri, Giulio; Toroš, Marko; Bassi, Angelo

    2017-09-08

    We characterize general non-Markovian Gaussian maps which are covariant under Galilean transformations. In particular, we consider translational and Galilean covariant maps and show that they reduce to the known Holevo result in the Markovian limit. We apply the results to discuss measures of macroscopicity based on classicalization maps, specifically addressing dissipation, Galilean covariance and non-Markovianity. We further suggest a possible generalization of the macroscopicity measure defined by Nimmrichter and Hornberger [Phys. Rev. Lett. 110, 16 (2013)PRLTAO0031-9007].

  18. Estimation methods for marginal and association parameters for longitudinal binary data with nonignorable missing observations.

    PubMed

    Li, Haocheng; Yi, Grace Y

    2013-02-28

    In longitudinal studies, missing observations occur commonly. It has been well known that biased results could be produced if missingness is not properly handled in the analysis. Authors have developed many methods with the focus on either incomplete response or missing covariate observations, but rarely on both. The complexity of modeling and computational difficulty would be the major challenges in handling missingness in both response and covariate variables. In this paper, we develop methods using the pairwise likelihood formulation to handle longitudinal binary data with missing observations present in both response and covariate variables. We propose a unified framework to accommodate various types of missing data patterns. We evaluate the performance of the methods empirically under a variety of circumstances. In particular, we investigate issues on efficiency and robustness. We analyze longitudinal data from the National Population Health Study with the use of our methods.

  19. ROC analysis in biomarker combination with covariate adjustment.

    PubMed

    Liu, Danping; Zhou, Xiao-Hua

    2013-07-01

    Receiver operating characteristic (ROC) analysis is often used to find the optimal combination of biomarkers. When the subject level covariates affect the magnitude and/or accuracy of the biomarkers, the combination rule should take into account of the covariate adjustment. The authors propose two new biomarker combination methods that make use of the covariate information. The first method is to maximize the area under the covariate-adjusted ROC curve (AAUC). To overcome the limitations of the AAUC measure, the authors further proposed the area under covariate-standardized ROC curve (SAUC), which is an extension of the covariate-specific ROC curve. With a series of simulation studies, the proposed optimal AAUC and SAUC methods are compared with the optimal AUC method that ignores the covariates. The biomarker combination methods are illustrated by an example from Alzheimer's disease research. The simulation results indicate that the optimal AAUC combination performs well in the current study population. The optimal SAUC method is flexible to choose any reference populations, and allows the results to be generalized to different populations. The proposed optimal AAUC and SAUC approaches successfully address the covariate adjustment problem in estimating the optimal marker combination. The optimal SAUC method is preferred for practical use, because the biomarker combination rule can be easily evaluated for different population of interest. Published by Elsevier Inc.

  20. Likelihood methods for regression models with expensive variables missing by design.

    PubMed

    Zhao, Yang; Lawless, Jerald F; McLeish, Donald L

    2009-02-01

    In some applications involving regression the values of certain variables are missing by design for some individuals. For example, in two-stage studies (Zhao and Lipsitz, 1992), data on "cheaper" variables are collected on a random sample of individuals in stage I, and then "expensive" variables are measured for a subsample of these in stage II. So the "expensive" variables are missing by design at stage I. Both estimating function and likelihood methods have been proposed for cases where either covariates or responses are missing. We extend the semiparametric maximum likelihood (SPML) method for missing covariate problems (e.g. Chen, 2004; Ibrahim et al., 2005; Zhang and Rockette, 2005, 2007) to deal with more general cases where covariates and/or responses are missing by design, and show that profile likelihood ratio tests and interval estimation are easily implemented. Simulation studies are provided to examine the performance of the likelihood methods and to compare their efficiencies with estimating function methods for problems involving (a) a missing covariate and (b) a missing response variable. We illustrate the ease of implementation of SPML and demonstrate its high efficiency.

  1. Hawking radiation and covariant anomalies

    SciTech Connect

    Banerjee, Rabin; Kulkarni, Shailesh

    2008-01-15

    Generalizing the method of Wilczek and collaborators we provide a derivation of Hawking radiation from charged black holes using only covariant gauge and gravitational anomalies. The reliability and universality of the anomaly cancellation approach to Hawking radiation is also discussed.

  2. Relative-Error-Covariance Algorithms

    NASA Technical Reports Server (NTRS)

    Bierman, Gerald J.; Wolff, Peter J.

    1991-01-01

    Two algorithms compute error covariance of difference between optimal estimates, based on data acquired during overlapping or disjoint intervals, of state of discrete linear system. Provides quantitative measure of mutual consistency or inconsistency of estimates of states. Relative-error-covariance concept applied, to determine degree of correlation between trajectories calculated from two overlapping sets of measurements and construct real-time test of consistency of state estimates based upon recently acquired data.

  3. Missing great earthquakes

    USGS Publications Warehouse

    Hough, Susan E.

    2013-01-01

    The occurrence of three earthquakes with moment magnitude (Mw) greater than 8.8 and six earthquakes larger than Mw 8.5, since 2004, has raised interest in the long-term global rate of great earthquakes. Past studies have focused on the analysis of earthquakes since 1900, which roughly marks the start of the instrumental era in seismology. Before this time, the catalog is less complete and magnitude estimates are more uncertain. Yet substantial information is available for earthquakes before 1900, and the catalog of historical events is being used increasingly to improve hazard assessment. Here I consider the catalog of historical earthquakes and show that approximately half of all Mw ≥ 8.5 earthquakes are likely missing or underestimated in the 19th century. I further present a reconsideration of the felt effects of the 8 February 1843, Lesser Antilles earthquake, including a first thorough assessment of felt reports from the United States, and show it is an example of a known historical earthquake that was significantly larger than initially estimated. The results suggest that incorporation of best available catalogs of historical earthquakes will likely lead to a significant underestimation of seismic hazard and/or the maximum possible magnitude in many regions, including parts of the Caribbean.

  4. Development of covariance capabilities in EMPIRE code

    SciTech Connect

    Herman,M.; Pigni, M.T.; Oblozinsky, P.; Mughabghab, S.F.; Mattoon, C.M.; Capote, R.; Cho, Young-Sik; Trkov, A.

    2008-06-24

    The nuclear reaction code EMPIRE has been extended to provide evaluation capabilities for neutron cross section covariances in the thermal, resolved resonance, unresolved resonance and fast neutron regions. The Atlas of Neutron Resonances by Mughabghab is used as a primary source of information on uncertainties at low energies. Care is taken to ensure consistency among the resonance parameter uncertainties and those for thermal cross sections. The resulting resonance parameter covariances are formatted in the ENDF-6 File 32. In the fast neutron range our methodology is based on model calculations with the code EMPIRE combined with experimental data through several available approaches. The model-based covariances can be obtained using deterministic (Kalman) or stochastic (Monte Carlo) propagation of model parameter uncertainties. We show that these two procedures yield comparable results. The Kalman filter and/or the generalized least square fitting procedures are employed to incorporate experimental information. We compare the two approaches analyzing results for the major reaction channels on {sup 89}Y. We also discuss a long-standing issue of unreasonably low uncertainties and link it to the rigidity of the model.

  5. RNA sequence analysis using covariance models.

    PubMed Central

    Eddy, S R; Durbin, R

    1994-01-01

    We describe a general approach to several RNA sequence analysis problems using probabilistic models that flexibly describe the secondary structure and primary sequence consensus of an RNA sequence family. We call these models 'covariance models'. A covariance model of tRNA sequences is an extremely sensitive and discriminative tool for searching for additional tRNAs and tRNA-related sequences in sequence databases. A model can be built automatically from an existing sequence alignment. We also describe an algorithm for learning a model and hence a consensus secondary structure from initially unaligned example sequences and no prior structural information. Models trained on unaligned tRNA examples correctly predict tRNA secondary structure and produce high-quality multiple alignments. The approach may be applied to any family of small RNA sequences. Images PMID:8029015

  6. Replacing a Missing Tooth

    MedlinePlus

    ... patient without a bone graft is a fixed bridge. The missing tooth is restored with an artificial ... be crowned to give adequate support to the bridge. This type of prosthesis is not removable. Its ...

  7. Inverse covariance simplification for efficient uncertainty management

    NASA Astrophysics Data System (ADS)

    Jalobeanu, A.; Gutiérrez, J. A.

    2007-11-01

    When it comes to manipulating uncertain knowledge such as noisy observations of physical quantities, one may ask how to do it in a simple way. Processing corrupted signals or images always propagates the uncertainties from the data to the final results, whether these errors are explicitly computed or not. When such error estimates are provided, it is crucial to handle them in such a way that their interpretation, or their use in subsequent processing steps, remain user-friendly and computationally tractable. A few authors follow a Bayesian approach and provide uncertainties as an inverse covariance matrix. Despite its apparent sparsity, this matrix contains many small terms that carry little information. Methods have been developed to select the most significant entries, through the use of information-theoretic tools for instance. One has to find a Gaussian pdf that is close enough to the posterior pdf, and with a small number of non-zero coefficients in the inverse covariance matrix. We propose to restrict the search space to Markovian models (where only neighbors can interact), well-suited to signals or images. The originality of our approach is in conserving the covariances between neighbors while setting to zero the entries of the inverse covariance matrix for all other variables. This fully constrains the solution, and the computation is performed via a fast, alternate minimization scheme involving quadratic forms. The Markovian structure advantageously reduces the complexity of Bayesian updating (where the simplified pdf is used as a prior). Moreover, uncertainties exhibit the same temporal or spatial structure as the data.

  8. A New Approach for Nuclear Data Covariance and Sensitivity Generation

    SciTech Connect

    Leal, L.C.; Larson, N.M.; Derrien, H.; Kawano, T.; Chadwick, M.B.

    2005-05-24

    Covariance data are required to correctly assess uncertainties in design parameters in nuclear applications. The error estimation of calculated quantities relies on the nuclear data uncertainty information available in the basic nuclear data libraries, such as the U.S. Evaluated Nuclear Data File, ENDF/B. The uncertainty files in the ENDF/B library are obtained from the analysis of experimental data and are stored as variance and covariance data. The computer code SAMMY is used in the analysis of the experimental data in the resolved and unresolved resonance energy regions. The data fitting of cross sections is based on generalized least-squares formalism (Bayes' theory) together with the resonance formalism described by R-matrix theory. Two approaches are used in SAMMY for the generation of resonance-parameter covariance data. In the evaluation process SAMMY generates a set of resonance parameters that fit the data, and, in addition, it also provides the resonance-parameter covariances. For existing resonance-parameter evaluations where no resonance-parameter covariance data are available, the alternative is to use an approach called the 'retroactive' resonance-parameter covariance generation. In the high-energy region the methodology for generating covariance data consists of least-squares fitting and model parameter adjustment. The least-squares fitting method calculates covariances directly from experimental data. The parameter adjustment method employs a nuclear model calculation such as the optical model and the Hauser-Feshbach model, and estimates a covariance for the nuclear model parameters. In this paper we describe the application of the retroactive method and the parameter adjustment method to generate covariance data for the gadolinium isotopes.

  9. Covariances from light-element r-martix analyses

    SciTech Connect

    Hale, Gerald

    2008-01-01

    We review the method for obtaining covariance information for light-element reactions using R-matrix theory. The general LANL R-matrix analysis code EDA provides accurate covariances for the resonance parameters at a solution due to the search algorithm it uses to find a local minimum of the chi-square surface. This information is used, together with analytically calculated sensitivity derivatives, in the first-order error propagation equation to obtain cross-section covariances for all reactions included in the analysis. Examples are given of the covariances obtained from the EDA analyses for n-p scattering and for the n+{sup 6}Li reactions used in the latest light-element standard cross section evaluation. Also discussed is a method of defining 'pure theory' correlations that could be useful for extensions to higher energies and heavier systems.

  10. Covariances from Light-Element R-Matrix Analyses

    SciTech Connect

    Hale, G.M.

    2008-12-15

    We review the method for obtaining covariance information for light-element reactions using R-matrix theory. The general LANL R-matrix analysis code EDA provides accurate covariances for the resonance parameters at a solution due to the search algorithm it uses to find a local minimum of the chi-square surface. This information is used, together with analytically calculated sensitivity derivatives, in the first-order error propagation equation to obtain cross-section covariances for all reactions included in the analysis. Examples are given of the covariances obtained from the EDA analyses for n-p scattering and for the n+{sup 6}Li reactions used in the latest light-element standard cross section evaluation. Also discussed is a method of defining 'pure theory' correlations that could be useful for extensions to higher energies and heavier systems.

  11. Covariance Matrix Evaluations for Independent Mass Fission Yields

    SciTech Connect

    Terranova, N.; Serot, O.; Archier, P.; De Saint Jean, C.

    2015-01-15

    Recent needs for more accurate fission product yields include covariance information to allow improved uncertainty estimations of the parameters used by design codes. The aim of this work is to investigate the possibility to generate more reliable and complete uncertainty information on independent mass fission yields. Mass yields covariances are estimated through a convolution between the multi-Gaussian empirical model based on Brosa's fission modes, which describe the pre-neutron mass yields, and the average prompt neutron multiplicity curve. The covariance generation task has been approached using the Bayesian generalized least squared method through the CONRAD code. Preliminary results on mass yields variance-covariance matrix will be presented and discussed from physical grounds in the case of {sup 235}U(n{sub th}, f) and {sup 239}Pu(n{sub th}, f) reactions.

  12. Sensitivity of missing values in classification tree for large sample

    NASA Astrophysics Data System (ADS)

    Hasan, Norsida; Adam, Mohd Bakri; Mustapha, Norwati; Abu Bakar, Mohd Rizam

    2012-05-01

    Missing values either in predictor or in response variables are a very common problem in statistics and data mining. Cases with missing values are often ignored which results in loss of information and possible bias. The objectives of our research were to investigate the sensitivity of missing data in classification tree model for large sample. Data were obtained from one of the high level educational institutions in Malaysia. Students' background data were randomly eliminated and classification tree was used to predict students degree classification. The results showed that for large sample, the structure of the classification tree was sensitive to missing values especially for sample contains more than ten percent missing values.

  13. Using analysis of covariance (ANCOVA) with fallible covariates.

    PubMed

    Culpepper, Steven Andrew; Aguinis, Herman

    2011-06-01

    Analysis of covariance (ANCOVA) is used widely in psychological research implementing nonexperimental designs. However, when covariates are fallible (i.e., measured with error), which is the norm, researchers must choose from among 3 inadequate courses of action: (a) know that the assumption that covariates are perfectly reliable is violated but use ANCOVA anyway (and, most likely, report misleading results); (b) attempt to employ 1 of several measurement error models with the understanding that no research has examined their relative performance and with the added practical difficulty that several of these models are not available in commonly used statistical software; or (c) not use ANCOVA at all. First, we discuss analytic evidence to explain why using ANCOVA with fallible covariates produces bias and a systematic inflation of Type I error rates that may lead to the incorrect conclusion that treatment effects exist. Second, to provide a solution for this problem, we conduct 2 Monte Carlo studies to compare 4 existing approaches for adjusting treatment effects in the presence of covariate measurement error: errors-in-variables (EIV; Warren, White, & Fuller, 1974), Lord's (1960) method, Raaijmakers and Pieters's (1987) method (R&P), and structural equation modeling methods proposed by Sörbom (1978) and Hayduk (1996). Results show that EIV models are superior in terms of parameter accuracy, statistical power, and keeping Type I error close to the nominal value. Finally, we offer a program written in R that performs all needed computations for implementing EIV models so that ANCOVA can be used to obtain accurate results even when covariates are measured with error. © 2011 American Psychological Association

  14. A Simulation Study of Missing Data with Multiple Missing X's

    ERIC Educational Resources Information Center

    Rubright, Jonathan D.; Nandakumar, Ratna; Glutting, Joseph J.

    2014-01-01

    When exploring missing data techniques in a realistic scenario, the current literature is limited: most studies only consider consequences with data missing on a single variable. This simulation study compares the relative bias of two commonly used missing data techniques when data are missing on more than one variable. Factors varied include type…

  15. A Simulation Study of Missing Data with Multiple Missing X's

    ERIC Educational Resources Information Center

    Rubright, Jonathan D.; Nandakumar, Ratna; Glutting, Joseph J.

    2014-01-01

    When exploring missing data techniques in a realistic scenario, the current literature is limited: most studies only consider consequences with data missing on a single variable. This simulation study compares the relative bias of two commonly used missing data techniques when data are missing on more than one variable. Factors varied include type…

  16. Covariate-free and Covariate-dependent Reliability.

    PubMed

    Bentler, Peter M

    2016-12-01

    Classical test theory reliability coefficients are said to be population specific. Reliability generalization, a meta-analysis method, is the main procedure for evaluating the stability of reliability coefficients across populations. A new approach is developed to evaluate the degree of invariance of reliability coefficients to population characteristics. Factor or common variance of a reliability measure is partitioned into parts that are, and are not, influenced by control variables, resulting in a partition of reliability into a covariate-dependent and a covariate-free part. The approach can be implemented in a single sample and can be applied to a variety of reliability coefficients.

  17. Covariance Localization with the Diffusion-Based Correlation Models

    DTIC Science & Technology

    2013-02-01

    133,1279-1294. Emerick, A., and A. Reynolds, 2011: Combining sensitivities and prior information for covariance localization in the ensemble Kaiman ...background error covariance esti- mates in an ensemble Kaiman filter. Mon. Wea. Rev., 129, 2776-2790. Herdin, M., N. Czink, H. Özcelik, and E. Bonek...using an ensemble Kaiman filter. Mon. Wea. Rev., 126, 796-811. , and , 2001: A sequential ensemble Kaiman filter for atmospheric data assimilation

  18. Levy Matrices and Financial Covariances

    NASA Astrophysics Data System (ADS)

    Burda, Zdzislaw; Jurkiewicz, Jerzy; Nowak, Maciej A.; Papp, Gabor; Zahed, Ismail

    2003-10-01

    In a given market, financial covariances capture the intra-stock correlations and can be used to address statistically the bulk nature of the market as a complex system. We provide a statistical analysis of three SP500 covariances with evidence for raw tail distributions. We study the stability of these tails against reshuffling for the SP500 data and show that the covariance with the strongest tails is robust, with a spectral density in remarkable agreement with random Lévy matrix theory. We study the inverse participation ratio for the three covariances. The strong localization observed at both ends of the spectral density is analogous to the localization exhibited in the random Lévy matrix ensemble. We discuss two competitive mechanisms responsible for the occurrence of an extensive and delocalized eigenvalue at the edge of the spectrum: (a) the Lévy character of the entries of the correlation matrix and (b) a sort of off-diagonal order induced by underlying inter-stock correlations. (b) can be destroyed by reshuffling, while (a) cannot. We show that the stocks with the largest scattering are the least susceptible to correlations, and likely candidates for the localized states. We introduce a simple model for price fluctuations which captures behavior of the SP500 covariances. It may be of importance for assets diversification.

  19. Restoration of HST images with missing data

    NASA Technical Reports Server (NTRS)

    Adorf, Hans-Martin

    1992-01-01

    Missing data are a fairly common problem when restoring Hubble Space Telescope observations of extended sources. On Wide Field and Planetary Camera images cosmic ray hits and CCD hot spots are the prevalent causes of data losses, whereas on Faint Object Camera images data are lossed due to reseaux marks, blemishes, areas of saturation and the omnipresent frame edges. This contribution discusses a technique for 'filling in' missing data by statistical inference using information from the surrounding pixels. The major gain consists in minimizing adverse spill-over effects to the restoration in areas neighboring those where data are missing. When the mask delineating the support of 'missing data' is made dynamic, cosmic ray hits, etc. can be detected on the fly during restoration.

  20. A relativistically covariant random walk

    NASA Astrophysics Data System (ADS)

    Almaguer, J.; Larralde, H.

    2007-08-01

    In this work we present and analyze an extremely simple relativistically covariant random walk model. In our approach, the probability density and the flow of probability arise naturally as the components of a four-vector and they are related to one another via a tensorial constitutive equation. We show that the system can be described in terms of an underlying invariant space time random walk parameterized by the number of sojourns. Finally, we obtain explicit expressions for the moments of the covariant random walk as well as for the underlying invariant random walk.

  1. Rats (Rattus norvegicus) flexibly retrieve objects' non-spatial and spatial information from their visuospatial working memory: effects of integrated and separate processing of these features in a missing-object recognition task.

    PubMed

    Keshen, Corrine; Cohen, Jerome

    2016-01-01

    After being trained to find a previous missing object within an array of four different objects, rats received occasional probe trials with such test arrays rotated from that of their respective three-object study arrays. Only animals exposed to each object's non-spatial features consistently paired with both its spatial features (feeder's relative orientation and direction) in the first experiment or with only feeder's relative orientation in the second experiment (Fixed Configuration groups) were adversely affected by probe trial test array rotations. This effect, however, was less persistent for this group in the second experiment but re-emerged when objects' non-spatial features were later rendered uninformative. Animals that had both types of each object's features randomly paired over trials but not between a trial's study and test array (Varied Configuration groups) were not adversely affected on probe trials but improved their missing-object recognition in the first experiment. These findings suggest that the Fixed Configuration groups had integrated each object's non-spatial with both (in Experiment 1) or one (in Experiment 2) of its spatial features to construct a single representation that they could not easily compare to any object in a rotated probe test array. The Varied Configuration groups must maintain separate representations of each object's features to solve this task. This prevented them from exhibiting such adverse effects on rotated probe trial test arrays but enhanced the rats' missing-object recognition in the first experiment. We discussed how rats' flexible use (retrieval) of encoded information from their visuospatial working memory corresponds to that of humans' visuospatial memory in object change detection and complex object recognition tasks. We also discussed how foraging-specific factors may have influenced each group's performance in this task.

  2. AUTOMATIC CLASSIFICATION OF VARIABLE STARS IN CATALOGS WITH MISSING DATA

    SciTech Connect

    Pichara, Karim; Protopapas, Pavlos

    2013-11-10

    We present an automatic classification method for astronomical catalogs with missing data. We use Bayesian networks and a probabilistic graphical model that allows us to perform inference to predict missing values given observed data and dependency relationships between variables. To learn a Bayesian network from incomplete data, we use an iterative algorithm that utilizes sampling methods and expectation maximization to estimate the distributions and probabilistic dependencies of variables from data with missing values. To test our model, we use three catalogs with missing data (SAGE, Two Micron All Sky Survey, and UBVI) and one complete catalog (MACHO). We examine how classification accuracy changes when information from missing data catalogs is included, how our method compares to traditional missing data approaches, and at what computational cost. Integrating these catalogs with missing data, we find that classification of variable objects improves by a few percent and by 15% for quasar detection while keeping the computational cost the same.

  3. Methods for Addressing Missing Data in Psychiatric and Developmental Research

    ERIC Educational Resources Information Center

    Croy, Calvin D.; Novins, Douglas K.

    2005-01-01

    Objective: First, to provide information about best practices in handling missing data so that readers can judge the quality of research studies. Second, to provide more detailed information about missing data analysis techniques and software on the Journal's Web site at www.jaacap.com. Method: We focus our review of techniques on those that are…

  4. Methods for Addressing Missing Data in Psychiatric and Developmental Research

    ERIC Educational Resources Information Center

    Croy, Calvin D.; Novins, Douglas K.

    2005-01-01

    Objective: First, to provide information about best practices in handling missing data so that readers can judge the quality of research studies. Second, to provide more detailed information about missing data analysis techniques and software on the Journal's Web site at www.jaacap.com. Method: We focus our review of techniques on those that are…

  5. Condition Number Regularized Covariance Estimation.

    PubMed

    Won, Joong-Ho; Lim, Johan; Kim, Seung-Jean; Rajaratnam, Bala

    2013-06-01

    Estimation of high-dimensional covariance matrices is known to be a difficult problem, has many applications, and is of current interest to the larger statistics community. In many applications including so-called the "large p small n" setting, the estimate of the covariance matrix is required to be not only invertible, but also well-conditioned. Although many regularization schemes attempt to do this, none of them address the ill-conditioning problem directly. In this paper, we propose a maximum likelihood approach, with the direct goal of obtaining a well-conditioned estimator. No sparsity assumption on either the covariance matrix or its inverse are are imposed, thus making our procedure more widely applicable. We demonstrate that the proposed regularization scheme is computationally efficient, yields a type of Steinian shrinkage estimator, and has a natural Bayesian interpretation. We investigate the theoretical properties of the regularized covariance estimator comprehensively, including its regularization path, and proceed to develop an approach that adaptively determines the level of regularization that is required. Finally, we demonstrate the performance of the regularized estimator in decision-theoretic comparisons and in the financial portfolio optimization setting. The proposed approach has desirable properties, and can serve as a competitive procedure, especially when the sample size is small and when a well-conditioned estimator is required.

  6. Condition Number Regularized Covariance Estimation*

    PubMed Central

    Won, Joong-Ho; Lim, Johan; Kim, Seung-Jean; Rajaratnam, Bala

    2012-01-01

    Estimation of high-dimensional covariance matrices is known to be a difficult problem, has many applications, and is of current interest to the larger statistics community. In many applications including so-called the “large p small n” setting, the estimate of the covariance matrix is required to be not only invertible, but also well-conditioned. Although many regularization schemes attempt to do this, none of them address the ill-conditioning problem directly. In this paper, we propose a maximum likelihood approach, with the direct goal of obtaining a well-conditioned estimator. No sparsity assumption on either the covariance matrix or its inverse are are imposed, thus making our procedure more widely applicable. We demonstrate that the proposed regularization scheme is computationally efficient, yields a type of Steinian shrinkage estimator, and has a natural Bayesian interpretation. We investigate the theoretical properties of the regularized covariance estimator comprehensively, including its regularization path, and proceed to develop an approach that adaptively determines the level of regularization that is required. Finally, we demonstrate the performance of the regularized estimator in decision-theoretic comparisons and in the financial portfolio optimization setting. The proposed approach has desirable properties, and can serve as a competitive procedure, especially when the sample size is small and when a well-conditioned estimator is required. PMID:23730197

  7. Covariation Neglect among Novice Investors

    ERIC Educational Resources Information Center

    Hedesstrom, Ted Martin; Svedsater, Henrik; Garling, Tommy

    2006-01-01

    In 4 experiments, undergraduates made hypothetical investment choices. In Experiment 1, participants paid more attention to the volatility of individual assets than to the volatility of aggregated portfolios. The results of Experiment 2 show that most participants diversified even when this increased risk because of covariation between the returns…

  8. Covariation Neglect among Novice Investors

    ERIC Educational Resources Information Center

    Hedesstrom, Ted Martin; Svedsater, Henrik; Garling, Tommy

    2006-01-01

    In 4 experiments, undergraduates made hypothetical investment choices. In Experiment 1, participants paid more attention to the volatility of individual assets than to the volatility of aggregated portfolios. The results of Experiment 2 show that most participants diversified even when this increased risk because of covariation between the returns…

  9. Multiple imputation of missing values was not necessary before performing a longitudinal mixed-model analysis.

    PubMed

    Twisk, Jos; de Boer, Michiel; de Vente, Wieke; Heymans, Martijn

    2013-09-01

    As a result of the development of sophisticated techniques, such as multiple imputation, the interest in handling missing data in longitudinal studies has increased enormously in past years. Within the field of longitudinal data analysis, there is a current debate on whether it is necessary to use multiple imputations before performing a mixed-model analysis to analyze the longitudinal data. In the current study this necessity is evaluated. The results of mixed-model analyses with and without multiple imputation were compared with each other. Four data sets with missing values were created-one data set with missing completely at random, two data sets with missing at random, and one data set with missing not at random). In all data sets, the relationship between a continuous outcome variable and two different covariates were analyzed: a time-independent dichotomous covariate and a time-dependent continuous covariate. Although for all types of missing data, the results of the mixed-model analysis with or without multiple imputations were slightly different, they were not in favor of one of the two approaches. In addition, repeating the multiple imputations 100 times showed that the results of the mixed-model analysis with multiple imputation were quite unstable. It is not necessary to handle missing data using multiple imputations before performing a mixed-model analysis on longitudinal data. Copyright © 2013 Elsevier Inc. All rights reserved.

  10. Covariant Formulations of Superstring Theories.

    NASA Astrophysics Data System (ADS)

    Mikovic, Aleksandar Radomir

    1990-01-01

    Chapter 1 contains a brief introduction to the subject of string theory, and tries to motivate the study of superstrings and covariant formulations. Chapter 2 describes the Green-Schwarz formulation of the superstrings. The Hamiltonian and BRST structure of the theory is analysed in the case of the superparticle. Implications for the superstring case are discussed. Chapter 3 describes the Siegel's formulation of the superstring, which contains only the first class constraints. It is shown that the physical spectrum coincides with that of the Green-Schwarz formulation. In chapter 4 we analyse the BRST structure of the Siegel's formulation. We show that the BRST charge has the wrong cohomology, and propose a modification, called first ilk, which gives the right cohomology. We also propose another superparticle model, called second ilk, which has infinitely many coordinates and constraints. We construct the complete BRST charge for it, and show that it gives the correct cohomology. In chapter 5 we analyse the properties of the covariant vertex operators and the corresponding S-matrix elements by using the Siegel's formulation. We conclude that the knowledge of the ghosts is necessary, even at the tree level, in order to obtain the correct S-matrix. In chapter 6 we attempt to calculate the superstring loops, in a covariant gauge. We calculate the vacuum-to -vacuum amplitude, which is also the cosmological constant. We show that it vanishes to all loop orders, under the assumption that the free covariant gauge-fixed action exists. In chapter 7 we present our conclusions, and briefly discuss the random lattice approach to the string theory, as a possible way of resolving the problem of the covariant quantization and the nonperturbative definition of the superstrings.

  11. Bayesian modeling of the covariance structure for irregular longitudinal data using the partial autocorrelation function.

    PubMed

    Su, Li; Daniels, Michael J

    2015-05-30

    In long-term follow-up studies, irregular longitudinal data are observed when individuals are assessed repeatedly over time but at uncommon and irregularly spaced time points. Modeling the covariance structure for this type of data is challenging, as it requires specification of a covariance function that is positive definite. Moreover, in certain settings, careful modeling of the covariance structure for irregular longitudinal data can be crucial in order to ensure no bias arises in the mean structure. Two common settings where this occurs are studies with 'outcome-dependent follow-up' and studies with 'ignorable missing data'. 'Outcome-dependent follow-up' occurs when individuals with a history of poor health outcomes had more follow-up measurements, and the intervals between the repeated measurements were shorter. When the follow-up time process only depends on previous outcomes, likelihood-based methods can still provide consistent estimates of the regression parameters, given that both the mean and covariance structures of the irregular longitudinal data are correctly specified and no model for the follow-up time process is required. For 'ignorable missing data', the missing data mechanism does not need to be specified, but valid likelihood-based inference requires correct specification of the covariance structure. In both cases, flexible modeling approaches for the covariance structure are essential. In this paper, we develop a flexible approach to modeling the covariance structure for irregular continuous longitudinal data using the partial autocorrelation function and the variance function. In particular, we propose semiparametric non-stationary partial autocorrelation function models, which do not suffer from complex positive definiteness restrictions like the autocorrelation function. We describe a Bayesian approach, discuss computational issues, and apply the proposed methods to CD4 count data from a pediatric AIDS clinical trial. © 2015 The Authors

  12. Bayesian modeling of the covariance structure for irregular longitudinal data using the partial autocorrelation function

    PubMed Central

    Su, Li; Daniels, Michael J

    2015-01-01

    In long-term follow-up studies, irregular longitudinal data are observed when individuals are assessed repeatedly over time but at uncommon and irregularly spaced time points. Modeling the covariance structure for this type of data is challenging, as it requires specification of a covariance function that is positive definite. Moreover, in certain settings, careful modeling of the covariance structure for irregular longitudinal data can be crucial in order to ensure no bias arises in the mean structure. Two common settings where this occurs are studies with ‘outcome-dependent follow-up’ and studies with ‘ignorable missing data’. ‘Outcome-dependent follow-up’ occurs when individuals with a history of poor health outcomes had more follow-up measurements, and the intervals between the repeated measurements were shorter. When the follow-up time process only depends on previous outcomes, likelihood-based methods can still provide consistent estimates of the regression parameters, given that both the mean and covariance structures of the irregular longitudinal data are correctly specified and no model for the follow-up time process is required. For ‘ignorable missing data’, the missing data mechanism does not need to be specified, but valid likelihood-based inference requires correct specification of the covariance structure. In both cases, flexible modeling approaches for the covariance structure are essential. In this paper, we develop a flexible approach to modeling the covariance structure for irregular continuous longitudinal data using the partial autocorrelation function and the variance function. In particular, we propose semiparametric non-stationary partial autocorrelation function models, which do not suffer from complex positive definiteness restrictions like the autocorrelation function. We describe a Bayesian approach, discuss computational issues, and apply the proposed methods to CD4 count data from a pediatric AIDS clinical trial. © 2015

  13. Recovery of information from multiple imputation: a simulation study.

    PubMed

    Lee, Katherine J; Carlin, John B

    2012-06-13

    Multiple imputation is becoming increasingly popular for handling missing data. However, it is often implemented without adequate consideration of whether it offers any advantage over complete case analysis for the research question of interest, or whether potential gains may be offset by bias from a poorly fitting imputation model, particularly as the amount of missing data increases. Simulated datasets (n = 1000) drawn from a synthetic population were used to explore information recovery from multiple imputation in estimating the coefficient of a binary exposure variable when various proportions of data (10-90%) were set missing at random in a highly-skewed continuous covariate or in the binary exposure. Imputation was performed using multivariate normal imputation (MVNI), with a simple or zero-skewness log transformation to manage non-normality. Bias, precision, mean-squared error and coverage for a set of regression parameter estimates were compared between multiple imputation and complete case analyses. For missingness in the continuous covariate, multiple imputation produced less bias and greater precision for the effect of the binary exposure variable, compared with complete case analysis, with larger gains in precision with more missing data. However, even with only moderate missingness, large bias and substantial under-coverage were apparent in estimating the continuous covariate's effect when skewness was not adequately addressed. For missingness in the binary covariate, all estimates had negligible bias but gains in precision from multiple imputation were minimal, particularly for the coefficient of the binary exposure. Although multiple imputation can be useful if covariates required for confounding adjustment are missing, benefits are likely to be minimal when data are missing in the exposure variable of interest. Furthermore, when there are large amounts of missingness, multiple imputation can become unreliable and introduce bias not present in a

  14. Missing proofs found.

    SciTech Connect

    Fitelson, B.; Wos, L.; Mathematics and Computer Science; Univ. of Wisconsin

    2001-01-01

    For close to a century, despite the efforts of fine minds that include Hilbert and Ackermann, Tarski and Bernays, Lukasiewicz, and Rose and Rosser, various proofs of a number of significant theorems have remained missing -- at least not reported in the literature -- amply demonstrating the depth of the corresponding problems. The types of such missing proofs are indeed diverse. For one example, a result may be guaranteed provable because of being valid, and yet no proof has been found. For a second example, a theorem may have been proved via metaargument, but the desired axiomatic proof based solely on the use of a given inference rule may have eluded the experts. For a third example, a theorem may have been announced by a master, but no proof was supplied. The finding of missing proofs of the cited types, as well as of other types, is the focus of this article. The means to finding such proofs rests with heavy use of McCune's automated reasoning program OTTER, reliance on a variety of powerful strategies this program offers, and employment of diverse methodologies. Here we present some of our successes and, because it may prove useful for circuit design and program synthesis as well as in the context of mathematics and logic, detail our approach to finding missing proofs. Well-defined and unmet challenges are included.

  15. Missing School Matters

    ERIC Educational Resources Information Center

    Balfanz, Robert

    2016-01-01

    Results of a survey conducted by the Office for Civil Rights show that 6 million public school students (13%) are not attending school regularly. Chronic absenteeism--defined as missing more than 10% of school for any reason--has been negatively linked to many key academic outcomes. Evidence shows that students who exit chronic absentee status can…

  16. Missing School Matters

    ERIC Educational Resources Information Center

    Balfanz, Robert

    2016-01-01

    Results of a survey conducted by the Office for Civil Rights show that 6 million public school students (13%) are not attending school regularly. Chronic absenteeism--defined as missing more than 10% of school for any reason--has been negatively linked to many key academic outcomes. Evidence shows that students who exit chronic absentee status can…

  17. Missing children found dead.

    PubMed

    Rodreguez, R D; Nahirny, C; Burgess, A W; Burgess, A G

    1998-06-01

    Forensic evidence in child homicide cases is critical to determine sexual abuse. Forensic evidence can help focus an investigation on a suspect through DNA results. Of 210 missing children found deceased, 68% were homicides, 16% had accidental causes, 12% were unknown, and 4% were suicides.

  18. Missed Diagnosis of Syrinx

    PubMed Central

    Oh, Chang Hyun; Kim, Chan Gyu; Lee, Jae-Hwan; Park, Hyeong-Chun; Park, Chong Oon

    2012-01-01

    Study Design Prospective, randomized, controlled human study. Purpose We checked the proportion of missed syrinx diagnoses among the examinees of the Korean military conscription. Overview of Literature A syrinx is a fluid-filled cavity within the spinal cord or brain stem and causes various neurological symptoms. A syrinx could easily be diagnosed by magnetic resonance image (MRI), but missed diagnoses seldom occur. Methods In this study, we reviewed 103 cases using cervical images, cervical MRI, or whole spine sagittal MRI, and syrinxes was observed in 18 of these cases. A review of medical certificates or interviews was conducted, and the proportion of syrinx diagnoses was calculated. Results The proportion of syrinx diagnoses was about 66.7% (12 cases among 18). Missed diagnoses were not the result of the length of the syrinx, but due to the type of image used for the initial diagnosis. Conclusions The missed diagnosis proportion of the syrinx is relatively high, therefore, a more careful imaging review is recommended. PMID:22439081

  19. Best practices for missing data management in counseling psychology.

    PubMed

    Schlomer, Gabriel L; Bauman, Sheri; Card, Noel A

    2010-01-01

    This article urges counseling psychology researchers to recognize and report how missing data are handled, because consumers of research cannot accurately interpret findings without knowing the amount and pattern of missing data or the strategies that were used to handle those data. Patterns of missing data are reviewed, and some of the common strategies for dealing with them are described. The authors provide an illustration in which data were simulated and evaluate 3 methods of handling missing data: mean substitution, multiple imputation, and full information maximum likelihood. Results suggest that mean substitution is a poor method for handling missing data, whereas both multiple imputation and full information maximum likelihood are recommended alternatives to this approach. The authors suggest that researchers fully consider and report the amount and pattern of missing data and the strategy for handling those data in counseling psychology research and that editors advise researchers of this expectation.

  20. Robust parametric indirect estimates of the expected cost of a hospital stay with covariates and censored data.

    PubMed

    Locatelli, Isabella; Marazzi, Alfio

    2013-06-30

    We consider the problem of estimating the mean hospital cost of stays of a class of patients (e.g., a diagnosis-related group) as a function of patient characteristics. The statistical analysis is complicated by the asymmetry of the cost distribution, the possibility of censoring on the cost variable, and the occurrence of outliers. These problems have often been treated separately in the literature, and a method offering a joint solution to all of them is still missing. Indirect procedures have been proposed, combining an estimate of the duration distribution with an estimate of the conditional cost for a given duration. We propose a parametric version of this approach, allowing for asymmetry and censoring in the cost distribution and providing a mean cost estimator that is robust in the presence of extreme values. In addition, the new method takes covariate information into account.

  1. The impact of sociodemographic, treatment, and work support on missed work after breast cancer diagnosis.

    PubMed

    Mujahid, Mahasin S; Janz, Nancy K; Hawley, Sarah T; Griggs, Jennifer J; Hamilton, Ann S; Katz, Steven J

    2010-01-01

    Work loss is a potential adverse consequence of cancer. There is limited research on patterns and correlates of paid work after diagnosis of breast cancer, especially among ethnic minorities. Women with non-metastatic breast cancer diagnosed from June 2005 to May 2006 who reported to the Los Angeles County SEER registry were identified and asked to complete the survey after initial treatment (median time from diagnosis = 8.9 months). Latina and African American women were over-sampled. Analyses were restricted to women working at the time of diagnosis, <65 years of age, and who had complete covariate information (N = 589). The outcome of the study was missed paid work (1 month, stopped all together). Approximately 44, 24, and 32% of women missed 1 month, or stopped working, respectively. African Americans and Latinas were more likely to stop working when compared with Whites [OR for stop working vs. missed missed

  2. The impact of sociodemographic, treatment, and work support on missed work after breast cancer diagnosis

    PubMed Central

    Mujahid, Mahasin S.; Janz, Nancy K.; Hawley, Sarah T.; Griggs, Jennifer J.; Hamilton, Ann S.; Katz, Steven J.

    2016-01-01

    Work loss is a potential adverse consequence of cancer. There is limited research on patterns and correlates of paid work after diagnosis of breast cancer, especially among ethnic minorities. Women with non-metastatic breast cancer diagnosed from June 2005 to May 2006 who reported to the Los Angeles County SEER registry were identified and asked to complete the survey after initial treatment (median time from diagnosis = 8.9 months). Latina and African American women were over-sampled. Analyses were restricted to women working at the time of diagnosis, <65 years of age, and who had complete covariate information (N = 589). The outcome of the study was missed paid work (≤ month, >1 month, stopped all together). Approximately 44, 24, and 32% of women missed ≤1 month, >1 month, or stopped working, respectively. African Americans and Latinas were more likely to stop working when compared with Whites [OR for stop working vs. missed ≤1 month: 3.0, 3.4, (P < 0.001), respectively]. Women receiving mastectomy and those receiving chemotherapy were also more likely to stop working, independent of sociodemographic and treatment factors [ORs for stopped working vs. missed ≤1 month: 4.2, P < 0.001; 7.9, P < 0.001, respectively]. Not having a flexible work schedule available through work was detrimental to working [ORs for stopped working 18.9, P < 0.001 after adjusting for sociodemographic and treatment factors]. Many women stop working altogether after a diagnosis of breast cancer, particularly if they are racial/ethnic minorities, receive chemotherapy, or those who are employed in an unsupportive work settings. Health care providers need to be aware of these adverse consequences of breast cancer diagnosis and initial treatment. PMID:19360466

  3. New capabilities for processing covariance data in resonance region

    SciTech Connect

    Wiarda, D.; Dunn, M. E.; Greene, N. M.; Larson, N. M.; Leal, L. C.

    2006-07-01

    The AMPX [1] code system is a modular system of FORTRAN computer programs that relate to nuclear analysis with a primary emphasis on tasks associated with the production and use of multi group and continuous energy cross sections. The module PUFF-III within this code system handles the creation of multi group covariance data from ENDF information. The resulting covariances are saved in COVERX format [2]. We recently expanded the capabilities of PUFF-III to include full handling of covariance data in the resonance region (resolved as well as unresolved). The new program handles all resonance covariance formats in File 32 except for the long-range covariance sub sections. The new program has been named PUFF-IV. To our knowledge, PUFF-IV is the first processing code that can address both the new ENDF format for resolved resonance parameters and the new ENDF 'compact' covariance format. The existing code base was rewritten in Fortran 90 to allow for a more modular design. Results are identical between the new and old versions within rounding errors, where applicable. Automatic test cases have been added to ensure that consistent results are generated across computer systems. (authors)

  4. [Clinical research XIX. From clinical judgment to analysis of covariance].

    PubMed

    Pérez-Rodríguez, Marcela; Palacios-Cruz, Lino; Moreno, Jorge; Rivas-Ruiz, Rodolfo; Talavera, Juan O

    2014-01-01

    The analysis of covariance (ANCOVA) is based on the general linear models. This technique involves a regression model, often multiple, in which the outcome is presented as a continuous variable, the independent variables are qualitative or are introduced into the model as dummy or dichotomous variables, and factors for which adjustment is required (covariates) can be in any measurement level (i.e. nominal, ordinal or continuous). The maneuvers can be entered into the model as 1) fixed effects, or 2) random effects. The difference between fixed effects and random effects depends on the type of information we want from the analysis of the effects. ANCOVA effect separates the independent variables from the effect of co-variables, i.e., corrects the dependent variable eliminating the influence of covariates, given that these variables change in conjunction with maneuvers or treatments, affecting the outcome variable. ANCOVA should be done only if it meets three assumptions: 1) the relationship between the covariate and the outcome is linear, 2) there is homogeneity of slopes, and 3) the covariate and the independent variable are independent from each other.

  5. Gaussian covariance matrices for anisotropic galaxy clustering measurements

    NASA Astrophysics Data System (ADS)

    Grieb, Jan Niklas; Sánchez, Ariel G.; Salazar-Albornoz, Salvador; Dalla Vecchia, Claudio

    2016-04-01

    Measurements of the redshift-space galaxy clustering have been a prolific source of cosmological information in recent years. Accurate covariance estimates are an essential step for the validation of galaxy clustering models of the redshift-space two-point statistics. Usually, only a limited set of accurate N-body simulations is available. Thus, assessing the data covariance is not possible or only leads to a noisy estimate. Further, relying on simulated realizations of the survey data means that tests of the cosmology dependence of the covariance are expensive. With these points in mind, this work presents a simple theoretical model for the linear covariance of anisotropic galaxy clustering observations with synthetic catalogues. Considering the Legendre moments (`multipoles') of the two-point statistics and projections into wide bins of the line-of-sight parameter (`clustering wedges'), we describe the modelling of the covariance for these anisotropic clustering measurements for galaxy samples with a trivial geometry in the case of a Gaussian approximation of the clustering likelihood. As main result of this paper, we give the explicit formulae for Fourier and configuration space covariance matrices. To validate our model, we create synthetic halo occupation distribution galaxy catalogues by populating the haloes of an ensemble of large-volume N-body simulations. Using linear and non-linear input power spectra, we find very good agreement between the model predictions and the measurements on the synthetic catalogues in the quasi-linear regime.

  6. Fission yield covariances for JEFF: A Bayesian Monte Carlo method

    NASA Astrophysics Data System (ADS)

    Leray, Olivier; Rochman, Dimitri; Fleming, Michael; Sublet, Jean-Christophe; Koning, Arjan; Vasiliev, Alexander; Ferroukhi, Hakim

    2017-09-01

    The JEFF library does not contain fission yield covariances, but simply best estimates and uncertainties. This situation is not unique as all libraries are facing this deficiency, firstly due to the lack of a defined format. An alternative approach is to provide a set of random fission yields, themselves reflecting covariance information. In this work, these random files are obtained combining the information from the JEFF library (fission yields and uncertainties) and the theoretical knowledge from the GEF code. Examples of this method are presented for the main actinides together with their impacts on simple burn-up and decay heat calculations.

  7. Realization of the optimal phase-covariant quantum cloning machine

    NASA Astrophysics Data System (ADS)

    Sciarrino, Fabio; de Martini, Francesco

    2005-12-01

    In several quantum information (QI) phenomena of large technological importance the information is carried by the phase of the quantum superposition states, or qubits. The phase-covariant cloning machine (PQCM) addresses precisely the problem of optimally copying these qubits with the largest attainable “fidelity.” We present a general scheme which realizes the 1→3 phase covariant cloning process by a combination of three different QI processes: the universal cloning, the NOT gate, and the projection over the symmetric subspace of the output qubits. The experimental implementation of a PQCM for polarization encoded qubits, the first ever realized with photons, is reported.

  8. Realization of the optimal phase-covariant quantum cloning machine

    SciTech Connect

    Sciarrino, Fabio; De Martini, Francesco

    2005-12-15

    In several quantum information (QI) phenomena of large technological importance the information is carried by the phase of the quantum superposition states, or qubits. The phase-covariant cloning machine (PQCM) addresses precisely the problem of optimally copying these qubits with the largest attainable 'fidelity'. We present a general scheme which realizes the 1{yields}3 phase covariant cloning process by a combination of three different QI processes: the universal cloning, the NOT gate, and the projection over the symmetric subspace of the output qubits. The experimental implementation of a PQCM for polarization encoded qubits, the first ever realized with photons, is reported.

  9. Szekeres models: a covariant approach

    NASA Astrophysics Data System (ADS)

    Apostolopoulos, Pantelis S.

    2017-05-01

    We exploit the 1  +  1  +  2 formalism to covariantly describe the inhomogeneous and anisotropic Szekeres models. It is shown that an average scale length can be defined covariantly which satisfies a 2d equation of motion driven from the effective gravitational mass (EGM) contained in the dust cloud. The contributions to the EGM are encoded to the energy density of the dust fluid and the free gravitational field E ab . We show that the quasi-symmetric property of the Szekeres models is justified through the existence of 3 independent intrinsic Killing vector fields (IKVFs). In addition the notions of the apparent and absolute apparent horizons are briefly discussed and we give an alternative gauge-invariant form to define them in terms of the kinematical variables of the spacelike congruences. We argue that the proposed program can be used in order to express Sachs’ optical equations in a covariant form and analyze the confrontation of a spatially inhomogeneous irrotational overdense fluid model with the observational data.

  10. 23 CFR Appendix B to Part 1240 - Procedures for Missing or Inadequate State-Submitted Information (Calendar Years 1996 and 1997)

    Code of Federal Regulations, 2011 CFR

    2011-04-01

    ... GRANTS FOR USE OF SEAT BELTS-ALLOCATIONS BASED ON SEAT BELT USE RATES Pt. 1240, App. B Appendix B to Part.... If State-submitted seat belt use rate information is unavailable or inadequate for both calendar years 1996 and 1997, State seat belt use rates for calendars year 1996 and 1997 will be estimated...

  11. Filling quality of the reports of adverse drug reactions received at the Pharmacovigilance Centre of São Paulo (Brazil): missing information hinders the analysis of suspected associations.

    PubMed

    Ribeiro, Adalton; Lima, Silvana; Zampieri, Maria-Elisa; Peinado, Mirtes; Figueras, Albert

    2017-08-23

    The completeness and accuracy of the reports of suspected adverse drug reactions is important in pharmacovigilance. The aim of the present study was to analyze the quality of the information included in the reports sent to the Pharmacovigilance Centre of São Paulo (Brazil). A sample of 999 reports received from January 2013 to December 2014 was selected. The quality of the filled information was evaluated according to a 'sufficiency' criterion to apply the Karch-Lasagna causality algorithm. There were 820 reports from manufacturers and 179 from health centres. Only 4.4% (44) were fully filled, thus allowing the adequate analysis of the causal relationship between the suspected medication and the adverse event. In 30% of the reports from manufacturers, the information about the critical variables was lacking or incomplete, preventing the adequate evaluation of the report. It was also noted that the reports' poor filling quality was not related with less severity or with old and well-known medicines. The poor quality of the information included in the reports received by this centre, especially those sent by pharmaceutical manufacturers, hampers the identification of potential safety signals. Measures to improve the quality of the reports must be urgently adopted.

  12. Understanding covariate shift in model performance

    PubMed Central

    McGaughey, Georgia; Walters, W. Patrick; Goldman, Brian

    2016-01-01

    Three (3) different methods (logistic regression, covariate shift and k-NN) were applied to five (5) internal datasets and one (1) external, publically available dataset where covariate shift existed. In all cases, k-NN’s performance was inferior to either logistic regression or covariate shift. Surprisingly, there was no obvious advantage for using covariate shift to reweight the training data in the examined datasets. PMID:27803797

  13. What's Missing? Anti-Racist Sex Education!

    ERIC Educational Resources Information Center

    Whitten, Amanda; Sethna, Christabelle

    2014-01-01

    Contemporary sexual health curricula in Canada include information about sexual diversity and queer identities, but what remains missing is any explicit discussion of anti-racist sex education. Although there exists federal and provincial support for multiculturalism and anti-racism in schools, contemporary Canadian sex education omits crucial…

  14. Mardia's Multivariate Kurtosis with Missing Data

    ERIC Educational Resources Information Center

    Yuan, Ke-Hai; Lambert, Paul L.; Fouladi, Rachel T.

    2004-01-01

    Mardia's measure of multivariate kurtosis has been implemented in many statistical packages commonly used by social scientists. It provides important information on whether a commonly used multivariate procedure is appropriate for inference. Many statistical packages also have options for missing data. However, there is no procedure for applying…

  15. What's Missing? Anti-Racist Sex Education!

    ERIC Educational Resources Information Center

    Whitten, Amanda; Sethna, Christabelle

    2014-01-01

    Contemporary sexual health curricula in Canada include information about sexual diversity and queer identities, but what remains missing is any explicit discussion of anti-racist sex education. Although there exists federal and provincial support for multiculturalism and anti-racism in schools, contemporary Canadian sex education omits crucial…

  16. Missing Data and Institutional Research

    ERIC Educational Resources Information Center

    Croninger, Robert G.; Douglas, Karen M.

    2005-01-01

    Many do not consider the effect that missing data have on their survey results nor do they know how to handle missing data. This chapter offers strategies for handling item-missing data and provides a practical example of how these strategies may affect results. The chapter concludes with recommendations for preventing and dealing with missing…

  17. Realistic Covariance Prediction for the Earth Science Constellation

    NASA Technical Reports Server (NTRS)

    Duncan, Matthew; Long, Anne

    2006-01-01

    Routine satellite operations for the Earth Science Constellation (ESC) include collision risk assessment between members of the constellation and other orbiting space objects. One component of the risk assessment process is computing the collision probability between two space objects. The collision probability is computed using Monte Carlo techniques as well as by numerically integrating relative state probability density functions. Each algorithm takes as inputs state vector and state vector uncertainty information for both objects. The state vector uncertainty information is expressed in terms of a covariance matrix. The collision probability computation is only as good as the inputs. Therefore, to obtain a collision calculation that is a useful decision-making metric, realistic covariance matrices must be used as inputs to the calculation. This paper describes the process used by the NASA/Goddard Space Flight Center's Earth Science Mission Operations Project to generate realistic covariance predictions for three of the Earth Science Constellation satellites: Aqua, Aura and Terra.

  18. Realistic Covariance Prediction For the Earth Science Constellations

    NASA Technical Reports Server (NTRS)

    Duncan, Matthew; Long, Anne

    2006-01-01

    Routine satellite operations for the Earth Science Constellations (ESC) include collision risk assessment between members of the constellations and other orbiting space objects. One component of the risk assessment process is computing the collision probability between two space objects. The collision probability is computed via Monte Carlo techniques as well as numerically integrating relative probability density functions. Each algorithm takes as inputs state vector and state vector uncertainty information for both objects. The state vector uncertainty information is expressed in terms of a covariance matrix. The collision probability computation is only as good as the inputs. Therefore, to obtain a collision calculation that is a useful decision-making metric, realistic covariance matrices must be used as inputs to the calculation. This paper describes the process used by NASA Goddard's Earth Science Mission Operations Project to generate realistic covariance predictions for three of the ESC satellites: Aqua, Aura, and Terra

  19. Realistic Covariance Prediction For the Earth Science Constellations

    NASA Technical Reports Server (NTRS)

    Duncan, Matthew; Long, Anne

    2006-01-01

    Routine satellite operations for the Earth Science Constellations (ESC) include collision risk assessment between members of the constellations and other orbiting space objects. One component of the risk assessment process is computing the collision probability between two space objects. The collision probability is computed via Monte Carlo techniques as well as numerically integrating relative probability density functions. Each algorithm takes as inputs state vector and state vector uncertainty information for both objects. The state vector uncertainty information is expressed in terms of a covariance matrix. The collision probability computation is only as good as the inputs. Therefore, to obtain a collision calculation that is a useful decision-making metric, realistic covariance matrices must be used as inputs to the calculation. This paper describes the process used by NASA Goddard's Earth Science Mission Operations Project to generate realistic covariance predictions for three of the ESC satellites: Aqua, Aura, and Terra

  20. Multiple imputation for an incomplete covariate that is a ratio.

    PubMed

    Morris, Tim P; White, Ian R; Royston, Patrick; Seaman, Shaun R; Wood, Angela M

    2014-01-15

    We are concerned with multiple imputation of the ratio of two variables, which is to be used as a covariate in a regression analysis. If the numerator and denominator are not missing simultaneously, it seems sensible to make use of the observed variable in the imputation model. One such strategy is to impute missing values for the numerator and denominator, or the log-transformed numerator and denominator, and then calculate the ratio of interest; we call this 'passive' imputation. Alternatively, missing ratio values might be imputed directly, with or without the numerator and/or the denominator in the imputation model; we call this 'active' imputation. In two motivating datasets, one involving body mass index as a covariate and the other involving the ratio of total to high-density lipoprotein cholesterol, we assess the sensitivity of results to the choice of imputation model and, as an alternative, explore fully Bayesian joint models for the outcome and incomplete ratio. Fully Bayesian approaches using Winbugs were unusable in both datasets because of computational problems. In our first dataset, multiple imputation results are similar regardless of the imputation model; in the second, results are sensitive to the choice of imputation model. Sensitivity depends strongly on the coefficient of variation of the ratio's denominator. A simulation study demonstrates that passive imputation without transformation is risky because it can lead to downward bias when the coefficient of variation of the ratio's denominator is larger than about 0.1. Active imputation or passive imputation after log-transformation is preferable.

  1. Multiple imputation for an incomplete covariate that is a ratio

    PubMed Central

    Morris, Tim P; White, Ian R; Royston, Patrick; Seaman, Shaun R; Wood, Angela M

    2014-01-01

    We are concerned with multiple imputation of the ratio of two variables, which is to be used as a covariate in a regression analysis. If the numerator and denominator are not missing simultaneously, it seems sensible to make use of the observed variable in the imputation model. One such strategy is to impute missing values for the numerator and denominator, or the log-transformed numerator and denominator, and then calculate the ratio of interest; we call this ‘passive’ imputation. Alternatively, missing ratio values might be imputed directly, with or without the numerator and/or the denominator in the imputation model; we call this ‘active’ imputation. In two motivating datasets, one involving body mass index as a covariate and the other involving the ratio of total to high-density lipoprotein cholesterol, we assess the sensitivity of results to the choice of imputation model and, as an alternative, explore fully Bayesian joint models for the outcome and incomplete ratio. Fully Bayesian approaches using Winbugs were unusable in both datasets because of computational problems. In our first dataset, multiple imputation results are similar regardless of the imputation model; in the second, results are sensitive to the choice of imputation model. Sensitivity depends strongly on the coefficient of variation of the ratio's denominator. A simulation study demonstrates that passive imputation without transformation is risky because it can lead to downward bias when the coefficient of variation of the ratio's denominator is larger than about 0.1. Active imputation or passive imputation after log-transformation is preferable. © 2013 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd. PMID:23922236

  2. Lorentz-covariant dissipative Lagrangian systems

    NASA Technical Reports Server (NTRS)

    Kaufman, A. N.

    1985-01-01

    The concept of dissipative Hamiltonian system is converted to Lorentz-covariant form, with evolution generated jointly by two scalar functionals, the Lagrangian action and the global entropy. A bracket formulation yields the local covariant laws of energy-momentum conservation and of entropy production. The formalism is illustrated by a derivation of the covariant Landau kinetic equation.

  3. Are Maxwell's equations Lorentz-covariant?

    NASA Astrophysics Data System (ADS)

    Redžić, D. V.

    2017-01-01

    It is stated in many textbooks that Maxwell's equations are manifestly covariant when written down in tensorial form. We recall that tensorial form of Maxwell's equations does not secure their tensorial contents; they become covariant by postulating certain transformation properties of field functions. That fact should be stressed when teaching about the covariance of Maxwell's equations.

  4. Lorentz-covariant dissipative Lagrangian systems

    NASA Technical Reports Server (NTRS)

    Kaufman, A. N.

    1985-01-01

    The concept of dissipative Hamiltonian system is converted to Lorentz-covariant form, with evolution generated jointly by two scalar functionals, the Lagrangian action and the global entropy. A bracket formulation yields the local covariant laws of energy-momentum conservation and of entropy production. The formalism is illustrated by a derivation of the covariant Landau kinetic equation.

  5. Analysis of cross-over studies with missing data.

    PubMed

    Rosenkranz, Gerd K

    2015-08-01

    This paper addresses some aspects of the analysis of cross-over trials with missing or incomplete data. A literature review on the topic reveals that many proposals provide correct results under the missing completely at random assumption while only some consider the more general missing at random situation. It is argued that mixed-effects models have a role in this context to recover some of the missing intra-subject from the inter-subject information, in particular when missingness is ignorable. Eventually, sensitivity analyses to deal with more general missingness mechanisms are presented.

  6. Multiple imputation of covariates by fully conditional specification: Accommodating the substantive model

    PubMed Central

    Seaman, Shaun R; White, Ian R; Carpenter, James R

    2015-01-01

    Missing covariate data commonly occur in epidemiological and clinical research, and are often dealt with using multiple imputation. Imputation of partially observed covariates is complicated if the substantive model is non-linear (e.g. Cox proportional hazards model), or contains non-linear (e.g. squared) or interaction terms, and standard software implementations of multiple imputation may impute covariates from models that are incompatible with such substantive models. We show how imputation by fully conditional specification, a popular approach for performing multiple imputation, can be modified so that covariates are imputed from models which are compatible with the substantive model. We investigate through simulation the performance of this proposal, and compare it with existing approaches. Simulation results suggest our proposal gives consistent estimates for a range of common substantive models, including models which contain non-linear covariate effects or interactions, provided data are missing at random and the assumed imputation models are correctly specified and mutually compatible. Stata software implementing the approach is freely available. PMID:24525487

  7. Multiple imputation of covariates by fully conditional specification: Accommodating the substantive model.

    PubMed

    Bartlett, Jonathan W; Seaman, Shaun R; White, Ian R; Carpenter, James R

    2015-08-01

    Missing covariate data commonly occur in epidemiological and clinical research, and are often dealt with using multiple imputation. Imputation of partially observed covariates is complicated if the substantive model is non-linear (e.g. Cox proportional hazards model), or contains non-linear (e.g. squared) or interaction terms, and standard software implementations of multiple imputation may impute covariates from models that are incompatible with such substantive models. We show how imputation by fully conditional specification, a popular approach for performing multiple imputation, can be modified so that covariates are imputed from models which are compatible with the substantive model. We investigate through simulation the performance of this proposal, and compare it with existing approaches. Simulation results suggest our proposal gives consistent estimates for a range of common substantive models, including models which contain non-linear covariate effects or interactions, provided data are missing at random and the assumed imputation models are correctly specified and mutually compatible. Stata software implementing the approach is freely available. © The Author(s) 2014.

  8. Specification of covariance structure in longitudinal data analysis for randomized clinical trials.

    PubMed

    Lu, Kaifeng; Mehrotra, Devan V

    2010-02-20

    Misspecification of the covariance structure for repeated measurements in longitudinal analysis may lead to biased estimates of the regression parameters and under or overestimation of the corresponding standard errors in the presence of missing data. The so-called sandwich estimator can 'correct' the variance, but it does not reduce the bias in point estimates. Removing all assumptions from the covariance structure (i.e. using an unstructured (UN) covariance) will remove such biases. However, an excessive amount of missing data may cause convergence problems for iterative algorithms, such as the default Newton-Raphson algorithm in the popular SAS PROC MIXED. This article examines, both through theory and simulations, the existence and the magnitude of these biases. We recommend the use of UN covariance as the default strategy for analyzing longitudinal data from randomized clinical trials with moderate to large number of subjects and small to moderate number of time points. We also present an algorithm to assist in the convergence when the UN covariance is used. (c) 2009 John Wiley & Sons, Ltd.

  9. Sharpening bounds on principal effects with covariates.

    PubMed

    Long, Dustin M; Hudgens, Michael G

    2013-12-01

    Estimation of treatment effects in randomized studies is often hampered by possible selection bias induced by conditioning on or adjusting for a variable measured post-randomization. One approach to obviate such selection bias is to consider inference about treatment effects within principal strata, that is, principal effects. A challenge with this approach is that without strong assumptions principal effects are not identifiable from the observable data. In settings where such assumptions are dubious, identifiable large sample bounds may be the preferred target of inference. In practice these bounds may be wide and not particularly informative. In this work we consider whether bounds on principal effects can be improved by adjusting for a categorical baseline covariate. Adjusted bounds are considered which are shown to never be wider than the unadjusted bounds. Necessary and sufficient conditions are given for which the adjusted bounds will be sharper (i.e., narrower) than the unadjusted bounds. The methods are illustrated using data from a recent, large study of interventions to prevent mother-to-child transmission of HIV through breastfeeding. Using a baseline covariate indicating low birth weight, the estimated adjusted bounds for the principal effect of interest are 63% narrower than the estimated unadjusted bounds. © 2013, The International Biometric Society.

  10. Recurrence Analysis of Eddy Covariance Fluxes

    NASA Astrophysics Data System (ADS)

    Lange, Holger; Flach, Milan; Foken, Thomas; Hauhs, Michael

    2015-04-01

    The eddy covariance (EC) method is one key method to quantify fluxes in biogeochemical cycles in general, and carbon and energy transport across the vegetation-atmosphere boundary layer in particular. EC data from the worldwide net of flux towers (Fluxnet) have also been used to validate biogeochemical models. The high resolution data are usually obtained at 20 Hz sampling rate but are affected by missing values and other restrictions. In this contribution, we investigate the nonlinear dynamics of EC fluxes using Recurrence Analysis (RA). High resolution data from the site DE-Bay (Waldstein-Weidenbrunnen) and fluxes calculated at half-hourly resolution from eight locations (part of the La Thuile dataset) provide a set of very long time series to analyze. After careful quality assessment and Fluxnet standard gapfilling pretreatment, we calculate properties and indicators of the recurrent structure based both on Recurrence Plots as well as Recurrence Networks. Time series of RA measures obtained from windows moving along the time axis are presented. Their interpretation is guided by three different questions: (1) Is RA able to discern periods where the (atmospheric) conditions are particularly suitable to obtain reliable EC fluxes? (2) Is RA capable to detect dynamical transitions (different behavior) beyond those obvious from visual inspection? (3) Does RA contribute to an understanding of the nonlinear synchronization between EC fluxes and atmospheric parameters, which is crucial for both improving carbon flux models as well for reliable interpolation of gaps? (4) Is RA able to recommend an optimal time resolution for measuring EC data and for analyzing EC fluxes? (5) Is it possible to detect non-trivial periodicities with a global RA? We will demonstrate that the answers to all five questions is affirmative, and that RA provides insights into EC dynamics not easily obtained otherwise.

  11. Neutrality tests for sequences with missing data.

    PubMed

    Ferretti, Luca; Raineri, Emanuele; Ramos-Onsins, Sebastian

    2012-08-01

    Missing data are common in DNA sequences obtained through high-throughput sequencing. Furthermore, samples of low quality or problems in the experimental protocol often cause a loss of data even with traditional sequencing technologies. Here we propose modified estimators of variability and neutrality tests that can be naturally applied to sequences with missing data, without the need to remove bases or individuals from the analysis. Modified statistics include the Watterson estimator θW, Tajima's D, Fay and Wu's H, and HKA. We develop a general framework to take missing data into account in frequency spectrum-based neutrality tests and we derive the exact expression for the variance of these statistics under the neutral model. The neutrality tests proposed here can also be used as summary statistics to describe the information contained in other classes of data like DNA microarrays.

  12. Impact of teamwork on missed care in four Australian hospitals.

    PubMed

    Chapman, Rose; Rahman, Asheq; Courtney, Mary; Chalmers, Cheyne

    2017-01-01

    Investigate effects of teamwork on missed nursing care across a healthcare network in Australia. Missed care is universally used as an indicator of quality nursing care, however, little is known about mitigating effects of teamwork on these events. A descriptive exploratory study. Missed Care and Team Work surveys were completed by 334 nurses. Using Stata software, nursing staff demographic information and components of missed care and teamwork were compared across the healthcare network. Statistical tests were performed to identify predicting factors for missed care. The most commonly reported components of missed care were as follows: ambulation three times per day (43·3%), turning patient every two hours (29%) and mouth care (27·7%). The commonest reasons mentioned for missed care were as follows: inadequate labour resources (range 69·8-52·7%), followed by material resources (range 59·3-33·3%) and communication (range 39·3-27·2%). There were significant differences in missed care scores across units. Using the mean scores in regression correlation matrix, the negative relationship of missed care and teamwork was supported (r = -0·34, p < 0·001). Controlling for occupation of the staff member and staff characteristics in multiple regression models, teamwork alone accounted for about 9% of missed nursing care. Similar to previous international research findings, our results showed nursing teamwork significantly impacted on missed nursing care. Teamwork may be a mitigating factor to address missed care and future research is needed. These results may provide administrators, educators and clinicians with information to develop practices and policies to improve patient care internationally. © 2016 John Wiley & Sons Ltd.

  13. Missing people, migrants, identification and human rights.

    PubMed

    Nuzzolese, E

    2012-11-30

    The increasing volume and complexities of migratory flow has led to a range of problems such as human rights issues, public health, disease and border control, and also the regulatory processes. As result of war or internal conflicts missing person cases and management have to be regarded as a worldwide issue. On the other hand, even in peace, the issue of a missing person is still relevant. In 2007 the Italian Ministry of Interior nominated an extraordinary commissar in order to analyse and assess the total number of unidentified recovered bodies and verify the extent of the phenomena of missing persons, reported as 24,912 people in Italy (updated 31 December 2011). Of these 15,632 persons are of foreigner nationalities and are still missing. The census of the unidentified bodies revealed a total of 832 cases recovered in Italy since the year 1974. These bodies/human remains received a regular autopsy and were buried as 'corpse without name". In Italy judicial autopsy is performed to establish cause of death and identity, but odontology and dental radiology is rarely employed in identification cases. Nevertheless, odontologists can substantiate the identification through the 'biological profile' providing further information that can narrow the search to a smaller number of missing individuals even when no ante mortem dental data are available. The forensic dental community should put greater emphasis on the role of the forensic odontology as a tool for humanitarian action of unidentified individuals and best practise in human identification.

  14. What are the best covariates for developing non-stationary rainfall Intensity-Duration-Frequency relationship?

    NASA Astrophysics Data System (ADS)

    Agilan, V.; Umamahesh, N. V.

    2017-03-01

    Present infrastructure design is primarily based on rainfall Intensity-Duration-Frequency (IDF) curves with so-called stationary assumption. However, in recent years, the extreme precipitation events are increasing due to global climate change and creating non-stationarity in the series. Based on recent theoretical developments in the Extreme Value Theory (EVT), recent studies proposed a methodology for developing non-stationary rainfall IDF curve by incorporating trend in the parameters of the Generalized Extreme Value (GEV) distribution using Time covariate. But, the covariate Time may not be the best covariate and it is important to analyze all possible covariates and find the best covariate to model non-stationarity. In this study, five physical processes, namely, urbanization, local temperature changes, global warming, El Niño-Southern Oscillation (ENSO) cycle and Indian Ocean Dipole (IOD) are used as covariates. Based on these five covariates and their possible combinations, sixty-two non-stationary GEV models are constructed. In addition, two non-stationary GEV models based on Time covariate and one stationary GEV model are also constructed. The best model for each duration rainfall series is chosen based on the corrected Akaike Information Criterion (AICc). From the findings of this study, it is observed that the local processes (i.e., Urbanization, local temperature changes) are the best covariate for short duration rainfall and global processes (i.e., Global warming, ENSO cycle and IOD) are the best covariate for the long duration rainfall of the Hyderabad city, India. Furthermore, the covariate Time is never qualified as the best covariate. In addition, the identified best covariates are further used to develop non-stationary rainfall IDF curves of the Hyderabad city. The proposed methodology can be applied to other situations to develop the non-stationary IDF curves based on the best covariate.

  15. Covariance Evaluation Methodology for Neutron Cross Sections

    SciTech Connect

    Herman,M.; Arcilla, R.; Mattoon, C.M.; Mughabghab, S.F.; Oblozinsky, P.; Pigni, M.; Pritychenko, b.; Songzoni, A.A.

    2008-09-01

    We present the NNDC-BNL methodology for estimating neutron cross section covariances in thermal, resolved resonance, unresolved resonance and fast neutron regions. The three key elements of the methodology are Atlas of Neutron Resonances, nuclear reaction code EMPIRE, and the Bayesian code implementing Kalman filter concept. The covariance data processing, visualization and distribution capabilities are integral components of the NNDC methodology. We illustrate its application on examples including relatively detailed evaluation of covariances for two individual nuclei and massive production of simple covariance estimates for 307 materials. Certain peculiarities regarding evaluation of covariances for resolved resonances and the consistency between resonance parameter uncertainties and thermal cross section uncertainties are also discussed.

  16. Provider-to-provider communication in dermatology and implications of missing clinical information in skin biopsy requisition forms: a systematic review.

    PubMed

    Comfere, Nneka I; Sokumbi, Olayemi; Montori, Victor M; LeBlanc, Annie; Prokop, Larry J; Murad, M Hassan; Tilburt, Jon C

    2014-05-01

    Various components of the skin biopsy requisition form (SBRF) may contribute to accurate dermatopathologic interpretation. A search of electronic databases, including those of Ovid MEDLINE In-Process & Other Non-Indexed Citations, Ovid MEDLINE, Ovid EMBASE, the Cochrane Database of Systematic Reviews, the Cochrane Central Register of Controlled Trials, and Scopus, was conducted from inception to October 2011. Two authors independently screened all articles for eligibility. Inclusion criteria required material to represent original studies on skin biopsy and pathology requisition forms. Data abstracted from each article that met the inclusion criteria included details of the study characteristics, including the study location, type of pathology practice, specimen type, type of dermatoses, medical specialty of the requesting provider, suggested clinical components, and format of the SBRF. Of 32 titles and abstracts reviewed, seven articles were included. From these, we determined that dermatologists, general practitioners and surgeons completed SBRFs. Commonly included components were patient demographics and requesting clinician characteristics. Clinical information and differential diagnosis were provided in 4% (two of 48 surgeons) to 36% (18 of 50 dermatologists) of requisitions. Most SBRFs did not include information on specimen type, clinical morphology, photographs or clinical history. The limited medical literature demonstrates variation in the content of SBRFs across clinicians and practices, and suggests an important target for improvement in the quality of communication and dermatologic care by requesting clinicians and pathologists. © 2013 The International Society of Dermatology.

  17. Patient Portals as a Means of Information and Communication Technology Support to Patient-Centric Care Coordination – the Missing Evidence and the Challenges of Evaluation

    PubMed Central

    Georgiou, Andrew; Hyppönen, Hannele; Ammenwerth, Elske; de Keizer, Nicolette; Magrabi, Farah; Scott, Philip

    2015-01-01

    Summary Objectives To review the potential contribution of Information and Communication Technology (ICT) to enable patient-centric and coordinated care, and in particular to explore the role of patient portals as a developing ICT tool, to assess the available evidence, and to describe the evaluation challenges. Methods Reviews of IMIA, EFMI, and other initiatives, together with literature reviews. Results We present the progression from care coordination to care integration, and from patient-centric to person-centric approaches. We describe the different roles of ICT as an enabler of the effective presentation of information as and when needed. We focus on the patient’s role as a co-producer of health as well as the focus and purpose of care. We discuss the need for changing organisational processes as well as the current mixed evidence regarding patient portals as a logical tool, and the reasons for this dichotomy, together with the evaluation principles supported by theoretical frameworks so as to yield robust evidence. Conclusions There is expressed commitment to coordinated care and to putting the patient in the centre. However to achieve this, new interactive patient portals will be needed to enable peer communication by all stakeholders including patients and professionals. Few portals capable of this exist to date. The evaluation of these portals as enablers of system change, rather than as simple windows into electronic records, is at an early stage and novel evaluation approaches are needed. PMID:26123909

  18. Phase-covariant quantum benchmarks

    SciTech Connect

    Calsamiglia, J.; Aspachs, M.; Munoz-Tapia, R.; Bagan, E.

    2009-05-15

    We give a quantum benchmark for teleportation and quantum storage experiments suited for pure and mixed test states. The benchmark is based on the average fidelity over a family of phase-covariant states and certifies that an experiment cannot be emulated by a classical setup, i.e., by a measure-and-prepare scheme. We give an analytical solution for qubits, which shows important differences with standard state estimation approach, and compute the value of the benchmark for coherent and squeezed states, both pure and mixed.

  19. Missing data? Plan on it!

    PubMed

    Palmer, Raymond F; Royall, Donald R

    2010-10-01

    Longitudinal study designs are indispensable for investigating age-related functional change. There now are well-established methods for addressing missing data in longitudinal studies. Modern missing data methods not only minimize most problems associated with missing data (e.g., loss of power and biased parameter estimates), but also have valuable new applications such as research designs that use modern missing data methods to plan missing data purposefully. This article describes two state-of-the-art statistical methodologies for addressing missing data in longitudinal research: growth curve analysis and statistical measurement models. How the purposeful planning of missing data in research designs can reduce subject burden, improve data quality and statistical power, and manage costs is then described.

  20. Examination of various roles for covariance matrices in the development, evaluation, and application of nuclear data

    SciTech Connect

    Smith, D.L.

    1988-01-01

    The last decade has been a period of rapid development in the implementation of covariance-matrix methodology in nuclear data research. This paper offers some perspective on the progress which has been made, on some of the unresolved problems, and on the potential yet to be realized. These discussions address a variety of issues related to the development of nuclear data. Topics examined are: the importance of designing and conducting experiments so that error information is conveniently generated; the procedures for identifying error sources and quantifying their magnitudes and correlations; the combination of errors; the importance of consistent and well-characterized measurement standards; the role of covariances in data parameterization (fitting); the estimation of covariances for values calculated from mathematical models; the identification of abnormalities in covariance matrices and the analysis of their consequences; the problems encountered in representing covariance information in evaluated files; the role of covariances in the weighting of diverse data sets; the comparison of various evaluations; the influence of primary-data covariance in the analysis of covariances for derived quantities (sensitivity); and the role of covariances in the merging of the diverse nuclear data information. 226 refs., 2 tabs.

  1. Posttraumatic stress disorder: the missed diagnosis.

    PubMed

    Grasso, Damion; Boonsiri, Joseph; Lipschitz, Deborah; Guyer, Amanda; Houshyar, Shadi; Douglas-Palumberi, Heather; Massey, Johari; Kaufman, Joan

    2009-01-01

    Posttraumatic stress disorder (PTSD) is frequently underdiagnosed in maltreated samples. Protective services information is critical for obtaining complete trauma histories and determining whether to survey PTSD symptoms in maltreated children. In the current study, without protective services information to supplement parent and child report, diagnosing PTSD was missed in a significant proportion of the cases. Collaboration between mental health professionals and protective service workers is critical in determining psychiatric diagnoses and treatment needs of children involved with the child welfare system.

  2. DISSCO: direct imputation of summary statistics allowing covariates

    PubMed Central

    Xu, Zheng; Duan, Qing; Yan, Song; Chen, Wei; Li, Mingyao; Lange, Ethan; Li, Yun

    2015-01-01

    Background: Imputation of individual level genotypes at untyped markers using an external reference panel of genotyped or sequenced individuals has become standard practice in genetic association studies. Direct imputation of summary statistics can also be valuable, for example in meta-analyses where individual level genotype data are not available. Two methods (DIST and ImpG-Summary/LD), that assume a multivariate Gaussian distribution for the association summary statistics, have been proposed for imputing association summary statistics. However, both methods assume that the correlations between association summary statistics are the same as the correlations between the corresponding genotypes. This assumption can be violated in the presence of confounding covariates. Methods: We analytically show that in the absence of covariates, correlation among association summary statistics is indeed the same as that among the corresponding genotypes, thus serving as a theoretical justification for the recently proposed methods. We continue to prove that in the presence of covariates, correlation among association summary statistics becomes the partial correlation of the corresponding genotypes controlling for covariates. We therefore develop direct imputation of summary statistics allowing covariates (DISSCO). Results: We consider two real-life scenarios where the correlation and partial correlation likely make practical difference: (i) association studies in admixed populations; (ii) association studies in presence of other confounding covariate(s). Application of DISSCO to real datasets under both scenarios shows at least comparable, if not better, performance compared with existing correlation-based methods, particularly for lower frequency variants. For example, DISSCO can reduce the absolute deviation from the truth by 3.9–15.2% for variants with minor allele frequency <5%. Availability and implementation: http://www.unc.edu/∼yunmli/DISSCO. Contact: yunli

  3. Covariance Structure Models for Gene Expression Microarray Data

    ERIC Educational Resources Information Center

    Xie, Jun; Bentler, Peter M.

    2003-01-01

    Covariance structure models are applied to gene expression data using a factor model, a path model, and their combination. The factor model is based on a few factors that capture most of the expression information. A common factor of a group of genes may represent a common protein factor for the transcript of the co-expressed genes, and hence, it…

  4. Diagnosis for Covariance Structure Models by Analyzing the Path

    ERIC Educational Resources Information Center

    Yuan, Ke-Hai; Kouros, Chrystyna D.; Kelley, Ken

    2008-01-01

    When a covariance structure model is misspecified, parameter estimates will be affected. It is important to know which estimates are systematically affected and which are not. The approach of analyzing the path is both intuitive and informative for such a purpose. Different from path analysis, analyzing the path uses path tracing and elementary…

  5. Covariance Structure Models for Gene Expression Microarray Data

    ERIC Educational Resources Information Center

    Xie, Jun; Bentler, Peter M.

    2003-01-01

    Covariance structure models are applied to gene expression data using a factor model, a path model, and their combination. The factor model is based on a few factors that capture most of the expression information. A common factor of a group of genes may represent a common protein factor for the transcript of the co-expressed genes, and hence, it…

  6. Relativistic covariance of Ohm's law

    NASA Astrophysics Data System (ADS)

    Starke, R.; Schober, G. A. H.

    2016-04-01

    The derivation of Lorentz-covariant generalizations of Ohm's law has been a long-term issue in theoretical physics with deep implications for the study of relativistic effects in optical and atomic physics. In this article, we propose an alternative route to this problem, which is motivated by the tremendous progress in first-principles materials physics in general and ab initio electronic structure theory in particular. We start from the most general, Lorentz-covariant first-order response law, which is written in terms of the fundamental response tensor χμ ν relating induced four-currents to external four-potentials. By showing the equivalence of this description to Ohm's law, we prove the validity of Ohm's law in every inertial frame. We further use the universal relation between χμ ν and the microscopic conductivity tensor σkℓ to derive a fully relativistic transformation law for the latter, which includes all effects of anisotropy and relativistic retardation. In the special case of a constant, scalar conductivity, this transformation law can be used to rederive a standard textbook generalization of Ohm's law.

  7. Addressing missing participant outcome data in dental clinical trials.

    PubMed

    Spineli, Loukia M; Fleming, Padhraig S; Pandis, Nikolaos

    2015-06-01

    Missing outcome data are common in clinical trials and despite a well-designed study protocol, some of the randomized participants may leave the trial early without providing any or all of the data, or may be excluded after randomization. Premature discontinuation causes loss of information, potentially resulting in attrition bias leading to problems during interpretation of trial findings. The causes of information loss in a trial, known as mechanisms of missingness, may influence the credibility of the trial results. Analysis of trials with missing outcome data should ideally be handled with intention to treat (ITT) rather than per protocol (PP) analysis. However, true ITT analysis requires appropriate assumptions and imputation of missing data. Using a worked example from a published dental study, we highlight the key issues associated with missing outcome data in clinical trials, describe the most recognized approaches to handling missing outcome data, and explain the principles of ITT and PP analysis.

  8. Bayesian modeling of air pollution health effects with missing exposure data.

    PubMed

    Molitor, John; Molitor, Nuoo-Ting; Jerrett, Michael; McConnell, Rob; Gauderman, Jim; Berhane, Kiros; Thomas, Duncan

    2006-07-01

    The authors propose a new statistical procedure that utilizes measurement error models to estimate missing exposure data in health effects assessment. The method detailed in this paper follows a Bayesian framework that allows estimation of various parameters of the model in the presence of missing covariates in an informative way. The authors apply this methodology to study the effect of household-level long-term air pollution exposures on lung function for subjects from the Southern California Children's Health Study pilot project, conducted in the year 2000. Specifically, they propose techniques to examine the long-term effects of nitrogen dioxide (NO2) exposure on children's lung function for persons living in 11 southern California communities. The effect of nitrogen dioxide exposure on various measures of lung function was examined, but, similar to many air pollution studies, no completely accurate measure of household-level long-term nitrogen dioxide exposure was available. Rather, community-level nitrogen dioxide was measured continuously over many years, but household-level nitrogen dioxide exposure was measured only during two 2-week periods, one period in the summer and one period in the winter. From these incomplete measures, long-term nitrogen dioxide exposure and its effect on health must be inferred. Results show that the method improves estimates when compared with standard frequentist approaches.

  9. Data Covariances from R-Matrix Analyses of Light Nuclei

    SciTech Connect

    Hale, G.M. Paris, M.W.

    2015-01-15

    After first reviewing the parametric description of light-element reactions in multichannel systems using R-matrix theory and features of the general LANL R-matrix analysis code EDA, we describe how its chi-square minimization procedure gives parameter covariances. This information is used, together with analytically calculated sensitivity derivatives, to obtain cross section covariances for all reactions included in the analysis by first-order error propagation. Examples are given of the covariances obtained for systems with few resonances ({sup 5}He) and with many resonances ({sup 13}C ). We discuss the prevalent problem of this method leading to cross section uncertainty estimates that are unreasonably small for large data sets. The answer to this problem appears to be using parameter confidence intervals in place of standard errors.

  10. Estimated Environmental Exposures for MISSE-3 and MISSE-4

    NASA Technical Reports Server (NTRS)

    Finckenor, Miria M.; Pippin, Gary; Kinard, William H.

    2008-01-01

    Describes the estimated environmental exposure for MISSE-2 and MISSE-4. These test beds, attached to the outside of the International Space Station, were planned for 3 years of exposure. This was changed to 1 year after MISSE-1 and -2 were in space for 4 years. MISSE-3 and -4 operate in a low Earth orbit space environment, which exposes them to a variety of assaults including atomic oxygen, ultraviolet radiation, particulate radiation, thermal cycling, and meteoroid/space debris impact, as well as contamination associated with proximity to an active space station. Measurements and determinations of atomic oxygen fluences, solar UV exposure levels, molecular contamination levels, and particulate radiation are included.

  11. The Impact of Missing Data on Sample Reliability Estimates: Implications for Reliability Reporting Practices

    ERIC Educational Resources Information Center

    Enders, Craig K.

    2004-01-01

    A method for incorporating maximum likelihood (ML) estimation into reliability analyses with item-level missing data is outlined. An ML estimate of the covariance matrix is first obtained using the expectation maximization (EM) algorithm, and coefficient alpha is subsequently computed using standard formulae. A simulation study demonstrated that…

  12. The Impact of Missing Data on Sample Reliability Estimates: Implications for Reliability Reporting Practices

    ERIC Educational Resources Information Center

    Enders, Craig K.

    2004-01-01

    A method for incorporating maximum likelihood (ML) estimation into reliability analyses with item-level missing data is outlined. An ML estimate of the covariance matrix is first obtained using the expectation maximization (EM) algorithm, and coefficient alpha is subsequently computed using standard formulae. A simulation study demonstrated that…

  13. Comparison of Modern Methods for Analyzing Repeated Measures Data with Missing Values

    ERIC Educational Resources Information Center

    Vallejo, G.; Fernandez, M. P.; Livacic-Rojas, P. E.; Tuero-Herrero, E.

    2011-01-01

    Missing data are a pervasive problem in many psychological applications in the real world. In this article we study the impact of dropout on the operational characteristics of several approaches that can be easily implemented with commercially available software. These approaches include the covariance pattern model based on an unstructured…

  14. Comparison of Modern Methods for Analyzing Repeated Measures Data with Missing Values

    ERIC Educational Resources Information Center

    Vallejo, G.; Fernandez, M. P.; Livacic-Rojas, P. E.; Tuero-Herrero, E.

    2011-01-01

    Missing data are a pervasive problem in many psychological applications in the real world. In this article we study the impact of dropout on the operational characteristics of several approaches that can be easily implemented with commercially available software. These approaches include the covariance pattern model based on an unstructured…

  15. A Two-Stage Approach to Missing Data: Theory and Application to Auxiliary Variables

    ERIC Educational Resources Information Center

    Savalei, Victoria; Bentler, Peter M.

    2009-01-01

    A well-known ad-hoc approach to conducting structural equation modeling with missing data is to obtain a saturated maximum likelihood (ML) estimate of the population covariance matrix and then to use this estimate in the complete data ML fitting function to obtain parameter estimates. This 2-stage (TS) approach is appealing because it minimizes a…

  16. A Two-Stage Approach to Missing Data: Theory and Application to Auxiliary Variables

    ERIC Educational Resources Information Center

    Savalei, Victoria; Bentler, Peter M.

    2009-01-01

    A well-known ad-hoc approach to conducting structural equation modeling with missing data is to obtain a saturated maximum likelihood (ML) estimate of the population covariance matrix and then to use this estimate in the complete data ML fitting function to obtain parameter estimates. This 2-stage (TS) approach is appealing because it minimizes a…

  17. Determining Predictors of True HIV Status Using an Errors-in-Variables Model with Missing Data

    ERIC Educational Resources Information Center

    Rindskopf, David; Strauss, Shiela

    2004-01-01

    We demonstrate a model for categorical data that parallels the MIMIC model for continuous data. The model is equivalent to a latent class model with observed covariates; further, it includes simple handling of missing data. The model is used on data from a large-scale study of HIV that had both biological measures of infection and self-report…

  18. Identifying Heat Waves in Florida: Considerations of Missing Weather Data.

    PubMed

    Leary, Emily; Young, Linda J; DuClos, Chris; Jordan, Melissa M

    2015-01-01

    Using current climate models, regional-scale changes for Florida over the next 100 years are predicted to include warming over terrestrial areas and very likely increases in the number of high temperature extremes. No uniform definition of a heat wave exists. Most past research on heat waves has focused on evaluating the aftermath of known heat waves, with minimal consideration of missing exposure information. To identify and discuss methods of handling and imputing missing weather data and how those methods can affect identified periods of extreme heat in Florida. In addition to ignoring missing data, temporal, spatial, and spatio-temporal models are described and utilized to impute missing historical weather data from 1973 to 2012 from 43 Florida weather monitors. Calculated thresholds are used to define periods of extreme heat across Florida. Modeling of missing data and imputing missing values can affect the identified periods of extreme heat, through the missing data itself or through the computed thresholds. The differences observed are related to the amount of missingness during June, July, and August, the warmest months of the warm season (April through September). Missing data considerations are important when defining periods of extreme heat. Spatio-temporal methods are recommended for data imputation. A heat wave definition that incorporates information from all monitors is advised.

  19. Identifying Heat Waves in Florida: Considerations of Missing Weather Data

    PubMed Central

    Leary, Emily; Young, Linda J.; DuClos, Chris; Jordan, Melissa M.

    2015-01-01

    Background Using current climate models, regional-scale changes for Florida over the next 100 years are predicted to include warming over terrestrial areas and very likely increases in the number of high temperature extremes. No uniform definition of a heat wave exists. Most past research on heat waves has focused on evaluating the aftermath of known heat waves, with minimal consideration of missing exposure information. Objectives To identify and discuss methods of handling and imputing missing weather data and how those methods can affect identified periods of extreme heat in Florida. Methods In addition to ignoring missing data, temporal, spatial, and spatio-temporal models are described and utilized to impute missing historical weather data from 1973 to 2012 from 43 Florida weather monitors. Calculated thresholds are used to define periods of extreme heat across Florida. Results Modeling of missing data and imputing missing values can affect the identified periods of extreme heat, through the missing data itself or through the computed thresholds. The differences observed are related to the amount of missingness during June, July, and August, the warmest months of the warm season (April through September). Conclusions Missing data considerations are important when defining periods of extreme heat. Spatio-temporal methods are recommended for data imputation. A heat wave definition that incorporates information from all monitors is advised. PMID:26619198

  20. Covariant diagrams for one-loop matching

    DOE PAGES

    Zhang, Zhengkang

    2017-05-30

    Here, we present a diagrammatic formulation of recently-revived covariant functional approaches to one-loop matching from an ultraviolet (UV) theory to a low-energy effective fi eld theory. Various terms following from a covariant derivative expansion (CDE) are represented by diagrams which, unlike conventional Feynman diagrams, involve gauge-covariant quantities and are thus dubbed "covariant diagrams." The use of covariant diagrams helps organize and simplify one-loop matching calculations, which we illustrate with examples. Of particular interest is the derivation of UV model-independent universal results, which reduce matching calculations of specifi c UV models to applications of master formulas. We also show how suchmore » derivation can be done in a more concise manner than the previous literature, and discuss how additional structures that are not directly captured by existing universal results, including mixed heavy-light loops, open covariant derivatives, and mixed statistics, can be easily accounted for.« less

  1. Covariant diagrams for one-loop matching

    NASA Astrophysics Data System (ADS)

    Zhang, Zhengkang

    2017-05-01

    We present a diagrammatic formulation of recently-revived covariant functional approaches to one-loop matching from an ultraviolet (UV) theory to a low-energy effective field theory. Various terms following from a covariant derivative expansion (CDE) are represented by diagrams which, unlike conventional Feynman diagrams, involve gauge-covariant quantities and are thus dubbed "covariant diagrams." The use of covariant diagrams helps organize and simplify one-loop matching calculations, which we illustrate with examples. Of particular interest is the derivation of UV model-independent universal results, which reduce matching calculations of specific UV models to applications of master formulas. We show how such derivation can be done in a more concise manner than the previous literature, and discuss how additional structures that are not directly captured by existing universal results, including mixed heavy-light loops, open covariant derivatives, and mixed statistics, can be easily accounted for.

  2. Missed Opportunities for Hepatitis A Vaccination, National Immunization Survey-Child, 2013.

    PubMed

    Casillas, Shannon M; Bednarczyk, Robert A

    2017-08-01

    To quantify the number of missed opportunities for vaccination with hepatitis A vaccine in children and assess the association of missed opportunities for hepatitis A vaccination with covariates of interest. Weighted data from the 2013 National Immunization Survey of US children aged 19-35 months were used. Analysis was restricted to children with provider-verified vaccination history (n = 13 460). Missed opportunities for vaccination were quantified by determining the number of medical visits a child made when another vaccine was administered during eligibility for hepatitis A vaccine, but hepatitis A vaccine was not administered. Cross-sectional bivariate and multivariate polytomous logistic regression were used to assess the association of missed opportunities for vaccination with child and maternal demographic, socioeconomic, and geographic covariates. In 2013, 85% of children in our study population had initiated the hepatitis A vaccine series, and 60% received 2 or more doses. Children who received zero doses of hepatitis A vaccine had an average of 1.77 missed opportunities for vaccination compared with 0.43 missed opportunities for vaccination in those receiving 2 doses. Children with 2 or more missed opportunities for vaccination initiated the vaccine series 6 months later than children without missed opportunities. In the fully adjusted multivariate model, children who were younger, had ever received WIC benefits, or lived in a state with childcare entry mandates were at a reduced odds for 2 or more missed opportunities for vaccination; children living in the Northeast census region were at an increased odds. Missed opportunities for vaccination likely contribute to the poor coverage for hepatitis A vaccination in children; it is important to understand why children are not receiving the vaccine when eligible. Copyright © 2017 Elsevier Inc. All rights reserved.

  3. Shrinkage approach for EEG covariance matrix estimation.

    PubMed

    Beltrachini, Leandro; von Ellenrieder, Nicolas; Muravchik, Carlos H

    2010-01-01

    We present a shrinkage estimator for the EEG spatial covariance matrix of the background activity. We show that such an estimator has some advantages over the maximum likelihood and sample covariance estimators when the number of available data to carry out the estimation is low. We find sufficient conditions for the consistency of the shrinkage estimators and results concerning their numerical stability. We compare several shrinkage schemes and show how to improve the estimator by incorporating known structure of the covariance matrix.

  4. ANL Critical Assembly Covariance Matrix Generation - Addendum

    SciTech Connect

    McKnight, Richard D.; Grimm, Karl N.

    2014-01-13

    In March 2012, a report was issued on covariance matrices for Argonne National Laboratory (ANL) critical experiments. That report detailed the theory behind the calculation of covariance matrices and the methodology used to determine the matrices for a set of 33 ANL experimental set-ups. Since that time, three new experiments have been evaluated and approved. This report essentially updates the previous report by adding in these new experiments to the preceding covariance matrix structure.

  5. Covariant constraints on hole-ography

    NASA Astrophysics Data System (ADS)

    Engelhardt, Netta; Fischetti, Sebastian

    2015-10-01

    Hole-ography is a prescription relating the areas of surfaces in an AdS bulk to the differential entropy of a family of intervals in the dual CFT. In (2+1) bulk dimensions, or in higher dimensions when the bulk features a sufficient degree of symmetry, we prove that there are surfaces in the bulk that cannot be completely reconstructed using known hole-ographic approaches, even if extremal surfaces reach them. Such surfaces lie in easily identifiable regions: the interiors of holographic screens. These screens admit a holographic interpretation in terms of the Bousso bound. We speculate that this incompleteness of the reconstruction is a form of coarse-graining, with the missing information associated to the holographic screen. We comment on perturbative quantum extensions of our classical results.

  6. What Darwin missed

    NASA Astrophysics Data System (ADS)

    Campbell, A. K.

    2003-07-01

    Throughout his life, Fred Hoyle had a keen interest in evolution. He argued that natural selection by small, random change, as conceived by Charles Darwin and Alfred Russel Wallace, could not explain either the origin of life or the origin of a new protein. The idea of natural selection, Hoyle told us, wasn't even Darwin's original idea in the first place. Here, in honour of Hoyle's analysis, I propose a solution to Hoyle's dilemma. His solution was life from space - panspermia. But the real key to understanding natural selection is `molecular biodiversity'. This explains the things Darwin missed - the origin of species and the origin of extinction. It is also a beautiful example of the mystery disease that afflicted Darwin for over 40 years, for which we now have an answer.

  7. Identifying sources of uncertainty using covariance analysis

    NASA Astrophysics Data System (ADS)

    Hyslop, N. P.; White, W. H.

    2010-12-01

    Atmospheric aerosol monitoring often includes performing multiple analyses on a collected sample. Some common analyses resolve suites of elements or compounds (e.g., spectrometry, chromatography). Concentrations are determined through multi-step processes involving sample collection, physical or chemical analysis, and data reduction. Uncertainties in the individual steps propagate into uncertainty in the calculated concentration. The assumption in most treatments of measurement uncertainty is that errors in the various species concentrations measured in a sample are random and therefore independent of each other. This assumption is often not valid in speciated aerosol data because some errors can be common to multiple species. For example, an error in the sample volume will introduce a common error into all species concentrations determined in the sample, and these errors will correlate with each other. Measurement programs often use paired (collocated) measurements to characterize the random uncertainty in their measurements. Suites of paired measurements provide an opportunity to go beyond the characterization of measurement uncertainties in individual species to examine correlations amongst the measurement uncertainties in multiple species. This additional information can be exploited to distinguish sources of uncertainty that affect all species from those that only affect certain subsets or individual species. Data from the Interagency Monitoring of Protected Visual Environments (IMPROVE) program are used to illustrate these ideas. Nine analytes commonly detected in the IMPROVE network were selected for this analysis. The errors in these analytes can be reasonably modeled as multiplicative, and the natural log of the ratio of concentrations measured on the two samplers provides an approximation of the error. Figure 1 shows the covariation of these log ratios among the different analytes for one site. Covariance is strongest amongst the dust element (Fe, Ca, and

  8. Expected estimating equations for missing data, measurement error, and misclassification, with application to longitudinal nonignorable missing data.

    PubMed

    Wang, C Y; Huang, Yijian; Chao, Edward C; Jeffcoat, Marjorie K

    2008-03-01

    Missing data, measurement error, and misclassification are three important problems in many research fields, such as epidemiological studies. It is well known that missing data and measurement error in covariates may lead to biased estimation. Misclassification may be considered as a special type of measurement error, for categorical data. Nevertheless, we treat misclassification as a different problem from measurement error because statistical models for them are different. Indeed, in the literature, methods for these three problems were generally proposed separately given that statistical modeling for them are very different. The problem is more challenging in a longitudinal study with nonignorable missing data. In this article, we consider estimation in generalized linear models under these three incomplete data models. We propose a general approach based on expected estimating equations (EEEs) to solve these three incomplete data problems in a unified fashion. This EEE approach can be easily implemented and its asymptotic covariance can be obtained by sandwich estimation. Intensive simulation studies are performed under various incomplete data settings. The proposed method is applied to a longitudinal study of oral bone density in relation to body bone density.

  9. Balancing continuous covariates based on Kernel densities.

    PubMed

    Ma, Zhenjun; Hu, Feifang

    2013-03-01

    The balance of important baseline covariates is essential for convincing treatment comparisons. Stratified permuted block design and minimization are the two most commonly used balancing strategies, both of which require the covariates to be discrete. Continuous covariates are typically discretized in order to be included in the randomization scheme. But breakdown of continuous covariates into subcategories often changes the nature of the covariates and makes distributional balance unattainable. In this article, we propose to balance continuous covariates based on Kernel density estimations, which keeps the continuity of the covariates. Simulation studies show that the proposed Kernel-Minimization can achieve distributional balance of both continuous and categorical covariates, while also keeping the group size well balanced. It is also shown that the Kernel-Minimization is less predictable than stratified permuted block design and minimization. Finally, we apply the proposed method to redesign the NINDS trial, which has been a source of controversy due to imbalance of continuous baseline covariates. Simulation shows that imbalances such as those observed in the NINDS trial can be generally avoided through the implementation of the new method. Copyright © 2012 Elsevier Inc. All rights reserved.

  10. Lorentz covariant {kappa}-Minkowski spacetime

    SciTech Connect

    DaPbrowski, Ludwik; Godlinski, Michal; Piacitelli, Gherardo

    2010-06-15

    In recent years, different views on the interpretation of Lorentz covariance of noncommuting coordinates have been discussed. By a general procedure, we construct the minimal canonical central covariantization of the {kappa}-Minkowski spacetime. Here, undeformed Lorentz covariance is implemented by unitary operators, in the presence of two dimensionful parameters. We then show that, though the usual {kappa}-Minkowski spacetime is covariant under deformed (or twisted) Lorentz action, the resulting framework is equivalent to taking a noncovariant restriction of the covariantized model. We conclude with some general comments on the approach of deformed covariance.

  11. Nucleon electromagnetic form factors from the covariant Faddeev equation

    NASA Astrophysics Data System (ADS)

    Eichmann, G.

    2011-07-01

    We compute the electromagnetic form factors of the nucleon in the Poincaré-covariant Faddeev framework based on the Dyson-Schwinger equations of QCD. The general expression for a baryon’s electromagnetic current in terms of three interacting dressed quarks is derived. Upon employing a rainbow-ladder gluon-exchange kernel for the quark-quark interaction, the nucleon’s Faddeev amplitude and electromagnetic form factors are computed without any further truncations or model assumptions. The form-factor results show clear evidence of missing pion-cloud effects below a photon momentum transfer of ˜2GeV2 and in the chiral region, whereas they agree well with experimental data at higher photon momenta. Thus, the approach reflects the properties of the nucleon’s quark core.

  12. The Impact of Nonignorable Missing Data on the Inference of Regression Coefficients.

    ERIC Educational Resources Information Center

    Min, Kyung-Seok; Frank, Kenneth A.

    Various statistical methods have been available to deal with missing data problems, but the difficulty is that they are based on somewhat restrictive assumptions that missing patterns are known or can be modeled with auxiliary information. This paper treats the presence of missing cases from the viewpoint that generalization as a sample does not…

  13. 26 CFR 601.901 - Missing children shown on penalty mail.

    Code of Federal Regulations, 2010 CFR

    2010-04-01

    ... and biographical information on hundreds of missing children. (b) Procedures for obtaining and disseminating data. (1) The IRS shall publish pictures and biographical data related to missing children in... photographic and biographical materials solely from the National Center for Missing and Exploited...

  14. Dealing with deficient and missing data.

    PubMed

    Dohoo, Ian R

    2015-11-01

    Disease control decisions require two types of data: data describing the disease frequency (incidence and prevalence) along with characteristics of the population and environment in which the disease occurs (hereafter called "descriptive data"); and, data for analytical studies (hereafter called "analytical data") documenting the effects of risk factors for the disease. Both may be either deficient or missing. Descriptive data may be completely missing if the disease is a new and unknown entity with no diagnostic procedures or if there has been no surveillance activity in the population of interest. Methods for dealing with this complete absence of data are limited, but the possible use of surrogate measures of disease will be discussed. More often, data are deficient because of limitations in diagnostic capabilities (imperfect sensitivity and specificity). Developments in methods for dealing with this form of information bias make this a more tractable problem. Deficiencies in analytical data leading to biased estimates of effects of risk factors are a common problem, and one which is increasingly being recognized, but options for correction of known or suspected biases are still limited. Data about risk factors may be completely missing if studies of risk factors have not been carried out. Alternatively, data for evaluation of risk factors may be available but have "item missingness" where some (or many) observations have some pieces of information missing. There has been tremendous development in the methods to deal with this problem of "item missingness" over the past decade, with multiple imputation being the most prominent method. The use of multiple imputation to deal with the problem of item missing data will be compared to the use of complete-case analysis, and limitations to the applicability of imputation will be presented.

  15. CMB lens sample covariance and consistency relations

    NASA Astrophysics Data System (ADS)

    Motloch, Pavel; Hu, Wayne; Benoit-Lévy, Aurélien

    2017-02-01

    Gravitational lensing information from the two and higher point statistics of the cosmic microwave background (CMB) temperature and polarization fields are intrinsically correlated because they are lensed by the same realization of structure between last scattering and observation. Using an analytic model for lens sample covariance, we show that there is one mode, separately measurable in the lensed CMB power spectra and lensing reconstruction, that carries most of this correlation. Once these measurements become lens sample variance dominated, this mode should provide a useful consistency check between the observables that is largely free of sampling and cosmological parameter errors. Violations of consistency could indicate systematic errors in the data and lens reconstruction or new physics at last scattering, any of which could bias cosmological inferences and delensing for gravitational waves. A second mode provides a weaker consistency check for a spatially flat universe. Our analysis isolates the additional information supplied by lensing in a model-independent manner but is also useful for understanding and forecasting CMB cosmological parameter errors in the extended Λ cold dark matter parameter space of dark energy, curvature, and massive neutrinos. We introduce and test a simple but accurate forecasting technique for this purpose that neither double counts lensing information nor neglects lensing in the observables.

  16. Adding local components to global functions for continuous covariates in multivariable regression modeling.

    PubMed

    Binder, H; Sauerbrei, W

    2010-03-30

    When global techniques, based on fractional polynomials (FPs), are employed for modeling potentially nonlinear effects of several continuous covariates on a response, accessible model equations are obtained. However, local features might be missed. Therefore, a procedure is introduced, which systematically checks model fits, obtained by the multivariable fractional polynomial (MFP) approach, for overlooked local features. Statistically significant local polynomials are then parsimoniously added. This approach, called MFP + L, is seen to result in an effective control of the Type I error with respect to the addition of local components in a small simulation study with univariate and multivariable settings. Prediction performance is compared with that of a penalized regression spline technique. In a setting unfavorable for FPs, the latter outperforms the MFP approach, if there is much information in the data. However, the addition of local features reduces this performance difference. There is only a small detrimental effect in settings where the MFP approach performs better. In an application example with children's respiratory health data, fits from the spline-based approach indicate many local features, but MFP + L adds only few significant features, which seem to have good support in the data. The proposed approach may be expected to be superior in settings with local features, but retains the good properties of the MFP approach in a large number of settings where global functions are sufficient.

  17. Efficient retrieval of landscape Hessian: Forced optimal covariance adaptive learning

    NASA Astrophysics Data System (ADS)

    Shir, Ofer M.; Roslund, Jonathan; Whitley, Darrell; Rabitz, Herschel

    2014-06-01

    Knowledge of the Hessian matrix at the landscape optimum of a controlled physical observable offers valuable information about the system robustness to control noise. The Hessian can also assist in physical landscape characterization, which is of particular interest in quantum system control experiments. The recently developed landscape theoretical analysis motivated the compilation of an automated method to learn the Hessian matrix about the global optimum without derivative measurements from noisy data. The current study introduces the forced optimal covariance adaptive learning (FOCAL) technique for this purpose. FOCAL relies on the covariance matrix adaptation evolution strategy (CMA-ES) that exploits covariance information amongst the control variables by means of principal component analysis. The FOCAL technique is designed to operate with experimental optimization, generally involving continuous high-dimensional search landscapes (≳30) with large Hessian condition numbers (≳104). This paper introduces the theoretical foundations of the inverse relationship between the covariance learned by the evolution strategy and the actual Hessian matrix of the landscape. FOCAL is presented and demonstrated to retrieve the Hessian matrix with high fidelity on both model landscapes and quantum control experiments, which are observed to possess nonseparable, nonquadratic search landscapes. The recovered Hessian forms were corroborated by physical knowledge of the systems. The implications of FOCAL extend beyond the investigated studies to potentially cover other physically motivated multivariate landscapes.

  18. Efficient retrieval of landscape Hessian: forced optimal covariance adaptive learning.

    PubMed

    Shir, Ofer M; Roslund, Jonathan; Whitley, Darrell; Rabitz, Herschel

    2014-06-01

    Knowledge of the Hessian matrix at the landscape optimum of a controlled physical observable offers valuable information about the system robustness to control noise. The Hessian can also assist in physical landscape characterization, which is of particular interest in quantum system control experiments. The recently developed landscape theoretical analysis motivated the compilation of an automated method to learn the Hessian matrix about the global optimum without derivative measurements from noisy data. The current study introduces the forced optimal covariance adaptive learning (FOCAL) technique for this purpose. FOCAL relies on the covariance matrix adaptation evolution strategy (CMA-ES) that exploits covariance information amongst the control variables by means of principal component analysis. The FOCAL technique is designed to operate with experimental optimization, generally involving continuous high-dimensional search landscapes (≳30) with large Hessian condition numbers (≳10^{4}). This paper introduces the theoretical foundations of the inverse relationship between the covariance learned by the evolution strategy and the actual Hessian matrix of the landscape. FOCAL is presented and demonstrated to retrieve the Hessian matrix with high fidelity on both model landscapes and quantum control experiments, which are observed to possess nonseparable, nonquadratic search landscapes. The recovered Hessian forms were corroborated by physical knowledge of the systems. The implications of FOCAL extend beyond the investigated studies to potentially cover other physically motivated multivariate landscapes.

  19. The Concept of Missing Incidents in Persons with Dementia

    PubMed Central

    Rowe, Meredeth; Houston, Amy; Molinari, Victor; Bulat, Tatjana; Bowen, Mary Elizabeth; Spring, Heather; Mutolo, Sandra; McKenzie, Barbara

    2015-01-01

    Behavioral symptoms of dementia often present the greatest challenge for informal caregivers. One behavior, that is a constant concern for caregivers, is the person with dementia leaving a designated area such that their whereabouts become unknown to the caregiver or a missing incident. Based on an extensive literature review and published findings of their own research, members of the International Consortium on Wandering and Missing Incidents constructed a preliminary missing incidents model. Examining the evidence base, specific factors within each category of the model were further described, reviewed and modified until consensus was reached regarding the final model. The model begins to explain in particular the variety of antecedents that are related to missing incidents. The model presented in this paper is designed to be heuristic and may be used to stimulate discussion and the development of effective preventative and response strategies for missing incidents among persons with dementia. PMID:27417817

  20. Covariate pharmacokinetic model building in oncology and its potential clinical relevance.

    PubMed

    Joerger, Markus

    2012-03-01

    When modeling pharmacokinetic (PK) data, identifying covariates is important in explaining interindividual variability, and thus increasing the predictive value of the model. Nonlinear mixed-effects modeling with stepwise covariate modeling is frequently used to build structural covariate models, and the most commonly used software-NONMEM-provides estimations for the fixed-effect parameters (e.g., drug clearance), interindividual and residual unidentified random effects. The aim of covariate modeling is not only to find covariates that significantly influence the population PK parameters, but also to provide dosing recommendations for a certain drug under different conditions, e.g., organ dysfunction, combination chemotherapy. A true covariate is usually seen as one that carries unique information on a structural model parameter. Covariate models have improved our understanding of the pharmacology of many anticancer drugs, including busulfan or melphalan that are part of high-dose pretransplant treatments, the antifolate methotrexate whose elimination is strongly dependent on GFR and comedication, the taxanes and tyrosine kinase inhibitors, the latter being subject of cytochrome p450 3A4 (CYP3A4) associated metabolism. The purpose of this review article is to provide a tool to help understand population covariate analysis and their potential implications for the clinic. Accordingly, several population covariate models are listed, and their clinical relevance is discussed. The target audience of this article are clinical oncologists with a special interest in clinical and mathematical pharmacology.

  1. A comparison of model-based imputation methods for handling missing predictor values in a linear regression model: A simulation study

    NASA Astrophysics Data System (ADS)

    Hasan, Haliza; Ahmad, Sanizah; Osman, Balkish Mohd; Sapri, Shamsiah; Othman, Nadirah

    2017-08-01

    In regression analysis, missing covariate data has been a common problem. Many researchers use ad hoc methods to overcome this problem due to the ease of implementation. However, these methods require assumptions about the data that rarely hold in practice. Model-based methods such as Maximum Likelihood (ML) using the expectation maximization (EM) algorithm and Multiple Imputation (MI) are more promising when dealing with difficulties caused by missing data. Then again, inappropriate methods of missing value imputation can lead to serious bias that severely affects the parameter estimates. The main objective of this study is to provide a better understanding regarding missing data concept that can assist the researcher to select the appropriate missing data imputation methods. A simulation study was performed to assess the effects of different missing data techniques on the performance of a regression model. The covariate data were generated using an underlying multivariate normal distribution and the dependent variable was generated as a combination of explanatory variables. Missing values in covariate were simulated using a mechanism called missing at random (MAR). Four levels of missingness (10%, 20%, 30% and 40%) were imposed. ML and MI techniques available within SAS software were investigated. A linear regression analysis was fitted and the model performance measures; MSE, and R-Squared were obtained. Results of the analysis showed that MI is superior in handling missing data with highest R-Squared and lowest MSE when percent of missingness is less than 30%. Both methods are unable to handle larger than 30% level of missingness.

  2. Group Theory of Covariant Harmonic Oscillators

    ERIC Educational Resources Information Center

    Kim, Y. S.; Noz, Marilyn E.

    1978-01-01

    A simple and concrete example for illustrating the properties of noncompact groups is presented. The example is based on the covariant harmonic-oscillator formalism in which the relativistic wave functions carry a covariant-probability interpretation. This can be used in a group theory course for graduate students who have some background in…

  3. Quality Quantification of Evaluated Cross Section Covariances

    SciTech Connect

    Varet, S.; Dossantos-Uzarralde, P.

    2015-01-15

    Presently, several methods are used to estimate the covariance matrix of evaluated nuclear cross sections. Because the resulting covariance matrices can be different according to the method used and according to the assumptions of the method, we propose a general and objective approach to quantify the quality of the covariance estimation for evaluated cross sections. The first step consists in defining an objective criterion. The second step is computation of the criterion. In this paper the Kullback-Leibler distance is proposed for the quality quantification of a covariance matrix estimation and its inverse. It is based on the distance to the true covariance matrix. A method based on the bootstrap is presented for the estimation of this criterion, which can be applied with most methods for covariance matrix estimation and without the knowledge of the true covariance matrix. The full approach is illustrated on the {sup 85}Rb nucleus evaluations and the results are then used for a discussion on scoring and Monte Carlo approaches for covariance matrix estimation of the cross section evaluations.

  4. Covariance Structure Analysis of Ordinal Ipsative Data.

    ERIC Educational Resources Information Center

    Chan, Wai; Bentler, Peter M.

    1998-01-01

    Proposes a two-stage estimation method for the analysis of covariance structure models with ordinal ipsative data (OID). A goodness-of-fit statistic is given for testing the hypothesized covariance structure matrix, and simulation results show that the method works well with a large sample. (SLD)

  5. Group Theory of Covariant Harmonic Oscillators

    ERIC Educational Resources Information Center

    Kim, Y. S.; Noz, Marilyn E.

    1978-01-01

    A simple and concrete example for illustrating the properties of noncompact groups is presented. The example is based on the covariant harmonic-oscillator formalism in which the relativistic wave functions carry a covariant-probability interpretation. This can be used in a group theory course for graduate students who have some background in…

  6. Position Error Covariance Matrix Validation and Correction

    NASA Technical Reports Server (NTRS)

    Frisbee, Joe, Jr.

    2016-01-01

    In order to calculate operationally accurate collision probabilities, the position error covariance matrices predicted at times of closest approach must be sufficiently accurate representations of the position uncertainties. This presentation will discuss why the Gaussian distribution is a reasonable expectation for the position uncertainty and how this assumed distribution type is used in the validation and correction of position error covariance matrices.

  7. Covariance Based Pre-Filters and Screening Criteria for Conjunction Analysis

    NASA Astrophysics Data System (ADS)

    George, E., Chan, K.

    2012-09-01

    Several relationships are developed relating object size, initial covariance and range at closest approach to probability of collision. These relationships address the following questions: - Given the objects' initial covariance and combined hard body size, what is the maximum possible value of the probability of collision (Pc)? - Given the objects' initial covariance, what is the maximum combined hard body radius for which the probability of collision does not exceed the tolerance limit? - Given the objects' initial covariance and the combined hard body radius, what is the minimum miss distance for which the probability of collision does not exceed the tolerance limit? - Given the objects' initial covariance and the miss distance, what is the maximum combined hard body radius for which the probability of collision does not exceed the tolerance limit? The first relationship above allows the elimination of object pairs from conjunction analysis (CA) on the basis of the initial covariance and hard-body sizes of the objects. The application of this pre-filter to present day catalogs with estimated covariance results in the elimination of approximately 35% of object pairs as unable to ever conjunct with a probability of collision exceeding 1x10-6. Because Pc is directly proportional to object size and inversely proportional to covariance size, this pre-filter will have a significantly larger impact on future catalogs, which are expected to contain a much larger fraction of small debris tracked only by a limited subset of available sensors. This relationship also provides a mathematically rigorous basis for eliminating objects from analysis entirely based on element set age or quality - a practice commonly done by rough rules of thumb today. Further, these relations can be used to determine the required geometric screening radius for all objects. This analysis reveals the screening volumes for small objects are much larger than needed, while the screening volumes for

  8. Missing data in a multi-item instrument were best handled by multiple imputation at the item score level.

    PubMed

    Eekhout, Iris; de Vet, Henrica C W; Twisk, Jos W R; Brand, Jaap P L; de Boer, Michiel R; Heymans, Martijn W

    2014-03-01

    Regardless of the proportion of missing values, complete-case analysis is most frequently applied, although advanced techniques such as multiple imputation (MI) are available. The objective of this study was to explore the performance of simple and more advanced methods for handling missing data in cases when some, many, or all item scores are missing in a multi-item instrument. Real-life missing data situations were simulated in a multi-item variable used as a covariate in a linear regression model. Various missing data mechanisms were simulated with an increasing percentage of missing data. Subsequently, several techniques to handle missing data were applied to decide on the most optimal technique for each scenario. Fitted regression coefficients were compared using the bias and coverage as performance parameters. Mean imputation caused biased estimates in every missing data scenario when data are missing for more than 10% of the subjects. Furthermore, when a large percentage of subjects had missing items (>25%), MI methods applied to the items outperformed methods applied to the total score. We recommend applying MI to the item scores to get the most accurate regression model estimates. Moreover, we advise not to use any form of mean imputation to handle missing data. Copyright © 2014 Elsevier Inc. All rights reserved.

  9. Adjoints and Low-rank Covariance Representation

    NASA Technical Reports Server (NTRS)

    Tippett, Michael K.; Cohn, Stephen E.

    2000-01-01

    Quantitative measures of the uncertainty of Earth System estimates can be as important as the estimates themselves. Second moments of estimation errors are described by the covariance matrix, whose direct calculation is impractical when the number of degrees of freedom of the system state is large. Ensemble and reduced-state approaches to prediction and data assimilation replace full estimation error covariance matrices by low-rank approximations. The appropriateness of such approximations depends on the spectrum of the full error covariance matrix, whose calculation is also often impractical. Here we examine the situation where the error covariance is a linear transformation of a forcing error covariance. We use operator norms and adjoints to relate the appropriateness of low-rank representations to the conditioning of this transformation. The analysis is used to investigate low-rank representations of the steady-state response to random forcing of an idealized discrete-time dynamical system.

  10. Treatment decisions based on scalar and functional baseline covariates.

    PubMed

    Ciarleglio, Adam; Petkova, Eva; Ogden, R Todd; Tarpey, Thaddeus

    2015-12-01

    The amount and complexity of patient-level data being collected in randomized-controlled trials offer both opportunities and challenges for developing personalized rules for assigning treatment for a given disease or ailment. For example, trials examining treatments for major depressive disorder are not only collecting typical baseline data such as age, gender, or scores on various tests, but also data that measure the structure and function of the brain such as images from magnetic resonance imaging (MRI), functional MRI (fMRI), or electroencephalography (EEG). These latter types of data have an inherent structure and may be considered as functional data. We propose an approach that uses baseline covariates, both scalars and functions, to aid in the selection of an optimal treatment. In addition to providing information on which treatment should be selected for a new patient, the estimated regime has the potential to provide insight into the relationship between treatment response and the set of baseline covariates. Our approach can be viewed as an extension of "advantage learning" to include both scalar and functional covariates. We describe our method and how to implement it using existing software. Empirical performance of our method is evaluated with simulated data in a variety of settings and also applied to data arising from a study of patients with major depressive disorder from whom baseline scalar covariates as well as functional data from EEG are available.

  11. Covariance matrices for use in criticality safety predictability studies

    SciTech Connect

    Derrien, H.; Larson, N.M.; Leal, L.C.

    1997-09-01

    Criticality predictability applications require as input the best available information on fissile and other nuclides. In recent years important work has been performed in the analysis of neutron transmission and cross-section data for fissile nuclei in the resonance region by using the computer code SAMMY. The code uses Bayes method (a form of generalized least squares) for sequential analyses of several sets of experimental data. Values for Reich-Moore resonance parameters, their covariances, and the derivatives with respect to the adjusted parameters (data sensitivities) are obtained. In general, the parameter file contains several thousand values and the dimension of the covariance matrices is correspondingly large. These matrices are not reported in the current evaluated data files due to their large dimensions and to the inadequacy of the file formats. The present work has two goals: the first is to calculate the covariances of group-averaged cross sections from the covariance files generated by SAMMY, because these can be more readily utilized in criticality predictability calculations. The second goal is to propose a more practical interface between SAMMY and the evaluated files. Examples are given for {sup 235}U in the popular 199- and 238-group structures, using the latest ORNL evaluation of the {sup 235}U resonance parameters.

  12. How many longitudinal covariate measurements are needed for risk prediction?

    PubMed

    Reinikainen, Jaakko; Karvanen, Juha; Tolonen, Hanna

    2016-01-01

    In epidemiologic follow-up studies, many key covariates, such as smoking, use of medication, blood pressure, and cholesterol, are time varying. Because of practical and financial limitations, time-varying covariates cannot be measured continuously, but only at certain prespecified time points. We study how the number of these longitudinal measurements can be chosen cost-efficiently by evaluating the usefulness of the measurements for risk prediction. The usefulness is addressed by measuring the improvement in model discrimination between models using different amounts of longitudinal information. We use simulated follow-up data and the data from the Finnish East-West study, a follow-up study, with eight longitudinal covariate measurements carried out between 1959 and 1999. In a simulation study, we show how the variability and the hazard ratio of a time-varying covariate are connected to the importance of remeasurements. In the East-West study, it is seen that for older people, the risk predictions obtained using only every other measurement are almost equivalent to the predictions obtained using all eight measurements. Decisions about the study design have significant effects on the costs. The cost-efficiency can be improved by applying the measures of model discrimination to data from previous studies and simulations. Copyright © 2016 Elsevier Inc. All rights reserved.

  13. Empirical Likelihood for Estimating Equations with Nonignorably Missing Data.

    PubMed

    Tang, Niansheng; Zhao, Puying; Zhu, Hongtu

    2014-04-01

    We develop an empirical likelihood (EL) inference on parameters in generalized estimating equations with nonignorably missing response data. We consider an exponential tilting model for the nonignorably missing mechanism, and propose modified estimating equations by imputing missing data through a kernel regression method. We establish some asymptotic properties of the EL estimators of the unknown parameters under different scenarios. With the use of auxiliary information, the EL estimators are statistically more efficient. Simulation studies are used to assess the finite sample performance of our proposed EL estimators. We apply our EL estimators to investigate a data set on earnings obtained from the New York Social Indicators Survey.

  14. Missed Nursing Care in Pediatrics.

    PubMed

    Lake, Eileen T; de Cordova, Pamela B; Barton, Sharon; Singh, Shweta; Agosto, Paula D; Ely, Beth; Roberts, Kathryn E; Aiken, Linda H

    2017-07-01

    A growing literature suggests that missed nursing care is common in hospitals and may contribute to poor patient outcomes. There has been scant empirical evidence in pediatric populations. Our objectives were to describe the frequency and patterns of missed nursing care in inpatient pediatric settings and to determine whether missed nursing care is associated with unfavorable work environments and high nurse workloads. A cross-sectional study using registered nurse survey data from 2006 to 2008 was conducted. Data from 2187 NICU, PICU, and general pediatric nurses in 223 hospitals in 4 US states were analyzed. For 12 nursing activities, nurses reported about necessary activities that were not done on their last shift because of time constraints. Nurses reported their patient assignment and rated their work environment. More than half of pediatric nurses had missed care on their previous shift. On average, pediatric nurses missed 1.5 necessary care activities. Missed care was more common in poor versus better work environments (1.9 vs 1.2; P < .01). For 9 of 12 nursing activities, the prevalence of missed care was significantly higher in the poor environments (P < .05). In regression models that controlled for nurse, nursing unit, and hospital characteristics, the odds that a nurse missed care were 40% lower in better environments and increased by 70% for each additional patient. Nurses in inpatient pediatric care settings that care for fewer patients each and practice in a professionally supportive work environment miss care less often, increasing quality of patient care. Copyright © 2017 by the American Academy of Pediatrics.

  15. Modelling categorical covariates in Bayesian disease mapping by partition structures.

    PubMed

    Giudici, P; Knorr-Held, L; Rasser, G

    We consider the problem of mapping the risk from a disease using a series of regional counts of observed and expected cases, and information on potential risk factors. To analyse this problem from a Bayesian viewpoint, we propose a methodology which extends a spatial partition model by including categorical covariate information. Such an extension allows detection of clusters in the residual variation, reflecting further, possibly unobserved, covariates. The methodology is implemented by means of reversible jump Markov chain Monte Carlo sampling. An application is presented in order to illustrate and compare our proposed extensions with a purely spatial partition model. Here we analyse a well-known data set on lip cancer incidence in Scotland. Copyright 2000 John Wiley & Sons, Ltd.

  16. Accounting for missing data in end-of-life research.

    PubMed

    Diehr, Paula; Johnson, Laura Lee

    2005-01-01

    End-of-life studies are likely to have missing data because sicker persons are less likely to provide information and because measurements cannot be made after death. Ignoring missing data may result in data that are too favorable, because the sickest persons are effectively dropped from the analysis. In a comparison of two groups, the group with the most deaths and missing data will tend to have the most favorable data, which is not desirable. Results based on only the available data may not be generalizable to the original study population. If most of the missing data are absent because of death, methods that account for the deaths may remove much of the bias. Imputation methods can then be used for the data that are missing for other reasons. An example is presented from a randomized trial involving frail veterans. In that dataset, only two thirds of the subjects had complete data, but 60% of the "missing" data were missing because of death. The available data alone suggested that health improved significantly over time. However, after accounting for the deaths, there was a significant decline in health over time, as had been expected. Imputation of the remaining missing data did not change the results very much. With and without the imputed data, there was never a significant difference between the treatment and control groups, but in two nonrandomized comparisons the method of handling the missing data made a substantive difference. These sensitivity analyses suggest that the main results were not sensitive to the death and missing data, but that some secondary analyses were sensitive to these problems. Similar approaches should be considered in other end-of-life studies.

  17. Concordance between criteria for covariate model building.

    PubMed

    Hennig, Stefanie; Karlsson, Mats O

    2014-04-01

    When performing a population pharmacokinetic modelling analysis covariates are often added to the model. Such additions are often justified by improved goodness of fit and/or decreased in unexplained (random) parameter variability. Increased goodness of fit is most commonly measured by the decrease in the objective function value. Parameter variability can be defined as the sum of unexplained (random) and explained (predictable) variability. Increase in magnitude of explained parameter variability could be another possible criterion for judging improvement in the model. The agreement between these three criteria in diagnosing covariate-parameter relationships of different strengths and nature using stochastic simulations and estimations as well as assessing covariate-parameter relationships in four previously published real data examples were explored. Total estimated parameter variability was found to vary with the number of covariates introduced on the parameter. In the simulated examples and two real examples, the parameter variability increased with increasing number of included covariates. For the other real examples parameter variability decreased or did not change systematically with the addition of covariates. The three criteria were highly correlated, with the decrease in unexplained variability being more closely associated with changes in objective function values than increases in explained parameter variability were. The often used assumption that inclusion of covariates in models only shifts unexplained parameter variability to explained parameter variability appears not to be true, which may have implications for modelling decisions.

  18. Reliability-based covariance control design

    SciTech Connect

    Field, R.V. Jr.; Bergman, L.A.

    1997-03-01

    An extension to classical covariance control methods, introduced by Skelton and co-workers, is proposed specifically for application to the control of civil engineering structures subjected to random dynamic excitations. The covariance structure of the system is developed directly from specification of its reliability via the assumption of independent (Poisson) outcrossings of its stationary response process from a polyhedral safe region. This leads to a set of state covariance controllers, each of which guarantees that the closed-loop system will possess the specified level of reliability. An example civil engineering structure is considered.

  19. Conformal Covariance and the Split Property

    NASA Astrophysics Data System (ADS)

    Morinelli, Vincenzo; Tanimoto, Yoh; Weiner, Mihály

    2017-08-01

    We show that for a conformal local net of observables on the circle, the split property is automatic. Both full conformal covariance (i.e., diffeomorphism covariance) and the circle-setting play essential roles in this fact, while by previously constructed examples it was already known that even on the circle, Möbius covariance does not imply the split property. On the other hand, here we also provide an example of a local conformal net living on the 2-dimensional Minkowski space, which—although being diffeomorphism covariant—does not have the split property.

  20. Application of pattern mixture models to address missing data in longitudinal data analysis using SPSS.

    PubMed

    Son, Heesook; Friedmann, Erika; Thomas, Sue A

    2012-01-01

    Longitudinal studies are used in nursing research to examine changes over time in health indicators. Traditional approaches to longitudinal analysis of means, such as analysis of variance with repeated measures, are limited to analyzing complete cases. This limitation can lead to biased results due to withdrawal or data omission bias or to imputation of missing data, which can lead to bias toward the null if data are not missing completely at random. Pattern mixture models are useful to evaluate the informativeness of missing data and to adjust linear mixed model (LMM) analyses if missing data are informative. The aim of this study was to provide an example of statistical procedures for applying a pattern mixture model to evaluate the informativeness of missing data and conduct analyses of data with informative missingness in longitudinal studies using SPSS. The data set from the Patients' and Families' Psychological Response to Home Automated External Defibrillator Trial was used as an example to examine informativeness of missing data with pattern mixture models and to use a missing data pattern in analysis of longitudinal data. Prevention of withdrawal bias, omitted data bias, and bias toward the null in longitudinal LMMs requires the assessment of the informativeness of the occurrence of missing data. Missing data patterns can be incorporated as fixed effects into LMMs to evaluate the contribution of the presence of informative missingness to and control for the effects of missingness on outcomes. Pattern mixture models are a useful method to address the presence and effect of informative missingness in longitudinal studies.

  1. Subsample ignorable likelihood for accelerated failure time models with missing predictors.

    PubMed

    Zhang, Nanhua; Little, Roderick J

    2015-07-01

    Missing values in predictors are a common problem in survival analysis. In this paper, we review estimation methods for accelerated failure time models with missing predictors, and apply a new method called subsample ignorable likelihood (IL) Little and Zhang (J R Stat Soc 60:591-605, 2011) to this class of models. The approach applies a likelihood-based method to a subsample of observations that are complete on a subset of the covariates, chosen based on assumptions about the missing data mechanism. We give conditions on the missing data mechanism under which the subsample IL method is consistent, while both complete-case analysis and ignorable maximum likelihood are inconsistent. We illustrate the properties of the proposed method by simulation and apply the method to a real dataset.

  2. Effects of expanding the look-back period to all available data in the assessment of covariates.

    PubMed

    Nakasian, Sonja S; Rassen, Jeremy A; Franklin, Jessica M

    2017-08-01

    A fixed baseline period has been a common covariate assessment approach in pharmacoepidemiological studies from claims but may lead to high levels of covariate misclassification. Simulation studies have recommended expanding the look-back approach to all available data (AAD) for binary indicators of diagnoses, procedures, and medications, but there have been few real data analyses using this approach. The objective of the study is to explore the impact on treatment effect estimates and covariate prevalence of expanding the look-back period within five validated studies in the Aetion system, a rapid cycle analytics platform. We reran the five studies and assessed covariates using (i) a fixed window approach (usually 180 days before treatment initiation), (ii) AAD prior to treatment initiation, and (iii) AAD with a categorized by recency approach, where the most recent occurrence of a covariate was labeled as recent (occurring within the fixed window) or past (before the start of the fixed window). For each covariate assessment approach, we adjusted for covariates via propensity score matching. All studies had at least one covariate that had an increase in prevalence of 15% or higher from the fixed window to the AAD approach. However, there was little change in treatment effect estimates resulting from differing covariate assessment approaches. For example, in a study of acute coronary syndrome in high-intensity versus low-intensity statin users, the estimated hazard ratio from the fixed window approach was 1.11 (95% confidence interval 0.98, 1.25) versus 1.21 (1.07, 1.37) when using AAD and 1.19 (1.05, 1.35) using categorized by recency. Expanding the baseline period to AAD improved covariate sensitivity by capturing data that would otherwise be missed yet did not meaningfully change the overall treatment effect estimates compared with the fixed window approach. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

  3. Kettlewell's Missing Evidence.

    ERIC Educational Resources Information Center

    Allchin, Douglas Kellogg

    2002-01-01

    The standard textbook account of Kettlewell and the peppered moths omits significant information. Suggests that this case can be used to reflect on the role of simplification in science teaching. (Author/MM)

  4. Kettlewell's Missing Evidence.

    ERIC Educational Resources Information Center

    Allchin, Douglas Kellogg

    2002-01-01

    The standard textbook account of Kettlewell and the peppered moths omits significant information. Suggests that this case can be used to reflect on the role of simplification in science teaching. (Author/MM)

  5. Central subspace dimensionality reduction using covariance operators.

    PubMed

    Kim, Minyoung; Pavlovic, Vladimir

    2011-04-01

    We consider the task of dimensionality reduction informed by real-valued multivariate labels. The problem is often treated as Dimensionality Reduction for Regression (DRR), whose goal is to find a low-dimensional representation, the central subspace, of the input data that preserves the statistical correlation with the targets. A class of DRR methods exploits the notion of inverse regression (IR) to discover central subspaces. Whereas most existing IR techniques rely on explicit output space slicing, we propose a novel method called the Covariance Operator Inverse Regression (COIR) that generalizes IR to nonlinear input/output spaces without explicit target slicing. COIR's unique properties make DRR applicable to problem domains with high-dimensional output data corrupted by potentially significant amounts of noise. Unlike recent kernel dimensionality reduction methods that employ iterative nonconvex optimization, COIR yields a closed-form solution. We also establish the link between COIR, other DRR techniques, and popular supervised dimensionality reduction methods, including canonical correlation analysis and linear discriminant analysis. We then extend COIR to semi-supervised settings where many of the input points lack their labels. We demonstrate the benefits of COIR on several important regression problems in both fully supervised and semi-supervised settings.

  6. Computation of the factorized error covariance of the difference between correlated estimators

    NASA Technical Reports Server (NTRS)

    Wolff, Peter J.; Mohan, Srinivas N.; Stienon, Francis M.; Bierman, Gerald J.

    1990-01-01

    A state estimation problem where some of the measurements may be common to two or more data sets is considered. Two approaches for computing the error covariance of the difference between filtered estimates (for each data set) are discussed. The first algorithm is based on postprocessing of the Kalman gain profiles of two correlated estimators. It uses UD factors of the covariance of the relative error. The second algorithm uses a square root information filter applied to relative error analysis. In the absence of process noise, the square root information filter is computationally more efficient and more flexible than the Kalman gain (covariance update) method. Both the algorithms (covariance and information matrix based) are applied to a Venus orbiter simulation, and their performances are compared.

  7. Conformally covariant parametrizations for relativistic initial data

    NASA Astrophysics Data System (ADS)

    Delay, Erwann

    2017-01-01

    We revisit the Lichnerowicz-York method, and an alternative method of York, in order to obtain some conformally covariant systems. This type of parametrization is certainly more natural for non constant mean curvature initial data.

  8. Earth Observing System Covariance Realism Updates

    NASA Technical Reports Server (NTRS)

    Ojeda Romero, Juan A.; Miguel, Fred

    2017-01-01

    This presentation will be given at the International Earth Science Constellation Mission Operations Working Group meetings June 13-15, 2017 to discuss the Earth Observing System Covariance Realism updates.

  9. Covariance Spectroscopy for Fissile Material Detection

    SciTech Connect

    Rusty Trainham, Jim Tinsley, Paul Hurley, Ray Keegan

    2009-06-02

    Nuclear fission produces multiple prompt neutrons and gammas at each fission event. The resulting daughter nuclei continue to emit delayed radiation as neutrons boil off, beta decay occurs, etc. All of the radiations are causally connected, and therefore correlated. The correlations are generally positive, but when different decay channels compete, so that some radiations tend to exclude others, negative correlations could also be observed. A similar problem of reduced complexity is that of cascades radiation, whereby a simple radioactive decay produces two or more correlated gamma rays at each decay. Covariance is the usual means for measuring correlation, and techniques of covariance mapping may be useful to produce distinct signatures of special nuclear materials (SNM). A covariance measurement can also be used to filter data streams because uncorrelated signals are largely rejected. The technique is generally more effective than a coincidence measurement. In this poster, we concentrate on cascades and the covariance filtering problem.

  10. Covariation bias in panic-prone individuals.

    PubMed

    Pauli, P; Montoya, P; Martz, G E

    1996-11-01

    Covariation estimates between fear-relevant (FR; emergency situations) or fear-irrelevant (FI; mushrooms and nudes) stimuli and an aversive outcome (electrical shock) were examined in 10 high-fear (panic-prone) and 10 low-fear respondents. When the relation between slide category and outcome was random (illusory correlation), only high-fear participants markedly overestimated the contingency between FR slides and shocks. However, when there was a high contingency of shocks following FR stimuli (83%) and a low contingency of shocks following FI stimuli (17%), the group difference vanished. Reversal of contingencies back to random induced a covariation bias for FR slides in high- and low-fear respondents. Results indicate that panic-prone respondents show a covariation bias for FR stimuli and that the experience of a high contingency between FR slides and aversive outcomes may foster such a covariation bias even in low-fear respondents.

  11. Modeling missing data in knowledge space theory.

    PubMed

    de Chiusole, Debora; Stefanutti, Luca; Anselmi, Pasquale; Robusto, Egidio

    2015-12-01

    Missing data are a well known issue in statistical inference, because some responses may be missing, even when data are collected carefully. The problem that arises in these cases is how to deal with missing data. In this article, the missingness is analyzed in knowledge space theory, and in particular when the basic local independence model (BLIM) is applied to the data. Two extensions of the BLIM to missing data are proposed: The former, called ignorable missing BLIM (IMBLIM), assumes that missing data are missing completely at random; the latter, called missing BLIM (MissBLIM), introduces specific dependencies of the missing data on the knowledge states, thus assuming that the missing data are missing not at random. The IMBLIM and the MissBLIM modeled the missingness in a satisfactory way, in both a simulation study and an empirical application, depending on the process that generates the missingness: If the missing data-generating process is of type missing completely at random, then either IMBLIM or MissBLIM provide adequate fit to the data. However, if the pattern of missingness is functionally dependent upon unobservable features of the data (e.g., missing answers are more likely to be wrong), then only a correctly specified model of the missingness distribution provides an adequate fit to the data.

  12. Noncommutative Gauge Theory with Covariant Star Product

    SciTech Connect

    Zet, G.

    2010-08-04

    We present a noncommutative gauge theory with covariant star product on a space-time with torsion. In order to obtain the covariant star product one imposes some restrictions on the connection of the space-time. Then, a noncommutative gauge theory is developed applying this product to the case of differential forms. Some comments on the advantages of using a space-time with torsion to describe the gravitational field are also given.

  13. Breeding curvature from extended gauge covariance

    NASA Astrophysics Data System (ADS)

    Aldrovandi, R.

    1991-05-01

    Independence between spacetime and “internal” space in gauge theories is related to the adjoint-covariant behaviour of the gauge potential. The usual gauge scheme is modified to allow a coupling between both spaces. Gauging spacetime translations produce field equations similar to Einstein equations. A curvature-like quantity of mixed differential-algebraic character emerges. Enlarged conservation laws are present, pointing to the presence of an covariance.

  14. Covariate analysis of bivariate survival data

    SciTech Connect

    Bennett, L.E.

    1992-01-01

    The methods developed are used to analyze the effects of covariates on bivariate survival data when censoring and ties are present. The proposed method provides models for bivariate survival data that include differential covariate effects and censored observations. The proposed models are based on an extension of the univariate Buckley-James estimators which replace censored data points by their expected values, conditional on the censoring time and the covariates. For the bivariate situation, it is necessary to determine the expectation of the failure times for one component conditional on the failure or censoring time of the other component. Two different methods have been developed to estimate these expectations. In the semiparametric approach these expectations are determined from a modification of Burke's estimate of the bivariate empirical survival function. In the parametric approach censored data points are also replaced by their conditional expected values where the expected values are determined from a specified parametric distribution. The model estimation will be based on the revised data set, comprised of uncensored components and expected values for the censored components. The variance-covariance matrix for the estimated covariate parameters has also been derived for both the semiparametric and parametric methods. Data from the Demographic and Health Survey was analyzed by these methods. The two outcome variables are post-partum amenorrhea and breastfeeding; education and parity were used as the covariates. Both the covariate parameter estimates and the variance-covariance estimates for the semiparametric and parametric models will be compared. In addition, a multivariate test statistic was used in the semiparametric model to examine contrasts. The significance of the statistic was determined from a bootstrap distribution of the test statistic.

  15. Combining biomarkers for classification with covariate adjustment.

    PubMed

    Kim, Soyoung; Huang, Ying

    2017-03-09

    Combining multiple markers can improve classification accuracy compared with using a single marker. In practice, covariates associated with markers or disease outcome can affect the performance of a biomarker or biomarker combination in the population. The covariate-adjusted receiver operating characteristic (ROC) curve has been proposed as a tool to tease out the covariate effect in the evaluation of a single marker; this curve characterizes the classification accuracy solely because of the marker of interest. However, research on the effect of covariates on the performance of marker combinations and on how to adjust for the covariate effect when combining markers is still lacking. In this article, we examine the effect of covariates on classification performance of linear marker combinations and propose to adjust for covariates in combining markers by maximizing the nonparametric estimate of the area under the covariate-adjusted ROC curve. The proposed method provides a way to estimate the best linear biomarker combination that is robust to risk model assumptions underlying alternative regression-model-based methods. The proposed estimator is shown to be consistent and asymptotically normally distributed. We conduct simulations to evaluate the performance of our estimator in cohort and case/control designs and compare several different weighting strategies during estimation with respect to efficiency. Our estimator is also compared with alternative regression-model-based estimators or estimators that maximize the empirical area under the ROC curve, with respect to bias and efficiency. We apply the proposed method to a biomarker study from an human immunodeficiency virus vaccine trial. Copyright © 2017 John Wiley & Sons, Ltd.

  16. Covariant action for type IIB supergravity

    NASA Astrophysics Data System (ADS)

    Sen, Ashoke

    2016-07-01

    Taking clues from the recent construction of the covariant action for type II and heterotic string field theories, we construct a manifestly Lorentz covariant action for type IIB supergravity, and discuss its gauge fixing maintaining manifest Lorentz invariance. The action contains a (non-gravitating) free 4-form field besides the usual fields of type IIB supergravity. This free field, being completely decoupled from the interacting sector, has no physical consequence.

  17. Vector Meson Property in Covariant Classification Scheme

    NASA Astrophysics Data System (ADS)

    Oda, Masuho

    2004-08-01

    Recently our collaboration group has proposed the covariant classification shceme of hadrons, leading to possible existence of two ground state vector mesons. One is corresponding to ordinary ρ nonet and the other is extra ρ nonet. We investigate the decay property of ω(1250) and ρ(1250) in the covariant classification scheme. And it is shown that ω(1250) is promising candidate of our extra ω meson.

  18. Phase-covariant quantum cloning of qudits

    SciTech Connect

    Fan Heng; Imai, Hiroshi; Matsumoto, Keiji; Wang, Xiang-Bin

    2003-02-01

    We study the phase-covariant quantum cloning machine for qudits, i.e., the input states in a d-level quantum system have complex coefficients with arbitrary phase but constant module. A cloning unitary transformation is proposed. After optimizing the fidelity between input state and single qudit reduced density operator of output state, we obtain the optimal fidelity for 1 to 2 phase-covariant quantum cloning of qudits and the corresponding cloning transformation.

  19. Covariance Modifications to Subspace Bases

    SciTech Connect

    Harris, D B

    2008-11-19

    Adaptive signal processing algorithms that rely upon representations of signal and noise subspaces often require updates to those representations when new data become available. Subspace representations frequently are estimated from available data with singular value (SVD) decompositions. Subspace updates require modifications to these decompositions. Updates can be performed inexpensively provided they are low-rank. A substantial literature on SVD updates exists, frequently focusing on rank-1 updates (see e.g. [Karasalo, 1986; Comon and Golub, 1990, Badeau, 2004]). In these methods, data matrices are modified by addition or deletion of a row or column, or data covariance matrices are modified by addition of the outer product of a new vector. A recent paper by Brand [2006] provides a general and efficient method for arbitrary rank updates to an SVD. The purpose of this note is to describe a closely-related method for applications where right singular vectors are not required. This note also describes the SVD updates to a particular scenario of interest in seismic array signal processing. The particular application involve updating the wideband subspace representation used in seismic subspace detectors [Harris, 2006]. These subspace detectors generalize waveform correlation algorithms to detect signals that lie in a subspace of waveforms of dimension d {ge} 1. They potentially are of interest because they extend the range of waveform variation over which these sensitive detectors apply. Subspace detectors operate by projecting waveform data from a detection window into a subspace specified by a collection of orthonormal waveform basis vectors (referred to as the template). Subspace templates are constructed from a suite of normalized, aligned master event waveforms that may be acquired by a single sensor, a three-component sensor, an array of such sensors or a sensor network. The template design process entails constructing a data matrix whose columns contain the

  20. Low-dimensional Representation of Error Covariance

    NASA Technical Reports Server (NTRS)

    Tippett, Michael K.; Cohn, Stephen E.; Todling, Ricardo; Marchesin, Dan

    2000-01-01

    Ensemble and reduced-rank approaches to prediction and assimilation rely on low-dimensional approximations of the estimation error covariances. Here stability properties of the forecast/analysis cycle for linear, time-independent systems are used to identify factors that cause the steady-state analysis error covariance to admit a low-dimensional representation. A useful measure of forecast/analysis cycle stability is the bound matrix, a function of the dynamics, observation operator and assimilation method. Upper and lower estimates for the steady-state analysis error covariance matrix eigenvalues are derived from the bound matrix. The estimates generalize to time-dependent systems. If much of the steady-state analysis error variance is due to a few dominant modes, the leading eigenvectors of the bound matrix approximate those of the steady-state analysis error covariance matrix. The analytical results are illustrated in two numerical examples where the Kalman filter is carried to steady state. The first example uses the dynamics of a generalized advection equation exhibiting nonmodal transient growth. Failure to observe growing modes leads to increased steady-state analysis error variances. Leading eigenvectors of the steady-state analysis error covariance matrix are well approximated by leading eigenvectors of the bound matrix. The second example uses the dynamics of a damped baroclinic wave model. The leading eigenvectors of a lowest-order approximation of the bound matrix are shown to approximate well the leading eigenvectors of the steady-state analysis error covariance matrix.

  1. Generating Covariance Data with Nuclear Models

    NASA Astrophysics Data System (ADS)

    Koning, A. J.

    2006-04-01

    A reliable assessment of the uncertainties in calculated integral reactor parameters depends directly on the uncertainties of the underlying nuclear data. Unfortunately, covariance nuclear data are scarce, not only because a significant experimental database for the isotope under consideration must be available, but also because the covariance evaluation process can be rather complex and time-consuming. We attack this problem with a systematical approach and developed, following the initial ideas of D. Smith (ANL), a method to produce a complete covariance matrix for evaluated data files on the basis of uncertainties of nuclear model parameters. This is accomplished by subjecting the nuclear model code TALYS to a Monte Carlo method for perturbing input parameters, an approach that is now possible with the available computer power. After establishing uncertainties for parameters of the optical model, level densities, gamma-ray strength functions, fission barriers etc., we produce random input files for the TALYS code. These deliver, provided enough calculations (samples) are performed, uncertainties + all off-diagonal elements for all open reaction channels. The uncertainties of the nuclear model parameter are tuned such that the calculated cross section uncertainties coincide, to a reasonable extent, with uncertainties obtained from covariance evaluations based on experimental data. If this method proves to be successful, and we will show here that we are not too far off, it will enable mass production of credible covariance data for isotopes for which no covariance data exists……and this constitutes a very significant part of the periodic table of elements.

  2. Lorentz covariance of loop quantum gravity

    NASA Astrophysics Data System (ADS)

    Rovelli, Carlo; Speziale, Simone

    2011-05-01

    The kinematics of loop gravity can be given a manifestly Lorentz-covariant formulation: the conventional SU(2)-spin-network Hilbert space can be mapped to a space K of SL(2,C) functions, where Lorentz covariance is manifest. K can be described in terms of a certain subset of the projected spin networks studied by Livine, Alexandrov and Dupuis. It is formed by SL(2,C) functions completely determined by their restriction on SU(2). These are square-integrable in the SU(2) scalar product, but not in the SL(2,C) one. Thus, SU(2)-spin-network states can be represented by Lorentz-covariant SL(2,C) functions, as two-component photons can be described in the Lorentz-covariant Gupta-Bleuler formalism. As shown by Wolfgang Wieland in a related paper, this manifestly Lorentz-covariant formulation can also be directly obtained from canonical quantization. We show that the spinfoam dynamics of loop quantum gravity is locally SL(2,C)-invariant in the bulk, and yields states that are precisely in K on the boundary. This clarifies how the SL(2,C) spinfoam formalism yields an SU(2) theory on the boundary. These structures define a tidy Lorentz-covariant formalism for loop gravity.

  3. A Comet's Missing Light

    NASA Astrophysics Data System (ADS)

    Kohler, Susanna

    2016-05-01

    On 28 November 2013, comet C/2012 S1 better known as comet ISON should have passed within two solar radii of the Suns surface as it reached perihelion in its orbit. But instead of shining in extreme ultraviolet (EUV) wavelengths as it grazed the solar surface, the comet was never detected by EUV instruments. What happened to comet ISON?Missing EmissionWhen a sungrazing comet passes through the solar corona, it leaves behind a trail of molecules evaporated from its surface. Some of these molecules emit EUV light, which can be detected by instruments on telescopes like the space-based Solar Dynamics Observatory (SDO).Comet ISON, a comet that arrived from deep space and was predicted to graze the Suns corona in November 2013, was expected to cause EUV emission during its close passage. But analysis of the data from multiple telescopes that tracked ISON in EUV including SDO reveals no sign of it at perihelion.In a recent study, Paul Bryans and DeanPesnell, scientists from NCARs High Altitude Observatory and NASA Goddard Space Flight Center, try to determine why ISON didnt display this expected emission.Comparing ISON and LovejoyIn December 2011, another comet dipped into the Suns corona: comet Lovejoy. This image, showingthe orbit Lovejoy took around the Sun, is a composite of SDO images of the pre- and post-perihelion phases of the orbit. Click for a closer look! The dashed part of the curve represents where Lovejoy passed out of view behind the Sun. [Bryans Pesnell 2016]This is not the first time weve watched a sungrazing comet with EUV-detecting telescopes: Comet Lovejoy passed similarly close to the Sun in December 2011. But when Lovejoy grazed the solar corona, it emitted brightly in EUV. So why didnt ISON? Bryans and Pesnell argue that there are two possibilities:the coronal conditions experienced by the two comets were not similar, orthe two comets themselves were not similar.To establish which factor is the most relevant, the authors first demonstrate that both

  4. Quantitative shape analysis with weighted covariance estimates for increased statistical efficiency

    PubMed Central

    2013-01-01

    Background The introduction and statistical formalisation of landmark-based methods for analysing biological shape has made a major impact on comparative morphometric analyses. However, a satisfactory solution for including information from 2D/3D shapes represented by ‘semi-landmarks’ alongside well-defined landmarks into the analyses is still missing. Also, there has not been an integration of a statistical treatment of measurement error in the current approaches. Results We propose a procedure based upon the description of landmarks with measurement covariance, which extends statistical linear modelling processes to semi-landmarks for further analysis. Our formulation is based upon a self consistent approach to the construction of likelihood-based parameter estimation and includes corrections for parameter bias, induced by the degrees of freedom within the linear model. The method has been implemented and tested on measurements from 2D fly wing, 2D mouse mandible and 3D mouse skull data. We use these data to explore possible advantages and disadvantages over the use of standard Procrustes/PCA analysis via a combination of Monte-Carlo studies and quantitative statistical tests. In the process we show how appropriate weighting provides not only greater stability but also more efficient use of the available landmark data. The set of new landmarks generated in our procedure (‘ghost points’) can then be used in any further downstream statistical analysis. Conclusions Our approach provides a consistent way of including different forms of landmarks into an analysis and reduces instabilities due to poorly defined points. Our results suggest that the method has the potential to be utilised for the analysis of 2D/3D data, and in particular, for the inclusion of information from surfaces represented by multiple landmark points. PMID:23548043

  5. Missing gene identification using functional coherence scores

    PubMed Central

    Chitale, Meghana; Khan, Ishita K.; Kihara, Daisuke

    2016-01-01

    Reconstructing metabolic and signaling pathways is an effective way of interpreting a genome sequence. A challenge in a pathway reconstruction is that often genes in a pathway cannot be easily found, reflecting current imperfect information of the target organism. In this work, we developed a new method for finding missing genes, which integrates multiple features, including gene expression, phylogenetic profile, and function association scores. Particularly, for considering function association between candidate genes and neighboring proteins to the target missing gene in the network, we used Co-occurrence Association Score (CAS) and PubMed Association Score (PAS), which are designed for capturing functional coherence of proteins. We showed that adding CAS and PAS substantially improve the accuracy of identifying missing genes in the yeast enzyme-enzyme network compared to the cases when only the conventional features, gene expression, phylogenetic profile, were used. Finally, it was also demonstrated that the accuracy improves by considering indirect neighbors to the target enzyme position in the network using a proper network-topology-based weighting scheme. PMID:27552989

  6. Depression and literacy are important factors for missed appointments.

    PubMed

    Miller-Matero, Lisa Renee; Clark, Kalin Burkhardt; Brescacin, Carly; Dubaybo, Hala; Willens, David E

    2016-09-01

    Multiple variables are related to missed clinic appointments. However, the prevalence of missed appointments is still high suggesting other factors may play a role. The purpose of this study was to investigate the relationship between missed appointments and multiple variables simultaneously across a health care system, including patient demographics, psychiatric symptoms, cognitive functioning and literacy status. Chart reviews were conducted on 147 consecutive patients who were seen by a primary care psychologist over a six month period and completed measures to determine levels of depression, anxiety, sleep, cognitive functioning and health literacy. Demographic information and rates of missed appointments were also collected from charts. The average rate of missed appointments was 15.38%. In univariate analyses, factors related to higher rates of missed appointments included younger age (p = .03), lower income (p = .05), probable depression (p = .05), sleep difficulty (p = .05) and limited reading ability (p = .003). There were trends for a higher rate of missed appointments for patients identifying as black (p = .06), government insurance (p = .06) and limited math ability (p = .06). In a multivariate model, probable depression (p = .02) and limited reading ability (p = .003) were the only independent predictors. Depression and literacy status may be the most important factors associated with missed appointments. Implications are discussed including regular screening for depression and literacy status as well as interventions that can be utilized to help improve the rate of missed appointments.

  7. A nonparametric multiple imputation approach for missing categorical data.

    PubMed

    Zhou, Muhan; He, Yulei; Yu, Mandi; Hsu, Chiu-Hsieh

    2017-06-06

    Incomplete categorical variables with more than two categories are common in public health data. However, most of the existing missing-data methods do not use the information from nonresponse (missingness) probabilities. We propose a nearest-neighbour multiple imputation approach to impute a missing at random categorical outcome and to estimate the proportion of each category. The donor set for imputation is formed by measuring distances between each missing value with other non-missing values. The distance function is calculated based on a predictive score, which is derived from two working models: one fits a multinomial logistic regression for predicting the missing categorical outcome (the outcome model) and the other fits a logistic regression for predicting missingness probabilities (the missingness model). A weighting scheme is used to accommodate contributions from two working models when generating the predictive score. A missing value is imputed by randomly selecting one of the non-missing values with the smallest distances. We conduct a simulation to evaluate the performance of the proposed method and compare it with several alternative methods. A real-data application is also presented. The simulation study suggests that the proposed method performs well when missingness probabilities are not extreme under some misspecifications of the working models. However, the calibration estimator, which is also based on two working models, can be highly unstable when missingness probabilities for some observations are extremely high. In this scenario, the proposed method produces more stable and better estimates. In addition, proper weights need to be chosen to balance the contributions from the two working models and achieve optimal results for the proposed method. We conclude that the proposed multiple imputation method is a reasonable approach to dealing with missing categorical outcome data with more than two levels for assessing the distribution of the outcome

  8. Missing data in medical databases: impute, delete or classify?

    PubMed

    Cismondi, Federico; Fialho, André S; Vieira, Susana M; Reti, Shane R; Sousa, João M C; Finkelstein, Stan N

    2013-05-01

    The multiplicity of information sources for data acquisition in modern intensive care units (ICUs) makes the resulting databases particularly susceptible to missing data. Missing data can significantly affect the performance of predictive risk modeling, an important technique for developing medical guidelines. The two most commonly used strategies for managing missing data are to impute or delete values, and the former can cause bias, while the later can cause both bias and loss of statistical power. In this paper we present a new approach for managing missing data in ICU databases in order to improve overall modeling performance. We use a statistical classifier followed by fuzzy modeling to more accurately determine which missing data should be imputed and which should not. We firstly develop a simulation test bed to evaluate performance, and then translate that knowledge using exactly the same database as previously published work by [13]. In this work, test beds resulted in datasets with missing data ranging 10-50%. Using this new approach to missing data we are able to significantly improve modeling performance parameters such as accuracy of classifications by an 11%, sensitivity by 13%, and specificity by 10%, including also area under the receiver-operator curve (AUC) improvement of up to 13%. In this work, we improve modeling performance in a simulated test bed, and then confirm improved performance replicating previously published work by using the proposed approach for missing data classification. We offer this new method to other researchers who wish to improve predictive risk modeling performance in the ICU through advanced missing data management. Copyright © 2013 Elsevier B.V. All rights reserved.

  9. Determination of Resonance Parameters and their Covariances from Neutron Induced Reaction Cross Section Data

    SciTech Connect

    Schillebeeckx, P.; Becker, B.; Danon, Y.; Guber, K.; Harada, H.; Heyse, J.; Junghans, A.R.; Kopecky, S.; Massimi, C.; Moxon, M.C.; Otuka, N.; Sirakov, I.; Volev, K.

    2012-12-15

    Cross section data in the resolved and unresolved resonance region are represented by nuclear reaction formalisms using parameters which are determined by fitting them to experimental data. Therefore, the quality of evaluated cross sections in the resonance region strongly depends on the experimental data used in the adjustment process and an assessment of the experimental covariance data is of primary importance in determining the accuracy of evaluated cross section data. In this contribution, uncertainty components of experimental observables resulting from total and reaction cross section experiments are quantified by identifying the metrological parameters involved in the measurement, data reduction and analysis process. In addition, different methods that can be applied to propagate the covariance of the experimental observables (i.e. transmission and reaction yields) to the covariance of the resonance parameters are discussed and compared. The methods being discussed are: conventional uncertainty propagation, Monte Carlo sampling and marginalization. It is demonstrated that the final covariance matrix of the resonance parameters not only strongly depends on the type of experimental observables used in the adjustment process, the experimental conditions and the characteristics of the resonance structure, but also on the method that is used to propagate the covariances. Finally, a special data reduction concept and format is presented, which offers the possibility to store the full covariance information of experimental data in the EXFOR library and provides the information required to perform a full covariance evaluation.

  10. Markov modulated Poisson process models incorporating covariates for rainfall intensity.

    PubMed

    Thayakaran, R; Ramesh, N I

    2013-01-01

    Time series of rainfall bucket tip times at the Beaufort Park station, Bracknell, in the UK are modelled by a class of Markov modulated Poisson processes (MMPP) which may be thought of as a generalization of the Poisson process. Our main focus in this paper is to investigate the effects of including covariate information into the MMPP model framework on statistical properties. In particular, we look at three types of time-varying covariates namely temperature, sea level pressure, and relative humidity that are thought to be affecting the rainfall arrival process. Maximum likelihood estimation is used to obtain the parameter estimates, and likelihood ratio tests are employed in model comparison. Simulated data from the fitted model are used to make statistical inferences about the accumulated rainfall in the discrete time interval. Variability of the daily Poisson arrival rates is studied.

  11. Model Order Selection Rules for Covariance Structure Classification in Radar

    NASA Astrophysics Data System (ADS)

    Carotenuto, Vincenzo; De Maio, Antonio; Orlando, Danilo; Stoica, Petre

    2017-10-01

    The adaptive classification of the interference covariance matrix structure for radar signal processing applications is addressed in this paper. This represents a key issue because many detection architectures are synthesized assuming a specific covariance structure which may not necessarily coincide with the actual one due to the joint action of the system and environment uncertainties. The considered classification problem is cast in terms of a multiple hypotheses test with some nested alternatives and the theory of Model Order Selection (MOS) is exploited to devise suitable decision rules. Several MOS techniques, such as the Akaike, Takeuchi, and Bayesian information criteria are adopted and the corresponding merits and drawbacks are discussed. At the analysis stage, illustrating examples for the probability of correct model selection are presented showing the effectiveness of the proposed rules.

  12. A Review On Missing Value Estimation Using Imputation Algorithm

    NASA Astrophysics Data System (ADS)

    Armina, Roslan; Zain, Azlan Mohd; Azizah Ali, Nor; Sallehuddin, Roselina

    2017-09-01

    The presence of the missing value in the data set has always been a major problem for precise prediction. The method for imputing missing value needs to minimize the effect of incomplete data sets for the prediction model. Many algorithms have been proposed for countermeasure of missing value problem. In this review, we provide a comprehensive analysis of existing imputation algorithm, focusing on the technique used and the implementation of global or local information of data sets for missing value estimation. In addition validation method for imputation result and way to measure the performance of imputation algorithm also described. The objective of this review is to highlight possible improvement on existing method and it is hoped that this review gives reader better understanding of imputation method trend.

  13. Should multiple imputation be the method of choice for handling missing data in randomized trials?

    PubMed

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

    2016-01-01

    The use of multiple imputation has increased markedly in recent years, and journal reviewers may expect to see multiple imputation used to handle missing data. However in randomized trials, where treatment group is always observed and independent of baseline covariates, other approaches may be preferable. Using data simulation we evaluated multiple imputation, performed both overall and separately by randomized group, across a range of commonly encountered scenarios. We considered both missing outcome and missing baseline data, with missing outcome data induced under missing at random mechanisms. Provided the analysis model was correctly specified, multiple imputation produced unbiased treatment effect estimates, but alternative unbiased approaches were often more efficient. When the analysis model overlooked an interaction effect involving randomized group, multiple imputation produced biased estimates of the average treatment effect when applied to missing outcome data, unless imputation was performed separately by randomized group. Based on these results, we conclude that multiple imputation should not be seen as the only acceptable way to handle missing data in randomized trials. In settings where multiple imputation is adopted, we recommend that imputation is carried out separately by randomized group.

  14. Some thoughts on positive definiteness in the consideration of nuclear data covariance matrices

    SciTech Connect

    Geraldo, L.P.; Smith, D.L.

    1988-01-01

    Some basic mathematical features of covariance matrices are reviewed, particularly as they relate to the property of positive difiniteness. Physical implications of positive definiteness are also discussed. Consideration is given to an examination of the origins of non-positive definite matrices, to procedures which encourage the generation of positive definite matrices and to the testing of covariance matrices for positive definiteness. Attention is also given to certain problems associated with the construction of covariance matrices using information which is obtained from evaluated data files recorded in the ENDF format. Examples are provided to illustrate key points pertaining to each of the topic areas covered.

  15. Implementation of optimal phase-covariant cloning machines

    SciTech Connect

    Sciarrino, Fabio; De Martini, Francesco

    2007-07-15

    The optimal phase-covariant quantum cloning machine (PQCM) broadcasts the information associated to an input qubit into a multiqubit system, exploiting a partial a priori knowledge of the input state. This additional a priori information leads to a higher fidelity than for the universal cloning. The present article first analyzes different innovative schemes to implement the 1{yields}3 PQCM. The method is then generalized to any 1{yields}M machine for an odd value of M by a theoretical approach based on the general angular momentum formalism. Finally different experimental schemes based either on linear or nonlinear methods and valid for single photon polarization encoded qubits are discussed.

  16. Covariation in the human masticatory apparatus.

    PubMed

    Noback, Marlijn L; Harvati, Katerina

    2015-01-01

    Many studies have described shape variation of the modern human cranium in relation to subsistence; however, patterns of covariation within the masticatory apparatus (MA) remain largely unexplored. The patterns and intensity of shape covariation, and how this is related to diet, are essential for understanding the evolution of functional masticatory adaptations of the human cranium. Within a worldwide sample (n = 255) of 15 populations with different modes of subsistence, we use partial least squares analysis to study the relationships between three components of the MA: upper dental arch, masseter muscle, and temporalis muscle attachments. We show that the shape of the masseter muscle and the shape of the temporalis muscle clearly covary with one another, but that the shape of the dental arch seems to be rather independent of the masticatory muscles. On the contrary, when relative positioning, orientation, and size of the masticatory components is included in the analysis, the dental arch shows the highest covariation with the other cranial parts, indicating that these additional factors are more important than just shape with regard to covariation within the MA. Covariation patterns among these cranial regions differ mainly between hunting-fishing and gathering-agriculture groups, possibly relating to greater masticatory strains resulting from a large meat component in the diet. High-strain groups show stronger covariation between upper dental arch and masticatory muscle shape when compared with low-strain groups. These results help to provide a clearer understanding of constraints and interlinkage of shape variation within the human MA and allow for more realistic modeling and predictions in future biomechanical studies. © 2014 Wiley Periodicals, Inc.

  17. Cross-Section Covariance Data Processing with the AMPX Module PUFF-IV

    SciTech Connect

    Wiarda, Dorothea; Leal, Luiz C; Dunn, Michael E

    2011-01-01

    The ENDF community is endeavoring to release an updated version of the ENDF/B-VII library (ENDF/B-VII.1). In the new release several new evaluations containing covariance information have been added, as the community strives to add covariance information for use in programs like the TSUNAMI (Tools for Sensitivity and Uncertainty Analysis Methodology Implementation) sequence of SCALE (Ref 1). The ENDF/B formatted files are processed into libraries to be used in transport calculations using the AMPX code system (Ref 2) or the NJOY code system (Ref 3). Both codes contain modules to process covariance matrices: PUFF-IV for AMPX and ERRORR in the case of NJOY. While the cross section processing capability between the two code systems has been widely compared, the same is not true for the covariance processing. This paper compares the results for the two codes using the pre-release version of ENDF/B-VII.1.

  18. Bounds on the average causal effects in randomized trials with noncompliance by covariate adjustment.

    PubMed

    Shan, Na; Xu, Ping-Feng

    2016-11-01

    In randomized trials with noncompliance, causal effects cannot be identified without strong assumptions. Therefore, several authors have considered bounds on the causal effects. Applying an idea of VanderWeele (), Chiba () gave bounds on the average causal effects in randomized trials with noncompliance using the information on the randomized assignment, the treatment received and the outcome under monotonicity assumptions about covariates. But he did not consider any observed covariates. If there are some observed covariates such as age, gender, and race in a trial, we propose new bounds using the observed covariate information under some monotonicity assumptions similar to those of VanderWeele and Chiba. And we compare the three bounds in a real example. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  19. Eddy Covariance Method: Overview of General Guidelines and Conventional Workflow

    NASA Astrophysics Data System (ADS)

    Burba, G. G.; Anderson, D. J.; Amen, J. L.

    2007-12-01

    received from new users of the Eddy Covariance method and relevant instrumentation, and employs non-technical language to be of practical use to those new to this field. Information is provided on theory of the method (including state of methodology, basic derivations, practical formulations, major assumptions and sources of errors, error treatment, and use in non- traditional terrains), practical workflow (e.g., experimental design, implementation, data processing, and quality control), alternative methods and applications, and the most frequently overlooked details of the measurements. References and access to an extended 141-page Eddy Covariance Guideline in three electronic formats are also provided.

  20. Convex Banding of the Covariance Matrix

    PubMed Central

    Bien, Jacob; Bunea, Florentina; Xiao, Luo

    2016-01-01

    We introduce a new sparse estimator of the covariance matrix for high-dimensional models in which the variables have a known ordering. Our estimator, which is the solution to a convex optimization problem, is equivalently expressed as an estimator which tapers the sample covariance matrix by a Toeplitz, sparsely-banded, data-adaptive matrix. As a result of this adaptivity, the convex banding estimator enjoys theoretical optimality properties not attained by previous banding or tapered estimators. In particular, our convex banding estimator is minimax rate adaptive in Frobenius and operator norms, up to log factors, over commonly-studied classes of covariance matrices, and over more general classes. Furthermore, it correctly recovers the bandwidth when the true covariance is exactly banded. Our convex formulation admits a simple and efficient algorithm. Empirical studies demonstrate its practical effectiveness and illustrate that our exactly-banded estimator works well even when the true covariance matrix is only close to a banded matrix, confirming our theoretical results. Our method compares favorably with all existing methods, in terms of accuracy and speed. We illustrate the practical merits of the convex banding estimator by showing that it can be used to improve the performance of discriminant analysis for classifying sound recordings. PMID:28042189

  1. A sparse Ising model with covariates.

    PubMed

    Cheng, Jie; Levina, Elizaveta; Wang, Pei; Zhu, Ji

    2014-12-01

    There has been a lot of work fitting Ising models to multivariate binary data in order to understand the conditional dependency relationships between the variables. However, additional covariates are frequently recorded together with the binary data, and may influence the dependence relationships. Motivated by such a dataset on genomic instability collected from tumor samples of several types, we propose a sparse covariate dependent Ising model to study both the conditional dependency within the binary data and its relationship with the additional covariates. This results in subject-specific Ising models, where the subject's covariates influence the strength of association between the genes. As in all exploratory data analysis, interpretability of results is important, and we use ℓ1 penalties to induce sparsity in the fitted graphs and in the number of selected covariates. Two algorithms to fit the model are proposed and compared on a set of simulated data, and asymptotic results are established. The results on the tumor dataset and their biological significance are discussed in detail.

  2. Convex Banding of the Covariance Matrix.

    PubMed

    Bien, Jacob; Bunea, Florentina; Xiao, Luo

    2016-01-01

    We introduce a new sparse estimator of the covariance matrix for high-dimensional models in which the variables have a known ordering. Our estimator, which is the solution to a convex optimization problem, is equivalently expressed as an estimator which tapers the sample covariance matrix by a Toeplitz, sparsely-banded, data-adaptive matrix. As a result of this adaptivity, the convex banding estimator enjoys theoretical optimality properties not attained by previous banding or tapered estimators. In particular, our convex banding estimator is minimax rate adaptive in Frobenius and operator norms, up to log factors, over commonly-studied classes of covariance matrices, and over more general classes. Furthermore, it correctly recovers the bandwidth when the true covariance is exactly banded. Our convex formulation admits a simple and efficient algorithm. Empirical studies demonstrate its practical effectiveness and illustrate that our exactly-banded estimator works well even when the true covariance matrix is only close to a banded matrix, confirming our theoretical results. Our method compares favorably with all existing methods, in terms of accuracy and speed. We illustrate the practical merits of the convex banding estimator by showing that it can be used to improve the performance of discriminant analysis for classifying sound recordings.

  3. Estimated Environmental Exposures for MISSE-3 and MISSE-4

    NASA Technical Reports Server (NTRS)

    Pippin, Gary; Normand, Eugene; Finckenor, Miria

    2008-01-01

    Both modeling techniques and a variety of measurements and observations were used to characterize the environmental conditions experienced by the specimens flown on the MISSE-3 (Materials International Space Station Experiment) and MISSE-4 space flight experiments. On August 3, 2006, astronauts Jeff Williams and Thomas Reiter attached MISSE-3 and -4 to the Quest airlock on ISS, where these experiments were exposed to atomic oxygen (AO), ultraviolet (UV) radiation, particulate radiation, thermal cycling, meteoroid/space debris impact, and the induced environment of an active space station. They had been flown to ISS during the July 2006 STS-121 mission. The two suitcases were oriented so that one side faced the ram direction and one side remained shielded from the atomic oxygen. On August 18,2007, astronauts Clay Anderson and Dave Williams retrieved MISSE-3 and-4 and returned them to Earth at the end of the STS-118 mission. Quantitative values are provided when possible for selected environmental factors. A meteoroid/debris impact survey was performed prior to de-integration at Langley Research Center. AO fluences were calculated based on mass loss and thickness loss of thin polymeric films of known AO reactivity. Radiation was measured with thermoluminescent detectors. Visual inspections under ambient and "black-light" at NASA LaRC, together with optical measurements on selected specimens, were the basis for the initial contamination level assessment.

  4. Estimated Environmental Exposures for MISSE-3 and MISSE-4

    NASA Technical Reports Server (NTRS)

    Pippin, Gary; Normand, Eugene; Finckenor, Miria

    2008-01-01

    Both modeling techniques and a variety of measurements and observations were used to characterize the environmental conditions experienced by the specimens flown on the MISSE-3 (Materials International Space Station Experiment) and MISSE-4 space flight experiments. On August 3, 2006, astronauts Jeff Williams and Thomas Reiter attached MISSE-3 and -4 to the Quest airlock on ISS, where these experiments were exposed to atomic oxygen (AO), ultraviolet (UV) radiation, particulate radiation, thermal cycling, meteoroid/space debris impact, and the induced environment of an active space station. They had been flown to ISS during the July 2006 STS-121 mission. The two suitcases were oriented so that one side faced the ram direction and one side remained shielded from the atomic oxygen. On August 18,2007, astronauts Clay Anderson and Dave Williams retrieved MISSE-3 and-4 and returned them to Earth at the end of the STS-118 mission. Quantitative values are provided when possible for selected environmental factors. A meteoroid/debris impact survey was performed prior to de-integration at Langley Research Center. AO fluences were calculated based on mass loss and thickness loss of thin polymeric films of known AO reactivity. Radiation was measured with thermoluminescent detectors. Visual inspections under ambient and "black-light" at NASA LaRC, together with optical measurements on selected specimens, were the basis for the initial contamination level assessment.

  5. Progress on Nuclear Data Covariances: AFCI-1.2 Covariance Library

    SciTech Connect

    Oblozinsky,P.; Oblozinsky,P.; Mattoon,C.M.; Herman,M.; Mughabghab,S.F.; Pigni,M.T.; Talou,P.; Hale,G.M.; Kahler,A.C.; Kawano,T.; Little,R.C.; Young,P.G

    2009-09-28

    Improved neutron cross section covariances were produced for 110 materials including 12 light nuclei (coolants and moderators), 78 structural materials and fission products, and 20 actinides. Improved covariances were organized into AFCI-1.2 covariance library in 33-energy groups, from 10{sup -5} eV to 19.6 MeV. BNL contributed improved covariance data for the following materials: {sup 23}Na and {sup 55}Mn where more detailed evaluation was done; improvements in major structural materials {sup 52}Cr, {sup 56}Fe and {sup 58}Ni; improved estimates for remaining structural materials and fission products; improved covariances for 14 minor actinides, and estimates of mubar covariances for {sup 23}Na and {sup 56}Fe. LANL contributed improved covariance data for {sup 235}U and {sup 239}Pu including prompt neutron fission spectra and completely new evaluation for {sup 240}Pu. New R-matrix evaluation for {sup 16}O including mubar covariances is under completion. BNL assembled the library and performed basic testing using improved procedures including inspection of uncertainty and correlation plots for each material. The AFCI-1.2 library was released to ANL and INL in August 2009.

  6. Mathematics Teachers' Covariational Reasoning Levels and Predictions about Students' Covariational Reasoning Abilities

    ERIC Educational Resources Information Center

    Zeytun, Aysel Sen; Cetinkaya, Bulent; Erbas, Ayhan Kursat

    2010-01-01

    Various studies suggest that covariational reasoning plays an important role on understanding the fundamental ideas of calculus and modeling dynamic functional events. The purpose of this study was to investigate a group of mathematics teachers' covariational reasoning abilities and predictions about their students. Data were collected through…

  7. Terrestrial gross carbon dioxide uptake: global distribution and covariation with climate.

    PubMed

    Beer, Christian; Reichstein, Markus; Tomelleri, Enrico; Ciais, Philippe; Jung, Martin; Carvalhais, Nuno; Rödenbeck, Christian; Arain, M Altaf; Baldocchi, Dennis; Bonan, Gordon B; Bondeau, Alberte; Cescatti, Alessandro; Lasslop, Gitta; Lindroth, Anders; Lomas, Mark; Luyssaert, Sebastiaan; Margolis, Hank; Oleson, Keith W; Roupsard, Olivier; Veenendaal, Elmar; Viovy, Nicolas; Williams, Christopher; Woodward, F Ian; Papale, Dario

    2010-08-13

    Terrestrial gross primary production (GPP) is the largest global CO(2) flux driving several ecosystem functions. We provide an observation-based estimate of this flux at 123 +/- 8 petagrams of carbon per year (Pg C year(-1)) using eddy covariance flux data and various diagnostic models. Tropical forests and savannahs account for 60%. GPP over 40% of the vegetated land is associated with precipitation. State-of-the-art process-oriented biosphere models used for climate predictions exhibit a large between-model variation of GPP's latitudinal patterns and show higher spatial correlations between GPP and precipitation, suggesting the existence of missing processes or feedback mechanisms which attenuate the vegetation response to climate. Our estimates of spatially distributed GPP and its covariation with climate can help improve coupled climate-carbon cycle process models.

  8. Modeling zero-inflated count data using a covariate-dependent random effect model.

    PubMed

    Wong, Kin-Yau; Lam, K F

    2013-04-15

    In various medical related researches, excessive zeros, which make the standard Poisson regression model inadequate, often exist in count data. We proposed a covariate-dependent random effect model to accommodate the excess zeros and the heterogeneity in the population simultaneously. This work is motivated by a data set from a survey on the dental health status of Hong Kong preschool children where the response variable is the number of decayed, missing, or filled teeth. The random effect has a sound biological interpretation as the overall oral health status or other personal qualities of an individual child that is unobserved and unable to be quantified easily. The overall measure of oral health status, responsible for accommodating the excessive zeros and also the heterogeneity among the children, is covariate dependent. This covariate-dependent random effect model allows one to distinguish whether a potential covariate has an effect on the conceived overall oral health condition of the children, that is, the random effect, or has a direct effect on the magnitude of the counts, or both. We proposed a multiple imputation approach for estimation of the parameters. We discussed the choice of the imputation size. We evaluated the performance of the proposed estimation method through simulation studies, and we applied the model and method to the dental data.

  9. Defining habitat covariates in camera-trap based occupancy studies.

    PubMed

    Niedballa, Jürgen; Sollmann, Rahel; bin Mohamed, Azlan; Bender, Johannes; Wilting, Andreas

    2015-11-24

    In species-habitat association studies, both the type and spatial scale of habitat covariates need to match the ecology of the focal species. We assessed the potential of high-resolution satellite imagery for generating habitat covariates using camera-trapping data from Sabah, Malaysian Borneo, within an occupancy framework. We tested the predictive power of covariates generated from satellite imagery at different resolutions and extents (focal patch sizes, 10-500 m around sample points) on estimates of occupancy patterns of six small to medium sized mammal species/species groups. High-resolution land cover information had considerably more model support for small, patchily distributed habitat features, whereas it had no advantage for large, homogeneous habitat features. A comparison of different focal patch sizes including remote sensing data and an in-situ measure showed that patches with a 50-m radius had most support for the target species. Thus, high-resolution satellite imagery proved to be particularly useful in heterogeneous landscapes, and can be used as a surrogate for certain in-situ measures, reducing field effort in logistically challenging environments. Additionally, remote sensed data provide more flexibility in defining appropriate spatial scales, which we show to impact estimates of wildlife-habitat associations.

  10. Defining habitat covariates in camera-trap based occupancy studies

    PubMed Central

    Niedballa, Jürgen; Sollmann, Rahel; Mohamed, Azlan bin; Bender, Johannes; Wilting, Andreas

    2015-01-01

    In species-habitat association studies, both the type and spatial scale of habitat covariates need to match the ecology of the focal species. We assessed the potential of high-resolution satellite imagery for generating habitat covariates using camera-trapping data from Sabah, Malaysian Borneo, within an occupancy framework. We tested the predictive power of covariates generated from satellite imagery at different resolutions and extents (focal patch sizes, 10–500 m around sample points) on estimates of occupancy patterns of six small to medium sized mammal species/species groups. High-resolution land cover information had considerably more model support for small, patchily distributed habitat features, whereas it had no advantage for large, homogeneous habitat features. A comparison of different focal patch sizes including remote sensing data and an in-situ measure showed that patches with a 50-m radius had most support for the target species. Thus, high-resolution satellite imagery proved to be particularly useful in heterogeneous landscapes, and can be used as a surrogate for certain in-situ measures, reducing field effort in logistically challenging environments. Additionally, remote sensed data provide more flexibility in defining appropriate spatial scales, which we show to impact estimates of wildlife-habitat associations. PMID:26596779

  11. Recent Advances with the AMPX Covariance Processing Capabilities in PUFF-IV

    SciTech Connect

    Wiarda, D. Arbanas, G.; Leal, L.; Dunn, M.E.

    2008-12-15

    The program PUFF-IV is used to process resonance parameter covariance information given in ENDF/B File 32 and point wise covariance matrices given in ENDF/B File 33 into group-averaged covariances matrices on a user-supplied group structure. For large resonance covariance matrices, found for example in {sup 235}U, the execution time of PUFF-IV can be quite long. Recently the code was modified to take advantage of Basic Linear Algebra Subprograms (BLAS) routines for the most time-consuming matrix multiplications. This led to a substantial decrease in execution time. This faster processing capability allowed us to investigate the conversion of File 32 data into File 33 data using a larger number of user-defined groups. While conversion substantially reduces the ENDF/B file size requirements for evaluations with a large number of resonances, a trade-off is made between the number of groups used to represent the resonance parameter covariance as a point wise covariance matrix and the file size. We are also investigating a hybrid version of the conversion, in which the low-energy part of the File 32 resonance parameter covariances matrix is retained and the correlations with higher energies as well as the high energy part are given in File 33.

  12. Recent Advances with the AMPX Covariance Processing Capabilities in PUFF-IV

    SciTech Connect

    Wiarda, Dorothea; Arbanas, Goran; Leal, Luiz C; Dunn, Michael E

    2008-01-01

    The program PUFF-IV is used to process resonance parameter covariance information given in ENDF/B File 32 and point-wise covariance matrices given in ENDF/B File 33 into group-averaged covariances matrices on a user-supplied group structure. For large resonance covariance matrices, found for example in 235U, the execution time of PUFF-IV can be quite long. Recently the code was modified to take advandage of Basic Linear Algebra Subprograms (BLAS) routines for the most time-consuming matrix multiplications. This led to a substantial decrease in execution time. This faster processing capability allowed us to investigate the conversion of File 32 data into File 33 data using a larger number of user-defined groups. While conversion substantially reduces the ENDF/B file size requirements for evaluations with a large number of resonances, a trade-off is made between the number of groups used to represent the resonance parameter covariance as a point-wise covariance matrix and the file size. We are also investigating a hybrid version of the conversion, in which the low-energy part of the File 32 resonance parameter covariances matrix is retained and the correlations with higher energies as well as the high energy part are given in File 33.

  13. Comparing Smoothing Techniques for Fitting the Nonlinear Effect of Covariate in Cox Models.

    PubMed

    Roshani, Daem; Ghaderi, Ebrahim

    2016-02-01

    Cox model is a popular model in survival analysis, which assumes linearity of the covariate on the log hazard function, While continuous covariates can affect the hazard through more complicated nonlinear functional forms and therefore, Cox models with continuous covariates are prone to misspecification due to not fitting the correct functional form for continuous covariates. In this study, a smooth nonlinear covariate effect would be approximated by different spline functions. We applied three flexible nonparametric smoothing techniques for nonlinear covariate effect in the Cox models: penalized splines, restricted cubic splines and natural splines. Akaike information criterion (AIC) and degrees of freedom were used to smoothing parameter selection in penalized splines model. The ability of nonparametric methods was evaluated to recover the true functional form of linear, quadratic and nonlinear functions, using different simulated sample sizes. Data analysis was carried out using R 2.11.0 software and significant levels were considered 0.05. Based on AIC, the penalized spline method had consistently lower mean square error compared to others to selection of smoothed parameter. The same result was obtained with real data. Penalized spline smoothing method, with AIC to smoothing parameter selection, was more accurate in evaluate of relation between covariate and log hazard function than other methods.

  14. Comparing Smoothing Techniques for Fitting the Nonlinear Effect of Covariate in Cox Models

    PubMed Central

    Roshani, Daem; Ghaderi, Ebrahim

    2016-01-01

    Background and Objective: Cox model is a popular model in survival analysis, which assumes linearity of the covariate on the log hazard function, While continuous covariates can affect the hazard through more complicated nonlinear functional forms and therefore, Cox models with continuous covariates are prone to misspecification due to not fitting the correct functional form for continuous covariates. In this study, a smooth nonlinear covariate effect would be approximated by different spline functions. Material and Methods: We applied three flexible nonparametric smoothing techniques for nonlinear covariate effect in the Cox models: penalized splines, restricted cubic splines and natural splines. Akaike information criterion (AIC) and degrees of freedom were used to smoothing parameter selection in penalized splines model. The ability of nonparametric methods was evaluated to recover the true functional form of linear, quadratic and nonlinear functions, using different simulated sample sizes. Data analysis was carried out using R 2.11.0 software and significant levels were considered 0.05. Results: Based on AIC, the penalized spline method had consistently lower mean square error compared to others to selection of smoothed parameter. The same result was obtained with real data. Conclusion: Penalized spline smoothing method, with AIC to smoothing parameter selection, was more accurate in evaluate of relation between covariate and log hazard function than other methods. PMID:27041809

  15. Filling the missing cone in protein electron crystallography.

    PubMed

    Dorset, D L

    1999-07-15

    The hyper-resolution property of the Sayre equation is explored for extrapolating amplitudes and phases into the missing cone of data left after tilting a representative protein (rubredoxin) to restricted limits in the electron microscope. At 0.6 nm resolution, a reasonable prediction of crystallographic phases can be made to reconstruct the lost information. Best results are obtained if the goniometer tilt value is greater than approximately +/-60 degrees, but some missing information can be restored if the tilt is restricted to +/-45 degrees.

  16. Covariant Lyapunov vectors for rigid disk systems.

    PubMed

    Bosetti, Hadrien; Posch, Harald A

    2010-10-05

    We carry out extensive computer simulations to study the Lyapunov instability of a two-dimensional hard-disk system in a rectangular box with periodic boundary conditions. The system is large enough to allow the formation of Lyapunov modes parallel to the x-axis of the box. The Oseledec splitting into covariant subspaces of the tangent space is considered by computing the full set of covariant perturbation vectors co-moving with the flow in tangent space. These vectors are shown to be transversal, but generally not orthogonal to each other. Only the angle between covariant vectors associated with immediate adjacent Lyapunov exponents in the Lyapunov spectrum may become small, but the probability of this angle to vanish approaches zero. The stable and unstable manifolds are transverse to each other and the system is hyperbolic.

  17. Incorporating covariates in skewed functional data models.

    PubMed

    Li, Meng; Staicu, Ana-Maria; Bondell, Howard D

    2015-07-01

    We introduce a class of covariate-adjusted skewed functional models (cSFM) designed for functional data exhibiting location-dependent marginal distributions. We propose a semi-parametric copula model for the pointwise marginal distributions, which are allowed to depend on covariates, and the functional dependence, which is assumed covariate invariant. The proposed cSFM framework provides a unifying platform for pointwise quantile estimation and trajectory prediction. We consider a computationally feasible procedure that handles densely as well as sparsely observed functional data. The methods are examined numerically using simulations and is applied to a new tractography study of multiple sclerosis. Furthermore, the methodology is implemented in the R package cSFM, which is publicly available on CRAN.

  18. FAST NEUTRON COVARIANCES FOR EVALUATED DATA FILES.

    SciTech Connect

    HERMAN, M.; OBLOZINSKY, P.; ROCHMAN, D.; KAWANO, T.; LEAL, L.

    2006-06-05

    We describe implementation of the KALMAN code in the EMPIRE system and present first covariance data generated for Gd and Ir isotopes. A complete set of covariances, in the full energy range, was produced for the chain of 8 Gadolinium isotopes for total, elastic, capture, total inelastic (MT=4), (n,2n), (n,p) and (n,alpha) reactions. Our correlation matrices, based on combination of model calculations and experimental data, are characterized by positive mid-range and negative long-range correlations. They differ from the model-generated covariances that tend to show strong positive long-range correlations and those determined solely from experimental data that result in nearly diagonal matrices. We have studied shapes of correlation matrices obtained in the calculations and interpreted them in terms of the underlying reaction models. An important result of this study is the prediction of narrow energy ranges with extremely small uncertainties for certain reactions (e.g., total and elastic).

  19. Covariant Lyapunov vectors for rigid disk systems

    PubMed Central

    Bosetti, Hadrien; Posch, Harald A.

    2010-01-01

    We carry out extensive computer simulations to study the Lyapunov instability of a two-dimensional hard-disk system in a rectangular box with periodic boundary conditions. The system is large enough to allow the formation of Lyapunov modes parallel to the x-axis of the box. The Oseledec splitting into covariant subspaces of the tangent space is considered by computing the full set of covariant perturbation vectors co-moving with the flow in tangent space. These vectors are shown to be transversal, but generally not orthogonal to each other. Only the angle between covariant vectors associated with immediate adjacent Lyapunov exponents in the Lyapunov spectrum may become small, but the probability of this angle to vanish approaches zero. The stable and unstable manifolds are transverse to each other and the system is hyperbolic. PMID:21151326

  20. Gram-Schmidt algorithms for covariance propagation

    NASA Technical Reports Server (NTRS)

    Thornton, C. L.; Bierman, G. J.

    1977-01-01

    This paper addresses the time propagation of triangular covariance factors. Attention is focused on the square-root free factorization, P = UD(transpose of U), where U is unit upper triangular and D is diagonal. An efficient and reliable algorithm for U-D propagation is derived which employs Gram-Schmidt orthogonalization. Partitioning the state vector to distinguish bias and coloured process noise parameters increase mapping efficiency. Cost comparisons of the U-D, Schmidt square-root covariance and conventional covariance propagation methods are made using weighted arithmetic operation counts. The U-D time update is shown to be less costly than the Schmidt method; and, except in unusual circumstances, it is within 20% of the cost of conventional propagation.

  1. Covariance for Cone and Wedge Complete Filling

    NASA Astrophysics Data System (ADS)

    Rascón, C.; Parry, A. O.

    2005-03-01

    Interfacial phenomena associated with fluid adsorption in two dimensional systems have recently been shown to exhibit hidden symmetries, or covariances, which precisely relate local adsorption properties in different confining geometries. We show that covariance also occurs in three-dimensional systems and is likely to be verifiable experimentally and in Ising model simulations studies. Specifically, we study complete wetting in wedge (W) and cone (C) geometries as bulk coexistence is approached and show that the equilibrium midpoint heights satisfy lc(h,α)=lw(h/2,α), where h measures the partial pressure and α is the tilt angle. This covariance is valid for both short-ranged and long-ranged intermolecular forces and identifies both leading and next-to-leading-order critical exponents and amplitudes in the confining geometries.

  2. Structural damage detection based on covariance of covariance matrix with general white noise excitation

    NASA Astrophysics Data System (ADS)

    Hui, Yi; Law, Siu Seong; Ku, Chiu Jen

    2017-02-01

    Covariance of the auto/cross-covariance matrix based method is studied for the damage identification of a structure with illustrations on its advantages and limitations. The original method is extended for structures under direct white noise excitations. The auto/cross-covariance function of the measured acceleration and its corresponding derivatives are formulated analytically, and the method is modified in two new strategies to enable successful identification with much fewer sensors. Numerical examples are adopted to illustrate the improved method, and the effects of sampling frequency and sampling duration are discussed. Results show that the covariance of covariance calculated from responses of higher order modes of a structure play an important role to the accurate identification of local damage in a structure.

  3. Bayesian source term determination with unknown covariance of measurements

    NASA Astrophysics Data System (ADS)

    Belal, Alkomiet; Tichý, Ondřej; Šmídl, Václav

    2017-04-01

    Determination of a source term of release of a hazardous material into the atmosphere is a very important task for emergency response. We are concerned with the problem of estimation of the source term in the conventional linear inverse problem, y = Mx, where the relationship between the vector of observations y is described using the source-receptor-sensitivity (SRS) matrix M and the unknown source term x. Since the system is typically ill-conditioned, the problem is recast as an optimization problem minR,B(y - Mx)TR-1(y - Mx) + xTB-1x. The first term minimizes the error of the measurements with covariance matrix R, and the second term is a regularization of the source term. There are different types of regularization arising for different choices of matrices R and B, for example, Tikhonov regularization assumes covariance matrix B as the identity matrix multiplied by scalar parameter. In this contribution, we adopt a Bayesian approach to make inference on the unknown source term x as well as unknown R and B. We assume prior on x to be a Gaussian with zero mean and unknown diagonal covariance matrix B. The covariance matrix of the likelihood R is also unknown. We consider two potential choices of the structure of the matrix R. First is the diagonal matrix and the second is a locally correlated structure using information on topology of the measuring network. Since the inference of the model is intractable, iterative variational Bayes algorithm is used for simultaneous estimation of all model parameters. The practical usefulness of our contribution is demonstrated on an application of the resulting algorithm to real data from the European Tracer Experiment (ETEX). This research is supported by EEA/Norwegian Financial Mechanism under project MSMT-28477/2014 Source-Term Determination of Radionuclide Releases by Inverse Atmospheric Dispersion Modelling (STRADI).

  4. Patient understanding of oral contraceptive pill instructions related to missed pills: a systematic review.

    PubMed

    Zapata, Lauren B; Steenland, Maria W; Brahmi, Dalia; Marchbanks, Polly A; Curtis, Kathryn M

    2013-05-01

    Instructions on what to do after pills are missed are critical to reducing unintended pregnancies resulting from patient non-adherence to oral contraceptive (OC) regimens. Missed pill instructions have previously been criticized for being too complex, lacking a definition of what is meant by "missed pills," and for being confusing to women who may not know the estrogen content of their formulation. To help inform the development of missed pill guidance to be included in the forthcoming US Selected Practice Recommendations, the objective of this systematic review was to evaluate the evidence on patient understanding of missed pill instructions. We searched the PubMed database for peer-reviewed articles that examined patient understanding of OC pill instructions that were published in any language from inception of the database through March 2012. We included studies that examined women's knowledge and understanding of missed pill instructions after exposure to some written material (e.g., patient package insert, brochure), as well as studies that compared different types of missed pill instructions on women's comprehension. We used standard abstract forms and grading systems to summarize and assess the quality of the evidence. From 1620 articles, nine studies met our inclusion criteria. Evidence from one randomized controlled trial (RCT) and two descriptive studies found that more women knew what to do after missing 1 pill than after missing 2 or 3 pills (Level I, good, to Level II-3, poor), and two descriptive studies found that more women knew what to do after missing 2 pills than after missing 3 pills (Level II-3, fair). Data from two descriptive studies documented the difficulty women have understanding missed pill instructions contained in patient package inserts (Level II-3, poor), and evidence from two RCTs found that providing written brochures with information on missed pill instructions in addition to contraceptive counseling significantly improved

  5. Parametric number covariance in quantum chaotic spectra.

    PubMed

    Vinayak; Kumar, Sandeep; Pandey, Akhilesh

    2016-03-01

    We study spectral parametric correlations in quantum chaotic systems and introduce the number covariance as a measure of such correlations. We derive analytic results for the classical random matrix ensembles using the binary correlation method and obtain compact expressions for the covariance. We illustrate the universality of this measure by presenting the spectral analysis of the quantum kicked rotors for the time-reversal invariant and time-reversal noninvariant cases. A local version of the parametric number variance introduced earlier is also investigated.

  6. Covariant version of Verlinde's emergent gravity

    NASA Astrophysics Data System (ADS)

    Hossenfelder, Sabine

    2017-06-01

    A generally covariant version of Erik Verlinde's emergent gravity model is proposed. The Lagrangian constructed here allows an improved interpretation of the underlying mechanism. It suggests that de Sitter space is filled with a vector field that couples to baryonic matter and, by dragging on it, creates an effect similar to dark matter. We solve the covariant equation of motion in the background of a Schwarzschild space-time and obtain correction terms to the noncovariant expression. Furthermore, we demonstrate that the vector field can also mimic dark energy.

  7. Estimating a Missing Examination Score

    ERIC Educational Resources Information Center

    Loui, Michael C.; Lin, Athena

    2017-01-01

    In science and engineering courses, instructors administer multiple examinations as major assessments of students' learning. When a student is unable to take an exam, the instructor might estimate the missing exam score to calculate the student's course grade. Using exam score data from multiple offerings of two large courses at a public…

  8. Filling in the Missing Links.

    ERIC Educational Resources Information Center

    Kemper, Susan

    1982-01-01

    Describes two experiments where readers were asked to restore missing actions and physical and mental states to short narratives. Although some deletions resulted in violations of the event chain taxonomy while others did not, in both cases readers used knowledge of possible causal sequences to repair gaps in stories. (Author/MES)

  9. The Board's missing link.

    PubMed

    Montgomery, Cynthia A; Kaufman, Rhonda

    2003-03-01

    If a dam springs several leaks, there are various ways to respond. One could assiduously plug the holes, for instance. Or one could correct the underlying weaknesses, a more sensible approach. When it comes to corporate governance, for too long we have relied on the first approach. But the causes of many governance problems lie well below the surface--specifically, in critical relationships that are not structured to support the players involved. In other words, the very foundation of the system is flawed. And unless we correct the structural problems, surface changes are unlikely to have a lasting impact. When shareholders, management, and the board of directors work together as a system, they provide a powerful set of checks and balances. But the relationship between shareholders and directors is fraught with weaknesses, undermining the entire system's equilibrium. As the authors explain, the exchange of information between these two players is poor. Directors, though elected by shareholders to serve as their agents, aren't individually accountable to the investors. And shareholders--for a variety of reasons--have failed to exert much influence over boards. In the end, directors are left with the Herculean task of faithfully representing shareholders whose preferences are unclear, and shareholders have little say about who represents them and few mechanisms through which to create change. The authors suggest several ways to improve the relationship between shareholders and directors: Increase board accountability by recording individual directors' votes on key corporate resolutions; separate the positions of chairman and CEO; reinvigorate shareholders; and give boards funding to pay for outside experts who can provide perspective on crucial issues.

  10. Monitoring: The missing piece

    SciTech Connect

    Bjorkland, Ronald

    2013-11-15

    The U.S. National Environmental Policy Act (NEPA) of 1969 heralded in an era of more robust attention to environmental impacts resulting from larger scale federal projects. The number of other countries that have adopted NEPA's framework is evidence of the appeal of this type of environmental legislation. Mandates to review environmental impacts, identify alternatives, and provide mitigation plans before commencement of the project are at the heart of NEPA. Such project reviews have resulted in the development of a vast number of reports and large volumes of project-specific data that potentially can be used to better understand the components and processes of the natural environment and provide guidance for improved and efficient environmental protection. However, the environmental assessment (EA) or the more robust and intensive environmental impact statement (EIS) that are required for most major projects more frequently than not are developed to satisfy the procedural aspects of the NEPA legislation while they fail to provide the needed guidance for improved decision-making. While NEPA legislation recommends monitoring of project activities, this activity is not mandated, and in those situations where it has been incorporated, the monitoring showed that the EIS was inaccurate in direction and/or magnitude of the impact. Many reviews of NEPA have suggested that monitoring all project phases, from the design through the decommissioning, should be incorporated. Information gathered though a well-developed monitoring program can be managed in databases and benefit not only the specific project but would provide guidance how to better design and implement future activities designed to protect and enhance the natural environment. -- Highlights: • NEPA statutes created profound environmental protection legislative framework. • Contrary to intent, NEPA does not provide for definitive project monitoring. • Robust project monitoring is essential for enhanced

  11. Missing data: prevalence and reporting practices.

    PubMed

    Bodner, Todd E

    2006-12-01

    Results are described for a survey assessing prevalence of missing data and reporting practices in studies with missing data in a random sample of empirical research journal articles from the PsychINFO database for the year 1999, two years prior to the publication of a special section on missing data in Psychological Methods. Analysis indicates missing data problems were found in about one-third of the studies. Further, analytical methods and reporting practices varied widely for studies with missing data. One may consider these results as baseline data to assess progress as reporting standards evolve for studies with missing data. Some potential reporting standards are discussed.

  12. Solving the differential biochemical Jacobian from metabolomics covariance data.

    PubMed

    Nägele, Thomas; Mair, Andrea; Sun, Xiaoliang; Fragner, Lena; Teige, Markus; Weckwerth, Wolfram

    2014-01-01

    High-throughput molecular analysis has become an integral part in organismal systems biology. In contrast, due to a missing systematic linkage of the data with functional and predictive theoretical models of the underlying metabolic network the understanding of the resulting complex data sets is lacking far behind. Here, we present a biomathematical method addressing this problem by using metabolomics data for the inverse calculation of a biochemical Jacobian matrix, thereby linking computer-based genome-scale metabolic reconstruction and in vivo metabolic dynamics. The incongruity of metabolome coverage by typical metabolite profiling approaches and genome-scale metabolic reconstruction was solved by the design of superpathways to define a metabolic interaction matrix. A differential biochemical Jacobian was calculated using an approach which links this metabolic interaction matrix and the covariance of metabolomics data satisfying a Lyapunov equation. The predictions of the differential Jacobian from real metabolomic data were found to be correct by testing the corresponding enzymatic activities. Moreover it is demonstrated that the predictions of the biochemical Jacobian matrix allow for the design of parameter optimization strategies for ODE-based kinetic models of the system. The presented concept combines dynamic modelling strategies with large-scale steady state profiling approaches without the explicit knowledge of individual kinetic parameters. In summary, the presented strategy allows for the identification of regulatory key processes in the biochemical network directly from metabolomics data and is a fundamental achievement for the functional interpretation of metabolomics data.

  13. Solving the Differential Biochemical Jacobian from Metabolomics Covariance Data

    PubMed Central

    Nägele, Thomas; Mair, Andrea; Sun, Xiaoliang; Fragner, Lena; Teige, Markus; Weckwerth, Wolfram

    2014-01-01

    High-throughput molecular analysis has become an integral part in organismal systems biology. In contrast, due to a missing systematic linkage of the data with functional and predictive theoretical models of the underlying metabolic network the understanding of the resulting complex data sets is lacking far behind. Here, we present a biomathematical method addressing this problem by using metabolomics data for the inverse calculation of a biochemical Jacobian matrix, thereby linking computer-based genome-scale metabolic reconstruction and in vivo metabolic dynamics. The incongruity of metabolome coverage by typical metabolite profiling approaches and genome-scale metabolic reconstruction was solved by the design of superpathways to define a metabolic interaction matrix. A differential biochemical Jacobian was calculated using an approach which links this metabolic interaction matrix and the covariance of metabolomics data satisfying a Lyapunov equation. The predictions of the differential Jacobian from real metabolomic data were found to be correct by testing the corresponding enzymatic activities. Moreover it is demonstrated that the predictions of the biochemical Jacobian matrix allow for the design of parameter optimization strategies for ODE-based kinetic models of the system. The presented concept combines dynamic modelling strategies with large-scale steady state profiling approaches without the explicit knowledge of individual kinetic parameters. In summary, the presented strategy allows for the identification of regulatory key processes in the biochemical network directly from metabolomics data and is a fundamental achievement for the functional interpretation of metabolomics data. PMID:24695071

  14. Nested-observation error covariance matrix in 1dVAR approach

    NASA Astrophysics Data System (ADS)

    Park, C.; Heidinger, A. K.

    2010-12-01

    Cloud-top Height (CTH) information is critical parameter derived from satellites. In recognition of this, CTH algorithms were included in the algorithm package developed by the NOAA GOES-R Algorithm Working Group (AWG) for application to the Advanced Baseline lmager (ABI). The ABI Cloud Height Algorithm (ACHA) uses an optimal estimation (OE) method with nested observations from three infrared channels (11, 12, 13.3 μm). The retrieval accuracy of this approach is highly dependent on the proper specification of the background error covariance and the observation error covariance matrices. The study introduces a new method to estimate all elements (including off-diagonal terms) of the observation error and its covariance matrix. In this study, we will demonstrate how proper specification of the off-diagonal terms in observation error covariance matrix with nested observation data set improves the ACHA approach. This improvement will be demonstrated through comparisons with CALIPSO, CLOUDSAT and MODIS CTH products.

  15. Partial covariance based functional connectivity computation using Ledoit-Wolf covariance regularization

    PubMed Central

    Brier, Matthew R.; Mitra, Anish; McCarthy, John E.; Ances, Beau M.; Snyder, Abraham Z.

    2015-01-01

    Functional connectivity refers to shared signals among brain regions and is typically assessed in a task free state. Functional connectivity commonly is quantified between signal pairs using Pearson correlation. However, resting-state fMRI is a multivariate process exhibiting a complicated covariance structure. Partial covariance assesses the unique variance shared between two brain regions excluding any widely shared variance, hence is appropriate for the analysis of multivariate fMRI datasets. However, calculation of partial covariance requires inversion of the covariance matrix, which, in most functional connectivity studies, is not invertible owing to rank deficiency. Here we apply Ledoit-Wolf shrinkage (L2 regularization) to invert the high dimensional BOLD covariance matrix. We investigate the network organization and brain-state dependence of partial covariance-based functional connectivity. Although RSNs are conventionally defined in terms of shared variance, removal of widely shared variance, surprisingly, improved the separation of RSNs in a spring embedded graphical model. This result suggests that pair-wise unique shared variance plays a heretofore unrecognized role in RSN covariance organization. In addition, application of partial correlation to fMRI data acquired in the eyes open vs. eyes closed states revealed focal changes in uniquely shared variance between the thalamus and visual cortices. This result suggests that partial correlation of resting state BOLD time series reflect functional processes in addition to structural connectivity. PMID:26208872

  16. Partial covariance based functional connectivity computation using Ledoit-Wolf covariance regularization.

    PubMed

    Brier, Matthew R; Mitra, Anish; McCarthy, John E; Ances, Beau M; Snyder, Abraham Z

    2015-11-01

    Functional connectivity refers to shared signals among brain regions and is typically assessed in a task free state. Functional connectivity commonly is quantified between signal pairs using Pearson correlation. However, resting-state fMRI is a multivariate process exhibiting a complicated covariance structure. Partial covariance assesses the unique variance shared between two brain regions excluding any widely shared variance, hence is appropriate for the analysis of multivariate fMRI datasets. However, calculation of partial covariance requires inversion of the covariance matrix, which, in most functional connectivity studies, is not invertible owing to rank deficiency. Here we apply Ledoit-Wolf shrinkage (L2 regularization) to invert the high dimensional BOLD covariance matrix. We investigate the network organization and brain-state dependence of partial covariance-based functional connectivity. Although RSNs are conventionally defined in terms of shared variance, removal of widely shared variance, surprisingly, improved the separation of RSNs in a spring embedded graphical model. This result suggests that pair-wise unique shared variance plays a heretofore unrecognized role in RSN covariance organization. In addition, application of partial correlation to fMRI data acquired in the eyes open vs. eyes closed states revealed focal changes in uniquely shared variance between the thalamus and visual cortices. This result suggests that partial correlation of resting state BOLD time series reflect functional processes in addition to structural connectivity. Copyright © 2015 Elsevier Inc. All rights reserved.

  17. Covariant formulation of pion-nucleon scattering

    NASA Astrophysics Data System (ADS)

    Lahiff, A. D.; Afnan, I. R.

    A covariant model of elastic pion-nucleon scattering based on the Bethe-Salpeter equation is presented. The kernel consists of s- and u-channel nucleon and delta poles, along with rho and sigma exchange in the t-channel. A good fit is obtained to the s- and p-wave phase shifts up to the two-pion production threshold.

  18. Scale covariant gravitation. V. Kinetic theory

    SciTech Connect

    Hsieh, S.; Canuto, V.M.

    1981-09-01

    In this paper we construct a scale covariant kinetic theory for particles and photons. The mathematical framework of the theory is given by the tangent bundle of a Weyl manifold. The Liouville equation is then derived. Solutions corresponding to equilibrium distributions are presented and shown to yield thermodynamic results identical to the ones obtained previously.

  19. Analysis of Covariance: A Proposed Algorithm.

    ERIC Educational Resources Information Center

    Frigon, Jean-Yves; Laurencelle, Louis

    1993-01-01

    The statistical power of analysis of covariance (ANCOVA) and its advantages over simple analysis of variance are examined in some experimental situations, and an algorithm is proposed for its proper application. In nonrandomized experiments, an ANCOVA is generally not a good approach. (SLD)

  20. Economical phase-covariant cloning of qudits

    SciTech Connect

    Buscemi, Francesco; D'Ariano, Giacomo Mauro; Macchiavello, Chiara

    2005-04-01

    We derive the optimal N{yields}M phase-covariant quantum cloning for equatorial states in dimension d with M=kd+N, k integer. The cloning maps are optimal for both global and single-qudit fidelity. The map is achieved by an 'economical' cloning machine, which works without ancilla.

  1. Covariant brackets for particles and fields

    NASA Astrophysics Data System (ADS)

    Asorey, M.; Ciaglia, M.; di Cosmo, F.; Ibort, A.

    2017-06-01

    A geometrical approach to the covariant formulation of the dynamics of relativistic systems is introduced. A realization of Peierls brackets by means of a bivector field over the space of solutions of the Euler-Lagrange equations of a variational principle is presented. The method is illustrated with some relevant examples.

  2. Errors and near misses in digestive endoscopy units.

    PubMed

    Minoli, Giorgio; Borsato, Paolo; Colombo, Enrico; Bortoli, Aurora; Casetti, Tino; de Pretis, Giovanni; Ferraris, Luca; Lorenzini, Ivano; Meggio, Alberto; Meroni, Rudy; Piazzi, Lucia; Terruzzi, Vittorio

    2012-11-01

    Not much is known about errors and near misses in digestive endoscopy. To verify whether an incident report, with certain facilitating features, gives useful information about unintended events, only excluding errors in medical diagnosis. Nine endoscopy units took part in this cross sectional, prospective, multicentre study which lasted for two weeks. Members of the staff were required to report any unintended, potentially dangerous event observed during the daily work. A form was provided with a list of "reminders" and facilitators were appointed to help. The main outcome measurements were type of event, causes, corrective interventions, stage of occurrence in the workflow and qualification of the reporters. A total of 232 errors were reported (two were not related to endoscopy). The remaining 230 amount to 10.3% of 2239 procedures; 66 (29%) were considered errors with consequences, 164 (71%) "near misses". There were 150 pre-operative errors (65%), 22 operative (10%) and 58 post-operative (25%). Corrective interventions were provided for 60 cases of errors and 119 near misses. Most of the events were reported by the nurses (106 out of 232, 46%). Short-term incident reporting focusing on near misses, using forms with lists of "reminders", and the help of a facilitator, can give useful information on errors and near misses in digestive endoscopy. Copyright © 2012 Editrice Gastroenterologica Italiana S.r.l. Published by Elsevier Ltd. All rights reserved.

  3. Statistical analysis with missing exposure data measured by proxy respondents: a misclassification problem within a missing-data problem.

    PubMed

    Shardell, Michelle; Hicks, Gregory E

    2014-11-10

    In studies of older adults, researchers often recruit proxy respondents, such as relatives or caregivers, when study participants cannot provide self-reports (e.g., because of illness). Proxies are usually only sought to report on behalf of participants with missing self-reports; thus, either a participant self-report or proxy report, but not both, is available for each participant. Furthermore, the missing-data mechanism for participant self-reports is not identifiable and may be nonignorable. When exposures are binary and participant self-reports are conceptualized as the gold standard, substituting error-prone proxy reports for missing participant self-reports may produce biased estimates of outcome means. Researchers can handle this data structure by treating the problem as one of misclassification within the stratum of participants with missing self-reports. Most methods for addressing exposure misclassification require validation data, replicate data, or an assumption of nondifferential misclassification; other methods may result in an exposure misclassification model that is incompatible with the analysis model. We propose a model that makes none of the aforementioned requirements and still preserves model compatibility. Two user-specified tuning parameters encode the exposure misclassification model. Two proposed approaches estimate outcome means standardized for (potentially) high-dimensional covariates using multiple imputation followed by propensity score methods. The first method is parametric and uses maximum likelihood to estimate the exposure misclassification model (i.e., the imputation model) and the propensity score model (i.e., the analysis model); the second method is nonparametric and uses boosted classification and regression trees to estimate both models. We apply both methods to a study of elderly hip fracture patients.

  4. Covariance modeling in geodetic applications of collocation

    NASA Astrophysics Data System (ADS)

    Barzaghi, Riccardo; Cazzaniga, Noemi; De Gaetani, Carlo; Reguzzoni, Mirko

    2014-05-01

    Collocation method is widely applied in geodesy for estimating/interpolating gravity related functionals. The crucial problem of this approach is the correct modeling of the empirical covariance functions of the observations. Different methods for getting reliable covariance models have been proposed in the past by many authors. However, there are still problems in fitting the empirical values, particularly when different functionals of T are used and combined. Through suitable linear combinations of positive degree variances a model function that properly fits the empirical values can be obtained. This kind of condition is commonly handled by solver algorithms in linear programming problems. In this work the problem of modeling covariance functions has been dealt with an innovative method based on the simplex algorithm. This requires the definition of an objective function to be minimized (or maximized) where the unknown variables or their linear combinations are subject to some constraints. The non-standard use of the simplex method consists in defining constraints on model covariance function in order to obtain the best fit on the corresponding empirical values. Further constraints are applied so to have coherence with model degree variances to prevent possible solutions with no physical meaning. The fitting procedure is iterative and, in each iteration, constraints are strengthened until the best possible fit between model and empirical functions is reached. The results obtained during the test phase of this new methodology show remarkable improvements with respect to the software packages available until now. Numerical tests are also presented to check for the impact that improved covariance modeling has on the collocation estimate.

  5. Stenting precision: "Image small, miss small".

    PubMed

    Goldstein, James A

    2016-09-01

    Stenting by angiography alone predisposes to geographic miss STEMI culprit lesions are most susceptible to Geographic Miss Direct coronary imaging assures procedural precision and perfection. © 2016 Wiley Periodicals, Inc.

  6. A covariance NMR toolbox for MATLAB and OCTAVE.

    PubMed

    Short, Timothy; Alzapiedi, Leigh; Brüschweiler, Rafael; Snyder, David

    2011-03-01

    The Covariance NMR Toolbox is a new software suite that provides a streamlined implementation of covariance-based analysis of multi-dimensional NMR data. The Covariance NMR Toolbox uses the MATLAB or, alternatively, the freely available GNU OCTAVE computer language, providing a user-friendly environment in which to apply and explore covariance techniques. Covariance methods implemented in the toolbox described here include direct and indirect covariance processing, 4D covariance, generalized indirect covariance (GIC), and Z-matrix transform. In order to provide compatibility with a wide variety of spectrometer and spectral analysis platforms, the Covariance NMR Toolbox uses the NMRPipe format for both input and output files. Additionally, datasets small enough to fit in memory are stored as arrays that can be displayed and further manipulated in a versatile manner within MATLAB or OCTAVE.

  7. Missed Appendicitis: Mimicking Urologic Symptoms

    PubMed Central

    Akhavizadegan, Hamed

    2012-01-01

    Appendicitis, a common disease, has different presentations. This has made its diagnosis difficult. This paper aims to present two cases of missed appendicitis with completely urologic presentation and the way that helped us to reach the correct diagnosis. The first case with symptoms fully related to kidney and the second mimicking epididymorchitis hindered prompt diagnosis. Right site of the pain, relapsing fever, frequent physical examination, and resistance to medical treatment were main clues which help us to make correct diagnosis. PMID:23326748

  8. Stochastic Complexity Based Estimation of Missing Elements in Questionnaire Data.

    ERIC Educational Resources Information Center

    Tirri, Henry; Silander, Tomi

    A new information-theoretically justified approach to missing data estimation for multivariate categorical data was studied. The approach is a model-based imputation procedure relative to a model class (i.e., a functional form for the probability distribution of the complete data matrix), which in this case is the set of multinomial models with…

  9. Missed Opportunities: But a New Century Is Starting.

    ERIC Educational Resources Information Center

    Corn, Anne L.

    1999-01-01

    This article describes critical events that have shaped gifted education, including: closing of one-room schoolhouses, the industrial revolution, the space race, the civil right movement, legislation for special education, growth in technology and information services, educational research, and advocacy. Missed opportunities and future…

  10. Methods for Mediation Analysis with Missing Data

    ERIC Educational Resources Information Center

    Zhang, Zhiyong; Wang, Lijuan

    2013-01-01

    Despite wide applications of both mediation models and missing data techniques, formal discussion of mediation analysis with missing data is still rare. We introduce and compare four approaches to dealing with missing data in mediation analysis including list wise deletion, pairwise deletion, multiple imputation (MI), and a two-stage maximum…

  11. Methods for Mediation Analysis with Missing Data

    ERIC Educational Resources Information Center

    Zhang, Zhiyong; Wang, Lijuan

    2013-01-01

    Despite wide applications of both mediation models and missing data techniques, formal discussion of mediation analysis with missing data is still rare. We introduce and compare four approaches to dealing with missing data in mediation analysis including list wise deletion, pairwise deletion, multiple imputation (MI), and a two-stage maximum…

  12. Characteristics of HIV patients who missed their scheduled appointments

    PubMed Central

    Nagata, Delsa; Gutierrez, Eliana Battaggia

    2016-01-01

    ABSTRACT OBJECTIVE To analyze whether sociodemographic characteristics, consultations and care in special services are associated with scheduled infectious diseases appointments missed by people living with HIV. METHODS This cross-sectional and analytical study included 3,075 people living with HIV who had at least one scheduled appointment with an infectologist at a specialized health unit in 2007. A secondary data base from the Hospital Management & Information System was used. The outcome variable was missing a scheduled medical appointment. The independent variables were sex, age, appointments in specialized and available disciplines, hospitalizations at the Central Institute of the Clinical Hospital at the Faculdade de Medicina of the Universidade de São Paulo, antiretroviral treatment and change of infectologist. Crude and multiple association analysis were performed among the variables, with a statistical significance of p ≤ 0.05. RESULTS More than a third (38.9%) of the patients missed at least one of their scheduled infectious diseases appointments; 70.0% of the patients were male. The rate of missed appointments was 13.9%, albeit with no observed association between sex and absences. Age was inversely associated to missed appointment. Not undertaking anti-retroviral treatment, having unscheduled infectious diseases consultations or social services care and being hospitalized at the Central Institute were directly associated to missed appointments. CONCLUSIONS The Hospital Management & Information System proved to be a useful tool for developing indicators related to the quality of health care of people living with HIV. Other informational systems, which are often developed for administrative purposes, can also be useful for local and regional management and for evaluating the quality of care provided for patients living with HIV. PMID:26786472

  13. Characteristics of HIV patients who missed their scheduled appointments.

    PubMed

    Nagata, Delsa; Gutierrez, Eliana Battaggia

    2015-01-01

    To analyze whether sociodemographic characteristics, consultations and care in special services are associated with scheduled infectious diseases appointments missed by people living with HIV. This cross-sectional and analytical study included 3,075 people living with HIV who had at least one scheduled appointment with an infectologist at a specialized health unit in 2007. A secondary data base from the Hospital Management & Information System was used. The outcome variable was missing a scheduled medical appointment. The independent variables were sex, age, appointments in specialized and available disciplines, hospitalizations at the Central Institute of the Clinical Hospital at the Faculdade de Medicina of the Universidade de São Paulo, antiretroviral treatment and change of infectologist. Crude and multiple association analysis were performed among the variables, with a statistical significance of p ≤ 0.05. More than a third (38.9%) of the patients missed at least one of their scheduled infectious diseases appointments; 70.0% of the patients were male. The rate of missed appointments was 13.9%, albeit with no observed association between sex and absences. Age was inversely associated to missed appointment. Not undertaking anti-retroviral treatment, having unscheduled infectious diseases consultations or social services care and being hospitalized at the Central Institute were directly associated to missed appointments. The Hospital Management & Information System proved to be a useful tool for developing indicators related to the quality of health care of people living with HIV. Other informational systems, which are often developed for administrative purposes, can also be useful for local and regional management and for evaluating the quality of care provided for patients living with HIV.

  14. Adjusting for covariate effects on classification accuracy using the covariate-adjusted receiver operating characteristic curve.

    PubMed

    Janes, Holly; Pepe, Margaret S

    2009-06-01

    Recent scientific and technological innovations have produced an abundance of potential markers that are being investigated for their use in disease screening and diagnosis. In evaluating these markers, it is often necessary to account for covariates associated with the marker of interest. Covariates may include subject characteristics, expertise of the test operator, test procedures or aspects of specimen handling. In this paper, we propose the covariate-adjusted receiver operating characteristic curve, a measure of covariate-adjusted classification accuracy. Nonparametric and semiparametric estimators are proposed, asymptotic distribution theory is provided and finite sample performance is investigated. For illustration we characterize the age-adjusted discriminatory accuracy of prostate-specific antigen as a biomarker for prostate cancer.

  15. Missing

    NASA Image and Video Library

    2009-04-30

    Here we see two different views of the spiral galaxy, Messier 81. On the left is an image taken in blue light, while on the right is a specially-processed version of an image taken with NASA Spitzer infrared array camera at 4.5 microns.

  16. Comparison of proportions for composite endpoints with missing components.

    PubMed

    Li, Xianbin; Caffo, Brian S

    2011-03-01

    Composite endpoints are commonly used in clinical trials. When there are missing values in their individual components, inappropriate handling of the missingness may create inefficient or even biased estimates of the proportions of successes in composite endpoints. Assuming missingness is completely at random or dependent on baseline covariates, we derived a maximum likelihood estimator of the proportion of successes in a three-component composite endpoint and closed-form variance for the proportion, and compared two groups in the difference in proportions and in the logarithm of a relative risk. Sample size and statistical power were studied. Simulation studies were used to evaluate the performance of the developed methods. With a moderate sample size the developed methods works satisfactorily.

  17. Combining contingency tables with missing dimensions.

    PubMed

    Dominici, F

    2000-06-01

    We propose a methodology for estimating the cell probabilities in a multiway contingency table by combining partial information from a number of studies when not all of the variables are recorded in all studies. We jointly model the full set of categorical variables recorded in at least one of the studies, and we treat the variables that are not reported as missing dimensions of the study-specific contingency table. For example, we might be interested in combining several cohort studies in which the incidence in the exposed and nonexposed groups is not reported for all risk factors in all studies while the overall numbers of cases and cohort size is always available. To account for study-to-study variability, we adopt a Bayesian hierarchical model. At the first stage of the model, the observation stage, data are modeled by a multinomial distribution with fixed total number of observations. At the second stage, we use the logistic normal (LN) distribution to model variability in the study-specific cells' probabilities. Using this model and data augmentation techniques, we reconstruct the contingency table for each study regardless of which dimensions are missing, and we estimate population parameters of interest. Our hierarchical procedure borrows strength from all the studies and accounts for correlations among the cells' probabilities. The main difficulty in combining studies recording different variables is in maintaining a consistent interpretation of parameters across studies. The approach proposed here overcomes this difficulty and at the same time addresses the uncertainty arising from the missing dimensions. We apply our modeling strategy to analyze data on air pollution and mortality from 1987 to 1994 for six U.S. cities by combining six cross-classifications of low, medium, and high levels of mortality counts, particulate matter, ozone, and carbon monoxide with the complication that four of the six cities do not report all the air pollution variables. Our

  18. Missing Data and Multiple Imputation: An Unbiased Approach

    NASA Technical Reports Server (NTRS)

    Foy, M.; VanBaalen, M.; Wear, M.; Mendez, C.; Mason, S.; Meyers, V.; Alexander, D.; Law, J.

    2014-01-01

    The default method of dealing with missing data in statistical analyses is to only use the complete observations (complete case analysis), which can lead to unexpected bias when data do not meet the assumption of missing completely at random (MCAR). For the assumption of MCAR to be met, missingness cannot be related to either the observed or unobserved variables. A less stringent assumption, missing at random (MAR), requires that missingness not be associated with the value of the missing variable itself, but can be associated with the other observed variables. When data are truly MAR as opposed to MCAR, the default complete case analysis method can lead to biased results. There are statistical options available to adjust for data that are MAR, including multiple imputation (MI) which is consistent and efficient at estimating effects. Multiple imputation uses informing variables to determine statistical distributions for each piece of missing data. Then multiple datasets are created by randomly drawing on the distributions for each piece of missing data. Since MI is efficient, only a limited number, usually less than 20, of imputed datasets are required to get stable estimates. Each imputed dataset is analyzed using standard statistical techniques, and then results are combined to get overall estimates of effect. A simulation study will be demonstrated to show the results of using the default complete case analysis, and MI in a linear regression of MCAR and MAR simulated data. Further, MI was successfully applied to the association study of CO2 levels and headaches when initial analysis showed there may be an underlying association between missing CO2 levels and reported headaches. Through MI, we were able to show that there is a strong association between average CO2 levels and the risk of headaches. Each unit increase in CO2 (mmHg) resulted in a doubling in the odds of reported headaches.

  19. Effects of increasing nurse staffing on missed nursing care.

    PubMed

    Cho, S-H; Kim, Y-S; Yeon, K N; You, S-J; Lee, I D

    2015-06-01

    Inadequate nurse staffing has been reported to lead nurses to omit required nursing care. In South Korea, to reduce informal caregiving by patient families and sitters and to improve the quality of nursing care, a public hospital operated by the Seoul Metropolitan Government has implemented a policy of increasing nurse staffing from 17 patients per registered nurse to 7 patients per registered nurse in 4 out of 13 general nursing units since January 2013. The study aims to compare missed nursing care (omission of required care) in high-staffing (7 patients per nurse) units vs. low-staffing (17 patients per nurse) units to examine the effects of nurse staffing on missed care. A nurse survey conducted in July 2013 targeted all staff nurses in all four high-staffing and all nine low-staffing units; 115 nurses in the high-staffing units (response rate = 94.3%) and 117 nurses in the low-staffing units (response rate = 88.6%) participated. Missed nursing care was measured using the MISSCARE survey that included 24 nursing care elements. Nurses were asked how frequently they had missed each element on a 4-point scale from 'rarely' to 'always'. Overall, nurses working in high-staffing units had a significantly lower mean score of missed care than those in low-staffing units. Seven out of 24 nursing care elements were missed significantly less often in high-staffing (vs. low-staffing) units: turning, mouth care, bathing/skin care, patient assessments in each shift, assistance with toileting, feeding and setting up meals. The findings suggest that increasing nurse staffing is associated with a decrease in missed care. Less omission of required nursing care is expected to improve nursing surveillance and patient outcomes, such as patient falls, pressure ulcers and pneumonia. Adequate nurse staffing should be ensured to reduce unmet nursing needs and improve patient outcomes. © 2015 International Council of Nurses.

  20. Ethical climate and missed nursing care in cancer care units.

    PubMed

    Vryonides, Stavros; Papastavrou, Evridiki; Charalambous, Andreas; Andreou, Panayiota; Eleftheriou, Christos; Merkouris, Anastasios

    2016-09-27

    Previous research has linked missed nursing care to nurses' work environment. Ethical climate is a part of work environment, but the relationship of missed care to different types of ethical climate is unknown. To describe the types of ethical climate in adult in-patient cancer care settings, and their relationship to missed nursing care. A descriptive correlation design was used. Data were collected using the Ethical Climate Questionnaire and the MISSCARE survey tool, and analyzed with descriptive statistics, Pearson's correlation and analysis of variance. All nurses from relevant units in the Republic of Cyprus were invited to participate. The research protocol has been approved according to national legislation, all licenses have been obtained, and respondents participated voluntarily after they have received all necessary information. Response rate was 91.8%. Five types identified were as follows: caring (M = 3.18, standard deviation = 1.39); law and code (M = 3.18, standard deviation = 0.96); rules (M = 3.17, standard deviation = 0.73); instrumental (M = 2.88, standard deviation = 1.34); and independence (M = 2.74, standard deviation = 0.94). Reported overall missed care (range: 1-5) was M = 2.51 (standard deviation = 0.90), and this was positively (p < 0.05) related to instrumental (r = 0.612) and independence (r = 0.461) types and negatively (p < 0.05) related to caring (r = -0.695), rules (r = -0.367), and law and code (r = -0.487). The reported levels of missed care and the types of ethical climates present similarities and differences with the relevant literature. All types of ethical climate were related to the reported missed care. Efforts to reduce the influence of instrumental and independence types and fostering caring, law and code, and rules types might decrease missed nursing care. However, more robust evidence is needed. © The Author(s) 2016.